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- Pamela DeLoatch
Celebrate Latinx Heritage Month with Colombian Cuisine

If hopping on a plane to Colombia is not in the plan this month, guess what? You can visit a local restaurant in your area or make a traditional Colombian recipe at home to celebrate National Latinx Heritage Month with some delicious authentic Colombian food. There’s lots to explore and love about this diverse South American cuisine.

Much like its culture and people, Colombian cuisine combines indigenous, Spanish, and West African influences and flavors. Colombia’s food is as diverse as its people and the land. As one of the most geographically and biologically diverse countries, you’ll encounter an array of different staple crops and dishes as you move throughout the country. 

From hearty, potato-rich stews in the mountains to fresh fish and rice in the coastal regions, there’s abundant color, flavor, and history on the plate.

Common Colombian foods

Colombia has hot, temperate, and cold climates,and its staple foods and dishes reflect them accordingly. Common foods include a variety of tropical fruits, rice, corn, cassava (yuca), potatoes, plantain, avocado, coconut, beef, and cheese. Colombia is well-known for its coffee and is a top producer of cocoa, sugar cane, and bananas. 

White rice is a staple throughout the country. And in the cooler mountainous regions, you’ll find warm soups and stews made with plenty of root vegetables such as potatoes, while fish and seafood dishes with plantain and coconut rice are common on the coast. 

Popular Colombian dishes to try

Each region of Colombia has its own traditional dishes, but here are a few popular ones throughout the country.

Colombian arepas are cornmeal cakes that are typically grilled but can also be fried or oven baked. Arepas might be served alongside a meal or topped or stuffed with a sauce, cheese, meat, and other fillings for a complete mealf. There are more than 40 different types of Colombian arepas—from the arepa paisa to the arepa Santandereana, made with bits of cassava and pork. They are prepared in many ways, and you’ll find variations depending on its departmento (similar to states) of origin. Some, like the arepa boyacense from the Boyacá región, are made with a sweet corn batter and stuffed with cheese. Another example, like the arepa de huevo, a fried corn cake with an egg inside, is popular in the Caribbean region. 

Tamales are also popular in Colombia. Lorena Drago, RDN, CDCES, and owner of Hispanic Foodways, shares that “Colombians eat tamales on weekends, Christmas, and special occasions.” She says each region prepares tamales differently. “The masa can be prepared with either corn or rice. The filling can have an assortment of vegetables, eggs, peanuts, and meats ranging from beef to pork.” For example, the Tamal Antioqueño, from the Colombian state of Antioquia, is prepared with corn flour and peas, carrots, sliced potatoes, olives, chicken, and pork.  

But Colombia is not all about handheld eats. Often called the national dish of Colombia, Bandeja Paisa is a traditional dish from Antioquia (home to Medellin and Guatape). Drago explains that this dish, which translates to “Paisa Platter,” gets its name from the people born in Antioquia, who are called Paisas. 

Though you’ll see some ingredient swaps from region to region, Drago explains that “Bandeja Paisa contains white rice, red beans seasoned with scallions, tomatoes, carrots, garlic, and pork, beef, corn arepa, fried egg, sweet plantain, and a slice of avocado.” This dish was born out of necessity. Drago describes how “in the mid-1900 century, Antioquian muleteers and men from the state of Caldas spent the entire day working in the field and brought the bandeja paisa to sustain them during the long hours of hard labor.”

There are far too many dishes to name, but other popular Colombian dishes to taste include ajiaco (a chicken and potato stew soup), sancocho (a soup made with corn, potatoes, plantain, and/or yuca, and chicken, pork, beef, or fish), sudado de pollo (chicken stew), and pescado frito (a whole fried fish from coastal Colombia commonly served with rice).

Sweet and nutritious Colombian fruit

Thanks to the country’s biodiversity, Colombia boasts one of the best fruit bounties in the world. So if you’re not quite ready to try your hand at arepas or a Colombian stew, you can get a taste of delicious, nutrient-dense Colombian fruits.

Drago suggests starting with soursop (guanábana), loquat (níspero), lulo (naranjilla), guava (guayaba), passion fruit (maracuya), tamarind (tamarindo), dragon fruit (pitahaya), or other typical fruits to sweeten the plate and refresh the palate. 

Fresh fruits like fiber-rich dragon fruit (pitahaya) might be fairly easy to find in major supermarkets, but others may take a bit more searching. If you live near an international farmers market, a Latinx market, or a specialty store, check for fresh guava, passion fruit, or tamarillo (a red or orange, egg-shaped fruit resembling a tomato). Ask a store produce manager if fresh options are available (even if for a short time each year). Also, check the frozen foods aisle for frozen fruit pulp and the grocery aisles for canned or dried options. 

Tropical fruits can be eaten fresh as a snack, with a meal, or whipped into fresh juice. Some fruits, like passion fruit, can often be found in juice form or made into ice cream or custard. The antioxidant-rich lulo is popular throughout Colombia, and its frozen pulp is widely available in many parts of the world. Use it to make lulada, a Colombian beverage made with muddled lulo fruit, lime juice, water, and sugar. 

Another way many Colombians enjoy fruit (particularly in the Andean region) is in an aromática. Making this fresh fruit infusion at home is easy. Simply steep fresh-cut pineapple, guava, or other fruit in hot, not boiling water until its infused with the fruit’s flavor. You can add fresh mint and honey or sugar to taste. Once it’s ready, sip and savor it like tea. 

The post Celebrate Latinx Heritage Month with Colombian Cuisine appeared first on Fitbit Blog.

- Jodi Helmer
6 Stress-Busting Strategies to Help You Regain Your Equilibrium

Feeling stressed? You’re not alone. A survey found that 84 percent of Americans are experiencing at least one stress-related emotion—but a culture of stress is not just a problem in the United States. In countries like Costa Rica, the Philippines, and Venezuela, more than half the population reports experiencing “a lot” of stress, making them among the most stressed nations in the world.

And as most of us know, COVID-19 exacerbated stress levels worldwide, with the World Health Organization (WHO) reporting a 25 percent increase in the global prevalence of anxiety, depression, and stress.

Regardless of where you live, your health and well-being depend on ensuring your  stress level is in check. These six research-backed strategies can help—and so can Fitbit.

Prioritize sleep. Stress can interfere with sleep. Your body pumps out adrenaline and cortisol, the stress hormone, during stressful periods, which increases your heart rate and core temperature, making it hard to fall asleep. On the flip side, lack of sleep can also leave you more vulnerable to stress. 

“Our brains sometimes want us to go down this unproductive rabbit hole, but chances are you’re not going to solve a big problem at one o’clock in the morning,” says Angela Ficken, LICSW, a Boston-based psychotherapist. “You need something boring to occupy your mind” so that you can fall asleep—and stay asleep. Try mentally cataloging all the blue shirts in your closet or listening to a storytime podcast to help you fall asleep. Find other techniques here

It’s also important to create a sleep sanctuary. Establish a bedtime routine; turn down the thermostat, install blackout shades to keep the room dark, turn on a white noise machine and use your Fitbit to track your sleep. Read more about the relationship between sleep and stress here

You can also try Fitbit’s advanced sleep tools, like the new personalized Sleep Profile with Premium, which goes beyond nightly sleep tracking to analyze your monthly sleep habits and trends  so that you can better understand your sleep health as well as work to improve it.

Meditate. It’s oft-cited advice for dealing with stress because it works. Studies show that practicing mindfulness meditation could reduce chronic stress levels up to 25 percent after six months.

Alfie Breland-Noble, Ph.D., MHSc, psychologist and founder of The AAKOMA Project, an organization supporting the mental health needs of BIPOC youth, calls deep breathing and mindfulness meditation “simple, portable, and feasible.” 

If the idea of a traditional mindfulness meditation feels overwhelming, Breland-Noble suggests a simpler exercise: Identify something you can see, hear, taste, touch, or smell for which you are grateful. “The focus it takes to list each of these things is often just enough to move our minds off what is stressing us and into the moment,” she says. 

There’s no need to take the conventional route when building your mindfulness practice—what matters is finding something that works for you. Try transforming a hike or walk into a moving meditation or turning to a furry friend. Looking for more unexpected ways to find mindfulness? Discover them here

Set boundaries. Sometimes it’s the news headlines regarding the state of the world that trigger stress and other times, it’s your to-do list. When it’s the latter, Ficken advises setting boundaries and saying no to things that will create additional stress. 

“We all have personal limits,” Ficken says. “It’s okay to say no to things that will create more stress.”

Ficken uses a few go-to boundary statements like, “Thank you so much for asking; I’m not able to do that right now,” or “I appreciate the invitation. Unfortunately, it doesn’t work for me.” Practice saying “no” to small things so you’ll feel more confident establishing bigger boundaries. Learn more about how to set and maintain healthy boundaries here.

Break a sweat. Try not to give in to the temptation to hide under a blanket and binge-watch crime dramas when you’re feeling stressed. Even a single session of exercise makes you less reactive to stress.

You don’t have to run a 5K or train for a triathlon to experience the benefits of exercise on stress. Ficken notes that all physical activity, from walking, swimming, and yoga—and, yes, triathlons—can have stress-busting benefits. “Making a conscious effort to take care of your body will have a direct impact on stress,” she says. Try engaging in a little friendly competition with the Fitbit community by participating in challenges with friends or attending group fitness classes to find accountability on your journey. 

Log off. Your devices are great for messaging friends, playing word games, catching up on the news, posting vacation pics, and watching crime dramas, but it’s possible to spend too much time on your screen. 

If watching cat videos for a few minutes helps restore your sense of calm, go for it, says Ficken. But be careful to minimize online activities that cause your blood pressure to rise. Disabling notifications, deleting social media apps, and setting timers to remind you to log off are all strategies that can help you reduce screen time.

No matter what you’re looking at on your screen, Ficken recommends avoiding scrolling before bed whenever possible.

Screen time could make it harder to fall asleep, reduce the quality of your sleep, and leave you feeling tired the next morning. 

Ask for help. Stress can be overwhelming. Instead of going it alone, make an appointment with a healthcare professional or call 988, the new dialing code, to connect with mental health professionals. “It is really important to normalize seeking help,” says Breland-Noble.

People of color and those with marginalized identities, including LGBTQIA+ and people with disabilities, face unique stressors related to racial trauma, homophobia, transphobia, and ability-based discrimination. Breland-Noble believes that acknowledging these stressors and seeking help if needed, is essential.

For anyone experiencing stress, she advises, “Start by identifying a trusted person to talk with and check in with them… Once we get to a place where we can acknowledge that there is something wrong that needs addressing and that we feel ready to address it, we are better prepared to seek out a mental health professional.” 

Learn more about how Fitbit can help you manage your stress here.

The post 6 Stress-Busting Strategies to Help You Regain Your Equilibrium appeared first on Fitbit Blog.

- Kelsey Maloney
Meet Kelley Green Johnson: A Force in the Wellness World and Inspiration To All

In our new monthly profile series, Meet the Trailblazers, Fitbit is seeking to amplify diversity in the world of wellness and fitness by featuring the voices of POC trailblazers at the helm of these industries—industries that have discredited voices like theirs for too long. 

For our September profile, we’re highlighting the incredible work of Kelley Green Johnson, a certified life and mindset coach, an International meditation instructor,  certified NLP and EFT practitioner, hypnotherapist, host of the “Hey Lovely!” podcast, and author to her book Perfectly Imperfect. We’re excited to share the conversation we had with Kelley about how she got started in wellness, her brand, and other impressive life work.

From the southside of Queens, New York, Kelley Green Johnson is the founder of Kelley Green Media LLC, a personal coaching and consulting brand focused on wellness, female empowerment, and self-love. Kelley inspires her clients by sharing her lifestyle and mindset and introducing the practice of self-love along with providing guidance and resources to those hoping to amplify their self-worth in life and business. 

“It’s been a blessing leaving my 10 year corporate journey directing human resources and communication departments,” she says. “I’ve always loved helping people and knew that if I left the confines of my desks and cubicles I could help change the world and manifest my dream life.”

This journey also included a surprising meet-cute, as Kelley recently married her husband, whom she met on a Zoom call while leading a meditation during the pandemic. Talk about the unexpected! 

Keep reading to learn more about Kelley, her business, and how she’s paving the way for women in her community.

FITBIT: How did you get started in the wellness industry and life coaching? 

KELLEY: I have a background in Human Resources and a degree in Business Administration and although after college I jumped into the corporate workforce, I’ve always wanted the freedom to travel and make an impact around the world. So when corporate life didn’t feel like it was for me anymore, I thought about how my expertise and love for helping people in Human Resources could be applied to peoples lives as a certified life coach. 

Then once I realized there are so many other healing and transformational modalities like neuro-linguistic programming, meditation, hypnotherapy, and more, I got super excited and made sure to indulge my interest in obtaining certifications in these, to provide even more positive change for my clients as well. 

As someone who was bullied growing up, I experienced a lot of self-doubt and a lack of confidence in my inner and outer beauty. I wish I knew about various mindset and mindfulness resources to help me change my view of myself and how I showed up in the world, which is one of the reasons why it’s so important for me to help others, especially women. 

FITBIT: Can you tell me about your personal coaching and professional consulting brand, Kelley Green Media? 

KELLEY: I help ambitious women release their mental blocks so they can transform their mindsets and confidently take aligned action in their lives and careers, all while practicing wellness and accomplishing their goals. I also work with brands and organizations leading wellness workshops to improve the performance, morale, and company experience of their employees. If there’s a brand or business that wants to discuss self-care, improving one’s mindset, manifestation, or be led in a powerful meditation or hypnosis, they will be sure to experience a life-changing session.

FITBIT: Through your life and mindset coaching work, you focus on women (specifically Black and POC women) empowerment, self-love, and self-care. What drew you to this work? 

KELLEY: By experiencing my own lack of self-love and overwhelm due to a lack of self-care, I knew it was important to help others avoid the pitfalls I’ve had in my life. Participating in unhealthy relationships, not prioritizing my mental or physical health, and living an unfulfilled life is not something I want for other women.  

At one point I was such a people pleaser at work that I avoided all the physical signs that told me to rest. I was at work ignoring the signs of my body and ended up having a panic attack one day. I was wheeled out of the office on a stretcher and brought to the hospital in the back of an ambulance. It was a lot and I decided to never let that happen again.  

As Black women, we often go above and beyond for others and neglecting our own needs. But enough is enough. It’s time we learn a better way of living and take action on what we learn. And this is why I’m so drawn to this work. 

FITBIT: Why, in your view, is it so important for there to be a safe space like yours that explores self-love and healing, for Black women and women of color? 

KELLEY: I feel like it’s important because I didn’t have that when I needed it most in life. Growing up, meditation was viewed as weird or wicked and was frowned upon in my community. Even therapy wasn’t something that was talked about because as a Black woman, I’d often hear my elders telling younger generations that “church was enough” if you’re having problems, and that if we went to therapy people would think we were crazy. It wasn’t a comfortable topic to discuss, so my healing and self-love journey was extremely delayed unfortunately. 

I want to help alleviate the shame and stigma surrounding wellness so people can comfortably access the tools, resources, and safe spaces that we need to heal and grow. There is so much emotional and even physical trauma in the Black community, if not directly, generationally—and it’s time to keep breaking these generational curses. Black Women deserve to love themselves, avoid settling for less, and start manifesting a life they love. 

FITBIT: Can you tell me about your first book, Perfectly Imperfect?

KELLEY: My first book is a collection of poetry and prose. It takes the reader on an alliterative journey to the intricate depths of the human psyche. From childhood hardships and ever-pervading self-doubt to the tumultuous waves of both love and hate, this collection of gripping poems and inspirational tidbits invites you to a reflective and cathartic experience.

Infused with valuable wisdom, each piece reminds the reader of life’s only constant: change and life’s key to happiness: self-love.

FITBIT: Not only are you a life coach, but you are a NLP practitioner and clinical hypnotherapist, public speaker, podcast host, author, and meditation teacher. What would you say is the most fulfilling part of your work? 

KELLEY:  I would say the most fulfilling part of my work is hearing from my clients about how their views of themselves and their lives have changed for the better. I love hearing how they love themselves more, how they’ve cultivated more peace in their lives, and how they can manifest with ease and love the positive transformation their lives are taking. It’s incredible and I’m so grateful to do this work. 

If you’d like to learn more about Kelley, you can follow her on Twitter, TikTok and IG at @kelleygreen_.

The post Meet Kelley Green Johnson: A Force in the Wellness World and Inspiration To All appeared first on Fitbit Blog.

- Pamela DeLoatch
Try This Gut-Friendly Cooking Technique to Up Your Kitchen Game

If you’re looking for a different and healthy way to enjoy your vegetables, try pickling them. Pickling isn’t new, but in recent years, the art of pickling and fermenting food and drinks has enjoyed renewed popularity.

Pickling preserves food like carrots, celery, or okra, in an acid like brine or vinegar, and may be healthier, especially when it is unprocessed. Fermenting is yet another way to preserve food and drink. The natural process converts carbs into alcohol or acids and promotes the growth of probiotics, which are healthy bacteria and good for your gut health

Taking a closer look at pickling and fermenting techniques is timely as we have entered Latinx Heritage Month, which is from September 15 to October 15. During this period, we celebrate the rich history, culture, and contributions made by Americans whose ancestors came from Spain, Mexico, the Caribbean, and Central and South America. 

The global appeal of pickling and fermenting 

Although pickling and fermenting didn’t originate in Latin culture, these techniques have become a staple in many Latin households. The Spanish word escabeche literally means to pickle, but it also describes preserved dishes including fish, which is cooked before it is pickled.

The flavor of escabeche can vary based on the pickling technique, explained Luz Payan, a Chicago native whose family roots are from Axochiapan, Morelos, Mexico. “Escabeche recipes differ from regions from Mexico, Spain, South America, or even the Caribbean islands. In Mexico, they can differ from state to state or even cities within a Mexican state.” 

But nothing is as good as Payan’s family recipe. “Our escabeche is pickled and spicy with some added flavors. It complements Mexican dishes, including tortas, tacos, and most grilled meats.” The taste can be addictive, Payan said. “Some people I know even add it to Flaming Hot Cheetos to give them that vinegar and spicy kick!”

Like pickling, fermenting is used in many countries. People worldwide drink fermented beverages like beer or kombucha, but tepache is a traditional Mexican probiotic drink with roots in Central and South America. It’s typically made from pineapple rind, but can contain corn and fruits like apples, guavas, oranges, and pears.

How pickling made its way across the globe

Archaeologists believe pickling has been around since 2400 BC, when Mesopotamians began soaking their food in brine to preserve them. Cucumbers were one of the foods soaked. The resulting pickle was popular because it was easy to transport, hardy, and tasty. 

Pickling and pickles made their way around the world as sailors stocked the food item on their journeys. The high level of vitamin C pickles contain helped prevent scurvy. 

Even as pickles grew in popularity, different regions developed their nuances. Dutch farmers and East Europeans popularized dill pickles in the US. In the Caribbean, escabeche PR is a favorite, said Lizette Watko, creator of the talk and cooking show, Lizette Invites You. “Something very popular in the Caribbean is green banana escabeche.” In the side dish, guineos en escabeche, green bananas are peeled, boiled, and then marinated with vinegar, onion, garlic, salt, peppercorn, and other flavors.

A look back at fermenting

Fermenting has an even longer history than pickling, going back to 6000 BC. Most cultures can boast some form of fermented food, including Korean kimchi and Indian chutney. Yogurt and cheese are fermented, as are beer and wine. 

In addition to preserving food, fermenting adds to the taste by offering more complexity. The process also adds more nutrition to the food. Many people eat and drink fermented foods because of the probiotics that can help digestion. Even though beers are fermented, the brewing process kills off probiotics. However, some beers, like strong Belgian beers, are fermented differently in a way that allows probiotics to grow.  

The resurgence of an old practice

Although people never stopped pickling and fermenting, the practice decreased in the last few decades. “I’m in between generations,” Watko explained. “The older folks used to make escabeche and still do.” But for the generations that followed and cooked less frequently, they stopped making escabeche for a time.  

Now, however, and in the last few years, the practice has seen a resurgence in more popularity. 

The practice of pickling and fermenting foods resonated with people interested in the farm-to-table movement and was a simple way to prepare vegetables. Then, when the pandemic began, many were stuck at home and wanted to try something new, said Watko. “People started cooking at home and going back to the dishes our aunties used to make.”

With many resources available to learn pickling and fermentation techniques, it’s easy to join the trend. 

In honor of Latinx Hispanic Heritage Month, try a few gut-friendly recipes, like an easy side dish of escabeche with carrots, onions, garlic and jalapeno, paired with a spicy and refreshing glass of tepache. 

Just be sure to prepare your recipes in advance, so the dishes will have time to reach their pickling and fermenting peak before you enjoy them!

The post Try This Gut-Friendly Cooking Technique to Up Your Kitchen Game appeared first on Fitbit Blog.

- Karen Ansel, MS, RDN
The Benefits of Barre Workouts

Barre exercises are one of the hottest fitness trends around. But they’re hardly new. These low-impact, total body workouts were developed more than 70 years ago by ballet dancer Lotte Berk to rehab her injured back. And they’re still going strong today.

Why is barre all the rage? “Barre workouts are great for everyone at any time in their fitness journey,” says Andrea Fornarola, founder and CEO of Elements Barre Fit in New York City and the Hamptons, NY. Based on a combo of yoga, Pilates, strength training, and (of course) ballet, barre has multiple benefits. In addition to better strength, flexibility, balance, and body alignment, fans swear by its ability to sculpt longer, leaner, and more defined muscles. And because it’s easy on the joints, it can also be a good fit for people recovering from injury or surgery or who are pregnant (with a doctor’s permission).

What is a barre workout like? Think lots of reps of small isometric movements that work muscles to fatigue. And you don’t need to be a dancer or have a ballet bar to do it. “At home barre workouts are great!” says Fornarola. “They require minimal equipment, many times all you need is a mat and a resistance band to get a highly effective workout.” But even without those, all you really need is the back of a chair for a bit of support.

If you’re intrigued, this 5-step workout can get you started—no equipment necessary.

Wide second position. Do you wish you had the glutes and thighs of a dancer? This foundational ballet move can help.

Stand with your feet slightly wider than hip distance apart. Then, pivot your feet so that your toes are pointing slightly outward. Slowly lower your body into a squat position, keeping your back straight and your core muscles contracted. Gradually pulse up and down 20 to 30 times.

Back dancing. You might be surprised to learn that some barre moves happen on the mat. Like this floor-based exercise, which works the core, glutes, hips, and thighs.

Lie down on a mat or the floor with your hands by your sides. Bend your knees and separate your feet so they’re hip distance apart. Keeping your upper back on the ground, slightly lift your hips up off the floor, about 2 inches. Squeeze your glutes together while contracting your abdominals, then release. Repeat for 30 reps.

Push-ups. Barre push-ups are a great way to work your back, chest, and shoulders. But when you don’t have a barre, the floor is equally effective.

Begin in a high plank position with your hands underneath your shoulders, your feet hip-distance apart, and your back flat. Keeping your abs contracted, slowly bend your elbows and lower your body toward the floor. Then, using your arms, push your body back up into a plank position. If that’s too challenging, try a modified push-up instead. Repeat 5 to 10 times.

Narrow V. This multitasking exercise works the inner and outer thighs in just one move.

Stand with your feet together. Slowly pivot your toes outward while keeping your heels connected so that your feet make a V-shape that’s about 4-inches wide. Bend your knees slightly and raise your heels off the ground about an inch. Aim to keep your weight on the balls of your feet. Then, place one hand on the back of a chair. Inhale, sink down, squeeze your heels together, and pulse 15 to 30 times.

Clamshells. If sculpted inner and outer thighs are a goal, clamshells are your friend.

Lie on your right side with your legs stacked and your knees bent at a right angle. Rest your head in your right hand. Then place your left hand flat on the ground in front of your waist for support. Keeping your feet together and your right hip on the mat, contract your abs and raise your left knee upward. Squeeze your glutes and lower your knee. Repeat 15 to 30 reps. Then take it to the other side. (If you’d like to kick things up a notch, repeat the exercise but instead of lowering your knee, pulse it upward.)

How does an at-home workout compare to a class? “While nothing replaces the energy of a live class, at-home barre workouts are a great way to get a taste of the exercises and the pace of a class before you visit the studio,” says Fornarola. So, grab a chair and get started!

The post The Benefits of Barre Workouts appeared first on Fitbit Blog.

- Deanna deBara
Why Resistance Training Might Be the Key to Better Sleep

Most people are aware that if you want to get better sleep, making exercise a priority is a great place to start. But while many people think that running, biking, and other forms of cardio are the key to catching more high-quality Zzz’s, it turns out that there’s another type of workout that may be the key to better sleep—and that’s resistance training.

Let’s take a look at how resistance training impacts sleep—and how to lift your way to a better night’s rest. 

How does resistance training promote better sleep?

First things first. How, exactly, does resistance training impact sleep—and how can your time in lifting weights and doing other forms of resistance training actually help you get better rest?

“This workout type is…linked to improved sleep quality overall and increased sleep duration,” says Alex Savy, certified sleep science coach and the founder of SleepingOcean.com.

There’s research to back that up. In a recent study, researchers from Iowa State University studied the impact of both resistance training and aerobic exercise on participants over the course of a year. Of the participants that reported getting less than 7 hours of sleep per night at the beginning of the study, the participants that regularly engaged in resistance training increased their sleep duration by 40 minutes over the course of the study—almost twice as much as the aerobic group (which increased their sleep duration by 23 minutes). 

In addition to increased sleep duration, the resistance training group also experienced better sleep quality, including being able to more easily fall and stay asleep.

There’s also research that suggests that “moderate-intensity resistance training can help patients with chronic insomnia sleep better,” says Savy.

Why does resistance training help you get better sleep?

Clearly, resistance training can help you get better sleep. But the question is—why? “Sleep is a necessary part of muscle recovery, so more taxing workouts encourage your body to sleep more deeply and longer throughout the night,” says Dr. Grant Radermacher, sports chiropractor at Ascent Chiropractic in Brookfield, WI. 

Resistance training may also help your body produce more sleep-supporting chemicals—which can make it easier to fall asleep. “Studies show that resistance training often boosts adenosine production,” says Savy. “This chemical causes that drowsy feeling that often helps people fall asleep easier and enjoy deeper, more restorative rest. So, a post-workout adenosine boost can help people prevent sleep offset and drift off easier, potentially catching more hours of sleep.”

Tips for using resistance training to get better sleep

Want to use resistance training to get better sleep? Here are some tips to help you lift your way to higher-quality Zzz’s (and all the benefits that go along with it).

Start slow. Now that you know the sleep-boosting benefits of resistance training, you may be tempted to jump right in and start lifting heavy weights. But if you’re new to resistance training, the best approach is to start slow. “Start slow, add weight gradually, and focus on proper form,” says Radermacher. “[By taking this approach], you’ll reduce your risk of injury and be less likely to suffer from the delayed-onset muscle soreness (DOMS) that most new lifters deal with.”

Don’t work out too close to bedtime. Resistance training may help you get better sleep—but if you work out too close to bedtime, it can actually have the opposite effect. “Resistance exercise too late in the day can increase heart rate and body temp in a way that’s disruptive to sleep,” says Radermacher. “Leave a gap of at least two hours after working out to allow your body to wind down before bed.”

Don’t abandon cardio. Just because resistance training may be a better form of exercise for improved sleep doesn’t mean you should completely abandon your morning run or weekly bike ride! Cardio offers a huge variety of benefits, from lower risk of heart disease and diabetes to improved mood—so to promote optimal sleep and overall health, consider making both cardio and resistance training a foundational part of your fitness routine.

The post Why Resistance Training Might Be the Key to Better Sleep appeared first on Fitbit Blog.

- Ethan Watters
Find Out How Content Creator Shaunise Price Uses Her Fitbit to Manage a Busy Life

By any measure, Shaunise Price’s life is busy. In addition to being a single mother of two elementary school-aged children, she works full-time and is finishing a bachelor’s degree in accounting. On top of all that, she’s a popular health and fitness influencer on Instagram. With all the demands in her day, she admits it would be easy to neglect the fundamentals like eating well and exercising.

“The truth is that I can’t let my health slip,” Shaunise says. “If I don’t prioritize diet and fitness, I’d never be able to keep up with everything I have to accomplish.” But like everyone, she admits, she needs a little extra motivation some days. That’s when she relies on her Fitbit Sense, which she calls “my life coach on my wrist.” 

When Shaunise first got a Fitbit Versa in 2018, she mostly used it to get her 10,000 steps each day, a goal she liked to share with her mother and some friends. When she bought her Fitbit Sense in 2021, she began to rely on the features that gave her little nudges and encouragement. It wasn’t long before she started using the guided coaching programs and video workouts to begin her day. She looked to the health metrics to chart her improvements over time. There are few Fitbit Premium features she hasn’t tried at least once.  

“I track my menstrual cycles and stress levels, and I log my water intake and what I ate during the day,” she says. “I’ve become a Fitbit evangelist around my friends, family, and coworkers. I’m always talking to people about my Fitbit and love the challenges I get to do with others where we can compete in a friendly way and keep ourselves accountable.”

Shaunise has always been active and athletic, but her Fitbit has given her new ways to achieve a healthy lifestyle. “With my Fitbit, I’ve tried some new activities,” she says. “I’ve even tried some mindfulness and meditation. I never realized how hard it was for me to sit silently, relax, and take deep breaths. I’ve also learned things about myself that I didn’t know. In particular, I now understand how important sleep is to my physical and mental health.”

Shaunise’s healthy journey hasn’t always been smooth or easy. As a teenager, she struggled with healthy eating and maintaining a positive body image. 

“I was a skinny kid, and for a while, I was restricting what I ate to the point that it was unhealthy,” she recalls. “I was probably borderline anorexic. Then I learned about bulimia on television, and I started that behavior. I hid that illness from my friends and family, but weirdly, I thought it was normal for a long time. It was like a kind of addiction.” 

One day Shaunise looked at herself in the mirror and saw herself clearly. She realized she was looking at a body that wasn’t getting enough nutrients and a young woman who so lacked energy that she sometimes nearly fainted. “I was frail and weak, and I didn’t like how my clothes hung on my body,” she remembers. “That’s when I began what I call my ‘self-love, self-care journey.'”

Logging what she eats into the Fitbit app helps remind Shaunise to get the nutrients and calories her body needs to function at a high level. She also uses Fitbit recipes to create balanced meals with healthy levels of macro and micronutrients. 

Shaunise has shared her journey to health through Instagram. She doesn’t try to present an idealized, perfect self. She believes it’s essential to be honest about her struggles and successes. “I was tired of hiding things,” she says, “I wanted to connect with the men and women who were also struggling to try to live healthier lives.”

When Shaunise became a mother, maintaining good eating and exercise habits became doubly important. She needed to stay healthy but also model healthy behavior for her children. 

“I want my kids to know that exercise and nutritious eating isn’t a burden—it can be fun and joyful,” she says. “When I’m feeling my best, I’m the type of person that laughs easily, smiles at everyone, and strikes up a conversation with the person next to me in the checkout line. That’s the person I want them to see. Staying healthy is not just about your body; it’s about your ability to stay upbeat, embrace life, and show love to the world.”

The post Find Out How Content Creator Shaunise Price Uses Her Fitbit to Manage a Busy Life appeared first on Fitbit Blog.

- Leandra Rouse
Healthy Recipe: Sun-Baked Tomato, Lentil, and Tuscan Kale Salad with Roast Chicken

This salad is a main dish that pulls out all the stops. Nutrient rich lentils, tasty roast chicken, and creamy feta cheese, paired with fresh leaf greens and sundried tomatoes. This single dish offers nourishment, is nutritionally balanced and tons of flavor. Best of all, it is great made in bulk because it saves well – in fact, the flavors get better overnight. Making it a great dinner, and an even better healthy lunch.

Lentils are packed with protein (a third of your daily requirements in one cup!), high in dietary fiber and other essential nutrients, especially the B-vitamins. With over a dozen varieties, the flavor can range from nutty to peppery to meaty or umami flavors. Colorful and an array of textures when cooked, they can thicken a sauce or add chewy excitement to a salad or fish dish. Lentils can even be sprouted on the window sill to create a leafy crunchy sandwich or salad topping. 

Lentils are a great pantry staple because they can be stored for a couple of years, they don’t require soaking and will cook in less than 30 minutes. They are an affordable source of good quality nutrition. 

INGREDIENTS:

For the chicken: 

1/2 cp olive brine 

1 lemon, zest and juice

1 Tbp olive oil 

2 cloves garlic, minced 

2 lbs chicken thighs, boneless, skinless

For the lentils: 

1 ½  lbs green lentils, dried 

2 bay leaves

1 clove garlic, large

½ bunch Tuscan kale, 

6 sun-dried tomatoes, drained, minced, with 3 Tbps of the oil reserved

4 Tbp fresh flat-leaf parsley, minced 

1 Tbp red wine vinegar

¼ lbs feta cheese, crumbled or cubed, with 1 Tbp set aside for garnish

INSTRUCTIONS:

Combine lemon zest and juice, garlic, olive brine, and olive oil in a large bowl and whisk to combine. Score the chicken thighs with two slits, then add to marinade and toss in marinade. Cover and set aside in the refrigerator to marinate. Toss occasionally. 

Preheat the oven to 425. 

In a large saucepan, bring 6 cups of water to a boil and then add the dried lentils, bay leaves and whole garlic clove. Return to a boil over medium heat, then reduce the heat to low and simmer, and cook uncovered until  the lentils are tender to the bite, approximately 30 to 40 minutes. Cook lentils like pasta, testing regularly, to be sure they are not over cooked. If any of the lentils are starting to break up – the lentils are cooked and should be removed from the heat. Once cooked, drain the lentils well and set aside. 

While the lentils are cooking, strip the kale leaves off their stems. Discard the stems (these can be saved in the freezer for stock or smoothies) and set the leaves aside in the large stack. Once all leaves have been stripped and stacked, roll the stack into a tight cylinder.  And cut crosswise to create uniform thin ribbons. Cut any very long stips in half and then transfer the ribbons to a large bowl. Season with a pinch of salt and massage until kale feels tender. Set aside. 

In a large bowl create the salad dressing by combining ½ teaspoon salt, the minced sun-dried tomatoes and the reserved sun-dried tomato oil, minced garlic, 3 tablespoons of chopped parsley and vinegar. Whisk to combine. 

Transfer the marinated chicken to a baking sheet and add to the oven. Roast for about 20 minutes or until a thermometer reads 165°F. Set aside to cool for 5 minutes. And then cut into 2 inch slices. 

When the lentils are fully drained add them to the large bowl, along with the marinated kale strips and the dressing and toss to combine. Top with the feta cheese, sliced chicken and garnish with an additional 1 tablespoon of parsley. Serve at room temperature. 

Serves 6-8 people.

NUTRITION FACTS (PER SERVING): 

Calories 420

Protein  37 g

Total fat  18 g

Saturated fat  4.5 g

Cholesterol  135 mg

Carbs 28 g

Fiber 7 g

Total sugars 3 g

Added sugars 0 g

Sodium 320 mg

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- Karen Ansel, MS, RDN
The Heart-Healthy Carb that You Need Now

Preventing heart disease means more than avoiding saturated fat, cholesterol, and sodium. It’s also about what you should be eating more of. Take fiber, for instance. “Fiber should be the first thing you think of getting more of for heart health,” says Libby Mills, MS, RD, a nutritionist in Philadelphia, PA. 

Getting the fiber you need is easier—and tastier—than you might think. Here’s how to effortlessly up your fiber game and give your heart some love in the process.

Aim high. Think of fiber as a nutritional jack of all trades. On top of keeping your heart happy, this multitasking nutrient helps control blood sugar, keeps your digestive system regular, and fills you up, so you automatically eat less. That’s good news. The National Academy of Sciences recommends women consume foods that provide about an ounce  (25 grams) of fiber daily and a third more for men (38 grams)

Why? Fiber is naturally present in whole, minimally processed foods such as fruits, vegetables, beans, and whole grains. When we eat more ultra-processed foods, like chicken nuggets, deli meats, salty snacks, cake, and cookies, we risk not consuming enough fiber.

Mix things up. Fiber may sound like it’s one thing. In reality, there are several types of fiber and they each appear to support heart health in distinct ways. So, it’s key to eat lots of different foods to get a variety of types of fiber.

For the most part, fiber falls into two general camps: soluble and insoluble. Soluble fiber, which is commonly found in foods like beans, oats, barley, apples, and pears, works like a sponge, helping to trap blood cholesterol-raising fats like saturated and Trans fats before they can enter your body. 

And that neat trick is just the beginning. “By slowing down digestion, soluble fiber can also prevent spikes in blood sugar which can elevate triglycerides and cause damage to blood vessels,” adds Mills.

Insoluble fiber is no slouch either. Found in foods like bran cereal and whole-wheat bread and pasta, insoluble fiber’s claim to fame is keeping your digestive system regular. But it can also do good things for your ticker. Because insoluble fiber provides a natural way to feel full after eating, it may also help you eat less, which in the long term can support maintaining a healthy body weight, and in turn translates to less strain on your heart. In fact, research has found that for every 7 daily grams of insoluble fiber a person eats, their risk of developing heart disease drops by 18 percent.   

Get the best of both worlds. If you can’t be bothered to keep tabs on whether you’re eating enough soluble and insoluble fiber, we hear you! Luckily there’s one quick, convenient type of fiber that delivers both kinds, namely whole grain fiber. If you’ve never heard of whole grain fiber, it’s essentially the bran of the grain kernel, and it is rich in—you guessed it—whole grain cereals. 

And according to a new study, eating an additional 5 grams of whole grain fiber to your daily diet may trim your risk of developing heart disease by 10 percent. The American Heart Association recommends making half your grains whole grains. 

Of course, you can always wolf down a big bowl of whole-grain cereal. But why stop there? If you’d like to harness the power of this helpful whole grain fiber, try these tips.

Blend ¼ cup quinoa flakes into your favorite smoothie.

For a crunchy spin on French toast, dust a slice of egg-dipped whole wheat bread with crushed, unsweetened whole grain cereal before cooking and serve with fresh fruit.

Roll energy balls in puffed brown rice.

Toss whole-grain cereal squares with tart-dried cherries and almonds for a sweet and savory snack mix.

Whip up a batch of Super Seed Granola.

Swap in crushed toasted oat cereal for breadcrumbs next time you make chicken nuggets or fish fingers.

Remember to add more fiber-rich foods to your diet, along with balancing your calories to maintain a healthy body weight, limiting saturated and Trans fats, and reducing excess salt and sodium. 

The post The Heart-Healthy Carb that You Need Now appeared first on Fitbit Blog.

- Kelsey Maloney
6 Tips to Help You Bounce Back Quickly After Being Sick

There’s never a convenient time to get sick. Between work, exercise, plans with friends or family, and just general life responsibilities, it can be hard to give up a few days to take the time you need to get better from a cold, mild virus, or stomach bug. An illness can last a few days or a few weeks and similarly it can take the same amount of time to get back to feeling fully yourself.

But once you’ve recovered from your illness and feel ready to get back into the regular rhythm of your life, it’s likely a good idea to be careful about picking up right where you left off. Jumping back in too hastily can often mean you end up stringing along a sickness or it may even land you right back in bed again. The most important thing is to listen to your body while you’re still recuperating.

Keep reading for tips that will help you ease back into your daily routine and restore health.

6 tips on how to bounce back

Ease back into exercise. Exercise requires energy, which you may not have a lot of since you started your recovery. Don’t push yourself too hard on your first few days back in your workout routine. Start slow, work yourself back up to where you were before, and remember to always stretch as your muscles may be stiff and achy at first.

Eat restorative foods. When you’re sick, your body needs all the nutrients it can get to get you back on your feet but it’s important to eat intentionally.  “Appetite sometimes wavers when you’re feeling lousy, but becoming underweight can put you at greater risk of infection,” says registered sports nutritionist Rob Hobson. “The body needs more energy to fight infection, so focus on small nourishing meals to help coax back your appetite. This might include soups, stews, eggs on toast, or smoothies. “

Hobson also says that vitamins and minerals such as vitamins A, B, and iron, all play a part in the recovery process as they are all involved in the normal functioning of the immune system. He recommends focusing on foods like green leafy vegetables, orange-colored fruits and vegetables, whole grains, meat, fish, and eggs.

Stay hydrated. It’s essential to stay hydrated while sick, but just as important during recovery as well. Drinking lots of water can help fend off headaches, nausea, or fatigue—and flush out any toxins leftover from an illness.

“Hydration is vital when you’re trying to fight infection and recover from illness,” says Hobson. “It’s essential to drink plenty of fluids to help organs like your kidneys to function well. Hydration also helps to loosen mucus and relieve congestion if these are symptoms of your infection.”

If you’re not a fan of regular water, try adding lemon or mint to add some flavor. You can also try water packets with electrolyte solution (no sugar) that usually have a selection of tasty flavors.

Make a to-do list. After taking time off to recoup, some might come back to their lives with an overwhelming amount of things to do. We recommend making a to-do list to help ease yourself back into daily life and work tasks, and to help organize yourself and prioritize your time. This way, you can avoid overexertion.

Get fresh air. Escape the stuffy room you were sick in and go outdoors to absorb some sunshine and fresh air. 

Good sleep hygiene. Even if you’re feeling better, that doesn’t mean your body is fully healed from being ill. Getting enough quality sleep is a key factor in getting back to being your healthiest self. You can manage your sleep hygiene by maintaining a regular sleep schedule, taking naps if needed, avoiding caffeine before bed, and limiting your screen time.

The post 6 Tips to Help You Bounce Back Quickly After Being Sick appeared first on Fitbit Blog.

- Michael Zourdos
Everything There is to Know About High-Load  versus Low-Load Training

Note: This article was originally published as the MASS Research Review cover story for October 2022 and is a review of two recent papers by Anderson et al and Dinyer et al. If you want more content like this, subscribe to MASS.

Key Points Researchers split untrained women into two groups. Both groups performed leg extensions, seated shoulder presses, leg curls, and lat pulldowns for eight weeks. One group performed three sets to failure at 30% of 1RM (low-load group), and the other performed three sets to failure at 80% of 1RM (high-load group). The researchers assessed 1RM strength and body composition pre-, mid-, and post-study. Effort-based rating of perceived exertion (RPE) was assessed after each set and each session (sRPE). The affective response was assessed via the feeling scale after each session, as was the intention to complete the same exercise within the next week or month.Both groups tended to improve strength but not body composition, and there were no group differences for either measure. Further, there were no group differences for either RPE measure or the affective response at any time point. Additionally, feeling scale ratings were positively related to intention to train.All measures were unaffected by the load. Importantly, since lifters similarly enjoyed high- and low-load training, this might mean that lifters would not have an issue adhering to low-load failure training over the long-term. However, the findings from this study were not in lockstep with the literature. Overall, it seems that higher loads are preferable for strength, but hypertrophy occurs mostly independent of loading.

Five years ago, an often-cited meta-analysis from Schoenfeld et al (3) found that strength gains were augmented with high- (>60% of one-repetition maximum (1RM)) versus low- (≤60% of 1RM) load training, but muscle growth was similar between training loads. Since then, more data (4, 5) seem to confirm those findings, suggesting that lifters can choose their preferred loading paradigm (high or low loads) and maximize hypertrophy. However, lifters must consider how other factors, such as fatigue, may influence long-term adherence with low loads. Ribeiro et al. (6) found that trained men reported high session rating of perceived exertion (RPE) scores and greater ratings of “displeasure” after low-load (25-30RM) than after high-load (8-12RM) training to failure. This study prompted me to write an article titled, “Most People Find Low-Load Training to Failure Miserable.” In this article, I questioned the utility of using low loads as a sole method of long-term training, since lifters seem to enjoy it less, which may lead to decreased adherence. However, in that article’s “Next Steps,” I called for a longitudinal study to assess acute perceptual responses to see if enjoyment increases over time with low-load training. This article reviews two papers from Anderson et al (1) and Dinyer et al (2), which were part of the same study that attempted to tackle my previous proposal. 

The reviewed study (12) split 23 untrained women into high- (n = 12, 80% of 1RM) and low- (n = 11, 30% of 1RM) load training groups for eight weeks. Both groups performed 2-3 sets of machine-based exercises (leg extensions, seated shoulder presses, leg curls, and lat pulldowns) to failure twice per week. The researchers assessed body composition and 1RM strength on each exercise before and after the training program. Effort-based RPE was assessed after each set, and sRPE was assessed immediately after each session. Feeling scale ratings (-5 – very bad to +5 very good) and intention to exercise within the next week and next month (0% – no intention to 100% – strong intention) were assessed immediately, 15 minutes, and 60 minutes after each training session. Findings showed no significant differences between groups for strength gains, body composition changes, set RPE, sRPE, feeling scale ratings, or intention to exercise at any time point. Set RPE and sRPE scores tended to increase over time across both groups. Feeling scale ratings were positively related to intention to exercise at various points throughout the study (i.e., more pleasure related to greater intention to exercise again). These findings suggest that untrained lifters can gain strength to a similar degree with both high- and low-load training. Further, these individuals had similar perceptual and affective responses to training, suggesting that long-term training adherence may not be compromised with low-load training. However, we should consider that some individuals may prefer one type of training, and that some exercises may be more tolerable with long-term low-load failure training. Moreover, there is a large body of literature comparing high and low-load training in recent years; thus, this article is also a good opportunity to thoroughly examine the topic as a whole. Therefore, this article will:

Thoroughly review the existing literature on high- and low-load training for strength and hypertrophy outcomes.Determine if the presently reviewed study’s findings are in agreement with the previous literature.Examine the research related to the perceptual and affective response to different loading paradigms and determine how these findings may influence long-term adherence.Provide practical examples of implementing both high- and low-load training into a program. Purpose and Hypotheses Purpose

As an overarching note, this review covers two different published papers (12) that came from data collected during a single study. The researchers published the strength and body composition data in one paper (2) and the perceptual and affective responses in another (1). Therefore, going forward in this article, I will refer to both papers together as “the study” or “the presently reviewed study.” 

The presently reviewed study compared long-term strength and body composition outcomes between high load (80% of 1RM) and low load (30% of 1RM) in untrained women. It also compared the perceptual and affective responses and intention to exercise between the training protocols. 

Hypotheses  

The researchers hypothesized the following:

Strength gains would not be significantly different between groups.Body composition would improve to a similar degree in both groups.There would be no significant differences between groups for perceptual or affective responses. There would be a positive relationship between affective responses and intention to exercise.  Subjects and Methods Subjects

23 untrained women between the ages of 18-27 completed the study. Additional subject details are presented in Table 1.

Graphics by Kat Whitfield Study Protocol

This study was a parallel-groups design in which the researchers split 23 untrained women into high-load and low-load training groups for 12 weeks. Subjects completed 2-3 sets to failure twice per week during weeks 2-4 and 6-11, while weeks 1, 5, and 12 served as pre-, mid-, and post-study testing. Subjects trained four machine-based exercises (leg extension, seated shoulder press, leg curl, and lat pulldown) during each session. The high-load group used 80% of 1RM, while the low-load group used 30% of 1RM, with the load in each group being adjusted in weeks 6-11 based on the mid-study 1RM testing.

Outcome Measures

Longitudinal outcome measures included 1RM strength, bone- and fat-free mass, and body fat percentage. The researchers also compared volume load and time under tension. Further, the researchers assessed set RPE, sRPE, feeling scale ratings (affective response), and intention to perform the same training session within the next week or the next month. Further description and the time points when each outcome measure was assessed can be seen in Table 2.

Graphics by Kat Whitfield Findings

The only significant differences between groups were for volume load and time under tension. There were no significant group differences for strength gains, body composition changes, set RPE or sRPE, the affective response, or intention to exercise. 

Volume, Time Under Tension, Body Composition, and Strength

The low-load group performed significantly more volume and had a greater time under tension when the researchers averaged all training sessions together (p < 0.05). In addition, strength increased significantly from pre- to mid-study and from mid- to post-study in both groups (p < 0.05), but with no significant differences between groups. Neither group significantly improved either metric of body composition (p > 0.05). However, subjects in the low-load group tended to increase bone- and fat-free mass (+1.1 kg) more than subjects in the high-load group (+0.1 kg) The findings for 1RM strength can be seen in Figure 1.

Graphics by Kat Whitfield Set and Session RPE

There were no significant group differences for set RPE or sRPE. However, both RPE metrics tended to increase over time. For example, when both groups were combined, set RPEs were significantly greater during sessions 1 and 2 of weeks 4 and 8 compared to the corresponding sessions in week 1 (Table 3). 

Graphics by Kat Whitfield

Similar to set RPE, there were also no between-group differences for sRPE, but when both groups were combined and both sessions per week were averaged, sRPE did tend to increase over time. The significant differences are in Table 4.

Graphics by Kat Whitfield Affective Response

There were no significant group differences for scores on the feeling scale. However, feeling scale scores tended to be lower (less pleasurable) immediately following training than at 15 and 60 minutes post-training (Table 5).

Graphics by Kat Whitfield Intention to Exercise

There were no significant group differences for intention to exercise at any time point. Further, when researchers combined all subjects and averaged the responses at all time points throughout the study, subjects had an intention of 81 ± 4% and 68 ± 5% to participate in resistance training to failure in the next month and week, respectively.

Feeling scale ratings were also positively related to intention to exercise when both groups were combined at various points throughout the study (i.e., more pleasure related to greater intention to exercise again). Specifically, feeling scale scores immediately post-training in week 1 were significantly related to intent to exercise within the next month (r = 0.416, p = 0.049) and feeling scale scores 15 minutes post-training during week 4 were significantly related to intent to exercise within the next week (r = 0.497, p = 0.016) and next month (r = 0.485, p = 0.019). Finally, feeling scale scores at all time points (immediately, 15 minutes, and 60 minutes post-training) were significantly related to the intention to exercise within the next week and next month. The relationships between feeling scale scores and intention to exercise in week eight can be seen in Figure 2AB.

Graphics by Kat Whitfield Interpretation

The previous section presented findings from two papers, Anderson et al (1) and Dinyer et al (2), which reported different outcomes from the same study. Overall, Anderson et al (1) found that untrained women reported low-load, high rep training to cause similar fatigue to high-load, moderate rep training. Further, the researchers reported that the women had a similar intent to train within the next week or month, regardless of which protocol they performed. Additionally, Dinyer et al found that strength gains and body composition changes were not significantly different between high- and low-load training. Together these findings suggest that lifters can use high or low loads for strength and potentially hypertrophy based upon preference. Further, similar sRPE and affective responses indicate that adherence to both loading zones might be similar over time. Previously, Ribeiro et al (6 – MASS Review) found that men reported higher sRPE following low-load versus high-load training; thus, the lack of group differences in this study for the perceptual and affective responses are intriguing.

Aside from the presently reviewed study, there is a relatively large body of literature comparing low- versus high-load training over the past decade, especially within the past 5-6 years. So, before getting back into both facets (performance and perceptual/affective) of the presently reviewed study, let’s take a deep dive into the totality of the high versus low-load literature. Therefore, this interpretation is split into four parts:

I will thoroughly review how muscle hypertrophy, strength, and endurance are affected by high and low load training, including how moderating factors (sex, proximity to failure, and upper or lower body) influence the responses.I will review the present study’s performance findings to determine how they fit with the total body of literature.I’ll review the previous data on the perceptual and affective response to high- and low-load training, followed by a discussion of how the reviewed study fits with the literature.I’ll provide practical examples of how to incorporate high- and low-load training into your training.

It’s going to be a long Interpretation, so let’s get started.

Main Findings to Date on Strength and Hypertrophy

In the last six years, there have been five meta-analyses and a systematic review evaluating high- versus low-load training. Specifically, four of the meta-analyses examined both strength and hypertrophy outcomes (3478), one meta-analysis examined only hypertrophy, including fiber type-specific outcomes (9), while the systematic review discussed both strength and hypertrophy (5). Additionally, three (101112) narrative reviews have covered high versus low-load training within the last five years. Before continuing, I would like to provide a working definition of high- versus low-load training; however, that’s difficult as meta-analyses used different criteria to categorize low- and high-load training. Further, some looked at training load as a continuum from 30-90% of 1RM (5), while others categorized training load as low, moderate, or high (48). Therefore, even though the categorization can be much more specific, for simplicity, I’ll generally refer to high- and low-load training as training at >60% of 1RM and ≤60% of 1RM (3) unless otherwise stated. However, no matter the categorization, the consensus in this literature is quite clear: muscle hypertrophy can be maximized independently of training load, while higher loads are needed to maximize strength gains. Table 6 summarizes all six meta-analyses/systematic reviews and distinguishes how each paper categorized high-, low-, and in some cases, moderate-load training.

Graphics by Kat Whitfield

The overarching theme of the meta-analyses is that strength gains are greater with moderate- and high-load training, and hypertrophy does not appear to be significantly affected by training load. Although it’s well-known that higher loads generally lead to greater strength outcomes, it’s worth noting that the first meta-analysis on the topic (7) did not quite find significance (p = 0.09) for strength. However, that meta-analysis included only 10 studies, and all were on untrained individuals. Future meta-analyses that analyzed strength had more total studies and included both trained and untrained subjects. Therefore, it seems that the lack of significant difference (although close) for high loads to lead to greater strength gains is due to the literature being underdeveloped at the time of the first meta-analysis. In other words, a meta-analysis is only as useful as the studies it includes, and when the first one was conducted there wasn’t nearly as much data to analyze.

On balance, the meta-analyses reveal that hypertrophy seems to be unaffected by training load. I agree with that position, and it’s the opinion I primarily espouse. However, there’s enough ambiguity in the literature that I think the hypertrophy findings warrant a closer look. For example, the Grgic 2020 (9) meta-analysis found a small effect size (0.30) and p-value that was close to significance (0.089), favoring high-load training for hypertrophy of type II muscle fibers. However, Grgic’s model only included five studies (1314151617), and only one of them (17) found a significant difference (p = 0.039) between groups for type II fiber hypertrophy. Further, although Lopez et al (4) reported no significant difference between high, moderate, and low loads for hypertrophy the authors did state the following, “the results of the consistency model indicate that moderate-load (84.5%) and high-load resistance training (75.8%) are the best load for muscle hypertrophy in overall and high-quality subgroup analyses, respectively.” In other words, when comparing high- versus low-load (19 total comparisons) and moderate- versus low-load (7 comparisons) it seemed that high and moderate loads were, on average, more likely to lead to larger increases in muscle growth than low-load training. A forest plot from Lopez’s meta-analysis comparing high- and moderate-load training versus low-load training for hypertrophy can be seen in Figure 3.

Graphics by Kat Whitfield

To be clear, the plot from Lopez is not convincing that high-load training leads to greater muscle growth. However, Lopez’s (4) note that higher loads tended to be better on average along with the Grgic meta (9) (albeit only five studies) suggests that the data might lean ever so slightly in the high load direction, but not to a degree which we can be confident. 

In reality, there’s evidence on both sides for hypertrophy. Specifically, Schuenke et al (17) found a 25.6% and 23.4% greater increase in type I and type IIa fiber cross-sectional area, respectively, among untrained women training with 6-10RM loads than those training with 25-30RM loads. However, Franco et al (18 – MASS Review) found that untrained women gained more fat-free leg mass with 25-30 reps per set (+4.6%) than with 8-10 reps per set (+1.5%). Lastly, Stefanski et al (19 – MASS Review) found a similar increase in biceps muscle thickness among untrained women completing sets with either 80% or 30% of 1RM training for six weeks. Moreover, narrative reviews from Grgic and Schoenfeld (10) and Fisher et al (12) suggest that load does not seem to affect muscle hypertrophy, especially when volume is equated between training protocols. Finally, the Carvalho meta-analysis (8), only included volume-equated studies and showed only high p values for all of its hypertrophy comparisons (p values = 0.559 – 0.938). Therefore, even though the Lopez et al (4) meta-analysis suggests the possibility that high-load training may provide additional hypertrophic benefits, I think the most appropriate interpretation is that, on the group level, muscle growth is maximized independently of training load.

Moderating Factors

A few of the meta-analyses (48) examined if factors such as training sex, training status, training upper or lower body training, and proximity to failure moderated the findings. For example, Lopez et al (4) reported that untrained individuals tended to experience more hypertrophy (p = 0.033) than trained individuals with low-load training compared to high-load training. Further, Lopez found that men tended to gain more strength than women with high-load training, but women tended to gain more strength than men with moderate-load training. In their sub-analyses, Carvalho et al found that when training not to failure, strength gains (p = 0.049) and hypertrophy (p = 0.002) were greater with high-load than with moderate-load training. In other words, when training with high loads, it seems to be preferable to train shy of failure. Notably, the Carvalho meta-analysis only included one study (20) which directly compared failure versus non-failure training; thus, Carvalho did not meta-analyze if training to failure was necessary with low loads.

Low Load Training To Failure

Despite the Carvalho meta-analysis (8) not comparing failure versus non-failure training, a few studies provide insight into the necessity of failure training with low loads to maximize hypertrophy. If you’re familiar with my content on training to failure, you may be rolling your eyes at this point and thinking, “here comes Zourdos again telling us to train 149 reps shy of failure.” If that’s you, then you can breathe a sigh of relief as I’m not here to do that this time. 

There are four studies providing insight into low-load non-failure training, and they include Lasevicius et al 2022 (20 – MASS Review), Terada et al 2022 (21 – MASS Review), Ikezoe et al 2020 (22), and Kapsis et al 2022 (23 – MASS Review). Lasevicius used a within-subjects design (one leg performed each condition) and had subjects perform leg extensions not to failure or to failure at 30% of 1RM for eight weeks. The researchers reported that quadriceps cross-sectional area increased by +7.7%% in the failure condition but only by 2.6% in the non-failure condition. Terada et al (21) compared pec and triceps hypertrophy over eight weeks in untrained men bench pressing at 80% of 1RM (8 reps per set), training to failure at 40% of 1RM, or benching to a 20% velocity loss threshold at 40% of 1RM. The difference between groups wasn’t significant, but the 80% (+4.4mm; +14.5%) and 40% to failure (+4.9mm; +16.8%) groups tended to increase triceps muscle thickness more than the 40% not to failure group (+2.4mm; +8.1%). Therefore, based upon the findings of Lasevicius et al and Terada et al there seems to be an added benefit to low-load training when it’s performed to failure.

The Ikezoe et al (22) and Kapsis et al (23) studies did not compare low-load non-failure training to low-load failure training; rather, they compared low- and high-load non-failure training. Ikezoe et al (22) found no significant differences in quadriceps muscle thickness in healthy men after leg extension training of either 12 (sets) × 8 (reps) at 30% of 1RM versus 3 × 8 at 80% of 1RM for eight weeks. While Ikezoe did not report significant group differences, it should be noted that the low-load group performed nine more sets than the high-load group. Of course, it cannot be known if hypertrophy would have been the same if sets were equated. However, it’s possible that if training far from failure with low loads then additional volume is needed for muscle growth to be similar to high-load training. Lastly, Kapsis et al (23) had both women and men perform circuit training for 12 weeks. Each session consisted of four rounds of five exercises. Each round consisted of one set performed for 30 seconds at either 30% or 70% of 1RM. The researchers reported that increases in lean body mass were not significantly different between groups (30%: +1.11 kg; 70%: +1.25kg). Overall, the current body of evidence may lean toward performing low-load training to failure, or at least closer to failure, to maximize hypertrophic benefits. However, it’s premature to make definitive conclusions on the necessity of failure training or how close to failure one needs to train with low loads. The lack of ability to draw conclusions is partly because the majority of non-failure low load studies have had subjects train really far (>7 RIR) from failure; thus, we cannot know if failure training would be necessary compared to a more moderate number of RIR (i.e., 2-5 RIR). 

Muscular Endurance

There isn’t a ton of data comparing high and low loads for muscular endurance; however, a new study from Fliss et al (24) was published just two days after the commencement of this article. Therefore, to be comprehensive, I wanted to briefly touch on the Fliss study. Fliss et al had untrained women perform unilateral dumbbell biceps preacher curl training and unilateral leg extensions for 10 weeks. This study was a within-subjects design, so the women performed training with one arm and one leg at a load corresponding to 80% of 1RM (6-12 reps per set) while the other side of the body performed sets with a load corresponding to 30% of 1RM (20-30 reps). The researchers assessed both absolute and relative muscular endurance. Relative muscular endurance tests the number of reps performed with a percentage of 1RM that corresponds to that specific day’s max. For example, if someone wants to test reps performed with 60% of 1RM and on that specific day their squat max is 100kg, then they would use 60kg to test muscular endurance. However, if their squat max is 105kg a week later, they would need to use 63kg to test their relative muscular endurance. To test absolute muscular endurance, this individual would always use their starting load, which was 60kg. Fliss had the women perform relative and absolute muscular endurance tests at pre- and post-study with both heavy (80% and 90% of 1RM) and light (30% and 60% of 1RM) loads. The main findings were that changes in leg extension muscular endurance tended to be specific to the training protocol. In other words, on average, the leg training at 80% of 1RM increased heavy-load absolute muscular endurance significantly more than the 30% leg; however, the 30% leg tended to improve absolute muscular endurance more with light-loads. One other note about low-load training and muscular endurance is that Terada et al (21) found that absolute muscular endurance at 40% of 1RM improved to a similar degree following bench press training to failure at 40% of 1RM and bench press training to a 20% velocity loss at 40% of 1RM. Therefore, it seems that low-load training to failure or not to failure is effective at producing improvements in muscular endurance at low loads. However, similar to low loads being inferior to high loads for strength gains, low loads also seem inadequate for increasing absolute muscular endurance at heavy loads, which, in part, demonstrates the principle of specificity. For more on the principle of specificity as it relates to muscular endurance, please see Greg’s research briefs from this month.

Where Does The Presently Reviewed Study Fit?

So now that we have thoroughly reviewed the literature on performance outcomes with high- and low-load training, does the presently reviewed study (1, 2) agree with the consensus positions? As a reminder, the reviewed study was a non-volume-equated study comparing high- (n = 12, 80% of 1RM) and low- (n = 11, 30% of 1RM) load training groups for eight weeks in untrained women. Both groups performed 2-3 sets of machine-based exercises (leg extensions, seated shoulder presses, leg curls, and lat pulldowns) to failure twice per week. Strength, on all exercises, was tested at pre-, mid-, and post-study as were fat-free mass and body-fat percentage. Both groups increased strength, but strength gains were not significantly different between groups. Further, neither group improved measures of body composition. These findings suggest that low loads are just as effective as high loads for increasing strength in untrained women and that muscle growth (although assessed indirectly) did not occur in either group. 

First, the strength findings conflict with the total body of literature. Of the six meta-analyses and systematic reviews, only the very first one (7) did not find strength gains to be significantly greater with high-load training, but it was close (p = 0.09). That meta-analysis was officially published in 2016 but was published online in 2014, and the study’s search procedures ceased in 2013. In other words, it came out too early to include some data (25), which has shown strength to be increased more with high load training among untrained women. It’s possible that there were no group differences for strength gains because the subjects were untrained; thus, they could progress with low-loading.

