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- Fitbit Staff
Combat the Cost of Groceries with a Plan

Food costs have skyrocketed in the past year, but that doesn’t mean you have to forego healthy eating. With just a little forethought, there are ways to counter this increase. Here are some tips for maintaining  a nutritious diet without breaking the bank. 

Think ahead 

Take inventory. Before heading to the store or submitting your online grocery order, shop for your refrigerator, freezer, and pantry. Take inventory and plan to make recipes using what you have on hand first. 

The most important tip is taking inventory and planning your menu accordingly. Have you ever tossed a bag of soggy spring salad mix? Welcome to the club! It’s happened to most of us at some point, but that’s throwing money away. 

Prioritize perishables. Take care to use up fresh produce like spinach or other greens that tend to spoil quickly. Toss greens into a soup or pasta sauce to use them up and simultaneously add a nutrient boost to your meals. Check the expiration dates for perishables like yogurt and create meals around those foods first. For example, you might use up yogurt in breakfast parfaits for the family.

Planning your meals, starting with what you have on hand, helps reduce waste and therefore save money. But you might also consider changing what’s on the menu altogether. 

Eat less meat. Meat is expensive. Beef, chicken, and fish can run up a grocery bill faster than anything else. Adding more protein-rich plants to your plate can help. This can mean using beans a few nights a week instead of meat–and thinking of ways to add more produce to your meals overall. This will automatically help increase your fiber and antioxidant intake.

“The cost of meat and meat products isn’t dropping, so think about incorporating more plant-based foods in your meals,” recommends Sara Haas, RDN, a Chicago-based chef, author, and food photographer.  “Whole grains, canned/frozen/fresh varieties of vegetables and fruits, nuts and seeds, beans, and legumes are all great options.” 

Though the price of eggs has more than doubled all over the country, eggs are still one of the least expensive sources of protein you can buy. Consider enjoying them for dinner for an inexpensive meal like Eggs in Purgatory. This recipe is made with canned tomatoes and is a great base for adding any vegetables you have in the fridge to use up, such as zucchini, peppers, and spinach.

List it out. Create a detailed grocery list and stick to it. Shop online (if you get free shipping) or go through the store as quickly as possible. Most of us know not to go to the supermarket hungry since that’s a recipe for impulse buys. However, studies show the longer you’re in the store, the more you buy. To save time in the store, organize your list by food aisle or departments to avoid backtracking—and make sure you have a snack before you shop! 

Shop smart 

Shop the sales–and stock up. Take a few minutes to peruse the weekly online ads for your favorite supermarket. It may help to get the deals delivered to your email as a reminder. Many sales are seasonal, and you might notice how some stores offer similar sales on a monthly cycle. Take note and buy accordingly. 

Haas reminds us to check out sale items and develop meals around those foods first. 

And don’t forget the staples. “If those shelf-stable basics are on sale, buy extra! Think rice, beans, and canned goods that can last at least one year if not opened,” she advises. 

Use coupons wisely. Physical coupons are great but not always available. Clip digital coupons to save time and money at the register—and remember, coupons are only a good idea if you buy food that you will actually eat. Though they exist, there aren’t many coupons for fresh fruits and vegetables or fresh meat, poultry, or seafood. Look instead for deals on frozen or canned produce, yogurt, eggs, and other healthy foods, plus household items like detergent and toothpaste.

Buy in bulk—if it makes sense. This strategy is common, but it only saves money if your family can eat the food before it expires. Large containers of olive oil or nut butter can go rancid and giant boxes of cereal can go stale if not used on time.

Switch things up 

Be flexible. If you have a recipe that calls for ground beef, but ground turkey is on sale, substitute ground turkey. There’s a wide range of substitutes that work well. If your recipe calls for chicken breasts but chicken thighs are on sale, consider making the swap. If you’re making stew, baked chicken, or soup, the chicken thighs will add even more flavor to the dish, so don’t be afraid to try something different!

Grow and regrow herbs and more. With fresh water and sunshine, you can grow a variety of foods indoors. We all know how expensive those little packages of fresh herbs can be. You might be surprised at how easy it is to grow them yourself. 

Common herbs, including basil, parsley, mint, and oregano, grow in plain water—no soil or potting needed. Simply place the stems of leftover fresh herbs in a jar of fresh water and place them in a sunny area such as a windowsill. Use as needed and refresh the water regularly. 

You can also regrow flavor-packed green onions using the same method. Place the white root end in a glass of fresh water and watch them regenerate in seven to 10 days. This is a fun project for kids to manage, but it also saves you from buying green onions again.

Though some of these tips may seem small, it all adds up in the end. Use these tiny tips to save big over time. 

The post Combat the Cost of Groceries with a Plan appeared first on Fitbit Blog.

- Leandra Rouse
New Year, Strong You: 3 Recipes for Mental Strength
RECIPE BY LEANDRA ROUSE | PHOTOGRAPHY BY SAM EMMONS

As you set about keeping your goals for the new year, consider how you fuel your brain for the day. Nutrition research shows people who consume breakfast do better in mental performance tests, have improved focus and even say they feel in a better mood when they have breakfast. What you eat matters too. Following a Mediterranean style of eating—which is rich in whole grains, pulses (like chickpeas), vegetables, fish, eggs, and olive oil, in addition to limiting saturated fats and sugars—has been found to protect our brain function as we age. 

Keep reading for three delicious and nourishing recipes that put these strategies into practice—and promote brain health, mental strength, and longevity. 

Turkish-Style Turmeric Eggs with Yogurt 

Inspired by a traditional Turkish breakfast consisting of eggs, thick yogurt, spices and fresh herbs, this healthy and filling dish is  eaten alongside pita or crusty bread. It is so simple, yet a very unusual food combination for an American diner. 

Bright colors of spices stand out against the white background of thick yogurt, which makes this meal beautiful enough to be the star of a special occasion brunch, but so ridiculously easy to prepare it could become your healthy weekday breakfast. The combination of eggs and thick Greek yogurt makes it rich in protein with about thirty grams per serving—the perfect start to a busy day. 

Adding ground turmeric to the hot olive oil flavors the oil and brings color to the egg whites. The result is a vibrant orange egg. And, when combined with the red paprika (or aleppo pepper), bright green kale, and fresh herbs, the colors of this dish really stand out from the crowd. Try it served along some sort of pita or crust bread, so that you can scoop up and absorb the yogurt and egg yolks with each bite. 

INGREDIENTS: 

2.5 tablespoons olive oil, divided
1 bunch kale (6 to 8 leaves), removed from stem and rough chopped  

1/2 teaspoon smoked paprika, or aleppo pepper

½ teaspoon salt

1 teaspoon fresh black pepper

½ teaspoon turmeric

4 large eggs

1 cup (250 mL) plain unsweetened low-fat Greek yogurt

1 lemon, juiced and zested

1 clove of garlic, shaved 

½ teaspoon chili flakes, optional   

1 round whole wheat pita, sliced in fourths 

2 teaspoons mixture of fresh herbs such as mint, dill and parsley, rough chopped 

INSTRUCTIONS: 

Begin by preheating the oven to 375F (190C). Toss the torn kale with ½ tablespoon of olive oil, ¼ teaspoon salt, and ¼ teaspoon fresh ground pepper. Toss to coat and add the kale to the hot oven. Cook until it begins to wilt and get a little crispy at the edges. Approximately 6 to 8 minutes. Carefully remove from the hot oven and set aside. 

Mix yogurt with 1 tablespoon lemon juice, ½ teaspoon of lemon zest, shaved garlic, and ¼ teaspoon salt. To prepare each plate, smear 1/4 cup of the yogurt mixture onto each plate using the back of a spoon and sprinkle with smoked paprika. Place half of the baked kale evenly around the yogurt on each plate. 

Heat two tablespoons of olive oil over medium heat in a large well seasoned cast iron pan or nonstick skillet. Once oil gets hot, add the ground turmeric and stir to combine. Cook the turmeric in the oil until it becomes fragrant and well incorporated, approximately one to two  minutes. Keep the oil just below smoking point. A small drop of water added to the pan should immediately sizzle. 

Carefully crack the eggs into the hot pan and season with remaining salt and pepper. As the eggs cook, tilt the pan to the side and use a spoon to baste the eggs with the turmeric olive oil. This will help the egg cook while keeping the center moist. It will also turn the whites bright yellow. Keep basting until the eggs are puffy and cooked, approximately one minute, or to your liking. Transfer to the plate and arrange on top of yogurt and kale. 

Finish with a teaspoon of fresh herbs and serve with pita. 

Makes 4 servings (one egg per person).

NUTRITION FACTS (PER SERVING): 

Calories 285 kcal/1194 kJ

Protein 16 g

Total fat 16 g

Saturated fat 3.5 g 

Cholesterol 190 mg

Carbs 24 g

Fiber 6 g

Total sugars 6 g 

Added sugars 0 g

Sodium 505 mg

Quinoa Egg Bowl with Smoked Salmon 

This simple and healthy quinoa breakfast bowl is sure to be in high rotation in your kitchen. Quinoa has eight grams of protein per cup and impressive levels of fiber and vitamin B. Try this quinoa breakfast bowl where the grains are topped with perfectly runny poached egg and the vibrant flavors of fermented veggies and smoked salmon..

The trick to cooking great quinoa is to add flavor to the cooking water. In this dish this is  achieved by cooking the grain in a broth, adding aromatics, and tossing in a little butter at the end. Another great trick for bringing out the unique nutty taste of quinoa is to toast the dry grain over medium heat before adding any liquid.

The protein in this breakfast bowl comes from the quinoa, egg, salmon, and even the tahini sauce. The flavor is rounded out with acidic pickled vegetables and the umami of the miso tahini sauce. You may want to consider making a double batch of the tahini sauce to maximize your efforts in the kitchen. It makes a great dressing or marinade for a future dish. 

There are many ways to poach eggs, and many strong opinions on which method is “correct”. In this recipe, it’s done by  adding vinegar to the cooking water, turning a complex process into an achievable weekday breakfast. Plus the added vinegar flavor adds a nice sharpness to the dish. 

Like any “bowl” recipe, it can easily be adapted to what you have in your refrigerator and your personal tastes. Don’t be afraid to experiment. That’s how great meals are invented! 

INGREDIENTS: 

1 cup (180g) dry quinoa 

2 cups (470ml) low sodium vegetable broth

½ tablespoon butter  

2 bay leaves

4 cloves garlic, whole and peeled

4 oz (110g) dry smoked salmon 

4 large eggs 

2 tablespoons vinegar—white, apple cider or red wine vinegar 

1 12 oz (340g) bunch spinach 

4 tablespoons pickled vegetable, we used a store bought beet sauerkraut 

Miso Tahini Drizzle Sauce (Makes 10 servings)

2 tablespoons miso, white preferred 

2 tablespoons tahini 

1 tablespoon fresh minced ginger 

1 garlic clove

2 teaspoon maple syrup 

1/4 cup (60ml) rice vinegar

2 tablespoons toasted sesame oil

2 tablespoons olive oil

INSTRUCTIONS: 

Toast quinoa over low heat until fragrant, being careful not to burn. Then add the stock, bay leaves, garlic cloves, and butter. Bring the pot to a boil, and then simmer on low for 15 minutes. Be sure to keep the lid on the entire time and avoid letting out any steam. Once the water is absorbed, remove the pot from the heat, fluff the quinoa with a fork and pull out the bay leaves and full garlic cloves. Cover and let stand for 5 to 10 more minutes. 

While the grain is cooking, fill a saucepan ⅔ of the way full and bring it to a simmer over medium heat. Once at a light simmer, add two tablespoons of vinegar to the water. 

Make an easy miso tahini drizzle by combining all sauce ingredients into a blender and blending on high until smooth. Pour into a jar and reserve. 

Dish quinoa into four bowls and split the smoked salmon into four servings among the bowls. Top each with one tablespoon of fermented vegetables. 

Once the grain and the sauce are prepared, you are ready to poach the eggs. Crack and carefully slide each egg into the simmering water leaving plenty of space between them. If the egg white spreads out, use a spoon to bring it closer to its yolk. Poach each egg until the white is firm and the yolk feels soft but contained. Use a spoon to carefully ladle hot water over the egg yolk until the top becomes opaque. When you believe the egg is poached, use a slotted spoon to carefully lift the egg from the water and touch the yolk to test for its desired consistency. A runny poached egg takes approximately three to four minutes. 

When the eggs are ready, use the slotted spoon to carefully remove from the water and nestle two eggs on top of each quinoa bowl. Drizzle the dish with a tablespoon of miso tahini sauce and a sprinkle of salt and pepper. 

Serve hot. 

Makes 4 servings.

NUTRITION FACTS (PER SERVING): 

Calories 360kcal/1506 kj

Protein 22 g

Total fat 14 g

Saturated fat 3.5 g

Cholesterol 193 mg

Carbs 72 g

Fiber 7 g

Total sugars 3 g

Added sugars 1 g

Sodium 530 mg

Coconut Ginger Chickpea Stew

This dish is warming and nourishing, satisfying and rich in spices. It’s reminiscent of Indian dal—hearty and flavorful, and is great served alongside warm flat bread or laddled over basmati rice. It has become our go to soup to bring to friends in need. And we recommend always doubling that recipe, because it freezes beautifully and makes a healthy meal in a pinch. The coconut milk may be totally or partially omitted and replaced with an additional low sodium vegetable broth. 