In the presently reviewed study, there was no significant main time effect (change over time collapsed across groups) for bone- and fat-free mass, but it was close (p = 0.079). Further, fat-free mass increased by 1.1 kg in the low-load group but only by 0.1 kg in the high-load group. A 1 kg difference between groups certainly could be meaningful; however, it’s hard to read too much into it since the totality of literature suggests that muscle growth is not different between high- and low-load training.

Overall, I’m not sure that performance findings from the presently reviewed study add much to the literature, and I’m comfortable siding with the consensus of the meta-analyses that high and low loads lead to similar hypertrophy. However, high loads are needed to maximize strength gains.

Main Findings on Perceptual and Affective Responses

Despite the plethora of research examining long-term strength and hypertrophy outcomes following high- and low-load training, there are far fewer studies comparing these training paradigms for perceptual and affective responses. Therefore, before diving into the existing literature on these topics, let’s briefly explain the perceptual and affective responses and why they are necessary measures to assess.

The perceptual response to training is traditionally assessed via effort-based RPE, which I’ve written about effort-based RPE before. In brief, effort-based RPE is assessed either after every set or after the entire session (e.g., sRPE) when used in resistance training. The presently reviewed study (1) used the original Borg 6-20 scale (26); however, the Borg 0-10 scale (27) is also commonly used to assess sRPE. Both scales are anchored on the low end with a descriptor of “little to no effort” and on the high end with “maximal effort.” In general, if two protocols lead to similar hypertrophy and strength outcomes, but lifters deem that one of the protocols took less effort, then the implication is that lifters will recover more quickly from the lower effort protocol and possibly increase their long-term adherence to training.

Similar to the perceptual response, the affective response can also be assessed via a simple scale and has been suggested to have long-term adherence implications. In the presently reviewed study, the affective response was assessed via the -5 (very bad) to +5 (very good) feeling scale. Negative ratings on the scale are seen as “displeasure” while positive ratings are viewed as a “pleasurable” experience. More broadly, feeling scale responses may encompass a variety of feelings related to mood, emotion, and someone’s general psychological state (2829 – MASS Review). It seems intuitive that feeling scale ratings of greater pleasure (more positive) would be related to a greater intention to exercise, and they were in the presently reviewed study. However, Ekkekakis (28) indicated in a review paper that ratings of displeasure might indicate a feeling of accomplishment and pride; thus, we shouldn’t be so quick to classify negative feeling scale ratings as a sign that the lifter wouldn’t want to continue with the training program. 

When comparing sRPE between high- and low-load training, some research has shown low-load training to elicit a greater sRPE (63031), and some research has indicated high-load training to elicit a greater sRPE (32333435). For example, Pritchett et al (30) found that 20 recreationally trained men reported a significantly higher sRPE following three sets on six exercises to failure at 60% than at 90% of 1RM. In agreement with Prichett (30), Shimano et al (31) found that both trained and untrained individuals reported higher sRPE following one set to failure at 60% of 1RM on the squat, bench press, and curl compared to one set to failure at both 80% and 90% of 1RM. Further, Ribeiro et al (6 – MASS Review) found that trained men reported higher sRPE, greater discomfort (on a 0-10 Likert scale), and lower feeling scale ratings (more displeasure) when following three sets to failure with a 25-30RM load on the bench press, hack squat, and lat-pulldown versus three sets to failure with an 8-12RM load. Based upon the above, I previously questioned the utility of using solely (more later on mixing high and low loads) low-load training to maximize hypertrophy because I theorized adherence and long-term enjoyment might be lower.

Other research has found that higher loads lead to a greater perceptual response than lower loads; however, those findings are likely a product of the higher load condition training closer to failure. For example, in a crossover design, Gearhart (32) had trained men and women perform 1 × 5 at 90% of 1RM in one condition and 1 × 15 at 30% of 1RM in another condition on seven different exercises. Subjects reported RPE after each rep in the 90% condition and after every three reps in the 30% condition. When all RPE scores were averaged together on each exercise, the RPE was significantly higher in the 90% condition. However, 90% of 1RM for five reps is far closer to failure or at failure (or past failure on some exercises), while 15 reps at 30% might have left some individuals with roughly 15 repetitions in reserve (RIR). Other studies have also found higher RPE following high- versus low-load training (333435), but all have had subjects train closer to failure in the high-load condition. 

Overall, load lifted may play some role in the acute perceptual and affective response, but at both high and low loads per set effort, independent of load, may be the determining factor. For instance, the previously discussed Lasevicius et al (20 – MASS Review) study had some subjects perform unilateral leg extensions to failure at 80% of 1RM on one leg and perform leg extensions shy of failure at 80% on the other leg. Another group of subjects performed unilateral leg extensions to failure and non-failure at 30% of 1RM. Importantly, in each group, the researchers had the non-failure leg perform more sets to equate volume load with the failure leg. The researchers assessed sRPE 30 minutes after each session. The subjects reported significantly higher sRPEs in both failure conditions with no difference between high-load failure and low-load failure conditions. These findings from Lasevicius can be seen in Figure 4, which is from a previous article written by Greg.

Graphics by Kat Whitfield Where Does The Presently Reviewed Study Fit?

Findings from the presently reviewed study (12) are, in part, at odds with the current consensus. First, the researchers found that both set and sRPE were not significantly different between high- and low-load training. This lack of difference is despite both groups training to failure and the low-load group performing significantly more volume load and spending more time under tension. As previously noted, Ribeiro et al (6) found that sRPE was significantly higher with low load than with high-load training when lifers performed both protocols to failure and sets were equated. Further, Ribeiro reported that subjects had feeling scale scores of displeasure after low-load failure training and scores of pleasure after high-load training. Yet, the presently reviewed study found similar scores of pleasure (Table 5) after both protocols.

The other findings from Dinyer et al (2) were that sRPE values tended to increase over the study while feeling scale ratings tended to decrease. Although the researchers did not statistically analyze it, Figure 4 from Lasevicius et al (20) shows that sRPE values did not seem to change, on average, from the beginning to the end of the study. Speculatively, the increase in sRPE in Dinyer could be due to accumulated fatigue since the subjects were untrained, while Lasevicius’ subjects were trained, but we cannot be sure. However, the decline in feeling scale ratings over time (Table 5) along with the increase in sRPE (Table 4), makes sense. In other words, the women tended to express lower ratings of pleasure when they perceived more effort. 

Perhaps the most critical finding of the presently reviewed study is that feeling scale scores at all time points (immediately, 15 minutes, and 60 minutes post-training) were positively related to the intention to exercise within the next week and month (Figure 2AB). My previous hesitation in recommending low loads over the long term was due to a potential lack of adherence; however, the presently reviewed study suggests that my position may have been unfounded. Interestingly, previous research has not always found feeling scale scores to be predictive of intent to exercise in the future. Specifically, Focht et al (36) observed trained women to record higher (more pleasurable) feeling scale scores following training at 40% of 1RM than at 70% of 1RM. However, despite lower feeling scale scores, subjects had a greater intention to exercise following the 70% of 1RM condition in the future. Importantly, when intent to exercise is assessed, researchers ask how likely someone is to perform the same exercise session again within the next week or month. So, even though subjects did not find the moderate-load 70% of 1RM training as pleasurable as the low-load 40% training, they indicated a greater likelihood to repeat the training. One explanation is that feeling scale scores pick up on various factors related to mood, emotion, physical fatigue, and a sense of accomplishment; thus, subjects may have been more fatigued after the 70% condition. However, that fatigue did not deter them from wanting to repeat the session. Additionally, the subjects in Focht’s study were trained; thus, it’s possible they knew that higher loads were preferable for strength gains; thus, their greater desire to continue performing the 70% training was partly based upon wanting to maximize improvement.

Ultimately, the presently reviewed findings are not in lockstep with previous literature; however, there isn’t much data on the long-term affective response to high- and low-load training. Therefore, I am not yet wholly convinced that using solely low loads over the long-term is a viable strategy, especially if low loads need to be performed to failure (or at least closer to failure than when using high loads) to maximize muscle growth, which is still open for debate. Importantly, and as with most training concepts, training with high or low loads is not an all or none principle; instead, it can be intertwined into the same training program.

Practical Implementation

If one thing is clear from this article, both high- (and moderate-) and low-load training have merit. Sure, if you’re interested in maximizing your squat or bench press strength, you must train heavy at some point. However, even someone interested in top-end strength could still use low load training for hypertrophy, especially on assistance work. Further, if you’re a physique athlete or just interested in generally growing muscle, then either low or high loads should work just fine.

As noted earlier, a lifter doesn’t have to make a binary choice between low or high loads. I think we too often think training decisions are a binary choice. For example, research has debated if it’s better to prescribe load with RIR or velocity; however, as I’ve pointed out before, those concepts can be intertwined, and the specific situation might dictate which autoregulation strategy is used. Further, suppose one training strategy does tend to work better than another. In that case, we often become antagonistic toward the inferior approach, but it’s important to remember that it might work to some degree. Besides, research mostly looks at mean data, and in most studies, at least a few individuals respond better to the “inferior” protocol. In the present context, high-load training leads to better strength gains than low-load training, but in research low-load training groups still get stronger. In fact, many of the strength tests in research are 1RM strength, and low loads are not specific to 1RM testing. For example, in the Fliss study (24), absolute muscular endurance (reps performed) improved more with low-load training than with high-load training. In other words, adaptations tend to be specific to the training protocol. Besides, if someone is just generally training to gain muscle, then 1RM strength is not of great importance to the person. Therefore, just because some are using low loads does not mean they won’t gain any strength.

The specific training phase or exercise may also dictate whether high- or low-load training is used. For example, if a powerlifter is in an intensity block close to a meet, the lifter likely wouldn’t use low-load training since it’s too unspecific to their current goal. However, if a powerlifter is in a volume block six months out from a meet, they might include some low-load training to accumulate volume. Specifically, a powerlifter may utilize low loads on assistance movements like curls, triceps extensions, or rows while training in a more traditional 6-15  hypertrophy rep range on the competition lifts. Another outside-of-the-box example would be for a powerlifter to work up to a heavy squat or bench press single (e.g., 1 rep at 1 RIR) a couple of times per week and then back off to 40% of 1RM for their volume work. The bottom line is that there are many ways to intertwine the different loading schemes.

Ultimately, suppose someone is training for general purposes (e.g., hypertrophy, body composition, general health, and fitness), their training should meet the main tenets of an appropriate program. In that case, the details (i.e., high or low loads, periodization type, programming strategies) can be filled with what they enjoy and will sustain. For example, if an individual likes the exhaustive feeling of performing low loads to failure but is worried that it will become too much over time, they should include various loading schemes. For others who have a specific goal (i.e., powerlifting, physique, etc.), the programming details will need to be filled with a strategy that will best prepare the lifer for that goal; however, this often includes various loading paradigms. Besides, even if you’re a powerlifter, performing sets of 20 reps on curls is still fun.

To finish up this monster of an article are tables showing examples of how to intertwine high- and low-load training. 

Graphics by Kat Whitfield

Table 7 demonstrates using heavy singles on the main lifts and low loads on the back-off sets. You’ll notice that the squat and bench press frequency is twice per week and sessions on the same exercise are separated by 72 hours. I chose a frequency of twice per week as opposed to three times per week and to spread out the sessions in case there’s any lingering fatigue for a couple of days from the low-load training. Of course, lifters can perform more assistance work after the main lifts and on an off day, but this table is a simple example of isolating the concept of heavy singles followed by performing volume with low-load training.

Graphics by Kat Whitfield

Table 8 demonstrates how to integrate high, moderate, and low loads throughout a week. This table also shows that the main lifts (squats and deadlifts in this example) are trained with moderate to high loads, and some assistance work is programmed with low loads. When intertwining multiple training strategies, some nuanced details must be manipulated to make everything work, and that is no exception here. For example, on Wednesday, I did not include squats, as there might still be some general fatigue from the low-load, high rep failure training on Monday; thus, I utilized leg press as the main lift. Further, Wednesday is largely devoid of low-load training to account for lingering fatigue from Monday. There is low load non-failure training for one exercise on Wednesday (seated row). Even though non-failure low load training may be suboptimal for muscle growth, it is still useful,  and is an easy way to add some volume if fatigue lingers from Monday’s session. Friday’s heavy squats and deadlifts are placed as far as possible (96 hours) away from the low-load failure training to ensure the lifter is fresh. For example, walking lunges don’t specify that the 20 steps are to actual failure because that’s extraordinarily difficult on walking lunges. Further, low-load assistance movements have a 10 rep range spread (20-30 RM) because, in practice, it may be difficult to know your 20RM, 25RM, or 30RM load. Further, a lifter may end up getting more or fewer reps than predicted on some assistance movements since lifters typically don’t keep the movement pattern as strict on those movements (i.e., rows, curls, etc.) as they do on the main lifts. Therefore, just generally aiming for that rep range should be sufficient to perform low-load training to failure effectively. Lastly, Table 8 is just a conceptual example, and there are many ways to intertwine high- and low-load training. Additionally, someone could include many other exercises instead of those chosen.

Next Steps

The last time I covered low- versus high-load training, I called for a long-term study on high- versus low-load training that assessed the perceptual and affective response. Well, we got that study, but I’m still unfulfilled. I think the next step is replicating the presently reviewed study using trained individuals. In a dream world, I’d like to see two replications in trained individuals, one that uses a compound exercise like the squat and another that uses single-joint exercises only (e.g., biceps curls and triceps extensions). 

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Application and Takeaways Anderson et al (1) and Dinyer et al (2) found that long-term strength and body composition changes were not different between groups of untrained women performing low-load (30% of 1RM) and high-load (80% of 1RM) training to failure. Further, this study found that the perceptual and affective responses to high and low loads were not significantly different.  The totality of literature in this area suggests that high loads are needed to maximize strength, but muscle growth can be maximized independently of load, as long as load is ≥ 30% of 1RM.Overall, using high or low loads does not have to be a binary choice for coaches and lifters. All loading schemes can, and probably should, be intertwined. For example, a powerlifter could use high loads on the main lifts but low loads on assistance work to accumulate volume and facilitate hypertrophy.For general training purposes, if someone enjoys one style of training and can adhere to that style over the long-term, I would encourage them to train as they see fit. Training for general fitness or muscle growth allows for considerable flexibility in programming; thus, programming based upon preference is perfectly fine if the programming is sustainable. References  Anderson OK, Voskuil CC, Byrd MT, Garver MJ, Rickard AJ, Miller WM, Bergstrom HC, McNeely TK. Affective and Perceptual Responses During an 8-Week Resistance Training to Failure Intervention at Low vs. High Loads in Untrained Women. The Journal of Strength & Conditioning Research. 2022 May 9:10-519.Dinyer TK, Byrd MT, Garver MJ, Rickard AJ, Miller WM, Burns S, Clasey JL, Bergstrom HC. Low-load vs. high-load resistance training to failure on one repetition maximum strength and body composition in untrained women. The Journal of Strength & Conditioning Research. 2019 Jul 1;33(7):1737-44.Schoenfeld BJ, Grgic J, Ogborn D, Krieger JW. Strength and hypertrophy adaptations between low-vs. high-load resistance training: a systematic review and meta-analysis. The Journal of Strength & Conditioning Research. 2017 Dec 1;31(12):3508-23.Lopez P, Radaelli R, Taaffe DR, Newton RU, Galvão DA, Trajano GS, Teodoro JL, Kraemer WJ, Häkkinen K, Pinto RS. Resistance training load effects on muscle hypertrophy and strength gain: Systematic review and network meta-analysis. Medicine and Science in Sports and Exercise. 2021 Jun;53(6):1206.Lacio M, Vieira JG, Trybulski R, Campos Y, Santana D, Filho JE, Novaes J, Vianna J, Wilk M. 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European journal of applied physiology. 2012 Oct;112(10):3585-95.de Castro Franco CM, da Silva Carneiro MA, Alves LT, de Oliveira Júnior GN, de Sousa JD, Orsatti FL. Lower-load is more effective than higher-load resistance training in increasing muscle mass in young women. The Journal of Strength & Conditioning Research. 2019 Jul 1;33:S152-8.Stefanaki DG, Dzulkarnain A, Gray SR. Comparing the effects of low and high load resistance exercise to failure on adaptive responses to resistance exercise in young women. Journal of sports sciences. 2019 Jun 18;37(12):1375-80.Lasevicius T, Schoenfeld BJ, Silva-Batista C, Barros TD, Aihara AY, Brendon H, Longo AR, Tricoli V, Peres BD, Teixeira EL. Muscle failure promotes greater muscle hypertrophy in low-load but not in high-load resistance training. Journal of strength and conditioning research. 2022 Feb 12;36(2):346-51.Terada K, Kikuchi N, Burt D, Voisin S, Nakazato K. Low-load resistance training to volitional failure induces muscle hypertrophy similar to volume-matched, velocity fatigue. The journal of strength & conditioning research. 2022 Jun 1;36(6):1576-81.Ikezoe T, Kobayashi T, Nakamura M, Ichihashi N. Effects of Low-Load, Higher-Repetition vs. High-Load, Lower-Repetition Resistance Training Not Performed to Failure on Muscle Strength, Mass, and Echo Intensity in Healthy Young Men: A Time-Course Study. The Journal of Strength & Conditioning Research. 2020 Dec 1;34(12):3439-45.Kapsis DP, Tsoukos A, Psarraki MP, Douda HT, Smilios I, Bogdanis GC. Changes in Body Composition and Strength after 12 Weeks of High-Intensity Functional Training with Two Different Loads in Physically Active Men and Women: A Randomized Controlled Study. Sports. 2022 Jan 4;10(1):7.Fliss MD, Stevenson J, Mardan-Dezfouli S, Li DC, Mitchell CJ. Higher-and lower-load resistance exercise training induce load-specific local muscle endurance changes in young women: a randomised trial. Applied Physiology, Nutrition, and Metabolism. 2022 Aug 26(ja).Jessee MB, Buckner SL, Mouser JG, Mattocks KT, Dankel SJ, Abe T, Bell ZW, Bentley JP, Loenneke JP. Muscle adaptations to high-load training and very low-load training with and without blood flow restriction. Frontiers in physiology. 2018 Oct 16;9:1448.Borg G. Perceived exertion as an indicator of somatic stress. Scand j rehabil med. 1970;2:92-8.Borg GA. Psychophysical bases of perceived exertion. Med sci sports exerc. 1982 Jan 1;14(5):377-81.Ekkekakis P. Pleasure and displeasure from the body: Perspectives from exercise. Cognition and Emotion. 2003 Jan 1;17(2):213-39.Emanuel A, Smukas IR, Halperin I. How one feels during resistance exercises: A repetition-by-repetition analysis across exercises and loads. International Journal of Sports Physiology and Performance. 2020 Aug 10;16(1):135-44.Pritchett RC, Green JM, Wickwire PJ, Kovacs MS. Acute and session RPE responses during resistance training: Bouts to failure at 60% and 90% of 1RM. South African Journal of Sports Medicine. 2009;21(1).Shimano T, Kraemer WJ, Spiering BA, Volek JS, Hatfield DL, Silvestre R, Vingren JL, Fragala MS, Maresh CM, Fleck SJ, Newton RU. Relationship between the number of repetitions and selected percentages of one repetition maximum in free weight exercises in trained and untrained men. The Journal of Strength & Conditioning Research. 2006 Nov 1;20(4):819-23.Gearhart JR RE, Goss FL, Lagally KM, Jakicic JM, Gallagher J, Gallagher KI, Robertson RJ. Ratings of perceived exertion in active muscle during high-intensity and low-intensity resistance exercise. The Journal of Strength & Conditioning Research. 2002 Feb 1;16(1):87-91.Day ML, McGuigan MR, Brice G, Foster C. Monitoring exercise intensity during resistance training using the session RPE scale. The Journal of Strength & Conditioning Research. 2004 May 1;18(2):353-8.Diniz RC, Martins-Costa HC, Machado SC, Lima FV, Chagas MH. Repetition duration influences ratings of perceived exertion. Perceptual and Motor Skills. 2014 Feb;118(1):261-73E.Sweet TW, Foster C, McGuigan MR, Brice G. Quantitation of resistance training using the session rating of perceived exertion method. The journal of strength & conditioning research. 2004 Nov 1;18(4):796-802.Focht BC, Garver MJ, Cotter JA, Devor ST, Lucas AR, Fairman CM. Affective responses to acute resistance exercise performed at self-selected and imposed loads in trained women. The Journal of Strength & Conditioning Research. 2015 Nov 1;29(11):3067-74.

The post Everything There is to Know About High-Load versus Low-Load Training appeared first on Stronger by Science.

- Greg Nuckols
A Guide to Detraining: What to Expect, How to Mitigate Losses, and How to Get Back to Full Strength

Note: This article was the MASS Research Review cover story for September 2022. If you want more content like this, subscribe to MASS.

I assume that if you’re reading Stronger By Science, training is an important part of your life. However, most people either have to take some time off of training, or choose to take some time away from training, at some point. Even if you never miss a training session for any reason whatsoever, you’ll occasionally need to take time away from training a particular body part due to injury.

So, what should you reasonably expect when you stop training for a while? How long does it take to experience a noticeable decrease in strength and muscularity? What can you do to mitigate losses in strength and muscle mass? And how should you go about returning to training?

This article will attempt to answer all of these questions, and probably a few more.

Impacts of Training Cessation On Performance

To start things off, let’s first explore the impact of training cessation (not training for a period of time) on performance.

A 2013 meta-analysis by Bosquet and colleagues summarized this literature nicely (1). This meta-analysis is nearly a decade old, but it included 103 studies, making it one of the largest meta-analyses conducted in our field. Importantly, the impact of additional studies gets smaller and smaller as meta-analyses get larger. If a meta-analysis only includes five studies, then accounting for an additional three studies may have a pretty large impact on its effect estimates. However, if a meta-analysis has 100 studies, the addition of 10 new studies is unlikely to have a meaningful impact on its effect estimates. In other words, this meta-analysis isn’t hot off the presses, but it’s also far from being outdated.

The researchers began by finding all of the studies that met three inclusion criteria:

The study needed to include a training intervention, followed by a period of training cessation.The study needed to measure muscular performance following the training intervention and following the period of training cessation.The study needed to report all of the necessary information for calculating standardized effect sizes.

The researchers were interested in the effects of training cessation on maximal strength, maximal power, and strength endurance. Maximal strength was assessed via 1-5RM strength or maximal force on a dynamometer in the included studies. Maximal power was assessed via jump height, sprint tests, peak torque during high-speed dynamometry, or power output during submaximal lifting tasks. Strength endurance was assessed via ≥ 6RM strength, time to exhaustion during isometric dynamometry, or total work completed during an isokinetic fatigue test.

Impacts of training cessation on maximal strength

The researchers found that maximal strength was mostly unaffected (pooled effect sizes were trivial; g < 0.2) following up to 28 days of training cessation (Figure 1). Strength losses accelerated after 28 days of training cessation, however.

Graphics by Kat Whitfield

The researchers performed sub-analyses to identify predictors of the rate of strength losses. They found that upper and lower body strength were lost at similar rates, and that males and females lost strength at similar rates. However, they did find that older adults (≥65 year old) lost nearly twice as much strength and younger adults (<65 years old). The pooled effect size for older adults was g = 0.76 (95% CI = 0.62-0.90), which was more than twice as large as the pooled effect size for younger adults: g = 0.31 (95% CI = 0.21-0.40).

Impacts of training cessation on maximal power

Losses in power were smaller than losses in maximal strength (Figure 2), especially for longer periods of training cessation (113-224 days of training cessation). However, I suspect that simply reflects differences in trainability for strength vs. power. In other words, you could easily add 100 pounds to your squat following a period of training, and lose 100 pounds off your squat following a period of detraining (which might represent a 20-40% swing in total squat strength). However, you might only add three  inches to your vertical jump following a period of jump training, and lose three  inches from your vertical jump following a period of detraining (which might represent a 10-20% swing in jump height). In other words, “losing all of your gains” for measures of maximal power generally corresponds to smaller standardized effect sizes than “losing all of your gains” for measures of maximal strength.

Graphics by Kat Whitfield

As with the strength findings, males and females experienced similar reductions in maximal power following training cessation. Furthermore, losses in upper and lower body power occurred at similar rates. However, once again, older adults experienced far larger losses in maximal power output than younger adults: g = 0.46 (95% CI = 0.21-0.72) for older adults, versus g = 0.18 (95% CI = 0.10-0.26) for younger adults.

Impacts of training cessation on strength endurance

Strength endurance was negatively impacted by training cessation sooner to a greater degree than maximal strength or power (Figure 3). I’ll explore the potential reasons for the larger decreases in strength endurance later in this article.

Graphics by Kat Whitfield

Once again, losses in strength endurance occurred at a similar rate for upper body and lower body tests of strength endurance, and for both males and females. Furthermore, losses in strength endurance were considerably larger for older adults than younger adults: g = 0.85 (95%CI = 0.57-1.12) for older adults, versus g = 0.48 (95%CI = 0.26-0.70) for younger adults.

My primary takeaway from this meta-analysis (1) is that younger adults can “get away with” about a month out of the gym before their performance suffers very much. Sure, you’ll probably get pretty sore after your first few workouts back in the gym, and it may take a couple of sessions to knock the rust off and get back in a good groove with your training, but you should expect to maintain your performance pretty well. However, older adults take a bigger hit when they spend some time away from the gym. Unfortunately, Bosquet and colleagues didn’t report the actual time course of strength, power, and strength endurance losses independently in younger vs. older adults (just pooled magnitude estimates across all studies), but I suspect that losses in performance start accelerating following about two weeks of detraining in older adults.

Impacts of training cessation on muscle mass

The Bosquet meta-analysis didn’t investigate the impact of training cessation on muscle mass, and I was unable to find a similar meta-analysis summarizing the research investigating the impact of training cessation on muscle mass (though a meta-analysis investigating the impact of training cessation on muscle mass in older adults appears to be in the works; 2).

When you delve into the literature, however, I think separate patterns emerge for young versus older adults. Here are four illustrative studies in young adults:

In a study by Staron and colleagues (3), college-aged women completed 20 weeks of lower body training, followed by 30-32 weeks of detraining. Following the detraining phase, lean mass (assessed via skinfolds), mid-thigh circumference, gluteal circumference, and type I and type IIa fiber cross-sectional area of the vastus lateralis didn’t significantly change (4).In a study by Psilander and colleagues (5), subjects in their mid-20s completed 10 weeks of quad training, followed by 20 weeks of detraining. Following the detraining phase, muscle thickness decreased toward baseline values, but fiber cross-sectional area didn’t significantly change (Figure 4).In a study by Bjørnsen and colleagues (reviewed in MASS; 6), subjects completed two 5-day blocks of intense quad training, separated by 10 days of rest. Fiber cross-sectional area was assessed for up to 10 days following the final training session, while rectus femoris cross-sectional area and vastus lateralis thickness were assessed for up to 10 days following the final training session. Fiber cross-sectional area continued increasing throughout the post-training period, while rectus femoris and vastus lateralis size regressed slightly (though the change wasn’t significant).In a study by Seaborne and colleagues (also reviewed in MASS; 7), subjects completed seven weeks of quad training, followed by seven weeks of detraining. Leg lean mass (assessed via DEXA) increased during the seven weeks of training, and regressed toward baseline values following the seven weeks of detraining.

Overall, it appears that measures of whole-muscle mass, thickness, or cross-sectional area tend to decline following detraining periods. The Staron study (3) presents an exception to this rule, but it assessed whole-muscle size using pretty inexact measures. The other three studies found, collectively, that measures of whole-muscle size decrease non-significantly within 20 days of training cessation, and decrease back near baseline values following 7-20 weeks of training cessation. However, it also appears that gains in fiber cross-sectional area are preserved quite well following a detraining period (Figure 4).

Graphics by Kat Whitfield

I’ll admit that interpreting these findings is pretty challenging. You could argue that the decreases in measures of whole muscle size are more reflective of what’s “truly” going on – muscle atrophy is occurring following training cessation. After all, a muscle biopsy only provides you with insight into a relatively small sample of the total muscle, so changes in whole-muscle thickness, cross-sectional area, or lower-body lean mass are more informative. Alternately, you could argue that preservation of muscle fiber cross-sectional area is more reflective of what’s “truly” going on – you don’t actually lose much muscle following training cessation. After all, we primarily care about the contractile elements of muscle tissue, right? Assessments of whole-muscle mass, thickness, or cross-sectional area may pick up on decreases in extracellular water or connective tissue content with training cessation, leading to the erroneous conclusion that muscles are shrinking. In actuality, the contractile elements of muscle tissue might be well-maintained, even following a period of training cessation.

I personally think the truth is somewhere in the middle. I do think most people overestimate the rate at which they lose muscle with training cessation. Due to decreases in muscle edema, decreases in muscle glycogen content, and potentially even decreases in muscle blood flow (since less oxygen would be needed to fuel muscle remodeling when the stimulus for elevated muscle remodeling is removed), muscles might start looking “flat” following a week of training cessation, but this perceived decrease in muscle size is unlikely to reflect a true loss of muscle tissue. However, I also strongly believe that a significant loss of contractile and structural protein occurs within 20-32 weeks of training cessation – I don’t think that the relative lack of change in fiber cross-sectional area tells the full story. For a deeper dive into the topic of assessing muscle hypertrophy (and, by extension, muscle atrophy), I’d strongly recommend this paper from Haun and colleagues (8). The short version is that assessing muscle hypertrophy and atrophy is a lot more complicated than most people realize.

On a practical level, I’d suggest that losses in muscle mass likely run roughly in parallel with losses in strength assessed via relatively simple exercises. In other words, if you’re out of the gym for two months, losses in squat 1RM probably aren’t a great indication of losses in lower body muscle mass. Squats have a significant skill component, so losses in squat strength might simply indicate that your motor patterns are a bit rusty. However, if your maximal leg press strength (or even better, your maximal knee extension strength) is down, those strength reductions probably reflect “true” losses in muscle mass. So, until more (and better) data is published, my assumption is that the pattern of strength losses observed in the Bosquet meta-analysis (1) are informative about the losses in muscle mass that occur – you probably maintain your muscle pretty well for about a month out of the gym, but losses in muscle mass accelerate after longer periods of training cessation.

Moving over to older adults, the research is much more straightforward. Losses in both whole-muscle size (9) and fiber cross-sectional area (10) occur with detraining. Once again, it’s hard to granularly assess the time course of muscle losses that occur with training cessation (since individual studies don’t assess muscle cross-sectional area or take biopsies every week during the detraining period). However, I suspect that the strength findings from the Bosquet meta-analysis are informative once again – older adults probably lose muscle about twice as fast as younger adults during a period of training cessation.

Why are losses in strength endurance larger than losses in maximal strength?

At first, it may seem unintuitive that strength endurance is lost at a faster rate than maximal strength during a period of training cessation. After all, we expect our motor patterns to be a bit rusty after time away from the gym, so it makes sense that maximal strength performance should take a hit. Higher rep training is a bit less dependent on your motor patterns being perfectly sharp, so it might seem like strength endurance should be better maintained than maximal strength. However, this finding should make a bit more sense when we zoom out and examine the metabolic de-adaptations that occur with training cessation.

As I’ve written about previously, resistance training can be a surprisingly metabolically taxing task – at least in short bursts. The energy expenditure of an entire training session may not be tremendously high compared to other forms of exercise (primarily due to the breaks you need to take between sets; 11), but the metabolic cost of each set can be quite high over a very short period of time.

To illustrate, Escamilla and colleagues (building upon prior work by Brown and colleagues; 12, 13) found that a set of 8 deadlifts with 175kg (385lb) burns about 25kcal. To put that in perspective, running 400m also burns about 25kcal for an average-sized person. If you’ve ever done an all-out 400m sprint, you know the metabolic cost of rapidly expending 25kcal; even if you’re well-trained for the task, you’ll be huffing and puffing like a freight train after a 400m sprint. If you’re not particularly well-trained for the task, you might vomit and need to lie down on the track for 5-10 minutes just to catch your breath. So, if you’ve ever wondered why you’re absolutely wrecked after completing a true 8-20RM set of squats or deadlifts (especially if you’re quite strong), that’s why – you may be expending energy at a rate that’s comparable to an Olympic-level 400m runner, but I doubt you’ve done nearly as much aerobic or anaerobic conditioning work as an Olympic-level 400m runner.

The raw energy expenditure values for smaller exercises (say, biceps curls) are considerably lower than the values observed for squats or deadlifts, simply because less muscle mass is being used, and less total work is being performed. However, the same principle applies in miniature – local energy usage of the active muscles is going to be extraordinarily high (relatively speaking), and performance is going to be limited by the ability of the active muscle tissue to produce enough energy. Once the muscle fibers can no longer produce enough ATP to maintain the required rate of cross-bridge cycling, or for the timely clearance of metabolites, you’ll fail to produce enough force to complete another rep.

So, strength endurance performance is affected by the same factors as maximal strength performance – your muscles’ ability to produce force and your nervous system’s ability to adequately coordinate muscle contraction – while additionally being constrained by your muscles’ ability to create enough energy throughout the set. Thus, if training cessation brings about a decrease in aerobic and anaerobic fitness (due to decreases in blood volume and hematocrit, decreases in mitochondrial density, decreases in concentrations of key enzymes involved in aerobic and anaerobic metabolism, decreases in capillary density, etc.), we should expect to see a larger decrease in strength endurance performance than maximal strength performance.

That’s precisely what we see. Most of the research investigating changes in aerobic and anaerobic performance focuses on team sport and endurance athletes going through a period of training cessation (14, 15), but I see no reason to anticipate that resistance trainees wouldn’t also experience a decrease in aerobic and anaerobic fitness. Resistance training brings about many of the same adaptations as more traditional anaerobic conditioning training (16), albeit to a lesser extent (17).

Since training cessation results in both strength loss and decreases in aerobic and anaerobic conditioning, and since strength endurance is (roughly speaking) the product of maximal strength and local aerobic and anaerobic conditioning, it’s unsurprising that strength endurance losses exceed losses in maximal strength during a period of training cessation (Figure 5).

Graphics by Kat Whitfield

As one final note, astute readers may have noticed that the actual pooled effect size estimates for reductions in maximal strength and strength endurance didn’t differ to a huge extent in the Bosquet meta-analysis: 0.76 vs. 0.85 for older adults, and 0.31 vs. 0.48 for younger adults. However, those pooled effect estimates are based on the effect sizes reported in studies examining periods of training cessation of different lengths. So, if there were a lot of studies examining the effects of relatively short-term training cessation on strength endurance, and a larger number of longer-term studies examining the effects of training cessation on maximal strength, you could easily wind up with comparable pooled effect estimates, despite also observing larger decreases in strength endurance performance over every discrete time scale. Based on the data reported in Figures 1 and 3, I strongly suspect that we’re observing this type of dynamic at play.

Muscle memory

After you take some time away from training, you’ll probably find that you can regain most (or all) of the muscle and strength you’d lost in a pretty short period of time. The “bros” have referred to this phenomenon as “muscle memory” for decades, and the term seems to be catching on in the scientific literature.

When I first started paying attention to the sciency side of the fitness industry in approximately 2010, I remember being told that muscle memory was mostly an illusion. At the time, the “orthodox” position was that a significant portion of lost strength was rapidly regained as lifters honed motor patterns that had grown rusty during their period of detraining (leading to the mistaken impression that muscle was also being rapidly rebuilt), but that lost muscle had to be rebuilt gradually. In other words, the muscles themselves didn’t actually “remember” how large they’d previously been, or possess any cellular mechanisms to facilitate the regrowth of lost muscle tissue. So, if it took you two years to build 5kg of muscle, and then you lost all of that muscle during a year away from the gym, it would take you an additional two years to rebuild the lost muscle.

Then, in 2013, a study by Egner and colleagues caused a pretty huge paradigm shift (18). In that study, mice were given supraphysiological doses of testosterone for 14 days, leading to considerable hypertrophy. After testosterone treatment was removed, the muscle fibers decreased in size over the next three weeks. However, following a period of overload exercise (achieved via synergist ablation), the mice rebuilt all of the muscle they’d lost during the “detraining” period. Furthermore, another cohort of mice that hadn’t been given testosterone and hadn’t previously experienced hypertrophy also underwent the same period of overload exercise. This second group of mice achieved less hypertrophy during the overload period than the group of mice that was merely rebuilding muscle (Figure 6).

Graphics by Kat Whitfield

This study both suggested that “muscle memory” was a real phenomenon – muscle can be rebuilt faster than it can be built initially – and it posited that a compelling pair of cellular mechanisms could explain this phenomenon: myonuclear permanence and myonuclear domain theory. It’s probably beyond the scope of this article to really get into the nitty-gritty of myonuclei regulation and the extent to which myonuclei regulate muscle size, but there’s a previous article on the topic that should bring you up to speed (19). In short, myonuclei are the “control centers” of muscle fibers. Unlike most human cells (which have a single nucleus), muscle fibers have multiple nuclei. As muscle fibers grow, they accrue more myonuclei. It appears that each myonucleus can “oversee” a finite volume of muscle fiber contents (its “myonuclear domain”). When myonuclei are stretched to their limits – the myonuclear domains are as large as each nucleus can manage – muscle growth becomes a slow process. However, when myonuclei are overseeing smaller myonuclear domains, they can rapidly ramp up gene transcription (leading to increased gene translation and increased protein synthesis), leading to considerably quicker muscle growth (or regrowth). Crucially, when muscle is lost during a period of detraining, it appears that the vast majority of those myonuclei stick around. So, when you get back under the bar, your myonuclei are overseeing smaller myonuclear domains, thus allowing you to quickly regain lost muscle tissue (32).

Graphics by Kat Whitfield

Research in the intervening years suggests that this myonuclei-mediated mechanism may be a factor contributing to muscle memory, but it’s not the only relevant mechanism (again, I’d recommend my previous article on the topic; 19). Notably, a 2018 study by Seaborne and colleagues found that epigenetic regulation of gene expression might also contribute to the phenomenon of muscle memory (7). A recent study hinted at another potential mechanism – resensitization of cellular signaling pathways associated with muscle growth following a period of training cessation (20). I wouldn’t be surprised if there are additional mechanisms waiting to be discovered. But for our purposes here, the precise mechanisms of muscle memory aren’t terribly important – just know that that concept of muscle memory is solid and scientifically supported (33).

Unfortunately, the precise time course of muscle memory-assisted strength re-gain and muscle regrowth isn’t well-understood. In other words, if you take six months out of the gym, and lose an amount of muscle and strength that it previously took you three years to build, we don’t know precisely how long it’ll take to rebuild all of the muscle and strength you lost. The primary reason for this gap in our knowledge is that research examining both detraining and retraining generally isn’t adequately designed to assess the time course of strength and hypertrophy adaptations during the retraining period. In other words, a study may involve 12 weeks of training, 24 weeks of detraining, and 12 weeks of retraining, with assessments of strength and muscularity at the start of the study, at the end of the training period, at the end of the detraining period, and at the end of the retraining period. The subjects may have more muscle and strength at the end of the retraining period than they had at the end of the initial training period, but we don’t know precisely how long it took for their strength and muscularity during the retraining period to equal their strength and muscularity at the end of the initial training period. The reason for this gap in our knowledge is that most studies don’t assess strength and hypertrophy on a weekly basis throughout the retraining period.

However, research does suggest that the period of time required to regain lost muscle and strength is shorter than the period of training cessation. For example, in the Seaborne study, subjects trained for seven weeks, detrained for seven weeks, and retrained for seven weeks (7). Subjects were substantially stronger and more muscular at the end of the retraining period than at the end of the initial training period, suggesting that it took less than seven weeks for the subjects to regain their lost muscle mass and strength. Similarly, a study by Henwood and Taffe involved 24 weeks of training, 24 weeks of detraining, and 12 weeks of retraining. The 12-week retraining period was sufficient to regain all of the strength lost during the detraining period (21). The aforementioned study by Psilander and colleagues had similar results (5). Subjects trained for 10 weeks, detrained for 20 weeks, and retrained for 5 weeks. The subjects were slightly stronger and slightly more muscular at the end of the retraining period than at the end of the initial training period. Similarly, a study by Ogasawara and colleagues compared two groups completing six months of bench press training (22). One group trained for six months straight, while the other group followed a pattern of training for six weeks, taking three weeks off, training for six more weeks, taking three weeks off, etc. Over the six-month training period, gains in bench press 1RM strength, pec cross-sectional area, and triceps cross-sectional area were similar in both groups. This suggests that the muscle and strength lost during the three-week detraining periods were rapidly rebuilt, allowing for each six-week training period to result in additional gains in strength and muscularity.

The Ogasawara study is particularly interesting because bench press 1RM was assessed every three weeks, thus allowing us to observe changes in strength over shorter time windows. After both three-week detraining periods, subjects experienced small decreases in 1RM strength. However, following three weeks of retraining, the subjects were (slightly) stronger than they’d been at the end of their prior six-week block of training. Unfortunately, pec and triceps thicknesses weren’t assessed as frequently as strength, thus giving us less insight into the precise time course of muscle re-growth.

Graphics by Kat Whitfield

A study by Taaffe and Marcus also assessed strength on a more frequent basis – every two weeks – over the course of a detraining and retraining study (10). Subjects trained for 24 weeks, detrained for 12 weeks, and retrained for 8 weeks. During the retraining period, it took the subjects six weeks to regain all of the strength they’d lost during the 12-week detraining period.

Graphics by Kat Whitfield

Until more granular data are published, I believe the research suggests that the period of time it takes to regain lost muscle and strength is approximately half as long as the preceding period of training cessation, with a rough confidence interval spanning from 1/3rd the length of the period of training cessation, up to 2/3rds the length of the period of training cessation. In other words, if you took three months (12 weeks) off of training, I suspect you’d be able to regain your lost muscle and strength within 4-8 weeks, with 6 weeks being my current best guess.

Mitigating the negative effects of training cessation

If you need to take time off from training, you’ll likely wonder what steps you can take to mitigate the negative impact of a period of training cessation. Is there anything you can do to minimize losses of strength and muscle mass?

Let me start by noting that training cessation exists on a spectrum. The Bosquet meta-analysis summarized the effects of “normal” training cessation (1) – subjects lifted weights for a period of time, and then stopped lifting weights while returning to their normal lifestyle. However, a period of training cessation might also be caused by a serious injury or illness, requiring bed rest or the complete immobilization of a limb. Research suggests that under these conditions, you don’t maintain muscle and strength reasonably well for up to a month. Instead, you hemorrhage muscle and strength at a pretty astounding rate – strength losses can exceed 1% per day, and muscle losses can be around half a percent per day (23). Conversely, you may put your training on pause because you start a very physically demanding job. For example, maybe you start work for a moving company, so you don’t want your back to be sore from deadlifting because you’re going to be moving couches and refrigerators up and down stairs for eight hours per day. Sure, this might be a period of “training cessation” while you adapt to the demands of your new job, but I strongly suspect that you’d maintain your muscle and strength quite well for quite a long time in this circumstance.

With that in mind, if the option is available to you, my best recommendation would be to not actually stop training entirely. It takes way less effort to maintain muscle and strength than to build additional muscle and strength – it doesn’t take a very large stimulus to maintain your muscle and strength for a very, very long time. For example, in a 2011 study by Bickel and colleagues young lifters (20-35 years old) and older lifters (60-75 years old) initially underwent a 16-week training phase, followed by a 32-week (8-month) phase of training with reduced volume, or complete detraining (24). A third of the lifters stopped training entirely, a third of the lifters reduced their volume by 2/3rds, and a third of the lifters reduced their volume by 8/9ths. The researchers found that the younger lifters could maintain their muscle and strength over 8 months by maintaining just 1/9th of their original training volume (Figure 10), and older lifters (60-75 years old) could maintain strength with just 1/3rd of their original training volume, though they may still experience some reduction in muscle mass.

Graphics by Kat Whitfield

A more recent study by Antunes had similar findings (25). Older women (> 60 years old) completed a 20-week training intervention involving three sets per week of multiple exercises. Following the 20-week training program, a third of the women continued training with three sets per exercise, a third of the women continued training with two sets per exercise, and a third of the women continued training with just one set per exercise for an additional 8 weeks. The group that cut their training volume down to one set per exercise was able to maintain (or slightly increase) their strength and lean soft tissue mass over the 8-week period of training with reduced volume.

So, even if you’re away from the gym, losses in muscle and strength should be minimal if you can just find a way to still do some resistance exercise. Something as simple as 2-3 sets of push-ups, pull-ups, split squats, and back raises or hip thrusts once or twice per week should be sufficient to maintain the vast majority of your muscle and strength for a long, long time. There are some muscle groups that are more challenging to train without any gym equipment (the spinal erectors and hamstrings, in particular), but if you can just carve out 30-45 minutes per week for a bit of bodyweight training, you can really put the brakes on muscle and strength losses when you’re away from the gym.

I realize that “still do a bit of training, actually” really stretches the definition of “training cessation,” but it actually meshes well with some of the reasons why someone might go through a period of full training cessation. Lack of time and lack of enjoyment are cited as two of the primary reasons people don’t participate in dedicated exercise (26). So, you might be staring down a period of training cessation because changes in your schedule severely curtail your leisure time (for example, maybe you recently became a new parent, you started a new job with a significantly longer commute, or you’re enrolling in night classes while still working a 9-to-5 job), or if you might be stepping away from serious training for a period of time because the grind of intense workouts is making your feel burnt out. In one of those situations, shorter-duration, less taxing workouts may allow you to reap the benefits of a period of training cessation, without losing the muscle and strength you’d worked so hard to build.

If you either can’t do any form of resistance training, or you simply don’t want to do any resistance training during a period of training cessation, then my primary recommendation would be to simply maintain a protein intake of approximately 1.3-1.4g of protein per kg of lean mass (27), and to avoid large caloric deficits or surpluses (28). We’re frequently asked if considerably higher protein intakes or any specific supplements can help with the maintenance of muscle mass during a period of training cessation, but I’m unaware of any research suggesting that extreme protein intakes or legal supplements can have a significant impact on muscle retention in the absence of a resistance training stimulus.

Returning to training

Assuming you don’t intend to give up on resistance training entirely, you’ll need to consider how you plan to return to training following a period of training cessation. Dr. Zourdos has already made a great video about returning to training after a layoff, and Dr. Jason Eure has written a great article about the risks of returning to training. I’d recommend those two pieces of content for in-depth examinations of this topic. However, I feel that this article about training cessation would be incomplete without at least touching on the subject of returning to training.

When you return to training, you’re probably going to be focused on regaining lost muscle and strength so that you can start making further gains. However, I think you should also be concerned with minimizing injury risk (since some evidence suggests that injury rates are elevated when athletes re-introduce intense training after an offseason; 29, 30) and re-conditioning your muscles. With that in mind, I think it makes sense to start with a rough, somewhat conservative plan for your return to serious training.

For your first week back under the bar, I’d recommend including all of the exercises you plan to perform in your “normal” training (once you’ve regained your strength), with the same set and rep volume you intend to use. However, for this first week of training, use very light weights. Using about 1/3rd as much weight as you used before your period of training cessation should provide you with a good starting point.

For example, maybe your typical upper body workout previously included 3 sets of 10 bench press with 225lb, overhead press with 150lb, pull-downs with 120lb, barbell rows with 150lb, curls with 30lb, and triceps extensions with 45lb. Eventually, you’d like to get back to those numbers.

For your first week of training, jump straight back to 3 sets of 10 reps for all of those exercises, but use 75lb for bench, 50lb for overhead press, 40lb for pull-downs, 50lb for barbell rows, 10lb for curls, and 15lb for triceps extensions.

This may seem like hilariously easy training, even after a prolonged period of training cessation, but it actually serves a purpose. Research has shown that training with just 10% of maximal force for a single session can dramatically attenuate soreness, post-training strength reductions, and blood markers of muscle damage when training ramps back up (31). As you’re returning to training, excessive soreness could derail early attempts to rebuild the habit and lifestyle of training consistently. If you planned to do upper body training on Tuesday and Friday, and lower body training on Wednesday and Saturday during your first week back in the gym, you may be demotivated to stick to that schedule if you can’t raise your arms over your head on Friday, and you can’t walk comfortably on Saturday due to the effects of your Tuesday and Wednesday workouts. By taking the first week of training really easy, you should significantly reduce the risk of excessively severe DOMS derailing your path back toward consistent training.

Graphics by Kat Whitfield

For your second week of training, your aim should be to feel out weights that are challenging but not hard for all of your exercises. Some prior experience with autoregulation using reps in reserve-based ratings of perceived exertion (RIR-RPE) helps considerably. For your first set of each exercise, you should aim to have at least 5 reps in reserve, and you should aim to still have at least 3-4 reps in reserve for your final set of each exercise. This is just your second week back in the gym, and your first week of somewhat challenging training – you’re still reconditioning your muscles and re-acclimating to training, so you should be disciplined and resist the urge to test your limits and, in doing so, potentially increase your injury risk. Don’t be afraid to reduce your working weight if you selected a weight that’s a bit too heavy for your first set, and don’t be afraid to increase your working weight if you selected a load that’s a bit too light for your first set. Also, don’t be surprised if your performance changes from set to set in an unpredictable manner. If your muscles are severely deconditioned, it’s entirely possible that your first set of an exercise will leave you with 6 reps in reserve, and your second or third set will leave you with just 1-2 reps in reserve due to the rapid onset of fatigue. Conversely, it’s also entirely possible that your second, third, or fourth set of an exercise will be noticeably easier than your first set, as your nervous system de-rusts old motor patterns in real-time. So, during this week of training, it’s very important to pay close attention to the feedback your body is giving you so that you can select appropriate training weights.

From there, you should be able to sketch out a rough plan for regaining the rest of your lost strength and muscle mass, following this process:

Add up the number of weeks you spent away from the gym. Divide by two. That’s roughly how long it should take to return to your prior levels of performance.Treating the week of training you just completed as week 1 (i.e., ignore the introductory week that involved training with ⅓ of your prior training weights), subtract your pre-training cessation training weights from your week 1 post-training cessation training weights.Divide your current strength deficit by the number of weeks it should take to regain your lost strength, minus 1. That will tell you how much your training weights should increase week-by-week.Repeat for all of your lifts. That should provide you with a rough blueprint for returning to training.

Here’s an illustration, which should help clarify this process:

First, let’s assume that your period of training cessation was 12 weeks. 12 ÷ 2 = 6. So, it should take about 6 weeks to regain your lost strength.

Next, let’s assume that you were previously squatting 405lb for 5 sets of 5 reps. During your first introductory week of training, you performed 5 sets of 5 reps with 135lb. We’re ignoring that week. During your first “real” week of training, you found that 255lb was a challenging but comfortable working weight for 5 sets of 5 reps. So, your current working weight is 405 – 255 = 150lb lower than your prior working weight.

Next, to calculate weekly load increases, divide your current strength deficit (150lb) by the number of weeks it should take to regain strength, minus 1 (6 – 1 = 5). So, 150 ÷ 5 = 30lb. So, your training loads for the squat should increase by 30lb per week. Repeat this process with each lift.

Of course, doing a lot of math by hand is no fun, so I’ve made a spreadsheet that will do all of these calculations for you. You can access it here to make a copy in Google Sheets, or here to download it for use with some other spreadsheet program.

As a general note, these return-to-training guidelines should be interpreted as a rough directional indicator, rather than a fixed roadmap that you can’t stray from. Monitor your level of exertion as you retrain. If reps in reserve start trending up (i.e., you had 3 reps in reserve on your final set during your first week of retraining, but you feel like you have 5+ reps in reserve on your final set during your third week of retraining, in spite of absolute loads increasing), you should be able to progress training loads a bit faster. Conversely, if you start consistently having just 0-1 reps in reserve, you may need to progress training loads a bit slower. Once you can’t increase loads week-to-week anymore, that indicates that your retraining period is over, and it’s time to shift back to “normal” training. For most folks, this should roughly coincide with the point at which your current training loads equal the training loads you were able to handle prior to your period of training cessation. However, some people will likely fall a bit short of their prior training loads (especially if they lost a significant amount of weight during their period of training cessation), and some people will be able to slightly exceed their prior training weights before linear progress slows to a halt.

If your period of training cessation was less than a month long, just treat it like it was an extended deload. Easing back into training shouldn’t need to be a big, multi-week process. In your first week back under the bar, just bump your training loads down by about 20% (relative to the loads you used in your last completed week of training). From there, you should be able to resume “normal” training without a hitch.

If your period of training cessation was more than a year long, I’d probably recommend treating yourself like an untrained lifter, and embarking on any training program employing a standard linear progression (adding 5-10lbs per week to lower body exercises, and 2.5-5lbs per week to upper body exercises).

The main thing I wouldn’t recommend is progressing in load as quickly as possible with very low training volumes, with the intention of increasing training volumes only after your strength has mostly recovered. For example, if you just worked up to a single hard set of 3-8 reps for each lift once or twice per week, your strength performance would rapidly increase. However, you’d also be leaving your muscles and tendons relatively deconditioned. I won’t pretend like I have solid references to back this up, but I think you’re better served to recondition your tissues with relatively low loads as you ease back into training, rather than reconditioning them with heavy loads once you’ve already regained most of your lost strength.

Wrapping things up

I realize this is a lengthy article, so let’s briefly recap the key points:

Younger adults can probably “get away with” about a month of training cessation before losing too much strength and muscle mass. Older adults may be able to get away with about two weeks of training cessation. After that, losses accelerate.Strength endurance seems to fade a bit faster than maximal strength, and older adults (>60-65 years old) seem to lose strength (and likely muscle) at about twice the rate of younger adults during a period of training cessation.Due to the phenomenon of “muscle memory,” the retraining period (the amount of time it takes to regain lost muscle and strength) following a period of training cessation seems to be about half as long as the period of training cessation. So, if you’re out of the gym for 12 weeks, you should be able to regain the vast majority of your lost strength and muscle mass in approximately 6 weeks.If you have the time, ability, and inclination to do any training, you can significantly mitigate the losses in strength and muscle mass you’d otherwise experience during a period of total training cessation. Get more articles like this

This article was the cover story for the September 2022 issue of MASS Research Review. If you’d like to read the full, 136-page September issue (and dive into the MASS archives), you can subscribe to MASS here.

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Repeated and Interrupted Resistance Exercise Induces the Desensitization and Re-Sensitization of mTOR-Related Signaling in Human Skeletal Muscle Fibers. Int J Mol Sci. 2022 May 12;23(10):5431. doi: 10.3390/ijms23105431. PMID: 35628242; PMCID: PMC9141560.Henwood TR, Taaffe DR. Detraining and retraining in older adults following long-term muscle power or muscle strength specific training. J Gerontol A Biol Sci Med Sci. 2008 Jul;63(7):751-8. doi: 10.1093/gerona/63.7.751. PMID: 18693231.Ogasawara R, Yasuda T, Ishii N, Abe T. Comparison of muscle hypertrophy following 6-month of continuous and periodic strength training. Eur J Appl Physiol. 2013 Apr;113(4):975-85. doi: 10.1007/s00421-012-2511-9. Epub 2012 Oct 6. PMID: 23053130.Campbell M, Varley-Campbell J, Fulford J, Taylor B, Mileva KN, Bowtell JL. Effect of Immobilisation on Neuromuscular Function In Vivo in Humans: A Systematic Review. Sports Med. 2019 Jun;49(6):931-950. doi: 10.1007/s40279-019-01088-8.Bickel CS, Cross JM, Bamman MM. Exercise dosing to retain resistance training adaptations in young and older adults. Med Sci Sports Exerc. 2011 Jul;43(7):1177-87. doi: 10.1249/MSS.0b013e318207c15d. PMID: 21131862.Antunes M, Kassiano W, Silva AM, Schoenfeld BJ, Ribeiro AS, Costa B, Cunha PM, Júnior PS, Cyrino LT, Teixeira DC, Sardinha LB, Cyrino ES. Volume Reduction: Which Dose is Sufficient to Retain Resistance Training Adaptations in Older Women? Int J Sports Med. 2022 Jan;43(1):68-76. doi: 10.1055/a-1502-6361. Epub 2021 Jul 13. PMID: 34256389.Hoare E, Stavreski B, Jennings GL, Kingwell BA. Exploring Motivation and Barriers to Physical Activity among Active and Inactive Australian Adults. Sports (Basel). 2017 Jun 28;5(3):47. doi: 10.3390/sports5030047. PMID: 29910407; PMCID: PMC5968958.Nunes EA, Colenso-Semple L, McKellar SR, Yau T, Ali MU, Fitzpatrick-Lewis D, Sherifali D, Gaudichon C, Tomé D, Atherton PJ, Robles MC, Naranjo-Modad S, Braun M, Landi F, Phillips SM. Systematic review and meta-analysis of protein intake to support muscle mass and function in healthy adults. J Cachexia Sarcopenia Muscle. 2022 Apr;13(2):795-810. doi: 10.1002/jcsm.12922. Epub 2022 Feb 20. PMID: 35187864; PMCID: PMC8978023.Hall KD. Body fat and fat-free mass inter-relationships: Forbes’s theory revisited. Br J Nutr. 2007 Jun;97(6):1059-63. doi: 10.1017/S0007114507691946. Epub 2007 Mar 19. PMID: 17367567; PMCID: PMC2376748.Agel J, Schisel J. Practice injury rates in collegiate sports. Clin J Sport Med. 2013 Jan;23(1):33-8. doi: 10.1097/JSM.0b013e3182717983. PMID: 23160274.Hootman JM, Dick R, Agel J. Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives. J Athl Train. 2007 Apr-Jun;42(2):311-9. PMID: 17710181; PMCID: PMC1941297.Huang MJ, Nosaka K, Wang HS, Tseng KW, Chen HL, Chou TY, Chen TC. Damage protective effects conferred by low-intensity eccentric contractions on arm, leg and trunk muscles. Eur J Appl Physiol. 2019 May;119(5):1055-1064. doi: 10.1007/s00421-019-04095-9. Epub 2019 Feb 18. PMID: 30778759.Gundersen K. Muscle memory and a new cellular model for muscle atrophy and hypertrophy. J Exp Biol. 2016 Jan;219(Pt 2):235-42. doi: 10.1242/jeb.124495. PMID: 26792335.I’m aware that a recent meta-analysis has called myonuclei-related mechanisms of muscle memory into question. However, I’d refer you to my previous article on the topic. Studies that employ the most rigorous measurement techniques are the studies most likely to report a preservation of myonuclei, leading me to suspect that the meta-analysis overestimates the extent of myonuclei loss that occurs with muscle atrophy. And, more generally, it’s less a question of whether myonuclei are retained forever, and more a question of whether myonuclei are lost at the same relative rate at which muscle atrophy occurs. A myonuclei-mediated mechanism of muscle memory doesn’t necessarily require true myonuclear permanence.

The post A Guide to Detraining: What to Expect, How to Mitigate Losses, and How to Get Back to Full Strength appeared first on Stronger by Science.

- Eric Trexler
Optimizing Bulking Diets To Facilitate Hypertrophy

Note: This article was the MASS Research Review cover story for August 2022. If you want more content like this, subscribe to MASS.

The fitness world is full of nutrition content related to fat loss, and for good reason.  Fat loss is a common objective among the general population, whether the underlying goal is health-related or aesthetic in nature. Furthermore, fat loss is a critical aspect of physique sports, and a noteworthy consideration for all strength sports involving weight classes. Nonetheless, there are ample reasons to optimize one’s diet for hypertrophy facilitation rather than fat loss. Physique athletes have to get lean, but they won’t be going far in the sport without a sufficient amount of muscle for their competitive class. Many strength athletes need to make weight, but there’s no point in cutting weight classes if you don’t have the strength (and prerequisite muscle mass) to lift at a competitive level. Finally, there are some great reasons for general population folks to be interested in lean mass accretion. For many people with aesthetic goals, attainment of their dream physique will involve adding some amount of muscle mass, and there are noteworthy health benefits associated with increased strength and muscularity, particularly as we age. For this reason, it’s very common for people to take a cyclical approach to dieting, with “bulking” phases consisting of an energy surplus with a focus on muscle gain, and “cutting” phases consisting of an energy deficit with a focus on fat loss.

Across a wide range of populations with varying fitness-related goals, there are many reasons to dedicate some time and attention to lean mass accretion, and a few key dietary adjustments and strategies can facilitate the process immensely. As such, the purpose of this article is to discuss how to optimize a “bulking” diet to maximally support hypertrophy.

Establishing an Energy Surplus to Facilitate Hypertrophy

It’s widely accepted that muscle hypertrophy is maximized in a state of positive energy balance. This describes a scenario in which the total amount of energy absorbed from the diet exceeds total daily energy expenditure, with the remainder of excess calories known as a caloric surplus or energy surplus. Despite the widespread acceptance of this idea, several questions persist. For example, why is an energy surplus advantageous? Is an energy surplus absolutely necessary for muscle growth in all circumstances? Exactly how large should an energy surplus be when hypertrophy optimization is the top priority? To achieve a deeper understanding of bulking diets, let’s dive into each of these questions.

Why is an energy surplus advantageous?

We can broadly categorize metabolic pathways as catabolic or anabolic. In catabolic pathways, energy-yielding nutrients (e.g., carbs, fats, proteins, and ketones) are broken down to yield energy-poor end products (e.g., carbon dioxide, water, and ammonia), and chemical energy (adenosine triphosphate, or ATP) is released in the process (1). For example, imagine that you begin exercising in a fasted state. Energy expenditure increases, and your body needs to break down some energy-rich substrates to adequately meet the rising demand for chemical energy (ATP). You’ll probably tap into a mixture of stored glycogen and stored fat, break them down to obtain ATP, and excrete the energy-poor end products of water and carbon dioxide. Greg gives an excellent overview of this process in a MASS article from Volume 1.

A brief overview of catabolic pathwaysGraphic by Kat Whitfield.

Anabolic pathways are the inverse of catabolic pathways. Rather than breaking down complex molecules into simpler end-products to extract energy, anabolic pathways involve building complex molecules (e.g., proteins, polysaccharides, lipids, and nucleic acids) from simpler precursors (e.g. amino acids, sugars, fatty acids, and nitrogenous bases), and chemical energy is actually required (used) to fuel the synthesis of these more complex end products (1). Muscle hypertrophy is an example of an anabolic pathway by which amino acids are assembled into muscle proteins, and ATP is required to power this process. Naturally, energy status is a critical regulator when it comes to both anabolic and catabolic pathways in the body. When demand for chemical energy exceeds the current supply, catabolic pathways are favored to liberate ATP. Intuitively, the body tends to scale down any unnecessary and energy-intensive anabolic pathways when catabolic pathways are being ramped up to solve an acute energy shortfall. Thus, at the surface level, we can see how maintaining a sufficient supply of accessible energy is an important factor dictating our capacity for muscle hypertrophy.

Is an energy surplus absolutely necessary for muscle growth?