INGREDIENTS: 

3 tablespoons olive oil 

3 garlic cloves, diced

1 large yellow onion, diced

3 garlic cloves, minced 

2 tablespoons fresh ginger, minced

1 teaspoon ground turmeric 

1 teaspoon red-pepper/chilli flakes, plus more for serving

½ teaspoon salt, divided 

¼  teaspoon freshly ground black pepper

2 medium carrots, peeled and diced   

2 15-ounce (425g) cans no-added salt chickpeas, drained and rinsed. Reserve ½ cup (130g) for serving

1 15 oz (425g) can of  light coconut milk, (optional) 

3 cups (700ml) of low sodium vegetable broth 

1 bunch 12 oz (340g) spinach, rough chopped

½ cup (125g) non-fat yogurt, for serving (optional)

1 cup mint leaves, torn into pieces for serving

INSTRUCTIONS: 

Preheat the oven to 425F (215C). Dry ½ a cup of the canned chickpeas on a paper towel removing any loose skins and place them on a small baking sheet with two teaspoons olive oil and a sprinkle of salt and pepper. Roast until crispy for approximately 5 to 7 minutes. Remove and set aside. 

Meanwhile, heat remaining olive oil in a large soup pot over medium heat. Add the diced onion, garlic, and ginger to the pot and cook until the onion is fragrant and translucent, about five minutes. Then add the ground turmeric, red pepper/chilli flakes, salt, and pepper and toss to combine. Let the spices heat for one to two minutes, give everything another good stir to incorporate. Then add the chickpeas and carrots and toss to combine. Sautee for 5 minutes until carrots begin to soften. Lastly add the vegetable stock and coconut milk. Bring to a boil and then reduce heat and cover, simmering for 15 to 20 more minutes. 

When you are ready to serve, ladle a cup or so of soup into a bowl. Top with a dollop of yogurt and a pile of fresh herbs. You may also finish the dish with a little drizzle of olive oil and a sprinkle of sea salt. 

Enjoy on its own, or alongside a salad, flat bread, or rice. 

Makes 6 servings.

NUTRITION FACTS (PER SERVING): 

Calories 310 kcal/ 1297 kj

Protein 12 g

Total fat 14 g

Saturated fat 5g 

Cholesterol 0 mg

Carbs 37 g

Fiber 11 g

Total sugars 9 g

Added sugars 0 g 

Sodium 360 mg

The post New Year, Strong You: 3 Recipes for Mental Strength appeared first on Fitbit Blog.

- Fitbit Staff
Fitbit’s Year in Review: Which Countries Took Their Health and Fitness to the Next Level in 2022?

As we head into a new year, it’s always important to reflect on the one gone by. 2022 was filled with highs and lows, wins and losses, learnings and celebrations. Above all else, it taught us that when we come together as a community, we can achieve great things such as the combined 47 trillion steps we took in 2022!

If you find that impressive, you’ll want to keep reading because Fitbit researchers analyzed data from Fitbit users around the world and found some of your most outstanding stats to date. Read on to discover which countries were the top contenders for highest step count, best rest, least stressed, and more.

Total Global Step Stats

The Fitbit community stepped up and showed out on May 27 taking a total of 162 billion steps, making it the most steps taken in a single day around the globe in 2022. Fitbitters also seemed to have an extra spring in their step during the month of May as it was our best step month of the year! 

Lastly, together we clocked 20 billion total miles in 2022. That’s enough to walk to Mars 143 times and enough to walk to Pluto 6.5 times!

Total Global Sleep Stats

How did Fitbitters fare when it came to hitting the hay? With an average of 6.5 hours of sleep and an average 76 Sleep Score, these metrics give you a “fair” score in snoozing. But not to worry, you can utilize your Fitbit sleep data to help get you back to an “excellent” sleep score in 2023! 

Another fun fact? The most common sleep animal in 2022 was the Giraffe, which means your sleep tends to be shorter, and you are more likely to sleep later and wake up earlier. You have a relatively good proportion of deep and REM sleep despite a shorter overall duration.¹ 

Total Global Exercise Stats

Active Zone Minutes, or AZM, is the heart-based metric that tracks the amount of time a user spends in heart-pumping activity. With a total of 173 billion Active Zone Minutes, Fitbit users certainly brought the heat in 2022—specially on May 14, with 612 million minutes total. Way to get moving!

Who got the highest Active Zone Minutes in 2022?

Based on recommendations from the World Health Organization, American Heart Association, and others, we should do at least 150 minutes of moderate-intensity activity or 75 minutes of vigorous-intensity activity each week. So, who took these activity targets to heart and got  the most AZMs in 2022? Drum roll please… Switzerland! Next up were Sweden and Denmark respectively. 

Who stepped it up in 2022?

But which country was the real MVP of steps? Let’s get a round of applause for Hong Kong! Following closely behind were Switzerland and Spain. Well played!

Who got the best rest in 2022?

Even with busy schedules and fluctuating routines, Fitbitters remembered that when you prioritize getting good sleep, your body and mind are healthier. According to our data, Finland hit the lights the most this past year. Other top snoozers include New Zealand and Belgium. 

Who had the best Stress Management Score in 2022?

You’ve been tapping into your mindfulness practice and putting your mental health first, even with the continued uncertainty in the world. And the country that won the best Stress Management Score in 2022? None other than Spain! Next up were Sweden, Netherlands, Ireland, and finally Denmark. How’s that for zen?

This new year will undoubtedly present a new series of opportunities and challenges as we all continue to refine and evolve our health and wellness routines. Through it all, it’s key to remember that the journey is just as, if not more, important than the destination. Cheers to 2023! 

¹ Requires Fitbit Premium membership. Not intended for medical purposes. Consult your healthcare professional for questions about your health. Must wear device to sleep for at least 14 nights over a month-long period.

The post Fitbit’s Year in Review: Which Countries Took Their Health and Fitness to the Next Level in 2022? appeared first on Fitbit Blog.

- Deanna deBara
What is the Sober Curious Movement—and Why Is It So Popular?

Many people think that, when it comes to alcohol use, it’s all-or-nothing; you either pursue full sobriety or you drink whatever and whenever you want. But it doesn’t have to be all-or-nothing. There’s a movement aimed at helping people want to better understand their relationship to alcohol and make healthier choices for themselves without necessarily giving up alcohol completely. It’s called the sober curious movement.

Let’s take a look at all things sober curious: what it is, how it differs from full sobriety or abstinence, and how (if you’re curious about it!) you can embrace the movement in your own life.

What is “sober curious”, and why is it having such a moment?

“The Sober Curious movement was launched by Ruby Warrington and her book, Sober Curious, in [late 2018],” says Elisa Peimer, LCSW, a therapist who has worked with a Sober Curious support group. “Sober Curious is a method of being mindful about drinking. People following it learn how to notice what triggers their drinking, what the act of drinking means to them, what needs it’s fulfilling, and how it’s adversely affecting their lives.”

The movement gained momentum quickly, in part because it allows people to explore living a more sober lifestyle without making the commitment to give up drinking entirely. “The movement encourages a sober (or more sober) lifestyle, but embraces and welcomes individuals who are not ready to quit alcohol entirely,” says Ian Andersen, co-founder of mindful drinking and moderation app Sunnyside.

The rise of the sober curious movement could also be viewed as an extension of health, wellness, and mindfulness in general becoming more mainstream. “As mindfulness in general has become more and more popular, being sober-curious feels like a natural extension of healthier lifestyle options like plant-based diets, yoga, and meditation,” says Molly Watts, author and host of The Alcohol Minimalist podcast.

Finally, the sober curious movement has gained quite the following on social media. And as more people and influencers have embraced the movement—and been willing to speak out about their choices around drinking less, or not at all—the movement has spread to more people. For example, “hashtags like #mindfuldrinking and #sobercurious are driving millions of views on social media,” says Andersen.

How is being sober curious different from being fully sober?

Being sober curious differs from traditional sobriety in a few ways—most notably that it doesn’t require abstinence from alcohol. Instead, it encourages people to, as the name suggests, get curious about their drinking—and aim to make better, more mindful choices around their alcohol use.

This allows people “to explore living a life without alcohol without having to fully commit to not drinking,” says Dr. Brooke Scheller, a Doctor of Clinical Nutrition who specializes in nutrition to support a sober or sober-curious journey. 

“The focus is not on just abstinence, but on the choices we make when the stressors in our lives drive us to relieve them with alcohol rather than in more healthy ways,” says Peimer.

It also appeals to a wider audience. While full abstinence is generally recommended for people with an alcohol use disorder, the sober curious movement is a fit for anyone who wants to have a better relationship to alcohol—or who want to cut back their drinking for reasons outside of addiction or alcohol use disorder, like improving their health.

“Before this, sobriety was mostly reserved for those who identified as having a problem with alcohol,” says Scheller. “Over the last two years, people are now exploring a sober-curious lifestyle by cutting back or even fully eliminating alcohol for reasons that include physical and mental health reasons, to improve their careers, relationships, or even just because they’re sick of the hangover habit.”

“Sober curious offers a flexibility to acknowledge your use of alcohol might not be healthy without the rigidity of an all-or-nothing approach,” says Peimer.

Feeling curious? Tips on how to embrace the movement

Practice mindfulness when you reach for a drink. Mindfulness is the foundation of the sober curious movement.

“Sober curiosity is about mindfulness—looking at your actions in the moment and being honest with yourself,” says Peimer. “Notice what’s going on for you when you decide to drink.” 

Before you take a drink, pause for a moment. Ask yourself “why am I taking this drink?”—and then, based on the answer, decide whether you want to move forward and have the drink. For example, when you stop to think about why you’re reaching for a drink, you may realize that it’s because you trying to manage challenging emotions, like boredom or anxiety—in which case you might opt to skip it in favor of a healthier coping mechanism, like calling a friend or going for a run. 

Practicing mindfulness in the moments that you want or reach for a drink can help you better understand the motivations behind your drinking—and whether those motivations are in line with the kind of relationship you want to have with alcohol.

Get curious and ask yourself some deeper questions. In addition to bringing mindfulness to moments when you want to drink, being sober curious means…well, getting curious. Ask yourself some deeper questions about your relationship to alcohol. “Be thoughtful about what drinking means to you,” says Peimer. “Is it helping you create a narrative that’s appealing? Is it a reminder of happy times? Is it an indication that the weekend is starting?”

Understanding the reasons behind your drinking can help you make better, healthier choices around if and when to drink. 

If you want to take things a step further, you may also want to ask yourself some questions about what it would be like to give up alcohol completely or consume less of it. “Ask yourself how your life would look without alcohol,” says Peimer. 

If you want to see how drinking may impact your Sleep Score, check it after a night out with friends. You can also try comparing your Sleep Score on a night you haven’t imbibed with that of a night when you have. (Learn more about how to tap into Fitbit’s sleep tools, including your Sleep Score, here.) 

Plan ahead. As mentioned, part of being sober curious is about being mindful when you reach for a drink. But while it’s important to be mindful in the moment, some are harder than others. And if there are moments you think it might be hard to say “no” to a drink—even if you want to? Plan ahead for them. “Try making a plan ahead of time for not drinking at an event that you normally would,” says Watts.

For example, do your Sunday catch-up brunches with your friends always end with you having one too many cocktails? Make a plan for what you’re going to drink instead (for example, a sparkling mocktail)—and for what you’re going to say.

As the old saying goes, “failing to plan is planning to fail”. Make sure to plan ahead for potentially challenging situations.

Try some non-alcoholic alternatives. If you want to change your relationship to alcohol—but also really enjoy the taste of beer, wine, or other alcoholic drinks? There’s good news.

“We’re…seeing a huge boom in the non-alcoholic beverage industry,” says Scheller. “While stores used to sell only one or two NA beers, we’re seeing a huge range of NA drinks in liquor stores, grocery stores, and even small markets—making it much easier to access alternatives to alcoholic beverages.”

There’s also been some serious improvements in the quality of non-alcoholic beverages—so you don’t have to sacrifice taste along with the buzz. “Many people are really surprised by how delicious some of the NA beverages are and you can have the mouth feel of your favorite drink without the negative aspects of alcohol,” says Watts.

Instead of having a drink every time you normally would (for example, with dinner or in the evening while you watch TV), “try to alternate your regular alcoholic drinks with an NA alternative,” says Watts. 

And have fun with it! As mentioned, there are tons of NA options on the market—so choose beverages that seem interesting, tasty, and that you’ll be genuinely excited to try. “Having options that you can get excited about that are not alcohol is a great way to modify your habits,” says Andersen.

The post What is the Sober Curious Movement—and Why Is It So Popular? appeared first on Fitbit Blog.

- Deanna deBara
Try a Mood Board for Your Fitness and Wellness Resolutions This Year

The New Year is one of the best times of the year to set fitness and wellness resolutions. And one of the best ways to ensure you keep those fitness and wellness resolutions all year long? A mood board.

Mood boards can be a great source of inspiration for making healthier decisions and moving towards your fitness and wellness goals. But what, exactly, is a mood board, how do they work,  and how can you use mood boards to become the happiest, healthiest version of yourself in 2023 (and beyond)?

Sometimes called a vision board, a mood board is a collection of images, graphics, words, and other visual imagery collaged together. They essentially create a visual representation of a chosen topic, idea, concept, or goal.

You can make a physical mood board (for example, by cutting out images from a magazine and collaging them on a piece of posterboard)—or, if you prefer the more tech-savvy route, you can create a virtual mood board using a digital design tool.

What can you use a mood board for?

Mood boards are extremely versatile. You can make one on just about every topic and for just about any reason (including just because you want to!). But one of the most effective ways to use them? To help you hit your goals.

For example, let’s say you just bought a new home. You might make a mood board full of interior design images to help guide your decorating process—including the colors, styles, textures, and patterns you want to include in your home design.

Or let’s say you’re still renting—but your goal is to buy a home in the near future. In that situation, you could make a mood board to keep you moving towards your goal, complete with photos of your dream neighborhood, the kind of home you’d like to buy, and images that will inspire positive financial habits.

Mood boards can be a helpful tool in working towards any goal. But as the New Year approaches, they can be especially effective in helping you work towards your New Year’s resolutions—including your health and fitness resolutions.

So how, exactly, can you use mood boards to help you hit your health and fitness goals—and keep you moving towards your healthiest, happiest self straight through 2023?

Make sure all of your health goals are represented—mental, physical, and emotional

When setting health resolutions for the coming year, it can be easy to focus on the physical—things like changing your diet or getting more exercise. And while you’ll 100 percent want to include those goals on your mood board, your physical health goals aren’t the only resolutions you want represented on your mood board. You’ll want to include your mental and emotional health goals as well.

Think about the resolutions you want to make to improve your overall health in 2023—physical, mental, and emotional. For example, maybe your health resolutions look something like this:

Physical: Run a 10k, learn to cook healthy meals at home, improve sleep hygiene and get 8 full hours of sleep each night Mental: Create a daily self-care practice, learn an instrument, read one book per month Emotional: Start seeing a therapist, schedule at least one “friend date” each week

All of those resolutions are going to help you become a healthier, happier person—and so you’ll want to include them on your mood board.

Include images that inspire you

Once you know all of the health resolutions you want to include on your mood board—physical, mental, and emotional—it’s time to start putting your mood board together. And the first step in that process? Gathering images to create your collage.

There’s no “right” way to search for, find, and collect imagery for a mood board. If you’re making a physical mood board, you might clip images from books and magazines or incorporate your own photos—while if you’re making a digital mood board, you might search the internet and take screenshots of images that align with your goals.

But however you conduct your image search, the most important thing to keep in mind? Choose images that inspire you.

Look for inspiring images that align with the goals you’re trying to hit. For example, is one of your fitness goals to finish your first marathon? Look for photos of people triumphantly crossing marathon finish lines or epic scenery shots of the location where you’d like to run. Are you planning on embracing a more plant-based lifestyle in the coming year? Make sure to incorporate lots of pictures of delicious, nutritious foods and meals to inspire your plant-based menu.

Bottom line? The purpose of your mood board is to act as a visual representation of your health and fitness resolutions—and to inspire you to keep working towards those resolutions, even when it gets challenging. So, to make sure your mood board inspires you when you need it most? Choose inspiring images.

Put your mood board on display

A mood board is a great reminder of the goals that you’re working towards and inspiration to help you work towards those goals. But it can only remind and inspire you if you can actually see it.

Display your mood board in a place where you’re sure to see it every day—for example, on your bedroom wall, on your dresser, or in your workout room. The more often you look at your mood board, the more visual reminders you’ll get of your fitness and wellness resolutions—and those reminders can help you stay accountable to hitting your goals and keeping your resolutions.

Add to your mood board as necessary

As the year progresses, your fitness and wellness resolutions may progress right along with it. So, if you want your mood board to continue to inspire you? Make sure to change and add to it as necessary.

For example, let’s say your major New Year’s health resolution is to get better sleep—and, as such, your mood board is focused on sleep-related imagery and inspiration. If you’re focused on that goal, chances are, your sleep is going to improve throughout the year—and by June, you may be getting such high-quality Zzz’s, improving your sleep no longer feels like as much of a focus.

In that situation, you may feel called to add new resolutions to your mood board, like getting more exercise or cooking more healthy meals—or you may be called to make a new mood board altogether!

The point is, in order for a mood board to be effective, it needs to be a reflection of your current fitness and wellness goals and resolutions—so don’t be afraid to change or add to your mood board as those goals and resolutions change.

The post Try a Mood Board for Your Fitness and Wellness Resolutions This Year appeared first on Fitbit Blog.

- Karen Ansel, MS, RDN
Should You Try a Ballet-Style Workout?

Barre classes may do great things for your glutes, thighs, and core, but if you really want to be strong like a ballerina, consider a ballet workout. This total body exercise doesn’t just strengthen and lengthen muscles; it boasts some decided fringe benefits, like better posture, balance, and confidence, says Victoria Marr, director and co-founder of Sleek Technique Ballet Fitness

What is a ballet workout, and how does it compare to barre? Read on to learn if this new spin on ballet is right for you. 

What is a ballet workout? 

“Ballet [workouts] take you through the entire journey of a ballet class,” says Chris Vo, director of programming, group fitness at Equinox and Equinox Media. But with a twist. In addition to plies, arabesques, and other classic moves, a typical ballet workout might include resistance bands to tone the arms and back or planks for core strength. Either way, the result is a gentle cardio workout that sculpts, tones, and leaves you feeling light, flexible, and more graceful.

A mind-body workout

Ballet-focused classes aren’t just about building a better body. “Dance can lead to a long list of benefits,” says Vo. In addition to improved flexibility, coordination, and balance, dance may also reduce stress and depression, he says. Escaping to the world of dance may also help you become more mindful, adds Marr. “Mentally, you absolutely have to focus for that 30 to 40 minutes and block out any other stresses and distractions,” she explains. Research backs up her theory. For example, one recent study found that dance students reported greater mindfulness and life satisfaction than students in other disciplines.

How do ballet workouts differ from barre classes?

On the surface, ballet and barre workouts may sound like the same thing, but there are some subtle—and not-so-subtle—distinctions. Here are the main ways they differ from one another. 

Coordination and rhythm. “A good barre workout will work on coordination and rhythm but focuses on the more basic ballet steps,” says Marr. “[However], there is even more of an opportunity to advance the work on your coordination and rhythm once you leave the barre as you start to work with a bigger vocabulary of movement and build longer dance sequences.” 

Upper body strength. Want ballerina arms? Then book a ballet class. While barre work can do magic for your lower body, it doesn’t always target the back and arms like sashaying across the floor with arms stretched outward or overhead does.

Cardiovascular endurance. “There is more of an option for larger range, dynamic movements off the barre,” says Marr. Plus, moving your arms and legs simultaneously really gets your heart pumping! Ballet is so effective for heart health that one recent study found that regular moderate-intensity dancing reduced a person’s risk of dying from heart disease by 46 percent. 

Perceived effort. “They both can be strenuous in different ways, but when you get lost in the artistry and the theatrical aspect of a ballet class, somehow one’s perceived exertion is much less,” says Vo. 

The fun factor. “Barre workouts tend to feel like workouts, [and are] usually focused on smaller range of motion, high repetition, and light resistance exercises,” says Vo. By contrast, the jumps, leaps, and turns of a ballet class make you feel like, well, a dancer. 

If you’re torn between the two, the good news is you don’t have to choose one over the other. “Both have their place and complement each other brilliantly,” says Marr. 

But if you’re still not convinced that ballet is really exercise, consider the results of a recent meta-analysis. When researchers reviewed the results of 28 studies, they found that dance was more effective than traditional exercise for improving flexibility and balance and reducing BMI, body fat, and triglycerides. And it was equally as beneficial as exercise for cardiovascular health. So go ahead and dance your heart out!

The post Should You Try a Ballet-Style Workout? appeared first on Fitbit Blog.

- Leandra Rouse
Healthy Recipe: Holiday Hot Pot

RECIPE BY LEANDRA ROUSE | PHOTOGRAPHY BY SAM EMMONS 

Hot Pot is a delicious family meal that is served with a steaming soup at the center of a table, where all guests can participate in flavoring the broth. This is a tradition that has been seen across Asia for thousands of years. Most notably in China (know as huǒguō), in Japan (known as Nabemono), and in Korea (known as Jeongol). It is a fun way to share a communal meal with loved ones, making it especially perfect for the  holidays. 

To ensure this dish is tasty as it is healthy, we will show you how to make the broth light and the vegetables abundant. The seafood and vegetable theme brings forward some of the best hot pot ingredients such as Asian mushrooms, tofu skin, and daikon. Although, you can swap in similar ingredients based on season and your preferences 

The key to a good hot pot is a great broth! We have a great hack to save time and deliver on flavor. Add shrimp and vegetable peels to your favorite pre-made vegetable stock then add This adds extra vitamins, minerals and a rich umami flavor into a packaged broth.  

Lastly, hot pot is traditionally served in the center of a dining table. You will need a heat source such as an electric hot pot, hotplate, or induction stoveplate to keep broth warm. (We’ve always used a plugged in hotplate, but this year added an electric hot pot to our holiday wish list.) 

Most importantly, share this dish with a group of loved ones. It is interactive, fun, easily tailored for personal tastes, and takes a flavorful departure from traditional American holiday flavors.  

INGREDIENTS: 

2 32 fl.oz (950ml) containers vegetable broth, sodium reduced or without added salt is preferred 

2 teaspoons powdered dashi *optional 

1 lb (450g) white fish filets, such as Mahi Mahi, Bass, Tilapia 

6 shrimp, peels removed and reserved 

1 14 oz (400g) package tofu, firm, cut into cubes 

6 shiitake mushrooms, sliced in half

2 enoki mushroom bundles 

4 baby (400 g) Bok Choy or Pak Choi, bottoms removed

2 cups (200g)  cabbage, Napa or green

2 carrots, peeled and sliced

1 cup (160g) daikon/mooli, peeled and sliced into 1” (2.5cm) half moons 

1 bunch (100g) green onions or spring onions, ends trimmed and reserved

½ lb (225g) vermicelli noodle, cooked

For the dipping sauce: 

1 tablespoon white miso paste

½ tablespoon toasted sesame oil

1 tablespoon rice vinegar 

1 ½  tablespoon tahini

1 tablespoon soy sauce

1 teaspoon sesame seeds, white, toasted

2 tablespoons water to thin sauce

INSTRUCTIONS: 

Stock: Begin by simmering shrimp peels, shiitake stems, and any other reserved vegetable peels in one cup (240ml) of water. Simmer over medium heat for 20 minutes. Strain and add this concentrated mixture to a large stock pot with the pre-made vegetable broth and powdered dashi. 

Hot pot: Chop and plate all the vegetable and seafood ingredients into approximate bite size pieces. Plate decoratively and arrange the ingredients based on the type. Place the ingredients across two or more plates to ensure that guests on all sides of the table have easy access. 

Dipping sauce: Whisk together the ingredients in a bowl. Portion the finished dipping sauce into several bowls and place around the table for guests. You may consider individual dipping sauce bowls. 

Set the table:  Put the heat source at center of the table, arrange the plates of ingredients and dipping sauce around the table so guests can reach them. Supply each guest with a bowl, soup spoon, and chopsticks. 

Hot pot meal: When you are ready to serve, carefully bring the large stock pot of broth and place it on the heat source at the center of the table. It should be kept at a low simmer during the meal. Guests can contribute to the flavoring of the broth by selecting raw ingredients, and carefully placing them into the hot pot using chopsticks or a spoon. Give the ingredients plenty of time to cook through and soften before spooning into bowls. The seafood typically will take 7 to 10 minutes to cook and the vegetables a minimum of 5 minutes.Once cooked, ladle the soup from the hotpot into guest bowls, including a little of each added ingredient. Guests can individually season with the dipping sauce. 

Eat and be merry!

Makes 15 servings. 

NUTRITION FACTS (PER SERVING): 

Calories 180 KCal

Protein 14g

Total fat 6g

Saturated fat 2g

Cholesterol 20mg

Carbs 19g

Fiber 3g

Total sugars 3g

Added sugars 0g

Sodium 600mg

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- Pamela DeLoatch
Here’s How You Can Brighten Winter with the Danish Practice of Hygge

Winter can be tough for many people, with fewer hours of daylight and plunging temperatures. Sure, there are many holidays and celebrations to look forward to, but they can come with over-the-top busyness, and expectations can be emotionally and physically draining.

This season, give yourself a break and perhaps elevate your happiness by following the Danish practice of hygge.

Hygge literally means—well, there is no direct translation into English! But it is a sense of cozy comfort, gratitude, and well-being. Pronounced “hoo-ga” or “hui-gah,” it is a common practice in Denmark to prioritize slowing down the pace of life and enjoying simple pleasures, such as close family and friends, food, nature, and relaxation.

Denmark is known for being one of the happiest countries in the world, and hygge may be the reason. With the average winter temperature hovering at the freezing mark and a mere seven hours of sunshine each day in December, the Danes use this time to comfort themselves and enjoy what they have.

History of hygge

The word hygge comes from the Norwegian language, where it means well-being. It was first seen in Danish writing in the 18th century. The concept of hygge fits well into Danish culture, which embraces genuine connection and a laid-back approach to life.

Although the concept of hygge grew in Denmark, an article published about it in 2015 began a spike in coverage around the globe. Subsequent articles and books about hygge followed. In 2016, the word hygge made the Oxford Dictionary shortlist for word of the year. It was defined as “a quality of cosiness  and comfortable conviviality that engenders a feeling of contentment or well-being (regarded as a defining characteristic of Danish culture).”

As the idea of hygge became more popular worldwide, it became more commercial. The Broadway production of the musical Frozen includes a song called Hygge—ensuring future generations will be well-versed in the concept. Lifestyles stores promote furniture, blankets, candles, and other accessories to make a home more hygge. Still, the original meaning of the word focuses on enjoying what you have, not necessarily needing to get more.

In addition to being a newly accepted word in Scrabble, hygge can be used as a verb, adjective, and noun.

Ways hygge may help happiness

While practicing hygge sounds good, can it really make you happy? Everyone is coping with different stressors and situations. However, hygge corresponds with the concepts of well-being and happiness.

Connection is essential to hygge, and good social relationships are a key predictor of happiness. Hygge is a perfect solution year-round, especially in winter when people socialize less and can feel more isolated without activities with close friends.