I chose my words very carefully in the previous sentence: maintaining a sufficient supply of accessible energy is an important factor dictating our capacity for muscle hypertrophy. It’s important to recognize that “maintaining a sufficient supply of energy” goes beyond what you ate within the last few hours or maintaining positive energy balance over a given 24-hour period. We store enormous amounts of energy in adipose tissue; for example, we can access over 100,000 kcals of energy by breaking down 11kg of fat (2). As such, the concept of maintaining a sufficient supply of energy is intrinsically linked to a combination of long-term energy status (adiposity) and short-term energy status (the day-to-day relationship between energy consumption and energy expenditure).

If you’re looking for a specific formula that quantifies “overall energy status” based on acute energy balance and stored adipose tissue, you won’t find it here. We’ve got enough scientific evidence to understand that there’s an interplay between the two, and researchers have identified a number of mechanisms by which the body senses and keeps tabs on indicators of both short-term and long-term energy status. However, we don’t (to my knowledge) have the necessary information and depth of understanding required to construct a unified formula that comprehensively summarizes the balance of long-term and short-term energy status in a manner that would inform the promotion of muscle hypertrophy. Nonetheless, we have some very useful empirical observations that can inform actionable takeaways. 

There is enough published research to render the following statement indisputable: it is possible to gain muscle mass without an energy surplus (3). In fact, it’s possible to gain muscle mass in a calorie deficit (4). However, it appears that adiposity is a major factor impacting the likelihood and magnitude of muscle gain in an energy deficit, which is also known as body recomposition. When long-term energy stores are high (e.g., we have plenty of stored body fat), it’s not particularly uncommon to observe noteworthy hypertrophy in the context of neutral, or even negative, energy balance. Conversely, recomposition is observed more rarely and in smaller magnitudes among individuals with very low body-fat levels. Another critical factor is the size of the energy deficit. As discussed in a previous MASS article, recomposition is routinely observed in the context of small energy deficits. However, as the energy deficit grows, the magnitude of hypertrophy increasingly tends to get blunted. A recent meta-regression (4) demonstrated that recomposition was quite common for calorie deficits up to around 200-300 kcal/day, but pretty atypical for calorie deficits larger than 500 kcal/day (Figure 2).

Relationship between estimated energy deficit and change in lean massGraphic by Kat Whitfield.

So, back to the original question: is an energy surplus absolutely necessary for muscle growth? Empirically, no. Hypertrophy is frequently observed in the presence of small-to-moderate energy deficits (3), and this is particularly true for people who have higher adiposity, less training experience, and a larger gap between their current level of muscularity and their maximal, genetically-determined limit for muscularity. However, there’s a more pertinent question for hypertrophy: is there a high likelihood of maximizing hypertrophy without an energy surplus? As reviewed by Slater and colleagues (5), evidence suggests that the answer is “probably not.” Research indicates that an energy surplus is generally advantageous when the goal is to maximize the rate and magnitude of muscle hypertrophy, and this is likely related to the simple relationship between energy status and the facilitation of energy-intensive anabolic processes (and, by extension, the hormonal milieu associated with positive energy balance). Some folks are in a position where they can achieve meaningful hypertrophy in spite of neutral or negative energy balance, but positive energy balance appears to be ideal if an individual is solely and exclusively focused on maximizing hypertrophy.

Guidelines for calorie intake and rate of weight gain

Now that we’ve established the value of a positive energy balance, the next step is to determine how large of a caloric surplus is necessary. If the only goal is maximizing hypertrophy at all costs, then larger is generally better, but real-world scenarios typically aren’t that simple. If we overshoot the caloric surplus necessary to maximize hypertrophy, we invite completely unnecessary fat gain, which might be viewed as unfavorable (depending on the context).

In an excellent, open-access review paper, Slater and colleagues describe the multifaceted reasons for increasing calorie intake to support hypertrophy goals (5). As previously mentioned, ATP is used in the process of synthesizing new muscle proteins, so we need extra calories to support that energy cost. In addition, resistance training itself costs energy, and energy expenditure tends to remain transiently elevated for hours following an exercise bout. In addition, we need to supply the raw materials (amino acids) for new muscle proteins through dietary intake of protein, and these amino acids contain roughly 4kcal/gram, on average. As calorie intake increases, many individuals experience an adaptive increase in energy expenditure (6), which further increases their energy needs. This is analogous to metabolic adaptation; while underfeeding causes adaptive reductions in energy expenditure, overfeeding has a tendency to cause adaptive increases in energy expenditure. Finally, as you start accruing substantial amounts of muscle mass, total daily energy expenditure will increase further, as muscle mass is a metabolically active tissue that burns around 13 kcal/kg/day at rest (7), and even more so during exercise and non-exercise physical activity.

As outlined in the previous paragraph, we have a general idea of the factors driving increased energy needs for hypertrophy optimization. Unfortunately, there still isn’t much research identifying exactly how large a caloric surplus should be in order to maximally promote hypertrophy without driving unnecessary fat gain. Slater and colleagues recommend aiming for a calorie surplus of around 1500-2000 kj/day (359-478 kcal/day), which they classify as a “conservative” starting point. However, they acknowledge that this estimate is a very rough approximation, and that we don’t currently have the evidence required to establish a precise target or range. They further recommend to “closely monitor response to the intervention, using changes in body composition and functional capacity to further personalize dietary interventions.” By closely monitoring changes in body composition, the hypertrophy-focused lifter (or their coach) can quickly course-correct if the starting calorie target was too high or too low.

I think that’s a sensible recommendation, but you have to know your total daily energy expenditure in order to turn that recommendation into an actual daily calorie target. With that in mind, I’ll present three different methods for identifying one’s calorie target while bulking. As I described in a previous Stronger By Science article, I refer to the three strategies as 1) assume, 2) estimate, and 3) observe. 

The “assume” approach is simple and straightforward: it assumes that one’s daily calorie target can be effectively dictated by their goal and current body weight. This strategy assumes that most people will generally maintain their current body weight if they consume roughly 15 kcals per pound of body mass. As a result, a general target for a moderate bulk would be around 17kcal/lb, and a general target for an aggressive bulk would be around 19 kcal/lb (Table 1). These bulking targets tend to work out relatively well for people with lower body weights (especially below 150lb or so), but start to get excessively aggressive (in my opinion) once body weight starts climbing into the 200s and beyond. It’s also important to recognize that total daily energy expenditure can vary considerably from person to person, even if they weigh exactly the same. For these reasons, I do not recommend using the “assume” approach.

Energy intake guidelines for bulking and cutting based on the "assume" approach Graphic by Kat Whitfield.

The “estimate” approach involves using validated equations to estimate one’s resting metabolic rate, then using activity factors to further estimate one’s total daily energy expenditure (TDEE). For a step-by-step guide through that estimation process, be sure to check out this article. In short, I recommend using the Cunningham 1980 equation to estimate resting metabolic rate based on fat-free mass (22 × fat-free mass [kg] + 500), and I recommend using the MacroFactor activity correction factors, which range from 1.2-1.6 for general (non-exercise) activity levels, and from 0-0.3 for the additive impact of structured exercise activity. Once TDEE is estimated, you’d aim to eat a certain percentage of that value in accordance with your goal. For example, someone with a maintenance goal would set a calorie target equal to 100% of TDEE, someone on a moderate bulk would aim for 105-110% of TDEE, and someone on an aggressive bulk would aim for 115-120% of TDEE (Table 2).

Energy intake guidelines for bulking and cutting based on the "estimate" approach Graphic by Kat Whitfield.

The “estimate” approach is great, and it’s certainly a viable strategy to use. However, I believe we can do better. The “observe” approach involves tracking your body weight every day (ideally measured immediately upon waking), while simultaneously tracking your daily caloric intake. After a couple weeks or so, you should be able to make very informative inferences about your energy needs. For example, if you’re consistently eating around 2400kcal/day and your bodyweight is very stable, then your maintenance calorie intake (and, by extension, TDEE) is around 2400kcal/day. If you’re slowly losing weight while consuming 2400kcal/day, then that intake is putting you in a small caloric deficit; if you’re rapidly gaining weight, then 2400kcal/day is putting you in a large caloric surplus. 

While this approach requires a little more time and effort than the other two, it is 100% individualized and circumvents the need for imprecise heuristics or equations that rely on population-level averages. Once you get a decent idea of how your body weight is fluctuating in response to your current daily calorie intake, the goal is to adjust your calorie intake until you achieve an intended rate of weight change. If you have a previous history of successful bulking, you can also get a “head start” on the process – instead of monitoring how your weight is responding to your habitual, baseline level of calorie intake, you can jump straight to a calorie target that has worked in the past to determine if it’s still an appropriate target based on your body weight response. Someone with a maintenance goal would aim to keep body weight stable, while someone on a moderate bulk would aim to gain 0.1-0.25% of body mass per week, and someone on an aggressive bulk would aim to gain >0.25% of body mass per week (Table 3). However, it’s important to note that these categories might be a bit too conservative for people who are starting at lower body weights, so lighter individuals with lofty bulking ambitions should err toward the more aggressive side of these targets.

Energy intake guidelines for bulking and cutting based on the "observe" approach Graphic by Kat Whitfield.

The “observe” approach is my personal favorite, and my default recommendation for two reasons. First, it’s completely individualized and requires the fewest possible assumptions. Second, it’s the only approach that has a built-in system for adjusting your calorie target over time. Once you identify an appropriate starting point for calorie intake, you continue to consistently monitor body weight to ensure that you’re staying on track with your intended rate of weight change. If you’re falling short of your weight gain goal, you’d increase your calorie target; if you’re exceeding your weight gain goal, you’d decrease your calorie target accordingly. This ongoing approach to calorie target adjustments is important because it directly accounts for changes in TDEE over time (which are to be expected), and allows the dieter to directly modulate their rate of weight gain in accordance with their current goal and comfort level (which could change over time). So, even if you use the “assume” or “estimate” approach to identify your initial calorie target, you’ll still want to begin monitoring weight changes to determine if this target is appropriate for you (and adjust it as needed). In other words, all roads should lead to the ongoing adjustment process implied by the “observe” approach if you intend to establish and maintain a goal-appropriate calorie target over time.

Throughout this section, I’ve mentioned bulking goals that fall on a spectrum. The most conservative approach is to aim for just slightly higher than maintenance calories (and, by extension, a slow rate of weight gain), while the most aggressive approach involves a very large surplus with a fast rate of weight gain. Choosing between a conservative, moderate, or aggressive approach will ultimately depend on a number of factors. If you’re a relatively inexperienced lifter, you can probably get away with a more aggressive approach to weight gain due to higher potential for substantial muscle growth. If you’re a very experienced lifter and near your genetic limit for muscularity, a more conservative approach would be advised, as substantial muscle gain is relatively unlikely. If your baseline weight is pretty low (relative to your goal), then you’ve got a lot of weight to gain, so a more aggressive approach is advised. If you’ve got a strong aversion to fat gain and are adamant about minimizing it, you’d want to go with a pretty conservative approach. Finally, if urgency is high and you’re in a major hurry to add muscle quickly, an aggressive approach would be your best bet. 

Table 4 presents the different characteristics influencing bulking “category” selections (ranging from approximate maintenance to very aggressive). Each characteristic (training status, starting weight, aversion to fat gain, and urgency) falls on a spectrum, and it’s important to recognize that the bulking “categories” fall on a spectrum as well. For example, a moderate bulk might involve aiming for 105-110% of TDEE and an aggressive approach might involve aiming for 115-120% of TDEE, but someone with a “kind of aggressive” approach could certainly set their target directly between these two categories. Finally, it’s important to acknowledge that the different characteristics influencing category selection are, in some cases, uncorrelated. For example, a new lifter with minimal training experience should be capable of pretty rapid hypertrophy, but they might also have a major aversion to fat gain. Their training status suggests that an aggressive bulk could be a suitable option, but their aversion to fat gain would theoretically nudge them toward a more conservative approach. As such, the only way to maneuver this individualized decision-making process is to strike a balance between one’s circumstances and top priorities.

Summary of contextualized bulking targetsGraphic by Kat Whitfield. What is a Hardgainer?

It’s difficult to discuss bulking diets without acknowledging the concept of “hardgainers.” This colloquial fitness term refers to individuals who find it very challenging to gain weight, despite their best efforts. While some can’t even fathom the concept of struggling to gain weight, it’s a reasonably common thing in the lifting world. There isn’t a ton of research on people who are relatively resistant to weight gain, but a very recent paper (8) sheds some light on the topic. Hu and colleagues sought to explore and quantify some characteristics of people they describe as “healthy underweight” adults, meaning their BMI is naturally below 18.5 for reasons unrelated to eating disorders or any other medical conditions. 

To achieve this objective, the researchers compared the weight-stable, healthy underweight adults (n = 150) to a control group of 173 weight-stable individuals with BMI values between 21.5-25. Due to smaller body size, the healthy underweight adults had lower values (in absolute terms) for resting energy expenditure and total daily energy expenditure. However, when scaled relative to their predicted energy expenditure values (which adjusts for body size), the healthy underweight participants had significantly higher resting and total energy expenditure, despite engaging in less physical activity and burning fewer calories from physical activity. The underweight individuals appeared to eat fewer calories than the normal weight control subjects in absolute terms, but they appeared to eat more total energy on a relative basis (scaled to body size). These findings suggest that higher-than-expected resting metabolic rates could contribute to weight gain resistance in naturally lean individuals. However, I am skeptical that this single characteristic tells the whole story, and I suspect that two additional factors can make it very challenging for an individual to intentionally gain weight.

As mentioned previously in this article, overfeeding can induce an increase in TDEE, largely by increasing non-exercise activity thermogenesis (6). However, the observed increase in TDEE varies considerably from person to person. In a 1999 study, Levine and colleagues fed volunteers an extra 1000kcal per day for eight weeks. Despite the standardized increase in calorie allowance, they found an enormous amount of variability in the amount of weight gained, with 10-fold differences separating the individuals with the most fat gain (4.23kg) from those with the least fat gain (0.36kg). Fat gain was inversely correlated with the increase in total energy expenditure (r = -0.86, p < 0.001) and the increase in non-exercise activity thermogenesis (r = -0.77, p < 0.001; Figure 3). This well-controlled study demonstrated that different individuals gain very different amounts of fat in response to identical calorie increases, and its results directly link overfeeding-induced increases in energy expenditure to resistance to fat gain (and total weight gain). 

The relation of the change in activity thermogenesis with fat gain after overfeedingGraphic by Kat Whitfield.

In summary, it’s very possible, if not likely, that many hardgainers are individuals who experience particularly large energy expenditure increases when they attempt to achieve a calorie surplus. This has important implications when it comes to setting a calorie target for a bulking diet. If a hardgainer tries to implement strategies that set calorie targets based on body mass or an estimated TDEE value (such as the “assume” or “estimate” approach), with no system in place to make incremental adjustments based on progress, they might find that their elevation in TDEE largely or entirely wipes out their planned surplus. This is yet another reason why I recommend the “observe” approach, which involves systematically adjusting your calorie target until a desired rate of weight gain is achieved. For hardgainers, the necessary level of calorie intake is often dramatically higher than expected. Imagine coaching some of the most weight-gain-resistant participants in Levine’s study – a well-planned increase of 1,000 kcal/day beyond maintenance needs, in a well-controlled intervention, yielded a minimum weight increase of only 1.4kg and a minimum fat mass increase of only 0.36kg across a two-month time period.

Aside from inter-individual differences in energy expenditure responses to overfeeding, I suspect that inter-individual differences in appetite regulation play a role as well. Back in Volume 3 of MASS, we had an excellent guest article by Dr. Anne-Kathrin Eiselt (if you haven’t read it yet, I highly recommend it). In that review, Dr. Eiselt describes the multifaceted nature of hunger and satiety regulation, in addition to the complex relationship between the consumption and reward systems of the brain. In short, there are distinct areas of the brain in which we are constantly processing information related to hunger, satiety, and reward sensations. These centers are in a state of ongoing neuroendocrine communication and coordination, and the net balance of these coordinated interactions has a direct impact on one’s appetite and energy intake. 

When it comes to hardgainers, I think it’s best to describe the relevance of these concepts within the context of the dual intervention point model, which Eric Helms described in this article. Within the fitness industry, it’s common to suggest that each individual has a body-fat “set point,” or an individualized body-fat percentage that their body actively works to defend. When taken literally, this theory would suggest that every person’s hunger, satiety, and reward center control is finely tuned to keep them stuck at a single specific body-fat percentage, and any deviation from that exact level of adiposity requires a substantial amount of ongoing intentional effort to maintain. As explained by Speakman et al (9), that theory does a poor job of explaining weight regulation. A more suitable model suggests that each person has a range of body-fat levels in which they generally feel comfortable. An individual’s hunger, satiety, and reward center control systems are tuned to keep them within that broad range of adiposity, but their habits and behaviors dictate whether they’re near the top, middle, or bottom of their genetically predetermined range. As a person starts getting near the bottom end of their comfortable range, also known as their lower intervention point, they start to receive some significant physiological feedback to prevent them from getting leaner (such as increased hunger, reduced satiety, and reduced energy expenditure). As a person starts getting near the top end of their comfortable range, they receive some physiological feedback to prevent them from getting heavier (such as blunted hunger, increased satiety, and increased energy expenditure). The dual intervention point model is presented in Figure 4.

Dual intervention modelGraphic by Kat Whitfield.

So, what does this all mean for hardgainers?

I suspect that many hardgainers exist in a “baseline state” that is quite close to their upper intervention point. For example, a hardgainer’s hunger and satiety circuitry might be wired in a way that sets their upper intervention point in a relatively “low” position, such that the slightest increase in body mass is met with a high degree of friction (in the form of a totally blunted appetite). This has a direct connection to the findings by Levine et al (6), who found that some non-obese individuals gained fat quite readily during overfeeding, while others were quite resistant to fat gain, despite falling in the same BMI range at baseline and receiving the same thousand-calorie increase beyond maintenance needs. We can imagine a very plausible scenario in which the weight-gain-resistant participants in Levine’s study were simply closer to their upper intervention point at the beginning of the study – not because they had dramatically higher adiposity levels, but because their genetically-determined upper intervention point was simply lower. This weight gain disadvantage can be overcome, but not without a focused and strategic effort.

Regardless of upper intervention point positioning, a hardgainer’s challenges might be exacerbated with a neurophysiological reward system circuitry that simply isn’t very responsive to hyperpalatable foods. As reviewed by Dr. Eiselt, hyperpalatable foods can cause robust neurophysiological reward responses that elicit a tremendous sensation of pleasure and enjoyment. However, a simple chat with your friends or family will make it very clear that different people have very different responses to food. Of course we all have specific flavor preferences that differ from one another, but upon closer examination, you’ll also find that the magnitude of pleasure derived from hyperpalatable food is quite variable from person to person. In fact, a growing body of evidence shows that the reward sensation, or magnitude of pleasure derived from eating, can vary over time and among different eating contexts (10), even for the same individual eating the same food. This is relevant to the plight of hardgainers, because stimulation of the brain’s reward system can override satiety cues, which directly enables intake of more calories. This is often viewed as the major “downside” of hyperpalatable foods within the context of weight loss, but robust reward responses to hyperpalatable foods are actually helpful when appetite is blunted during intentional weight gain.

In summary, hardgainers are individuals who struggle to induce intentional weight gain, and they certainly exist in considerable numbers. A number of factors might contribute to this difficulty, such as a higher-than-expected resting metabolic rate, an exaggerated increase in energy expenditure during overfeeding, or a balance of hunger and satiety regulatory circuits that generally lean toward a lack of appetite. Within the context of the dual intervention point model, we might view these individuals as having a baseline status that is already quite close to their upper intervention point, which makes it very difficult to sustainably increase body weight. It’s also quite possible that some hardgainers may simply experience blunted reward sensations in response to hyperpalatable food consumption, which might nudge them toward lower calorie intakes due to lack of interest and an inability to overcome satiety signals via pleasure and reward signaling. 

Strategies for Hardgainers

On paper, the challenges faced by hardgainers are easy to solve. Set a suitable calorie target, and hit it consistently. If that calorie target fails to promote your intended rate of weight gain, incrementally increase your calorie target until you start gaining weight at the intended rate. If your weight gain slows or stalls entirely, incrementally increase your calorie target again. Easy stuff, in theory. In practice, it’s far more challenging. Many hardgainers carry out this incremental process of calorie target adjustment until they inevitably reach a major hurdle: due to extreme fullness and an absence of hunger, it becomes very difficult to reach the daily target for calorie intake. 

Unfortunately, overcoming weight gain challenges isn’t commonly viewed as a major public health priority. With obesity prevalence exceeding 40% in the United States, weight loss has been prioritized extensively in the scientific literature. A great deal of research has been conducted for the purpose of identifying eating habits, patterns, and strategies that increase satiety and reduce hunger to facilitate passive weight loss. As reviewed in a previous MASS article, the evidence generally indicates that hunger can be attenuated by eating more slowly, eating more mindfully in the absence of distractions, avoiding hyperpalatable meals, and structuring meals with low energy density and plenty of unprocessed or minimally processed foods with harder textures. If we invert these findings, we can flip the satiety promotion literature to yield some very helpful strategies for satiety attenuation.

If appetite suppression is a major hurdle preventing a hardgainer from consistently consuming enough energy to gain weight, they’ll likely benefit from incorporating more energy-dense foods. These types of foods will provide a large number of calories while taking up less space on their plate (and in their stomach), which may confer both psychological and physiological advantages favoring increased energy intake. By opting for foods with a high degree of processing and softer textures, a hardgainer may be able to eat more quickly, which appears to facilitate higher calorie intakes before reaching a given satiety level (11). Selection of hyperpalatable foods appears to override some intrinsic satiety signals; this can be counterproductive for weight loss goals, but advantageous for hardgainers. If nothing else, hyperpalatable food selection gives hardgainers a more compelling reason to eat when appetite is low; a tasty meal is inherently rewarding from a neurophysiological perspective, whereas it’s often difficult to compel yourself to force down another plate of plain chicken, broccoli, and sweet potatoes. Finally, there is some evidence to suggest that energy-dense snacking can lead to increased calorie intake over time (12). While the snacking literature is a bit inconsistent (13), it appears that energy-dense snacking is associated with either no change or increases in body weight, and snacking lends itself to a more distracted, less mindful form of eating that could passively facilitate increased energy intake.

In summary, hardgainers who are struggling to hit their daily calorie target should aim to incorporate more foods with higher energy density, greater palatability, softer textures, and a higher degree of processing. Furthermore, meals should be supplemented with palatable, energy-dense snacks that can be consumed somewhat mindlessly throughout the day to encourage passive increases in energy intake. In other words, make a list of the most common hunger-fighting strategies for fat loss diets, then do the exact opposite.

Macronutrient Distribution While Bulking

Once a calorie target is selected, the next step is to address macronutrient distribution (after all, those calories have to come from somewhere). I’ll address protein first, because that’s the simplest of them all. The “standard” evidence-based protein recommendations will do just fine for bulking purposes, whether you’re taking a conservative or aggressive approach. There are some situations where these recommendations might require some adjustments, such as a scenario in which a very lean person is dieting pretty hard (14), but protein is very simple when energy balance is neutral or positive. As a result, individuals on a bulking diet are likely to fully optimize their hypertrophy progress by aiming for around 1.6-2.2 g/kg/day of protein (15), which should scale to roughly 2-2.75 g/kg of fat-free mass (rather than total body mass) per day. If those two different ranges give you very different protein intakes (which may be observed, depending on your weight and body composition characteristics), my recommendation is to scale your protein intake to fat-free mass rather than total body mass. Furthermore, you should split this daily protein target roughly evenly among 3-6 meals per day (one, two). If you want a hyper-optimized meal schedule that relies on a little bit of mechanistic speculation but leaves nothing to chance, you might consider restricting this even further, with an eating schedule that splits protein intake across 4-5 meals per day, with at least 2-3 hours between meals. However, a relevant note for bulkers: if you’re eating relatively low-protein snacks throughout the day to facilitate high daily calorie intakes, these low-protein snacks wouldn’t be counted as “meals.” In this context, a meal will generally provide at least 0.3g/kg of protein per day, or an absolute dose of at least 20-30g of protein.

When it comes to carbohydrate and fat intake, the conversation gets a little more interesting. First, I think it’s defensible to suggest that extreme carbohydrate restriction is generally inadvisable while bulking. Previous MASS articles have noted that ketogenic diets tend to have either similar or slightly worse effects on hypertrophy when compared to more balanced macronutrient distributions, and there is mechanistic evidence to suggest that maintaining an abundant supply of glycogen is generally favorable for lifters. In addition, a very recent meta-analysis indicates that carbs are ergogenic for lifters who complete training sessions that include plenty of maximal-effort sets and/or last longer than 45 minutes in duration (16). There are certainly some scenarios in which lifters can get by with very low carb intakes, but it’s hard to broadly suggest that extreme carbohydrate restriction is an optimal approach to bulking diets for lifters.

On the completely opposite end of the spectrum, some folks suggest that lifters should follow bulking diets with very high carb intakes and pretty aggressive fat restriction. The reasoning for this relatively common recommendation is based on a few distinct observations. First, there is evidence that carb overfeeding increases TDEE more than fat overfeeding (17). This means that a high-carb overfeeding diet would, calorie-for-calorie, lead to slightly less fat gain than a high-fat overfeeding diet, which has been observed in the published literature (18). Second, it has become fairly common knowledge that de novo lipogenesis (the process by which our bodies convert carbohydrate to fat for long-term storage) is rarely observed in real-world scenarios, such that de novo lipogenesis typically makes negligible contributions to the storage of additional fat mass (19). Many folks interpret this to mean that people who overshoot their calories on a high-carb bulking diet will neglect to store the excess calories as fat, thus allowing for an aggressively high calorie target without the risk of excessive fat storage. Third, proponents of this high-carb bulking strategy often point to a particular piece of empirical evidence that seems to lend support. An abstract published in 2011 seems, at the surface level, to suggest that high-carb, high-calorie bulking with aggressive fat restriction leads to more hypertrophy and less fat gain than a very similar diet with less aggressive fat restriction. While the abstract itself is hard to find these days, it was covered in an excellent write-up on the SuppVersity blog several years ago.

Personally, I am skeptical that high-carb bulking with extreme fat restriction is the “cheat code” that some proponents make it out to be. First, I’ll acknowledge that high-carb overfeeding does increase TDEE more than calorie-matched high-fat overfeeding (17), which is primarily due to the fact that carbs have a higher thermic effect of feeding than fat (18), particularly when consumed in large quantities. However, this isn’t necessarily an advantage in all contexts. If you’re perpetually hungry and looking for a more satiety-inducing diet while bulking, this might be a helpful and actionable observation, and you might consider opting for a relatively high-carb, high-fiber, high-protein approach. However, this is actually an extra challenge from the perspective of a hardgainer who’s struggling to consume enough calories to support weight gain. There is definitely a difference in the thermic effect of carb versus fat overfeeding, but whether or not that’s an advantage or disadvantage depends on the context, and the magnitude of the effect isn’t particularly large – for example, Dirlewanger et al (17) found that a 40% energy surplus (140% of TDEE) increased TDEE by about 140 kcal/day during high-carb overfeeding, whereas high-fat overfeeding increased TDEE by almost half of that. A similarly small difference between high-fat and high-carb overfeeding was observed by Horton et al (18), which suggests that this difference is more interesting than it is impactful.

Next, it’s important to contextualize the claim that de novo lipogenesis is rarely observed in real-world applications, to the extent that we can largely disregard its role in the maintenance of human fat stores. It is true that “real-world scenarios” (that is, diets with relatively balanced macronutrient contents) generally don’t lead to meaningful amounts of de novo lipogenesis. For example, an overfeeding study by McDevitt et al (19) concluded that de novo lipogenesis “does not contribute greatly to total fat balance,” and the results of an overfeeding study by Horton et al (18) indirectly suggest that de novo lipogenesis did not occur to an extent that would meaningfully impact total fat storage. However, there’s a huge caveat to keep in mind with these studies: fat intake was not aggressively restricted. De novo lipogenesis is a convoluted and energetically costly pathway. As a result, the human body prefers not to use it unless it’s actually necessary. If you’ve got tons of carbohydrate and fat available after a meal, your body is inclined to take the easiest and most efficient path, which involves burning the carbs for immediate energy and storing the fat for later use. 

It’d be hard to justify the process of converting extra carbs to fat for storage while you’re simultaneously burning fat to meet immediate energy demands – a more straightforward and energy-efficient strategy is to store the stuff that’s already in a storable form (the dietary fat from the previous meal). To draw on an analogy, imagine that I owe you $20 USD and you owe me $15 USD. It would be possible for me to pay you $20 USD and request that you mail me $15 USD worth of Euros, which I could then take to the bank, convert back to USD, and deposit into my account. Or I could just give you five bucks. 

Your body is more than capable of converting extra carbs to fat for storage if absolutely necessary, and if you’ve got a huge surplus of carbs and fully saturated glycogen stores, that’s exactly what will happen. In a high-carb overfeeding study, Acheson et al (20) implemented a multiple-day glycogen depletion protocol, followed by seven days of high-carb overfeeding. Notably, fat intake was aggressively restricted to only 3% of total energy. In short, the extra calories were handled exactly how you’d expect them to be handled. At first, a bunch of the carbs were allocated toward refilling the recently depleted glycogen stores. Once glycogen stores were topped off, participants had to deal with a huge surplus of calories that were almost exclusively coming from carbohydrates. Even after sending the small amount of dietary fat straight to storage and burning carbs to meet immediate energy needs, there were still a ton of carbs left over. As a result, the participants used the de novo lipogenesis pathway to convert the carbs to fat and store the extra energy for later. As a result, the researchers concluded that glycogen stores “can accommodate a gain of approximately 500 g before net lipid synthesis contributes to increasing body fat mass.”

In summary, it’s true that de novo lipogenesis is pretty negligible in most real-world scenarios and nutrition studies. However, that’s mostly because real-world scenarios and nutrition studies rarely involve massive amounts of carbohydrate overfeeding combined with aggressive fat restriction. When possible, the default preference of the human body is to allocate extra dietary fat toward storage and to burn extra dietary carbohydrate. For example, imagine a scenario in which you’ve overshot your energy surplus a bit, and you’re eating an extra 300 kcal/day beyond the energy needed to support muscle growth. If you’re eating 80g of fat per day (which is 720 kcal/day from fat), the path of least resistance is to simply store 300kcal worth of the dietary fat that was consumed. However, if we try to “cheat the system” by creating a bulking scenario in which our leftover energy greatly exceeds our glycogen storage capacity and daily fat intake, the extra calories from carbs aren’t going to disappear – we’re more than capable of converting them to fat and storing them. 

So, my carb and fat guidelines for bulking are pretty simple: get at least 3-4g/kg/day of carbohydrate, and calculate your daily fat minimum (in grams) by subtracting 150 from your height (in cm), then dividing the outcome by 2, and adding 30. So, someone who is 180cm tall would have a daily fat minimum of (180-150)/2 + 30, which equals 45g/day. If you’re under 150cm tall, you probably want to ignore this equation and set your lower boundary to a default value of 30g/day. These guidelines should help to ensure that most dieters are getting enough carbohydrate to fuel their training and enough fat to support good health. Notably, these guidelines are bare minimums, and bulking diets tend to involve pretty high calorie targets, which means you have a ton of wiggle room to work with. As long as you’re meeting or exceeding the bare minimums for carb and fat intake, their exact ratio is pretty inconsequential while bulking, so you should feel free to eat in accordance with your preferences. 

Should I Bulk, Cut, or Recomp?

For the huge number of folks whose goal physique involves more muscle and less fat mass, it can be challenging to construct a plan for tackling these distinct subgoals. When determining if the best immediate course of action should involve bulking, cutting, or trying to achieve recomposition, it’s hard to provide a generalizable answer for everyone. However, there are some answers that we can categorize as generally inadvisable. 

Some folks might answer by indicating that recomping is virtually impossible, then nudging you toward a large energy deficit or a large energy surplus. As we’ve already covered, this isn’t true, and it’s especially untrue for people with high levels of adiposity or relatively minimal training experience. As such, there are some folks who might wish to begin by recomping rather than bulking or cutting, whereas others might opt for a sequential, multi-step approach that starts with a dedicated phase to explicitly focus on either fat loss (cut) or muscle gain (bulk). As noted previously, some people can also “split the difference” – if you can’t decide between cutting or recomping, you can just do a very conservative cut and try to get the best of both worlds. Similarly, if you’re torn between bulking or recomping, you can just do a very conservative bulk. 

Some folks might answer by indicating that you should cut first, because weight loss will potentiate future hypertrophy by enhancing insulin sensitivity or reducing basal inflammation levels. This response is tied to the concept of p-ratios, which was first proposed by Forbes as a way to model relative changes in fat mass and fat-free mass among people who do not lift weights (21). If you’re new around here, this is a topic I’ve covered extensively – first as a MASS article, and then as a three-part Stronger By Science article series (one, two, three). Needless to say, there’s plenty of content to dig into if you’d like to explore this topic in detail. The short version of the conclusion is that this p-ratio concept has minimal relevance to people who are regularly lifting weights, and the overwhelming majority of evidence in lifters contradicts the idea that getting leaner will increase the magnitude or rate of hypertrophy achieved during a subsequent bulk. In fact, we did our own participant-level meta-analysis with full data sets from seven different resistance training studies, resulting in complete data from over 160 study participants. We created a “lean gains” metric, which is simply the change in fat-free mass minus the change in fat mass, and found that leanness did not confer the theoretical advantage implied by the p-ratio concept (Figure 5).

Relationship between baseline body-fat percentage and change in "lean gains" metricGraphic by Kat Whitfield.

After digging deeper into the data, it became clear that participants with lower and higher body-fat percentages were achieving similar magnitudes of hypertrophy, whether you’re looking at changes in fat-free mass or direct measurements of muscle thickness. The primary difference was that individuals with higher baseline body-fat levels were more likely to lose a little bit of fat during resistance training interventions, but they were still able to achieve substantial hypertrophy in the absence of fat gain, or even in the presence of simultaneous fat loss. Thus, we concluded that getting leaner does not potentiate hypertrophy in a subsequent bulk, and that people with higher baseline body-fat are more capable of recomping. If anything, you could justify speculating that individuals with higher body-fat levels had slightly greater capacity for hypertrophy, given that they achieved similar amounts of hypertrophy in spite of less positive energy balance (as demonstrated by the tendency for fat loss).

A third inadvisable answer would encourage an individual (whose long-term goal involves being very lean) to get to a very low body-fat level (<10% body-fat for males, or <18% body-fat for females), then bulk from there while maintaining their hard-earned leanness. The participant-level analysis from our p-ratio article found that every single person under 8% body-fat at baseline had some degree of fat gain in the seven resistance training studies for which we had subject-level data, and only one of these individuals gained more than 1kg of fat-free mass. Based on these observations, in addition to the broader body recomposition literature (3), the probability of a very lean person gaining meaningful amounts of muscle mass without some degree of concomitant fat gain appears to be fairly low, which defeats the purpose of getting shredded on the front end of a bulk.

When deciding to bulk, recomp, or cut (and, by extension, how aggressively to bulk or cut), a lifter should consider several factors. As listed in Table 4, they should first reflect on their training status, starting weight, aversion to fat gain, and urgency. In doing so, they might clarify their own priorities well enough to make their decision quite easily. If not, I can offer my own perspective on how to navigate this dilemma. There are definitely some folks who feel that their starting level of adiposity is very incompatible with their day-to-day aesthetic goals, or contributing to some cardiometabolic health markers that are currently outside of their preferred ranges. If you’re starting in a spot where weight gain has a high probability of fueling some mild dissatisfaction related to body image, or ongoing concerns related to cardiometabolic health, then starting with a cut makes all the sense in the world (as a side note, it’d be a good idea to address any severe instances of body image dissatisfaction with a qualified mental health professional). 

However, for lifters who are comfortable with their current body-fat level, generally fine with a little bit of additional fat gain, and know they want to gain a considerable amount of muscle over the remainder of their lifting journey, my general preference is to err toward bulking first and cutting later. Anecdotally, my observation is that many lifters’ “ideal body-fat level” (based on their personal goals and preferences) is either close to or below their lower intervention point (Figure 4). This means that the later stages of the cutting process is likely to get pretty tough, and the likelihood of sustaining that level of leanness during a subsequent (presumably conservative) bulking phase is fairly unlikely. I’ve also noticed that many folks who take the “cut first, bulk later” approach tend to be a bit dissatisfied with the results of their first cut. They often feel more “thin” and less “shredded” than they initially anticipated, largely because they underestimated exactly how much muscularity is required for a physique to have a “shredded” appearance. Furthermore, if their “ideal body-fat level” is absolutely shredded, or substantially below their lower intervention point, it’s quite likely that hypertrophy might be impaired. As noted previously, our p-ratio analysis seemed to indicate that it’s very hard to make lean gains at low body-fat levels. 

With these considerations in mind, it’s very possible that a “cut first” approach could lead to some initial dissatisfaction when the initial cut is complete, and could also make the long-term goal striving process a little more challenging and a little more uncomfortable than it needs to be. However, that doesn’t mean it’s always a bad plan. For example, you might have a client whose lower intervention point is around 10% body-fat, would like to eventually be as lean as they can sustainably maintain, and generally dislikes to get above 16% body-fat while bulking (based on their personal aesthetic or health-related preferences). If they’re currently around 18% body-fat, it would be very defensible to cut to around 12-13% body-fat (comfortably above their lower intervention point), bulk until they reach about 15-16%, then oscillate back and forth between cutting and bulking phases until they’ve reached their ideal level of muscularity. At that point, they can cut down to around 10-11% body-fat as a reasonably comfortable maintenance range that is just above their lower intervention point. If they wish to be extra lean for certain special occasions (like a wedding, vacation, photo shoot, competition, etc.), they can temporarily cut down to a leaner body-fat level for a brief period of time, then settle back to their comfortable maintenance level when the special occasion has passed. 

In summary, there are many factors to consider when deciding to bulk, cut, or recomp, and there is no one-size-fits-all approach. It’s important to thoughtfully reflect on individualized factors related to one’s hypertrophy potential, short-term priorities, and long-term goals prior to making a decision. Furthermore, the decision about where to start is, by definition, just the beginning. A lifter is likely to be consistently bouncing between short-term recomping, bulking, and cutting phases throughout the entirety of their fitness journey. So, with that in mind, don’t overthink the decision too much – the impact on body composition will become functionally irrelevant as enough time passes and a lifter shifts from phase to phase. The only way to totally screw this decision up is to choose a path that stifles a lifter’s ability to enjoy the process. Anything that stifles enjoyment or enthusiasm early in a lifter’s fitness journey has the potential to thwart motivation and derail the entire process.

One Last Thing: What About Cardio?

There’s one last topic I’d like to briefly address before wrapping things up. A common misconception is that bulking necessarily requires an intentional avoidance of cardio and other non-lifting physical activity. On the surface, it’s an intuitive conclusion – people who are struggling to achieve an energy surplus aren’t eager to increase their energy expenditure, and many people are at least vaguely aware of the “interference effect,” which describes the attenuation of resistance training adaptations caused by concurrent cardio training. Fortunately for people who enjoy non-lifting physical activity (or simply value its health benefits), bulkers don’t necessarily need to avoid cardio at all costs.

First, let’s address the interference effect. This is a topic that’s been covered numerous times in MASS, so I’ll simply restate the main conclusions here. It is very true that studies have observed an attenuation of resistance training adaptations when cardio is added to the mix. However, this interference is far more pronounced for power adaptations than strength adaptations, and even less pertinent to hypertrophy adaptations. Furthermore, the cardio “dose” required to meaningfully interfere with resistance training adaptations tends to be pretty large (e.g., pretty arduous sessions at least 5-6 days per week). As Greg highlighted in one of his Research Spotlight articles, the interference effect isn’t quite as scary as some make it out to be, especially for people with hypertrophy-focused goals and light-to-moderate doses of weekly cardio training. 

In contrast to the large amounts of cardio required to meaningfully attenuate hypertrophy, noteworthy health benefits can be obtained from very modest amounts of cardio or non-lifting physical activity. For example, walking a mere 8,000 steps per day has been associated with a sizable reduction in all-cause mortality (22). In addition, the US guidelines for physical activity call for for 150-300 weekly minutes of exercise at “moderate” MET levels (3.0-5.9 METs), 75-150 weekly minutes of exercise at “vigorous” MET levels (≥6.0 METs), or a combination of the two. For context, some household chores like sweeping the floor or “general kitchen activity” are above 3 METS (i.e., in the “moderate” category), and a very brisk walk (4.5mph) can get you into the “vigorous” category (23).

In summary, a relatively small amount of cardio is needed for meaningful health benefits, and a very large cardio dose is needed to meaningfully interfere with hypertrophy adaptations. As a result, the typical bulker who’s doing non-lifting physical activity for the purpose of enjoyment or general health is unlikely to be racking up cardio doses large enough to impair hypertrophy. Similarly, they’re unlikely to be racking up cardio doses large enough to dramatically increase TDEE, so a little bit of extra activity shouldn’t make it prohibitively difficult to establish an energy surplus large enough to support hypertrophy. In conclusion, a successful bulk does not necessarily require that individuals alter their cardio or non-lifting physical activity habits. As long as you’re able to consume a suitable amount of calories and you aren’t doing cardio doses that resemble a highly competitive endurance athlete, additional physical activity should be pretty irrelevant. 

Application and Takeaways

While recomposition is definitely possible in a variety of circumstances, the majority of lifters will eventually find themselves in a position where a dedicated bulking phase is warranted to optimize hypertrophy.

The first priority when bulking is to establish a state of positive energy balance (i.e., a calorie surplus), as extra energy is needed to accommodate the energy cost of building and maintaining new muscle tissue. It’s certainly important to get enough protein while bulking (1.6-2.2g/kg of body mass, or 2-2.75g/kg of fat-free mass), but the ratio of carbohydrate to fat in the diet is highly flexible.

For many individuals, bulking is a fairly manageable process of estimating one’s total daily energy expenditure, setting a calorie target, and adjusting it to maintain the intended rate of weight gain. However, there are many hardgainers who run into considerable friction while bulking, which may be related to elevated resting metabolic rate, exaggerated increases in energy expenditure, inter-individual differences in hunger and satiety regulation, or blunted reward responses to hyperpalatable food. We can conceptualize hardgainers as being near their “upper intervention point” at baseline, and they may need to lean on dietary strategies that completely invert the guidelines that would typically increase satiety and reduce hunger.

Bulkers need not worry about getting lean before their bulk or aggressively restricting their non-lifting physical activity, but they should carefully consider their current circumstances and priorities when deciding when (and how aggressively) to bulk.

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3. Barakat C, Pearson J, Escalante G, Campbell B, De Souza EO. Body Recomposition: Can Trained Individuals Build Muscle and Lose Fat at the Same Time? Strength Cond J. 2020 Oct;42(5):7–21.

4. Murphy C, Koehler K. Energy Deficiency Impairs Resistance Training Gains In Lean Mass But Not Strength: A Meta-Analysis And Meta-Regression. Scand J Med Sci Sports. 2022 Jan;32(1):125-137.

5. Slater GJ, Dieter BP, Marsh DJ, Helms ER, Shaw G, Iraki J. Is an Energy Surplus Required to Maximize Skeletal Muscle Hypertrophy Associated With Resistance Training. Front Nutr. 2019;6:131.

6. Levine JA, Eberhardt NL, Jensen MD. Role Of Nonexercise Activity Thermogenesis In Resistance To Fat Gain In Humans. Science. 1999 Jan 8;283(5399):212–4.

7. McClave SA, Snider HL. Dissecting The Energy Needs Of The Body. Curr Opin Clin Nutr Metab Care. 2001 Mar;4(2):143–7.

8. Hu S, Zhang X, Stamatiou M, Hambly C, Huang Y, Ma J, et al. Higher Than Predicted Resting Energy Expenditure And Lower Physical Activity In Healthy Underweight Chinese Adults. Cell Metab. 2022 Jul 14; ePub ahead of print.

9. Speakman JR, Levitsky DA, Allison DB, Bray MS, Castro JM de, Clegg DJ, et al. Set Points, Settling Points And Some Alternative Models: Theoretical Options To Understand How Genes And Environments Combine To Regulate Body Adiposity. Dis Model Mech. 2011 Nov;4(6):733.

10. Rolls ET. Reward Systems in the Brain and Nutrition. Annu Rev Nutr. 2016 Jul 17;36:435–70.

11. de Graaf C, Kok FJ. Slow Food, Fast Food And The Control Of Food Intake. Nat Rev Endocrinol. 2010 May;6(5):290–3.

12. Tey SL, Brown RC, Gray AR, Chisholm AW, Delahunty CM. Long-Term Consumption Of High Energy-Dense Snack Foods On Sensory-Specific Satiety And Intake. Am J Clin Nutr. 2012 May;95(5):1038–47.

13. Njike VY, Smith TM, Shuval O, Shuval K, Edshteyn I, Kalantari V, et al. Snack Food, Satiety, and Weight. Adv Nutr. 2016 Sep;7(5):866–78.

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15. Morton RW, Murphy KT, McKellar SR, Schoenfeld BJ, Henselmans M, Helms E, et al. A Systematic Review, Meta-Analysis And Meta-Regression Of The Effect Of Protein Supplementation On Resistance Training-Induced Gains In Muscle Mass And Strength In Healthy Adults. Br J Sports Med. 2018 Mar;52(6):376–84.

16. King A, Helms E, Zinn C, Jukic I. The Ergogenic Effects of Acute Carbohydrate Feeding on Resistance Exercise Performance: A Systematic Review and Meta-analysis. Sports Med. 2022 Jul 9; ePub ahead of print.

17. Dirlewanger M, di Vetta V, Guenat E, Battilana P, Seematter G, Schneiter P, et al. Effects Of Short-Term Carbohydrate Or Fat Overfeeding On Energy Expenditure And Plasma Leptin Concentrations In Healthy Female Subjects. Int J Obes Relat Metab Disord. 2000 Nov;24(11):1413–8.

18. Horton TJ, Drougas H, Brachey A, Reed GW, Peters JC, Hill JO. Fat And Carbohydrate Overfeeding In Humans: Different Effects On Energy Storage. Am J Clin Nutr. 1995 Jul;62(1):19–29.

19. McDevitt RM, Bott SJ, Harding M, Coward WA, Bluck LJ, Prentice AM. De Novo Lipogenesis During Controlled Overfeeding With Sucrose Or Glucose In Lean And Obese Women. Am J Clin Nutr. 2001 Dec;74(6):737–46.

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The post Optimizing Bulking Diets To Facilitate Hypertrophy appeared first on Stronger by Science.

- Eric Helms
Can You Stay Shredded?

Note: This article was the MASS Research Review cover story for July 2022. If you want more content like this, subscribe to MASS.

Getting really lean is a common goal, which a fair number of people regularly achieve, and it’s easy to find trainers and books to help you do so. However, getting lean and staying lean is often viewed as a holy grail, at least in bodybuilding-centric circles. People pursue this goal for many reasons, and it’s something many can relate to. As a bodybuilder and fan of bodybuilding, I’m awestruck by physiques lean enough to display all the anatomical muscular details of the human body. Thus, when I go through the grueling process of contest prep to get shredded, there’s always a part of me that wonders if maybe I could stay shredded, or at least stay something close to shredded. If you look around the fitness industry, and see what people buy, click on, and try, it’s apparent I’m not alone. 

The question is, why is it so hard for people to stay shredded once they get there? Speaking generally, regardless of the end-point body composition achieved, the difficulty of maintaining clinically meaningful, long-term weight loss is well established (1). For those interested in learning how difficult it is (and why), I highly recommend reading Dr. Ben House’s excellent, in-depth MASS guest article (MASS subscription required) on this topic. However, while Dr. House’s review covers a related question, the present article isn’t about how hard it is to maintain weight loss. Rather, it specifically addresses the question of whether it’s sustainable to maintain a very low body fat. 

To discuss sustainability, however, I must acknowledge the subjectivity of the word. Everyone can technically sustain an extremely low level of body fat. Hypothetically, if you were locked in a room and only fed sufficient energy to lose weight until you got to essential levels of body fat, and then subsequently only fed enough to maintain those levels of body fat, you’d sustain a shredded physique. Whether or not you’d have full physiological functionality doing so, and whether you’d enjoy the experience enough for it to be worth it, however, are the more relevant questions. Indeed, if you’ve ever spoken to bodybuilders, they almost universally express sentiments of how difficult it is to get shredded for competition. At 3DMJ, we’ve collectively prepped thousands of drug free physique competitors in the last decade, and we’ve been intimately involved in bodybuilding culture. When discussions of contest prep come up, we hear the same anecdotal reports time and time again of how it gets harder and harder as the weeks pass. Physique athletes report getting hungrier, more food focused, lethargic, tired, and irritable, and veterans notice they seem to get ill and injured more frequently as they get leaner. Indeed, many of these anecdotal experiences are mirrored in studies of physique competitors during contest preparation and recovery. A collection of these findings are shown in Table 1, adapted from a review I led on the challenge of making physique sport a sustainable practice (2). However, it’s difficult to parse out whether these experiences and observations are caused by the state of being really lean, the process of getting really lean, or a combination of the two.

Graphic by Kat Whitfield. Energy availability and RED-S

To better understand the causes of the negative symptoms associated with getting really lean, we must discuss “relative energy deficiency in sport” (RED-S). RED-S describes the “impaired physiological functioning caused by relative energy deficiency, and includes but is not limited to impairments of metabolic rate, menstrual function, bone health, immunity, protein synthesis, and cardiovascular health” (3). Importantly, research directly links RED-S to being in a chronic state of low energy availability, defined as the amount of calories consumed relative to lean body mass (LBM) when taking exercise activity into account. Mathematically, this is expressed as: (total energy intake – exercise expenditure) / LBM. If this value gets too low, athletes experience increased prevalence of RED-S symptoms. As reviewed by Anne Loucks (4, 5), a seminal researcher in this field, signs of metabolic and reproductive hormonal downregulation associated with RED-S are observed in diverse populations from lean, male Army Rangers during training, to exercising and sedentary normal weight women, to women with obesity undergoing rapid weight loss, when energy availability falls below ~30kcal/kg of LBM/day through any combination of increased exercise energy expenditure and/or decreased energy intake. 

While 30kcal/kg/LBM/day is a decent rule of thumb to keep in mind, it should not be seen as a universal threshold that applies to all (6). Furthermore, most physique athletes in my experience simply won’t get into adequate contest shape without going lower than 30kcal/kg of LBM/day at a certain point, and even if you can stay above it, there is substantial individual variation as to when symptoms of RED-S crop up (in many cases, the threshold among athletes is higher, in the 30-45kcal/kg of LBM/day range). Differences in baseline non-exercise activity, one’s composition of LBM, and other individual physiological differences cause the appropriate energy availability for a given person to vary (6). Regardless of where an individual’s threshold for low energy availability lies, you can view going below it as there not being enough “left over” energy for physiological function. When this continues chronically, adaptive downregulation across various aspects of physiology occur, which can impact performance and health (Figure 1). 

Graphic by Kat Whitfield.

RED-S is relatively common among athletes with a high energy output, such as endurance athletes, or among athletes who are likely to restrict energy intake (3), such as physique athletes, weight class athletes, or athletes who benefit from a high power-to-weight ratio. When reflecting on the effects of RED-S and how energy availability is calculated, you might notice two things: 1) RED-S symptoms line up with the experiences of bodybuilders during contest prep, and 2) body fatness is not part of the energy availability equation. So, does this mean if a bodybuilder was to diet down to stage condition, then simply increase their calories or decrease their training energy expenditure to get out of a deficit, they’d be able to avoid all the RED-S symptoms and stay lean consequence free? Well, despite the current understanding that the singular cause of RED-S is low energy availability, independent of leanness, it is a little more complicated than that. 

Adaptive thermogenesis 

Stronger By Science and MASS readers are likely more familiar with the concept of metabolic adaptation, known more commonly in the literature as “adaptive thermogenesis,” than they are with RED-S and energy availability. Briefly, adaptive thermogenesis refers to a reduction in total energy expenditure following weight loss (or the increase following weight gain) beyond what would be predicted by changes in body composition (7, 8). For a deep dive, Dr. Trexler has a fantastic article that outlines its mechanisms and how to address it while dieting, and during maintenance post-diet. While the study of adaptive thermogenesis is distinct from the study of low energy availability, the two fields are interrelated and describe the same phenomena from different perspectives. The fitness industry focuses on adaptive thermogenesis because this research has been around longer and it attempts to understand how reductions in energy expenditure manifest, and how they impact efforts to lose weight and maintain weight loss. This lines up with the interests of the fitness industry, while low energy availability research doesn’t line up quite as well, as it addresses how to adequately fuel athletes for health and performance. 

Since adaptive thermogenesis is studied in relation to weight loss, the focus is on energy balance, rather than energy availability. People often have a difficult time conceptually integrating the two concepts, especially if they are new to the latter. The way to understand the link between the two is to consider the effects of adaptive thermogenesis beyond the simple quantitative reduction in energy expenditure. The causes of reduced energy expenditure are due to reduced sympathetic and increased parasympathetic nervous system tone and downregulation of the hypothalamic pituitary-thyroid and -gonadal axes, resulting in decreases in heart rate, thyroid hormone production, increases in skeletal muscle work efficiency at low intensities, decreases in non-exercise activity expenditure, and reductions in sex hormone production (8). But these physiological changes don’t just reduce energy expenditure in a vacuum. Many of these changes also cause the symptoms associated with RED-S. Adaptive thermogenesis describes the degree to which the downregulation of physiological systems impacts energy expenditure, while RED-S describes how the downregulation impacts health and performance. 

Importantly, you can be at energy balance while being in a state of low energy availability and experiencing symptoms of RED-S. Unfortunately, adaptive thermogenesis doesn’t only occur during weight loss, but can persist during weight maintenance. In a classic study by Rosenbaum (7), seven trios of weight and sex matched participants spanning a range of bodyweights were compared. Each trio consisted of a participant who had lost at least 10% of their bodyweight and was maintaining that loss for 5-8 weeks, a participant who had lost at least 10% of their bodyweight and was maintaining it for at least a year, and a participant at their usual weight. Total energy expenditure was significantly lower among the weight-reduced participants compared to the participants at their usual weight, regardless of whether the weight loss had been maintained for 5-8 weeks, or a year or longer. Further, the reductions in energy expenditure were similar between the two weight-reduced groups. This seems to be a consistent trend when assessing the literature broadly (8), as 10% weight-reduced study participants display a ~15% lower total daily energy expenditure on average compared to their non-weight-reduced counterparts.

Considering the above, let’s do a little bit of math. Using this calculator (9), a 170cm (~5’6”), 70kg (~154lbs), 25 year old, very lean woman at 12% body fat, who performs moderate exercise 4-5 days per week, has an estimated daily energy expenditure of 2491kcals. If she was sedentary, she would instead have an expenditure of 2041kcals; the difference between these two values can be used to represent her average exercise energy expenditure of 450kcals per day. If this woman was previously 77kg, and had lost 10% of her bodyweight to reach 70kg, we could reasonably expect a ~15% reduction in energy expenditure based on the literature. Thus, her daily energy expenditure of 2491kcals would instead be ~2117kcals. At 70kg and 12% body fat, she has 61.6kg of LBM. Thus, if she was eating at maintenance following weight loss, we could calculate her energy availability using the previously mentioned equation ([total energy intake – exercise expenditure] / LBM) as follows: (2117kcals – 450kcals) / 61.6kg = 27.1kcal/kg of LBM/day.

As you can see, this intake, despite being her maintenance calories, is below the ~30kcal/kg of LBM/day average threshold for low energy availability where we’d anticipate symptoms of RED-S would occur. 

Certainly, not everyone experiences a 15% reduction in total energy expenditure after weight loss; some experience less, some more. But, on average, if we accept the current understanding that energy availability is the sole cause of RED-S with no influence of body composition, it seems unlikely that the majority of individuals would be able to maintain a very low body fat after weight loss without experiencing some symptoms of RED-S. However, this begs the question: if it just comes down to energy availability, and body fat doesn’t enter the equation, why does physiological function remain downregulated in weight-reduced individuals in the first place?   

Body fat “set points” and leptin

To answer the question I just posed, I don’t think it comes down to energy availability exclusively. I think body fat plays a role, and it’s hard to think otherwise when you understand the physiology at play. If you’ve observed discussions on dieting in the evidence-based fitness space, you might have heard the concept of a “body fat set point.” Generally, the idea is that people have a level of body fat that is “defended” (i.e., adaptive thermogenesis occurs) when fat loss takes you below it, or when fat gain takes you above it. This concept originated from scientific research that’s been ongoing for the better part of 70 years. Indeed, the set point concept describes the original “lipostatic” model of body weight regulation proposed by Kennedy in 1953 (10). This model states that, like a thermostat, adipose tissue sends signals to the brain indicating whether body fat stores are below, at, or above a person’s body fat set point. In response, the brain sends signals to downregulate, maintain, or upregulate energy expenditure, and increase, maintain, or decrease energy intake, respectively, to get back to the body fat set point. This model was largely theoretical until the discovery of leptin in the 1990s (11), a hormone that seemed to act as the proposed signal from the lipostatic model. 

Leptin is a hormone secreted by adipose tissue in proportion to the amount of adipose tissue present (12), and, in initial animal models, leptin would decrease with weight loss, increase with weight gain, and returned to baseline when animals compensatorily increased or decreased food intake following these states to return to a seeming “set point” (13). However, the pure lipostatic model has a lot of problems, and is not the current model used to understand body weight regulation. From an observational perspective, the lipostatic model fails to explain the obesity epidemic, and from a mechanistic perspective, leptin doesn’t behave exactly like the lipostatic model’s signal is supposed to. Specifically, it seems leptin release from adipose tissue is impacted by metabolic hormones, such as insulin and others, that respond acutely to feeding and fasting (14). Leptin decreases precipitously upon the initiation of fasting, and this response precedes (and is disproportionate to) changes in body fat. Further, as research on leptin continued, it was discovered that, while leptin is primarily produced by adipose tissue (15), it is produced (and there are receptors for it) in other tissues as well, notably the stomach. Gastrically produced leptin is thought to be a signaller of short-term energy availability, while adipose tissue derived leptin may act as a long term signal of energy availability (16). Indeed, changes in macronutrients and energy intake can acutely change leptin (17). Also out of step with the lipostatic model is that leptin is much more effective at encouraging weight gain when levels are low, as opposed to encouraging weight loss when levels are high. Indeed, circulating leptin is quite high in those with common forms of obesity, but does not suppress excess energy consumption enough to cause weight loss (18).  

As reviewed by Speakman and colleagues (13; notably this is open access and very informative), to account for these observations and complexities, the “dual-intervention model” of body weight regulation was eventually proposed, which arguably is the best fit for the currently available data. It accounts for environmental factors that can overcome physiological set points, which lines up with the obesity epidemic and the nuances of leptin physiology. As shown in Figure 3, there are upper and lower points where physiological factors primarily influence energy intake and expenditure, modifying adiposity. Between these points, however, environmental factors dominate. These upper and lower points are thought to exist due to evolutionary predation and famine selection pressures (i.e., being too heavy and slow made you more likely to be eaten, being too lean made you more vulnerable to famine), respectively (13). Arguably, the latter was a greater threat to humans, resulting in a better defended lower intervention point, hence the struggles many have with weight gain and regain. 

Graphic by Kat Whitfield.

This model provides hope for those interested in maintaining a lower body fat. Based on the model, if you can modify your environment to do the opposite of what the modern, obesogenic environment has done to our collective waist lines, you should be able to hang out closer to your lower, rather than your upper, intervention point. In fact, by examining people living in a non-modern environment, we can see this is probably the case. One such group, the Amish, live in traditionalist communities that typically don’t adopt most conveniences of modern technology. Bassett and colleagues (19) examined the physical activity and body composition of a sample of 98 Amish men and women from a community in Ontario that did not use electricity or gas power, and of whom the majority of men were farmers (78%) and the majority of women were homemakers (69%). The researchers gave the Amish participants pedometers to track their step count, and assessed their body composition via bioelectrical impedance measurements. In this agricultural community, they made their own food, and the requisite activity levels for day-to-day work were very high compared to modern standards. The men walked an average of 18,425 ± 4,685 steps per day, and the women an average of 14,196 ± 4,078. Interestingly, the men had an average body fat percentage of 9.4 ± 4.3%, and the women an average of 25.3 ± 6.7%. Importantly, these are bioimpedance measurements, so they aren’t as accurate or reliable, even at the group level, as alternative measurement options like DXA. However, with a sample of nearly 100 individuals, they are likely close to the true values. Notably, the more active men were maintaining, on average, a single digit body fat percentage. The women weren’t as lean relatively, even taking sex differences into account (the rough female equivalent to a male at ~9-10% body fat is ~17-18%), and also weren’t as active. While it’s tempting to isolate this difference in body fat percentage to the men being more active, it’s not as though the women weren’t reasonably active as well. Rather, other cultural or environmental aspects were likely at play, which led to the women being relatively higher in body fat (for example, the authors noted Amish women have an average of seven children, which can result in a higher average body fat). So, if we assume the men didn’t have RED-S – a reasonable assumption as the community had an ample food supply (earlier research on Amish men reports a daily energy intake of ~3600kcal/day [20]) and they weren’t athletes trying to stay lean – this suggests your environment plays a major role in how lean you stay. In support of this contention, decreases in sedentary activity (21) and ultra-processed food consumption (22) can lead to maintaining lower body fat levels. However, it’s important to point out that 9.4 ± 4.3% body fat is not 5 ± 1%  body fat. These Amish dudes are lean, some more and some less than others, but on average they aren’t ready to don posing trunks to show off their striated glutes. 

Putting it all together

If we put these models and observational data together, we can construct a relatively clear, albeit simplified (23), theoretical explanation of what determines the level of leanness you can sustainably maintain. Starting with the dual intervention model as the backdrop, when you are between your lower and upper intervention points of adiposity, you’ll likely feel fine. However, bringing in the RED-S model, this is only true until you reduce your energy intake or increase your energy expenditure to the point where you reach your threshold for low energy availability. When this happens, regardless of where your body fat level is between your intervention points, RED-S and adaptive thermogenesis may occur. However, if you can manipulate your body fat gradually, so that you don’t reduce energy intake to the point where you reach a state of low energy availability, you can mitigate adaptive thermogenesis and symptoms of RED-S. That is, until you pass your lower intervention point, which is where body fat comes into the picture. 