A significant part of hygge is gratefulness, an appreciation of what you have. Research shows that gratitude is strongly associated with greater happiness. According to Harvard Medical School, “gratitude helps people feel more positive emotions, relish good experiences, improve their health, deal with adversity, and build strong relationships.” Practicing hygge provides regular opportunities to appreciate the people and things around you.

Rest is another aspect of hygge that translates to well-being and happiness. Taking time from overloaded schedules to slow down and relax reduces stress, boosts creativity and productivity, and helps decision-making. Instead of waiting until burnout occurs, hygge creates built-in downtime.

Adding hygge to your life

If you don’t think you’ve practiced hygge before, there is no need for FOMO—you probably have! Think of the last cold, dreary day when you and your bestie wore sweats all day, piled on the blankets, binge-watched a Netflix series, and talked about anything and everything. Perhaps it was when you had a game night or Friendsgiving with a few of your favorite people. Or when you went on a nature walk with your pup, appreciating the open space and chance to breathe fresh air.

There are many ways to hygge. But it’s not just about the activity; it’s about intention and attitude. Because hygge is part of Denmark’s culture, the people there hygge intentionally and consistently. They allow their schedules to include downtime and appreciate the restorative aspects of hanging out with friends and family. And they don’t just do this on special occasions. They do this weekly.

Meik Wiking, CEO of the Happiness Research Institute and author of The Little Book of Hygge, highlights the central tenets of hygge:

Get together with a few close friends in a trusting environment. Danes believe the ideal number of people to hygge with is three or four. Enjoy good food and drink. This can be simple food at home, a local coffeehouse, or a casual and relaxing restaurant. Disconnect from digital devices and distractions to savor the moment. This includes leaving work on time to be with family and friends and turning off emails and social media when you’re with people. Turn the lights down. Candles are an important aspect of creating a hygge environment. Dress comfortably. Now isn’t the time for suits and heels. Think soft sweats and thick, warm socks. Have a hygge spot at home where you can light candles, snuggle under a blanket, and drink hot tea, coffee, or cocoa.

While hygge is often practiced indoors, it doesn’t have to be, even in winter. A brisk walk or run outside, a snowball fight, or ice skating with friends are excellent ways to hygge. Activities like picnics, barbecues, canoeing, and camping are popular in warmer weather.

Although hygge can help improve happiness, it isn’t a substitute for psychological support. Still, with its multiple benefits, practicing hygge may help this winter be a little brighter, warmer, and more fulfilling!

The post Here’s How You Can Brighten Winter with the Danish Practice of Hygge appeared first on Fitbit Blog.

- Fitbit Staff
Googler Zahra Barnes Tried Fitbit Premium’s Sleep Profile for Two Months

Googler Zahra Barnes, an editorial content manager and contributor to the Google Keyword blog, was immediately intrigued by Fitbit Premium’s Sleep Profile feature when it launched in June—and the thought of understanding more about her sleep quality, not just quantity. That’s why, after being set up with a device by Fitbit, she decided to test out our Sleep Profile for the next two months. 

Sleep Profile is determined by analyzing 10 key metrics identified by the Fitbit research team to be most important to your sleep health, including sleep schedule variability, sleep start time, sleep duration, time in deep and REM sleep, and more. Plus, Sleep Profile will reveal which animal represents a user’s most recent sleep habits. The options are Bear, Dolphin, Hedgehog, Parrot, Tortoise, and Giraffe. 

Read on for Zahra’s takeaways: 

Setting up her Sleep Profile was simple. Once her device and the Fitbit app were set up, all she had to do was wear it.  She thought her Inspire 3’s 10-day battery life was the stuff of dreams. “I’m frankly still not over this!” Zahra shared. She found looking through her sleep data fascinating, especially her Sleep Score, and was able to improve her sleep as a result.  Smart Wake made her mornings less groggy by waking her with gentle vibrations at the lightest point in the sleep cycle. Fitbit’s breathing exercise and guided meditations helped her wind down before bed. On nights when she couldn’t drift off, she found that Fitbit helped.   Getting her sleep animal, the Giraffe, was as rewarding as she’d hoped. She discovered that like her other fellow Giraffes (the most common Sleep Animal), she went to bed later, got less sleep than women her age, and did not have much time spent awake while sleeping.  Fitbit’s workout encouragement helped her tire herself out. “If I’d known my Fitbit would basically be a life coach and cheerleader right on my wrist, I’d have tried one out a lot sooner!” Zahra wrote. 

Interested in trying it yourself? If you’re a Premium member, all you have to do is wear your Fitbit device to bed for at least 14 nights of the previous month, and on the first day of the month, you’ll get your monthly Sleep Profile. Available on Google Pixel Watch, Sense 2, Sense, Versa 4, Versa 3, Versa 2, Charge 5, Luxe, Inspire 2, or Inspire 3. 

Want to find out more about Zahra’s experience? You can read the full story on the Google Keyword.

The post Googler Zahra Barnes Tried Fitbit Premium’s Sleep Profile for Two Months appeared first on Fitbit Blog.

- Elisa van der Plas
Fitbit Research Findings Show that Users Who Meet Physical Activity Recommendations Are Able to Improve Their Resting Heart Rate, Sleep, and More

Did you know that the famous 10K steps per day target wasn’t originally based in science? Manpo-kei, translated as “10,000-steps-meter,” was introduced by a Japanese pedometer manufacturer in 1965. As we know at Fitbit, a wide range of research has occurred since then, indeed suggesting that hitting this daily target can improve sleep duration and quality, have a positive impact on self-reported mental health, boost blood oxygen levels, and decrease resting heart rate

Research shows that it’s not only step count, but also intensity that matters. Since 2020, Fitbit has inspired Fitbit users to push up their physical activity levels with the introduction of personalized Active Zone Minutes (AZMs) minutes of high-intensity activity that are based on heart rate targets achieved for each minute spent on any workout that gets your heart pumping.

For this analysis, we investigated whether hitting the American Heart Association’s recommended physical activity target of 150 minutes per week of moderate intensity aerobic activity leads to measurable improvements in Fitbit users. We also took a look at approximately how long users should meet these physical activity targets to get the highest return on investment on these aspects of their health.

We analyzed 471 million AZMs and 106 billion steps of anonymous and consenting users who met the physical activity targets in February 2022, but not in January 2022, and assessed whether they saw corresponding improvements in their health compared with users who did not meet the targets in the same period. The results show positive health impacts across resting heart rate, HRV, sleep and stress management scores so long as at least one threshold is reached. Health benefits are even further pronounced when users achieve multiple recommendations.

Users who met both 10K steps per day and the 150 AZMs per week target saw improvements in multiple metrics compared to those who did not meet those thresholds. Specific improvements were as follows: 

Heart rate variability improved by 20 percent (6.1 millisecond or ms. difference) Resting heart rate lowered by 8.1 percent (4 bpm difference) Stress management scores lowered by 7.3 percent (5.4 difference)1 

In addition, users that met or exceeded only the 10K steps per day recommendation still showed a 3.44 millisecond higher heart rate variability (higher is better), 3.05 beats per minute lower resting heart rate, and 3.97 improvement in their stress management score than comparable users. 

Users that met or exceeded only the 150 AZMs per week recommendation showed a 3.08 ms higher heart rate variability, 1.35 beats per minute lower resting heart rate, and 5.08 higher stress management score than comparable users. These findings suggest that meeting even one of the targets may still yield improvements in your health.

Next, we looked at how long the same user who initially does not meet the physical activity targets needs to be active to start reaping the health benefits:

Reaching the 150 AZMs per week and 10K steps per day targets for as little as two weeks increased heart rate variability by 20 percent, decreased RHR by 4.3 percent, and increased sleep scores by 4.2 percent compared to remaining at below-target physical activity levels Users that managed to hit the physical activity targets for an additional two weeks (6 weeks total) also saw a 4.9 percent decrease in their resting heart rate² Importantly, these positive effects on health lasted for over 4 weeks even if activity later dropped!

Key recommendation: Shoot for 150 AZMs per week in addition to 10K steps per day for the biggest benefit. If that’s too much, aim for activity consistency balanced with some higher intensity workouts for measurable benefits. Use Fitbit’s Activity goals to set daily targets for steps and AZMs and remember to turn on those reminders to move! By enabling these features, Fitbit can help you set targets and achieve your health goals. 

1 This analysis was not designed to directly compare the AZM and step count physical activity targets as these distinct workouts are subject to different variables that affect health, such as measurement error. So it is possible that the associations we found with health are attributable to some other unobserved characteristic of the workout.

² As these analyses were observational in nature, we were unable to control for all confounding variables, so it is possible that the associations we found with physical activity and health are attributable to other, unobserved characteristics in the groups. However, other studies, including prospective randomized controlled trials, have shown comparable changes in RHR and HRV over a similar time period.

The post Fitbit Research Findings Show that Users Who Meet Physical Activity Recommendations Are Able to Improve Their Resting Heart Rate, Sleep, and More appeared first on Fitbit Blog.

- Eric Helms
Is Regional Hypertrophy Predictable?

Note: This article was the MASS Research Review cover story for January 2023 and is a review of a recent paper by Albarello et al. If you want more content like this, subscribe to MASS.

Key Points  The present study (1) measured acute EMG and muscle swelling (thickness and cross-sectional area, assessed via ultrasound) responses in the upper and mid chest after flat and incline benching.  A previous study (2) reported greater longitudinal upper chest hypertrophy and similar mid chest hypertrophy when comparing incline to flat benching. EMG responses differed in a predictable manner in the present study, with incline bench causing higher upper chest activity and flat bench causing higher mid chest activity. However, muscle swelling outcomes differed from the EMG outcomes, and also failed to comport with the longitudinal hypertrophy data from the prior study.   

Regional hypertrophy is the well-established phenomena (demonstrated herehere, and here) where non-uniform growth occurs in a muscle or muscle group. For example, if your biceps grew more near the elbow in response to training than they did near the shoulder, that would be an example of regional hypertrophy. In another example, Greg previously reviewed a study by Chavez and colleagues in which a group of untrained males performing incline bench press only, flat bench press only, or a mixture of both, experienced different hypertrophy patterns in the upper and mid pecs (2). Interestingly, there were no significant differences between groups, except that the incline bench press only group had a significantly greater (and quite substantial) increase in their upper chest thickness (i.e., the clavicular head of the pectoralis major) compared to the other two groups. To my knowledge, this is the only study examining longitudinal regional pec hypertrophy differences between groups performing different pressing exercises; however, there are a fair number of studies looking at acute responses (such as EMG) that you might think would provide some indication of what regional hypertrophy responses you’d get long term (34). The present study (1) in an interesting example, as the participants performed flat and incline bench press sessions while the researchers examined their upper chest and mid chest (i.e., the sternal head of the pectoralis major) EMG activity, and ultrasound-derived muscle swelling (i.e., acute muscle thickness and cross-sectional area changes) following training. This design allows us to compare these two acute proxy measures to see how they comport with one another, but we can also see if either acute measure lines up with the one existing study on longitudinal hypertrophy following incline and flat bench pressing (2). As one might expect, EMG activity was greater for the upper than the mid chest during incline bench press and the opposite was observed during flat bench, and these differences were significant between exercises. However, as I’ll discuss in this article, the normalization procedures used in the present study make it difficult to conclude much from these findings. Muscle swelling increased to a greater extent in the upper than the mid chest following the incline bench, and the opposite pattern was observed following the flat bench. However, when comparing absolute muscle swelling between exercises, the only significant difference was that mid chest swelling was greater following flat bench than incline bench, but notably, upper chest swelling was similar after both bench press variations. Therefore, acute muscle swelling did not follow the pattern of longitudinal regional hypertrophy observed in the one existing training study (2). In this review I’ll discuss the detailed findings of the current study and how they inform our ability (or lack thereof) to predict regional hypertrophy in response to specific exercise variations.

Purpose and Hypotheses Purpose

The purpose of this study was to determine if there are differences in surface EMG during, and muscle thickness and cross-sectional area after the incline and flat bench press in specific regions of the pectoralis major. Further, this study aimed to clarify whether “the muscle region of the pectoralis major with the highest sEMG [surface EMG] amplitude during exercise corresponds to the one with the greatest acute variations in cross-sectional area and/or muscle thickness.”

Hypothesis

The authors hypothesized “that the pectoralis major head with the greatest sEMG amplitude during exercise will be the one with the greatest acute variations in muscle architecture [i.e., muscle thickness and cross-sectional area].” 

Subjects and Methods Subjects

Thirteen injury-free, resistance-trained males (28.79 ± 4.46 years old; 174.64 ± 5.60cm tall; weighing 79.43 ± 8.99kg) participated in this study. Participants needed to have at least one year of resistance training experience, not be regularly performing other forms of physical activity, and have a 1RM flat bench of at least their bodyweight. 

Study Design

The participants attended four lab sessions. The first two were 1RM sessions and the last two were experimental training sessions. Specifically, the first session was a familiarization which included ultrasound measurements and a 1RM assessment where the participants tested both the flat and 45° degree incline bench press with 30 minutes rest between the two. The second session was a repeat of the first session that was performed for reliability purposes. I believe the exercise order of the 1RM test days was randomized, as exercise order was randomized in the training sessions, but this was not explicitly clear. The third session occurred 72 hours after the second 1RM testing session, and consisted of four sets of the flat or incline bench press (the exercise that was performed on each day was randomly determined) to failure with 60% of 1RM, during which pec EMG activity was recorded, and after which muscle swelling was captured. The fourth and final experimental session occurred 96 hours after the third session (the additional day was provided to allow for muscle damage recovery), and was a repeat of session three with the bench variation that had not yet been performed. The participants rested at least three minutes between these four sets on both days. Participants were asked to avoid any strenuous activity during the study. 