As discussed, leptin transiently fluctuates in response to meals and acute changes in energy balance, and each time you eat you can get a nice bump in leptin. However, the largest contributor to your circulating leptin levels is far and away fat mass. To put a specific number to it, Considine and colleagues reported a strong correlation (r = 0.85, p < 0.001) between serum leptin and body fat percentage across a combined sample of 136 normal-weight participants and 139 participants with obesity (12). Meaning, in this large, diverse sample, body fat percentage explained ~72% of the variance in leptin. Thus, even if you’re eating at maintenance, when you’re between meals (which is most of the day), your leptin will fall to low levels when below your lower intervention point. As a consequence, total energy expenditure will remain suppressed, keeping you in a state of low energy availability, leading to symptoms of RED-S. Indeed, we can’t discount the important effect of chronic leptin levels; the only known intervention besides regaining lost body fat that alleviates adaptive thermogenesis (and likely RED-S symptoms for some) in weight-reduced individuals are multiple daily leptin injections (8).

I know what some of you are thinking: “But Eric, I know some people who walk around shredded who are just fine!” So do I, and this still lines up with the theoretical understanding I’ve proposed. Importantly, there is a ton of individual variation at play. Individual variation exists in where one’s lower intervention point is (some people have a leaner lower end point), the energy threshold for when RED-S symptoms crop up (some people do okay at lower values), and whether and how much a person experiences adaptive thermogenesis during and after weight loss (some people don’t experience much at all). Thus, you’ll see people who maintain a variety of different body fat levels, despite living in similar environments. For example, not everyone in our modern obesogenic environment has obesity. Likewise, the Amish men had a body fat standard deviation of 4.3%, meaning (if we trust the bioelectrical impedance measurements) some were walking around at 5% body fat, but just as many were walking around at 14% (maybe; 24).

Also consider that when there are strong rewards at play, people might be okay with living with mild or even moderate RED-S symptoms. In a prior MASS article, I reviewed a paper on energy availability in a group of elite female sprinters who were maintaining reasonably lean (~20% body fat) physiques (article; 25). Interestingly, the sprinters with more indicators of low energy availability had higher fat mass (13.0 ± 2.3kg vs. 11.2 ± 1.6kg, p = 0.03) compared to the leaner sprinters with fewer indicators. While speculative, I guessed this was due to the selection pressures of being an elite sprinter, where having less fat mass means you can run faster. Thus, there were those with a lower intervention point at a lower body fat level who were able to stay leaner without issue, while the rest who weren’t so lucky had to stay in a perpetual weight-reduced, low energy availability state to stay lean (but not quite as lean). Simply put, athletes like to win, and they are often okay with some health and comfort trade-offs if being leaner will improve their performance (I would also note that influencers like your money and attention, and being leaner helps them get that too). This is why athletes in sports where a lower body fat improves performance tend to be leaner (26), and, while many of these athletes have the genetics to be naturally lean, not all of them do, which is why athletes in these sports are also more likely to experience RED-S (2).

Testing the hypothesis that body fat matters

We can assess the veracity of the theoretical explanation I’ve presented that it’s not just energy availability, but also your lower body fat intervention point that dictates how lean you can maintain. If body fat played no role, and it just came down to energy availability, you’d expect dieting to impact people in similar ways, regardless of their body fat level when starting the diet, but it doesn’t. For example, authors of a recently published meta-analysis reported that caloric restriction resulted in an increase in testosterone in the majority of studies on men with overweight or obesity, while the majority of studies on men with normal weight reported a decrease (27). Likewise, muscle protein synthesis is blunted during an energy deficit in overweight dieters (28), but, in lean dieters, not only is protein synthesis blunted, but protein breakdown increases as well (29). Furthermore, lean individuals utilize two to three fold more energy from protein when fasting compared to individuals with obesity (30) and are more likely to lose lean mass while dieting (31). 

However, the most direct evidence we have to test my hypothesis that body fat matters are observations of what happens when people get very lean, and then try to stay very lean. In a case series on physique athletes by Longstrom and colleagues, some of the competitors did just that, following conservative “reverse diets” to minimize fat gain post-contest by slowly increasing calories and decreasing cardio (32). Longstrom measured body composition and metabolic hormones 1-2 weeks prior to competition, as well as 8-10 weeks post-contest once the competitors had carried out their post competition strategies. Generally, Longstrom reported that those who increased fat and body mass the most experienced larger increases in leptin and resting metabolic rate, while smaller increases or no changes occurred in those who gained very little fat and body mass. 

If you examine Figures 3 and 4 from this study, you can see that two of the male competitors (M1 and M2) only increased their body fat by ~2%, staying below 10% body fat even 8-10 weeks post competition. Likewise, one female competitor (F4) increased her body fat by just 2.7%, only getting up to ~15% body fat 8-10 weeks post show, which was the body fat that the other three females achieved at the end of their diets. Notably, these two male competitors experienced no appreciable change in leptin, and F2 had the lowest leptin value of the female competitors. Likewise, resting metabolic rate only slightly increased (M3), stayed the same (M2), or slightly decreased (F2) among these competitors. Finally, at the group level, the observations were also consistent with the hypothesis that fat mass does indeed play a role in hormonal and metabolic recovery. The change in fat mass was strongly associated (33) with the change in resting metabolic rate (τ  = 0.90; p = 0.001) and the change in body fat percentage was strongly associated with changes in leptin (τ = 0.88; p = 0.003). 

Graphics by Kat Whitfield. Takeaways

It’s difficult to piece together complex, distinct lines of research on how humans adapt to changes in short and long term energy availability. Different models tell a piece of the story, but not all of it.

A pure focus on low energy availability can lead one to think that body fat plays no role in the symptoms we associate with RED-S, but effectively ignores ~70 years of research on body composition regulation.

Similarly, a pure focus on adaptive thermogenesis can neglect the effect of these adaptations on health and performance, focusing only on how it changes energy expenditure.

In totality, it’s likely that energy availability is the dominant variable impacting your physiology when you’re between your upper and lower body fat intervention points. However, when you go below your lower intervention point, you’ll be persistently fought by your body and you probably won’t be able to get your calories high enough (without fat gain) to alleviate the negative effects you experience.

With that said, some people can stay really lean, as they happen to have a leaner lower intervention point. For those of us that are not so lucky, that doesn’t mean all hope is lost. Rather, it just means that we have to respect wherever our lower intervention points might be.

Further, you can do all the things we’ve talked about time and time again (like eating sufficient protein and lots of low energy density, high fiber fruits and vegetables, increasing activity and reducing sedentary time, reducing ultra-processed and highly palatable food intake, and of course, lifting lots of weights) to modify your environment so you can stay close to it.    

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D., Ayuketah, A., Brychta, R., Cai, H., Cassimatis, T., Chen, K. Y., et al. (2019). Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metabolism, 30(1), 67–77.e3.I call this “simplified” because it holds up when conceptualizing what happens with normal weight individuals attempting to get lean and stay lean; however, it does not for individuals with obesity and/or metabolic disease. Large amounts of fat gain can change one’s intervention points, and leptin resistance, which is common in those with obesity, can impair the physiological responses which attempt to prevent further weight gain.  Standard deviations only accurately represent normally distributed data (i.e., shaped like a bell curve). It’s quite possible, given how the dual intervention model works, that body fat wasn’t normally distributed. There may have been just a few outlier men who were close to 5%, and then a lot clustering around 9-12% to produce the mean. Sygo, J., Coates, A. M., Sesbreno, E., Mountjoy, M. L., & Burr, J. F. (2018). Prevalence of Indicators of Low Energy Availability in Elite Female Sprinters. International Journal of Sport Nutrition and Exercise Metabolism, 28(5), 490–496.Jeukendrup, A. and Gleeson, M., (2018). Sport Nutrition. Human Kinetics.Smith, S. J., Teo, S., Lopresti, A. L., Heritage, B., & Fairchild, T. J. (2022). Examining the effects of calorie restriction on testosterone concentrations in men: a systematic review and meta-analysis. Nutrition Reviews, 80(5), 1222–1236.Hector, A. J., McGlory, C., Damas, F., Mazara, N., Baker, S. K., & Phillips, S. M. (2018). Pronounced energy restriction with elevated protein intake results in no change in proteolysis and reductions in skeletal muscle protein synthesis that are mitigated by resistance exercise. 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Physiological, Psychological and Performance-Related Changes Following Physique Competition: A Case-Series. Journal of Functional Morphology and Kinesiology, 5(2), 27.For those unfamiliar with the “τ” symbol, it represents Kendall’s tau, which is a nonparametric correlation coefficient, interpreted similarly to Pearson’s r. A value of zero reflects no correlation, and values closer to 1 or -1 represent stronger correlations, with the sign of the tau value (positive or negative) reflecting the direction of the association.

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- Cameron Gill
The Most Commonly Neglected Movements and Muscles (and Exercises to Address Weak Links)

Resistance training for improving physical performance, aesthetics, and health does not need to be complicated. The vast majority of people can experience substantial progress in these three areas by consistently performing a few multi-joint exercises that train the major movement patterns. Variations of horizontal presses and pulls, vertical presses and pulls, squats, and hip hinges are certainly effective for strengthening and hypertrophying a large amount of muscle mass, but some muscles will be neglected to a rather meaningful degree. Consequently, we can use assistance exercises to fill in the gaps, but a sizable amount of muscle mass may still be neglected by the assistance exercises commonly selected by many lifters. 

Identifying which muscles are or are not adequately overloaded by widely utilized multi-joint exercises can help guide efficient selection of assistance exercises due to the law of diminishing returns. As the set volume for a particular muscle group increases up to a certain threshold, the magnitude of the hypertrophic response will tend to increase in turn (31). Given that strength can improve through neural adaptations (e.g. enhanced motor unit recruitment), muscular hypertrophy is not necessary to experience an increase in strength (17, 35). Nonetheless, muscle size is a major contributor to maximal force production potential, and inducing muscle hypertrophy is of great value for achieving both long-term strength and aesthetic goals (12). You can read more about how strength is influenced by muscle size in Stronger By Science articles by Greg Nuckols. The relationship between training volume and hypertrophic adaptations certainly varies among different individuals and also among different time points in a lifter’s life. Nutrition, sleep, genetics, performance enhancing drug usage, prior training history, training frequency, exercise selection, proximity to failure during each set, and inter-set rest intervals all likely have the potential to impact how someone will be affected by changes in volume. This wide degree of possible variation can result in apparently contradictory research findings with regard to how volume influences hypertrophy. 

However, I am confident in asserting that the law of diminishing returns applies to everyone with respect to volume’s effect on gains. At some point, increasing volume for a particular muscle group will result in only a marginal return relative to the time and effort required to perform the higher volume training, and no additional increases in muscle size or strength may be induced with further increases in volume (1, 24). For instance, many lifters who train their hamstrings twice per week may experience a meaningful benefit if they progress from performing three sets to six sets per session for their hamstrings. However, progressing from 10 to 13 hamstring sets per session may not result in any detectable benefit for the majority of lifters if sets are performed fairly close to failure and rest intervals are sufficiently long to mostly recover between sets. Some individuals may experience slightly greater progress with these very high volumes, while others may experience a slightly slower rate of progress if their recovery capacities are exceeded.    

Rather than investing your finite time and energy into very high volumes for some select major muscle groups, you may achieve greater overall muscle growth through reaping the benefits of picking low-hanging fruit by performing a few sets of exercises that target otherwise neglected muscles. Even if the muscles that you allocate training volume toward are smaller than those whose volume is somewhat reduced, you can still experience a net increase in total body muscle mass due to the law of diminishing returns. For example, the calves are smaller than the quads, but performing three sets of calf raises might stimulate greater overall hypertrophy than three sets of hack squats if you already perform five sets of leg presses and five sets of leg extensions in your lower body training sessions, but you don’t currently do any calf training. In this case, adding more hack squats has the potential for some individuals to experience slightly greater quad development, but the magnitude of effect is likely eclipsed by the hypertrophic response that would be stimulated by the addition of three sets for the otherwise neglected calves.

Beyond contributing to a greater total body muscle mass, adding a minimal effective dose of volume for muscles which were previously not targeted may help enhance your resiliency by eliminating weak links and increasing stability of the joints they cross. A wide variety of muscles function isometrically as stabilizers during different movements, but these movements may provide a rather poor stimulus for increasing the size and strength of such muscles, particularly for people who are beyond the novice phase. For instance, the rotator cuff muscles help stabilize the shoulder joint while bench pressing, but bench pressing may not be an effective means of developing the rotator cuff muscles.   

Let’s take a look at three commonly neglected movements that train muscles that may otherwise not be effectively targeted in many programs. Keep in mind that if you have not been consistently training some of these muscles, you likely can make meaningful gains with the addition of rather low volumes, such as performing two sets twice per week. A number of the muscles which will be covered lie deep to other muscles, so they will not be directly visible. Nonetheless, increasing the size of these muscles can still result in visible changes by increasing the total thickness of the region in which they are situated. For example, the rhomboids are nearly completely covered by the trapezius, but hypertrophying the rhomboids can result in a noticeable increase in upper back thickness.

The human body has over 600 muscles, many of which are quite small, so it would be nonsensical to advocate or discuss direct training for all of them. However, some of the muscles which may commonly go untrained are larger than you likely think and, when grouped together with other muscles performing the same function, they may constitute a sizable amount of total muscle with a meaningful degree of growth potential. To help understand the size of these muscles, the volume of lower body muscles will be compared to the volume of the gluteus maximus (the largest lower body muscle), while the mass of upper body muscles will be compared to the mass of the lats (the largest back muscle), just to give you a point of comparison (8, 36). 

If you add a low volume of one exercise for a particular movement into your program, I recommend selecting a variation that provides a meaningful amount of tension to the exercise’s prime movers in a stretched position. A growing body of evidence indicates that doing so can allow a muscle to experience stretch-mediated hypertrophy (13, 16, 25, 30). As a result, partial ROM exercises performed at short to moderate muscle lengths may be suboptimal for inducing hypertrophy compared to a full ROM or partial ROM exercise which loads a muscle at long peak lengths. For each of the three movements that will be discussed in this article, I will mention a variety of possible exercise variations because not everyone has access to the same training tools, but keep in mind that selecting an exercise that applies tension to the working muscles in a stretched position will likely be optimal. 

Scapular Protraction Serratus Anterior       Pectoralis Minor

The serratus anterior and pectoralis minor produce scapular protraction – forward movement of the shoulder blade (6). Together, the mass of these muscles has been measured to be 9% greater than the lats, primarily due to the serratus anterior, which has a volume just 10% smaller than the lats (36). Before I ever read through the actual data on the sizes of different muscles, I would have never imagined that the serratus anterior is this large, and consequently, that the absence of any scapular protraction exercise in my program resulted in such a sizable amount of muscle mass being neglected. The serratus anterior also plays a major role in maintaining a functional and healthy shoulder by upwardly rotating the scapula as the arm is elevated to enable full range of motion of the shoulder (20). Weakness of this muscle may contribute to the development of scapula winging, a painful disorder characterized by a protruding scapula which restricts shoulder mobility and strength (14). 

You can directly target the serratus anterior and pectoralis minor with multi-joint exercises that incorporate scapular protraction into a horizontal press, or with a single joint exercise where scapular protraction exclusively occurs. The “pushup plus,” where scapular protraction is performed during the concentric phase of a pushup while scapular retraction occurs during the eccentric phase, is a viable means of loading these muscles at the same time as the pectoralis major, triceps, and the deltoid’s anterior (i.e. front) head. If you do not yet have the strength to achieve full scapular protraction during the standard pushup plus, you can use a modified version where your hands are placed onto an elevated surface such as a bench. If this variation is too difficult, you can further regress the intensity by performing the movement against a wall with a forward lean of the torso. Similar to the pushup plus, you can also add scapular protraction to a cable or elastic band horizontal press to train the serratus anterior and pectoralis minor with a multi-joint exercise. Alternatively, you can perform scapular protraction as its own exercise by utilizing either your bodyweight or an external load provided by cables or elastic bands. In these instances, you would assume the same starting position as the aforementioned multi-joint exercises, and perform the same scapular movement without motion occurring at the shoulder or elbow joints.

Pushup Plus Bodyweight Scapular Protraction Cable Scapular Protraction Press Cable Scapular Protraction Dual Cable Scapular Protraction

You can also perform a pure scapular protraction exercise or a multi-joint scapular protraction exercise with free weights when lying down on your back in the supine position. In contrast to the other variations, a supine scapular protraction exercise can restrict dynamic scapular range of motion if the scapula contacts the ground or bench before full retraction occurs. 

Supine Dumbbell Partial ROM Scapular Protraction Supine Dumbbell Partial ROM Scapular Protraction Press

While this may inevitably occur if the exercise is performed bilaterally, you can perform a unilateral variation with a dumbbell in a manner that enables a greater scapular retraction ROM. To do so, you would maintain a back arch and a fully retracted position for the scapula on the side that is not being trained.

Supine Dumbbell Full ROM Scapular Protraction Supine Dumbbell Full ROM Scapular Protraction Press

A scapular protraction exercise performed through a partial ROM from a resting to protracted scapula position can certainly be used to strengthen and hypertrophy the serratus anterior and pectoralis minor. Nonetheless, I recommend finishing the eccentric phase of each rep in a position of full scapular retraction in order to load these muscles at long lengths where stretch-mediated hypertrophy may be induced.

Hip Flexion Iliacus Psoas Major Rectus Femoris Sartorius

Hip Flexion, which is the forward movement of the thigh, is performed by the iliacus, psoas major, rectus femoris, tensor fasciae latae, and sartorius, which collectively constitute a volume of muscle approximately 12% greater than the gluteus maximus (7, 8, 19, 26). 

Tensor Fasciae Latae

When the hip is in a low angle of flexion, the adductor longus and pectineus, which together have a volume that is 27% of the gluteus maximus, also have rather favorable moment arms for flexing the hip (7, 8, 19, 26). A muscle’s moment arm is the perpendicular distance between the line of force a muscle produces and the rotational axis of the joint upon which it acts. Torque is the product of a force multiplied by a moment arm, so a muscular moment arm quantifies the leverage a muscle has for generating torque in a particular plane of motion and consequently its ability to contribute to a joint movement. When the hip is near a neutral position, the adductor brevis and to a lesser degree the gracilis can also secondarily assist in generating hip flexion torque while exhibiting a combined volume that is about 24% of the gluteus maximus (8, 19, 26). As the hip flexion angle increases, the moment arms these four hip adductor muscles have for flexing the hip steadily decrease, so they will likely no longer be effectively trained as hip flexors once the angle of hip flexion exceeds 30° (7, 26). 

Adductor Longus Pectineus Adductor Brevis Gracilis

A very similar relationship exists between hip flexion torque and the angle of hip flexion, such that maximal hip flexion torque peaks near a neutral hip position and progressively declines at higher angles of hip flexion (4, 9). Consequently, strength at high angles of hip flexion will likely be the limiting factor during a hip flexion exercise unless the resistive torque steadily decreases as the angle of hip flexion increases throughout the concentric phase of the exercise.   

“Maximum voluntary hip flexion torque during isometric contraction at four hip joint angles. Values are presented as mean ± standard deviation. *p <0.05 vs. 0°” from Jiroumaru et al. (9)

Some abdominal-focused movements such as hanging leg raises or reverse crunches can involve both hip flexion and trunk flexion simultaneously and are therefore a viable means of training hip flexor muscles and the rectus abdominis (i.e. the “six-pack” muscle) at the same time. An advantage of these exercises is their accessibility, because bodyweight alone can provide a sufficiently challenging level of resistance for many individuals. Due to its ability to provide peak resistive torque near a neutral hip position, the reverse crunch may effectively train a greater amount of muscle mass than a hanging leg raise where peak resistive torque occurs at 90° of hip flexion if full ROM reps are exclusively utilized. During the initial 30° of hip flexion where the hip adductor muscles can meaningfully contribute to generating hip flexion torque, resistive torque is minimal during a hanging leg raise but maximal during a reverse crunch. A possible method to amplify hip adductor muscle stimulation during a set of hanging leg raises is to perform progressively shorter ROM reps after you can no longer perform full ROM reps due to fatigue accumulation. If you terminate the set once you can no longer reach a high degree of hip flexion, the hip adductor muscles may be suboptimally stimulated. However, if you continue the set until you can no longer achieve even a low degree of hip flexion, all of the hip flexor muscles may be effectively targeted. You can readily adjust the intensity of hanging leg raises to your current level of strength by altering your knee angle. Compared to a flexed knee position, an extended knee position provides a greater magnitude of resistive torque during this exercise by shifting the body’s center of mass further out in front of the hip and trunk. Consequently, you can progress by decreasing the angle of knee flexion used for this exercise over time. 

Hanging Leg Raise Reverse Crunch

On a side note, it is worth mentioning that exercises like reverse crunches and hanging leg raises are sometimes performed with a technique that causes hip flexion to be the only dynamic joint action that is trained. If trunk flexion does not occur during these exercises, the rectus abdominis will still be activated, as it functions isometrically to resist the anterior pelvic tilt, which may otherwise be produced by contraction of the hip flexor muscles. For some individuals with prior injuries that result in discomfort or pain being experienced when flexing the lumbar spine, this technique may be preferable to one that utilizes dynamic trunk flexion. Like dynamic training, isometric training certainly has the potential to induce muscle hypertrophy if it provides a potent enough stimulus to the working fibers (23). To my knowledge, the research examining changes in muscle size after similarly designed dynamic or isometric resistance training interventions is limited to just two studies, neither of which assessed trained subjects (10, 27). They reported different outcomes after using rather dissimilar training protocols and have a combined age of 98 years, so a firm conclusion cannot be reasonably drawn from the available evidence. I am skeptical that merely resisting anterior pelvic tilt during a hip flexion exercise would be as effective in inducing rectus abdominis hypertrophy as an exercise which includes dynamic trunk flexion, particularly for trained individuals. However, empirical data on this matter is presently lacking. 

When opting to utilize a single-joint hip flexion exercise, a multi hip machine can be a very useful tool due to its ability to apply resistance in an extended hip position with a readily adjustable load that facilitates incremental progression. Alternatively, you can use ankle weights or elastic bands to perform hip flexion exercise from a supine (i.e. lying on your back) or standing position. While hip flexion exercise may also be performed from a seated position, this variation would not be my first choice due to it training less overall muscle mass.

Multi Hip Hip Flexion Lying Ankle Weight Hip Flexion Lying Banded Hip Flexion Seated Ankle Weight Hip Flexion Hip Abduction Gluteus Medius Gluteus Minimus

In a neutral hip position, hip abduction, which is the movement of the thigh out away from the midline of the body, is primarily produced by the gluteus medius, gluteus minimus, and tensor fasciae latae, which together have a volume equivalent to 58% of the gluteus maximus (8, 18, 19, 26). The sartorius, piriformis, and rectus femoris also assist with abducting the hip and collectively have a volume that is 56% of the gluteus maximus (8, 18, 19, 26). Similar to how greater hip flexion torque can be generated near a neutral hip position compared to a moderately high degree of hip flexion, maximal hip abduction torque peaks in an adducted hip position and decreases as these muscles shorten with increasing angles of hip abduction (2, 11, 21, 22, 29, 38). 

“Maximal-effort isometric hip abduction torque as a function of frontal plane range of abduction in 30 healthy persons” from Neumann (19)

Additionally, as the angle of hip flexion increases, the hip abduction moment arms of the gluteus medius and gluteus minimus steadily decline until these muscles can no longer contribute to producing hip abduction torque when the hip flexion angle nears 90° (7, 26, 37). In contrast, hip abductor moment arms of the piriformis, obturator internus, and gemellus superior increase as the hip flexes to the extent that they function as primary hip abductors in 75-105° of hip flexion (7, 26, 34). These muscles belong to the short hip external rotator group, which as the name suggests, externally rotates the hip when the hip is in or near a neutral position (19). The changes in muscular moment arms that occur at different angles of hip flexion will result in some hip abduction exercises training a meaningfully greater amount of muscle mass than others. While the piriformis, obturator internus, and gemellus superior help stabilize the hip joint, they are rather small with a collective volume equivalent to approximately 10% of the gluteus maximus (8, 39). Together the gluteus medius and gluteus minimus have a volume which is five times greater than these muscles, so a hip abduction exercise performed with close to 0° of hip flexion will target a noticeably greater amount of muscle than a hip abduction exercise performed with close to 90° of hip flexion (8). 

Short Hip External Rotators Muscles (& Gluteus Minimus)

The tensor fasciae latae, rectus femoris, and sartorius can generate hip abductor torque at either 0° or 90° of hip flexion (7). However, the tensor fasciae latae is quite small with a volume which is about 8% of the gluteus maximus, and the rectus femoris and sartorius have meaningfully better leverage for producing other joint movements such as hip flexion (8). 

My recommendations for selecting a hip abduction exercise mirror those previously discussed for hip flexion exercises. For the sake of maximizing training efficiency, I recommend selecting a hip abduction that involves close to a neutral hip position, if you opt to perform only one hip abduction exercise. Consequently, an exercise using a side lying or standing position would be preferable to a seated hip abduction exercise for training the greatest amount of muscle. This is not to say that seated hip abduction exercises are bad by any means. They simply target less total muscle mass than other variations. 

For the same reasons that a multi hip machine is a very effective implement for training hip flexion, so too is it very well-suited to train hip abduction. With a slightly flexed hip position, you can use this machine to train the hip abductor muscles in a stretched position which may be difficult to achieve with other exercises. Similar to hip flexion, you can also use ankle weights or elastic bands to perform a hip abduction exercise in a standing or side-lying position. 

Multi Hip Hip Abduction Side Lying Ankle Weight Hip Abduction Side Lying Banded Hip Abduction

Additionally, you can train hip abductor muscles without the need for any equipment through side plank variations. The exercise can be completely isometric in nature if the side plank position is statically maintained, which requires the hip abductor muscles nearest to the ground to act isometrically in order to prevent the pelvis from dropping. If the standard version where the elevated foot remains on top of the bottom foot is too challenging, you can regress the intensity by placing your knees or both feet on the ground. Alternatively, you can progress the intensity by keeping the top hip in an isometrically abducted position or by dynamically abducting and adducting this side while the bottom hip abductors continue to function isometrically.

Side Plank Regression Side Plank Progression

Enhancing isometric hip abduction strength may be particularly advantageous to strength athletes who perform loaded carries and/or walk out heavy squats such as strongmen and powerlifters who compete without a monolift. At any moment when only one foot is in contact with the ground, the hip abductors which are on the same side as the stance limb must stabilize the pelvis by functioning isometrically to resist pelvic drop in the frontal plane (19). Successfully doing so during a squat walkout, farmer’s walk, or yoke walk with maximal or near maximal loads can be a considerable challenge, and insufficient hip abduction strength may limit performance (15). Even if an athlete possesses the minimum amount of hip abduction strength required for a squat walkout, further enhancing hip abduction strength may still be beneficial for some powerlifters. Instead of struggling to perform a maximal walkout, an athlete may feel psychologically encouraged in his/her ability to complete a squat if a reserve of hip abduction strength is present and the walkout requires less relative effort. 

Programming Recommendations

If you do not have experience performing any of these three movements and now wish to incorporate some or all of them into your resistance training program, I recommend that you begin doing so by adding a low volume, such as two sets for each movement twice a week. Untrained individuals have been measured to experience significant strength and hypertrophic adaptations by performing a single set of an exercise to volitional fatigue 2-3 times per week (5, 32, 33). Even if you have been consistently resistance training for many years, muscles which have not been effectively loaded by the exercises you have been performing may still respond to the addition of direct training in a manner similar to a novice.        

None of the exercises for these three movements impart meaningful axial loading and most of the muscles which function as prime movers during these movements do not act as prime movers for commonly performed multi-joint exercises. Consequently, adding a low volume of the aforementioned movements is unlikely to interfere with your current training, and you can readily integrate the exercises which train these muscles into any session, or even an active recovery day. While adding a couple of sets of these movements at the end of an existing workout would not require much extra time training, it could still extend the time commitment for a session and impose an opportunity cost. To get the most out of your finite training time, you can incorporate these movements into a dynamic warmup or non-competing supersets along with other exercises. If you have a low work capacity and are not accustomed to these techniques, you may notice a minor reduction in performance on some of the pre-existing exercises in your program when you initially incorporate these new exercises. However, any interference will likely subside as your work capacity improves, after you acclimate to using these methods for a few weeks.

With a non-competing superset, you can perform a set of one of the three movements after completing a set of another exercise training different muscles. For instance, you can directly follow a bench press set with a hip abduction set during the rest period before the next bench press set. If you use rest intervals which are sufficiently long to recover between sets, this technique should not require any additional workout time or reduce the quality of the exercises already included in your training program. You can also utilize non-competing giant sets when you sequentially perform sets of three or more exercises which target different muscle groups. For instance, you can include a barbell row, hip flexion exercise, and scapular protraction exercise together in a giant set which targets a large amount of muscle mass in a brief period of time. 

The main constraint that may present in non-competing superset and particularly giant set exercise selection is equipment availability. For instance, if you train in a busy commercial gym, utilizing a barbell, multi hip machine, and cable station for the aforementioned giant set example may provoke ire among other lifters. If you crank up the volume in your headphones, avoid making eye contact with other humans, forgo the application of deodorant, and aggressively talk to yourself between sets, the likelihood that someone will approach you and interfere with your giant set will be substantially minimized. Alternatively, you can utilize variations which require only one piece of shared equipment for a giant set. For example, you can perform a barbell row, reverse crunch, and pushup plus together without needing multiple types of equipment.     

In addition to including the three movements into supersets/giant sets, programming them into a dynamic warmup is an efficient strategy. As the name suggests, increased body temperature is a key benefit of a general warmup due to the favorable physiological effects that result in enhanced force production and oxygen delivery (3, 28). While 5-10 minutes of low intensity aerobic exercise can induce this increased temperature, so too can a low volume of some of the exercises discussed in this article with the added benefit of hypertrophying and strengthening some otherwise neglected muscles. You can use any of the three movements as part of your dynamic warmup, but you may find that hip flexion and hip abduction are particularly suitable to begin a lower body workout, and scapular protraction works similarly well to start an upper body session.

With a scapular protraction press, such as the pushup plus, strength of the serratus anterior and pectoralis minor will be the limiting factor rather than strength of the pectoralis major, triceps, and deltoid’s anterior head when proper technique is used. Consequently, even if you perform sets of this exercise until technical failure (i.e. when full scapular protraction can no longer be achieved) during a dynamic warmup, these pressing muscles should not be meaningfully fatigued for subsequent pressing exercises.

Conclusions

Regardless of where they are implemented, I recommend adding a low volume of some of these exercises to your program if increasing whole body muscularity and strength is a goal of yours. Scapular protraction, hip flexion, and hip abduction may not be the most popular movements, nor would I consider them to be indispensable components for most resistance training plans to be effective. Nonetheless, they can provide the distinct advantage of targeting a notable amount of muscle mass that may otherwise go neglected and allow you to reap the benefits of picking these low-hanging fruits from the tree of gains.  

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The tensor fasciae latae anatomy image was published in Henry Gray’s Anatomy of the Human Body (1918), is in the public domain, and can be found at https://commons.wikimedia.org/wiki/File:Gray430_with_TensorFasciaLata.png.

The short hip external rotator muscle anatomy image was created by Beth O’Hara, is licensed as a Creative Commons work, and can be found at https://commons.wikimedia.org/wiki/File:Posterior_Hip_Muscles_1.PNG.

All other muscle anatomy images were published by “BodyParts3D, © The Database Center for Life Science”, are licensed as Creative Commons works, and can be found at http://lifesciencedb.jp/bp3d/.

The post The Most Commonly Neglected Movements and Muscles (and Exercises to Address Weak Links) appeared first on Stronger by Science.

- Greg Nuckols
Where are all the Female Participants in Strength, Hypertrophy, and Supplement Research?

Note: This article was the MASS Research Review cover story for June 2022. If you want more content like this, subscribe to MASS.

For as long as I can remember, inter-individual differences in training responses have been one of my biggest research interests. It doesn’t take enormous powers of observation to see that two people can undergo the same type of training, but attain wildly different results. I’ve always been interested in learning more about this for a few reasons. First, I simply want to understand the phenomenon better: how much variability exists (1)? In my experience, people tend to underestimate how much training responses differ between individuals. Second, I want to learn more about the factors that are predictive of responsiveness to training. If we know what factors promote above-average training responses, we may eventually be able to use that knowledge to improve training results for everyone.

This interest in inter-individual differences naturally led to an interest in sex differences. Sex is a unique variable, in that it’s bimodal (most observable traits are more normally distributed), and it’s either associated with, or causally linked to, a host of other traits that differ between individuals, which may be predictive of training responsiveness (hormone levels, body size, muscle fiber types, etc.). So, learning more about sex differences seemed to be worthwhile, in order to better understand inter-individual differences more broadly. This interest led me to study sex differences in fatigability for my thesis research (2).

However, once you start trying to learn more about sex differences in domains related to resistance training, one thing becomes immediately apparent: most of the research in our field is solely conducted with male research subjects. When the topic of sex differences in research participation comes up, my go-to citation has always been a 2014 study by Costello and colleagues, showing that about 61% of the research subjects in our field are male, and 39% are female (3; the title of this article is an intentional homage to that study). However, those figures never quite sat right with me. In the research I was reading, it seemed like female research subjects made up considerably less than ~40% of the total research subjects.

To understand why there might be a disconnect, it’s worth understanding how Costello and colleagues came up with their estimate. They monitored three of the most prestigious journals in our field – Medicine and Science in Sports & Exercise (MSSE), the British Journal of Sports Medicine (BJSM), and the American Journal of Sports Medicine (AJSM) – for three years (2011-2013), noting the total number of male and female subjects in each article contained within each issue. Across that three-year span, 1,328 articles were published, containing nearly 6.1 million subjects, including about 2.4 million female subjects and about 3.7 million male subjects. This was a truly impressive undertaking, but it has one notable drawback for our purposes: a minority of the research published in those journals is directly related to the subjects we discuss most often in MASS and Stronger by Science, and a small minority of the research we discuss is published in those three journals. The most prestigious journals in our field will publish really cool research related to maximizing strength, hypertrophy, and resistance training performance from time to time, but a larger chunk of their publications focus on general health, injury prevention or rehabilitation, and aerobic fitness.

A couple years ago, I happened across an article in ScienceNews by Bethany Brookshire – a journalist with a PhD in physiology and pharmacology – who had similar concerns. She wasn’t specifically interested in strength and hypertrophy research, but she wanted to know if the proportion of male versus female research subjects differed by study type. She kept tabs on MSSE and the AJSM for the first five months of 2015, sorting studies into six categories: disease, basic physiology, metabolism and diet/obesity, injury, social, and performance. She found that female participants made up between 40-60% of the research participants in all six categories, but the “performance” category had a major caveat. One study on marathon pacing contained more than 90,000 subjects, accounting for the majority of the total research subjects in the “performance” category. When that single study was excluded, only 3% of the subjects in “performance” studies were female (Figure 1).

Graphic by Kat Whitfield

However, Brookshire’s analysis also has a couple of drawbacks for our purposes. First, and most notably, it was still centered around two journals that don’t prioritize strength and hypertrophy research. If I had to wager a guess, I doubt that most of the “performance” studies were focused on resistance training performance. Second, her analysis of performance studies had a pretty small sample. In the first five months of 2015, just 30 studies related to performance were published in the two journals she was monitoring. After excluding a single enormous study, she found that only 3% of the subjects in the other 29 studies were female. While that’s certainly concerning, it may not be representative – 29 studies published in two journals over five months in 2015 may not be a reliable reflection of all performance-related research in the field. It’s entirely possible that MSSE and AJSM just had a random run of very male-dominated performance studies during that five-month span. However, happening across Brookshire’s article reassured me that my suspicions probably weren’t misplaced: female subjects probably don’t account for 39% of the total subjects in the exercise research that I (and most of you, I assume) care the most about. After reading Brookshire’s article, I determined that I’d eventually do my own analysis. That’s what you’re reading now.

Finally, a 2021 paper by Cowley and colleagues (4) gave me another issue to ponder: what if the sex disparity in exercise research participation is actually getting worse over time? Cowley and colleagues updated and expanded on Costello et al’s analysis. They analyzed the research published in six journals (they looked at MSSE, BJSM, and AJSM like Costello, and added the European Journal of Sport Science, the Journal of Sports Science & Medicine, and the Journal of Physiology) over a seven-year span – 2014-2020. Approximately 5,300 studies with about 12.5 million participants were published in those six journals over the analyzed time span, including about 8.25 million (66%) male subjects and 4.25 million (34%) female subjects.

This gave me pause, because there’s a general belief that sex disparities in research participation are shrinking over time. In other words, there’s a general assumption that very early exercise science research used predominately male subjects, but that female subjects accounted for nearly 40% of all research subjects by 2011-2013 (the time period of Costello’s analysis), meaning that the sex disparity in research participation was decreasing over time. The natural assumption is that this disparity would be expected to decrease further until it became a non-issue. However, Cowley and colleagues found that female research subjects accounted for a smaller proportion of the total research subjects in the 2014-2020 time period (34%) than Costello and colleagues observed in the 2011-2013 time period (39%). These aren’t purely apples-to-apples comparisons, since Cowley and colleagues investigated more journals than Costello and colleagues did, but it at least suggests that sex disparities in exercise research participation aren’t continuing to shrink over time; in fact, they may be getting larger.

Purpose and Strategy

So, with that preamble out of the way, I decided to do my own analysis of sex disparities in research participation in the areas of research that MASS and Stronger by Science readers care the most about. Here’s what I wanted to accomplish:

I wanted to be able to cast a wide net. Our monthly journal sweep combs through >140 journals (thanks, Kedric and Colby), so restricting my analysis to 3-6 journals wasn’t going to cut it.I wanted to be able to analyze trends over time, so I needed to have a way to see how sex disparities in research participation had shifted (or not shifted) over a period of decades, rather than a 3-7 year period.I wanted to be able to restrict my analysis to the sorts of research MASS and Stronger by Science readers care the most about. I’ll admit that this isn’t a completely objective criterion, but after putting out content, answering questions, and monitoring chatter in the fitness industry for over a decade, I think I have a pretty decent grasp on the research topics y’all care the most about.I wanted to be able to see whether the results of studies with female subjects systematically differed from the broader literature. I’ll discuss my reasoning for this below.I needed to be mindful of time- and labor-intensiveness. I’m writing this article on a deadline, so I needed to select a strategy that would allow me to do a thorough, representative analysis in about two weeks (not two months or two years). Furthermore, as a non-academic, I no longer have institutional journal access (5). I’m fortunate enough to have people who will send me papers when I request them, but I don’t want to push my luck. Requesting a few dozen papers is asking for a favor. Requesting a few thousand papers is asking someone to start a part-time job.

To expand on my fourth criterion a bit, seeing whether the results of studies on female subjects differ from the broader literature helps us answer a couple of important questions.

First, it helps us generate informed assumptions about the generalizability of research conducted on single-sex samples. In areas of research where most findings come from studies on male subjects, it would be nice to know whether or not we can assume that those findings will generalize to female lifters. And, conversely, it would be nice to know whether research findings on female-only samples are likely to generalize to male lifters. Research interpretation always involves making assumptions about generalizability – better-informed assumptions help make more research more useful to more people.

Second, it can help inform research recruitment strategies. If we see that studies on female subjects commonly reach different results than male-dominated studies, that would imply that single-sex cohorts are probably preferable most of the time. In that situation, you should assume that a new intervention will produce different results in male and female lifters, meaning that early studies in the area should use male-only and female-only samples to generate effect estimates for each sex independently. However, if we see that studies on female subjects typically have similar results to those observed in male-dominanted studies, that would imply that mixed-sex cohorts are probably preferable for both practical reasons and logistical reasons.  Why invest double the time and double the energy to generate male- and female-specific effect estimates, if those effect estimates are likely to be similar? You can just used mixed-sex cohorts to generate a generalized effect estimate that should apply across the board. And why struggle trying to recruit 30 male subjects or 30 female subjects for a study? You’d have an easier time just recruiting 30 humans of any sex.

So, with all of that in mind, I decided to use recent systematic reviews and meta-analyses to do a lot of the heavy lifting for me. This approach fulfills the five criteria listed above:

Systematic reviews and meta-analyses start with a comprehensive literature search, pulling in research from all of the indexed journals in our field, rather than restricting the search to a handful of journals.Systematic reviews and meta-analyses generally aren’t time-limited. They pull in research going back decades, allowing me to analyze trends in sex disparities over time.There are now systematic reviews and meta-analyses covering damn near every topic that MASS and Stronger by Science readers care about, which we catalog here. So, mining systematic reviews and meta-analyses was a convenient method of pulling in all of the research on highly relevant topics, while excluding research on less relevant topics.By comparing effect estimates from female-only research to pooled effect estimates in the included meta-analyses, I’d be able to see whether research on female subjects typically has meaningfully different results than research on male or mixed-sex cohorts.This strategy saves an enormous amount of time and labor, without sacrificing the scope of the project. Systematic reviews and meta-analyses generally contain a table listing the characteristics of the studies included, including the number of subjects and sex of the subjects in the study. Thus, a single meta-analysis can provide all of the relevant information about 20 studies, rather than needing to pull data from all 20 studies one-by-one. Systematic reviews and meta-analyses are also more likely to be open-access than original research. As a result, I only needed a kind soul (named Eric Helms) to hook me up with 12 papers I didn’t have access to, rather than (likely) 400+ papers.

After identifying an initial pool of 45 systematic reviews and meta-analyses, I whittled the list down slightly based on two factors. First, if two reviews covered very similar topics, I’d select the review that included the most studies. I wanted to maximize the scope of topics included in this analysis, while minimizing the overlap between reviews. Second, I’d exclude a systematic review or meta-analysis if it didn’t include a table listing the characteristics of the studies included. Fortunately, these two exclusion criteria only whittled my initial pool of systematic reviews and meta-analyses down by 6 papers, leaving me with 39 systematic reviews and meta-analyses for my final analysis.

After finalizing my list of systematic reviews and meta-analyses, I went through each one to extract the following information from the studies included:

The title and author of the study.The number of male and female subjects in the study, along with the total number of participants. If the sex of the participants in a study wasn’t reported, or if a study was reported as mixed-sex without specific counts of male and female subjects, the subjects were assumed to be 50% male and 50% female.Whether the study used a male-only, female-only, or mixed-sex cohort.The publication year of the study.

Furthermore, I isolated all of the forest plots from the meta-analyses, and highlighted the effect estimates from the female-only studies in each forest plot. From the forest plots, I extracted the following information:

The pooled effect estimate.The standard error of the pooled effect estimate.The effect estimate of female-only studies.Whether the 95% confidence interval of each female-only effect estimate overlapped with the 95% confidence interval for the corresponding pooled effect estimate.

Finally, I’d just like to make a note about the assumption that subjects were 50% male and 50% female when meta-analyses didn’t report sex of the subjects in a particular study, or when studies were reported to be mixed-sex without a precise delineation of the number of male and female subjects. Technically speaking, this is a cut corner, but I don’t think it materially impacts the value of this analysis for a few reasons: 1) over three-quarters of the studies included in these systematic reviews and meta-analyses were single-sex studies, 2) sex wasn’t reported in a very small minority of studies, and 3) the precise numbers of male and female subjects were reported for most of the mixed-sex studies. So, if the subjects in those studies were 70/30 or 30/70 male/female instead of 50/50, that would only shift the estimated proportions of male and female subjects by 1-2%, which is pretty immaterial for the purpose of interpreting this analysis. As I’ll cover in the next section, about 25% of the subjects included in these studies were female – if the “true” figure is actually 23% or 27%, I don’t think that’s an error that actually matters. Any figure within that range would lend itself to the same set of conclusions.

Results

As previously mentioned, 39 systematic reviews and meta-analyses were used for this analysis, covering topics ranging from training volume to rest intervals to ketone supplementation. They’re listed in Table 1.

Graphic by Kat Whitfield

These systematic reviews and meta-analyses covered 628 unique studies, with an average of 16.8 studies per systematic review or meta-analysis (range: 6-49 studies). Just 28 studies (4.5%) were included in multiple meta-analyses, suggesting that I did a pretty good job of selecting topics that would cover a broad range of topics while minimizing the overlap between topics.

Of these 628 unique studies, 408 (65.0%) had all-male samples, 133 (21.2%) had mixed-sex samples, 73 (11.6%) had all-female samples, and 14 (2.2%) did not specify the sex of the subjects (Figure 2).

Graphic by Kat Whitfield

The box and whisker plot in Figure 3 shows the proportion of male-only, mixed-sex, and female-only studies included in each systematic review and meta-analysis.

Graphic by Kat Whitfield

The studies contained within these systematic reviews and meta-analyses had 16,683 total subjects, including 12,501 males (75.01%) and 4,182 females (24.99%). Only one meta-analysis included studies with more total female subjects than male subjects (34), while three meta-analyses didn’t include any studies with female subjects (8, 13, 27).

From the 1990s onward, the proportion of studies with male-only cohorts has actually increased, while the proportion of studies with female-only cohorts has slightly decreased. The proportion of studies with mixed-sex cohorts has basically remained flat since the 1990s (Figure 4). 

Graphic by Kat Whitfield

Finally, when analyzing the forest plots contained within these meta-analyses, I didn’t find evidence that the female-only studies systematically differed from the broader literature. There were 67 total forest plots that contained at least one effect estimate from a female-only study, and there were 185 effect estimates from female-only studies contained within these forest plots. The 95% confidence interval of female-only effect estimates overlapped with the 95% confidence interval for the corresponding pooled effect estimate … 94.6% of the time. The confidence intervals overlapped 175 times and didn’t overlap 10 times. Furthermore, on all 67 discrete forest plots, the confidence intervals from female-only studies overlapped with the confidence interval of the pooled effect estimate a majority of the time. Finally, across all of these meta-analyses, the average female effect estimates differed from pooled effect estimates by an average of -0.042 standard errors. In other words, if the pooled effect estimate from a meta-analysis was d = 0.5 (95% CI = 0.1-0.9), the mean effect estimates from female-only studies would be d = 0.49, on average – a completely inconsequential difference.

If the last paragraph sounded like it was written in a foreign language, Figure 5 illustrates what I’m talking about.

Graphic by Kat Whitfield

In this forest plot from García-Valverde’s meta-analysis (18), the pooled effect estimate is 0.86, with a 95% confidence interval from 0.51-1.21. The studies by Ayers (45) and Slovak (46) are female-only studies. Both of the confidence intervals from the Ayers study (95% CIs from 0.13-1.32 and 0.02-1.05) overlap with the confidence interval of the pooled effect estimate. However, the confidence interval from the Slovak study does not overlap with the confidence interval of the pooled effect estimate. Of note, the Slovak effect estimate in Figure 5 was one of the biggest outliers of any female-only effect estimate in any of these meta-analyses, and it’s not even the biggest outlier in that particular forest plot. The effect estimate from the Moore study (47) is quite literally off the chart.

Just to solidify this point, Figure 6 shows the “worst” forest plot of the bunch, from Heidel et al’s meta-analysis (24). There are five female-only effect estimates; three of them have confidence intervals that overlap with the pooled effect estimate (Nautilus leg press, Nautilus chest press, and Soloflex chest press), while two don’t overlap (Nautilus shoulder press and Soloflex shoulder press).

Graphic by Kat Whitfield

As you can see, all five effect estimates come from a single study (48). While the confidence intervals of the lowest and highest effect estimates from this study don’t overlap with the confidence interval of the pooled effect estimate, the mean effect of all measures in the Boyer study was –0.88, which is very close to the pooled effect estimate (-0.78).

In short, it appears that studies on female lifters produce results that are similar to the broader literature across every topic examined in these meta-analyses.

Interpretation

To summarize, this analysis found that female subjects are heavily under-represented in areas of research that are relevant to lifters. Furthermore, it found that female under-representation may actually be getting worse in recent decades. Finally, it found that studies on female lifters typically have results that are similar to those observed in mixed-sex and male-only cohorts.

On its face, mere under-representation doesn’t necessarily imply that there’s a bias against studying female lifters. After all, you could argue that males are more likely to participate in resistance training than females – if you studied, say, 10% of all male lifters and 10% of all female lifters, you’d still be studying more males than females. Therefore, you should expect there to be more male subjects than female subjects in areas of research that are relevant to lifters. Furthermore, you could argue that studies exclude female subjects for valid logistical reasons. For example, you may be concerned that performance fluctuations throughout the menstrual cycle would either increase the logistical complexity of a research project (i.e., it might require you to ensure female participants are always assessed during the same phase of their cycles – that’s not a concern with male subjects) or add noise to your results (if you didn’t account for menstrual cycle phase during assessments). Or, you might be concerned that results of a particular intervention would differ between normally menstruating women and women using hormonal contraceptives (again, not a concern with male subjects). Finally, you might be concerned that a particular intervention would affect male and female subjects differently, such that using a mixed-sex sample would simply result in noisier data. However, I think I can reasonably counter all of these concerns.

For starters, males are more likely to participate in resistance training than females. A recent review by Nuzzo found that women are between 9.4-44% less likely than men to meet public health recommendations related to participation in muscle-strengthening activities (49). However, even if we were to assume that research participation in resistance training-related research should match trends of general participation in resistance training, female lifters would still be under-represented in the scientific literature as it currently stands. If research participation scaled with general resistance training participation, you should expect female subjects to comprise 36-47.5% of the total pool of research subjects. The current proportion (25%) falls well below the bottom end of that range. Even if I cherry-picked the 20 systematic reviews and meta-analyses with the highest proportion of female subjects (out of my initial pool of 39), the proportion of female subjects in the studies included in those reviews is still just 33%, which would still fall below the bottom end of that range. In short, differing levels of participation in resistance training don’t explain the degree to which female subjects are under-represented in resistance training research.

Next, let’s address logistical concerns. Some researchers may opt to study male-only cohorts due to fears that including female subjects will add complexity to a research project or add noise to your results (largely relating to concerns about the menstrual cycle and hormonal contraceptives). Fortunately, while those concerns are certainly reasonable in a vacuum, recent research should significantly alleviate those concerns in most contexts. Meta-analyses by McNulty et al (50) and Elliot-Sale et al (51) have found that performance fluctuations throughout the menstrual cycle are typically trivial, and that hormonal contraceptives have little impact on most measures of performance. Furthermore, as I discussed in a recent article, hormonal contraceptives seem to have little impact on longitudinal muscle growth and strength gains following resistance training. These findings should mitigate most of the logistical concerns people raise when discussing the prospect of studying female lifters. Assessing female lifters at different points in the menstrual cycle or including female lifters who both use and don’t use hormonal contraceptives is unlikely to meaningfully alter your results or add unmanageable amounts of noise to your data in most contexts.

Finally, let’s address the concern that male and female subjects are likely to attain different results following some resistance training or supplementation intervention. This is a perfectly reasonable concern since there are fairly large visible differences (there are obvious anthropometric differences between the sexes) and invisible differences (52) between males and females. However, in most research contexts, we typically don’t care too much about baseline differences between subjects. Rather, we care if subjects experience different responses to a particular intervention. If subjects do experience meaningfully different responses to a particular intervention, that increases the variance in your data, and makes it harder to reliably detect the effect of the intervention. The present analysis found that studies on female lifters typically attain results that are in line with the broader literature (across numerous different bodies of research). Thus, while there are certainly baseline differences between males and females, it seems that male and female subjects have very similar responses to most interventions that would be relevant to MASS and Stronger by Science readers. Furthermore, looking beyond this analysis, we know that male and female lifters typically experience comparable muscle growth and strength gains in response to identical training interventions (53), and that protein needs are very similar between the sexes when scaled to lean body mass (54, 55). Thus, using mixed-sex samples should not be problematic in most contexts. Using mixed-sex samples should make subject recruitment easier, without making it more difficult to reliably detect treatment effects in most contexts.

With all of that said, there are still circumstances when it would make sense to use single-sex cohorts. For starters, there are some research topics that are specifically relevant to a single sex (research related to the menstrual cycle, pregnancy, hormonal contraceptives, menopause, prostate cancer, etc.). Furthermore, there are areas of research with known, notable sex differences where single-sex cohorts may offer advantages – research related to concussion and non-contact ACL injury risk immediately come to mind, as well as research related to osteoporosis and iron deficiency/supplementation. There can also be situations where you only have access to a single-sex cohort. For example, if you’re given the unique chance to study an elite male rugby team, turning down the opportunity because you don’t also have access to study an elite female rugby team doesn’t make a ton of sense. There are also research topics where cultural norms may dictate that a single-sex cohort would be preferable. For example, if you want to study pec hypertrophy, and all of the trained ultrasound technicians in a particular lab are male, studying a male-only cohort may be preferable. There are also research questions for which a mixed-sex cohort would increase the complexity of your statistical approach at best, or massively increase the noise in your results at worst. For example, if you were interested in the correlation between testosterone levels and some particular training outcome, employing a mixed-sex subject pool with a bimodal distribution of testosterone levels would present you with significant analytical challenges. Finally, there may be good a priori reasons to assume that sex would have a notable impact in some brand new area of research – in that context, it might make sense to conduct a couple of male-only and female-only studies first to validate or disprove your assumption.

Moving on, I want to briefly address the areas of research where female subjects are the most and least under-represented.

There were three meta-analyses in which female subjects accounted for at least 45% of the total subject pool: a meta-analysis by Schoenfeld and colleagues investigating the impact of eccentric vs. concentric muscle actions on muscle growth (40; 47% female subjects), a meta-analysis by Grønfeldt and colleagues investigating the impact of blood-flow restriction training on strength gains (22; 49.5% female subjects), and a meta-analysis by Murphy and colleagues investigating the impact of energy deficits of strength and lean mass changes (34; 82% female subjects). Furthermore, there were three meta-analyses that exclusively included studies with male-only cohorts (56): a meta-analysis by Cuthbert and colleagues investigating the impact of training frequency on strength gains (13), a meta-analysis by Baz-Valle and colleagues investigating the impact of training volume on muscle growth and strength gains (8), and a meta-analysis by Kassiano and colleagues investigating the impact of exercise variation on muscle growth and strength gains (27).

The three male-only meta-analyses cover research questions that are highly relevant to most lifters: how frequently should I train each muscle, how much training volume do I need to maximize my results, and can I improve my results by training each muscle group with multiple exercises (57)? Conversely, the two meta-analyses with near-parity cover more niche topics: blood-flow restriction training isn’t a staple in most people’s training arsenals, and few lifters do much eccentric-only or concentric-only training. Thus, while this is obviously subjective, the present analysis may still overstate the degree to which female subjects are represented in the research most people would use to make training decisions. In other words, females may be 25% of the total subject pool, but they may comprise 20% of the subject pool in the most practically relevant areas of research, and 30% of the total subject pool in more niche areas of research (58). Furthermore, I’ll note that the only meta-analysis I reviewed specifically investigating weight loss was also the only meta-analysis with more female subjects than male subjects. Take from that what you will.

Takeaways

I’ll start by stating the obvious: female lifters are significantly under-represented in the research that’s most relevant to lifters. Furthermore, female subjects are more under-represented in resistance training research than in general exercise science research, and the problem seems to be growing over time.

Thankfully, that doesn’t necessarily mean that the research in this area is uninformative for female lifters. The finding that studies on female-only cohorts reach results that are similar to those observed in the broader literature cuts both directions. It doesn’t just mean that researchers can confidently include female lifters in their studies without fearing that their male and female subjects will have meaningfully different responses to study interventions. It also means that, in general, research on male-only or mixed-sex cohorts should be expected to generalize to female lifters. As someone who has discussed research in public for a decade, I frequently encounter male lifters who disregard research findings from studies on female cohorts, and female lifters who disregard research findings from studies on male cohorts. I can certainly understand that impulse, but at least in the context of resistance training research, it’s probably unfounded most of the time. Most research findings generalize between the sexes quite well.

Finally, my biggest takeaway from this analysis is that more research should be conducted on mixed-sex cohorts. Most of the time, using a mixed-sex cohort comes with clear benefits (making subject recruitment easier and potentially increasing the generalizability of your findings), and it rarely has obvious downsides. As previously acknowledged, there are certainly situations where it makes sense to study single-sex cohorts, but there’s absolutely no reason why 65% of the studies in this area should use male-only cohorts. When female lifters account for approximately 40% of the general lifting population, there’s no reason why they should only account for 25% of the research subjects in the area. Thankfully, we know this is a solvable problem – as Brookshire found in 2016, most areas of exercise-related research have already achieved something resembling equal research representation for both sexes. It’s time for strength training research to follow suit.

Drawbacks

While I think the approach I took to addressing this problem is sound, it still has its drawbacks. If you’re primarily interested in practical takeaways, feel free to stop reading here. If you’re really hankering for several pages of self-criticism, then read on.

First, this analysis likely underestimates the overall proportion of studies on female lifters, for one simple reason: I only looked at “neutral” bodies of literature where participants can be either male or female. There are several bodies of research where all of the subjects are female: research looking at the impact of the menstrual cycle, hormonal contraceptives, and pregnancy necessarily have female-only cohorts. Conversely, there are a handful of topics that necessarily employ male-only cohorts (most notably, studies on exercise in subjects with prostate cancer). There are more sex-specific topics that require female-only cohorts than male-only cohorts, and the female-specific topics tend to garner more research attention because they affect more people in total (almost all females will menstruate, but most males won’t get prostate cancer). Cowley’s paper found that 20% of studies on female subjects investigate female-specific research questions, whereas only 0.6% of studies on male subjects investigate male-specific research questions (4). However, I don’t necessarily view this as a weakness of the analysis. There’s no reason why there shouldn’t be plenty of studies on contraceptives, and also plenty of studies on training volume with female cohorts. The fact that female-specific bodies of research exist doesn’t imply that female lifters shouldn’t be better represented in “neutral” bodies of literature.

Second, the precise result of this type of analysis will necessarily depend on the systematic reviews and meta-analyses you select as your starting point. If someone wanted to cherry pick reviews to make it appear that female lifters are more under-represented or less under-represented, it wouldn’t be hard to put your thumb on the scale. For example, if I only analyzed the 20 systematic reviews and meta-analyses with the lowest proportions of female subjects, I could have estimated that female subjects compose just 14% of the total subject pool in the area. However, if I only analyzed the 20 systematic reviews and meta-analyses with the highest proportions of female subjects, I could have estimated that female subjects compose nearly 33% of the total subject pool in the area. To be clear, I didn’t do this; I didn’t pre-screen the original batch of 45 reviews I looked into, and the reviews I excluded were either excluded by necessity (due to insufficient reporting about the studies going into the review), or they would have had minimal impact on the analysis due to extensive overlap with reviews that were included. However, this general problem applies to essentially any approach one could take to addressing this basic research question. For example, if you used Costello’s and Cowley’s approach (screening all studies published in a fixed number of journals), you could decide to bin the results from a journal or two with particularly high or low proportions of female research subjects (to be clear, I don’t think that happened in the Costello and Cowley studies). I attempted to defray this risk by casting a really wide net. When you scan the list of systematic reviews and meta-analyses in Table 1, I suspect you won’t be able to think of very many highly active areas of research that are unrepresented in this analysis and that are highly relevant to lifters. That should ensure that I obtained a representative sample of studies.

Third, this approach is blind to areas of research that haven’t yet been systematically reviewed and meta-analyzed. So, it’s theoretically possible that less active areas of research or cutting-edge areas of research – bodies of literature with too few studies to warrant a systematic review or meta-analysis – have a proportionally greater number of male or female subjects than more established areas of research. If anything, I suspect this source of bias would result in a net overestimate of the proportion of female research subjects using the approach I took for this article. Based on my observations, it seems that the first couple of studies in a new niche tend to use male-only samples, with mixed-sex and female-only studies trickling in later. However, less active areas of research also tend to be areas of research that aren’t quite as interesting to lifters and coaches – topics with greater general interest also tend to attract greater research interest (and vice versa). So, I don’t think this potential drawback fundamentally impacts my ability to do what I set out to do with this article: analyze male and female representation in the areas of research that are most relevant to lifters.

Fourth, the approach I took in this article – letting systematic reviews and meta-analyses do a lot of the heavy lifting for me – may be slow to pick up on very recent trends. Research output is increasing every year, but this analysis only included nine studies from 2021 and one study from 2022 (compared to 63 studies from 2019 and 57 studies from 2020). The reason for this under-representation of very recent research is simple – there aren’t new meta-analyses about every topic, every month. If a meta-analysis was published in 2021, it may be based on a systematic search conducted in mid-2020, which would include all of the research output through 2019, half of the research output in 2020, and none of the research output in 2021 and 2022. To mitigate the impact of this drawback, I attempted to select the most recent systematic reviews and meta-analyses possible: none pre-dated 2017, and 27 of the 39 reviews were published in 2021 or 2022. However, it’s possible that a seismic shift in sex representation has occured within the past 18 months; if that has happened, the strategy I employed for this analysis would be unable to identify such a shift. For what it’s worth, I think this possibility is quite unlikely, but I still feel compelled to mention it in the interest of thoroughness.

Fifth, I could have been a bit more thorough and rigorous in my analysis of whether results from female-only studies differ from the rest of the literature. In theory, you could re-create every meta-analysis, and generate pooled effect estimates for male-only studies, mixed-sex studies, and female-only studies, and more rigorously compare those pooled effect estimates. In practice, doing this would have increased the time burden of this analysis by approximately 100-fold. Since I analyzed 67 forest plots, I would have needed to re-code all 628 studies and perform approximately 67 × 3 = 201 unique meta-analyses if I went this route. If someone wants to walk down that road, then more power to them. For practical purposes, just counting the number of overlapping versus non-overlapping confidence intervals is a quick and dirty method of analysis that’s way more feasible.

Sixth, if a meta-analysis made any data reporting errors (for example, reporting that a study had 28 subjects when it actually had 18 subjects, or reporting that a study had a mixed-sex cohort when it actually had a male-only cohort), those data reporting errors would be preserved in my analysis. I have to assume that such errors are rare and unlikely to be large enough to meaningfully change the outcomes of this analysis. Furthermore, this isn’t a unique drawback to this method of analysis – it’s not like original research never has data reporting errors, and it’s not like I’m immune to transcription errors. However, it’s worth mentioning in the interest of thoroughness.

Seventh, if the authors of a systematic review or meta-analysis failed to identify a relevant study or two when conducting their literature search, those studies would also be excluded from my analysis. Again, this isn’t a unique drawback – if I conducted my own systematic literature search, there’s no guarantee that I’d find every single relevant paper. However, I also don’t view this as a major drawback. Even if we were to assume that there’s a vast ocean of resistance training studies with female subjects that meta-analysts failed to find, the functional takeaways of this analysis wouldn’t change. If conventional systematic search strategies can’t find a study, that study doesn’t exist for all practical purposes. Research has utility insofar as it can be discovered, read, and used to inform future research and real-world application. If a study is indexed in any of the major databases, or if it’s ever been cited by other studies in its niche, a systematic literature search should be able to find it. If it’s not indexed and has never been cited, it basically doesn’t exist.