EMG 

Electrodes were placed on the clavicular and sternal head of the pectoralis major as shown here. EMG data were reported as the root mean square (RMS) of activity during the concentric phase of each rep within each set. Essentially, the RMS tells you the average power of the electrical signal from the muscle over the time period sampled. These RMS values for each head of the pec were normalized within each exercise separately to the highest RMS value observed for the specific pectoral head during each set. Meaning, the average EMG values for each pec region were scaled to their own peak within each exercise. Unfortunately, this presents a substantial limitation which prevents meaningful comparisons between muscle regions within, and between exercises. If you’re interested, the specifics of what the EMG findings in this study can tell you I discuss in the Criticisms and Statistical Musings section. 

Ultrasound-Derived Muscle Swelling

Changes in muscle thickness and cross-sectional area (which I refer to collectively as changes in “muscle swelling” in this article) were derived from panoramic ultrasound images guided by a custom made support to increase probe stability and standardize probe placement as shown here. The percentage change in cross-sectional area and muscle thickness was calculated by comparing the average thickness and cross-sectional area values from before to after each training session. The same technician analyzed all the images. 

Findings Reliability

The authors ensured an accurate relative load selection by recruiting trained participants with experience in the bench press, by having a familiarization session, and by repeating their 1RM tests twice to ensure reliability. Likewise, they took ultrasound measurements on each 1RM testing day to ensure muscle thickness and cross-sectional area reliability. They reported their reliability statistics, which aren’t worth discussing in detail in this review. But, in summary, reliability was quite good, indicating that we can have confidence in the reported values. 

EMG

As shown in Figure 1, within each exercise there was a significant difference reported in sternal and clavicular head EMG activity. The authors reported that flat bench press produced more activity (~30 %; p < 0.001) in the sternal than the clavicular head in every set, while the incline bench press produced more activity (~34%; p < 0.001) in the clavicular than the sternal head in every set. While these findings are intuitive, due to the normalization procedures, we actually can’t make inferences about relative excitation between the pectoral regions. 

Likewise, the authors reported that the flat bench produced significantly more sternal activity than the incline bench press (~35%, p < 0.001), while the incline bench press produced significantly more clavicular activity than the flat bench press (~28%; p < 0.001). However, once again, these between-exercise muscle activity comparisons should also be interpreted with caution, if made at all (see the Criticisms section). 

Muscle Swelling

As shown in Figure 2, muscle swelling responses differed between pectoral regions within-exercise, and between exercises. After flat benching, the participants’ sternal head increased in thickness significantly more than their clavicular head (~11.06%; p < 0.005) and this pattern held true for cross-sectional area (~5.42%; p < 0.005). Conversely, after incline benching, the participants’ clavicular head cross-sectional area increased significantly more than their sternal head (~10.81%; p < 0.001), and although this pattern roughly held true for muscle thickness, it was not significant (~6.83%; p = 0.08). Notably, however, the only between-exercise differences in muscle swelling were that the flat bench press produced significantly greater increases in muscle thickness (~3-fold; p < 0.001) and cross-sectional area (~17.89%; p < 0.001) in the sternal head compared to the incline bench press. Further, the increases in clavicular pec muscle swelling were not significantly different between incline and flat bench (p = 0.842-0.992), and in fact, appeared quite similar visually.  

Criticisms and Statistical Musings

As I’ve mentioned earlier in this article, the EMG data from this study don’t allow many useful inferences to be made. Initially when I wrote this article, I thought only the between-exercise data was subject to this limitation, but Greg thankfully (but unfortunately) caught that it also applied to the within-exercise data. To understand this limitation, we need to discuss normalization. Normalization is the process by which you scale raw EMG data into more usable information. But, which raw EMG data you choose to be scaled and what you scale it to, is very important. Notably, in this study, the authors used the average EMG activity for each pec region during each set for each exercise, and normalized it to the highest EMG activity produced by that same pec region, within that same set. This gives you the ratio of the average EMG activity of each pec region to its own peak EMG, during each set of each exercise. So, if a pec region reached a peak that actually was equal to its theoretical max excitability (which we wouldn’t know), and stayed at 90% of that peak value on average during each set, its value would be 90% (out of 100%). However, if the pec region reached a peak that actually was equal to only half of its theoretical max excitability (which again, we wouldn’t know), and stayed at 90% of that peak value on average, its value would also be 90%. Meaning, all these values really tell us are how close to the highest EMG activity (which may or may not have been very high relative to the muscle’s maximum capacity) recorded in each set was the average EMG activity in each set. If you’re wondering “ok, so what does that tell me?” Unfortunately, the answer is not very much. If you wanted to know the relative differences in muscle-specific EMG activity within and between the two lifts, EMG activity would need to be normalized to a theoretical muscle-specific maximum value, like a maximum voluntary isometric contraction (MVIC). An MVIC is exactly what it sounds like, an isometric contraction done with maximum effort using a joint angle that should provide a muscle-specific theoretical maximal EMG excitation value which can then be used as a reference across exercises (as I discussed here in more detail in the “limitations of EMG” section). Since the authors didn’t use this approach, you can’t actually make any inferences about relative EMG activity between pec regions, within or between exercises. With all that said, it’s worth mentioning that normalizing data to an MVIC introduces other limitations. There are assumptions as to whether MVIC performances represent true maximums, which also extend to the exercises they are compared against. Joint angle differences between exercises acutely alter muscle morphology and position, subsequently impacting EMG signal strength and acquisition. In addition, not all muscles have the same recruitment pattern. Some follow a superficial to deep pattern, while others deep to superficial, which can understandably impact surface EMG (5). Therefore, comparisons between different exercises which train the same muscle, even when based on MVICs, may not be valid representations of relative signal strengths, and therefore of expected longitudinal hypertrophy differences (6).

I have one final note, unrelated to EMG, which is a defense against potential criticisms rather than a criticism of this article. Some could reasonably disagree with how I’ve positioned differential hypertrophy in the upper and mid chest due to exercise selection as “regional hypertrophy.” While the clavicular and sternal heads of the pecs are both part of the pectoralis major, and not considered separate muscles, they are innervated by different motor nerves (7). Thus, they arguably function more like separate muscles than regions of the same muscle. In a prior era, say a decade ago, when the “evidence-based” community was generally skeptical of the concept of regional hypertrophy, you might hear someone point this out and claim a differential hypertrophy response in the upper versus mid chest was therefore not evidence of regional hypertrophy. However, at this point, with more awareness of the many studies showing the occurrence of regional hypertrophy, I didn’t think it was a distinction worth quibbling over. For completeness I wanted to acknowledge this nuanced point, but from a bodybuilding perspective, it’s immaterial, as it’s simply useful to know what exercises might be more effective for growing the upper and mid chest.

Interpretation

It would be nice to be able to predict which exercises reliably produce specific regional hypertrophy in specific muscles. As a competitive bodybuilder and a coach of physique athletes, I want that to be our reality. Unfortunately, we just aren’t there yet. Moreso, we need more data to determine which acute proxy measures might be able to predict hypertrophy long term, but we also have to accept that there will always be inherent limitations to such predictions.

To start, let’s summarize where we are with predicting regional hypertrophy based upon exercise selection. As I mentioned in the Methods and Criticisms sections, the EMG data in this study unfortunately can’t tell us very much. Despite this, it is still an important reminder regarding the challenges and complexities of predicting long term adaptations from acute measurements which each have their own unique limitations. Notably, while EMG activity is useful in other ways, it is limited in its ability to compare the hypertrophy stimuli between exercises (56). On the other hand, I can’t think of any reason why you can’t make valid muscle swelling comparisons between exercises. Interestingly, the muscle swelling findings suggest that flat bench press is an all around better exercise than incline for both upper and mid chest development (if we assume muscle swelling is indicative of the stimulus). Specifically, the flat bench produced greater increases in the thickness and cross-sectional area of the mid chest compared to incline, and similar increases in the upper chest. But what does acute muscle swelling really tell us? Well, a rationale to use it as a proxy measure for hypertrophy might be that increases in swelling could indicate increased blood flow, edema, and metabolic activity (which might be experienced as a greater pump) in a given region, likely due to work being performed by those specific muscle fibers. Indeed, there are some (albeit not strong) relationships between acute changes in muscle size and longitudinal hypertrophy (8). 

So, if we take the muscle swelling results at face value, we’d conclude that flat bench press is an overall superior chest builder compared to incline, as it better stimulates the mid chest and provides a similar stimulus to the upper chest. Further, we’d conclude that the anecdotal experiences of lifters that incline is a better upper chest developer are therefore incorrect. This could be true. Anecdotal experiences can be confounded by a lot of factors, and furthermore, individual differences can be amplified by loud or prominent voices and unduly influence what becomes “collective wisdom.” For example, imagine that a prominent bodybuilder with a strong influence in the community did actually get more upper pec hypertrophy from incline compared to flat bench due to their individual biomechanics, and then reported their experience as if it was the norm, when in fact, most people wouldn’t respond the same way. This type of thing happens, however, we don’t have to make this shaky, speculative comparison between a proxy measure and anecdotal evidence. Rather, we can make a slightly less shaky and speculative comparison (but just slightly) between the present study with existing data on hypertrophy.

As I mentioned in the introduction, Greg previously reviewed the only study I’m aware of in which the authors compared longitudinal changes in regional hypertrophy between groups performing only flat bench, incline bench, or a combination of the two (2). Unfortunately, their findings were directly contrary to what you’d expect based on the muscle swelling data in the present study, as the incline only group experienced significantly greater upper pec hypertrophy than both of the other two groups, with no other differences between groups. Therefore, while the present acute data indicates the flat bench is a hands-down better exercise for pec development in all regions compared to incline, the actual research on hypertrophy suggests the opposite. If your reaction to this comparison is that we should place more faith in the actual longitudinal hypertrophy findings, in principle, I agree with you. However, it’s worth noting that we only have this one study by Chavez (2) to go on, and, as Greg noted in his interpretation, the findings are a little odd. Specifically, upper pec thickness increased by more than 62% in the incline pressing group, which is an unheard of increase that stands out as an outlier compared to other studies. The reason for this massive degree of hypertrophy reported by Chavez is unknown, but to be clear, neither I nor Greg are calling foul. As discussed in Greg’s article, besides the magnitude of change, the data don’t look “funky.” So this finding could be accurate, but just confounded by other factors which inflate its magnitude such as muscle swelling (the measurement was taken within 24-48 hours of the last training session), the use of untrained subjects, poor measurement reliability, a combination of these factors or perhaps other, unknown variables. Nonetheless, we need more research to clearly determine the effects of incline and flat bench press on regional pec hypertrophy. 

To give you my personal take, it doesn’t make sense to me that flat bench press is just as effective at inducing upper chest hypertrophy as incline bench and it also doesn’t make sense that incline bench is just as good as flat bench at inducing mid chest hypertrophy, and of course, the two are mutually exclusive conclusions. This means I have to question both the findings of Chavez and the relevance of the muscle swelling findings in the present study. This puts me in a difficult spot, as I actually think it’s reasonable to speculate that differences in acute muscle swelling might be grossly predictive of long term hypertrophy. However, I think there might be a plausible explanation for why muscle swelling favored flat bench press. If you look at the inclusion criteria, the participants had a strength requirement for flat bench, but not incline. Thus, it’s possible that the trained participants were more trained on flat bench press than incline, and just couldn’t induce as much of a stimulus with the less familiar movement. While this sounds plausible, it’s admittedly not a rock solid explanation. If you look at the reliability data for the 1RM tests, both exercises were similarly reliable, and the participants’ incline bench 1RM was ~85% of their flat bench press 1RM, which seems like a reasonable percentage. Another possibility that Greg brought up, is that it could be related to posture. Since you’re sitting more upright with incline, that could just promote better venous drainage/less blood pooling during exercise, resulting in less swelling at the time of measurement. While possible, ultimately, I don’t have a good explanation for the (in my opinion) non-intuitive between-exercise muscle swelling differences, but it might just be that acute muscle swelling (and by proxy, your acute perception of a pump) just aren’t that useful as proxies for the hypertrophy stimulus. 

On that note, I think we simply need to test more proxy measurements such as T2 MRI (as discussed here), and perhaps muscle oxygenation (as discussed here) in addition to EMG activity (normalized differently) and muscle swelling, as well as more practical measurements like “perceived pump” and subjective assessments of muscle soreness after training to see if and how they associate with longitudinal changes in regional hypertrophy. However, even if we conduct such experiments, we have to set realistic (i.e., low) expectations for these proxies’ predictive abilities. To illustrate why, imagine an experienced lifter who needs a given amount of volume and effort to maintain their muscle size. Then, imagine that the lifter reduced their training to 1/10th of that volume for an extended period of time while maintaining high effort. Likely, they would slowly start getting smaller; however, if you took a battery of measurements during and after one of their effortful, reduced-volume sessions, you’d see plenty of EMG activity in the trained muscles, increased T2 MRI and muscle oxygenation in those same muscles, the lifter would get a pump and get sore afterwards (probably more than normal due to a degradation of the repeated bout effect), and they’d also experience large increases in acute muscle swelling. However, they’d still be getting smaller. My point is, there is a lot that goes into the outcome of hypertrophy, and no matter how good a proxy is, it simply can’t capture all the variables which influence it. 

To conclude, the present study is really interesting. It demonstrates unique EMG activity in the upper and mid chest during flat and incline bench pressing and distinct regional-specific muscle swelling responses. While these responses don’t comport with the current incline and flat bench longitudinal hypertrophy data we have, this area is ripe for further research.   