Eighth, general biases present in the scientific publishing industry will be preserved by any approach that analyzes published research output. Publication bias is the most prominent issue here: studies deemed to be more interesting by editors and reviewers are generally more likely to be published than studies deemed to be less interesting. Publication bias is mostly discussed in relation to statistical significance – statistically significant findings have an easier time getting published than null results. However, publication bias can also apply to novelty – studies addressing new research questions, or studies addressing old research questions in new populations are more likely to get published than studies addressing old research questions in well-trod populations. If publication bias affected this analysis, it would likely result in a slight overestimation of the proportion of female research subjects in the area. Since most lines of research start with male-only samples, studies with female samples are typically novel or under-represented in a particular body of research, which would tend to make it a bit easier to publish new studies with female cohorts than male cohorts.

To be clear, I don’t think any of the potential drawbacks of my analysis strategy fundamentally alter the validity of my findings. Rather, I considered the potential drawbacks before I started, and determined that they were all either acceptable (i.e., they were unlikely to meaningfully change the results) or unavoidable (drawbacks inherent to sampling a portion of the published literature). Furthermore, since I took a somewhat novel approach to analyzing sex representation within the literature (rather than cribbing Costello’s and Cowley’s approach), I wanted to preempt some of the potential questions and criticisms my strategy might provoke. I also just have a tendency to excessively fixate on the potential weaknesses of my own work.

References This curiosity about the sheer magnitude of variability has actually paid dividends. A couple years ago, I was involved in a project that helped expose implausible results coming from a highly productive exercise science researcher, which has resulted in several retractions. The first thing that made me skeptical of this research was the incredibly homogeneous training responses reported in this researcher’s papers.Nuckols G. The effects of biological sex on fatigue during and recovery from resistance exercise. Thesis, University of North Carolina at Chapel Hill (2019).Costello JT, Bieuzen F, Bleakley CM. Where are all the female participants in Sports and Exercise Medicine research? Eur J Sport Sci. 2014;14(8):847-51. doi: 10.1080/17461391.2014.911354. Epub 2014 Apr 25. PMID: 24766579.Cowley ES, Olenick AA, McNulty KL, Ross EZ. “Invisible Sportswomen”: The Sex Data Gap in Sport and Exercise Science Research. 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Journal of Strength and Conditioning Research: August 1990 – Volume 4 – Issue 3 – p 88-94Nuzzo JL. Sex Difference in Participation in Muscle-Strengthening Activities. J Lifestyle Med. 2020 Jul 31;10(2):110-115. doi: 10.15280/jlm.2020.10.2.110. PMID: 32995338; PMCID: PMC7502892.McNulty KL, Elliott-Sale KJ, Dolan E, Swinton PA, Ansdell P, Goodall S, Thomas K, Hicks KM. The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta-Analysis. Sports Med. 2020 Oct;50(10):1813-1827. doi: 10.1007/s40279-020-01319-3. PMID: 32661839; PMCID: PMC7497427.Elliott-Sale KJ, McNulty KL, Ansdell P, Goodall S, Hicks KM, Thomas K, Swinton PA, Dolan E. The Effects of Oral Contraceptives on Exercise Performance in Women: A Systematic Review and Meta-analysis. Sports Med. 2020 Oct;50(10):1785-1812. doi: 10.1007/s40279-020-01317-5. PMID: 32666247; PMCID: PMC7497464.Haizlip KM, Harrison BC, Leinwand LA. Sex-based differences in skeletal muscle kinetics and fiber-type composition. Physiology (Bethesda). 2015 Jan;30(1):30-9. doi: 10.1152/physiol.00024.2014. PMID: 25559153; PMCID: PMC4285578.Roberts BM, Nuckols G, Krieger JW. Sex Differences in Resistance Training: A Systematic Review and Meta-Analysis. J Strength Cond Res. 2020 May;34(5):1448-1460. doi: 10.1519/JSC.0000000000003521. PMID: 32218059.Malowany JM, West DWD, Williamson E, Volterman KA, Abou Sawan S, Mazzulla M, Moore DR. Protein to Maximize Whole-Body Anabolism in Resistance-trained Females after Exercise. Med Sci Sports Exerc. 2019 Apr;51(4):798-804. doi: 10.1249/MSS.0000000000001832. PMID: 30395050.Bandegan A, Courtney-Martin G, Rafii M, Pencharz PB, Lemon PW. Indicator Amino Acid-Derived Estimate of Dietary Protein Requirement for Male Bodybuilders on a Nontraining Day Is Several-Fold Greater than the Current Recommended Dietary Allowance. J Nutr. 2017 May;147(5):850-857. doi: 10.3945/jn.116.236331. Epub 2017 Feb 8. PMID: 28179492.I double checked, and none of them included the presence of female subjects as an exclusion criterionA 2020 meta-analysis by Hagstrom and colleagues surveyed the literature examining longitudinal resistance training outcomes in females. They found that, at the time of publication, only 24 such studies existed. It feels like 24 longitudinal resistance training studies in male subjects are published every month. So, I do think it’s likely that female lifters are even more under-represented in the most relevant and impactful areas of resistance training research.The finding that female subjects account for just 25% of the total subject pool in resistance training-related research comports well with a recent analysis by Smith and colleagues. They investigated sex representation in supplement research, and found that female subjects accounted for just 23% of the total subject pool in studies investigating the effects of β-alanine, caffeine, creatine, glycerol, nitrate/beetroot juice and sodium bicarbonate. 1826 studies with 34,889 participants were included in their analysis.

The post Where are all the Female Participants in Strength, Hypertrophy, and Supplement Research? appeared first on Stronger by Science.

- Greg Nuckols
Do Oral Contraceptives Affect Your Gains?

A quick note about this article before we dive in:

This article was first published in MASS Research Review and is a review and breakdown of a recent study.  The study reviewed is Molecular Markers of Skeletal Muscle Hypertrophy Following 10 Weeks of Resistance Training in Oral Contraceptive Users and Non-Users. Oxfeldt et al. (2020)

Key Points Two groups of untrained females participated in a 10-week training study. One group was composed of oral contraceptives users, and the other group was composed of non-users.Strength gains and hypertrophy were not significantly different between groups. However, the subjects using oral contraceptives tended to have larger increases in lean body mass, along with some molecular indicators of anabolism.When analyzing these results within the context of the rest of the literature, it doesn’t seem that second- or third-generation oral contraceptives have a meaningful effect on strength or hypertrophy outcomes.

Many female lifters use hormonal contraceptives, but we’ve only discussed them twice in MASS. I previously reviewed a study by Myllyaho and colleagues which found that oral contraceptives didn’t have a significant impact on strength gains or changes in lean body mass (2), and a meta-analysis by Elliott-Sale and colleagues which found that hormonal contraceptives don’t have a particularly notable effect on acute performance measures (3). So, we’ve only reviewed one study that examined longitudinal outcomes, and as we all know, it’s dangerous to put too much faith in the results of a single study.

The presently reviewed study (1) assessed strength, hypertrophy, body composition, and cellular signaling outcomes before and after 10 weeks of training in users and non-users of oral contraceptives. Strength gains and hypertrophy were not significantly different between groups. However, the subjects using oral contraceptives tended to have larger increases in lean body mass, along with some molecular indicators of anabolism. Based on these results, it may initially be tempting to conclude that oral contraceptives might have a small positive effect on hypertrophy, but when examining the rest of the research on the topic, it seems that hormonal contraceptives simply don’t have much of an effect (positive or negative) on strength, hypertrophy, or lean mass outcomes.

Purpose and Hypotheses Purpose

The purpose of this study was to investigate whether oral (hormonal) contraceptives influence hypertrophy, molecular signaling markers, and satellite cell responses to resistance training.

Hypotheses

The authors hypothesized that oral contraceptives would “potentiate the anabolic response to resistance training.”

Subjects and Methods Subjects

38 untrained young women completed this study. 20 used second-generation oral contraceptives (containing 30µg ethinyl estrogen and 0.15mg levonorgestrel, or 35µg ethinyl estrogen and 0.25mg norgestimate), and 18 did not use hormonal contraceptives. All subjects were healthy but untrained and menstruated regularly. You can see more information about the subjects in Table 1.

All graphics and tables in this review are by Kat Whitfield. Experimental Design

Subjects trained for 10 weeks, and vastus lateralis muscle biopsies were collected before and after the 10-week training period.

The training consisted of three weekly training sessions. During each session, subjects performed leg press, knee extensions, leg curls, back extensions, pull-downs, and incline crunches. The subjects performed each exercise for 3 sets of 12 reps during weeks 1-5, 3 sets of 10 reps during weeks 6-8, and 4 sets of 8 reps during weeks 9 and 10. The subjects were “encouraged to use maximal effort and train near momentary muscle failure,” and “adjusted weights throughout the entire training period to maintain muscle loading as muscle strength increased.” I’d prefer more details about proximity to failure and the process for progressing loads, but it sounds like the training program likely provided an adequate stimulus for untrained subjects. 

From the biopsies, the researchers examined changes in muscle fiber cross-sectional area, myosin heavy chain isoform ratios (the same procedure used in this study), satellite cells per fiber, myonuclei per fiber, mRNA levels for various muscular regulatory factors (Pax7, MYF5, MyoD1, MRF4, and MyoG, which are generally reflective of growth, and FOXO1, FOXO3, FOXO4, TNF-α, Atrogin-1, and MURF-1, which are generally reflective of protein breakdown), and levels of various proteins associated with anabolism (mTOR, alpha estrogen receptor, androgen receptor, Pax7, and MyoD).

Notably, the present study (1) is the second paper published from a single investigation. The first paper (4) also tested 5RM leg press strength, maximal knee extension and knee flexion torque, quadriceps cross-sectional area, countermovement jump height, Wingate test average power, and body composition (via DEXA) pre- and post-training. I’ll report those findings as well.

Findings

Quadriceps cross-sectional area at three different sites along the quadriceps increased significantly in both groups, without significant differences between groups.

Fat-free mass also increased significantly in both groups. It tended to increase to a greater extent in the subjects using oral contraceptives (3.7 ± 3.8% vs. 2.7 ± 3.5%; p = 0.08), but the difference between groups wasn’t statistically significant.

Muscle fiber cross-sectional area significantly increased in both groups for both major fiber types, with no significant differences between groups.

Fat mass decreased to a significantly greater extent in the subjects not using hormonal contraceptives, but the raw changes were tiny in both groups (-0.1kg vs. -0.8kg).

Knee flexion torque, knee extension torque, leg press strength, countermovement jump height, and Wingate test average power increased significantly in both groups, without significant differences between groups.

From pre- to post-training, the oral contraceptive users had a significantly larger increase in type IIa myosin heavy chain isoform proportion (+6.9% vs. -0.1%; p < 0.01), probably due to the fact that they had a larger proportion of type IIx myosin heavy chain protein pre-training (7.5% vs. 3.8%).

Myonuclei per fiber changed to a similar degree in both groups, as did satellite cells per fiber. When expressed as satellite cells per unit of fiber cross-sectional area (basically the inverse of myonuclear domain), the increase in the subjects using hormonal contraceptives tended to be larger than the change in the controls, but the difference didn’t quite reach statistical significance (p = 0.055).

The only significant difference between groups for changes in mRNA levels of generally anabolic regulatory factors was for MRF4, which increased to a significantly greater extent in the oral contraceptive users. For generally catabolic regulatory factors, TNF-α mRNA significantly increased in the oral contraceptive users but not the non-users (though the difference between groups wasn’t significant). Furthermore, both groups experienced a significant increase in MURF-1 mRNA, with no significant differences between groups. There were no other significant within-group or between group changes for the various mRNAs examined (Pax-7, MYF5, MyoD1, MyoG, FOXO1, FOXO3, FOXO4, and Atrogin-1).

There was only one significant change in protein levels. Androgen receptor protein levels significantly increased in the oral contraceptive users, but not the non-users; however, there wasn’t a significant difference between groups. No other protein examined significantly changed in either group, or significantly differed between groups.

Interpretation

In previous issues of MASS, we’ve reviewed another study examining the effects of oral contraceptives on strength and body composition outcomes (2), and a meta-analysis investigating the effects of oral contraceptives on exercise performance (3). The study investigating body composition and performance outcomes failed to find any significant differences between users and non-users, and the meta-analysis found that non-users may perform slightly better, but any mean difference is likely to be trivial, if one exists at all.

In that light, the results of the present studies are unsurprising (14). Hormonal contraceptives didn’t seem to affect hypertrophy or strength gains, their effect on body composition seemed to be trivial, and most molecular markers were unaffected. Thus, especially in light of the studies we’ve previously reviewed on the topic, the conservative interpretation of these results is that hormonal contraceptives are unlikely to have much of an impact (either positive or negative) on strength gains or hypertrophy.

However, if you squint just right, you could possibly make the case that oral contraceptives may be a weak ergogenic. In the present studies, direct measures of hypertrophy (type I and type II fiber cross-sectional area and quadriceps cross-sectional area) all leaned in favor of the oral contraceptives group, though the differences weren’t statistically significant. The subjects using oral contraceptives also tended to gain more lean mass (p = 0.08). Furthermore, in type II fibers, satellite cells per unit of cross-sectional area tended to increase more in the subjects using oral contraceptives (p = 0.055); increases in satellite cells suggest that muscle fibers are “setting the stage” to future growth. The subjects using oral contraceptives also had a significantly greater increase in mRNA levels for a pro-anabolic regulatory factor (MRF4), and non-significant differences in mRNA levels for all other pro-anabolic regulatory factors tended to favor the subjects using oral contraceptives. The oral contraceptive group also significantly increased androgen receptor protein levels; previous research has found that androgen receptor density is a positive predictor of hypertrophy (albeit in males; 5). The authors of the present study also argue that the significant increase in TNF-α mRNA levels in the subjects using oral contraceptives may reflect an increase in adaptive protein remodeling (1).

On the other hand, the non-users may have been slightly more “trained” at baseline (all of the subjects were untrained, but not all untrained subjects are equally untrained). They had lower proportions of type IIx myosin (which decreases as training status increases). Furthermore, body composition changes reveal that the oral contraceptive users were in a slight caloric surplus, on average (+1.5kg of body mass; -0.1kg decrease in fat mass), while the non-users were in a slight deficit (+0.3kg of body mass, -0.8kg of fat mass), which may be sufficient to explain the non-significant differences in hypertrophy. Thus, while some of the findings regarding molecular markers are certainly interesting, I wouldn’t read too much into them yet.

Let’s now look beyond the presently reviewed studies. When I last wrote about a study examining the effects of oral contraceptives on performance and body composition outcomes, there weren’t many other longitudinal studies to analyze. There are a few more now, so I think we’re ready for a preliminary summary of the literature.

Graphics in this review are by Kat Whitfield.

I was able to find 10 papers from 8 distinct studies (two studies were responsible for two papers apiece) that investigated the effects of oral contraceptives on strength, lean mass, or hypertrophy outcomes (1246789101112). You can see a summary of their results in Table 5. It’s not really worth breaking down every study in depth, because the overall pattern is clear: oral contraceptives don’t seem to have a consistent, notable impact on the sorts of outcomes most SBS or MASS readers care about. The strength outcomes are clearly a wash, as are the lean body mass outcomes. For hypertrophy outcomes, only two studies (spanning three papers) have directly investigated the effects of oral contraceptives on direct measures of muscle growth (1411), and they’re both chock full of non-significant results favoring the use of hormonal contraceptives. In fact, the difference in type I fiber CSA in Dalgaard’s 2019 study was actually statistically significant, favoring the subjects using oral contraceptives (11). Personally, I’m not incredibly impressed by one significant difference out of eight outcome measures, especially when all we have to go on is a pair of relatively small studies, and especially when the entire body of research doesn’t suggest that there are meaningful differences in strength gains or lean mass accretion.

With that being said, there are two little nuggets in the “notes” column of Table 5 that are worth dwelling on for a moment. A study by Lee and colleagues found a significant difference in lean mass accretion between people taking oral contraceptives with weakly androgenic progestins, compared to people taking oral contraceptives with more strongly androgenic progestins (6). Furthermore, a study by Dalgaard (11) found that subjects taking oral contraceptives with a higher estrogen dose (30µg) experienced more vastus lateralis hypertrophy than subjects taking oral contraceptives with a lower estrogen dose (20µg). When discussing these findings, I’ll reverse the orders of “good cop” and “bad cop” for a change.

These findings deserve some degree of skepticism because they appear to be the result of exploratory analyses; in other words, it’s unlikely that they’re comparisons the researchers had in mind when designing their studies. The reason I think these were exploratory analyses is that the dominant subject recruitment paradigm revolves around developing a research question, predicting the magnitude of the effect you’ll find (or assuming whatever magnitude of effect you think will be “meaningful”), and recruiting enough subjects to be able to reliably detect an effect of your predicted magnitude. If you can’t recruit enough people or you overestimate the actual effect size, your study is underpowered. If you underestimate the actual effect size, and recruit way more people than you need, your study is overpowered. In both of these studies, the primary comparisons were between people using versus not using oral contraceptives; thus, the studies would be powered to detect differences between those two groups. In other words, in the Dalgaard study, there were 14 subjects per group meaning the researchers were likely anticipating that 14 subjects per group would provide adequate statistical power to detect differences between users and non-users of oral contraceptives (11). But then, the comparison between high-estrogen and low-estrogen oral contraceptive users compared groups of just seven subjects apiece. That would only make sense as an a priori decision if the researchers had reason to believe that the difference between high- and low-estrogen oral contraceptive users would be considerably larger than the difference between users and non-users. Furthermore, for the low- versus high-estrogen comparison to be a planned analysis, the researchers would have needed to make an effort to recruit similar numbers of subjects who used low- and high-estrogen oral contraceptives; they wound up with 7 and 7, but they could have easily wound up with 3 and 11, which would make traditional significance testing basically impossible. The authors don’t state that they went out of their way to recruit similar numbers of high- and low-estrogen oral contraceptive users.

So, what does that mean? Well, for starters, I’m certainly not claiming the authors did anything wrong. Running exploratory analyses is a perfectly normal part of science. However, we inherently need to be more skeptical of findings that are a result of exploratory analyses. Why? Because there are an almost infinite amount of analyses you can run and comparisons you can make when you’re working with data. There are a huge number of statistically significant “findings” lurking in every dataset, and a non-negligible proportion of them are bound to be illusory and spurious. If you pre-specify a data analysis plan, you run a study correctly, you stick to your pre-specified data analysis plan, and you get a statistically significant result, there’s a low probability that your significant result is spurious. However, once you start running exploratory analyses, you’re almost guaranteed to stumble upon some statistically significant “discoveries” that are completely spurious. As one example, in my thesis study, I found that soreness 24 hours post-training was significantly positively associated with hours of sleep the night following the training session (p = 0.017). I was dealing with a rich dataset, which would lend itself to literally thousands of comparisons, and examining the relationship between sleep and soreness was not part of my pre-specified data analysis plan. So what’s more likely? Sleeping more is predictive of greater soreness following training? Or I stumbled across one of the (likely hundreds of) spurious findings lurking in my dataset? My money’s on option 2. Now, don’t get me wrong. I’m certainly not claiming that all significant results that result from exploratory analyses are spurious. I’m not even claiming that a majority of them are. I’m simply stating that the probability of a “false positive” is greater when you see a significant finding resulting from an exploratory analysis, than when you see a significant finding resulting from researchers’ primary analyses.

The value of exploratory analyses is that they help you generate hypotheses for future research. If you see that, in subgroups of seven subjects apiece, people who use high-estrogen oral contraceptives experience more hypertrophy than people who use low-estrogen oral contraceptives, you might want to design a study to test that preliminary finding more rigorously. If you get similar results in a study specifically designed to make that comparison, then you can start having considerably more confidence in the finding. If a study designed to test that comparison fails to find significant or meaningful differences, then you know there’s a decent chance that your exploratory analysis found a false positive and sent you on a wild goose chase.

In a roundabout way, all I’m saying is that you always need to be cautious of findings that are only supported by one study, and you need to be doubly cautious of findings that are only supported by exploratory analysis in one study.

So, now that I’m done with the bad cop routine, let’s discuss why you should maybe have a little confidence in these exploratory findings, suggesting that hormonal contraceptives with higher levels of estrogen and less androgenic progestins might be beneficial for hypertrophy.

Starting with estrogen, we’ve previously discussed the beneficial effects of estrogen for muscle remodeling. However, it appears that most of the popular oral contraceptives on the market induce less total estrogenic activity on a monthly basis than females would naturally be exposed to (13). Ethinyl estrogen is the form of estrogen used in the vast majority of hormonal contraceptives, and the typical monthly dose of ethinyl estrogen contained in oral contraceptives is a little less than half the amount of estradiol (the primary estrogen humans produce) naturally menstruating women produce on a monthly basis, on average. However, ethinyl estrogen’s affinity for the estrogen receptor is approximately 90% greater than estradiol’s, so the total estrogenic activity of the ethinyl estrogen in a month’s supply of oral contraceptives is probably around 10-12% lower than the total estrogenic activity of the estradiol that naturally menstruating women produce. There’s a pretty broad range, though, with some oral contraceptives providing more than 50% less monthly estrogenic activity than would be present in an average natural menstrual cycle, and others providing almost 50% more (13). Given the positive effects of estrogen on skeletal muscle and the wide range of estrogen doses one could possibly derive from oral contraceptives, I do think it’s plausible that formulations with higher estrogen content could be meaningfully ergogenic. Though, to reiterate, I’d want to see future research confirm Dalgaard’s exploratory findings.

Now, let’s move on to the matter of progestins. How much stock should we place in Lee et al’s finding that oral contraceptives with less androgenic progestins could be beneficial for hypertrophy? Quite a bit, actually.

Psych.

Bad cop’s back, baby.

First off, I need to eat some crow. I’ve previously made the claim that progestins with greater androgenicity were a negative for hypertrophy, because the progestins with a high affinity for the androgen receptors would essentially “clog up” the receptors without actually causing downstream androgenic signalling, and thus keep androgens from being able to do their job (competitive antagonism, if you prefer the fancy biochem jargon). In my defense, I vividly remember learning this in my undergraduate exercise physiology class, and that is how progesterone functions (though progesterone has a relatively low affinity for the androgen receptor). However, that’s not how most of the progestins present in oral contraceptives function. First-, second-, and third-generation progestins (second- and third-generation progestins are present in most oral contraceptives currently on the market) are truly androgenic, binding to the androgen receptor and functioning like an androgen when bound to the receptor. Fourth-generation progestins, on the other hand, do function more like progesterone, functioning as androgen receptor antagonists (14).

So, with that in mind, I suspect Lee et al’s finding (smaller gains in lean mass with less androgenic progestins; 6) is likely spurious, for three reasons. First, androgenic signaling is generally a positive thing for hypertrophy, and one would assume that it would be especially positive for people using oral contraceptives: oral contraceptives tend to decrease free testosterone levels, so getting an androgenic signalling boost from a highly androgenic progestin seems like it would be a good thing. Second, it’s hard to square Lee et al’s findings with the results of the present study (1): 17 of the 20 oral contraceptive users in the present study used formulations featuring the progestin levonorgestrel, which is either the most androgenic progestin commonly used in oral contraceptives, or one of the most androgenic progestins commonly used in oral contraceptives (depending on the measure of androgenicity you look at; 14). As previously mentioned, the hormonal contraceptive users in the present study grew just fine (1), and basically all (mostly non-significant) hypertrophy differences between the users and non-users leaned in favor of the users. At minimum, if more androgenic progestins blunt hypertrophy, the Lee study likely overestimates the effect (the mean increase in lean body mass was 3.5% in non-users and 0.3% in hormonal contraceptive users whose pills included moderately-to-highly androgenic progestins). Lastly, in re-examining Lee’s results, I think the exploratory analysis comparing the low- versus moderate-to-highly androgenic progestins was inappropriate in the first place. The average increase in lean body mass for the group of hormonal contraceptive users was 2.1% (n = 34). The sub-group using progestins with low androgenicity had an average increase of 2.5%, while the sub-group using progestins with moderate-to-high androgenicity had an average increase of 0.3%. What should jump out at you (and what should have jumped out at me sooner) is the fact that 2.1% is nowhere near the midpoint of 2.5% and 0.3%. That’s relevant, because you’d expect the group average to be the midpoint of the two subgroup averages if the subgroups were the same size. So, I did a little algebra, and calculated how large the two subgroups were. As it turns out, there were 28 subjects using progestins that were deemed to have low androgenicity, and just 6 subjects using progestins that were deemed to have moderate-to-high androgenicity (15). The authors don’t state what statistical test they used to compare the two groups (though, in their defense, the only published results from this study are in the from of a conference abstract, and abstracts don’t generally have sprawling statistics sections), but most parametric tests assume that the number of datapoints are roughly similar between groups. That’s arguably less important with large sample sizes, but it seems pretty darn important when you’re dealing with relatively small groups that differ in size by more than four-fold. And, on a more basic level, I don’t really see the point in using inferential statistics on a group of six subjects in the first place. With a group that small, one or two new subjects that differ substantially from the mean can completely change your results.

So, just to wrap this sucker up, I don’t think you need to be too concerned about how oral contraceptives will affect your strength or hypertrophy goals. Now that the body of evidence is growing, you could possibly make a very tentative case that hormonal contraceptives potentially improve hypertrophy results, but I’d want to see stronger evidence before stating that confidently. There’s also some (quite weak) evidence suggesting that formulations with higher estrogen doses may be beneficial for hypertrophy, so if you use oral contraceptives and you’re willing to do anything for a slight edge, you could consider asking your doctor about oral contraceptives with higher estrogen doses (though, as someone with no skin in the game, the risks seem to outweigh the rewards; if estrogen levels get too high, they can cause headaches, nausea, and lethargy. At minimum, those are symptoms worth monitoring if you and your doctor decide to change to a new oral contraceptive). Ultimately, no matter what you do, you shouldn’t expect a night-and-day difference. Based on the current state of the research, the most commonly discussed reasons for using or not using oral contraceptives (contraception, more control over your period, managing menstrual symptoms, etc.) seem like the most justifiable reasons. Future research may tip the balance of evidence toward oral contraceptives being meaningfully ergogenic or ergolytic, or research on fourth-generation oral contraceptives may have results that differ substantially from the research on primarily second- and third-generation oral contraceptives (which dominate the literature currently). But for now, the research suggests that you probably don’t need to think about your gains when you’re deciding whether to start, stop, or change oral contraceptives.

As always, you should talk to your doctor about drugs, and nothing in this article should be construed as medical advice. 

Next Steps

There are a lot of forms of hormonal contraception that haven’t yet been studied in a resistance training context. We don’t know how the minipill (progestin-only oral contraception), fourth-generation combination pills, hormonal IUDs, intravaginal inserts, or progestin injections affect strength and hypertrophy. A straightforward training study with any of the un-researched forms of hormonal contraception would fill a significant hole in the literature. 

Application and Takeaways

To this point, it doesn’t seem like second- or third-generation oral contraceptives have much of an effect on strength or hypertrophy outcomes following resistance training. If you choose to use hormonal contraceptives, you probably don’t need to worry about your gains.

References Oxfeldt M, Dalgaard LB, Jørgensen EB, Johansen FT, Dalgaard EB, Ørtenblad N, Hansen M. Molecular markers of skeletal muscle hypertrophy following 10 weeks of resistance training in oral contraceptive users and non-users. J Appl Physiol (1985). 2020 Oct 15. doi: 10.1152/japplphysiol.00562.2020. Epub ahead of print. PMID: 33054662.Myllyaho MM, Ihalainen JK, Hackney AC, Valtonen M, Nummela A, Vaara E, Häkkinen K, Kyröläinen H, Taipale RS. Hormonal Contraceptive Use Does Not Affect Strength, Endurance, or Body Composition Adaptations to Combined Strength and Endurance Training in Women. J Strength Cond Res. 2018 Jun 20. doi: 10.1519/JSC.0000000000002713. Epub ahead of print. PMID: 29927884.Elliott-Sale KJ, McNulty KL, Ansdell P, Goodall S, Hicks KM, Thomas K, Swinton PA, Dolan E. The Effects of Oral Contraceptives on Exercise Performance in Women: A Systematic Review and Meta-analysis. Sports Med. 2020 Oct;50(10):1785-1812. doi: 10.1007/s40279-020-01317-5. PMID: 32666247; PMCID: PMC7497464.Dalgaard LB, Jørgensen EB, Oxfeldt M, Dalgaard EB, Johansen FT, Karlsson M, Ringgaard S, Hansen M. Influence of Second Generation Oral Contraceptive Use on Adaptations to Resistance Training in Young Untrained Women. J Strength Cond Res. 2020 Jul 20. doi: 10.1519/JSC.0000000000003735. Epub ahead of print. PMID: 32694286.Morton RW, Sato K, Gallaugher MPB, Oikawa SY, McNicholas PD, Fujita S, Phillips SM. Muscle Androgen Receptor Content but Not Systemic Hormones Is Associated With Resistance Training-Induced Skeletal Muscle Hypertrophy in Healthy, Young Men. Front Physiol. 2018 Oct 9;9:1373. doi: 10.3389/fphys.2018.01373. PMID: 30356739; PMCID: PMC6189473.Lee CW, Newman MA, Riechman SE. Oral Contraceptive Use Impairs Muscle Gains in Young Women. FASEB. 2009 Apr;23:51. doi: 10.1096/fasebj.23.1_supplement.955.25.Ihalainen JK, Hackney AC, Taipale RS. Changes in inflammation markers after a 10-week high-intensity combined strength and endurance training block in women: The effect of hormonal contraceptive use. J Sci Med Sport. 2019 Sep;22(9):1044-1048. doi: 10.1016/j.jsams.2019.04.002. Epub 2019 May 30. PMID: 31186194.Nichols AW, Hetzler RK, Villanueva RJ, Stickley CD, Kimura IF. Effects of combination oral contraceptives on strength development in women athletes. J Strength Cond Res. 2008 Sep;22(5):1625-32. doi: 10.1519/JSC.0b013e31817ae1f3. PMID: 18714222.Romance R, Vargas S, Espinar S, Petro JL, Bonilla DA, Schöenfeld BJ, Kreider RB, Benítez-Porres J. Oral Contraceptive Use does not Negatively Affect Body Composition and Strength Adaptations in Trained Women. Int J Sports Med. 2019 Dec;40(13):842-849. doi: 10.1055/a-0985-4373. Epub 2019 Sep 6. PMID: 31491790.Ruzić L, Matković BR, Leko G. Antiandrogens in hormonal contraception limit muscle strength gain in strength training: comparison study. Croat Med J. 2003 Feb;44(1):65-8. PMID: 12590431.Dalgaard LB, Dalgas U, Andersen JL, Rossen NB, Møller AB, Stødkilde-Jørgensen H, Jørgensen JO, Kovanen V, Couppé C, Langberg H, Kjær M, Hansen M. Influence of Oral Contraceptive Use on Adaptations to Resistance Training. Front Physiol. 2019 Jul 2;10:824. doi: 10.3389/fphys.2019.00824. PMID: 31312144; PMCID: PMC6614284.Wikström-Frisén L, Boraxbekk CJ, Henriksson-Larsén K. Effects on power, strength and lean body mass of menstrual/oral contraceptive cycle based resistance training. J Sports Med Phys Fitness. 2017 Jan-Feb;57(1-2):43-52. doi: 10.23736/S0022-4707.16.05848-5. Epub 2015 Nov 11. PMID: 26558833.Lovett JL, Chima MA, Wexler JK, Arslanian KJ, Friedman AB, Yousif CB, Strassmann BI. Oral contraceptives cause evolutionarily novel increases in hormone exposure: A risk factor for breast cancer. Evol Med Public Health. 2017 Jun 5;2017(1):97-108. doi: 10.1093/emph/eox009. PMID: 28685096; PMCID: PMC5494186.Louw-du Toit R, Perkins MS, Hapgood JP, Africander D. Comparing the androgenic and estrogenic properties of progestins used in contraception and hormone therapy. Biochem Biophys Res Commun. 2017 Sep 9;491(1):140-146. doi: 10.1016/j.bbrc.2017.07.063. Epub 2017 Jul 12. PMID: 28711501; PMCID: PMC5740213.[(0.3 × 6) + (2.5 × 28)] ÷ 34 ≅ 2.1

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- Eric Trexler
Building Muscle in a Caloric Deficit: Context is Key

This article was first published in MASS Research Review and is a review and breakdown of a recent study. The study reviewed is  Energy Deficiency Impairs Resistance Training Gains in Lean Mass but Not Strength: A Meta-Analysis and Meta-Regression by Murphy et al. (2021). Graphics in this review are by Kat Whitfield.

Key Points The presently reviewed meta analysis (1) quantified the impact of an energy deficit on strength and lean mass gains in response to resistance training.Energy deficits led to significant impairment of lean mass gains (effect size [ES] = -0.57, p = 0.02) and non-significant impairment of strength gains (ES = -0.31, p = 0.28). As the energy deficit grew by 100kcals/day, lean mass effect size tended to drop by 0.031 units; a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0).  “Recomposition” (simultaneous fat loss and muscle gain) is possible in certain scenarios, but a sizable calorie deficit typically makes lean mass accretion an uphill battle.

Three of the most common goals among lifters are to lose fat, gain muscle, and get stronger. This presents a noteworthy challenge, as these goals can lead to contradictory recommendations for total energy intake. Lifters with fat loss goals are virtually always advised to establish a caloric deficit (2), whereas a caloric surplus is typically recommended to support recovery and anabolic processes for lifters aiming to get stronger and more muscular (3). If similar hypertrophy could occur in the presence of a calorie deficit, then this apparent dilemma would be resolved. 

That brings us to the presently reviewed meta-analysis (1), which sought to determine if calorie deficits impair gains in strength and lean mass in response to resistance training. Compared to a control diet, energy deficits led to significantly smaller gains in lean mass (effect size [ES] = -0.57, p = 0.02). Energy deficits also led to smaller gains in strength, but the effect size was smaller, and the effect was not statistically significant (ES = -0.31, p = 0.28). Impairment of lean mass gains became more pronounced as the caloric deficit got larger, and a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0). Meta-analyses are great for identifying a general, overall effect, but the feasibility of body recomposition (simultaneous fat loss and muscle gain) is impacted by a number of nuanced contextual factors. Read on to learn more about who might be able to achieve substantial lean mass gains during a calorie deficit, and how to maximize the likelihood of success when pursuing fat loss, hypertrophy, strength, or recomposition goals.

Purpose and Hypotheses Purpose

The primary purpose of the presently reviewed meta-analysis (1) was “to quantify the discrepancy in lean mass accretion between interventions prescribing resistance training in an energy deficit and interventions prescribing resistance training without an energy deficit.” The secondary purpose was to investigate the same question, but with a focus on strength gains rather than lean mass gains. The researchers also conducted additional analyses to determine if effects were meaningfully impacted by potentially important variables including age, sex, BMI, and study duration.

Hypotheses

The researchers hypothesized that “lean mass gains, but not strength gains, would be significantly attenuated in interventions conducted in an energy deficit compared to those without.”

Methods Search and Study Selection

These researchers wanted to do a meta-analysis comparing resistance training in a caloric deficit to resistance training with a control diet. However, they knew ahead of time that there would be a limited number of studies directly comparing both types of diets in longitudinal research designs. So, they cast a broad net with their literature search and committed to doing two separate analyses. The search strategy aimed to identify English-language studies evaluating relevant resistance training adaptations (lean mass or fat-free mass measured via DXA or hydrostatic weighing, and strength measured via low-repetition strength tests [e.g., 1RM or 3RM] or maximal voluntary contraction). In order to be considered for inclusion, studies needed to implement resistance training protocols that were at least three weeks long, utilized a training frequency of at least two sessions per week, and did not involve aerobic training.

Analysis A

Analysis A involved only studies that directly compared two groups within the same longitudinal resistance training study, with one group consuming a calorie deficit, and another group consuming a control diet. Seven such studies were identified; six involved female participants only, while the seventh involved a mixed-sex sample of males and females. A total of 282 study participants were represented across 16 treatment groups, with an average age of 60 ± 11 years old. Participants were generally sedentary or physically inactive prior to study participation, but one of the studies did not specify activity level. In terms of study characteristics, the researchers described that the studies in analysis A included full-body resistance training programs that “lasted between 8 and 20 weeks (13.3 ± 4.4 weeks) and involved 2-3 sessions per week (2.9 ± 0.3 sessions) with 4-13 exercises per session (8.3 ± 2.4 exercises), 2-4 sets per exercise (2.7 ± 0.4 sets), and 8-20 repetitions per set (11.3 ± 4.1 repetitions).” The researchers used standard meta-analytic techniques to separately compare the effects of calorie deficits and control diets on strength gains and lean mass gains. 

Analysis B

In order to expand the pool of studies, analysis B included studies with participants completing resistance training in an energy deficit or completing resistance training without an energy deficit. It’s easy to do a meta-analysis when you’ve got two different diets tested within the same study, because the two diet groups are effectively matched in terms of key subject characteristics and training programs. However, it’s not quite as easy when you’re analyzing separate studies that involve one type of diet or the other. In order to ensure that results from studies with and without energy deficits were being compared on approximately equal footing, the researchers began by identifying studies that assessed the effects of resistance training with an energy deficit and met the previously listed inclusion criteria (they found 31). Then, they scoured the much, much larger body of research assessing the effects of resistance training without an energy deficit. The purpose of this expanded search was to find suitable “matches” for the 31 energy deficit studies based on age, sex, BMI, and characteristics of the resistance training interventions completed. 

They weren’t able to find perfect matches for every study, but they ended up with 52 total studies that were effectively matched for age, sex, study duration, and resistance training characteristics (but not BMI). One study included resistance-trained participants, one study did not specify the training status of their participants, and the rest of them included participants that were sedentary or physically inactive prior to study participation. This collection of 52 studies included 10 with male subjects, 24 with female subjects, and 18 with mixed-sex samples, for a total of 57 treatment groups and 1,213 participants with an average age of 51 ± 16 years. The researchers described that the studies in analysis B included full-body resistance training programs that “lasted between 3 and 28 weeks (15.8 ± 6.0 weeks) and involved 2-4 sessions per week (2.9 ± 0.5 sessions) with 4-14 exercises per session (8.2 ± 2.6 exercises), 1-4 sets per exercise (2.7 ± 0.6 sets), and 1-16 repetitions per set (10.1 ± 1.9 repetitions).” 

Analysis B began with a visual comparison of changes in lean mass and strength. For each treatment group among the included studies, an effect size was calculated, and the effect sizes from each group were plotted in a “waterfall plot.” This type of plot arranges the effect sizes from smallest (or most negative) to largest (or most positive), which allows for some surface-level inferences based on visual assessment. Analysis B also included a meta-regression component, in which the energy deficit in each treatment group was calculated based on the assumption that each kilogram of fat lost in the study represented a cumulative calorie deficit of ~9,441kcals (4). As such, the daily energy deficit was back-calculated based on the cumulative energy deficit and the length of the trial, and meta-regression was used to assess the relationship between daily energy deficits and changes in lean mass, while controlling for age, sex, study duration, and BMI. 

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Findings

In analysis A, energy deficits led to significantly smaller gains in lean mass when compared to a control diet (effect size [ES] = -0.57, p = 0.02). Energy deficits also led to smaller gains in strength, but the effect size was smaller, and the effect was not statistically significant (ES = -0.31, p = 0.28). Forest plots for both analyses are presented in Figure 1.

The waterfall plots for analysis B are presented in Figure 2. For studies involving an energy deficit, the pooled effect size for lean mass was negative (ES = -0.11, p = 0.03), while it was positive for studies that did not involve an energy deficit (ES = 0.20, p < 0.001). For strength gains, effect sizes were positive and similar in magnitude whether studies did (ES = 0.84, p < 0.001) or did not (ES = 0.81, p < 0.001) involve an energy deficit.

As for the meta-regression component of analysis B, the relationship between energy deficits and changes in lean mass (when controlling for age, sex, study duration, and BMI) is presented in Figure 3. The slope of the line was -0.00031 (p = 0.02), which means there was a statistically significant negative relationship between the size of the energy deficit and the magnitude of changes in lean mass. As the energy deficit grew by 100kcals/day, the effect size for lean mass tended to drop by 0.031 units. By extension, a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0), and estimated changes in lean mass became negative for energy deficits beyond ~500kcals/day.

Criticisms and Statistical Musings

I wouldn’t call these “criticisms,” but there are a few important limitations and contextual factors to keep in mind when interpreting these results. The first point pertains to the pool of participants for this meta-analysis. In analysis A, the majority of participants were untrained individuals in their 50s, 60s, or 70s. Compared to a young, healthy, resistance-trained “control” subject, their untrained status boosts their propensity for short-term hypertrophy, while their age (specifically combined with their untrained status) might limit their propensity for short-term hypertrophy. The participant pool for analysis B is a little more heterogeneous in terms of age, but the untrained status is still a factor to consider when generalizing these findings to well-trained people. More advanced lifters tend to require greater optimization of training and nutrition variables to promote further training adaptations, so the untrained participants in this meta-analysis might theoretically be able to achieve better growth in suboptimal conditions (in this case, a caloric deficit). On the other hand, this analysis did not account for protein intake and did not require included studies to achieve any particular threshold for minimum protein intake. Insufficient protein consumption would impair hypertrophy and make recomposition less feasible, which could potentially exaggerate the impact of caloric deficits on lean mass accretion.

The next points pertain to analysis B. This analysis was a bit unconventional when compared to the typical meta-analysis, but I really like it and feel that it strengthens the paper. It’s important to recognize that the energy deficit quantified in analysis B is estimated based on the energy value of changes in fat mass. While this analysis did not incorporate the energy value of changes in lean mass, the researchers provided an excellent explanation for this choice, and confirmed that the choice did not meaningfully impact outcomes of the analysis. As noted previously, analysis B included a pool of 52 studies that were effectively matched for age, sex, study duration, and resistance training characteristics, but the researchers were unable to match the studies based on BMI. The studies involving an energy deficit reported an average BMI of 32.7 ± 3.0, while the studies without an energy deficit reported an average BMI of 27.5 ± 3.6. The meta-regression analysis did identify a relationship between BMI and changes in lean mass, but I am neglecting to interpret that as a meaningful relationship due to the confounding effect of this study matching discrepancy. 

Finally, a general note on meta-analyses. They sit atop our hierarchy of evidence, which means we consider them to be the most robust type of evidence available (when done correctly). However, we still have to apply their findings carefully and judiciously. For example, if a meta-analysis finds no benefit of micronutrient supplementation but virtually all of the studies recruited participants with adequate baseline levels of the nutrient in question, we can’t use that evidence to conclude that supplementation would be ineffective for individuals with a deficiency. For many research questions, context is critically important; some meta-analyses are well suited to sort through those contextual factors, while others are not. A lot of people will scan the presently reviewed study, see that predicted lean mass gains reached zero at a deficit of 500kcals/day, and will interpret that cutoff point as a widely generalizable “rule.” We should resist that temptation, and hesitate before applying a literal interpretation of these results for individuals who are substantially leaner or substantially more trained than the participants included in this meta-analysis.

Interpretation

A surface-level interpretation of analysis A is pretty straightforward: if gaining lean mass is your priority, you should avoid a calorie deficit. This general concept is easy to digest; low energy status leads to increased activation of 5’-adenosine monophosphate-activated protein kinase (AMPK), which generally promotes catabolic processes and impedes anabolic processes (5). Further, as reviewed by Slater and colleagues (3), maximizing hypertrophy is an energy-intensive process. The process of building muscle involves the energy cost of resistance training, the energy cost of post-exercise elevations in energy expenditure, the energy cost of increased protein turnover (which includes both degradation and synthesis), and several other aspects of increased expenditure that result from gaining more metabolically active tissue and consuming more calories to fuel training. As such, muscle hypertrophy is an energy-intensive process that is optimally supported by a state of sufficient energy availability. Having said that, a deeper interpretation of analysis B suggests that our conclusions probably require a little more nuance regarding how much energy is “enough.”

Figure 3 shows the relationship between estimated energy deficits and gains in lean mass. The regression line crosses zero at about 500kcals/day, which is informative. It tells us that, in a sample of people who are mostly untrained and have BMIs in the overweight-to-obese categories, a daily energy deficit of ~500kcals/day is predicted to fully attenuate gains in lean mass. However, Figure 3 includes individual data points from studies, which adds further depth and nuance to our interpretation. With exactly one exception, all of the studies reporting fairly substantial gains in lean mass involved an estimated deficit of no more than 200-300 kcals/day. Furthermore, every study reporting an effect size clearly below zero (that is, a loss of lean mass) involved an estimated deficit larger than 200-300 kcals/day. As such, we should acknowledge and understand that the ~500kcals/day number is not a rigid cutoff; the relationship between energy deficits and lean mass changes is continuous in nature, and there appears to be (for example) a substantive difference between 100 and 400 kcals/day. 

Since we can’t treat every deficit below 500kcals/day as being functionally equivalent, a dieter with ambitions related to recomposition will have to decide exactly how large of a deficit they can manage without meaningfully impairing hypertrophy potential. As Slater and colleagues have noted (3), simultaneous fat loss and skeletal muscle hypertrophy is “more likely among resistance training naive, overweight, or obese individuals.” Along those lines, readers who are well-trained or substantially leaner than the participants in this meta-analysis might need to adjust their interpretation and expectations, erring toward a smaller daily energy deficit if they wish to accomplish appreciable hypertrophy along the way. While an untrained individual with a BMI over 30 is an obvious candidate for successful recomposition, it would be inaccurate to suggest that body recomposition is completely unattainable for individuals with leaner physiques or more training experience. 

As reviewed by Barakat and colleagues (6), there are several published examples of resistance-trained individuals achieving simultaneous fat loss and lean mass accretion in the absence of obesity. Nonetheless, these researchers also acknowledged that the feasibility and magnitude of recomposition are impacted by training status and baseline body composition, and that trained individuals have an increased need to optimize training variables, nutrition variables, and other tertiary variables (such as sleep quality and quantity) in order to achieve practically meaningful recomposition. While having some resistance training experience or a BMI below 30 does not automatically render recomposition impossible, it’s also important to acknowledge that significant recomposition might not be attainable for people who have already optimized (more or less) their approach to training and nutrition and are absolutely shredded or near their genetic ceiling for muscularity. 

I think this meta-analysis was conducted very effectively, and its results are quite informative for setting energy intake guidelines that are suitable for a wide range of goals. So, to wrap up this article, I want to concisely review how to adjust energy intake for lifters with strength goals, recomposition goals, hypertrophy goals, and fat loss goals. Please note that these recommended targets for rates of weight loss and weight gain throughout the following section are admittedly approximate and imprecise, as hypertrophic responses to training can be quite variable. There are innumerable “edge cases” and circumstances in which these recommendations start to become less advisable; unfortunately, I can’t (at this time) think of a way to provide a totally robust set of concise recommendations without an individualized assessment of body composition, diet history, training experience, and responsiveness to training.

Practical Guidance for Adjusting Energy Intake

For Strength Goals

The results of the presently reviewed meta-analysis could be perceived as suggesting that energy restriction does not meaningfully impair strength gains. However, the analysis generally included untrained participants in relatively short-term trials. As we know, much of the early strength adaptations experienced by novice lifters can be attributed to factors that are entirely unrelated to hypertrophy, such as neural adaptations and skill acquisition (7). When it comes to long-term capacity for strength, creating an environment suitable for hypertrophy plays an important role in maximizing muscle mass, and creating an environment suitable for rigorous training and recovery plays an important role in maximizing longitudinal training adaptations. In both cases, a state of chronic energy insufficiency counters these goals, so lifters should generally aim to spend the majority of their training career in a state that reflects adequate energy status. Energy status is reflected by both short-term energy availability and long-term energy stores (i.e., fat mass), so lifters with higher body-fat levels can probably make considerable strength gains while losing fat, as long as the acute deficit isn’t large enough to threaten hypertrophy, training performance, or recovery capacity. This is particularly true for lifters who are relatively new to training or have a lot of room for additional strength gains. 

So, lifters with relatively high body-fat levels should not feel like they’re unable to cut to their ideal weight if it happens to be lower than their current weight. I would expect that many lifters can maintain a satisfactory rate of progress while losing up to (roughly) 0.5% of body mass per week. However, as one gets leaner and leaner, stored body energy is reduced, and the acute presence of an energy deficit probably has a larger impact on the body’s perceived energy status. Once a strength-focused lifter is at their ideal body-fat level, they’ll want to shift their focus away from fat loss and toward hypertrophy, training capacity, and recovery. In this context, they’ll generally want to minimize their time spent in an energy deficit and set their calorie target at a level that allows for weight maintenance or modest weight gain over time (for example, ~0.1% of body mass per week for relatively experienced lifters, or ~0.25% of body mass per week for relatively inexperienced lifters). As they get closer to their genetic limits for strength and muscularity, they might find it difficult to make continued progress at approximately neutral energy balance, and then might shift toward oscillating phases of bulking (a caloric surplus) and cutting (a modest caloric deficit). This approach is also suitable for less experienced lifters who simply prefer to see more rapid increases in strength and hypertrophy during their bulking phases, and are comfortable with the tradeoff of requiring occasional cutting phases. It’s also important to note that strength-focused lifters don’t always need to be in neutral or positive energy balance; in fact, short-term energy restriction is commonly implemented in order to make the weight class that offers the lifter their greatest competitive advantage. Fortunately, these transient periods of energy restriction don’t tend to have a huge impact on strength performance (8), provided that the lifter is adequately refueled and recovered in time for competition. 

For Recomposition Goals

I’d like to mention two caveats before providing recommendations for recomposition. First, you should assess the feasibility of recomping before you set up a recomposition diet. If you’ve got plenty of body-fat to lose and are untrained, your recomp potential is very high. If you’re shredded and near your genetic ceiling for muscularity, your recomp potential is extremely low. Everyone else will find themselves somewhere in the middle, but the general idea is that you can get away with a larger energy deficit during recomposition if you have higher body-fat or less advanced training status. Second, these recommendations are going to seem a bit superficial. The presently reviewed meta-analysis discussed the specific caloric value of energy deficits, but I will focus on the rate of body weight changes. This is because the recommendations are intended to be practical in nature; few people will have the ability to perform serial DXA scans to allow for up-to-date energy deficit calculations based on changes in total body energy stored as lean mass and fat mass. Plus, and even if they could, the margin of error for DXA (and other accessible body composition measurement devices) is so large as to render this calculation functionally unreliable at the individual level.  

One factor that could guide your approach to recomposition is hypertrophy potential. If you’ve got plenty of body-fat to lose and you’re relatively untrained, you should be able to recomp very effectively with an energy intake that allows for a slow rate of weight loss (up to 0.5% of body mass per week), weight maintenance, or even a slow rate of weight gain (up to 0.1% of body mass per week). I know it seems paradoxical to suggest that you could be gaining weight while in a caloric deficit, but the math works out. If, for example, you gain 1.5kg of lean mass while losing 1kg of fat mass, the estimated cumulative change in body energy would be in the ballpark of around -6,700 kcals (so, body weight increased, but the total metabolizable energy content of the body decreased, thereby representing a caloric deficit). For lifters with lower body-fat levels or more advanced training status, it becomes increasingly critical to optimize diet and training variables in order to promote hypertrophy. Even when these variables are optimized, the anticipated rate of hypertrophy shrinks. As a result, the “energy window” for recomposition most likely tightens; even a moderate energy deficit has potential to threaten hypertrophy, and the anticipated rate of hypertrophy becomes too low to suggest that rapidly trading a few pounds of fat for several pounds of muscle is a realistic goal. So, for these individuals, I would advise keeping body weight as steady as is feasible.

A separate factor that could guide your approach to recomposition is the degree to which you prioritize fat loss versus hypertrophy. In many cases, a lifter interested in recomposition might have goals that are a bit skewed. In other words, some lifters might feel that recomposition would be fantastic if possible, but they’re particularly adamant about losing fat, even if it comes at the expense of optimizing hypertrophy along the way. Conversely, others will be particularly adamant about making some big strides toward lean mass accretion, even if it comes at the expense of losing fat along the way. For a lifter who wishes to recomp but prioritizes fat loss, aiming for a relatively slow rate of weight loss would be a sensible approach (for example, losing somewhere between 0.1% and 0.5% of body mass per week). 

For a lifter who wishes to recomp but prioritizes hypertrophy, aiming for a relatively slow rate of weight gain would be advisable (for example, gaining somewhere between 0.05% and 0.1% of body mass per week). It’s obviously difficult to track some small changes in weekly intervals without using some method of data smoothing, but just to contextualize those numbers, a 180lb lifter would gain between 4.32-8.64 pounds over the course of a year if gaining between 0.05% and 0.1% of body mass per week. Within this set of recommendations, a lifter with lower perceived potential for recomping would be advised to aim for the lower ends of the weight gain and weight loss ranges, or to simply aim for approximate weight stability.

For Hypertrophy Goals (Bulking)

Finally, moving on to simpler stuff. For hypertrophy-focused lifters who are relatively experienced and comparatively closer to their genetic limit for muscularity, aiming to gain around 0.1% of body mass per week is a decent starting point. For hypertrophy-focused lifters who are relatively inexperienced and pretty far from their genetic limit for muscularity, aiming to gain around 0.25% of body mass per week is a good place to start. Obviously, if one were adamant about avoiding unnecessary fat gain, they could go a little below these recommended rates. You’ll notice that the guidelines for a hypertrophy-focused recomp and a very conservative bulk are not mutually exclusive. Sometimes, people will embark on a conservative bulking phase and find that they ended up losing a little fat along the way (as Bob Ross would call it, a happy accident). Conversely, a lifter who was eager to maximize their rate of hypertrophy and unconcerned about fat gain could push their rate of weight gain a little higher. There are probably diminishing returns for the hypertrophy-supporting effects of a caloric surplus as the surplus grows larger and larger, but to my knowledge, the “ideal surplus” for hypertrophy has not yet been conclusively identified (3).  

For Fat Loss Goals (Cutting)

Choosing a rate of fat loss involves striking a balance; as mentioned in a previous MASS article, favoring a slower rate of weight loss confers plenty of benefits. However, going too slow with the process can delay goal completion, threaten motivation, and lead to unnecessary time spent in a deficit. If maintaining strength, lean mass, and training capacity is of utmost importance, losing up to 0.5% of body mass per week would be advisable. Once again, the guidelines for a recomp that prioritizes fat loss and a very conservative cut are not mutually exclusive, and some individuals will embark on a conservative fat loss phase and be pleasantly surprised to find that they gained a little bit of muscle along the way. If you’re in a bit of a hurry, you could bump your rate of weight loss closer to 1% of body mass per week. However, it’s important to note that the higher this rate gets, the higher the potential to negatively impact strength, lean mass, and training capacity, especially for lifters with less fat mass to lose. From a practical perspective, it might not be a bad idea to cap weight loss at around a kilogram or so per week, even if that ends up being <1% of body mass. Losing a kilogram of fat requires establishing a cumulative energy deficit of ~9,441kcals, which would equate to a daily energy deficit of ~1350kcals/day. As such, when lifters who weigh over 100kg or so aim for 1% of body mass loss per week, they can often find themselves in a scenario that demands daily calorie intakes that might be considered unsustainably low relative to their body size.

Next Steps

Rates of weight gain and weight loss appear to be quite impactful, and they’re topics of considerable interest in the fitness world. As a result, the dearth of studies directly comparing different rates of weight gain and weight loss in resistance-trained participants is a bit surprising. In the short term, we could probably gain some useful insight related to this question if researchers took an approach like the meta-regression component of “analysis B” in the presently reviewed study, but restricted the search to studies with resistance-trained samples and included studies assessing caloric surpluses and caloric deficits of varying magnitudes. An even better way to address this topic would involve a series of well controlled trials directly comparing different rates of weight loss and gain within the same study. These types of studies would yield more robust results, but it would take a while to run enough of these studies to develop nuanced conclusions with a high level of confidence.

Application and Takeaways

The most direct path to fat loss is a caloric deficit, and a caloric surplus offers the smoothest path to gains in strength and lean mass. Nonetheless, we want the best of both worlds from time to time. Large energy deficits threaten lean mass accretion, and extended periods of excessive energy restriction can impair strength gains as well. However, these issues can largely be circumvented by utilizing a caloric deficit that is appropriately scaled to the individual’s goal, training status, and body-fat level. Simultaneous fat loss and muscle gain is indeed possible, although it becomes less feasible as an individual’s body-fat level decreases and training status increases. “Recomping” can theoretically be achieved in the context of weight loss, gain, or maintenance, but the dietary approach should be individualized based on the lifter’s body composition, training status, and priorities. 

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References   Murphy C, Koehler K. Energy deficiency impairs resistance training gains in lean mass but not strength: A meta-analysis and meta-regression. Scand J Med Sci Sports. 2021 Oct 8; ePub ahead of print.  Roberts BM, Helms ER, Trexler ET, Fitschen PJ. Nutritional Recommendations for Physique Athletes. J Hum Kinet. 2020 Jan;71:79–108.  Slater GJ, Dieter BP, Marsh DJ, Helms ER, Shaw G, Iraki J. Is an Energy Surplus Required to Maximize Skeletal Muscle Hypertrophy Associated With Resistance Training. Front Nutr. 2019;6:131.  Hall KD. What is the required energy deficit per unit weight loss? Int J Obes. 2008 Mar;32(3):573–6.  Thomson DM. The Role of AMPK in the Regulation of Skeletal Muscle Size, Hypertrophy, and Regeneration. Int J Mol Sci. 2018 Oct 11;19(10):3125.  Barakat C, Pearson J, Escalante G, Campbell B, De Souza EO. Body Recomposition: Can Trained Individuals Build Muscle and Lose Fat at the Same Time? Strength Cond J. 2020 Oct;42(5):7–21.  Taber CB, Vigotsky A, Nuckols G, Haun CT. Exercise-Induced Myofibrillar Hypertrophy is a Contributory Cause of Gains in Muscle Strength. Sports Med. 2019 Jul;49(7):993–7.  Helms ER, Zinn C, Rowlands DS, Naidoo R, Cronin J. High-protein, low-fat, short-term diet results in less stress and fatigue than moderate-protein moderate-fat diet during weight loss in male weightlifters: a pilot study. Int J Sport Nutr Exerc Metab. 2015 Apr;25(2):163–70.

The post Building Muscle in a Caloric Deficit: Context is Key appeared first on Stronger by Science.

- Cameron Gill
Does Your Rowing Grip Actually Affect Back Development?

If you’ve been in the iron game for more than a few months, you’ve likely heard how the grip you use during rowing exercises can affect back development. Lifters have sought to target the lats with a supinated (i.e. underhand) or neutral (i.e. palms facing each other) grip and target their upper back with a pronated (i.e. overhand) grip during rows. Similarly, a close grip width is often used for the objective of boosting lat involvement while a wide grip width is commonly utilized to emphasize the upper back. In this article, we’re going to discuss the variables that really affect back development from rows, and how to most effectively target the different muscles in your back.

Supinated, neutral, and pronated grips. Anatomical Planes and Joint Movements 

The ability of grip position to influence which muscles are preferentially trained during a row ultimately stems from how the selected grip affects which actions are occurring at the shoulder joint. Anatomical movements can occur in three planes: sagittal, frontal, and transverse. As a ball and socket joint, the shoulder can move in all three planes. 

Anatomical Planes

In the sagittal plane, which divides the midline of the body front to back into symmetrical halves, shoulder flexion occurs as the upper arm is raised in front of the body, while shoulder extension occurs as the upper arm is pulled toward the body’s backside. For example, during a straight arm cable pulldown, the shoulder extends as the arm moves toward the back of the body during the concentric phase, while the shoulder flexes as the arm is raised overhead during the eccentric phase.

Extended vs Flexed

In the transverse plane, which divides the body into top and bottom halves, shoulder horizontal flexion (AKA horizontal adduction) occurs as the upper arm is moved toward the midline of the body, while shoulder horizontal extension (AKA horizontal abduction) occurs as the upper arm is moved away from the midline of the body. For example, during a reverse fly with an elastic band, shoulder horizontal extension occurs as the arm moves outwards throughout the concentric phase, while shoulder horizontal flexion occurs as the arm moves inwards throughout the eccentric phase.

Horizontally Extended vs Horizontally Flexed

In the frontal (AKA coronal) plane, which divides the body into front and back halves, shoulder abduction occurs as the upper arm is moved away from the midline of the body, while shoulder adduction occurs as the upper arm is moved towards the midline of the body. For example, during a cable lateral raise, shoulder abduction occurs as the arm is raised out to the side during the concentric phase, while shoulder adduction occurs as the arm is lowered during the eccentric phase.

Abducted vs Adducted The Effect of Grip on Shoulder Joint Actions during Rows

The grip width used during a row does not directly determine which back muscles are preferentially targeted; however, it may influence the type of movement that occurs at the shoulder joint, which can influence the primary musculature being used. As the angle of shoulder abduction used for a row decreases (i.e. elbows become closer to the trunk), a row will involve more shoulder extension, while more shoulder horizontal extension will occur as the angle of shoulder abduction increases (i.e. elbows flare out).   

With a close grip, you may be more likely to have your elbows pinned close to your sides and primarily perform shoulder extension. If you maintain a position of 0° of shoulder abduction in the frontal plane during a row, shoulder movement will be exclusively comprised of extension in the sagittal plane. A row with anywhere between 0-30° of shoulder abduction can be considered to be shoulder extension-dominant.        

Supinated Close Grip Shoulder Extension Row 

In contrast, with a wide grip, you may be more likely to have your elbows flared out and primarily perform shoulder horizontal extension. If you maintain a position of 90° of shoulder abduction in the frontal plane during a row, shoulder movement will be exclusively comprised of horizontal extension in the transverse plane. A row with anywhere between 90-60° of shoulder abduction can be considered to be shoulder horizontal extension-dominant.          

Pronated Wide Grip Shoulder Horizontal Extension Row

While rowing with 45° of shoulder abduction, equal parts shoulder extension and shoulder horizontal extension will occur as the shoulder moves through the sagittal and transverse planes in balanced proportions.  

Pronated Close Grip Row with Shoulder Extension and Shoulder Horizontal Extension

As with grip width, the decision to utilize either a pronated, supinated, or neutral grip will only affect preferential activation of different back muscles if it alters which types of movement occur at the shoulder joint. For instance, during a wide grip shoulder horizontal extension row, a pronated grip may be the most comfortable and practical to use, while a neutral grip can only be used with a specialized attachment, and a supinated grip is physically impossible for this variation (unless you’ve got some really freaky wrist mobility). When the hands are free to move during a row, such as when utilizing a cable machine with a rope attachment, a close grip can also be used to perform a pronated or neutral grip shoulder horizontal extension row. 

Pronated Close Grip Shoulder Horizontal Extension Row

Any of the three grip types can be used to perform a close grip shoulder extension row, though the supinated or neutral grip may feel more natural and comfortable. 