Next Steps

As I alluded to in the interpretation, there are two avenues for future research I’m interested in. For one, we need additional longitudinal research on hypertrophy following incline compared to flat bench press training to see if the observations of Chavez and colleagues (2) can be replicated. Additionally, we need longitudinal hypertrophy research that follows baseline proxy measures that might predict hypertrophy like T2 MRI, muscle oxygenation, and practical measures of subjective soreness and pump quality to see what relationships are strongest, and if they are regionally accurate. With this research conducted we might be able to potentially make better inferences about long term adaptation based on acute studies and possibly use certain proxies for training monitoring purposes as well. 

Application and Takeaways We don’t yet have good acute proxy measures to accurately predict hypertrophy broadly, let alone in a region specific manner. The present study demonstrates differences in muscle activity during flat and incline bench training, and muscle swelling following flat and incline bench training in the upper and mid chest. While intriguing, we need further research to determine if these data are reasonable proxies for predicting long term adaptation.          Get more articles like this

This article was the cover story for the January 2023 issue of MASS Research Review. If you’d like to read the full, 126-page January issue (and dive into the MASS archives), you can subscribe to MASS here.

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References Albarello JCDS, Cabral HV, Leitão BFM, Halmenschlager GH, Lulic-Kuryllo T, Matta TTD. Non-uniform excitation of pectoralis major induced by changes in bench press inclination leads to uneven variations in the cross-sectional area measured by panoramic ultrasonography. J Electromyogr Kinesiol. 2022 Dec;67:102722.  Chaves SFN, Rocha-Júnior VA, Encarnação IGA, Martins-Costa HC, Freitas EDS, Coelho DB, et al. Effects of Horizontal and Incline Bench Press on Neuromuscular Adaptations in Untrained Young Men. Int J Exerc Sci. 2020 Aug 1;13(6):859-872. Rodríguez-Ridao D, Antequera-Vique JA, Martín-Fuentes I, Muyor JM. Effect of Five Bench Inclinations on the Electromyographic Activity of the Pectoralis Major, Anterior Deltoid, and Triceps Brachii during the Bench Press Exercise. Int J Environ Res Public Health. 2020 Oct 8;17(19):7339. Trebs AA, Brandenburg JP, Pitney WA. An electromyography analysis of 3 muscles surrounding the shoulder joint during the performance of a chest press exercise at several angles. J Strength Cond Res. 2010 Jul;24(7):1925-30. Vigotsky AD, Halperin I, Lehman GJ, Trajano GS, Vieira TM. Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences. Front Physiol. 2018 Jan 4;8:985. Vigotsky AD, Halperin I, Trajano GS, Vieira TM. Longing for a Longitudinal Proxy: Acutely Measured Surface EMG Amplitude is not a Validated Predictor of Muscle Hypertrophy. Sports Med. 2022 Feb;52(2):193-199. Haley CA, Zacchilli MA. Pectoralis major injuries: evaluation and treatment. Clin Sports Med. 2014 Oct;33(4):739-56. Franchi MV, Longo S, Mallinson J, Quinlan JI, Taylor T, Greenhaff PL, et al. Muscle thickness correlates to muscle cross-sectional area in the assessment of strength training-induced hypertrophy. Scand J Med Sci Sports. 2018 Mar;28(3):846-853.

The post Is Regional Hypertrophy Predictable? appeared first on Stronger by Science.

- Eric Trexler
Meta-Analyses Are the Gold Standard for Evidence, but What’s the Value of Gold These Days?

Note: This article was the MASS Research Review cover story for December 2022 and is a review of a recent paper by Kadlec et al. If you want more content like this, subscribe to MASS.

Key Points The presently reviewed paper assessed the 20 most-cited meta-analyses in the field of strength and conditioning. After critically appraising these meta-analyses, the researchers found that 85% of them contained at least one statistical error. In exercise science and sports nutrition, it’s very common for meta-analyses to contain errors. The most common errors include ignoring outliers, conflating standard errors and standard deviations, ignoring within-study correlations, focusing on within-group results, and failing to account for within-study variance. The present article leans on previous MASS articles to show examples of these errors, and provides a thorough checklist to guide the process of critically reading meta-analyses in the future.

It’s time to talk about the big vaccine argument. I know these discussions get heated, but we can’t shy away from these types of debates just because they’re controversial or contentious. One side says the jab is a gamechanger, and that vaccine skeptics require an impractical standard of evidence. In fact, a reputable vaccine proponent accused a notable skeptic of building their argument on “cooked” statistics. The other side openly questions the quality of the data, and even accuses a key vaccine proponent of cherry-picking and being eager to “accept mythical reports” supporting his argument. By now, it’s probably pretty obvious which vaccine I’m talking about.

The year was 1904, and typhoid fever was top of mind for the British military. Intrepid bacteriologist Almroth Wright had introduced his first version of a typhoid vaccine in 1896, and it was rolled out slowly in subsequent years. By 1904, Karl Pearson was asked to conduct a statistical analysis of the available typhoid vaccine data. You might recognize that name from the Pearson correlation, which is mentioned in numerous MASS and Stronger By Science articles (but beware: if you wish to maintain a favorable, or even neutral view of esteemed statisticians of the early 1900s, don’t read any biographical material about them whatsoever). This task sounds simple enough, but there was one major issue: Wright absolutely despised statistics, saw little value in the statistical analysis of vaccine efficacy, and kept records accordingly – much of the available data were scattered, disorganized, and messy (2). Pearson attempted to aggregate the data from several distinct sources into a singular pooled analysis, in a project that is broadly viewed as the first iteration of contemporary meta-analysis (3). 

On the positive side, this pioneering work introduced new methods and highlighted key considerations that set the future trajectory for the refinement of meta-analyses as we know them today. It was rudimentary and violated many “best practices” by current standards, but it was a critical first step toward modern meta-analysis. On the negative side, you could argue that Pearson’s conclusion was incorrect. He called for more evidence (which is typically a defensible stance to take), and was working with some pretty crappy data (and the computing power of a pencil and paper), but ultimately suggested that the use of this typhoid inoculation should not yet be a “routine method” for the military. This initiated a fierce and public war of words between Wright and Pearson, as I alluded to previously. Ultimately, the British army did administer a large number of typhoid vaccines, and this decision is estimated to have saved hundreds of thousands of lives by 1915 (2). After World War I, it was abundantly clear that the vaccine worked, and its administration became a routine practice. 

Now, a very reasonable question: Why bother with the history lesson to kick off a Stronger By Science article?

First, it’s interesting, and interesting things are valuable for their own sake. Second, as systematic reviews and meta-analyses have become all the rage in the last 20-30 years, assuming the highest position in the hierarchy of evidence, it’s important to acknowledge the humble beginnings of the meta-analysis, and to remember that a meta-analysis is only as good as the quality of the underlying data and the suitability of the analytical approach. Nonetheless, over a century has passed, and technological leaps have unlocked previously unimaginable possibilities with regards to data reporting, management, and analysis. As such, modern meta-analyses are sure to be free from the errors that plagued the initial attempt in 1904… right?

That’s exactly what the presently reviewed paper (1) sought to explore. The researchers identified the 20 most frequently cited meta-analyses in the field of strength and conditioning, then checked them for statistical errors. Their findings were a bit sobering, as a staggering 85% of these papers contained errors. This article will not devolve into an overly technical, jargon-filled dissertation on statistics, though. Rather, we’re going to look at real-world examples of the most common statistical errors in meta-analyses, and focus on practical ways to easily identify these errors to avoid being misled by erroneous meta-analyses in the future.

Purpose and Hypotheses Purpose

The purpose of the presently reviewed paper was “to review common statistical errors in meta-analyses and to document their frequency in highly cited meta-analyses from strength and conditioning research.”

Hypotheses

The researchers did not state specific hypotheses. If we consider the purpose of the paper, this makes a lot of sense. It’s very doubtful that these researchers woke up one day and randomly pondered whether or not meta-analyses in the strength and conditioning literature tend to contain statistical errors. Rather, I assume that these researchers saw a ton of statistical errors in meta-analyses over the last few years, and decided that it was time to bring some focused attention to this issue in an organized manner. We can infer that they expected to see a high prevalence of errors in this literature, because we can safely assume that they decided to initiate this project as a direct result of routinely observing so many errors.

Methods

The researchers began by reviewing a single published meta-analysis containing five separate errors that are commonly observed in the field of exercise science. Coincidentally, the paper they reviewed in this example happened to be the ninth-most-cited strength and conditioning meta-analysis. The presently reviewed paper was “initially conceived as a teaching article,” but it looks like the process of working through the errors in this meta-analysis piqued their curiosity. So, they did a systematic literature search aimed at gathering the 20 most-cited meta-analyses related to strength and conditioning topics. They had to omit one of them (it was already retracted because of statistical errors), so they included the 21st most-cited meta-analysis to take its place and round out the even list of 20. They worked through each meta-analysis to quantify the prevalence of the five common statistical errors at the heart of their initial inquiry.

Findings

Looking at the 20 most-cited meta-analyses in the field of strength and conditioning, 85% contained at least one statistical error. The five common errors they screened for, and the prevalence of each individual error (% of meta-analyses committing the error), are as follows:

Ignoring outliers: 25% Miscalculated effect sizes that arise from using standard errors instead of standard deviations: 45% Ignoring within-study correlation (failing to account for correlated observations): 45% Focusing on within-group rather than between-group results: 45% Failing to account for within-study variance (failing to weight studies): 40%

If you’re a bit uncertain about the precise meaning of some of these errors, fear not – that’s what the rest of this article is for. Rather than wrestling with jargon-filled textbook definitions, we’ll take a look at real-world examples of these errors, with a focus on previous MASS Research Review articles. 

Criticisms and Statistical Musings

This is certainly not a criticism, but it’s an important detail to keep in mind: these researchers stacked the deck in a way that might actually understate the prevalence of the problem at hand. By focusing on the top 20 most-cited meta-analyses in the field, they theoretically gave themselves a bit of an uphill battle in terms of error identification. Citations are a type of currency among researchers; whereas social media influencers might view content engagement statistics as markers of success, researchers lean on citations as an analogous, indirect (and imperfect) form of evidence that their research is making an impact and being favorably appraised by their peers. As meta-analyses have become more and more popular in our field, there has been a huge influx of low-quality meta-analyses that are essentially flying under the radar – they aren’t making a big enough splash to garner widespread citations or generate substantial chatter, so the papers aren’t getting enough attention for errors to be detected and flagged. 

I know what you’re thinking – Eric, these are peer-reviewed papers, so the reviewers are likely to catch potential errors in these low-profile papers. Well, here’s the worst-kept secret in exercise science: very few people truly know how to conduct a meta-analysis start-to-finish, which is the level of familiarity and expertise needed to provide a sufficiently thorough review of a meta-analysis. I know a great many researchers; in fact, for about 10 years, academics were the only people I spent a meaningful amount of time with. I only know a handful who would be totally comfortable firing up their computer and running a meta-analysis that is fully aligned with current statistical best practices. So, who is doing and reviewing all these meta-analyses in exercise science? To answer that question, I’ll refer you to the 85% error rate, and once again remind you that this error prevalence pertains to, theoretically, the cream of the crop in exercise science meta-analyses. The error rate is astronomical, and it would be illogical to expect otherwise.

Interpretation

As a writer for Stronger By Science and MASS, my primary goal is to ensure that readers are fully up-to-speed with the most relevant exercise and nutrition literature. However, my more ambitious goal is to help readers stay ahead of the curve (when possible), which is a very dangerous game. The fitness industry is full of people who say their anecdote-driven methods allow them to stay “ahead of the science,” while science will eventually contradict at least 80-90% of their “innovations” in due time. In other words, most people who try to stay ahead of the curve are fooling themselves and have an unacceptably low success rate with their speculative practices. Once your success rate falls below a certain threshold, it becomes hard to argue that you’re even leaning on evidence-based principles anymore, so attempts to outpace the science should be approached with a high level of discernment and caution. In this example (common errors in meta-analyses), I am pleased to say that MASS readers were truly ahead of this particular curve. All the way back in Volume 4, I wrote the following:

“We can’t uncritically accept the results of meta-analyses at face value. Of course, I would agree in theory that meta-analyses belong at the top of the hierarchy of evidence. However, that placement is based on a number of very, very critical assumptions: the search identified all of the relevant studies, the authors weeded out the studies that shouldn’t be included, the individual studies were carried out and reported effectively, an appropriate selection of outcomes was extracted from the studies, effect sizes were calculated effectively, and the statistical approach to the meta-analysis was appropriate. If one or more of these assumptions are violated, that place atop the hierarchy of evidence doesn’t mean much anymore, and it’s quite common to see one or more of these assumptions violated.”A New Meta-Analysis Says… Arginine Works Now?, MASS Research Review, Vol 4 Issue 7

With this in mind, let’s look back at some previous MASS articles to explore the most common errors flagged in the presently reviewed paper.

Ignoring outliers

Back in Volume 4 of MASS, I reviewed a meta-analysis about arginine supplementation (4). In doing so, I noticed some outliers that the researchers didn’t really account for. To visually examine the impact of these outliers, I provided two versions of the funnel plot: one with outliers included, and one with outliers removed. In ideal scenarios, a funnel plot should look like – you guessed it – a funnel. Studies with more precise effect size estimates (which tend to be larger studies) should cluster very closely around the overall, pooled effect size from the meta-analysis. Studies with less precise effect size estimates (which tend to be smaller studies) should be expected to display greater variation. This causes them to “fan out” in a symmetrical pattern, which gives the plot its “funnel” shape. Without getting too bogged down in the details (more on this later), a funnel plot gives us a visual representation of some key statistical characteristics of the literature being summarized in a meta-analysis. So, let’s see how outliers impacted the arginine funnel plot (Figure 1):

Graphic by Kat Whitfield

The original funnel plot (A) was, frankly, a disaster. After removing the outliers (B), it looked very suitable. When you get a funnel plot that looks like version A, it’s next to impossible to take the pooled effect seriously at face value. But that leads to a valid question – how did funnel plot A come to be so disastrous? That leads us to error #2.

Miscalculated effect sizes that arise from using standard errors instead of standard deviations

We saw this exact error in the arginine meta-analysis I reviewed back in Volume 4 of MASS. The forest plot from this study is presented in Figure 2.

Graphic by Kat Whitfield

When you examine Figure 2, three data points should catch your attention. There are three separate data points representing effect sizes well above 3.0, which would be large enough for the authors of the presently reviewed paper to call them “outliers” without even looking at the rest of the data. Upon closer inspection, it was clear that the researchers conducting the arginine meta-analysis had misinterpreted some standard error values, believing them to be standard deviations. This is a pretty huge deal, because the effect sizes reported in this meta-analysis were calculated by dividing the difference in means (arginine group versus placebo group) by the pooled standard deviation. We can convert standard error to standard deviation by multiplying standard error by the square root of the sample size; based on this equation, it’s clear that standard error values are much smaller than standard deviation values, and this difference gets larger as the sample size increases.

To illustrate, imagine a study comparing the effects of two training interventions on gains in squat 1RM. At baseline, the subjects had an average squat 1RM of 100 ± 20kg. At the end of the study, we observe that one training protocol led to a 30kg increase in squat strength, while the other protocol led to a 20kg increase in squat strength, resulting in a between-group difference of 10kg. To correctly calculate the effect size, you divide that 10kg difference by the pre-training standard deviation for squat 1RM strength (20kg), resulting in an effect size of 0.5. However, if there were 16 subjects per group, you’d divide the pre-training standard deviation by the square root of 16 (four) to calculate the standard error: 20/4 = 5kg. So, if you erroneously calculated an effect size by dividing the difference in strength gains (10kg) by the standard error (5kg) instead of the standard deviation (20kg), you’d calculate an effect size of 2.0, thus inflating the magnitude of the effect four-fold. If there were 100 subjects per group, the standard error would be 2kg, and the erroneously calculated effect size would be 5.0.

So, if you check out a forest plot and you see an effect size (or a few) that looks unusually huge, it’s always a good idea to make sure the researchers didn’t mistake a standard error for a standard deviation. In fact, the authors of the presently reviewed paper found that about 60% of the effect sizes >3.0 that they observed were a direct result of this error.

Ignoring within-study correlation (failing to account for correlated observations)

Back in Volume 4 of MASS, I reviewed a meta-analysis about the effects of carnitine supplementation on recovery from exercise (5). Within that article, I included a forest plot (Figure 3).

Graphic by Kat Whitfield

When examining Figure 3, you might notice that there are multiple studies, as identified by the combination of author and year in the left-hand column, contributing several different effect sizes to the same analysis. When a meta-analysis treats (from a mathematical perspective) a single study as if it were two separate studies, this is known as “double counting,” and treating a single study as if it were three separate studies is known as “triple counting.” The more times you count a single study in a meta-analysis (without appropriate mathematical adjustments), the worse it gets. Imagine you’re in a group of five total people voting on an important decision, but one person in the group gets three votes. If the decision ends up being 5 “yes” votes to 2 “no” votes, the triple-counted voter has made the decision appear far more lopsided than it truly is. If the triple-counted voter decided to vote “no” while everyone else voted “yes,” the result would be 4-3, but this would make the decision seem a lot closer than it really was. When researchers fail to account for studies that are using “extra votes” in a meta-analysis, the studies in question have an inappropriately exaggerated impact on the pooled effect estimate, and may lead researchers toward an inappropriately inflated confidence level in the pooled effect estimate. For more details about double counting, along with an in-depth example, be sure to check out Greg’s article from Volume 5.

Focusing on within-group rather than between-group results

Back in Volume 4 of MASS, I reviewed a meta-analysis about the effects of vitamin D supplementation on strength outcomes (6). In my article, I noted the following:

“Typically, for this type of literature, you’d calculate the effect size based on the change in the placebo group (from pre-testing to post-testing), the change in the vitamin D group, and then some form of standard deviation for each group– either the standard deviation of the pre-test or post-test value, or the standard deviation of the change from pre- to post-testing. For the current meta-analysis, they took a very different approach. Effect sizes were calculated using only the pre-test value in the vitamin D group, the post-test value in the vitamin D group, and the standard deviations at each time point. This is quite atypical, and totally ignores a key, defining feature of these studies, which is that they included a placebo group. The strength of the placebo-controlled design is that we can directly evaluate the effect of the treatment above and beyond the effect of the placebo; to ignore this in the effect size calculation is to adopt a less informative interpretation of each study’s individual results.”Shedding Some Light on Vitamin D Supplementation: Does It Increase Strength In Athletes?, MASS Research Review, Vol 4 Issue 2

From a theoretical perspective, this error detracts from key concepts that make randomized, placebo-controlled trials so robust and informative. From a practical perspective, this error typically leads to inflated effect sizes. In a placebo-controlled trial, we often (but not always) expect that the placebo group will make some degree of positive progress, and our research question focuses on the added benefit of the experimental treatment, above and beyond the positive progress experienced in the placebo group. Failing to frame the treatment group’s results relative to the results observed in the placebo group often leads to a situation where the meta-analysis is not purely quantifying the independent effect of the experimental treatment, but is actually quantifying the additive effects of the experimental treatment plus the gains that would have been observed in a placebo group.

Failing to account for within-study variance (failing to weight studies)

All the way back in Volume 3 of MASS, Dr. Zourdos reviewed a systematic review about the effects of sodium bicarbonate supplementation on intermittent exercise performance (7). When interpreting this body of literature, the researchers used a semi-quantitative analytical technique known as “vote counting.” Basically, they tallied up the studies that found a statistically significant effect, tallied up the studies that didn’t find a statistically significant effect, and compared the numbers. We shouldn’t judge this decision too harshly, as the researchers did not frame their paper as a meta-analysis, used this technique for descriptive purposes rather than framing it as a nuanced quantitative analysis, and interpreted their results with an appropriate level of caution. Nonetheless, it’s an example of drawing “unweighted” inferences from a body of literature. With this approach, every study gets exactly 1.0 “votes,” regardless of how large the study was, or how precise its effect size estimate was. 

If you live in the United States, we can lean on the US Congress as a practical example of weighted versus unweighted approaches. The Senate is unweighted; every single state gets two representatives. In contrast, the House of Representatives is weighted; larger states get more representatives (based on their state population), and therefore have more relative influence within the House of Representatives. With a meta-analysis, we want to use weighted analyses whenever possible, because it’s statistically appropriate for some studies to have far less influence on the pooled results than others. Most meta-analyses in our field use “inverse variance weighting,” which functionally means that studies are weighted by sample size. Sample size and the variability of the observed results both influence variance, but, in general, larger studies have lower variance, and are thus more heavily weighted in meta-analyses. If one study has five subjects per group, and another study has 100 subjects per group, you’d probably want to give more weight to the study with 100 subjects per group. The presently reviewed study found that 40% of the most-cited meta-analyses didn’t do this type of weighting, and thus implicitly assume that a study with 100 subjects per group and a study with 5 subjects per group should receive equal weight.

A major caveat

The purpose of this article is to be helpful and pragmatic rather than nitpicking for the sake of pedantry. With this in mind, I want to acknowledge that there are varying degrees of meta-analysis sins, and that the severity of the sin often depends on the surrounding contextual factors and interpretation. For example, mixing up standard errors and standard deviations is a major, unforgivable sin – it totally messes up your analysis, and it’s wrong in all contexts. Double-counting or triple-counting samples (failing to account for correlated observations) typically isn’t quite as egregious, but there’s still no context where it would actually be justifiable, and there are way too many viable ways to deal with this issue in user-friendly softwares for researchers to keep committing this error. However, there are situations where certain “statistical errors” are forgivable, or even acceptable, as long as they’re interpreted appropriately.

For example, I previously mentioned the concept of “vote counting” when discussing a systematic review on sodium bicarbonate. If the researchers interpreted this as a robust, nuanced, quantitative analysis, that’d be incorrect and inappropriate. However, if you’re tallying up votes just to describe the general landscape of the literature, then following up with a very nuanced qualitative analysis, there’s really nothing wrong with that. For another example, I think some people will be surprised to note that one of the most frequently cited protein analyses in the evidence-based fitness world (8) technically commits two of the five errors reviewed in this article – and I’m totally fine with it.

Graphic by Kat Whitfield

You’ve probably seen Figure 5 before. It’s the figure (and analysis) mostly commonly used to support the daily protein recommendation of 1.6-2.2 g/kd/day for lifters. I think it’s a very helpful and informative figure, and I think it provides a nice, surface-level overview of the literature linking daily protein intakes to increases in fat-free mass. However, unless I’ve misunderstood the description of this analysis in the original paper, it appears to focus on within-group rather than between-group results, and it doesn’t appear to account for within-study variance (that is, it doesn’t weight studies). So, does this figure perfectly conform with the most rigorous statistical principles in the realm of meta-regression? No. Does it provide an informative and insightful look at the data? Absolutely.

I spend a great deal of time and effort exploring the interpretation of this figure in my other full-length article this month, so I don’t want to belabor the point by reprinting that information here. However, the concise summary is that this figure is very informative, but needs to be interpreted in a nuanced and contextualized manner. It would be incorrect to say “we know that the optimal range for daily protein intake begins at precisely 1.6g/kg/day, due to a robust statistical analysis that is unimpeachable and beyond reproach.” Even if we ignore the lack of weighting and the focus on within-group effects, the 95% confidence interval spans from around 1.0-2.2 g/kg/day anyway. 

To more appropriately interpret this figure, we need to begin by acknowledging an unstated assumption. Before we even look at it, we are assuming the following: “if protein has such a big impact on hypertrophy, we should be able to see glimpses of that relationship, even if we neglect to weight studies and control for study characteristics that differ among individual studies.” In other words, this figure isn’t actually filtering out the “signal” from the “noise,” but the signal should be detectable nonetheless. Then, upon scanning the figure, we can broadly conclude that higher protein intakes (greater than around 1.2 g/kg/day) seem to be better than lower intakes (less than around 1.2 g/kg/day), but returns start to diminish at a certain point. Our “best estimate” of that point would be 1.6 g/kg/day based on this relatively surface-level analysis, but this estimate is extremely imprecise, with a 95% confidence interval spanning all the way from 1.0 to 2.2 g/kg/day. Visually, it looks like intakes above 1.2 g/kg/day are pretty solid, but lifters who would rather overshoot than undershoot their protein needs might prefer to aim for a daily intake target above 1.4 or even 1.6 g/kg/day. I walk through this example to illustrate an important point: it’s okay to visualize, examine, and even analyze data using methods that are a bit unconventional or slightly incongruent with the most rigorous statistical standards possible. However, the resulting interpretation should transparently reflect the nuance and rigor of the analytical approach. Surface-level analyses can be informative and useful without being precise, but we have to interpret the conclusions with an appropriate level of caution and imprecision.

The meta-analysis checklist

Now that we’ve gone through the most common errors in meta-analyses, I’d like to take the practical application one step further by going through a list of items to check as you’re reading through a meta-analysis on your own. For this list, I am assuming that the researchers thoughtfully selected a defensible research question and conducted their literature search proficiently, which frees us up to focus on the most pertinent issues that impact the analysis itself. This list certainly isn’t exhaustive or fully comprehensive, but it’s a practical guide that hits the most practically important highlights.

1. Are the data reported at the group-level or participant-level?

In exercise science meta-analyses, data are almost always reported and analyzed at the group level. For example, the overall effect size of a study will be determined by comparing the results in the supplement group to the results in the placebo group, and the whole study contributes one single data point to a meta-analysis. However, there are sometimes opportunities to conduct participant-level meta-analyses, where each individual data point from raw studies is included in a massive meta-analysis that retains individual, person-level data points instead of collapsing everything into a group-level summary. So, if you’re pooling the results from 10 studies with 100 participants each, a “standard” (group-level) meta-analysis would have 10 data points, but a participant-level meta-analysis would have 1,000 data points. When you come across a participant-level meta-analysis, you’re in luck! This is about as good as it gets in the world of meta-analyses (9), but all standard caveats apply – crappy data or crappy analytical techniques still yield a crappy meta-analysis, even when participant-level data are involved. If you’re reading a participant-level meta-analysis, check the methods to make sure that the researchers dealt with correlated data (within-study correlations) appropriately. In my opinion, linear mixed models (which may be described as multilevel models, hierarchical models, or general linear models with random effects) are the best way to go with a participant-level meta-analysis. 

2. Are there enough studies or participants to draw strong conclusions?

The theoretical advantage of a meta-analysis comes down to strength in numbers. As we gather more and more studies on a particular topic, we have opportunities to become more confident in the typical effect of the intervention, and to explore key factors that might modify the effectiveness of the intervention. But where exactly is the inflection point – how many studies do you need before a meta-analysis actually becomes meaningfully more informative than looking at the one or two randomized controlled trials that are most pertinent to your intended application?

It’s truly hard to settle on a generalizable heuristic or rule of thumb, but let’s look at an example. Back in Volume 3, I reviewed a meta-analysis of studies investigating the effects of betaine supplementation on fat loss (10). There were multiple outcomes analyzed (body weight,  BMI, waist circumference, fat mass, and body-fat percentage), but for each individual outcome, there were only 2-5 effect sizes to actually pool and analyze. These studies differed across a range of important study characteristics, but there weren’t nearly enough studies to actually sort through the impact of those study characteristics in a meaningful way. Furthermore, for most outcomes, a pair of studies by the same author carried around 70-75% of the weight for the analysis. In other words, the results of the meta-analysis were basically the results of the two studies by Cholewa et al. In some cases, a meta-analysis with only a few small, heterogeneous studies tells us no more, and potentially even less, than a large, well-controlled randomized trial. 