How the Back Muscles are Affected by Shoulder Movement

Shoulder extension and shoulder horizontal extension are primarily performed by different muscles, so the degree to which you perform each joint movement during a row will consequently affect the degree to which these back muscles are trained. However, the concept of targeting either the lats or upper back muscles is only part of the larger picture that can be painted by exploring the functions of the back musculature.   For the purpose of this article, I will define upper back muscles as muscles whose majority of muscle fibers attach to the back surface of the scapula (i.e. shoulder blade), which excludes the lats. As the widest muscle in the human body, the lats’ expansive bony origin sites descend as low the pelvis and as high as the lowest tip of the scapula (9). Given that only a minuscule proportion of its muscle fibers attach to the lowest part of the scapula, I will not consider the lats to be part of the upper back. 

Scapula

Note that our examination of upper back muscles will exclude the intrinsic muscles of the back, whose largest muscle group is the erector spinae. These muscles, which run along the entire length of the spine, primarily stabilize the spine and are certainly recruited during standing bent-over rows, but they are a topic of their own for another day (10, 23).

Intrinsic Back Muscles

Shoulder horizontal extension is primarily produced during a row by the deltoid’s posterior (i.e rear) head and three of the four muscles comprising the rotator cuff, namely the teres minor, infraspinatus, and supraspinatus (16). While these rotator cuff muscles would rarely be included in a list of the top 25 sexiest muscles, they play a pivotal role in stabilizing the shoulder joint (29). Consequently, developing the teres minor, infraspinatus, and supraspinatus can help a lifter develop all-around strength while potentially improving his/her likelihood of staying healthy.

Posterior Deltoid (Blue)Teres MinorInfraspinatus Supraspinatus

During a row, shoulder extension is primarily produced by the latissimus dorsi, teres major, and posterior head of the deltoid (1, 16). To a lesser extent, the teres minor can also assist in extending the shoulder, although it is better suited to perform shoulder horizontal extension (1, 16). 

Latissimus DorsiTeres Major

The teres major, deltoid’s posterior head, teres minor, infraspinatus, and supraspinatus all originate on the back surface of the scapula above the origin of the lats. Consequently, a shoulder extension-dominant row will preferentially target the lats and one upper back muscle (i.e. teres major) to a greater degree than a shoulder horizontal extension-dominant row. On the other hand, a shoulder horizontal extension-dominant row will preferentially target three other upper back muscles (i.e. teres minor, infraspinatus, and supraspinatus) to a greater degree than a shoulder extension-dominant row.

Given that the deltoid’s posterior head can meaningfully function as both a shoulder extensor and shoulder horizontal extensor, it will be effectively trained during either type of row. However, it is quite plausible that a shoulder horizontal extension-dominant row could yield somewhat greater development of this muscle. The deltoid’s posterior head has greater leverage for producing shoulder horizontal extension than shoulder extension, and it has greater leverage for producing shoulder horizontal extension than any other muscle in the human body (16). In contrast, the teres major and possibly the lats (contradictory research findings exist) have greater leverage for producing shoulder extension than the posterior deltoid (1, 16).

While the teres minor can also contribute to shoulder horizontal extension and shoulder extension to a lesser degree, the infraspinatus and supraspinatus lack the capacity to aid in extending the shoulder in the sagittal plane, so they will not be trained to a large extent by a shoulder extension row (1, 16). Similarly, the lats and teres major have close to non-existent leverage for horizontally extending the shoulder during a row and will consequently not be adequately strengthened during a shoulder horizontal extension row (16). 

Primarily due to the lats being the largest back muscle, a shoulder extension-dominant row can effectively train more overall muscle mass than a shoulder horizontal extension-dominant row (13, 28). Nevertheless, the rotator cuff muscles are greater in size than many people may realize, so a shoulder horizontal extension row still preferentially targets a sizeable amount of muscle. According to two studies, the combined volume of the teres minor, infraspinatus, and supraspinatus has been measured to be 55-67% of the volume of the lats and teres major together (13, 28). The teres minor and supraspinatus are slightly smaller and larger, respectively, than the fairly small teres major, but the infraspinatus is a sizable muscle, along with the posterior head of the deltoid (4, 13, 28). In fact the combined volume of these two primary shoulder horizontal extensor muscles has been measured to be essentially equivalent (ranging from slightly lower to mildly higher) to the volume of the lats (4, 13, 28). Much to my surprise and likely yours as well, the lats have actually been consistently measured by three separate MRI studies to have a lower volume than the deltoid when accounting for all three of its heads (4, 13, 28). The research from which all of this data on muscle size was obtained did not assess subjects who regularly performed resistance training, so the proportional size differences among muscles for experienced lifters may differ from the findings of these studies. To my knowledge, a detailed examination of upper body muscle size in experienced lifters unfortunately has yet to be conducted, so information derived from general population subjects is currently the highest quality available evidence.

How the Back Muscles are Affected by Scapular Movement

The other muscles that constitute the upper back, namely the trapezius, rhomboids, and levator scapulae, do not cross the shoulder joint and consequently cannot directly perform shoulder extension or shoulder horizontal extension. Rather, the recruitment of these muscles will be dictated by the type of scapular motion which occurs during a row. 

TrapeziusRhomboid MajorRhomboid MinorLevator Scapulae

The trapezius, rhomboid major, and rhomboid minor will be prime movers during scapular retraction (i.e. squeezing the shoulder blades back and together) (21). 

Protracted vs Retracted

While scapular retraction can be incorporated into nearly any type of row, you can preferentially target these muscles by loading them through a full range of motion. To do so, you can end the eccentric phase of a row in a position of scapular protraction (i.e. where your shoulder blades are pulled forwards) and emphasize full scapular retraction at the end of the concentric phase. If you opt to return your scapula to a neutral position rather than protracting them at the end of the eccentric phase, you will be training your trapezius and rhomboids at shorter muscle lengths through a lower range of motion. This could be akin to performing half squats as opposed to full squats for glute development. While hypertrophy may be induced by any type of squat with a sufficient volume and intensity, full squats (with a peak knee flexion angle of 140°) train the gluteus maximus in a more stretched position and yield greater growth of this muscle than partial squats (with a peak knee flexion angle of 90°) (15). So too may the trapezius and rhomboids experience the same benefit of stretch-mediated hypertrophy by loading them in a lengthened position of scapular protraction each rep. Some people may find that they can most effectively utilize this technique when performing rows with moderate to light loads given that full control of scapular motion can be more challenging when using relatively heavy loads.     

Protracted Start vs Retracted Start

An additional stimulus can be provided to these muscles by immediately following up a set of rows with pure scapular retraction reps. When a row can no longer be performed due to fatigue, strength of other prime movers rather than the trapezius and rhomboids may be the limiting factor. If your scapular retractor muscles are not the limiting factor during a multi-joint row, you can perform isolated scapular retraction directly after a set of rows to ensure that these muscles are maximally stimulated. This technique, along with utilizing full scapular protraction and retraction during each rep of rows, can readily be performed with a shoulder horizontal extension-dominant row or a shoulder extension-dominant row as demonstrated in the accompanying videos.

Regardless of the type of shoulder movement which occurs during a row, the trapezius and rhomboids will be trained when the scapula is retracted. Nonetheless, it is possible that some individuals may achieve better activation of these muscles during a shoulder horizontal extension row than a shoulder extension row. When shoulder movement is performed in isolation (i.e. not as part of a row), the trapezius and rhomboid major (rhomboid minor was not assessed) have been measured to be more active on average during shoulder horizontal extension than shoulder extension (2, 8). Similarly, somewhat greater activation of the trapezius has been measured to occur during single joint shoulder horizontal extension than a neutral grip shoulder extension row where untrained subjects were not instructed to actively retract the scapula (3). This may arise from people, particularly those with limited experience actively training scapular movement, being naturally more likely to perform a greater degree of scapular retraction during shoulder horizontal extension than shoulder extension. If a similar trend occurs when performing a row, some lifters who are not consciously focusing on scapular movement may also be more inclined to retract the scapula during a shoulder horizontal extension row and train their trapezius and rhomboids more effectively. However, similar activation of the trapezius has been measured to occur when performing a shoulder horizontal extension-dominant pronated wide grip row, shoulder extension-dominant neutral close-grip row, and pronated close-grip row with a fairly even balance of shoulder horizontal extension and shoulder extension (12). A supinated close-grip shoulder extension-dominant row and a pronated close-grip row with approximately even proportions of shoulder extension and shoulder horizontal extension have also been measured to produce comparable activation of the trapezius (30).        

Data obtained by using electromyography [EMG] to measure muscle activation during different movements is more valuable than no data at all when the anatomy of a muscle does not clearly indicate if it will be trained more effectively during one exercise compared to another. However, the scope of practical conclusions which can be drawn from this type of research is limited in part due to the phenomenon known as “cross-talk,” where the EMG value from one muscle partially reflects the electrical activity of an adjacent muscle. Furthermore, surface EMG cannot be used to measure the activation of a muscle which lies underneath another, such as the rhomboid minor, which is situated beneath the trapezius. Even if an EMG study reports that a muscle is more active during one exercise compared to another, this snapshot finding does not necessarily mean that the exercise that yields greater EMG activation will produce greater hypertrophy of the measured muscle during an actual block of training. One example relates to the aforementioned difference between using full or half squats for inducing glute hypertrophy. EMG research has reported the gluteus maximus to be substantially more active during an isometric back squat with a knee flexion angle of 90° (half squat) or 20° (not even a quarter squat) than 140° (full squat) (18). Despite these EMG findings, the full squat group in a different study experienced three times as much gluteus maximus growth as the half squat growth after 10 weeks of actual resistance training (15). For a deeper dive into the utility of EMG research, you can check out Dan Ogborn’s article on Stronger By Science. 

To my knowledge, no resistance training study has been conducted which examines trapezius and/or rhomboid hypertrophy after performing a mesocycle of shoulder extension-dominant or shoulder horizontal extension-dominant rows. Presently it is unclear whether the type of grip or shoulder movement utilized while rowing affects the training stimulus presented to these muscles if a lifter intentionally protracts and retracts the scapula to the same degree during different variations. In light of this uncertainty, I recommend that you employ the full scapular protraction and retraction technique with different row variations to subjectively assess if you feel a more vigorous contraction of the trapezius and rhomboids with one style of rowing compared to another. 

Scapular elevation (i.e. shrugging) is primarily produced by the levator scapulae and trapezius (9). In contrast to scapular retraction where all regions of the trapezius have been measured to be quite active, the lower region of the trapezius is not meaningfully involved during scapular elevation, which is to be expected from the direction of its muscle fibers (3, 11, 19). The most direct way to train scapular elevation is, unsurprisingly, with any type of shrug variation, but scapular elevation can also be trained during some row variations. 

Elevated Scapula  | Neutral Scapula | Depressed Scapula

During a seal row or seated machine row, the line of resistive torque will be applied perpendicular to your trunk position and scapular retraction can be effectively loaded, but no external resistance will be applied against scapular elevation. However, as the line of resistive torque becomes closer to being parallel with your trunk position during a row, greater resistance can be applied to scapular elevation. Consequently, as your trunk position becomes more upright during a free weight row where the line of resistive torque is directed downwards, scapular elevation can be loaded to a greater degree. 

     < Easier to Load Retraction –––––– Easier to Load Elevation >

Of all the back muscles, the trapezius is the second largest, with a mass that is over 80% of the lats (27). The rhomboids and levator scapulae are noticeably smaller, with a combined mass of these three muscles equivalent to a little over half of the trapezius (27). Approximately half of the muscle constituting the trapezius is found within its lower region, while the middle region contains slightly more than a quarter, and the upper region contains slightly less than a quarter (14). Due to this distribution, scapular retraction will effectively train a greater amount of muscle mass than scapular elevation. Both for the efficiency of targeting more muscle and for longevity, I recommend that most lifters focus on training scapular retraction rather than scapular elevation during their rows. 

In addition to functioning as a scapular retractor, the lower trapezius plays a pivotal role, along with the upper trapezius, in rotating the scapula to enable safe and effective shoulder movement, particularly when nearing an overhead position (14). As the arm elevates, motion occurs not only at the glenohumeral (i.e. shoulder) joint but also at the scapula (20). The importance of scapular rotation for full shoulder movement can be readily observed if you consciously depress your scapula into a packed position while attempting to elevate your arm via shoulder flexion or abduction. When the scapula cannot freely move, shoulder range of motion is severely limited. Individuals experiencing neck pain and shoulder impingement have been measured to have impaired strength and activation of the lower trapezius on the injured side, and those with shoulder impingement have also been found to exhibit a high upper trapezius to lower trapezius activation ratio (5, 6, 7, 22, 24, 25). Correlation does not equate to causation, so it is presently unclear whether directly strengthening the lower trapezius can contribute to a reduced risk of injury. Nonetheless, prioritizing scapular retraction, which strengthens the entire trapezius, over scapular elevation, which neglects the lower trapezius, may be worthwhile to promote balanced back development and address a common strength discrepancy. To be clear, I am not advocating that you avoid training scapular elevation, but rather that you should account for the overall context of your training to determine which focus will provide the greatest benefit for you.    

Programming Recommendations

Because different types of rows have their own distinct advantages in terms of preferential muscle involvement, no single way to row can be considered superior to another in isolation. Rather, your priorities and the overall context of the rest of your training will determine which variations are best suited for a particular training session. Additionally, it is important to consider the other functions of the back muscles that are trained during rows. Notably, the lats and teres major also function as primary shoulder adductors, while the supraspinatus also functions as a primary shoulder abductor (1, 16). This means that vertical pull variations (even those where minimal shoulder extension occurs, such as pronated grip pullups) can effectively train the lats and teres major, and lateral raises will effectively train the supraspinatus. 

Shoulder Adduction Vertical Pull

The levator scapulae also functions as a cervical extensor, along with a handful of intrinsic back muscles, so it can be directly trained with any type of neck extension exercise (26). Additionally, the infraspinatus and teres minor are the two strongest shoulder external rotators in the body, so a face-pull, during which the shoulder simultaneously externally rotates and horizontally extends, will effectively train both primary functions of these muscles (17). 

Externally Rotated vs Internally Rotated

Exercise selection does not need to be an “either-or” scenario where you exclusively choose to perform a single type of row, and a combination of different variations is often optimal. No two individuals will respond identically to the same exercise, and how a single person is affected by a given movement can change over time as training experience and lifestyle factors change. However, if you prefer to utilize a more minimalist exercise selection and want to maximize the benefit you receive when selecting a single type of row for a period of time, there are certain situations when one type of row likely provides greater advantages than another. 

For instance, I would recommend a:

Shoulder horizontal extension-dominant row if you are also performing a type of vertical pull (e.g. lat pulldown or pullup) or single joint shoulder extension movement (e.g. straight arm cable pulldown or machine pullover) but are not performing a movement that trains shoulder horizontal extension or shoulder external rotation (e.g. face-pulls, rear delt flies, or band pull aparts).Shoulder extension-dominant row if you are not also performing a type of vertical pull but are already performing a movement that trains shoulder horizontal extension or shoulder external rotation. Row with an even balance of shoulder extension and shoulder horizontal extension if you are just performing a single back exercise.Row with a scapular retraction focus, unless you are already performing a moderately high volume of direct scapular retraction exercise.

At the end of the day, you should focus on the type of shoulder movement and scapular movement you perform during a row. The type of grip you select is merely a means to an end. The grip you select may affect how you perform these movements, which can meaningfully affect which muscles are predominantly targeted when training rows.

Image sources

The anatomical plane and intrinsic back muscle anatomy images were published by “OpenStax,” are licensed as a Creative Commons work, and can be found here.

All other muscle anatomy images were published by “BodyParts3D, © The Database Center for Life Science,” are licensed as Creative Commons works, and can be found at here.

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Comparison of Upper, Middle, and Lower Trapezius Strength in Individuals with Unilateral Neck Pain. spine 1, 2012.Cools, A, Witvrouw, E, Declercq, G, Vanderstraeten, G, and Cambier, D. Evaluation of isokinetic force production and associated muscle activity in the scapular rotators during a protraction-retraction movement in overhead athletes with impingement symptoms. Br J Sports Med 38: 64–68, 2004.Cools, AM, Declercq, GA, Cambier, DC, Mahieu, NN, and Witvrouw, EE. Trapezius activity and intramuscular balance during isokinetic exercise in overhead athletes with impingement symptoms. Scand J Med Sci Sports 17: 25–33, 2007.Cools, AM, Dewitte, V, Lanszweert, F, Notebaert, D, Roets, A, Soetens, B, et al. Rehabilitation of Scapular Muscle Balance: Which Exercises to Prescribe? The American Journal of Sports Medicine , 2017. Cowan, PT, Mudreac, A, and Varacallo, M. Anatomy, Back, Scapula. In: StatPearls.Treasure Island (FL): StatPearls Publishing, 2021 [cited 2021 Oct 3].Edelburg, H. 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Dynamic Scapulohumeral Rhythm: The Effects of External Resistance During Elevation of the Arm in the Scapular Plane. J Orthop Sports Phys Ther 27: 125–133, 1998.Paine, R and Voight, ML. THE ROLE OF THE SCAPULA. Int J Sports Phys Ther 8: 617–629, 2013.Petersen, SM and Wyatt, SN. Lower Trapezius Muscle Strength in Individuals With Unilateral Neck Pain. J Orthop Sports Phys Ther 41: 260–265, 2011.Saeterbakken, A, Andersen, V, Brudeseth, A, Lund, H, and Fimland, MS. The Effect of Performing Bi- and Unilateral Row Exercises on Core Muscle Activation. Int J Sports Med 94: 900–905, 2015.Shinohara, H, Urabe, Y, Maeda, N, Xie, D, Sasadai, J, and Fujii, E. Does shoulder impingement syndrome affect the shoulder kinematics and associated muscle activity in archers? J Sports Med Phys Fitness 54: 772–779, 2014.Smith, M, Sparkes, V, Busse, M, and Enright, S. Upper and lower trapezius muscle activity in subjects with subacromial impingement symptoms: Is there imbalance and can taping change it? 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- Eric Trexler
Diet Tracking and Disordered Eating: Which Comes First?

This article was first published in MASS Research Review and is a review and breakdown of a recent study. The study reviewed is Introducing Dietary Self-Monitoring to Undergraduate Women via a Calorie Counting App Has No Effect on Mental Health or Health Behaviors: Results From a Randomized Controlled Trial by Hahn et al. (2021). Graphics in this review are by Kat Whitfield.

Key Points In the presently reviewed study (1), 200 female college students who did not closely monitor their diet were randomly assigned to one month of diet tracking with MyFitnessPal or no intervention (control).The researchers did not observe significant negative effects on eating disorder risk, anxiety, depressive symptoms, body satisfaction, quality of life, eating behaviors, physical activity, screen time, or other forms of weight-related self-monitoring. For individuals without a current or previous eating disorder diagnosis, tracking with a diet app did not negatively impact psychological outcomes or increase eating disorder risk. On the other hand, the mere act of tracking did not significantly improve other health-related behaviors.

Eating disorders are not to be trifled with, as they can have extremely deleterious effects on physical health, mental health, and quality of life. Unfortunately, eating disorder symptoms and other subclinical indicators of disordered eating can often manifest as actions and behaviors that are common among many health and fitness enthusiasts, who may engage in these actions and behaviors in the absence of psychological symptoms that are pathological in nature. For example, I once distributed some eating disorder questionnaires to a group of physique athletes during contest preparation, and some of the questions included: 

“Have you been deliberately trying to limit the amount of food you eat to influence your shape or weight (whether or not you have succeeded)?”

 “Have you tried to follow definite rules regarding your eating (for example, a calorie limit) in order to influence your shape or weight (whether or not you have succeeded)?”

“Have you had a strong desire to lose weight?”

Needless to say, if you ask a physique athlete any of those questions during their contest preparation, their only answer is a blank, confused stare. Questions related to these behaviors find their way onto eating disorder questionnaires, but the behaviors themselves are not inherently deleterious when completed in the absence of unfavorable psychological symptoms. Along these lines, the definition of “disordered eating” is a bit ambiguous, and there doesn’t seem to be a unanimous consensus. Broad definitions make it seem like just about any intentional dietary modification intended to influence body composition could qualify as “disordered eating,” while the more strict definitions can be difficult to distinguish from clinical eating disorder diagnoses such as “other specified feeding or eating disorders” and “unspecified feeding or eating disorder.” 

So, for the purposes of this article, I intend to refer to “disordered eating habits” as potentially pathological dietary attitudes and behaviors that are accompanied or driven by deleterious psychological symptoms related to weight or body image. With this operational definition, an “increase in disordered eating” among a group of individuals could pertain to an increased prevalence of eating disorder diagnoses, an increase in scores on questionnaires designed to quantify the severity of eating disorder symptoms, or an increase in the frequency or severity of potentially pathological dietary attitudes and behaviors that are accompanied or driven by deleterious psychological symptoms related to weight or body image. In this context, someone with an eating disorder diagnosis will display disordered eating habits, but a subclinical increase in disordered eating habits does not necessarily warrant an eating disorder diagnosis, and goal-oriented dietary modifications that are implemented safely and in the absence of deleterious psychological symptoms (such as a powerlifter modifying their diet to move up or down a weight class for competitive purposes) would not fit the description. I’m not necessarily suggesting that this is the one “true” definition of disordered eating that should be adopted broadly, but this is the most useful definition for the purpose of this article.

It is often hard to draw the line between healthy and unhealthy dietary manipulation, so fitness enthusiasts and fitness professionals must be vigilant to avoid doing harm to themselves or others. Whenever this discussion comes up in fitness circles, people often wonder if encouraging someone to track their food intake, calories, or macros is a risky directive that may cause eating disorders or subclinical (but still unfavorable) disordered eating behaviors. This concern is largely based on cross-sectional observations indicating that the use of diet and fitness monitoring devices is correlated with eating disorder symptomatology (2) and that people with eating disorders track their dietary intake at a higher rate than people without eating disorders and tend to report the perception that their app usage contributes to their eating disorder symptoms (3). However, with these types of associations, it’s hard to say whether diet tracking led to the development of eating disorders, or whether people with eating disorders were drawn to diet tracking. We also can’t rule out the possibility that the relationship between diet tracking and eating disorder development or symptom severity is moderated by the individual’s level of susceptibility to eating disorders, or the possibility that the relationship between diet tracking and eating disorder development is substantially more complex than any of these proposed explanations. 

The presently reviewed study (1) was a randomized controlled trial that sought to determine if one month of diet tracking with MyFitnessPal would significantly impact eating disorder questionnaire scores, prevalence of eating disorder behaviors, mental health, or health behaviors. Results indicated that tracking with a diet app did not negatively impact psychological outcomes or increase eating disorder risk. However, tracking also failed to significantly improve health behaviors related to physical activity and nutrition.

Before you read the rest of this article, I want to disclose a clear conflict of interest: Greg and I (and the rest of the team at Stronger By Science Technologies) have a diet app. The reality is that it’s nearly impossible to operate in the fitness space with an absolute absence of conflicts, whether those conflicts are directly or indirectly related to financial incentives. Every fitness professional favors particular approaches to eating or training (hopefully based on an unbiased appraisal of strong scientific evidence), and those preferences will be (and should be) reflected in that professional’s content, partnerships, products, and services. In my opinion, the goal shouldn’t be to get information from someone with absolutely no biases or conflicts of interest (good luck with that). Rather, I try to get my information from people who clearly and transparently disclose their conflicts and make an earnest effort to suspend their biases when creating content. So, with that out of the way, let’s dig into this study.

Purpose and Hypotheses Purpose

The purpose of the presently reviewed study was “to identify the effects of dietary self-monitoring on eating disorder risk among college women via a randomized controlled trial.”

Hypotheses

The researchers hypothesized that “women assigned to use an app for self-monitoring dietary intake would report an increase in eating disorder risk relative to women assigned to the control condition.” They also hypothesized that “dietary self-monitoring would lead to poorer mental health outcomes given the impacts of self-weighing on mental health among this population.”

Subjects and Methods Subjects

To recruit for this study, the researchers sent out emails to 4,601 female undergraduate students, indicating they were seeking participants for a study evaluating the impact of smartphone apps on the wellbeing of college students. The email did not specifically mention anything about eating disorder risk as an outcome, in an effort to avoid influencing study results. They specifically recruited female undergraduate college students based on previous research indicating that the prevalence of eating disorders and disordered eating behaviors are particularly high within this population.

Participants were eligible to participate if they were a female undergraduate student, were fluent in English, had a smartphone, and were at least 18 years old. Participants were excluded if they reported a current or previous eating disorder diagnosis, reported a history of any medical condition that directly impacted the type or amount of food they eat, or had tracked their food intake within the past year. Participants were also excluded if they had a score ≥2 on a preliminary questionnaire used to gauge eating disorder symptoms and behaviors (EDE-QS). The longer version of this questionnaire has twice as many survey items, with scores ≥4 commonly classified as “within the clinical range.” So, the researchers decided that a cutoff of  ≥2 would be analogous when using the shortened version of the questionnaire. In theory, this participant sampling procedure and screening process should have allowed the researchers to investigate the research question within a population (female college students) with a heightened propensity for expressing disordered eating habits and eating disorder symptoms, while weeding out participants who were already in the clinical range for questionnaire scores related to eating disorder symptoms, which is an ethically defensible approach to take.

Of the 4,601 students emailed, 808 completed the screening survey, and 411 were deemed eligible for participation. The first 201 eligible participants were invited to enroll in the study. One participant was removed due to a deviation from the study protocol, so 200 participants were randomly assigned to one of two groups: the intervention group tracked their diet for a month using the MyFitnessPal smartphone app, while the control group maintained their typical habits and did not monitor their diet. Eight participants from the intervention group dropped out prior to study completion, so the study yielded data from 100 participants in the control group and 92 participants in the intervention group. The full sample had an average age of 20.2 ± 2.4 years, and an average BMI of 23.1 ± 4.8 kg/m2.

Methods

The methods for this study were very straightforward. The study consisted of two visits, separated by about a month. At the pre-testing visit, participants had their height and weight measured, and completed some surveys related to eating disorder risk, anxiety, depressive symptoms, body satisfaction, quality of life, eating behaviors, physical activity, screen time, and other health-related outcomes and behaviors. After that, participants in the intervention group were given instructions about how to track their food and beverage intake using MyFitnessPal, and the app was downloaded to their phones with energy requirements entered based on the Mifflin St. Jeor equation. They were instructed to log everything they ate or drank immediately after consumption for the following month, whereas the control group made no modifications to their daily habits. After the month was over, participants returned to the laboratory for post-testing, and the same procedures carried out in the pre-testing visit were repeated. At the end of the post-testing visit, participants were informed about the purpose of the study, and were provided a list of locally available mental health resources.

Eating disorder risks and behaviors were assessed using the “EDE-QS,” depressive symptoms were assessed using the “Center for Epidemiologic Studies Depression Scale Revised,” state anxiety was assessed using the state subscale of the “State-Trait Anxiety Inventory,” body image was assessed using the “Body Image States Scale,” overall quality of life was assessed using the “Brunnsviken Brief Quality of Life Scale,” nutrition and physical activity behaviors were assessed using questions adapted from the “Youth Risk Behavior Surveillance System Survey,” and other miscellaneous sets of questions were used to assess social media use, screen time, self-weighing frequency, and physical activity self-monitoring. For dichotomous outcomes, statistical analyses sought to calculate the odds of participants in the intervention group experiencing the outcome in comparison to participants in the control group. For continuous outcomes, statistical analyses sought to numerically quantify the impact of group membership (intervention or control) on a given outcome.

Findings

Participants in the intervention group used the diet app an average of 89.1% of the days between pre-testing and post-testing (median = 94.1% of days). For the total overall score on the eating disorder questionnaire, there was no significant difference between groups (p = 0.17). Scores were actually a little lower in the diet tracking group, but not to a degree that would be considered practically or statistically significant. Furthermore, as shown in Table 1, there were no significant differences between groups for prevalence of any of the individual eating disorder behaviors. 

As shown in Table 2, there were no significant differences between groups for state anxiety (p = 0.48), depressive symptoms (p = 0.66), body image (p = 0.81), or quality of life (p = 0.36). 

In the original study, there was a huge table presenting very detailed outcomes related to eating behavior, dietary intake, physical activity, social media use, and screen time. However, these outcomes can be summarized quite concisely, as no significant differences were observed between the two groups (all p > 0.05). The only significant between-group difference in the study is presented in Table 3, which shows that self-weighing frequency decreased from 0.66 to 0.33 times per week in the tracking group, while self-weighing frequency increased from 0.44 to 0.60 times per week in the control group. In the absence of other changes related to eating disorder questionnaire scores, prevalence of eating disorder behaviors, self-monitoring habits, and mental health outcomes, this isolated finding doesn’t seem to be particularly impactful. 

Interpretation

This is an important study, because the concerns giving rise to the research question are plausible and have high potential for widespread impact. Observational evidence tells us that diet and fitness tracking is correlated with eating disorder symptomatology (2) and that diet tracking is far more prevalent among people with eating disorders than the general population (3), so it’s natural to wonder if tracking one’s diet might lead to a pathological degree of focus and fixation on dietary intake, body weight, body image, and so on. However, a major shortcoming of observational research reporting correlations is that we can’t make confident inferences about causation. For example, one might plausibly speculate that higher rates of diet tracking among people with eating disorders could suggest that diet tracking causes eating disorders. Conversely, in the absence of additional evidence, one could suggest with a similar degree of plausibility that people with eating disorders are simply more likely to track their diet as a consequence, not a cause, of their eating disorder. One could also suggest that the relationship between diet tracking and eating disorder development or symptom severity is moderated by the individual’s level of susceptibility to eating disorders, or that there is a far more complicated chain of phenomena that indirectly link diet tracking to eating disorders, without one directly causing the other. 

Fortunately, the presently reviewed study is a randomized controlled trial, which circumvents this issue and gives us more stable footing for making claims about causation. This study had a large sample of participants that were drawn from the same population, then randomly assigned to track their diet or maintain their normal habits. This means we can have a reasonable degree of confidence that both groups had generally similar characteristics, with the key difference between them being the introduction of diet tracking. As a result, we can observe the temporal impact of changing one particular behavior, while comparing these observations to a group of very similar people who did not make that change. The presently reviewed results indicate that the mere act of diet tracking did not meaningfully impact BMI or a variety of health-related behaviors, but it also didn’t do any measurable harm with regards to mental health or disordered eating. 

Of course, we never want to place all of our confidence in a single study. As reviewed by Helms and colleagues (4), the evidence linking a variety of self-monitoring strategies to eating disorder symptoms is a bit mixed, but the presently reviewed study is not the first to report fairly benign effects. In a study by Jospe et al (5), 250 adults seeking treatment for overweight or obesity were randomly assigned to one of five self-monitoring conditions: daily self-weighing, diet tracking with MyFitnessPal, monthly consultations, self-monitoring of hunger, or control (no monitoring). After 12 months of actively trying to lose weight, the groups did not significantly differ in terms of eating disorder questionnaire scores or prevalence of binge eating, self-induced vomiting, laxative misuse, or excessive exercise. While there haven’t been many randomized controlled trials assessing the impact of dietary monitoring with smartphone apps, some randomized controlled trials evaluating other self-monitoring interventions have reported pretty negligible effects with regards to outcomes related to eating disorders. For example, Bailey and Waller reported that frequent body checking did not generally impact body dissatisfaction or disordered eating attitudes to a significant degree (6). They did observe a significant effect by which body checking increased one specific survey item (fear of uncontrollable weight gain after eating), but their analyses demonstrated that this effect was specifically driven by unfavorable responses in people with more pathological baseline eating attitudes. In other words, body checking generally didn’t have a deleterious effect, but did negatively impact one particular cognition related to eating pathology, specifically in predisposed individuals. In addition, Steinberg et al reported that daily self-weighing did not negatively affect mental health or outcomes related to disordered eating (including depressive symptoms, anorectic cognitions, disinhibition, susceptibility to hunger, and binge eating) to a significant degree in overweight individuals undergoing a weight loss intervention (7). 

This is positive news for coaches who like to use diet tracking as a tool for their clients, and for individuals who are interested in tracking (or already tracking) but are a bit nervous about the correlation between diet tracking and disordered eating. However, it’s important to acknowledge that there might be scenarios where tracking could be part of a plan with potential to do harm. In the presently reviewed study, the researchers excluded participants with baseline eating disorder questionnaire scores in the clinical range, which means these results can’t be extrapolated to people who have an active eating disorder or elevated predisposition to eating disorder development. So, despite the findings of the presently reviewed study, it’s most likely a bad idea to introduce diet tracking without professional guidance if you have a history of disordered eating or suspect that you’re at an elevated risk for developing an eating disorder. As someone who manages a team of fitness coaches, I have procedures in place to ensure that all applicants who appear to have an elevated eating disorder risk are directed toward a registered dietitian with clinical training in the area of disordered eating. Unfortunately, you don’t have to look far to find “horror stories” of people who’ve had bad experiences with diet tracking, and I would suspect that many of these unfavorable experiences involve a convergence of three factors: diet tracking, a predisposition to disordered eating, and an approach to dieting that reinforces rigid restraint. 

In the context of dieting, rigid restraint describes an approach that sets a lot of inflexible and dichotomous boundaries, with clear delineations between acceptable and unacceptable intakes. For example, someone dieting with rigid restraint would only eat a small list of “diet foods,” insist upon hitting macronutrient or calorie targets with exceptional precision, and maintain a regimented and hyper-specific meal schedule. With this approach, perfection is the goal, and there is little room for flexibility, adaptability, or approximation. There are also very few gray areas, so behaviors can be quite easily categorized as unequivocal successes or failures. You could argue that rigid restraint reinforces some “perfectionist concerns” that were covered in a previous MASS article (MASS subscription required to read) by Dr. Helms. While that article focused on training and performance, there are some pretty clear parallels to nutrition, and perfectionist concerns were a recipe for burnout and distress. In contrast, someone dieting with flexible restraint would allow for a wide variety of food sources, accept a goal-appropriate margin of error with regards to daily macronutrient or calorie targets, and shift meal composition and timing when necessary. 

Broadly speaking, rigid restraint creates a dieting environment that emphasizes precision, perfection, and a stark delineation between success and failure, whereas flexible restraint creates a dieting environment that is adaptable, malleable, and accommodating. In more practical terms, a person with rigid restraint might “miss a meal” or be “off their diet,” whereas a person with flexible restraint might shift calories from lunch to dinner, or notice that they’re over their carbohydrate target and lower their fat intake a little bit to account for it. When a person with rigid restraint deviates from their strict plan, it’s categorized and internalized as a failure that gets paired with a negative emotion, whereas someone with flexible restraint might simply shift their focus to a pragmatic adjustment that can be made to accommodate the small deviation within their flexible plan. Unsurprisingly, as reviewed by our very own Dr. Helms (and colleagues), rigid dietary restraint is associated with a wide range of negative outcomes, including disordered eating behaviors and attitudes, body image concerns, psychological distress, and poorer well-being (4).

Diet tracking and other forms of self-monitoring can be helpful tools. When a new dieter learns the skill of tracking, it can reinforce the flexible nature of constructing a diet, the importance of portion sizes, the misguidedness of fad diets and weight loss “tricks,” and the arbitrary nature of rigid lists outlining which foods are acceptable or off limits. Aside from this utility during active dieting phases, tracking can also support weight maintenance after a given body composition goal is achieved. The National Weight Control Registry was developed to study and understand characteristics of individuals who are able to successfully lose substantial amounts of weight and keep it off. More than 10,000 people have joined this registry, and research on registry members indicates that decreased frequency of self-weighing is associated with weight regain (8). Self-monitoring also appears to have a high level of feasibility; in the presently reviewed study, participants used the diet app on an average of 89.1% of days (median = 94.1% of days), and daily food tracking in MyFitnessPal can be a bit cumbersome, particularly for individuals with no prior tracking experience. In addition to the benefits of diet tracking with a flexible approach that have already been described, Dr. Helms has previously covered studies documenting slightly better body composition outcomes and micronutrient intakes (MASS subscription required to read) when using flexible diets with macro tracking compared to more rigid, rule-based diets. 

However, it’s important to note that – just like any other tool – the effects of diet tracking depend on how it is used. As the presently reviewed study indicates, merely tracking alone does not automatically impart a favorable impact on other health-related behaviors. A diet app can support self-regulation, but if your aim is to make some major changes related to your health, fitness, or physique, you’ll want to pair it with other components of successful behavior change interventions. For example, diet tracking could be used in conjunction with intentional modifications to your diet or physical activity habits, in addition to other intervention components that aim to increase nutrition-related knowledge, bolster self-efficacy, and provide social support. You’ll also want to avoid a plan of action that involves excessively rigid restraint, as the “horror stories” of diet tracking seem to have a lot more to do with rigid restraint, perfectionist concerns, excessively restrictive guidelines, and internalization of perceived failures than diet tracking per se. It’s also important to recognize that tracking is not for everyone, all the time. As stated previously, anyone with a history of disordered eating or significantly elevated eating disorder risk probably shouldn’t venture into the world of diet tracking or diet manipulation without guidance from a qualified professional. I don’t have any clinical training or experience in the realm of disordered eating, so that’s not a professional opinion, but a better-safe-than-sorry opinion that errs on the side of doing no unintentional harm. For all others seeking a practical breakdown of circumstances in which diet tracking makes sense, and how to go about learning the process, Dr. Helms has a great three-part video series covering the topic in the MASS archive (onetwothree – MASS subscription required to view). 

Next Steps

There are a couple ways I’d like to see this work built upon in the future, with varying degrees of ethical acceptability. I’d be interested to see a study very similar to this one, but with one small change: Rather than simply giving participants (with no history of diet tracking) access to the app and passively putting in their estimated energy needs, participants would self-select a weight-related goal (gain, lose, or maintain) and receive a specific set of macro targets to aim for each day. This would crank up the intensity, and shift the intervention from a more passive state of observation to a more active state of manipulation. On the slightly-less-ethical (but still probably ethical-enough-to-justify) side, I’d also be interested to see a study in which dietary monitoring on smartphone apps was evaluated in people with no prior tracking history, with half of the participants receiving instructions that reinforce rigid restraint and the other half receiving instructions that reinforce flexible restraint. I would expect the results to indicate that dietary tracking is still benign (in terms of mental health and eating disorder symptoms) for the majority of individuals within the context of flexible restraint, but more likely to induce unfavorable effects when rigid restraint is applied, specifically in individuals who are particularly predisposed to eating disorders.

Application and Takeaways

Quantitative diet tracking is a tool; no more, no less. Tracking dietary intake on a smartphone app did not lead to deleterious effects related to mental health, eating disorder questionnaire scores, or prevalence of eating disorder behaviors. On the other hand, the mere act of tracking nutrition alone did not lead to the improvement or adoption of other health-related behaviors. While a lot of people with eating disorders track their diet, diet tracking did not appear to increase the frequency or severity of eating disorder symptoms in this sample of participants with baseline eating disorder questionnaire scores below the clinical range. As a result, diet tracking within the context of dietary guidelines that encourage flexible restraint can be generally viewed as an effective method of modifying dietary intake without inducing disordered eating symptoms or other negative effects on mental health. The huge caveat is that some individuals are particularly predisposed to developing eating disorders, and these individuals should not undergo any intervention involving weight monitoring, diet monitoring, or dietary manipulation without guidance from a qualified medical professional with ample training and experience in the area of disordered eating.

References   Hahn SL, Kaciroti N, Eisenberg D, Weeks HM, Bauer KW, Sonneville KR. Introducing Dietary Self-Monitoring to Undergraduate Women via a Calorie Counting App Has No Effect on Mental Health or Health Behaviors: Results From a Randomized Controlled Trial. J Acad Nutr Diet. 2021 Aug 19;S2212-2672(21)00734-6.  Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eat Behav. 2017 Aug;26:89–92.  Levinson CA, Fewell L, Brosof LC. My Fitness Pal calorie tracker usage in the eating disorders. Eat Behav. 2017 Dec;27:14–6.  Helms ER, Prnjak K, Linardon J. Towards a Sustainable Nutrition Paradigm in Physique Sport: A Narrative Review. Sports. 2019 Jul 16;7(7):172.  Jospe MR, Brown RC, Williams SM, Roy M, Meredith-Jones KA, Taylor RW. Self-monitoring has no adverse effect on disordered eating in adults seeking treatment for obesity. Obes Sci Pract. 2018 Jun;4(3):283–8.  Bailey N, Waller G. Body checking in non-clinical women: Experimental evidence of a specific impact on fear of uncontrollable weight gain. Int J Eat Disord. 2017 Jun;50(6):693–7.  Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. Daily self-weighing and adverse psychological outcomes: a randomized controlled trial. Am J Prev Med. 2014 Jan;46(1):24–9.  Thomas JG, Bond DS, Phelan S, Hill JO, Wing RR. Weight-loss maintenance for 10 years in the National Weight Control Registry. Am J Prev Med. 2014 Jan;46(1):17–23.

The post Diet Tracking and Disordered Eating: Which Comes First? appeared first on Stronger by Science.

- Greg Nuckols
Does Muscle Growth Increase Your Potential for Strength Gains?

This article was first published in MASS Research Review and is a review and breakdown of a recent study. The study reviewed is Do Exercise-Induced Increases in Muscle Size Contribute to Strength in Resistance-Trained Individuals by Buckner et al. (2021)

Key Points 18 trained subjects completed two phases of training. For the first phase (lasting eight weeks), one arm did biceps hypertrophy training twice per week, while the other arm performed 1RM biceps curls twice per week. In the second phase (lasting four weeks), both arms performed 1RM biceps curls twice per week, followed by two “hypertrophy” sets for each arm.During the first phase of the study, more hypertrophy occurred in the arms doing hypertrophy training, but strength gains were similar between arms. During the second phase of the study, no meaningful change in muscle thickness occurred in either arm, and strength gains did not substantially differ between arms.These findings suggest that hypertrophy during the first phase of the study did not increase the “strength potential” of the arms initially doing hypertrophy training. However, I contend that not enough actual hypertrophy occurred for us to be able to make that inference. I also think the case in favor of hypertrophy increasing strength potential is strong enough that it would take substantially stronger empirical evidence than was provided by this study in order to disprove it.

When the presently reviewed study (1) was published, my inbox was inundated with people telling me I needed to review it for MASS, since I’m on the record arguing that muscle growth does, in fact, contribute to strength gains (2). I always aim to please, so here we are. I’m pretty sure this was the longest MASS article to date, and as I was writing it, my computer crashed no fewer than a dozen times as I tried to wrangle with a spreadsheet with nearly 100,000 rows. I hope all of you that asked for this review are pleased with yourselves.

However, I do think this was a really neat study, and I appreciate the researchers’ experimental approach to the question. Briefly, subjects completed a two-part training protocol. During the first part of the protocol, one arm did hypertrophy training, while the other arm merely practiced 1RMs. During the second part of the protocol, both arms practiced 1RMs. In theory, if hypertrophy increases one’s potential for strength gains, you should expect the arms that initially did hypertrophy training to experience larger gains in strength than the 1RM practice arms during the second phase of the study. However, there weren’t meaningful differences in strength gains between conditions during the second part of the study. Is this study the nail in the coffin? Does this mean hypertrophy really doesn’t contribute to strength gains? In this article I’ll argue that no, it doesn’t, and we still have solid reasons for believing that muscle growth does, in fact, increase one’s “strength potential.”

Purpose and Hypotheses Purpose

The purposes of this study were to:

Investigate the effects of hypertrophy training versus 1RM practice on biceps growth and strength gains.Investigate whether previous hypertrophy promoted larger subsequent strength gains following a period of 1RM practice. Hypotheses

No hypotheses were directly stated. However, based on familiarity with the researchers, I presume they anticipated that hypertrophy training and 1RM practice would lead to similar strength gains, and that hypertrophy training would cause more muscle growth. Furthermore, I suspect that they anticipated that previous hypertrophy would not promote larger strength gains following a period of 1RM practice.

Subjects and Methods Subjects

The presently reviewed paper presents the results of two separate experiments. The first experiment included 25 individuals who all had at least 6 months of experience with biceps training (13 males, 12 females). The second experiment included 18 of the subjects from the first experiment (10 males, 8 females).

Experimental Design

As mentioned, this paper details the results of two experiments, but the experiments ran back-to-back, so I’ll describe the full experimental protocol of both studies together.

Subjects initially completed eight weeks of biceps training, using a within-subject, unilateral design. Each subject’s arms were randomized to two different training conditions. One arm performed hypertrophy training (four sets of dumbbell curls to failure with an 8-12RM load), and one arm performed 1RM practice (they were allowed up to five attempts to establish a dumbbell curl 1RM). They performed all of their training with their back against a wall, in order to keep their form strict. The subjects trained twice per week for eight weeks during this first phase.

During the second phase of the study, 18 of the original 25 subjects completed an additional four weeks of training. During this phase, they worked up to a 1RM with both arms on each training day, followed by two sets to failure with an 8-12RM load.

Testing took place before the start of the first eight weeks of training, 3-5 days after the first eight weeks of training, and 3-5 days after the last four weeks of training. Four outcome variables were assessed: unilateral biceps curl 1RM, unilateral isometric elbow flexion torque (at a 90° elbow angle), unilateral biceps strength endurance (reps to failure with 40% of the subjects’ baseline unilateral biceps curl 1RM), and elbow flexor muscle thickness (assessed via ultrasound, at 50%, 60%, and 70% of the distance between the shoulder and the elbow). Strength endurance was not assessed after the last four weeks of training.

It’s worth noting that subjects were allowed to continue their habitual resistance training during the duration of this study. They were asked to refrain from direct biceps training (any type of curl), but they were still allowed to perform indirect biceps training (like rows, pull-ups, or pull-downs). I think this is a fairly notable confounder.

The researchers used Bayesian statistics to analyze their results. I’m only mentioning that because the statistical evidence in the “Findings” section may be presented in a way you’re not familiar with. With Bayesian statistics, you’re interested in the relative level of support of a hypothesis, rather than the relative lack of support for the null hypothesis. For example, if you’re comparing muscle thicknesses in two groups, you could have three different hypotheses: 1) changes in muscle thickness did not differ between groups, 2) changes in muscle thickness are greater in group 1 than group 2, and 3) changes in muscle thickness were greater in group 2 and group 1. Based on your results, you could have some level of support for all three hypotheses. If results leaned in favor of the second hypothesis (larger gains in group 1 than group 2), but the difference wasn’t particularly large, you could wind up with results along the lines of:

Support for hypothesis 1 (no difference between groups ) = 0.18

Support for hypothesis 2 (group 1 > group 2) = 0.80

Support for hypothesis 3 (group 2 < group 1) = 0.02

This could be interpreted as probable support for hypothesis 2 – you’re 80% sure that gains were larger in group 1 than group 2, but there’s a 18% chance that there was no real difference. However, it’s quite unlikely (2% probability) that group 2 achieved better results.

If, on the other hand, gains were WAY larger in group 1 than group 2, you could wind up with results along the lines of:

Support for hypothesis 1 (no difference between groups ) = 0.001

Support for hypothesis 2 (group 1 > group 2) = 0.999

Support for hypothesis 3 (group 2 < group 1) = 0.000

In this scenario, you have very strong support for hypothesis 2 – you’re 99.9% sure that gains were larger in group 1 than group 2.

In the present study, sometimes two hypotheses were tested (no difference, or larger gains in one condition versus the other), and sometimes three hypotheses were tested (no difference, larger gains with hypertrophy training, and larger gains with 1RM training). For the purposes of the “Findings” section, I’ll make statements along the lines of “muscle thickness increased to a greater degree in the hypertrophy training condition (probability = 0.82).” You can interpret that as “the hypothesis that hypertrophy training would produce larger increases in muscle thickness than 1RM training was most strongly supported by the results (posterior probability for this hypothesis = 0.82).” Also note, probabilities weren’t reported for all comparisons, so if I don’t report the largest probability, it’s probably because it wasn’t stated.

Findings

During the first experiment, unilateral 1RM biceps curl strength and maximal isometric elbow flexion torque increased similarly in both conditions, while larger changes in strength endurance (probability > 0.9999) and muscle thicknesses occurred in the hypertrophy training condition.

The 18 subjects who completed both experiments experienced changes in muscle thicknesses and strength measures during the first study that were similar to the full 25-person cohort of the first study. Muscle thicknesses increased to a greater degree for arms in the hypertrophy training condition (probabilities = 0.773-0.999), while 1RM biceps curl strength (probability = 0.886) and maximal isometric torque (probability 0.858) increased to a similar degree in both conditions.

During the second experiment, muscle thicknesses changed to a similar extent (very little) in the arms that had previously completed hypertrophy training, and the arms that had previously completed 1RM training (probability = 0.646-0.677). Evidence also supported the null hypothesis (no difference between conditions) for changes in strength. For 1RM strength, the probability of no difference between conditions was 0.503, while the probability that strength gains were larger in the group previously completing hypertrophy training was 0.496. For isometric strength, the probability of no difference between conditions was 0.517, while the probability that strength gains were larger in the group previously completing hypertrophy training was 0.482.

Interpretation

I’m forgoing a “criticisms and statistical musings” section for this article, because most of my discussion of this article will be statistical. I’m primarily reviewing this article based on popular demand. In 2019, I was involved in a written debate on this topic (does muscle growth contribute to strength gains?) with some of the authors of the presently reviewed study. Our side (headed up by Dr. Chris Taber) argued that muscle hypertrophy does contribute to strength gains, while the other side (headed by up Dr. Jeremy Loenneke) contended that hypertrophy does not contribute to strength gains (23). Since I was involved in that back-and-forth, quite a few MASS readers requested that I review this study, and I was more than happy to comply.

In our 2019 back-and-forth, our side contended that there are multiple factors contributing to muscular strength, and muscle size is merely one such factor. Based on the influence of other factors (neural factors, technique, changes in connective tissue, etc.), changes in muscle size don’t necessarily increase strength on a 1:1 basis; however, we contended that a larger muscle has the potential to be a stronger muscle. In other words, if you increase your biceps thickness, your 1RM biceps curl may not automatically increase (and, similarly, you may be able to increase your 1RM biceps curl without increasing the thickness of your biceps); however, if you absolutely maxed the strength of your biceps at their current thickness, your biceps strength would be lower than it could be if you maxed out the strength of your biceps after they were considerably thicker. We also argued that the other factors contributing to muscle strength (everything beyond changes in muscle size) change to a greater degree in untrained lifters than trained lifters, overpowering the gains in strength that can primarily be attributed to hypertrophy. Therefore, we argued that the impact of hypertrophy on strength development would be easier to measure and establish in trained subjects than untrained subjects.

With this in mind, I first want to applaud Dr. Buckner and colleagues for conducting the presently reviewed study. Previous studies in the area either used untrained subjects, or didn’t use experimental models that would be adequate to determine whether larger muscles truly had greater strength potential than smaller muscles (they mostly looked at whether, in the short-to-moderate term, hypertrophy-style training increases strength to a greater extent than 1RM practice; 45). The subjects in the presently reviewed study did have at least a bit of prior training experience, and the experimental model the researchers used was far more appropriate for addressing the “larger muscles have more strength potential” argument.

In short, here’s the logic of the experimental model:

One set of arms does hypertrophy training, which should increase both strength and muscle thickness.

Another set of arms does 1RM practice, which should increase strength, but not muscle thickness.

After enough training time has elapsed for these divergent adaptations to occur, the arms that had been doing 1RM practice should be closer to their strength potential (given their current biceps thickness), and the arms that had been doing hypertrophy training should be further from their strength potential (at their post-training biceps thickness), due to increases in biceps thickness and smaller changes in the “other factors” that contribute to maximal strength. Therefore, following a period of 1RM practice in both arms, you should expect the arms previously doing hypertrophy training to experience a larger increase in strength than the arms previously doing 1RM practice, if you assume that hypertrophy increases a muscle’s potential strength. In other words, if the arms previously doing hypertrophy training do gain more strength during this period of 1RM practice, that would be evidence that a larger muscle has the potential to be a stronger muscle; conversely, if both sets of arms increase strength to a similar degree following this period of 1RM practice, that would be evidence suggesting that larger muscles actually don’t have the potential to be stronger muscles.

Here’s an illustration to make this concept more tangible, assuming that increases in muscle size do increase strength potential. Let’s say you have two people who are identical in every way. At baseline, their biceps are 3cm thick, and they can DB curl 15kg. We can get an idea of how strong their biceps are, relative to their size, by dividing their DB curl 1RM by their biceps thickness: 15 ÷ 3 = 5kg per cm (in actuality, muscle cross-sectional area would be a slightly better size measure than muscle thickness for this purpose, but I’m using thickness in this example because thickness was used in the present study). Let’s assume that, when all non-hypertrophic factors are maximized, biceps are capable of curling 7kg per cm of thickness.

One person does hypertrophy training for a few months, and they increase their biceps thickness to 3.6cm. Because they weren’t training to maximize strength, their biceps curl increases proportionally to their muscle thickness: a 20% increase, for a new 1RM of 18kg. The other person does 1RM practice for a few months, and they also increase their curl 1RM to 18kg. However, they didn’t experience an increase in muscle thickness, so now they’re capable of curling 18 ÷ 3 = 6kg per cm of muscle thickness.

Then, both people do 1RM practice for a few months, until they wring every bit of strength out of their biceps, achieving a DB curl 1RM equal to 7kg per cm of muscle thickness. During this time period, neither person experiences an increase or decrease in biceps thickness. After this period of 1RM practice, the person previously doing hypertrophy training now curls 3.6cm × 7 kg per cm = 25.2kg, while the person who’d been doing 1RM practice the whole time now curls 3cm × 7 kg per cm = 21kg. During the final period of 1RM practice, the subject who previously did hypertrophy training increased their 1RM biceps curl by 7.2kg, while the subject who did 1RM practice the whole time only increased their 1RM biceps curl by 3kg. In total, the subject that started with hypertrophy training increased their 1RM by 10.2kg, while the subject that did 1RM practice the whole time increased their 1RM by 6kg. That would be a pretty striking difference, which would strongly suggest that hypertrophy increases the potential for strength gains. However, that’s not what was observed in the present study. The difference in 1RM strength gains between conditions during the second period of the study was just 0.37kg (with the 95% credible interval crossing zero). So, where did this illustration deviate from reality? And what does all of this mean about whether hypertrophy contributes to strength gains?

The first key difference between my illustration and the actual data in the presently reviewed study is the total amount of hypertrophy that took place. As anticipated, very little biceps growth occurred in the arms doing 1RM practice for the entire study (using a composite measure of biceps thicknesses at 50%, 60%, and 70% of humerus length, average biceps thickness only increased by a total of 0.63mm, on average). However, the arms that started with hypertrophy training didn’t grow very much either. Biceps thickness increased by 1.7mm during the period of hypertrophy training, and by an additional 0.47mm during the period where both sets of arms were doing 1RM practice. As a result, biceps thickness only increased by 2.2mm over the course of the entire study (about 7.4%, on average). Thus, even if we assume that hypertrophy increases strength potential, we should not anticipate that strength potential would have increased by very much in the group initially doing hypertrophy training, because the subjects simply didn’t grow very much.

The second key difference between my illustration and the actual data relates to the changes in 1RM strength per unit of muscle thickness. In my example, the capacity for change was fairly large (from 5kg per cm to 7kg per cm – a 40% increase), the person doing hypertrophy training did not increase their biceps curl 1RM strength per unit of biceps thickness during their period of hypertrophy training, and both theoretical individuals increased their biceps curl 1RM strength per unit of biceps thickness to a considerable degree during the final period of 1RM practice (from 5 to 7 kg per cm in the hypertrophy subject, and from 6 to 7 kg per cm in the subject who did 1RM practice the whole time). In reality, the arms doing hypertrophy training increased their biceps curl strength per unit of biceps thickness during the period of hypertrophy training by about 0.4 kg per cm (from about 5.7 to 6.1 kg per cm), which was a bit less than the arms doing 1RM practice (5.5 to 6.2 kg per cm), but it was still a notable increase. Furthermore, neither set of arms experienced much of an increase in strength per unit of muscle thickness during the second part of the study (when both sets of arms were doing 1RM practice) – the increase was only about 0.2kg per cm in both conditions. In total, the relative increase in 1RM strength per centimeter of muscle thickness was about 11.3% in the arms initially doing hypertrophy training, and about 16.0% in the arms that did 1RM practice for the entire study.

When you combine those first two deviations between the illustration and reality, the third deviation is predictable: the subjects in the present study simply didn’t get that much stronger. The arms doing 1RM practice the whole time increased 1RM strength by an average of 3.1kg over the entire course of the study, while the arms that started with hypertrophy training increased 1RM strength by an average of 3.3kg (compared to 6kg and 10.2kg in the illustration).

So, you may be wondering why I used the illustration in the first place, if it doesn’t comport particularly well with reality. I used it for two reasons. First, so everyone would be on the same page about why the experimental design was appropriate for answering the question at hand, and second, to illustrate a point about magnitudes. In reality, all of the results of this study were in line with what one would expect if hypertrophy does increase strength potential; the magnitudes of changes were all just very small. The arms that started with hypertrophy training grew more (but neither group grew by very much), and they gained more strength during the final four weeks of 1RM practice (but neither condition increased strength by very much). In fact, the researchers’ own Bayesian analysis provides some degree of support for hypertrophy increasing “strength potential.” The posterior probabilities for changes in strength during the final four weeks of 1RM practice basically represent a coin flip – about 50.3% probability in favor of the null hypothesis (prior hypertrophy training did not increase “strength potential”) and about 49.6% probability in favor of the alternate hypothesis (prior hypertrophy training did increase “strength potential”).

Let’s tweak the illustration above, using the actual data from the study (muscle thickness can increase from 2.97 to 3.19cm instead of 3 to 3.6cm, strength per unit of muscle thickness can increase from 5.5 to 6.4kg per cm, instead of 5 to 7kg per cm), while still making idealized assumptions (strength per unit of thickness won’t increase from hypertrophy training, no hypertrophy will occur when doing 1RM practice, and both people will wind up with the same relative increase in strength per unit of thickness). The person starting with hypertrophy training would increase their 1RM from 2.97cm × 5.5kg per cm = 16.3kg to 3.19 × 5.5 = 17.5kg following hypertrophy training, and then to 3.19 × 6.4 = 20.5kg following 1RM practice, while the person who did 1RM practice the whole time would increase their 1RM from 16.3 to 2.97 × 6.4 = 19kg over the entire course of the study, for a net difference between groups of just 1.5kg. In other words, even if we explicitly assume that hypertrophy is directly and causally linked with strength gains, and that hypertrophy training doesn’t contribute to strength development in any way other than via the actual hypertrophy it induces, the overall magnitude of improvements observed in this study were so small that the difference in strength gains that could be attributed to differences in hypertrophy still would have been quite small. Just to illustrate this point further, composite biceps thickness increased by only 2.2mm in the hypertrophy group; reliability studies suggest that the limits of agreement for ultrasound biceps thickness measurements (test-retest and interrater reliability) is approximately 3mm (67). Quite simply, it’s hard to know if hypertrophy increases a muscle’s strength potential if you don’t observe much hypertrophy in the first place. This reminds me a bit of a human study designed to see whether myonuclear addition from resistance training would aid the re-growth of muscle following a period of de-training (8), inspired by the design of this rodent study by Egner and colleagues (9). The researchers designed a good study and executed it well, but they had a problem: the subjects simply didn’t accrue more myonuclei during their initial period of resistance training, so the researchers couldn’t really test whether myonuclear addition aided muscle regrowth. There’s a similar issue here (though not quite as extreme) – it’s hard to know if hypertrophy increases strength potential, unless a pretty fair amount of hypertrophy actually takes place (unless you either assume “strength potential” and “hypertrophy” scale perfectly on a 1:1 basis and your measurements are outrageously accurate and precise, or you have an enormous sample size and insane statistical power).

In short, I think the researchers used a very interesting experimental model, but our ability to take much away from this study was hamstrung by four major factors:

Very little muscle growth actually occurred.The hypertrophy training still increased the subjects’ 1RM strength per unit of biceps thickness. As such, the conditions didn’t produce truly divergent responses during the first phase of the study. While the hypertrophy condition did produce more hypertrophy, both conditions seem to have improved the “other” factors that contribute to maximal strength (neural adaptations, the skill of maxing, etc.).The second phase of the study likely didn’t last long enough to truly ascertain whether hypertrophy increased “strength potential.” As far as I can tell, there was no procedure in place to ensure that the subjects actually attained their “strength potential” at their given level of muscle mass by the end of the study. Arms in the 1RM practice condition seemed to still be getting stronger after 12 straight weeks of 1RM practice. How can we know that the arms in the hypertrophy condition had truly reached their “strength potential” after just four weeks of 1RM practice?The subjects were still allowed to perform their normal resistance training, with the exception of direct biceps training. However, we know that pull-downs and rows can increase biceps strength and thickness (1011; there’s every reason to suspect that pull-ups also increase biceps strength and thickness. I just don’t know of a citation to back up that claim). That’s a major confounding factor.

I think the study could be improved with a few simple (though not easy) tweaks:

Let the first phase of the study run for a longer period of time, so that more total hypertrophy can occur in the hypertrophy condition. I recognize this would be a substantial burden (the 12-week total length of the present study was probably chosen to fit neatly within a semester), but I really don’t see any way around it when using trained subjects.For the first phase of the study, use low-load training (30-50% of 1RM) to induce hypertrophy in the hypertrophy condition, instead of moderate-load (8-12RM) training. We know that low-load training can be just as effective for promoting hypertrophy (12), and it shouldn’t cause the substantial increases in strength per unit of size during the period of the initial period of the study, when the goal is to induce divergent adaptations (primarily “neural” adaptations with minimal hypertrophy in the 1RM practice condition, and primarily hypertrophy with minimal “neural” adaptations in the hypertrophy condition).Use some sort of objective cut-off for the second phase of the study to establish that subjects have truly reached their “strength potential” at their current level of muscle size. For example, they could do 1RM practice twice per week, until they fail to achieve a new 1RM for three weeks in a row.Use a more expansive training program, such that you can require an abstinence from all upper body training outside of the study. For example, instead of just training biceps curls, subjects could train curls, triceps extensions, unilateral chest press, and unilateral rows. That would have the added benefit of allowing you to test whether hypertrophy contributes to strength gains in multiple different muscles (biceps, triceps, and potentially pecs), instead of just one.

As is likely clear by now, I don’t agree with the researchers’ contention that hypertrophy is not a contributory factor for strength gains. However, instead of just arguing against their position, I’d like to present the case for why hypertrophy is a contributing factor for strength gains.

First, as I recently discussed on the Iron Culture podcast, there’s a reasonably straightforward case for why you should expect hypertrophy to increase the potential for strength gains. In short, we know that contractile force is produced via actin-myosin crossbridges within muscles. Ultimately, that is the mechanistic basis for muscle contraction. As such, it’s very reasonable to simply assume that muscle hypertrophy will contribute to strength gains, merely by increasing the sheer amount of basic contractile units in each cross-section of the muscle. Thus, for hypertrophy to not contribute to strength gains (or at least increase the potential for strength gains), one of two things would need to be true.

As muscle size increased, the density of contractile proteins within the muscle would need to decrease substantially. In fact, virtually 100% of muscle growth would need to occur via sarcoplasmic hypertrophy, so that the absolute amount of contractile proteins in the muscle’s cross-section remained constant.As muscle size increased, the maximal possible level of the “other factors” contributing to strength would need to decrease. For example, if you were necessarily able to master motor skills to a greater degree with smaller muscles, or if you were necessarily able to activate motor units better with smaller muscles, or if lateral force transmission to connective tissue was necessarily greater with smaller muscles than with larger muscles, that could theoretically mean that hypertrophy wouldn’t increase strength potential, in spite of increased absolute amounts of contractile proteins.

As far as I’m aware, there’s no strong evidence for any of these possible complicating factors. There is some evidence suggesting that sarcoplasmic hypertrophy can occur (13), but as far as I know, most evidence still leans firmly in favor of myofibrillar hypertrophy being the dominant factor in long-term muscle growth. In fact, there’s some evidence against the existence (or importance) of these complicating factors, at least collectively. If these complicating factors exist and are relevant, you’d anticipate that whole-muscle and muscle fiber strength per unit of cross-section area would decrease as hypertrophy occurred. However, a 2018 meta-analysis (which included the same senior author as the presently reviewed study) found that strength per unit of whole-muscle size and per unit of fiber size tended to increase with hypertrophy (14). That being the case, there’s a very strong logical case one could make for why hypertrophy should be a contributory cause of strength gains.

However, logic can only get you but so far. Plenty of logical things don’t pan out in the real world. So, what sort of empirical evidence suggests that hypertrophy may contribute to strength gains? Well, for starters, muscle size and lean mass are strongly associated with muscle strength on a cross-sectional basis. In fact, among lifters, the association is very strong: r = 0.8+ (151617). That’s not the strongest evidence in the world, however; as we know, you can’t infer causation from correlation. We also see that changes in muscle size or lean mass are associated with changes in strength, especially in trained subjects (181920). The association is much weaker in untrained subjects, suggesting that non-hypertrophic factors are driving strength gains early in one’s training career (2122). However, that also doesn’t necessarily imply causation. So, what else do we have beyond associations?

Well, in a perfect world, we’d have a study like the presently reviewed study where more hypertrophy occurred in the hypertrophy condition, or we’d have a randomized controlled trial where “hypertrophy” could be randomized. For example, if hypertrophy could be induced in one group but not another, while equating all other factors that might independently influence strength gains (training volume, intensity, frequency, etc.), a difference in strength gains between groups would suggest that hypertrophy independently contributes to strength gains. I don’t think such a study exists, but I can think of a relevant body of literature that comes reasonably close. In studies where subjects are given exogenous androgens (steroids), they build more muscle than people not on steroids, and they also increase their strength to a greater degree. That applies when a steroid group and steroid-free group aren’t lifting and when they are lifting (on the same training program). There’s even a close dose-response relationship between blood testosterone levels following testosterone administration and changes in strength, without any resistance training stimulus. In a 2001 study by Bhasin and colleagues, subjects given 300 or 600 mg of testosterone per week increased their leg press 1RMs by 70-80 kg, without any resistance training stimulus (23). One could potentially argue that steroids simultaneously increase muscle mass and increase strength, with those two adaptations being completely independent and unrelated, but that feels like a pretty big stretch to me. I’m unaware of any non-hypertrophic mechanisms by which steroids could independently increase strength to such a notable degree. Unless you’re going to contend that steroids cause neural adaptations that rival those of exercise (for example, in a study in trained lifters, resistance training drug-free led to a 10kg increase in bench press 1RM; steroid administration without any lifting caused a 9kg increase; 24), it certainly seems that the hypertrophy induced by steroids contributes to steroids’ effects on strength gains.

I wanted to look into this a bit deeper, so I downloaded the OpenPowerlifting IPF dataset (downloaded 4/6/2021). I wanted to see whether changes in body mass between meets were predictive of changes in people’s powerlifting totals between meets. Again, such an association wouldn’t conclusively establish a causal relationship, but it would provide some support for the idea that hypertrophy contributes to strength gains. To be clear, if we observed a positive association between changes in body mass and changes in strength among powerlifters, that would suggest that one (or more) of these possibilities may be true:

Muscle growth contributes to strength gains, and muscle loss contributes to strength loss.Adipose tissue has contractile properties that have hitherto remained elusive.Hypertrophy-focused training is more effective for strength development than strength-focused training that’s not intended to cause hypertrophy, but the reasons it’s more effective for strength development are not related to hypertrophy.Training that’s effective for strength development happens to increase body mass, in a manner where it’s completely irrelevant if hypertrophy occurs in the process.Merely being in a sustained calorie deficit mechanistically decreases strength, and merely being in a sustained calorie surplus mechanistically increases strength, in a clear dose-response relationship. Basically, if you’ve been in a cumulative calorie surplus, you’ll get stronger (regardless of whether you gained muscle or fat), and if you’ve been in a cumulative calorie deficit, you’ll get weaker (regardless of whether you lost muscle or fat).Changes in body mass increase or decrease your strength due to changes in leverages, independent of whether you gain or lose muscle in the process.Changes in body mass generally reflect how hard someone is training. When people are training hard, they gain strength, and also happen to gain body mass (though it would be better if they didn’t, since not gaining body mass would improve their competitiveness), and when people aren’t training hard, they lose strength, and also happen to lose body mass.

Of those seven possibilities, I think the first is clearly the most plausible. I actually think the fifth and sixth possibilities also contribute to some degree, but I don’t think they’re sufficient explanations to explain the magnitude of effect we’re about to see in the data.

Once I downloaded the dataset, I did some light cleaning. First, I removed lifters who competed in single-ply powerlifting gear or knee wraps, filtering it down to the raw division. Then, I removed lifters who competed in bench-only, deadlift-only, or push-pull meets, filtering it down to lifters who competed in all three lifts (squat, bench press, and deadlift). Then, I removed lifters who were competing before or after the typical “prime years” for strength (20-35 years old), so that age effect wouldn’t affect strength changes too much, and puberty wouldn’t affect body weight changes. Then, I removed lifters for whom actual body mass wasn’t recorded (some entries just had the lifter’s weight class, but not their actual body mass). Then, I removed lifters who hadn’t competed in at least two competitions, since I was interested in changes between meets. Finally, I removed lifters whose change in body mass or change in strength were at least four standard deviations above or below the mean (these were mostly obvious typos, or lifters who clearly took some token lifts, probably due to entering a meet injured or injuring themselves during the squat), and meets that took place less than 28 days apart (since we want to see “true” changes in strength; if lifters compete twice in a month, you’re probably just seeing normal fluctuations in strength, rather than true increases or decreases).

I was left with 86,084 meet results from 27,871 individual lifters (about two-thirds males and one-third females). People improved their totals by 15.0 ± 27.1 kg between meets, gained 0.72 ± 3.61 kg of body mass between meets, and went 233 ± 226 days between meets, on average.

I started by seeing whether changes in strength were associated with changes in body mass between meets. They were: r = 0.375 (this means that changes in body weight explain about 14.1% of the variability of changes in strength). Then, I wanted to see how predictive every other factor in the OpenPowerlifting dataset was. So, I crammed them all into one big multiple regression model: r = 0.284 (this means that literally every other factor – sex, time between meets, age, body weight on meet day, powerlifting total, how many meets the person had competed in, and the person’s IPF points – cumulatively explained 8.1% of the variability of changes in strength). In other words, merely knowing how much someone’s body mass changes between meets tells you more about how their total will change between meets than knowing how strong or competitive they already are, how many meets they’ve done, how long they’ve been training between meets, their sex, and their age … combined.

However, those are pretty “naive” analyses. The biggest problem with them is that subjects who competed more times were given more weight. For example, if someone competed twice, they were included once (changes in their total and their body mass between meets one and two), but if someone competed 15 times, they were included 14 times (changes in their total and their body mass between each consecutive pair of meets). How many meets someone had done was also predictive of their change in strength. The association wasn’t as strong as the association between changes in body mass and changes in strength, but people did tend to gain more strength between their first and second meets than between, say, their 9th and 10th meets. So, I filtered the data down further to folks who had competed at least four times and tried to analyze the data using linear mixed models, with change in weight versus change in strength nested within subject. Trex and I spent an afternoon on it and couldn’t get a model to converge, so I went with an alternate approach.

Between each pair of meets – meet 1 to meet 2, meet 2 to meet 3, meet 3 to meet 4, etc. (out to 10 meets) – I wanted to see if there was still an association between changes in body mass and changes in strength. One possibility is that newer lifters are more likely to gain a lot of strength and experience a lot of hypertrophy (though hypertrophy isn’t actually contributing to those strength gains), while more seasoned lifters are likely to experience little hypertrophy and small strength gains – that would have the effect of creating a positive association between gains in body mass and gains in strength within the full dataset, which would break down when looking at each pair of meets individually. However, that’s not what I found. The strength of the association between changes in body mass and changes in strength was fairly stable between each pair of meets (r = 0.29-0.40), and the slope of the relationship (the degree to which you’d expect a lifter’s total to change for each kilogram that body mass increases) was also pretty stable between each pair of meets (slope = 2.2-3.1). This is basically the same result I got from the initial linear regression analysis I ran on the full dataset, but it’s more robust and statistically justifiable.

To make the relationship easier to intuitively grasp, between each pair of meets, I calculated the average change in strength if someone lost at least 10kg between meets, if they lost 5-10kg, if they lost 2-5kg, if they lost 0-2kg, if they gained 0-2kg, if they gained 2-5kg, if they gained 5-10kg, and if they gained 10+ kg. From meet 5 to meet 6 onward, I pooled the two largest weight loss buckets (people who lost at least 5kg), and from meet 7 to meet 8 onward, I pooled the two largest weight gains buckets (people who gained at least 5kg) in order to ensure each “bucket” had at least 30 lifters in it. You can see the results in Figure 3. As you can see, as rates of weight loss decrease or rates of weight gain increase, strength gains get larger and larger.

Finally, just as a bit of evidence against the seventh hypothesis I outlined above (“changes in body mass generally reflect how hard someone is training. When people are training hard, they gain strength, and also happen to gain body mass”), I also looked at the strength of the association between changes in body mass and changes in IPF points (a scaled scoring system that accounts of body mass and sex; two lifters of different body masses with the same amount of IPF points should be similarly skilled lifters). There was virtually no relationship (r = -0.03). In other words, it doesn’t seem that people are training particularly well and becoming objectively more competitive as they gain weight, or that people are slacking off and becoming less competitive as they lose weight, on average.

If you’re inclined to be skeptical about the proposition that hypertrophy contributes to strength gains, you’re probably not impressed by any of these analyses of powerlifter data. “It’s just more associations (which don’t necessarily imply causation),” you might say, “and you’re looking at changes in body mass, not even changes in muscle mass.” And, while those are both very fair points, I think this is stronger evidence than mere associations. If hypertrophy truly didn’t contribute to strength gains, this dataset would be the place for such a lack of relationship to shine. Powerlifters are incentivized to be as strong as possible while also being as light as possible – that’s literally the entire point of the sport. If we could maximize our strength gains without building more muscle (or even while being able to lose muscle), that would be the holy grail for powerlifting training. Furthermore, powerlifters have generally been training for at least a few years before they step on the platform (and certainly before they compete in their 10th meet) – long enough that hypertrophy no longer occurs by accident; it’s hard to chalk gains in body mass and simultaneous gains in strength up to the effects of strength training that also happens to cause a bunch of hypertrophy as an unnecessary byproduct. Powerlifters also employ a wide array of training styles; if heavy, low-volume, non-hypertrophic strength training year-round was truly capable of maximizing long-term strength gains, you’d expect that people who maintained weight would gain as much strength as lifters who gained weight. Furthermore, as simple as the sport is, and as highly specific as most powerlifting training tends to be, you shouldn’t anticipate major improvements in neural or technique factors (on the whole) between someone’s 9th and 10th meets, after someone’s been competing in the sport for years. If gains in body mass are still associated with gains in strength in that context, but you want to contend that hypertrophy has nothing to do with strength gains, you’re going to have a hard time explaining this relationship, unless you chalk it all up to leverages or some inherent, causal effect of calorie deficits or surpluses (or hitherto unobserved contractile properties of fat mass).

With all of that being said, I do have to acknowledge that it’s never been conclusively, empirically proven that hypertrophy either causes or increases the potential for strength gains. I also genuinely appreciate the authors of the presently reviewed study for conducting a very interesting study using a very clever experimental model. I just wish more hypertrophy would have actually occurred so that the results would have been clearer and more easily interpretable.

So, what should we do with all of this?

For starters, I do think that if you want to maximize strength gains over time, you should also be interested in maximizing hypertrophy over time. That could take one of two forms: 1) interspersing strength-focused training blocks and hypertrophy-focused training blocks, or 2) doing hybrid training (or, if you insist, “powerbuilding”), with a decent emphasis on hypertrophy work, while including enough heavy (>80% 1RM) strength work to hone and improve your skills with heavy loads. I find that two approaches to hybrid training can work really well. First, you can basically just do hypertrophy-focused training, but add in 1-3 heavy but sub-maximal single-rep sets (with ~85-95% of your max, or at RPE 7-9ish) before your main working sets for your most important compound exercises. Second, you can train your primary compound lifts like a powerlifter (mostly pretty heavy sets of <8 reps), but make sure you do a fair bit of additional “bodybuilding” training within the same workout. For example, you could start a workout with 4 sets of 3 squats at 85% of 1RM, followed by leg press for sets of 10-15 reps, and walking lunges for sets of 20-25 reps.

The more interesting question is whether hypertrophy training will maximize your competitiveness in powerlifting. As I mentioned earlier, change in body mass between meets was not associated with change in IPF points between meets in the OpenPowerlifting dataset. Does that mean that, while muscle growth may contribute to maximizing total strength, it doesn’t actually make you a better powerlifter? That’s certainly one possibility, but I think the nature of how people tend to gain and lose weight in powerlifting clouds the true relationship between hypertrophy and competitiveness in powerlifting over time. Powerlifting has weight classes, and a lot of lifters try to move up or down a full weight class between meets. For example, if someone is competing in the 83kg class, and they try to move up to the 93kg class, they’ll often try to “fill out” their new weight class between meets. Unless they’re waiting at least two or three years between meets, they probably aren’t gaining 10kg of pure muscle. More likely, they’ll gain 3-4kg of muscle and 6-7kg of fat between their last meet at 83kg and their first meet at 93kg. In the process, they’ll probably get stronger, but they may actually be less competitive. This would register as no change in their IPF points, or maybe even a slight decrease. However, as they fill out their class over time, they’ll lose some fat, gain some muscle, and improve their IPF score, which would register as an increase in IPF points with no change in body weight between meets. The opposite can occur when losing weight; if you were near the top of the 93kg weight class, and tried to cut to 83kg between meets, a relatively speedy 10kg loss of weight could lead to some muscle loss, leading to a decrease in IPF points. However, as you stabilize in your new weight class, you’ll probably start building strength again, improving your IPF points. These relationships I’ve just described would create an inverted-U relationship – increases in IPF score at weight maintenance, and decreases when gaining or losing weight. However, there’s more to it than that. If someone does gain weight gradually, such that most of the weight gain is muscle, their IPF score should improve as they gain weight. Conversely, if someone has a fair amount of fat to lose, and they cut gradually so that they don’t sacrifice muscle mass, their IPF score should improve as they lose weight. There are also a lot of people in the middle, who stay in the same weight class, but don’t have meaningful changes in strength between meets. Put all of that together, and it’s a bit unsurprising that changes in body weight don’t predict changes in IPF score from meet to meet.

However, I do think there’s a relatively straightforward case for hypertrophy improving competitiveness in powerlifting over time. If we assume that contractile force per unit of muscle cross-sectional area doesn’t decrease with hypertrophy, muscle growth should increase relative strength (which is what IPF points are assessing; strength scaled to body mass). As long as your level of body fat remains constant, hypertrophy increases the fraction of your total body mass composed of muscle. Just using some round numbers, the average untrained person has about 30kg of skeletal muscle mass. If they can total 300kg (10kg per kg of muscle mass), and they weigh 80kg, their total would be worth 42.31 IPF points. If they can add 5kg of muscle mass without increasing body fat, even if their strength per unit of muscle mass doesn’t increase, their total would increase to 350kg at a body mass of 85kg, which would be worth 47.87 IPF points. This works for any set of assumptions I’m aware of. Instead of an untrained lifter, let’s assume a high-level 105kg lifter has 48kg of muscle mass at 105kg, and they can total 864kg (18kg per kg of muscle mass), for 106.67 IPF points. They decide they’re comfortable water cutting 3kg, so they gain 3kg of muscle mass. Now they should be able to total 918 in the gym at 108kg, for 111.87 IPF points. If they can hit that same total on the platform after weighing in at 105, their 918kg total would be worth 113.34 IPF points. Now, things would clearly be a bit more complicated in the real world. Training may increase bone mass a bit, your strength per unit of muscle mass will increase over time, etc., but as long as fat mass either stays constant or decreases, hypertrophy should increase your competitiveness in powerlifting, unless it can be shown that the contractile force of muscle necessarily decreases following hypertrophy. As I mentioned previously, there’s evidence that the opposite actually occurs – strength per unit of muscle actually increases following hypertrophy (14).

Anyway, that’s all I’ve got. To wrap up, let me admit that I may just be blinded by my biases on this topic. I’m well aware that I turned the discussion of a study purporting to show no impact of hypertrophy on “strength potential” into a very long-winded defense of hypertrophy’s impact on strength development. If I can psychoanalyze myself for a moment, I think I’m inclined to be verbose on this topic, because I’m slightly insecure about how discussing it forces me to adopt an argumentative style that’s pretty foreign to me. I generally default to empirical reasoning (“what does the data show us?”), but the strongest argument in favor of hypertrophy contributing to strength gains is a rationalist argument (actin-myosin cross-bridges are what cause muscle contraction, and they scale in number with increases in muscle size). I’m also generally happy to assume the typical null hypothesis (“there’s no difference between these things” or “there’s no relationship between these things” or “this thing doesn’t cause that thing”), but in this case, the relationship between hypertrophy and strength development seems so self-evident, that I tacitly accept this positive relationship as the null hypothesis that needs to be disproven. So, since I feel like I’m arguing from my back foot to some degree, I may cross a few more “T”s and dot a few more “I”s than I typically would. If you made it to the end of this article, I appreciate your patience, and I hope that at least a bit of this rambling was interesting or informative. 

Next Steps

I already laid out my ideal next step in the “Interpretation” section. Namely, I’d like to see a similarly designed study, where hypertrophy is initially induced via low-load training, the initial phase runs long enough for more hypertrophy to occur, the second phase has some sort of objective cutoff to ascertain if subjects have truly reached their “strength potential” at their given level of muscle mass, and the subjects aren’t allowed to train the muscle(s) being assessed outside of the study’s training program.

Application and Takeaways

If you want to maximize your strength long-term, I’m still firmly of the opinion that you should also aim to maximize your muscularity. Practically, that could involve performing phases of hypertrophy-focused training throughout the year, integrating heavy strength work into a generally hypertrophy-focused, or integrating hypertrophy-focused accessory training into a generally strength-focused program.

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- Eric Trexler
An Evidence-Based Approach to Goal Setting and Behavior Change

If you’re tapped into the fitness world, it’s now the time of year that you’ll be seeing plenty of claims like this: “You’ll fail because you don’t care enough. If you really cared, you wouldn’t have waited to start.” It’s early January, which means a lot of people are just getting started on their New Year’s resolutions. Just to give an idea of what “a lot” means, a 2002 study randomly called a bunch of people at the very end of December and found that 41% of the 434 survey respondents were making a New Year’s resolution that year. So, does that mean they found 178 people who were setting themselves up for catastrophic failure and shame?

Fortunately, no.

Success Rates of New Year’s Resolutions

It’s become a bit fashionable for fitness pros to discount New Year’s resolutions as a tool for the uncommitted, and to reinforce the perspective that it’s extremely rare for New Year’s resolutions to be successful or fruitful. They lean on messaging that the time to start is always NOW, and that your reluctance to act before January 1st is a clear indicator that you’re unfocused, uncommitted, and unserious – you don’t want it badly enough, so you’re unlikely to succeed. This trend has been observed (and humorously mocked) in the peer reviewed literature; from 2009 to 2019, the number of scientific papers suggesting that “the time is now” in their title increased precipitously. In other words, this phrasing has effectively become a literary device that allows authors to project the level of importance they attribute to the topic of discussion. But is the underlying concept actually true? Are success rates of New Year’s resolutions really that low?

Again, fortunately, no.

A 2002 study found that six months into the year, 69% of resolutioners self-reported that they were still successful at the time of contact, and 46% reported that they maintained continuous success all throughout the six-month time period. In a more recent study, self-reported success rates after 12 months were around 55%. Without question, these are overestimates of true success rates, given that they’re self-reported and response rates tend to decline over time in a biased manner (people who have stayed on track with their goal are more likely to answer the follow-up phone call and share the good news). So, I wouldn’t stake my reputation on those exact numbers being robust and representative of objectively true success rates, but it’s quite clear that plenty of people are content with their progress and success over large portions of the year. Another great thing about these studies is their relevance to fitness applications – in both studies, the most popular types of resolutions (by far) pertained to weight loss, exercise habits, nutrition habits, and other fitness-related endeavors.

Image adapted from Oscarsson et al.

The reported success rates give us plenty of optimism related to this year’s batch of New Year’s resolutions, but it’s also important to recognize that those statistics also reflect plenty of self-reported failure. The reality is that changing behaviors and achieving ambitious goals is hard, whether you’re starting the process in January or July. 

New Year’s resolutions appear to be popular because they reflect a common tendency to embrace temporal landmarks. Broadly speaking, temporal landmarks are distinct events that stand out relative to our ordinary, continuous stream of daily occurrences. Research on human behavior has suggested that we tend to use temporal landmarks, such as a new week, new month, new quarter, or new year, as a method of segmenting life into discrete accounting periods that allows us to incrementally keep tabs on our progress and success. 

We also tend to make a separation between our “present self” and our “future self” that will inhabit the new, forthcoming accounting period. In other words, our previous lack of physical activity belonged in 2021, with our 2021 self that didn’t sufficiently value or prioritize activity. However, our new, improved, reinvigorated self will inhabit the 2022 accounting period – we believe that this future self is not responsible for the things that occurred in 2021, and can be trusted to turn things around in 2022. 

This combination of factors leads to what researchers call the “fresh start effect,” which describes the scenario in which a person has renewed enthusiasm and increased self-efficacy in close proximity to a big temporal landmark. This concept is entirely baked into the common phrase, “New Year, New You” – the direct implication is that your future self, which exists immediately after the temporal landmark, will be more capable of accomplishing this task than your previous self.

The fresh start effect can promote enthusiasm and self-efficacy, which can definitely fuel momentum early in the process of pursuing a New Year’s resolution. However, the fresh start effect alone is not sufficient to reliably ensure successful behavior change or goal attainment, which is why a decent percentage of New Year’s resolutions yield lackluster results in the long run. 

There are a few notable drawbacks of leaning too heavily on temporal landmarks. First, they can subconsciously lead to unfavorable actions in the time period preceding the temporal landmark. In other words, the current self relegates goal-related responsibility to the future self, so an individual who plans to increase their physical activity in 2022 might actually become a bit more sedentary in the closing weeks of 2021. The thought process is that we have a little more leeway now, because our future self will be responsible for dealing with any ramifications of our present actions. This can reduce our motivation, as presented in the following theoretical model created by Koo et al:

temporal landmarksImage Credit: Koo et al.

Second, if a temporal landmark is not complemented by several ongoing strategies to support behavior change and goal attainment, the short-lived enthusiasm will not have the staying power to propel long-term success. Third, the distinction between our current self and future self is, ultimately, a fabrication of our mind. If we fail to implement ongoing strategies to support behavior change and goal attainment, we are likely to eventually learn that our future self has some of the same tendencies as our present self; this can be extremely discouraging and demotivating if our self-efficacy was entirely tied to our belief in a more ideal future self.

So, what we need here is the best of both worlds. We need to capitalize on the renewed enthusiasm that comes with the fresh start effect, but we also need to support that enthusiasm by leaning on more substantive strategies that can promote ongoing and sustainable success. As such, the rest of this article will discuss how to successfully implement a number of these evidence-based strategies to translate a short-term New Year’s resolution into long-term success.

Should I Set a SMART Goal?

The “SMART goal” acronym has been very popular for a long time, and many discussions related to goal setting will start (and often end) there. If you’re wondering what “SMART” stands for, the answer isn’t nearly as straightforward as you might think. You can find quite a few versions of this acronym floating around the internet. For example, the “A” could mean achievable, attainable, or assignable, and the “R” could mean relevant, realistic, or resourced. In fact, these aren’t even exhaustive lists for the “A” and “R” components – the following image fuels even more uncertainty about what “SMART” actually means in the first place.

SMART goalsImage credit

The terms are unclear because “SMART” goal criteria have been applied to many different health behavior applications, but that’s simply not what they were meant for. SMART goals were designed to help managers keep their employees on task in a corporate setting, which is not a strong foundation for behavior change applications.

At best, you could view SMART goal criteria as a repurposed concept that kind of makes sense for fitness-related goals. At worst, you could view fitness- and health-related applications of the SMART goal framework as insufficient and ineffective attempts at generalizing a concept to a fundamentally incompatible context. In fact, you could justifiably suggest that applying the SMART goal framework was a bit counterproductive in a study on resolutioners who were mostly aiming to change health-related behaviors (rather than aiming to keep a corporation in the black). 

So, rather than leaning on the old SMART acronym, we’ll now explore a number of evidence-based strategies to support New Year’s resolutions by promoting successful behavior change and goal achievement.

Key Factors of Successful Goal Setting Establishing a Goal Hierarchy

A lot of people recommend keeping goals small, manageable, and restricted to a very specific action or behavior. This is partially good advice, but might actually be a bit short-sighted in the long run. A more robust approach to goal setting involves establishing an entire goal network – a hierarchy of goals including superordinate, intermediate, and subordinate components. 

A superordinate goal is, frankly, the type of goal that has become unfashionable in most of the goal-setting advice you see these days. It sits at the top of your goal hierarchy, and refers to idealized, big-picture concepts of one’s self. In other words, they’re more reminiscent of a value than a goal, and tend to be very identity-based. In a fantastic review paper by Höchli et al, they use “be healthy” as an example of a superordinate goal.

The next level of the hierarchy includes intermediate goals. These are less abstract, more specific, and provide a general direction that leads someone toward their superordinate goal. For the superordinate goal of “being healthy,” Höchli et al provide intermediate goals related to sleeping more, eating better, exercising more, and managing stress more effectively.

The lowest level of the hierarchy includes subordinate goals. They are precise goals that specify exactly what you’re going to do (and how you’re going to do it) in order to achieve your intermediate goals. For the intermediate goal of “exercise more,” a subordinate goal would be a specific plan to do 45 minutes of resistance training before work on Mondays, Wednesdays, and Fridays at the gym near your place of work.

Image credit: Höchli et al.

Like I said, the common advice to keep your goals small, manageable, and restricted to a very specific action or behavior is partially good advice. It’s excellent advice for your subordinate goals, but if you only have subordinate goals, you’re missing out on some key advantages provided by a robust goal hierarchy.

The importance of superordinate goals can’t be overstated. These are goals that are tightly linked to our identity and sense of self; they represent the “why” behind our intermediate and subordinate goals, and give us direction for which intermediate and subordinate goals to pursue. They exist over a longer time scale, which leads us to continuous and ongoing goal striving and reduces our likelihood of sacrificing long-term goal pursuit in exchange for short-term rewards that lead us astray. They also exist across broader contexts, which reduces the likelihood that we’ll compensate by indulging in a totally separate, unhealthy behavior when we successfully achieve a subordinate goal related to another health-related behavior. These are just a few advantages of superordinate goals, but a more comprehensive list is provided in the following image (note: If you follow the link to the full text, it does an excellent job breaking down some of the technical jargon into very digestible terms):

Image credit: Höchli et al.

Superordinate goals also introduce a great deal of equifinality and multifinality to the goal hierarchy. Put simply, equifinality means that a goal can be supported via multiple distinct goals that are lower on the hierarchy. For example, the goal of “being healthier” can be supported through efforts to eat better, sleep better, or manage stress more effectively. So, even when motivation to exercise is low, you can still stay on track with the superordinate goal by shifting more focus to your dietary habits. This provides an element of flexibility that can be extremely helpful in reinforcing long-term goal striving. 

Red arrows demonstrate an example of equifinality, with multiple intermediate goals that all support the same superordinate goal. Image adapted from Höchli et al.

Multifinality means that a single goal can support multiple different goals that are higher on the hierarchy. For example, you might start your day with an outdoor jog. This would obviously support the intermediate goal of getting more physical activity, but it could also favorably impact your sleep habits by reinforcing a consistent wake-up time and promoting more restful sleep. Exercise has also been shown to reduce psychogenic stress levels and promote better hunger and appetite regulation. So, this single action supports all four intermediate goals; when your motivation to enhance your cardiovascular fitness is low, you might still go for that run because of how favorably it impacts your sleep quality or stress levels. As a result of multifinality, we have several reasons to complete the subordinate goal, which provides a robust and multifaceted cluster of motivators to reinforce adherence.

Red arrows demonstrate an example of multifinality, with a single subordinate goal that supports multiple distinct intermediate goals. Image adapted from Höchli et al.

In summary, superordinate goals are important, but they aren’t independently sufficient to get the job done. A well-constructed goal hierarchy with superordinate, intermediate, and subordinate goals will equip you with the overarching values that give your goals purpose, the granular details of how you’ll pursue your goals on a day-to-day basis, and the interconnecting elements required to tie the web together in a cohesive manner.

Prefer to listen?

Greg and Eric recently discussed goal setting and behavior change on an episode of the Stronger By Science Podcast. You can listen with the player below, or check out the episode’s page for more listening options.

Characteristics of Effective Goals

We’ve established that intermediate and subordinate goals are critical for success, but that knowledge alone does not facilitate effective goal-setting. In the following sections, we’ll discuss some critical characteristics to consider when you’re formulating your intermediate and subordinate goals.

Approach Versus Avoidance

The distinction between “approach goals” and “avoidance goals” can be very effectively explained in terms of nutrition goals. For someone wishing to improve the quality of their diet, approach goals might include eating more vegetables or eating more protein. In contrast, avoidance goals might include eating dessert less frequently or consuming fewer sugar-sweetened beverages. Approach goals inherently frame changes in a more positive light, and have been associated with more positive emotions, thoughts, and self-evaluations, leading to greater overall psychological well-being. If that’s not enough to win you over, there’s also empirical evidence from 2020 indicating that people with (mostly fitness-oriented) New Year’s resolutions were significantly more successful if they set approach-oriented goals rather than avoidance-oriented goals. So, if you’d like to increase your chances of success and enjoy yourself more along the way, you’ll want to favor approach-oriented goals whenever possible. 

Flexible Restraint Versus Rigid Restraint

As thoroughly reviewed by Helms et al, rigid restraint is bad news. This is yet another topic in which leaning on nutrition examples is instructive for establishing operational definitions. Someone who applies rigid restraint to their diet will tend to set a lot of inflexible rules and boundaries, and will typically evaluate their adherence in dichotomous terms (success or failure, with no gray area). This type of dieter will often restrict themselves to a very small list of “diet foods,” aim to hit daily macro or calorie targets with a fairly impractical level of precision, and will aim for perfection at the expense of flexibility, adaptability, and practicality. In contrast, someone applying flexible restraint to their diet will take a more pragmatic approach that allows more flexible food selection and more tolerance for small deviations from predetermined macro or calorie targets. Flexible restraint abandons perfectionism in exchange for a more adaptable, malleable, and accommodating approach. As such, it’s unsurprising that previous research has shown rigid restraint to be associated with disordered eating symptoms, body image concerns, poorer well-being, and a wide range of negative psychological outcomes. While this concept is most frequently discussed in terms of dietary restraint, it does apply to other contexts, and a general tendency to favor flexible rather than rigid restraint is often advisable for a variety of goals related to health behaviors.

Process Versus Outcome

It can be very tempting to get fixated on the outcome during the goal striving process. In many cases, the outcome is ultimately what we’re striving for, and it’s often the measuring stick that determines whether we’ve successfully accomplished our objective. However, as reviewed by Kaftan and Freund, there’s a fairly sizable body of literature suggesting that this outcome-oriented approach might be counterproductive. The alternative is to take a process-oriented approach, in which we focus on the tasks, behaviors, and habits that should lead us toward our desired outcome rather than fixating on the outcome itself. With this perspective, we’re more inclined to focus on what we can do in the short term to promote and support our own success, rather than constantly focusing on the large discrepancy between the small amount of progress we’ve made and the large amount of progress required to achieve the desired outcome, and subsequently dreading the long and challenging road that we’ll need to traverse to make our outcome goal a reality.

For example, you might decide that you have a superordinate goal of improving your health and wellness, and an intermediate goal involving weight loss. Rather than exclusively focusing on scale weight with a specific goal weight in mind (an outcome focus), it would probably be more fruitful to focus on the processes that will support your body composition goals, such as getting more physical activity, tracking your nutritional intakes more regularly, or making more prudent food selections. You’ll be making progress toward your weight loss goal either way, but a process-focused approach will make your day-to-day objectives more concrete and attainable. Adopting a process-focused approach will certainly support your ability to achieve the goals at the higher levels of your goal hierarchy, but is also likely to make the experience more enjoyable and positive. As Kaftan and Freund argue: “focusing more on the means of goal pursuit (i.e., adopting a process focus) is more beneficial for goal progress and subjective well-being than focusing more on its ends (i.e., adopting an outcome focus).”

Mastery Versus Performance

As reviewed by Bailey, performance goals “involve judging and evaluating one’s ability.” On the surface, that might sound like a great plan of action that holds you accountable for successful outcomes during the goal striving process. However, this approach can backfire – if your self-assessment is purely based on performance and you run into some challenges, then even small setbacks can be interpreted as a major failure that threatens your confidence and self-efficacy. As such, research suggests that mastery goals are generally a better option than performance goals. 

With a mastery goal, you focus on learning new skills or refining abilities that you already possess. Much like the distinction between process and outcome goals, this dichotomy of mastery and performance goals reflects two different approaches with different focal points and priorities. Mastery goals are fantastic because learning new skills or dramatically improving an existing skill provides a huge boost for self-efficacy, yet challenges and setbacks are to be expected when you’re learning something new or refining a skill that is very difficult. As a result, challenges and setbacks during mastery goals are not viewed as failures, but inevitable parts of a process that reflect a need for creative solutions and novel strategies. 

For example, a mastery goal related to nutrition might be to improve your cooking skills. A mastery goal related to exercise might be to learn how to swim. Both endeavors will be challenging, and setbacks are inevitable, but both will bring a tremendous sense of fulfillment as mastery is achieved. If you make a bland dish, you won’t immediately doubt your ability to achieve your weight loss goal. Rather, you’ll search for a few different recipes to see what might have been missing. If you make a fantastic dish, you’ll enjoy a sense of accomplishment, and have even greater confidence that you will be able to achieve your weight loss goal without permanently sacrificing flavorful meals. As an added bonus, you’ll be making significant fitness-related strides while you pursue your mastery goals, as your cooking improvements can lead to more nutritious food options and your increasingly efficient swims will still burn plenty of calories. So, whenever possible, mastery goals are preferable when compared to performance goals.

Goal Difficulty

When it comes to goal difficulty, research suggests we should aim to strike a delicate balance. Goals that are too hard will lead to demotivating instances of failure, whereas excessively easy goals will be too mundane and unchallenging to capture our interest and motivate us to strive for success. Nobody likes to feel like a failure, and it’s hard to get too much of a buzz from unchallenging accomplishments, but a feasible challenge that aligns with our superordinate goals should be sufficient to stir up some excitement and motivation. One strategy that can make challenging goals feel more feasible (and, by extension, reinforce continued motivation) involves introducing “slack with a cost.” This phrase, which can be used interchangeably with “emergency reserve,” comes from a study by Sharif and Shu that demonstrates how useful this strategy can be for effective goal setting. 

To give a concrete example of what “slack with a cost” means, one of their experiments challenged participants to complete an annoying task (35 CAPTCHAs) on their computer, with successful adherence rewarded via monetary compensation ($1/day for that day’s efforts, plus a $5 bonus for successful weekly goal completion). Some participants were instructed to complete the task 5 days per week (easy), some were instructed to complete the task 7 days per week (hard), and others were told that their “official” goal was to complete the task 5 days per week, but they should aim to complete the task 7 days per week. Finally, the fourth group was given “slack with a cost”: they were told to complete the task 7 days per week, but that up to 2 days per week would be excused, if they needed it. 

They’d still lose the dollar for the days they skipped, but the weekly bonus for successful goal completion wouldn’t be impacted until they missed their third day. So, the goal was generally on the more challenging end of the spectrum, and missing a day did have a cost associated with it, but it was not perceived as a total failure. The participants who received the goal with an emergency reserve built in, or “slack with a cost,” were more persistent and more likely to receive their bonus than all other conditions. On top of that, it was the most preferred condition of the four.

Across six experiments, this study by Sharif and Shu demonstrated that people preferred goals that involved slack with a cost, perceived them to have higher attainability than hard goals but higher value than easy goals, and also experienced more sustained persistence when striving for them. In practical terms, this strategy could involve setting a daily calorie goal for weight loss, but have a “reserve budget” of calories that you can chip away at throughout the week (for example, 1900kcal/day, plus an extra 800kcals to be allocated anywhere throughout the week, if needed). Tapping into those 800kcals would not be indicative of failure, but would slightly impact your rate of progress if habitually utilized. 

A similar approach might be having one (or a few) days per week with an elevated calorie allowance that is optional in nature. You aren’t required to eat more than your typical daily target on those days, but they are predetermined days in which calorie reserves are built into the goal. Indeed, these types of planned hedonic deviations have been shown to promote better self-regulatory ability, higher positive affect, better motivation to pursue goals, and development of more numerous strategies to overcome temptations. So, it appears that the goal difficulty “sweet spot” is to aim for a goal that is challenging, but includes a little bit of slack that comes with a cost. 

Setting Specific Goals

Now that we’ve covered the general characteristics of effective goals, it’s time to determine which specific subordinate and intermediate goals will set us on the right trajectory. A good first step involves something called “Mental Contrasting.”

Mental Contrasting

In previous sections of this article, we’ve talked about how goals often involve an idealized concept of the future – a future in which you are thriving and achieving whatever superordinate goals are of value to you. As it turns out, simply getting to this step means you’re halfway to mental contrasting.

With mental contrasting, you envision a desired future (in which you’ve successfully achieved your goal), then you contrast it with the present reality. Doing so encourages you to wrestle with the contrast between the idealized future and the present reality, which can spur action and boost enthusiasm about goal completion. More importantly, it appears to activate expectations of success and help you identify specific obstacles that are standing between you and your desired future. As you might expect, a recent meta-analysis found that mental contrasting led to a positive, statistically significant effect on health behavior improvements.

So, after mental contrasting, you’re likely to feel energized about pursuing your goal, confident in your ability to do so successfully, and focused on the specific barriers you need to overcome in order to make your desired future a reality. Once the specific barriers or obstacles are identified, the next step is to create specific plans to target each one. That’s where implementation intentions come into play.

Implementation Intentions

Implementation intention is defined as “an if–then plan that specifies when, where, and how the behavior will lead to the achievement of a goal.” For example, you might say, “If it’s a weekday, then I will exercise for at least 45 minutes before work at the gym located nearest my office, which will promote my intermediate goal of increasing my physical activity level, which will support my superordinate goal of improving my health.” Mental contrasting helps us get a better idea of what our subordinate goals should be targeting, but implementation intentions turn those ideas into highly specific plans of action. These two strategies pair together quite nicely, and a recent meta-analysis found that combining the two strategies led to statistically significant improvements in goal attainment. 

A couple of common applications of implementation intentions include “temptation bundling” and “habit stacking.” In temptation bundling, a goal-directed behavior that we should do (but involves delayed gratification) is paired with a very enjoyable (but not necessarily goal-directed) activity that provides instant gratification. For example, you may recognize that you’re less likely to exercise because you’d prefer to go home, relax, and watch a television show. You could pair the show with exercise, such that you can only watch the show while you’re cycling or walking on the treadmill at the gym. In this way, a barrier that previously kept you from the gym becomes a source of motivation to get there more regularly. A study by Milkman et al used this approach with audiobooks, and found that pairing gym attendance with the opportunity to listen to an enjoyable audiobook increased the frequency of gym visits among study participants.

Habit stacking (first popularized by BJ Fogg, and double-popularized by James Clear) is a slight deviation from traditional implementation intention. Instead of associating an action or behavior with a specific time and place, you associate it with another habit and stack them together. For example, you might decide to stretch for five minutes before each time you brush your teeth, or to complete a five-minute relaxation exercise before each time you travel to the gym. The advantage is that the first (pre-existing) habit is already formed with some degree of regularity and automaticity, so it should theoretically be easy to tack on a second habit without too much additional effort. 

Action plans and coping plans are conceptually quite similar to implementation intentions. Action plans involve specifying where, when, and how a goal will be implemented. Action plans and implementation intentions are functionally quite similar, but action plans don’t necessarily take the form of an if-then statement. According to a review by Bailey, some important characteristics of action plans are that they’re supposed to be shared with others, are supposed to be short-duration plans that are evaluated weekly, and planners are supposed to have a high level of confidence in their ability to carry out the action plan.

Coping plans are complementary to action plans. Rather than planning for actions to be completed under ideal circumstances, coping plans aim to anticipate barriers and challenges that could interfere with successful completion of goal-directed behaviors, and to identify specific strategies to overcome these barriers and challenges. For example, you might have a fantastic action plan that involves going for a walk around a nearby park to get some additional exercise every morning. Your coping plan might involve specific solutions to deal with heavy rain or snow.

Additional Tools for Goal Support

Up to this point, we’ve covered evidence-based strategies for effective goal setting, which should be applicable to a variety of goals involving behavior change. However, setting up a great plan is just the beginning. There are some additional strategies that can facilitate successful goal striving after your well-formulated goal hierarchy is constructed.

Modifying Your Environment

We’d like to believe that our non-habitual, goal-directed behaviors are purely dictated by rational decisions that are fully compatible with our goals. However, that’s not quite the case. Our behaviors are always subject to some degree of influence from stimuli, whether they are internal or external. Taking this a step further, habits are heavily impacted by stimuli, to the extent that we hardly devote any thought to these relatively automated actions.

Eating behaviors offer some very relatable examples of these pesky stimuli that can alter our decisions and actions. For example, you might have a goal-compatible dinner planned out, but your route home from work takes you right past one of your favorite restaurants. As you walk past the restaurant, the enticing smell of cooking food and convenience of a quick carryout order might be enough to derail your dinner plans. This would be an example of external stimuli, but we are often led astray by internal stimuli as well. For example, we might become accustomed to seeking out hedonically pleasing food options when we’re stressed; when this internal stimulus appears, we may once again find that our dinner plans crumble, and we resort to eating behaviors that are less compatible with our goals. Other stimuli could be purely habitual associations with a given environment; for example, you may snack on popcorn when you go to see a movie, even when you aren’t hungry and don’t have a particularly strong craving for the flavor of popcorn. These are examples of something called stimulus control, which is a phenomenon related to the fact that our actions and behaviors are impacted by a variety of situational and environmental cues and stimuli.

When armed with the knowledge that environmental cues and situational contexts can influence our behavior, we can proactively act upon this information to support our own success. If the tedious nature of tracking calories tends to derail your dieting efforts, it might be time to find a more efficient tool for tracking. If equipment issues or a crappy gym environment are impacting your enthusiasm for training, it might make sense to switch over to another gym, even if it’s a little farther away from home. If boredom and monotony are causing you to cut your runs short, loading some interesting podcasts to your phone or driving a little bit further to a more stimulating running environment might be warranted. If you’re losing valuable gym time in the morning because you have a tendency to mindlessly scroll through your phone before getting out of bed, perhaps it’s time to leave the phone in a different room and invest in an old fashioned alarm clock. If there’s a particular food that consistently leads you to over-consume calories, you might want to reconsider the decision to keep it regularly stocked in your pantry or refrigerator. If stress eating routinely leads to overconsumption, it could be helpful to ensure easy access to alternative options for stress relief, such as enjoyable video games, books, or puzzles. 

As you can see from this long list of examples, the general idea is quite simple: you want to modify your environment to reduce friction between you and your goal-compatible actions, increase friction between you and your goal-incompatible actions, and introduce tools (or facilitate access to strategies) that make your goal striving process easier.

Social Support and Feedback

Our environment does not only consist of the stimuli and resources we surround ourselves with, but also the people we surround ourselves with. Unsurprisingly, social support is linked to more successful behavior change outcomes across a wide variety of behaviors ranging from physical activity and nutrition choices to smoking cessation. Social support can take several different forms, so there are many different ways to utilize your social support system in a helpful manner. 

You might simply tell someone about your goal to facilitate social support, whether it’s someone with skin in the game (like a coach) or an outside observer within your social circle. This is a small step to take, but increases the stakes in terms of accountability. Alternatively, you might seek a friend to simultaneously pursue a goal that is similar to yours. You and your friend can hold each other accountable, troubleshoot challenges, and learn from each others’ successes along the way. In the age of the internet, you can also lean on support from online communities, which are extremely plentiful. There’s an online community to support just about any goal you could imagine, and there’s real value in pursuing a goal while having access to a large number of people who have previously been (or are currently) in your shoes, striving for the same goals and experiencing the same challenges.

People in your social support network can provide periodic feedback and reminders related to your goal, but as people increasingly utilize technological tools to support their goal striving, these technologies can provide feedback and reminders in the absence of human interaction. Of course, not all feedback or reminders are equivalent in nature – a systematic review by Fry and Neff concluded that these types of nudges were most effective when they provided personalized information (rather than generic educational information) and were delivered with high frequency (approximately weekly). The systematic review did not include any studies that implemented reminders or feedback more frequently than once per week; some research has suggested that higher frequencies are valuable, but other research has suggested that there may be drawbacks to excessively high frequencies. 

Researchers studying a weight management phone app found that when delivering an average of 1.44 messages per day, only 66.5% were viewed over the course of a 12-week study. In addition, there was a significant effect of time; each week, the probability of viewing a message dropped by 0.15, which means participants were ignoring messages more and more as the study progressed. So, if you’re using technology to facilitate your goal pursuit, you’ll want to utilize tools that provide personalized feedback with a relatively high frequency of updates, and send reminders frequently enough to keep you engaged, but infrequently enough to avoid annoying you.

How MacroFactor Supports Your Diet Goals (Scientifically)

If you’ve been keeping up with the Stronger By Science Cinematic Universe, it’s no secret that we recently launched a diet app called MacroFactor, and we’re pretty proud of it. The following section of this article will demonstrate how evidence-based strategies related to goal setting and behavior change can be put into action by detailing how MacroFactor implements and supports these strategies. It will provide some concrete, practical examples to reinforce the concepts discussed previously in this article, but it will vaguely resemble a sales pitch at the same time. If you don’t have the stomach for that type of content, there’s no shame in skipping ahead to the “Practical Application” section.

Approach-Oriented

MacroFactor does not implement any rigid restrictions on specific foods or macronutrients. Users are free to choose what type of macronutrient distribution they prefer, and free to select the foods that meet their preferences. The app makes no effort to categorize specific foods or macronutrients as good or bad, and the logging experience in MacroFactor is all about what you choose to include in your diet, not what you are required to exclude from your diet.

Process-Oriented

While some diet apps may function like a mysterious black box that spits out generic recommendations and asks if you happened to comply with them, MacroFactor works entirely differently. At its core, MacroFactor’s algorithm works by maintaining a running estimate of your total daily energy expenditure that is continuously updating based on your daily weight and nutrition data. Every piece of information you enter is allowing the app to refine its understanding of your personal metabolic phenotype. As such, users often report that they view data logging as “feeding the algorithm” – this promotes a process-oriented approach that emphasizes the importance of feeding accurate data to the algorithm, and provides a uniquely positive source of motivation to track regularly and accurately.

When you set a weight gain or weight loss goal in MacroFactor, you select a goal rate of weight change. The goal of the app is to continuously support this intended rate of weight change, which has a huge impact on the user experience. If you happen to overeat one day, MacroFactor will not overreact by drastically reducing your calorie target – instead, it will use the information you logged to steer you toward reestablishing your intended rate of weight change moving forward. In other words, the goal is to get you back on track, not to overcompensate at a time when dietary adherence was already challenging. Whereas a more outcome-focused approach could lead to large swings in energy intake and an unpleasant and counterproductive degree of volatility, the process-oriented approach of MacroFactor leads to much smoother (and less stressful) sailing. 

Flexible Restraint Reinforcement

Rigid dietary restraint leads to rigid rules related to food selection, meal number, and meal timing, and emphasizes hitting daily or weekly calorie and macronutrient targets with an impractical and unrealistic level of precision. With rigid restraint, any small deviation from these rules or expectations is classified as a categorical failure. You won’t find any of these characteristics in MacroFactor, because it heavily emphasizes flexible restraint. Food choice is flexible, meal number is flexible, meal timing is flexible, and MacroFactor is totally adherence-neutral.

Based on the way MacroFactor’s algorithm works, there is no need to set an arbitrary threshold for categorizing a day as “adherent” or “non-adherent.” There is no boundary at which success turns to failure – you simply aim for your nutrition targets, report your actual intakes, and the app handles the rest. As long as you continue to log your weight and nutrition data, MacroFactor will continue functioning optimally and making updates based on what you actually did, rather than what you were supposed to do. Within this framework, no rigid expectation for precise target adherence is set or reinforced, and the app continues to provide support when you need it the most. 

Within the app, you won’t see numbers turning red when you exceed a target, nor will you see any negative banners or alerts trying to reinforce rigid guidelines. The app provides guidance, support, and data, without guilt, shame, and negative reinforcement. This is why we often call MacroFactor a “diet sidekick” – it’s a tool designed to support you, not to reprimand you or to reinforce rigid, unhelpful, and unproductive forms of restraint.

Mastery Promotion

Nutrition goals involve a lot of mastery – as you move forward with the process, you develop skills related to food selection, meal building, and meal preparation. MacroFactor provides support throughout the journey, with a robust food database, efficient custom food features, and a convenient recipe builder that facilitates your process of building mastery in the kitchen.

Moving beyond the basics, MacroFactor offers an extensive set of features. While it’s easy to pick up the app and get rolling, there’s a lot of depth beneath the surface, which leads to a user experience that is progressive in nature. As users become increasingly familiar with MacroFactor’s flexibility, functionality, and comprehensive set of analytics, they build mastery related to the app itself. MacroFactor is a powerful and flexible tool, and users get better and better at leveraging this tool and maximizing its impact as they gain more experience with it. Focusing on mastery facilitates goal attainment while making the journey more enjoyable and rewarding, and MacroFactor is built to facilitate continuous skill development and mastery building along the way.

Slack With a Cost

MacroFactor can accommodate the “slack with a cost” strategy in a few separate ways. In coached mode, the app allows you to specify higher-calorie days to build some “slack” into your week of dieting. In collaborative mode or manual mode, you have even greater flexibility to allocate some extra calories to specific days of the week. Fortunately, even if you have an unplanned deviation from your daily targets, MacroFactor will continue functioning properly without any hiccups or interruptions. Since the algorithm functions in an adherence-neutral manner, just about any deviation from the plan can be viewed as slack with a cost; large and consistent departures from targets can impact the timeline to goal completion, but these deviations are not categorized or handled as failed days or failed weeks of dieting.

Habit Reinforcement

Our development team has put (and continues to put) a tremendous amount of time, thought, and effort into making MacroFactor’s food logger more efficient than any logger on the market. This includes robust food database coverage, barcode scanning functionality, voice-to-text logging capability, the ability to copy and paste foods, meals, and full days of eating, and much more. The logger also includes a Smart History feature that remembers your food choices at specific times of the day. So, when you go to log a meal, MacroFactor considers the current time of day to ensure that your most likely food selections are exceptionally easy to access, which reinforces prior habits related to food selection and meal planning.

Collectively, these functions make logging as quick, convenient, and efficient as possible, which reduces friction as you develop the habit of regular food tracking. In fact, logging a meal is so quick and easy that you can utilize “habit stacking,” such that logging your food becomes a routine part of the meal preparation or meal clean-up process. You can also utilize habit stacking by linking MacroFactor with your smart scale or wearable technologies; the data resulting from habits you’ve already developed with these technologies can be automatically imported and synced with MacroFactor, which makes self-monitoring even easier and more convenient.

On top of that, the MacroFactor dashboard has a “habits” tile that helps you stay on track of your consistency and streaks related to weight tracking and nutrition tracking. These analytics provide some positive reinforcement to promote the habit of regular self-monitoring, which is an important predictor of an individual’s likelihood of successfully achieving and maintaining long-term changes in body composition.

Social Support and Feedback

As mentioned previously in this article, frequent and personalized feedback is important for reinforcing enthusiasm and adherence during the process of behavior change. MacroFactor offers a rich set of analytics related to energy expenditure, weight trends, habit streaks, and more, which provide personalized feedback on a daily basis. Collectively, these analytics function to keep you motivated, enthusiastic, and fully informed about your progress toward your chosen goal. The occasional (but not too frequent) nudge can be helpful as well, which is one of the reasons that MacroFactor prompts a weekly check-in, which is followed by personalized feedback and program adjustments.

Of course, we can’t get all of our support from in-app analytics and prompts, so MacroFactor users are welcome and encouraged to join our supportive online community. MacroFactor has its own subreddit and Facebook group so users can ask questions, share success stories, crowdsource recommendations to overcome challenges, and generally support one another. The entire MacroFactor team also has a regular presence in these online communities, so they are great places to get recommendations about how to most effectively utilize the app, straight from the people who designed it.

If those avenues don’t provide a strong enough sense of community for you, you can even participate in the future development of MacroFactor. Our development team utilizes a public roadmap in which users can share their recommendations, requests, and feedback related to future updates and new features, which are continuously rolling out. So, whether you’re interested in sharing your successes, asking for help, or guiding the development of brand new features, we encourage all users to become active and supportive members of MacroFactor’s online community.

Practical Application

To wrap things up and connect some of these goal setting concepts using a practical example, let’s revisit the goal hierarchy presented by Höchli et al. As a reminder, here’s what it looked like:

Image credit: Höchli et al.

First, they identified “be healthy” as a superordinate goal. This is excellent, because it’s aspirational, reflective of values, and identity-based. With that type of goal, you aren’t just trying to adopt a new workout schedule; you’re trying to be the type of person who engages in positive behaviors that support their health and wellness. So far, so good.

Next, the hierarchy includes several intermediate goals. If they weren’t sure what to focus on, mental contrasting would’ve been an excellent strategy to help them identify specific barriers between their ideal future and their present reality. As a reminder, this group of intermediate goals provides a great example of equifinality – there are multiple roads that all lead to the destination of “being healthy.” In this case, the intermediate goals are “be in good physical shape,” “get enough sleep,” “avoid stress,” and “eat a healthy diet.” Not bad, but there is a little room for improvement here, in my humble opinion. “Get enough sleep” is a great example of an approach-oriented goal, but “avoid stress” is quite clearly avoidance-oriented. This could easily be converted to an approach-oriented goal if it were reframed as “practice more effective stress management techniques.” 

On the positive side, you could make the argument that this group of intermediate goals generally leans toward being more process-oriented (rather than outcome-oriented). “Eat a healthy diet” is definitely process-oriented, but the process-oriented nature of the other three could probably be reinforced by reframing the goals as “exercise regularly,” “maintain excellent sleep hygiene,” and “practice more effective stress management techniques.” Intermediate goals also give us a great opportunity to introduce some mastery goals into our hierarchy (such as “learn how to bench press” or “become more proficient at yoga”), although none are present in the hierarchy example provided by Höchli et al. 

Finally, the hierarchy presents a few examples of subordinate goals. While the chart doesn’t explicitly draw the connections, these subordinate goals provide examples of multifinality. Going to yoga class on Thursdays can directly impact the goal to “be in good physical shape,” but might also impact the goal to “avoid stress.” Due to the concepts of equifinality and multifinality, we might be better off viewing this as a goal web rather than a goal hierarchy, as the various elements at each level will ideally be interconnected in a fairly extensive manner. 

As we further explore these subordinate goals, we see that they provide very specific plans for making strides toward the higher-level goals. As a result, they provide excellent opportunities to utilize implementation intentions and other related strategies like habit stacking, temptation bundling, action planning, and coping planning. Some of the subordinate goals presented by Höchli et al did a great job of providing adequate specificity (“do 40 push-ups on Wednesday afternoon”), while others fell a bit short (“exercise once a week”). When writing out a very specific goal, it can be tempting to make the goal extremely ambitious, and to pursue it with a great deal of rigid restraint. However, a more advisable approach would be to set goals that are challenging but have a little bit of slack built in, and to practice flexible restraint throughout the goal striving process.

Finally, while pursuing this hierarchy of goals, an individual may support their own success by leaning on their network for social support, utilizing technologies or processes that provide periodic reminders and individualized feedback on progress, and manipulating their environment to reinforce goal-compatible cues and habits. These might not show up directly in the diagram of a goal hierarchy, but they are critically important nonetheless. 

This article began by taking a deep dive into a wide range of goal setting and behavior change concepts that might have seemed miscellaneous and unconnected. However, as we take a step back and focus on practical application, you can see how they seamlessly fit together in the goal setting process.

Conclusion

Successful goal attainment and behavior change are possible, but leaning exclusively on willpower and determination isn’t likely to get the job done. By following the simple evidence-based strategies presented in this article, you can set yourself up for a much higher likelihood of success in your next attempt to achieve a goal or change a behavior. So, if you happen to be pursuing a New Year’s Resolution, remember that success isn’t as rare as you might think, and remember to implement these evidence-based strategies: establish a solid goal hierarchy, favor approach goals over avoidance goals, utilize flexible restraint rather than rigid restraint, adopt a process-focused approach rather than an outcome-focused approach, favor mastery goals over performance goals, set ambitious goals with a little bit of slack, lean on helpful implementation intention strategies, and modify your environment to promote social support and enable periodic reminders and feedback while facilitating goal-compatible habit formation.

The post An Evidence-Based Approach to Goal Setting and Behavior Change appeared first on Stronger by Science.

- Cameron Gill
Reverse Nordic Curls: How to Perform and Progress this Bodyweight Exercise for Quad Strength and Hypertrophy

Throughout the past year and a half, having the ability to effectively train outside the gym has become more valuable than ever before in recent history. While COVID-related gym closures have now mostly ended throughout the globe, not all lifters have returned to training in gyms. Many lifters have opted to continue training at home where equipment is often limited but the monetary and time costs are lower than renewing gym memberships and regularly commuting. Survey data from over 11,000 participants who were previously attending gyms found that approximately 35% of Americans and 28% of people globally did not intend to return to training at gyms (24). For those who have returned (or intend to in the future), knowing how to train effectively with minimal equipment can still be immensely beneficial when traveling, and minimal equipment does not have to translate to minimal results. For the majority of people, most muscle groups can be successfully stimulated to strengthen and grow from training at home with a couple resistance bands, a pullup bar, and proper exercise selection. However, some muscle groups such as the quads may pose more of a challenge to effectively train at a home that lacks a barbell, power rack, and weight plates. Performing a typical quad workout consisting of some combination of barbell squats, leg presses, hack squats, and machine leg extensions may simply not be possible.     

If you have sufficiently heavy dumbbells, rear foot elevated split squats are a viable means of building the quads at home. However, many people lack dumbbells heavy enough to reasonably load this exercise if they are stronger than the novice level, and heavy dumbbells can be a pricey purchase. One way to expose the quads to a higher magnitude of tension without heavy weights is to perform a truly unilateral exercise such as a pistol squat where the entirety of loading is transmitted into one leg rather than having the rear leg provide assistance with a split squat or lunge. However, the vast majority of individuals lack the mobility required to properly execute a pistol squat. Skater squats are a viable alternative that do not necessitate the same degree of mobility as pistol squats do, and they may challenge the quads when performed with a pair of relatively light dumbbells. Unfortunately, balance rather than quad strength may be the limiting factor for some people when performing skater squats, providing a poor stimulus for hypertrophy. Additionally, complete quad development cannot be achieved from exclusively using compound exercises due to the anatomy of this muscle group. 

Dumbbell Skater Squats

Multi-joint exercises where knee extension is performed simultaneously with hip extension, such as squat and lunge variations, effectively train the vastus lateralis, vastus medialis, and vastus intermedius. These three monoarticular (i.e. crossing only one joint) muscles originate from the femur and exclusively function as knee extensors (25). In contrast, the other muscle of the quadriceps, the biarticular (i.e. crossing two joints) rectus femoris, originates from the pelvis and functions as both a knee extensor and hip flexor (25).

Notably, the rectus femoris does not just weakly assist in flexing the hip, but rather functions as a primary hip flexor that has greater leverage for producing hip flexion torque than any other muscle in the human body at certain joint angles (21). Because contraction of the rectus femoris can produce a substantial amount of hip flexion torque, its recruitment during a movement that requires both hip and knee extension is less biomechanically efficient than recruitment of the other muscle bellies of the quadriceps. As the rectus femoris contracts more forcefully during a movement like the squat, the hip extensors must work harder to resist the hip flexion torque it produces. Striving to maximize efficiency, the nervous system preferentially recruits the three monoarticular muscle bellies of the quads to a greater degree than the rectus femoris during compound exercises where extension simultaneously occurs at the hip and knee joints (4, 7, 8, 10, 12, 22). For this reason, squats, leg presses, and lunges can effectively increase the size of the vastus lateralis, vastus medialis, and vastus intermedius, but fail to induce any significant hypertrophy of the rectus femoris (6, 16). However, during a single joint knee extension exercise where no movement occurs at the hip joint, the rectus femoris is just as active as the monoarticular muscles of the quad, if not more so (11, 20, 23, 27). Consequently, machine seated leg extensions have been demonstrated to produce a considerable degree of hypertrophy of the rectus femoris that is similar to or even more pronounced than that of the three vastii muscles (9, 14, 18, 20, 26, 27).

While you may not have a leg extension machine at home, the reverse Nordic curl serves the same function without requiring any equipment. To perform the reverse Nordic curl, kneel on a padded surface such as a Pilates mat or foam pillow to provide cushioning for your knees. Begin the movement by tensing your abs and glutes to brace your core while your torso is in a vertical position. While keeping your hips extended and torso rigid, gradually lean backwards by flexing at the knees. Descend as far as you can control the movement and then return to the starting position by extending your knees.   

During this exercise, the training stimulus presented to the quads is provided by resistive torque acting on the knee joints. Torque is the product of a force (i.e. the product of mass multiplied by acceleration) multiplied by its moment arm length (i.e. the perpendicular distance between the line of force and the axis of rotation). During a reverse Nordic curl, the knee joint is the axis of rotation, and the resistive force is the product of gravitational acceleration multiplied by body mass located above the knee joint. The moment arm, also known as the lever arm, is the perpendicular distance between the knee joint and the body’s center of mass. When standing, the body’s center of mass is typically located near the navel. When kneeling during the reverse Nordic curl, the center of mass will lie higher up in the abdominal region. As the angle of knee flexion increases during the descent, your center of mass moves a greater perpendicular distance from your knee joint. Consequently, the magnitude of resistive torque increases, requiring your quads to produce greater knee extension torque in turn. 

The following two images depict the aforementioned biomechanics of the reverse Nordic curl. The red circle represents an approximation of the body’s center of mass. The green circle represents the knee joint’s axis of rotation. The yellow line represents the line of gravitational force directed downward from the center of mass. The purple line represents the moment arm length of the resistive torque acting on the knee joint.

reverse nordic curl reverse nordic curl

The increase in resistive torque throughout the eccentric phase results in the reverse Nordic curl having an ascending exercise strength curve. With this strength curve, the end range of motion (ROM), corresponding to the start of the concentric phase and peak angle of knee flexion, is the most challenging portion of the movement. 

reverse nordic curl reverse nordic curl reverse nordic curl

As a result, the exercise can be tailored to an individual’s strength by being performed with a partial ROM by less experienced lifters or with a full ROM where the upper back contacts the ground by more advanced lifters. Rather than increasing the load, intensity progression can occur by increasing the ROM until a full ROM is achieved. Alternatively, the angle of hip flexion may be manipulated to vary intensity. As the angle of hip flexion increases, the body’s center of mass will shift forward toward the knee joint, resulting in lower knee extension torque demands during the exercise. If an individual lacks the strength required to perform the reverse Nordic curl through a full ROM with an extended hip position, full ROM reps may still be performed by utilizing a more flexed hip position. Similar to progression via increasing ROM, reverse Nordic curl intensity may be increased by decreasing the angle of hip flexion maintained throughout the exercise until a neutral hip position is achieved. 

If you can perform the reverse Nordic curl with an extended hip position through a full ROM for a moderately high number of reps, you can increase the intensity by raising your arms above your head, which shifts your body’s center of mass further away from your knee joint. Alternatively, you can hold a weight plate or dumbbell to your chest, but this additional load will not be required for the vast majority of people who ever attempt to perform this exercise through a full ROM with arms held overhead. Mechanical drop sets can also be readily performed with this exercise. After fatigue accumulates during a set and you can no longer perform full ROM reps, you can perform reps with a progressively shorter ROM or more flexed hip position if you wish to train your quads until failure.             

Beyond its convenience, the reverse Nordic curl also provides some advantages over machine seated leg extensions. With the extended hip position involved with a reverse Nordic curl, the gluteus maximus is trained isometrically (since you are tensing your glutes to lock your pelvis in place) in a manner similar to holding the position of peak contraction during a glute bridge. Furthermore, during this exercise, the rectus femoris can be trained at longer muscle lengths than with a seated leg extension. Because the rectus femoris functions as a hip flexor, it is shortened at the hip joint in a seated position and therefore operates at short to moderate muscle lengths during a seated leg extension. During the end ROM of a seated leg extension’s concentric phase, the rectus femoris is shortened at both the hip and knee joints. 

When a biarticular muscle is simultaneously shortened at two joints, it may experience active insufficiency, a phenomenon which limits how much force can be produced by the shortened muscle. Force is generated within the functional units of muscle, known as sarcomeres, when the contractile proteins actin and myosin are able to bind together to form crossbridges (13). At very short sarcomere lengths, little overlap between actin and myosin is present, and relatively few crossbridges can be formed, resulting in low force production capacity (5). This decreased force production at short lengths can readily be observed if you make a firm fist and then fully flex your wrist while attempting to maintain the tight fist. Because of active insufficiency, you can flex your fingers more forcibly with a neutral wrist position compared to a flexed wrist position. In addition to functioning as wrist flexors, the flexor pollicis longus can flex the thumb, and the flexor digitorum superficialis can flex the other four fingers. When the wrist is in a neutral position, actin-myosin overlap within the constituent sarcomeres of these two muscles is high enough to form a large number of crossbridges during finger flexion, therefore facilitating high force production. When the wrist and fingers are simultaneously flexed, the sarcomeres of these muscles are shortened to the extent that relatively little actin-myosin overlap is present. In this state of active insufficiency, fewer crossbridges can be formed within the sarcomeres of the flexor pollicis longus and flexor digitorum superficialis, resulting in diminished force production capacity.

Because muscle fibers experiencing a high magnitude of tension is a primary determinant of hypertrophy, training a biarticular muscle as it is simultaneously shortened at two joints is suboptimal for inducing hypertrophy. In contrast to seated leg extensions, when knee extension is performed with an extended hip position during reverse Nordic curls, the rectus femoris can be loaded at long muscle lengths, where active insufficiency does not occur but stretch-mediated hypertrophy may be induced. This may result in a greater magnitude of rectus femoris growth than could otherwise be achieved from training at shorter muscle lengths with seated leg extensions (17,19). Indeed, research has found seated leg curls, which train the biarticular hamstrings at long muscle lengths, to yield greater hypertrophy than lying leg curls, which train the biarticular hamstrings at short muscle lengths (17). For a deeper dive into the details of this hamstring training study conducted by Maeo et al., check out the analysis (subscription required to access) written by Greg Nuckols in Volume 4, Issue 12 of the MASS Research Review

For individuals without experience performing reverse Nordic curls, the volume and intensity of this exercise can be gradually progressed as they build up more strength and proficiency with the exercise. Alonso-Fernandez et al. conducted an 8-week reverse Nordic curl intervention with 26 recreationally active subjects and found that performing 2-3 sets 2-3 times per week was sufficient to induce significant increases in rectus femoris thickness and cross-sectional area (1). More experienced individuals will likely require a greater stimulus for further hypertrophy, but initially employing a conservative progression may be prudent because both the quads and knee joint experience a high magnitude of loading during this exercise. In response to training, neuromuscular function may improve more rapidly than adaptations occur to connective tissues such as tendons (15). Correspondingly, a sudden spike in reverse Nordic curl volume and intensity may result in knee extensor strength increasing at a faster rate than the load tolerance of the knee joint’s connective tissue. An athlete’s risk of injury has been found to be elevated when his acute workload meaningfully exceeds the chronic workload to which his body has been accustomed, so a gradual progression of the training stimulus is warranted (2, 3). Additionally, not every exercise is right for every person even when a conservative progression is utilized. For instance, deficit deadlifts and full ROM weighted dips are quality exercises, but they may be inappropriate selections for some individuals due to previous injury or mobility restrictions. Just as lying tricep extensions may be suboptimal for some lifters with prior elbow joint issues, so too may reverse Nordic curls not be suitable for some people with prior knee injuries. 

In conclusion, whether your goals are to enhance the strength or aesthetics of your quads, reverse Nordic curls are a valuable exercise, especially when training at home.

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- Greg Nuckols
MacroFactor’s Algorithms and Core Philosophy

Of the three articles in this series, this is probably going to be the most important one. The first was basic background information, and the third will primarily address some of the most common questions people have about MacroFactor – good information for users and potential users to be aware of, but nothing critically important. This article, however, addresses the two most important aspects of MacroFactor: how the app actually works, and the overall philosophy that infuses the app.

The first part of this article will discuss the algorithm we use to adjust calorie and macronutrient targets, as transparently as reasonably possible. I think that once people understand how the algorithm works, prospective users will have an easier time intuitively understanding the utility of the app, and current users will have an easier time understanding the logic behind their calorie and macronutrient adjustments week-to-week

The second part of this article will discuss, in broad terms (and some specific terms) why MacroFactor is the way it is, and why it feels the way it feels. A lot of new users report that, for reasons they can’t quite put their finger on, other nutrition trackers stress them out, but MacroFactor doesn’t. That’s not an accident. It’s the result of decisions driven by our core philosophy, which more-or-less boils down to, “what if nutrition apps didn’t try to guilt people into losing weight?” With that in mind, the second part of the article will discuss some of the key design and functionality decisions flowing from this philosophy.

How MacroFactor Actually Works

The first article in this series covered the broad strokes of how our algorithms works, but this article will discuss the system that MacroFactor uses to set and adjust calorie and macronutrient targets in quite a bit more depth.

Our algorithms are purely deterministic, and rest on a simple premise: changes in body mass over time reflect changes in stored chemical energy in the body, which are directly linked to energy balance (the difference between energy consumed and energy expended). Thus, deterministic algorithms make the most sense for an app that seeks to make appropriate adjustments to calorie and macronutrient targets to help people gain or lose weight at their desired rate, or maintain weight over time despite fluctuations in energy expenditure.

When you first start using MacroFactor, you’re taken through a brief series of questions that aims to do two things: 1) get a rough estimate of your energy expenditure, and 2) get you set up with an initial set of calorie and macronutrient targets that suit your goals, needs, and preferences. I’m going to ignore the actual calorie and macronutrient targets for now, because estimating energy expenditure is really the primary thing the rest of the app revolves around.

This initial estimate of daily energy expenditure is based on two factors: we use the Cunningham equation to estimate your basal metabolic rate (the amount of energy your body would use in a day, merely to stay alive), and a series of custom activity multipliers to estimate your maintenance energy needs (the amount of energy you’d need, per day, to neither gain nor lose weight, given your activity level).

Our initial estimate is a rough estimate, but it’s the best starting point we can generate before we have more data. However, I’ll note that even though this initial estimate of daily energy expenditure is easily the least bit of information for our algorithms, we still put a lot of thought into it. Most other fitness products use the Harris-Benedict equation to estimate basal metabolic rate; the Cunningham equation “feels” too simplistic because its estimate of basal metabolic rate is only based on one variable (lean body mass) rather than the four variables used by Harris-Benedict (sex, age, height, and weight), but it performs about as well the Harris-Benedict equation in most populations, and may be particularly useful for people with greater than average lean mass.

We also developed our own set of custom activity factors, which can be seen below. The standard activity multipliers have a notable drawback: they don’t separate activity associated with day-to-day life (work, school, or any waking time spent not engaging in purposeful exercise) from exercise activity (purposeful, moderate-to-intense weight lifting, cardio, etc.). So, if you have a sedentary job, but you lift weight six days per week, which activity multiplier should you choose? Or how about someone with a reasonably active job or lifestyle, who doesn’t engage in any purposeful exercise? The standard activity multipliers don’t account for a lot of different lifestyles, whereas ours remove quite a bit of unnecessary confusion.

At this point, we’ve generated a starting energy expenditure estimate. It’s the best estimate we can generate with limited information, but it’s not a great estimate because these types of estimates are inherently imprecise – even when accounting for lean body mass, basal metabolic rate differs considerably between individuals, and the amount of energy expended when performing a given level of physical activity will also differ considerably between individuals. Our initial estimate of energy requirements will hit the nail squarely on the head for some people, but it will be “wrong,” to varying degrees, for most people. If it wasn’t, that would imply that accurately estimating the energy requirements of individuals at a given point in time was quite easy; if that were the case, there would be no reason for MacroFactor to exist. This initial estimate of energy expenditure will change. Don’t get too attached to it.

Once we’ve generated a rough starting point, you just need to log your weight (ideally daily) and food intake (as thoroughly and accurately as is reasonable for you). Your initial energy expenditure estimate will “stick around” for a few days while our algorithms get to know you. After a week of using the app, your energy expenditure estimate will start moving, and the color of the bar representing your daily energy expenditure estimate will turn darker and darker orange over time, indicating that we have greater and greater confidence in our estimate of your daily energy needs. After about 14-30 days (depending on how consistently you track your weight and nutrition, and how close your initial energy expenditure estimate was to your “true” energy expenditure), the rate at which your energy expenditure estimate changes day-to-day should slow down, and more-or-less plateau. That’s when our algorithms have figured you out. From there, everything should be smooth sailing. Your estimated energy expenditure will increase or decrease over time as your actual energy expenditure increases or decreases over time, and we should be able to make excellent calorie and macronutrient recommendations and adjustments to help you reach your goals.

This is the start of my personal expenditure graph. My actual energy expenditure was 700kcal/day higher than the initial estimate (based on lean mass and activity levels), but MacroFactor figured me out in about 20 days.

So, what’s going on here? Let’s dig into the nuts and bolts of the algorithm.

The first key variable is your weight trend. Body weight is a pretty “noisy” measurement in the short term – you may wake up a few pounds or kilos heavier or lighter than normal because you ate a lot of sodium yesterday, you had a hard exercise session and depleted some glycogen, you have more or less undigested food in your intestinal tract than normal, or for any number of other reasons. However, over the longer term, there’s a lot of signal in all of that noise: long-term changes in weight reflect changes in chemical energy stored in the body, in the form of fats and proteins. So, our first job is to isolate the signal, but remove the noise.

This is my personal weight trend graph, so you can see how our weight trending algorithm handles fluctuations over time.

 

This article in the MacroFactor knowledge base explains how our weight trending algorithm works conceptually, but in more concrete terms, we use an averaging approach that accounts for the influence of weight values over a pretty long time scale, while placing more weight on more recent values. This is similar to (but not identical to) the approach I’d proposed in a YouTube comment years ago, which initially caught Cory’s eye.

With this more sophisticated approach to weight trending, you “care” about data over a fairly long time period, but you “care” more about relatively recent data than relatively old data, because more recent data is more informative about the system you’re trying to describe. This is pretty easy to understand conceptually. Since scale weight is noisy day-to-day, we don’t want to trust it completely, but if I were to ask you, “how much do you weigh?”, the number on the scale this morning would certainly be more informative than the number on the scale a week ago, and the number on the scale a week ago would certainly be more informative than the number on the scale a month ago. However, older data isn’t completely uninformative. If you’re been approximately the same weight for a few weeks, but you randomly woke up five pounds heavier today, your relatively stable weight over the past few weeks probably tells you more about your “true” weight than the number on the scale this morning.

For measuring real changes in weight (and therefore stored chemical energy) over time, our approach to weight trending accomplishes the two things we want to accomplish: 1) it doesn’t overreact to short-term fluctuations in weight, but 2) it isn’t slow to pick up on real trends as they develop.

If you’re trying to maintain your weight, and you’re a bit heavier or lighter than average for a day (or two, or three, or possibly even four), we don’t care too much, especially if your weight is moving back toward its longer-term baseline, but if you’re still quite a bit heavier or lighter than normal (relatively speaking) by day four or five, especially if the downward or upward trend is accelerating, we want to be able to pick up on the trend and start adjusting. This same logic applies if you’re gaining or losing weight, except we’re interested in deviations from the longer-term trend, rather than the longer-term baseline.

In short, we use a pretty sophisticated approach to weight trending, which allows us to pick up the signal (true changes in weight over time), while filtering out the noise (short-term fluctuations in weight that don’t reflect real changes in chemical energy storage).

At this point, if you’re starting to feel like I’ve gotten lost or sidetracked, don’t worry. We’re getting very close to our destination: explaining how MacroFactor adjusts calorie and macronutrient targets week to week.

Remember, changes in weight reflect changes in stored chemical energy over time. Luckily for us, stored chemical energy can be described by the amount of heat that would be released if that energy was utilized. In other words: calories.

This means we can estimate the caloric content of weight gained or lost once we’ve identified your weight trend, and that tells us how large of a calorie deficit or surplus you’ve been in. If we know that information, and we know your energy intake over time (i.e. if you’ve been consistently and accurately logging your nutrition intake), we can estimate your daily energy expenditure with a high degree of accuracy. This estimate of your daily energy expenditure, based on your actual body weight and nutrition data, will be far more accurate and reliable than the energy expenditure estimate you started with (based on an equation that just considered your lean body mass and activity level).

From there, calorie and macronutrient recommendations and adjustments are a breeze. When you set up a new goal, we simply look at the rate at which you’d like to gain or lose weight, convert that rate of weight gain or loss to the size of the calorie surplus or deficit it represents, and add or subtract that figure from your daily energy expenditure. Once a goal is locked in, week-to-week adjustments move in concert with changes in your energy expenditure.

So, for example, if your activity levels increase, that increase will be reflected by the combination of your nutrition and weight trend data: either your weight will keep changing (or not changing) at the rate it was previously while your calorie intake increases, or your weight trend will bend downward (increasing at a slower rate, decreasing it was previously flat, or decreasing at a faster rate if it was already decreasing) while your calorie intake remains constant. When this happens, your calculated energy expenditure will increase, and your calorie recommendations will increase. The opposite is also true; if metabolic adaptation occurs while you’re trying to lose weight, that will also be reflected by the combination of your nutrition and weight data, which will decrease your calculated energy expenditure, in turn decreasing your calorie targets.

Once calorie recommendations have been set or adjusted, macronutrient recommendations and adjustments are also a cinch. They’re largely (or completely) based on preference. If you’re on a “collaborative” program, the algorithm will adjust your weekly calorie budget, and you’ll have all the freedom in the world to set and adjust your own macronutrient targets from there. If you’re using a “coached” plan – the default for new users – your protein targets will be based on the type of exercise you do (no serious exercise, just endurance training, just resistance training, or a combination of endurance and resistance training), and your preferences (whether you want to be near the bottom, middle, or upper end of the advisable range for the type of exercise you do). Carbohydrate and dietary fat targets are purely preference-based. When calorie adjustments occur, we respect your macronutrient preferences, to the greatest extent possible. If, for whatever reason, you have a very low calorie target, we prioritize maintaining fat intake at a safe level for normal metabolic and hormonal health, followed by maintaining protein intake to preserve lean mass, followed by carb intake. 

And…that’s about it! I held a few key details back (what, exactly, is the calorie content implied by a particular rate of weight gain or loss? How does it differ in different situations?), I didn’t discuss a few other small adjustments that make our systems more robust to a wider variety of scenarios than the “vanilla” version would be, and there’s also a very light dusting of magic, but that’s a pretty thorough explanation of how our algorithms and “coaching” system work. I’m trying to strike a balance of making sure you can have a really solid conceptual understanding of our system’s inner workings, without making it easy to completely reverse-engineer.

(I’ll also note that our algorithms are only going to improve over time; we already have the most sophisticated system out there, but as soon we have enough user data, the possibilities for fine-tuning are virtually limitless. Professors regularly consult with Eric for advice regarding statistical analysis, and Rebecca has a graduate-level education in data science and machine learning from MIT. As I mentioned in the first article, we have the best team for transforming your weight and nutrition data into actionable recommendations and advice.)

This may have been a more thorough explanation than you were expecting, but I think this level of detail is worthwhile, for several reasons.

First, we’re committed to being as transparent as is reasonable, as I mentioned in the first part of this series. That’s an easy thing for someone to bullshit about in the hopes that it will create positive brand associations, but if my willingness to divulge a large chunk of our “secret recipe” (with the rest of the team’s blessing, of course) doesn’t illustrate that we’re serious about our commitment, I don’t know what will.

Second, this explanation should illustrate how our “adherence neutral” coaching adjustments are possible. Some other apps with functionality that’s similar to MacroFactor require the users to have more-or-less perfect adherence to calorie and macro targets in order to receive weekly calorie and macro adjustments. Since that’s an established pattern within this niche, I’ve seen quite a bit of skepticism about whether “adherence neutral” calorie and macro adjustments are even possible; if you don’t actually need to require an extreme degree of adherence to make appropriate program adjustments, why would you? Is the MacroFactor team lying about the capabilities of their algorithms to get a competitive edge?

However, now that you understand how our MacroFactor works, the reason we don’t need to require perfect adherence should be pretty obvious: what we’re actually doing is solving for energy expenditure. If you can do a good job of solving for energy expenditure (and you can therefore track changes in energy expenditure over time), the final step of actually recommending and adjusting calorie and macro targets is simple. In our system for estimating energy expenditure, “adherence” is irrelevant. The algorithms don’t even need to know your goal (much less whether you’re sticking to it); you could use MacroFactor for the simple purpose of tracking your energy expenditure over time, with no weight-related goals at all. As long as you track your weight and nutrition, accurately and consistently, MacroFactor will have no trouble calculating your energy expenditure, so it will have no problem generating and adjusting calorie and macronutrient targets for any weight-related goal.

In this long-term expenditure graph, the ups and downs correspond with periods of different goals, different activity levels, etc., and the changing colors represent days with missing data. You can see that MacroFactor handles things pretty well without wild swings. For scale, this covers about eight months.

 

Third, I think it’s worthwhile for illustrating the sorts of scenarios where our algorithms struggle, versus situations where they may be expected to struggle, but actually perform really well.

Assuming you track your weight and nutrition data accurately and consistently, there are two scenarios where our algorithms really struggle. First, if you have a relatively large, persistent change in body weight that doesn’t reflect actual changes in energy storage, we’ll wind up providing reasonably inappropriate calorie and macro adjustments for a week (or possibly two weeks, if the weight change is large enough). For example, if you’re someone who gains a significant amount of weight when you start using creatine, that change in weight doesn’t reflect significant changes in energy storage; we’d (erroneously) believe your energy expenditure was dropping, and decrease our calorie recommendations. The same would apply if you switched from a very low-carb diet to a high-carb diet (and stayed on a high-carb diet), or if you went from consuming virtually no dietary fiber to a considerable amount of dietary fiber (and kept consuming a considerable amount of dietary fiber). Conversely, if you switched from a high-carb to a low-carb diet (and lost weight due to glycogen depletion), the opposite would occur – calorie recommendations may be a bit too high for a week or two.

Our algorithms also struggle in the face of a very large, somewhat protracted, anomalous increase in energy expenditure. For example, if you’re a recreational cyclist, and you embark on a once-per-year, weeklong cycling excursion where you’re burning a couple thousand additional calories per day, our algorithms won’t know that this true increase in energy expenditure is a momentary aberration, rather than the “new normal.” Once you get home and return to your normal schedule, it may take a couple of weeks for your estimated energy expenditure to return to normal.

I don’t mind admitting that our algorithms don’t produce absolutely perfect recommendations in every conceivable scenario, for several reasons. First, transparency. Second, I don’t want users to be surprised by (or unprepared for) one of these situations. Third, MacroFactor has a very simple solution for these rare scenarios: just don’t do your weekly check-in for a week (or two). Once the rare event has been “priced in,” your energy expenditure estimate will stabilize, and you can start checking in again.

Conversely, our algorithms will handle most situations splendidly.

Did you eat a huge meal with a ton of carbs and sodium, so you wake up several pounds/kilos heavier than normal? That’s totally fine, and I hope it was delicious! Your weight will trend back down, our algorithms won’t over-react, and you won’t get slapped with a huge reduction in recommended calories.

Are you up a couple pounds/kilos on the scale for a few days each month due to your period? No worries! Our algorithms will handle it beautifully. I knew that this situation would theoretically be handled well, but since approximately half of all people on this planet either menstruate, have menstruated, or will menstruate, we were on high alert during alpha and beta testing to make sure there was no negative feedback about how our algorithm made adjustments around peoples’ monthly cycles. Thus far, feedback has been absolutely flawless.

Were you unable to track your weight and nutrition for several days (or even several weeks)? That’s completely okay. We don’t expect you to track your macros when you’re sick, or take your scale (and ESPECIALLY not your food scale) on vacation. Just start tracking again when it makes sense, and we’ll carry forward your last “high confidence” energy expenditure value to get things rolling again.

Were you far more active than normal for a single day? That’s also totally fine; it takes a large, multi-day excursion from the norm for your energy expenditure estimate to start trending up due to large increases in activity. The algorithm will handle this just fine.

Want to just take a break from your diet for a day? Or a week? Or two weeks? There have been a lot of questions about how to take a “diet break” using MacroFactor. You could absolutely change your goal from “lose” to “maintain” for the duration of your diet break, or you could simply stick with your current weight loss goal in the app, but use your estimated energy expenditure as your new calorie target, and basically ignore your macronutrient targets for a while.

Furthermore, there’s one scenario where the algorithms theoretically struggle, but where I think they actually behave quite well, for practical purposes. Remember, the algorithms do struggle in situations where notable changes in weight don’t necessarily reflect changes in energy storage. However, there’s one such situation where I actually like the way the algorithms behave: when you start a new weight loss or weight gain phase. When you transition into a caloric deficit from a prior state of maintenance or a caloric surplus, your weight will generally drop quite a bit, pretty quickly. This initial change primarily reflects a bit of glycogen depletion, combined with a decrease in intestinal contents. The opposite occurs when you transition into a caloric surplus. When this happens, the algorithms will initially think you’re losing or gaining weight a bit faster than desired, and therefore increase or decrease (respectively) your calorie targets a bit. Over the next week or two, your calorie targets will ease themselves to the appropriate levels of your desired rate of weight gain or loss. In effect, this behavior grants you a week or two to “ease into” your weight loss or weight gain phase. It’s a behavior that’s theoretically slightly “incorrect,” but I think that practically, it grants people a bit of time to adapt their lifestyle to their new goal, rather than being tossed in the deep end all at once.

Finally, I think that explaining the algorithms serves the purpose of illustrating why certain things are possible with our app, and for explaining certain behaviors of the coaching algorithm that may initially appear to be counterintuitive.

Because our energy expenditure calculation is at the heart of MacroFactor, and since the energy expenditure calculation is based solely on weight and nutrition data, the algorithm can “get to know you” from day 1 if you have prior, high quality data you can fill in. If you don’t, no worries! Everything will be fine-tuned in 14-30 days. But if you have 14-30 days of prior data, you can enter it in on the “Habits” tab to get everything humming along nicely, right out of the gate. As a note: if you back-fill old weight and nutrition data, create a new macro program so that your calorie and macronutrient targets will reflect your updated energy expenditure value, rather than our initial, imprecise estimate.

Because our algorithms only require weight and nutrition data, that explains why MacroFactor can provide “coaching” to people who still prefer using other food loggers. As long as you feed your data to the algorithm (yum yum), we can provide the same calorie and macro adjustments to you as we provide to people who use the native MacroFactor food logger.

Finally, the nature of our algorithms helps explain calorie and macro updates that some users find to be counterintuitive. For example, in our Facebook group, users will occasionally make posts along the lines of, “I went over my calorie targets last week, but in my last check-in, the app increased my calories this week, instead of decreasing them. What’s going on?” Those are completely understandable questions, because that’s how some apps (and a lot of nutrition coaches) operate. The thinking more-or-less goes: “if I was over my calorie target last week, I’m going to be forced to ‘make up’ for it next week.” However, that’s not how our system operates at all!

At the start of each week, we give you targets that will help you gain or lose weight at your desired rate, based on everything we know about you at the time we make our recommendations. Over the next week, we gather more information. At your next check-in, we make adjustments based on updated information, with the goal of helping you gain or lose weight, at your desired rate, over the following week. Each week is, therefore, its own self-contained unit. The goal of the algorithm is to help you meet your goal for that week, and that week alone.

So, in the example above, we’re not going to force people to “make up for” deviating from their calorie and macro targets for the week! We’re just going to use what you actually ate, and how your weight actually changed to make recommendations for the next week. For example, if we thought you needed to eat 2500kcal/day to lose weight at your desired rate, but you consistently ate 2600kcal/day, and you wound up losing weight at your desired rate, why would we bump your calorie target down to 2400kcal/day to “make up for” eating 100kcal/day more than your previous targets? We just learned that 2600kcal/day is a more appropriate target than 2500kcal/day was, so of course we’re going to bump your calorie targets up! The same logic applies across the board – we’re constantly updating and refining our estimate of your energy expenditure, and using that information to adjust your targets for the next week. Ultimately, it’s all just information. Our recommendations are informative, not punitive.

That’s actually an excellent segue into…

Our core philosophy

Our philosophy is simple: we want to help you reach your goals, while taking as much stress as possible out of food logging. In a lot of insidious little ways, other food loggers seem to be designed to cause stress and encourage neurotic behaviors. We’ve designed MacroFactor, top-to-bottom, to break that mold.

The most obvious way we try to take the stress out of logging is via the way our core feature functions: adherence-neutral calorie and macro adjustments. You’re completely in control of every step of the process. You set a goal, and we tell you how to get there, but it’s totally fine if you don’t follow our advice. We’ll make appropriate adjustments to your calorie and macro targets based on what you log, regardless of how close you came to hitting your targets from the previous week.

Our algorithms don’t function any worse if you deviate from your targets. Lord knows I don’t hit my numbers squarely on the head every day (or sometimes even for weeks at a time)! There are plenty of reasons you may want to deviate from your calorie and macro targets – a big social event, a coworker bringing doughnuts into the office, a new restaurant opening in town, or simply not feeling like thinking too hard about your nutrition for a day – and our algorithms will take them all in stride, continuing to provide appropriate recommendations and adjustments for you and your goals.

You don’t have to eat like a robot and perfectly adhere to your daily or weekly targets for us to make appropriate adjustments to your weekly calorie and macro targets over time. We believe that you shouldn’t have to work for your nutrition app; we believe your nutrition app should work for you. It’s our job to tell you how to reach your goals, but everything else is in your hands.

However, our philosophy of reducing stress extends well beyond our adherence-neutral calorie and macro adjustments.

For starters, independent of the “adherence-neutral” framework we use for calorie and macro adjustments, simply having targets that are clearly derived from your actual data really puts many people’s minds at ease. One of the things that’s stressed me out when trying to lose weight previously was the feeling that I was never quite sure if my personal calorie targets were correct. Were they unnecessarily low, which could make me less sharp, more lethargic, and more likely to lose muscle while dieting? Were they too high, meaning I’d take longer to reach my goal weight than intended? When would I need to update my targets, based on reductions in energy expenditure that naturally occur when dieting?

I personally know enough about nutrition that I had the capacity to answer those questions, and I should have felt confident in my answers … but I just didn’t. Dieting can fuck with your head. And for some people, it goes the opposite direction – “hardgainers” are often unsure if they’re actually eating enough to gain weight at the rate they want (weight loss just happens to be the thing I’ve personally struggled with). With that in mind, when you have calorie and macro targets that make sense, and are clearly being informed and updated based on your actual nutrition and weight data, that can remove virtually all of the guesswork (and therefore a lot of the stress) from a diet.

For another example of how we aim to take some of the stress out of food logging, when you’re setting your first goal in MacroFactor, you’ll notice that we don’t encourage you to select any particular goal; you can aim to gain weight, lose weight, or maintain your current weight, and nothing about our user interface tries to hassle you into choosing any particular goal – whatever you want to do is fine with us!

Our weight trending feature was also designed to reduce stress. As someone who experiences larger-than-average day-to-day weight swings, this has been huge for my psyche. If your weight is trending the way you want it to, but the scale gives you “bad” news one day (you’re trying to gain weight and your weight is below normal, or you’re trying to lose weight and your weight is above normal), it’s not hard to start catastrophizing. However, the nice, smooth, gradual procession of the weight trending line puts my mind at ease. It’s not uncommon for my weight on the scale to be heavier than yesterday, but my weight trend to still tick down (and, again, I’m personally losing weight at the moment, but the same principle applies in reverse if you’re trying to gain weight) due to how the weight trending feature works. The weight trend is also very resilient in the face of large weight swings that are short-lived. As you log your weight in MacroFactor, you realize more and more that your trended weight reported in the app is more indicative of reality than the day-to-day vicisitudes of the scale. This has helped me out tremendously.

One of the first things users notice about MacroFactor is the way we handle things when you go over your calorie or macro targets for a day. And by that, I mean we simply don’t do anything. We don’t throw a warning up on the screen to shame you for exceeding your calorie target or your carbohydrate target, or anything of that nature. We don’t turn any numbers red (and we all know that on the user interface of a nutrition app, red means “bad”) when you exceed a particular target. There are a few main reasons for that.

The numbers on the screen change, but that’s it: just neutral information with no implied judgments attached.

First, it’s just a stupid design decision. A user presumably has a particular calorie goal (I’m just going to use calories for this example to keep things simple) because that calorie goal is in line with some weight goal – it will help them maintain weight, gain weight at the desired rate, or lose weight at the desired rate. With that in mind, there’s a similar “cost” associated with any deviation from your target. Being under your calorie target has undesired effects (losing weight faster than desired, losing weight when the goal is to maintain weight, or gaining weight slower than desired), and being over your calorie target has undesired effects (losing weight slower than desired, gaining weight when the goal is to maintain weight, or gaining weight faster than desired). If you use pop-ups and changes to the user interface (turning numbers red) that indicate a user has done something bad when they exceed their targets, but you don’t do anything when a user is below their targets, you’re giving your true intentions away – you’re not trying to help people reach their goals on their terms. You’re just trying to cajole everyone into losing weight. And if the user already wants to lose weight, you’re trying to cajole them into losing weight faster than they said they want to. If the purpose of a nutrition app is truly to help the user reach their goals, there are only two defensible options: shame people for any deviations from their targets, or don’t shame people at all. We think the latter is the superior option, because a) shame doesn’t work, b) expecting people to hit a particular target on the head every day is wildly unrealistic and completely unnecessary and c) we’re not assholes.

Second, we don’t use pop-ups or UI changes to tell people they’re doing something “bad,” because we believe that what you want to do is your own damn business, and it’s not our job to judge you for it. Again, this is most relevant when people are trying to lose weight. When you put yourself in the shoes of someone who’s trying, and struggling, to lose weight (i.e. when I put myself in my own shoes), it becomes pretty obvious that design decisions that shame people for exceeding their calorie and/or macro targets are incredibly unhelpful. When you’re trying really hard to stick to a diet, you probably already feel a little bad when you exceed your calorie target for the day, simply because our society attaches so much stigma to weight and difficulties losing weight; speaking for myself, I certainly don’t need an app to tell me to feel even worse. I’m already suitably disappointed in myself, thank you very much. Furthermore, when you purposefully deviate from your diet – you’re celebrating a special occasion, going out with friends, or simply not in the mood to worry about it for a day – the last thing you need is an app telling you, “sorry. No fun for you. You wanted to relax and have a good time, but guess what? You fucked up, buddy.” For what it’s worth, I’ve never had that bad of a relationship with food or that bad of a relationship with my body – the notifications and red numbers were generally little more than an annoyance – but some of our users have told us that those UI decisions in other apps have really messed with their heads.

We don’t expect people to eat like robots, and we don’t shame people for the choices they make. One user called this approach, “depressingly revolutionary.” We’ll take it.

This philosophy of aiming to remove unnecessary stress influences virtually every decision we make. We aim to give you all of the information and guidance you need to reach your goals, without trying to shame you or force your hand.

Finally, you may be wondering why this article covers both our algorithm and our philosophy. At first, they may seem like strange bedfellows or, at minimum, strange topics to discuss in the same article. After all, the algorithm is the most data-driven, technical part of the app, which seems very divorced from our feel-good “don’t be mean to people” philosophy.

However, our philosophy, and the decisions flowing from our philosophy, are crucial for ensuring the algorithm works as well as possible, for as many users as possible.

One thing that a lot of our users report is that they’re simply doing a better job of logging their nutrition since they started using MacroFactor, and I think there’s a reason for that. The most common systematic food logging error people make is under-counting – they don’t log all of their foods, they underestimate portion sizes, etc. I think there are several reasons for that, but a major factor, for people coming from a calorie-counting background, is that they’re used to being met with negative feedback when they eat more than they’re “supposed” to eat. I assume that the intention of such feedback is to convince people to eat less, and maybe it accomplishes that goal to some degree, for some people (though, to be clear, that’s not always the best goal). However, it also encourages logging less – logging with less consistency, logging with less accuracy, and simply logging fewer calories, to avoid being met by feedback that says, in so many words, “you fucked up.”

When people start using MacroFactor, and they find there’s no penalty for exceeding calorie targets, and there’s no design elements or pop-ups or warnings telling them they did something wrong, that removes a fairly significant disincentive against accurate food logging. Furthermore, when you log everything, and log accurately (which means logging more calories for most people, most of the time), our algorithm responds appropriately by increasing calorie targets. Especially when you’re dieting, being told to eat more is generally preferable to being told to eat less, so beyond removing a disincentive, our system passively (but pretty strongly) incentivizes accurate food logging.

That’s where our algorithm and our philosophy meet. If we were jerks, our algorithms would be analyzing less accurate data from more users, and would therefore be making worse recommendations. A kinder approach to food logging doesn’t just make for happier users because their interactions with the app are more pleasant. A kinder approach also makes for happier users because it tangibly improves the recommendations we provide, and therefore makes it more likely that more people will reach their goals (and we’re glad to see that other people agree).

The post MacroFactor’s Algorithms and Core Philosophy appeared first on Stronger by Science.

- Greg Nuckols
The History and Team Behind MacroFactor

As I write this, MacroFactor (our food logger and nutrition coach app) has been out for a little more than a week. This is the first article in a three-part series, which I’m writing for a few reasons. First, I believe in being as transparent as possible. If we’re going to ask you to open up your wallet, I think it’s important that there’s enough public information about it for you to make an informed purchasing decision. There’s plenty of information about the app on the sales page, obviously, but my primary aim for this article is to inform you (not to sell you; however, that will be hard, because I love this app). Second, I’d like to explain why some things about MacroFactor are the way they are; some of the decisions we made may initially seem a bit confusing if you don’t know the reasoning and considerations that went into those decisions. Third, I’d like to briefly discuss how we aim to improve the app over time. Fourth, I (selfishly) want to have a repository of thorough answers to many of the questions we’re asked the most frequently, so I can copy and paste those answers moving forward.

The Genesis. Or, the story of a spreadsheet.

The story of MacroFactor, like all great stories, starts with a spreadsheet.

On March 25th, 2015, I released a spreadsheet called the “Self-Correcting Macro Plan” as a freebie for people who preordered a pair of books we published. In June of 2015, I added it to a little bundle called the “Training Toolkit,” where it lived for several years. The idea behind the spreadsheet was simple: log your daily calorie intake, log your weight each day, and the spreadsheet would calculate how large of an energy deficit or energy surplus you were in. From there, you could calculate your daily energy expenditure by subtracting your energy deficit or surplus from your average calorie intake. Once you knew your daily energy expenditure, you could calculate your calorie targets for the next week to gain or lose weight at the desired rate.

To illustrate, let’s say your average weight last week was 180lb, your average weight this week was 179.2lb, you consumed an average of 2500kcal per day over the past week, and you wanted to lose a pound per week.

To start with, you just calculate the rate of weight loss: 0.8lb per week. From there, I used the standard rule of thumb that a pound of weight loss required a 3500kcal deficit for the week to calculate the energy deficit represented by 0.8lb of weight loss: 3500 * 0.8 = 2800kcal for the week, or 400kcal per day. Since you consumed 2500kcal per day, and you were in a 400kcal deficit, that means you were burning 2900kcal per day. Finally, you want to lose a pound per week (which means inducing a weekly deficit of 3500kcal, or a daily deficit of 500kcal), so you could calculate your calorie target for the next week: 2900 – 500 = 2400kcal per day.

It was a nifty little product, and I’m still quite proud of it. Back then, I was even dumber than I am now (if you can even believe that), and this little spreadsheet demonstrated the limits of both my knowledge and, more embarrassingly, my MS Excel skills.

Unfortunately, the product wasn’t very good. I’d say it “worked” about 75% of the time, but a 25% failure rate is pretty bad. The problem was that the spreadsheet freaked out if you experienced any large weight swings. If you stuck to your diet pretty well, and you’d been bulking or cutting for at least two weeks, the sheet was surprisingly effective at manipulating calorie targets to keep you on track. But when you changed your goal, and experienced a large shift in water weight? Whoo Nellie, hold onto your hat, because my little spreadsheet was about to take you on a rollercoaster ride. If you were aiming for one pound of weight loss, but you shed four pounds of water weight, the spreadsheet would bump your calorie target up by 1500kcal per day. That’s … a grossly inappropriate adjustment.

So, I just started telling people “if you switch from a bulk to a cut (or vice versa), stick with the initial calorie targets for three weeks before making any adjustments.” With that advice in place, my little spreadsheet did a pretty admirable job. It would still occasionally make adjustments that were larger than may be advisable (maybe resulting in swings in daily calorie targets of 200-300 calories or so), but it did a fairly good job most of the time. However, since smaller swings in body weight do randomly occur, even if you stick to your diet like a robot, it would still make bad recommendations with some regularity. It was also basically unusable for women who experience notable swings in body weight associated with their menstrual cycle.

I felt like I was on the right track overall, and I had an idea to fix the issues that remained: I needed a way to smooth out the weight values, without smoothing them out so much that the sheet would be slow to pick up on trends as they were developing. I mentioned this in a YouTube comment in a video about the spreadsheet. That comment would turn out to be very fortuitous.

However, right around the time the lightbulb was starting to come on, I was finally starting to grow up and learn the meaning of “stay in your lane.” I was not a “nutrition guy,” nor did I have any desire to be. I let the spreadsheet go dormant, dropped it from the bundle when we released new training programs, and stopped writing about nutrition almost entirely.

One of the reasons we hired Eric Trexler (and subsequently made him a partner at Stronger By Science) was so he could manage the nutrition side of the business. When I was trying to convince him that being a blogger is a lot cooler than being a tenure-track professor and explaining the things we’d want him to work on, I showed him my dusty old spreadsheet. I floated the idea that it could potentially help with the coaching program (making macro adjustments a bit easier for our coaches who do nutrition coaching), could potentially function as part of a new lead magnet (since the 28 free training programs have been our email hook for AGES now), or could potentially be a new product to sell, if he put some work in to improve their functionality. He seemed interested, but other plans came up – Eric took over managing the coaching program, took on a ton of coaching clients, and joined the MASS research review – so the spreadsheet once again fell by the wayside.

Then one day, out of the blue, I woke up to a very long message on Reddit about my old, tattered, neglected spreadsheet.

I was intrigued. I missed that old spreadsheet, and I knew it could be something great with a little love and a lot of polish. I was even more intrigued, since these developers were offering a partnership rather than a work-for-hire system (that’s how I prefer to do things whenever it makes sense; everyone has a real stake in the product, and everyone’s interests are aligned), and since they lived nearby (if I’m going to work with someone long-term, I want to be able to meet them and shake their hand). It also sounded like their areas of expertise were perfect for this sort of project.

After that initial contact, we chatted, they showed me the rough v0.0.0.0.0.0.1 version of the app, and I immediately knew this app had the potential to be something special. The first build was WAY pre-food logger. It basically answered the question, “hey Greg, how well would that old spreadsheet manipulate calorie targets if we ironed out all of the kinks and weird behaviors?” The answer: it worked like a charm.

We formed a partnership, and it’s been a mad dash rush to get a great product to market. Our alpha and beta testing periods were especially productive. We love all of our early testers. They provided amazing feedback to help us make sure the launch-day feature set would make most people happy. That’s more-or-less the story of MacroFactor, from inception to the present day. 

My biggest lesson from all of this: commenting on YouTube videos is a great use of time. You never know which YouTube comment will drop a pair of outrageously talented app developers into your lap. The more you comment on YouTube videos, the more chances you have to get lucky. It’s like the lottery: if you buy enough tickets, you’re guaranteed to win eventually.

In all seriousness, I think it’s worth recounting the story of MacroFactor to head off a question we’ve already been asked and an … insinuation … I’ve already seen percolating: MacroFactor was not (to use some of the exact terms being thrown around) “heavily inspired by” other apps on the market with similar functionality, and we’re certainly not “imitating” them; I’m sure you can understand what’s being implied. My humble little spreadsheet, on which the core functionality of MacroFactor is based, came to market before the three apps we view as our most direct competitors: Avatar Nutrition, Carbon Diet Coach, and RP Diet Coach.

To be clear, I’m absolutely not trying to flip the implication. I’m certainly not implying that any of them copied me (and I’m sure I wasn’t even the first person to have the idea upon which the spreadsheet was based). My understanding is that the functionality and logic of all four apps in this space are distinctly different. However, since we’re the new kids on the block, devoted users of other apps have made some less-than-charitable assumptions about the inspiration behind MacroFactor, so I want to make it clear: The app itself is the new kid on the block, but the machinery at the heart of the MacroFactor? It’s the oldest game in town.

However, at the end of the day, we believe that the entire category of “diet coaching apps” is under-discovered at the moment. I think a lot of people who currently use MyFitnessPal or LoseIt or FatSecret want to be using an app like MacroFactor, or like RP, or like Carbon, or like Avatar; they just don’t know we exist yet. We think that having more apps in the space, along with some friendly competition, will bring more awareness to this space, serving as a rising tide that raises all boats.

The team

I truly think the MacroFactor team has the perfect dynamic for the project of making the best nutrition app imaginable.

First, I’ve already alluded to this, but the structure of the team is crucially important. The MacroFactor team – Cory Davis, Rebecca Kekelishvili, Eric Trexler, Lyndsey Nuckols, and myself – is a coequal partnership. No one is the boss, pulling the strings. When major decisions need to be made, we make them collaboratively, which means every bad idea would need to go through four layers of vetting (i.e. make it past four other intelligent people) before it makes it into the app. As a result, it’s very hard for bad ideas to make it into the app. Furthermore, no one is just punching the clock and collecting a paycheck. We all have the same incentive structure: if we create a great product and continue to improve it quickly and efficiently, we all benefit equally.

That last part is crucial. I have to imagine that most app developers are good at what they do, but I couldn’t possibly recount all of the horror stories I’ve heard from people who’ve hired outside companies to build, maintain, and improve apps for them. Sometimes the project is delayed, sometimes it goes over budget, sometimes the developers simply don’t deliver the features that were promised, etc. Then, once the app is out, bug fixes can take longer (or bring an additional cost), any significant new feature requires a major outlay of cash (so improving the app is at least somewhat disincentivized), and if you decide you’re fed up with one developer and hire someone new to clean up the mess … good luck. The people who wrote the initial code are the only people who truly understand it on a deep level, inside and out, so small improvements can turn into big projects, and introduce a ton of unexpected bugs.

Again, I’m sure that the process of getting an app built by an outside company has gone well for someone, somewhere, but the benefits of partnering with developers are enormous. I think most people just don’t want to give up a slice of ownership to their devs, but that comes at a pretty steep cost. And, to be clear, Rebecca and Cory aren’t “just” the devs – this project is a completely shared, collaborative vision.

Second, we all have complementary skill sets.

Cory and Rebecca are both astoundingly talented developers. In the early going, though, Cory has brought a unique set of perspectives and insights to the table: I’ve never met a person who’s more obsessed with tracking nutrition data (all data, really. He joked that he may add a poop tracker to MacroFactor eventually, but I’m not completely sure he was joking), and I’ve never met someone more obsessed with reducing clicks and input friction. We’re really proud of the app’s food logger. The basic user experience of food logging has been more-or-less the same across virtually all apps, for at least the past decade. Cory thought up so many little innovations to save a tap here and a click there, that we eventually landed on a food logging experience that’s fundamentally different from other apps, and it just feels so nice to use once you get the hang of it. For almost every action you’d want to take in the food logger, your muscle memory from other apps will guide you toward a normal (i.e. slow) way to do things, but we typically also have a faster method of accomplishing the same purpose. Everyone brought some ideas to the table, but the food logger really is Cory’s baby.

Rebecca is the reason that MacroFactor feels incredible to use. That may sound like small potatoes after I just gushed over Cory’s efforts on the food logger, but it’s absolutely huge. MacroFactor is only about a week old, so it still has some kinks and bugs to work out, but the overall experience feels like that of an app that’s been on the market for half a decade. Part of that comes down to design, and a lot of it just comes down to interactions with the app feeling natural: when you tap a field for data entry, does the app consistently recognize that you’re tapping the field, rather than the surrounding area? When you look at a screen with a lot of text on it, does the weighting of the text versus the white space just feel nice? Do all of these things scale appropriately across devices of all sizes, leading to a great and consistent experience for everyone? Those are the sorts of things that you probably won’t think about when they’re done well; when they’re done poorly, that leads to frustrating interactions with the app. At this point in its life, MacroFactor feels far more mature than it has any right to.

More than anything else, Rebecca and Cory are the reason I’m sure MacroFactor will be successful. They’re absolutely incredible.

Eric is the nutrition and body composition expert for the app. When we started working on MacroFactor, Cory fixed the most fucked up thing about my nutrition spreadsheet on day 1, and Eric cleaned up all of the additional things I’d messed up, including (but not limited to): doing a better job of accounting for the difference in metabolizable energy in fat versus lean mass, doing a better job of accounting for different estimations of fat versus lean mass gain or loss based on the rate at which you’re gaining or losing weight, and generating sets of macro targets that account for a wide array of preferences, goals, and (especially) edge cases where “standard” advice would produce recommendations that would likely run counter to one’s goals and needs. His impact on the core algorithms and the calorie/macro recommendations we generate is immeasurable. I’d put Eric’s combination of lab and practical experience up against anyone.

Lyndsey keeps the lights on, and keeps everyone on track and organized. She’s an administrative and marketing genius. Her impact isn’t felt as directly in the app itself, but this whole operation simply would not function without her. I mentioned before that we’re all co-equal, but if anyone really calls the shots at the end of the day, it’s Lyndsey. There are a million little tasks that go into running a business, and she manages all of them, while keeping us all focused on the most critical tasks, and while actually selling the product.

And me? Sometimes I feel like I’m just coasting on one good-but-poorly-executed idea that I had six years ago. Realistically, my day-to-day work just relates to being the group’s resident extrovert. I keep lines of communication flowing freely between the devs and the “Stronger by Science” wing of the business, and I keep the vibes good in our subreddit and Facebook group (I’m really proud of the fledgling communities we’re building. They’re outspoken, but also helpful and supportive). I like to think of myself as the glue guy – I have a good enough grasp of the business and marketing operation to help Lyndsey with business strategy and messaging, a good enough grasp of the nutrition side of things to help Trex out, and a good enough grasp of our technical capabilities and roadmap to assist Cory and Rebecca (on big picture things, at least; if I had to write a line of code, the app would disintegrate). Everyone needs a #2, and I like to think of myself as everyone’s #2. Or, it’s possible that I’m full of shit, and I really am just coasting on one good-but-poorly-executed idea that I had six years ago. Regardless, if you’re aware of my work, you know I’m a reasonably competent person; if there’s a team for a fitness-related project in which I’m the least useful member, it’s a damn good team.

Specific to the core functionality of the app – generating good nutrition recommendations and making appropriate updates – I think our team is particularly well-positioned, for one simple reason: we’re bigger data nerds than any of our competitors. The first hurdle is getting good data from users. I don’t trust anyone more than Cory to develop features that make good logging fast and simple, or bring in new integrations to pull even more data into the app. And then, once we have data, who are you going to trust to analyze it and generate useful recommendations, predictions, and insights? I’d feel pretty comfortable going toe to toe with the biggest data nerd working for any of our direct competitors, and I’m the dumb guy (relatively speaking) on our team. Eric is a level above me, and Rebecca is several levels above him. At the end of the day, making a good “diet coach in an app” requires an adequate understanding of nutrition and metabolism, but it also requires a lot of math. The algorithms we use to generate and adjust nutrition recommendations are already head-and-shoulders better than our competitors (in my humble opinion), but they’re also as bad as they’re ever going to be. As we get rolling and start collecting more data, we have the expertise to meaningfully improve our algorithms on both an individual and group level, and generate useful insights over time that no one else will be able to touch.

At this point I’m getting ahead of myself. To wrap up this section: we have a great team.

What IS MacroFactor?

MacroFactor’s core functionality is its ability to take your goals, preferences, nutrition intake, and weight data, and transform that data into dynamically updating calorie and macronutrient recommendations. Most importantly, we can generate solid recommendations and adjustments even if you don’t perfectly adhere to the calorie and macronutrient targets the app recommends – everything you log is just data, and all (accurate) data is useful. If you log your weight and nutrition intake diligently, and get within the same general ballpark of our recommendations, you’ll get where you want to go.

However, that’s not everything MacroFactor does. We have a great food logger (the best, in my opinion). We let you track your period. We show you sophisticated weight trending data. We have convenient integrations, if you want to pull weight or nutrition data from other apps. We have a full-fledged knowledge base that explains how to use all of the app’s features, and explains how we generate our recommendations and adjustments. We have habit-tracking functionality. We allow you to track micronutrient intake.

We have a lot of functionality, revolving around one core feature: you log your weight, you log your nutrition, you tell us your goal, and we show you how to get there.

I’ll discuss how we adjust calorie and macronutrient targets in part 2 of this series.

MacroFactor is a user-guided app

We have a very vocal community, that’s shared plenty of ways we can improve MacroFactor, and we love the suggestions and feature requests! I think, if anything, we’re feeling a little overwhelmed at the moment with the amount of feedback we’re getting, but it’s the good kind of overwhelmed. When I look at our feature tracker, my first thought is “oh no, there’s so much here.” Then my second thought is, “holy shit, I thought the app was already great, but once most of these features and enhancements are added, it’ll be sooo much better.”

Since the app just launched, we don’t yet have a public track record to support this statement (though our beta testers could vouch for this), but we have every intention of rolling out cool new features and enhancements pretty frequently. Users have already given us an extensive list to choose from. Honestly, after just one week of being someone “behind” a nutrition app, I’m shocked at how infrequently other nutrition apps roll out meaningful updates (even the major players) – a lot of the features our users have requested don’t exist elsewhere, they’re completely feasible to add, and they’re excellent ideas!

We view ongoing development as a collaboration with our users. There will be user-suggested features that we don’t want to add – and in those situations, we’ll be happy to explain why they’re either not feasible, or not in line with our philosophy. We don’t want to shoot down any ideas without providing a respectful explanation. There will also be some features we cooked up that no one has specifically asked for. However, the Venn Diagram of “features we add” and “features that users suggest” is going to have a LOT of overlap. We also intend to prioritize features that are the most highly suggested, so if someone shares a feature request in the subreddit or Facebook group that you agree with, make sure you drop your two cents in the comments. We’re very active in those groups, and we do “+1” features that are suggested or agreed with more than once (the Facebook group is “private” to cut down on trolls, but we’ll add anyone who’s just curious about the app and the community).

With that being said, we request a bit of grace and patience (but only a bit; that’s all we’ll need). We have a lot of features we want to add, so it may take us a while to get around to yours. Cool new features will be rolling out very soon, though.

Within the next two months, we also plan to release a public roadmap, once we’ve fully collated and considered the first tidal wave of user feedback. This will allow users to see where we’re headed, and it will allow them to provide even more feedback regarding the path they’d like to see from point A to point Z.

Ultimately, we’re committed to making MacroFactor the app our users want it to be. Within the next month or two, as new features and significant enhancements start rolling out the door, you’ll see. For now, you just have to take my word for it. Very soon, you won’t.

That concludes Part 1. If all goes well, Part 2 should be done within the next week. It will discuss how our “coaching” algorithm actually works (so you can see it’s not some inscrutable black box) and our core philosophies.

The post The History and Team Behind MacroFactor appeared first on Stronger by Science.

- Fitnessista
weekend things

Hi hi! Happy Monday! Thank you so much to those of you who let me know you were missing the old school blogging recaps, so I’m bringing them back. It won’t be every day, but will definitely be scattered more frequently on the blog. Thank you so much for sharing your feedback with me. It’s my goal to create a blog that you want to read (that I also enjoy writing). I always appreciate your perspective, and take your thoughts and comments to heart.

It was a packed and fun-filled weekend around here. Friday afternoon, I met up with some friends to get everything ready for our school auction, and headed straight from that to a meeting with the dance moms to plan out Trunk or Treat. We have a lot to look forward to this month!

(I saw this tree at Hobby Lobby and didn’t end up buying it because it’s huge and tacky, but also SO perfect.)

We had Greek bowls for dinner, which is one of our frequent staples.

It’s grilled chicken, rice, hummus, cucumbers, olives, grape leaves, salad, and tzatziki bread. I totally forgot to add falafel to this meal, which takes it over the top. (I just air fry the frozen falafel from Whole Foods or Trader Joe’s.) The kids always go wild for this one – they like crafting their own bowls- so I cling tightly to the recipes they genuinely love. (They’re not picky, but are definitely vocal about which meals they like more than others.)

Saturday morning, we cheered for P at her soccer game, dropped Liv at dance, and I headed back to the auction venue to help set up decorations and last-minute to-dos. I look forward to our school’s auction each year, especially because our community is truly amazing. The kids SO lovely and kind, their families are incredible, and the teachers are the best of the best.

We’ve been able to make friends through our school that have become like family, and I feel blessed that after some school hunting, we’ve certainly found the right spot. (We switched schools two months before the world shut down, so we didn’t get to have a true experience until last year!)

Auction attire:

(Dress is here and world to the world: SIZE UP. I didn’t read reviews as carefully as I should have and welp, it was a little painful to breathe.)

Sunday morning, we were all moving slowly, so we took it easy. I made cinnamon rolls with chicken sausage and eggs for breakfast and I filmed some workout tutorials for a 1:1 client.

We also took a little trip to Spirit Halloween and brought all of our decorations out of storage. I’ll share a pic of the finished product in Friday Faves. The older the more I’m leaning into “cute Halloween” vs. “scary/gory Halloween.” 

We had family movie night with a themed cheese board (totally got the idea from this post!) to go along with it.

This week, I’m really looking forward to a lil Sakara delivery, some podcast interviews, and planning the Pilot and P’s birthday parties. I hope your morning is off to a great start and I’ll see ya soon!

Thank you for checking in on the blog today <3

xoxo

Gina

The post weekend things appeared first on The Fitnessista.

- Fitnessista
Friday Faves

Hi friends! Happy weekend! What do you have going on? We have an event for the girls’ school and I’m looking forward to teaching barre and hopefully catching a hike. The weather has been a dream this week! I’d love to hear what you’re up to. I also wanted to add a little note that I’m praying for safety for my friends in Hurricane Ian’s path. <3

It’s time for the weekly Friday Faves party! This is where I share some favorite finds from the week and around the web. I always love to hear about your faves, too, so please shout out something you’re loving in the comments section below.

A random note: I ended up canceling our fall break trip to NYC. We have a lot of reasons for deciding to postpone the trip, but decided we’d rather do a Disney cruise in the new year instead. When I told the kids, they were SO pumped, so I know we made the right choice! I’m also kind of glad that fall break will be more low-key, especially since we’re heading into the Pilot’s birthday, P’s birthday, Halloween, my birthday and a wedding, Thanksgiving through the New Year into Liv’s birthday. It’s all fun stuff – my fave time of year- but it can definitely be a lot. Do you have any upcoming trips planned?

Pic from our last cruise!

family in costumes

Friday Faves 9.30 Read, watch, listen:

I loved reading about these happy moments.

Five meditation retreat practices to try at home.

Don’t forget to listen to this week’s podcast episode about why diets don’t work.

If you’re looking to start a daily journal practice, check out this 5-minute journal. I’m ordering one to use in 2023.

Fitness + good eats:

Thai peanut chicken thighs.

Apple cider donut loaf CAKE?! I’m in.

Full fall fitness plan here!

Family dinner at Calle Tepa is always a winner. I feel like it’s one of the most underrated Mexican spots in Tucson; it’s been a go-to for years.

Calle Tepa tacos

Fashion + beauty:

If you’ve been wanting to take advantage of the 30% off for new Beautycounter clients, it ends tonight! The discount will go back down to 20% on the 1st. Click here and use the code CLEANFORALL30. I highly recommend the All Bright C serum, AHA mask, color intense lipstick, charcoal mask, and supreme cream.

Got these Chelsea boots on sale at Nordstrom. I think they’ll be super cute with skirts, dresses, and leggings. (Still not sure how I feel about these with jeans…I’m not on board with the wide leg/baggy trend.)

Nordstrom chelsea boots - Friday Faves 9.30

(Dress is here in a different print)

I FINALLY created my Amazon storefront. I’m going to be adding in goodies this weekend – fashion, fitness, and for the kids is already available – and will also post more holiday gift ideas here. Check it out!

Gina Harney Amazon storefront

Just for kicks:

Friday Faves 9.30 vince mcmahon meme

 

Happy Friday, friends!

xoxo

Gina

The post Friday Faves appeared first on The Fitnessista.

- Fitnessista
111: Why diets don’t work with Lisa Moskovitz, RD

Hi friends! Happy Thursday! I have a new podcast episode and can’t wait to hear your thoughts on this one.

Today, I’m chatting with Lisa Moskovitz, RD, all about quieting the diet noise and why diets don’t work.

Here’s what we talk about on today’s episode:

How to quiet the diet noise: how become a more confident, autonomous, and intuitive eater, and improve your relationship with food

How to move the needle towards fitness and weight loss goals in a healthy way

Why  diets don’t work: how they actually bring you further away from finding your healthiest, happiest weight

How diets impact your metabolism

Her tips for being Healthy in Real Life

and so.much.more.

I LOVED today’s conversation and hope that you’ll take a listen!

111: Why diets don’t work with Lisa Moskovitz, RD

Here’s a bit more about Lisa and her background:

Lisa Moskovitz, RD, is a registered dietitian, the CEO of NY Nutrition Group, a large group nutrition practice and the author of The Core 3 Healthy Eating Plan, a personalized, science-based guide to finding your healthiest, happiest weight. She received a BS in nutrition from Syracuse University and then went on to complete an intensive dietetic internship at NYPresbyterian Hospital. Since then, she has accumulated over a decade of experience in private practice, providing nutrition workshops and working with the media. Lisa is regularly featured in major publications such as Well + Good, Eat This Not That, Yahoo Health and is often interviewed for popular news channels such as Fox 5 NY, CBS News and Inside Edition.

Check out her website here, her Instagram here, and get a copy of The Core 3 Healthy Eating Plan (use Core20 for 20% off!).

Resources from this episode:

I love love love the meals from Sakara LifeUse this link and the code XOGINAH for 20% off their meal delivery and clean boutique items. This is something I do once a month as a lil treat to myself and the meals are always showstoppers.

Get 15% off Organifi with the code FITNESSISTA. I drink the green juice, red juice, gold, and Harmony! (Each day I might have something different, or have two different things. Everything I’ve tried is amazing.)

The weather is cooling down, and I’m still obsessed with my sauna blanket. It feels even BETTER when it’s chilly outside and you can use the code FITNESSISTA15 for 15% off! This is one of my favorite ways to relax and sweat it out. I find that it energizes me, helps with aches and pains, I sleep better on the days I use this, and it makes my skin glow. Link to check it out here. You can also use my discount for the PEMF Go Mat, which I use every day!

If any of my fellow health professional friends are looking for another way to help their clients, I highly recommend IHP. You can also use this information to heal yourself and then go one to heal others, which I think is a beautiful mission.

You can use my referral link here and the code FITNESSISTA for up to $250 off the Integrative Health Practitioner program. I just finished Level 1 and have started Level 2. I highly recommend it! You can check out my initial thoughts on IHP here!

Thank you so much for listening and for all of your support with the podcast! Please be sure to subscribe, and leave a rating or review if you enjoyed this episode. If you leave a rating, head to this page and you’ll get a little “thank you” gift from me to you. 

xo

The post 111: Why diets don’t work with Lisa Moskovitz, RD appeared first on The Fit