Graphic by Kat Whitfield

When it comes to interpreting meta-analysis, I think the size of a meta-analysis falls on a spectrum that greatly impacts interpretation. A meta-analysis with a few heterogeneous studies can lead to some unreliable conclusions, and should be approached very cautiously. A meta-analysis with a few homogeneous studies can actually give us a more reliable effect estimate than any of the individual papers, but we can’t generalize the conclusion to other contexts (the meta-analysis is meaningful because the studies are so similar, but the conclusion is necessarily tied to the characteristics that made the studies so similar, such as population, duration, intervention details, outcome of interest, and so on). As a meta-analysis gets even bigger (let’s say more than a handful of studies, but less than two dozen), we find ourselves in a middle ground. These analyses can help us generate some very insightful hypotheses for future research, but we shouldn’t draw any overly confident conclusions. For example, I did a meta-analysis on citrulline supplementation (11), which was reviewed in MASS before I became part of the team. In that paper, I emphasized that, due to the small number of studies included (13 independent samples from 12 total studies), the results should be viewed as fairly preliminary, and should be used to generate hypotheses more so than conclusions. Eventually, meta-analyses get large enough to start really shining. Once you’ve got a few dozen studies or more, you can start to use a wide range of statistical tools to sort out nuanced interpretations of the literature. For example, you eventually get enough data to meaningfully analyze whether a particular dosage is more effective than another, whether women experience larger effects than men, whether the intervention works better for people who are exercising than those who are not, and so on. 

Now, what should researchers do when they systematically review the literature, apply their inclusion and exclusion criteria, but find that there isn’t enough data to provide a meaningful and informative quantitative analysis of pooled data?

That’s easy. They should move forward with the systematic review, and forgo the calculation of a pooled, quantitative analysis. This would involve going through the individual studies to provide a nuanced interpretation of the relevant research, and trying to provide a thorough, qualitative analysis of the literature as a whole. A common misconception is that meta-analyses are a “step up” from systematic reviews – this is categorically incorrect. Systematic reviews exist at the very top of the hierarchy of evidence, and meta-analyses are simply a particular type of systematic review that happens to include a quantitative analysis of pooled data. Along these lines, you might notice that many meta-analysis titles include the phrase “a systematic review and meta-analysis,” or “a systematic review with meta-analysis.” A systematic review may or may not include a meta-analysis component, and the inclusion or omission of a meta-analysis does not make a systematic review inherently better or worse.

The decision to include a quantitative analysis is not dictated by ambition or preference, but by the characteristics of the literature that you’re systematically reviewing. If adding a quantitative meta-analysis would be informative and the data have the necessary characteristics to enable a pooled analysis, then researchers should go for it. If the systematic review returns a set of data that are unsuitable for a pooled analysis, then researchers should keep the systematic review qualitative instead of forcing a flawed analysis. In other words, systematic reviews are the top level of the hierarchy of evidence, and researchers should make their systematic review only as quantitative as the characteristics of the data indicate – no more, and no less. A systematic review with no meta-analysis provides an unequivocally better summary of the evidence than an ill-advised meta-analysis that yields an unreliable effect estimate.

3. Are these studies similar enough to even think about combining their data?

Let’s keep rolling with the previous example about betaine and fat loss. This analysis included a very small number of studies, and the included studies varied considerably. Some studies sampled untrained subjects, others sampled trained subjects; some studies sampled lean and active participants, while others sampled relatively inactive people with obesity; some studies included resistance training, others did not; some studies were less than 2 weeks long, others were in the 6-12 week range. So, when you pool these findings to look at an “average” result, what exactly are you looking at? It’s kind of like taking a sweet meal, a savory meal, and a spicy meal, throwing them into a blender, and trying to judge the chef’s skill level based solely on the disgusting smoothie that should’ve never been made in the first place. There’s some stuff that we simply shouldn’t mix if we’re trying to judge the chef fairly, and if we wish to get an informative and unbiased summary of a body of research, we have to ensure that the elements should actually be combined in the first place.

So, what should researchers do when they systematically review the literature, apply their inclusion and exclusion criteria, but find that the studies aren’t similar enough to justify combining their data? Once again, they should move forward with their systematic review, and simply forgo the calculation of a pooled quantitative analysis. 

4. Are the measured outcomes similar enough to even think about combining them?

Every now and then, you’ll come across meta-analyses that cast a very, very wide net when searching the literature, or use very lax inclusion and exclusion criteria to determine which studies “make the cut” for analysis. In some cases, you’ll end up with studies that measure meaningfully different outcomes. For example, I wrote the following when reviewing the arginine meta-analysis in Volume 4:

“The analysis also includes at least one outcome that probably shouldn’t be categorized as a ‘performance’ outcome. In the study by Pahlavan, the Harvard Step Test was used to create a performance metric for the sample of trained soccer (football) players. For this test, you step up and down from a 20cm box for 5 minutes, at a cadence of 30 step-ups per minute. Your score for this test is calculated solely based on how rapidly your heart rate returns to normal after exercise. While this might be a decent proxy for general cardiovascular fitness levels in the general population, I’d hardly consider this a performance test in a sample of soccer (football) players, so I think this study probably should’ve been excluded from consideration.”A New Meta-Analysis Says… Arginine Works Now?, MASS Research Review, Vol 4 Issue 7

This is one particularly extreme example in which the purported performance outcome is simply not a performance outcome, but we wrestle with less extreme examples all the time. For example, a meta-analysis on strength outcomes has some important, context-specific decisions to make: based on the research question, can we really treat maximal isometric handgrip strength as being similar to a 10RM squat test? In an endurance-focused meta-analysis, can we really treat a three-minute endurance test as being similar to a two-hour endurance test? The answers to these questions are heavily context-dependent, but they should always be top of mind when reviewing an exercise-focused meta-analysis.

For another example, let’s revisit Greg’s review of a periodization meta-analysis (12) from Volume 1. When looking at the data, it was clear that there were some major outliers, which were subsequently removed from one version of the analysis. As Greg noted:

“Several of the effects eliminated weren’t ‘fair’ comparisons of periodized and non-periodized training. A few studies compared periodized resistance training versus non-periodized resistance training roughly equated for average intensity and volume versus single-set non-periodized resistance training. In those studies, you’d extract one effect for challenging periodized versus challenging non-periodized training, and one effect for challenging periodized versus incredibly easy non-periodized training. The first effect may have shown a slight benefit for periodized training, while the second effect may have shown a massive benefit for periodized training, though the second effect would be comparing apples and oranges. A couple other studies compared challenging periodized training to only single set non-periodized training.”Does Periodization Lead to Faster Strength Gains?, MASS Research Review, Vol 1 Issue 4

In this example, the meta-analysis would basically contain two different groups of effect sizes – the ones that compare periodized training to relatively similar non-periodized programs, and the ones that compare periodized training to dramatically less arduous training programs. In simple terms, these different types of effect sizes represent truly different things, and it would be ill-advised to combine them in a manner that treats them as equivalent metrics.

So, what should researchers do when they systematically review the literature, apply their inclusion and exclusion criteria, but find that the measured outcomes aren’t similar enough to justify combining their data? Once again, they should move forward with their systematic review, and simply forgo the calculation of a pooled quantitative analysis. 

5. Have effect sizes been calculated correctly?

Effect sizes can take many forms – far too many to thoroughly explore in this section. If you’re interested in getting a sense of the common effect size outcomes used when comparing groups (the most common type of comparison observed in exercise science meta-analyses), be sure to check out this paper by Lakens (13). If you’re interested in exploring the broader spectrum of effect size metrics for meta-analyses (including observational correlations, dichotomous outcomes, and a variety of other analytical approaches), check out the supporting documentation for the R package called “metafor.” However, I want to provide a few key details to look out for when assessing effect size calculations.

First, there are standardized and unstandardized effect sizes. Every now and then, you’ll be dealing with a meta-analysis in which every study is truly measuring the same outcome (for example, weight loss in kilograms). When you can leave an effect size in its raw, unstandardized units, it’s the ideal route to take. However, we rarely have this luxury in exercise science; a group of “strength-focused studies” might measure, for example, bench press 1RM, squat 3RM, or maximal isokinetic leg extension. When this is the case, meta-analysts calculate a standardized effect size, which converts each metric into generic units that aim to describe the size of the effect relative to the standard deviation of the measurement. Most commonly, you’ll see papers report Cohen’s d values, Hedges’ g values, or generic “standardized mean differences.” These are all interpreted similarly, but you can read this paper to explore the distinctions among the various effect size metrics that fall under this umbrella.

Practically speaking, there are two primary issues to look out for. As discussed previously, some meta-analysts will calculate within-group effect size (change in treatment group, ignoring placebo group) when it would actually be more appropriate to calculate a between-group effect size (change in treatment group, relative to change in placebo group). A second common issue involves using the incorrect standard deviation value. Consider a scenario in which two groups (treatment group and placebo group) complete strength testing before and after an intervention. For each group, you could look at the baseline strength value, post-test strength value, or the change in strength from baseline to post-testing; for each value, you can calculate a mean and a standard deviation. In most cases, we’re primarily interested in the difference between the change in the treatment group and the change in the placebo group, scaled relative to the pooled (averaged) baseline standard deviation of both groups combined. Sometimes researchers will divide by the pooled standard deviation of the change value, rather than the pooled standard deviation of the baseline value. When reviewing the meta-analysis on arginine, I discussed an example of this error:

“It appears as if the analysis includes multiple different types of effect sizes, but treats them as if they’re the same. For Cohen’s d effect sizes (or in this case, Hedges’ g, which is very similar), you have the option to standardize based on raw standard deviations or change score standard deviations. Think of it this way: a group might bench 100 ± 20 kg at baseline, and a supplement might increase their bench press values by an average of 3 ± 5 kg. This gain of 3kg could be presented as an effect size of 3/20kg (raw score standardization) or 3/5kg (change score standardization). Obviously, these are different metrics with very different values.”A New Meta-Analysis Says… Arginine Works Now?, MASS Research Review, Vol 4 Issue 7

This is an oversimplified example calculation that focused on a single group (rather than comparing two groups and using pooled standard deviations), but you get the idea – this error leads to the calculation of a fundamentally different effect size metric, which tends to distort interpretation by inflating the apparent size of the effect. 

6. Have they properly accounted for any samples contributing multiple effect sizes?

We’ve already discussed this error in detail, so I won’t elaborate much on what it is or why it’s an issue. However, we haven’t specifically discussed how researchers might address it. Some researchers might go into the software and manually adjust the sample size entry, standard error, or weighting of the analysis as a “rough and dirty” way of dealing with it. For example, instead of double-counting a study with 20 subjects, they might include both effect sizes but adjust the same sizes to be 10 each. A more nuanced approach would involve calculating a single aggregated effect size for the study that combines all of the individual effect sizes, such that it contributes only one “composite,” properly weighted effect size to the meta-analysis. An even more nuanced approach involves using a multi-level model, where multiple effect sizes can be statistically “nested” within a given study, such that each effect size contributes information to the model while being handled in a statistically appropriate manner (that is, this nested approach allows samples to contribute multiple effect sizes without exaggerating their influence on the model or inflating the precision of the effect size estimates). There are many different ways to address this issue with varying levels of nuance and sophistication, but the most important thing is that the researchers acknowledge it and take some defensible steps toward addressing it.

7. Have they used an appropriate statistical model?

This is a particularly vague question, but there are two important sub-questions that come to mind. 

First, did they make the appropriate decision to use a fixed effect or random effects model? Most of the time, the random effects model is the way to go. Some people use a simplified heuristic that you should use a random effects model when the calculated heterogeneity value is high and a fixed effect model when calculated heterogeneity is low, but this decision is more theoretical than empirical, and should (in my opinion) be made before the numbers are crunched in the first place. The two approaches rest on fundamentally different assumptions about the nature of the analysis taking place, so researchers should wrestle with those assumptions before they complete the analysis itself. 

As a reminder, studies recruit a sample from a population, and the hope is that the study will approximately estimate the population-level effect size. It won’t be perfect, but we can’t actually test the entire population of interest, so we make do. A meta-analysis tries to combine several different samples to obtain an even more refined estimate of the population-level effect size. This is where fixed effect and random effects models start to diverge. 

A random effects analysis assumes that the studies in the meta-analysis are individually trying to estimate one effect size from a larger distribution or spectrum of “true” effect sizes. For example, the “true,” population-level effect size of an intervention might differ a little bit when comparing males to females, or younger subjects to older subjects. Or, key study characteristics might truly impact the “true,” population-level effect of an intervention, such as the dosage of a supplement or the duration of the study period. As such, a short study that sampled younger males might actually be trying to approximate a “true” effect size that is different from that of a long study sampling older females. A random effects meta-analysis calculates the statistical heterogeneity among these individual studies, incorporates that heterogeneity into the model, and aims to estimate the average value of the distribution of effect sizes in this literature, rather than estimating the singular “true” effect size. As you’ve probably inferred, a fixed effect analysis is the opposite. It assumes that there is one “true” effect being estimated by all of the studies in the analysis – one might even say that this effect size is fixed in nature, rather than varying based on key characteristics of the individual studies. This topic is subject to considerable debate, but my very oversimplified perspective is as follows: for most meta-analyses in our field, the random effects model is the more defensible choice, and should probably be the default selection unless you’ve got a particularly good reason to opt for a fixed effect model. However, when statistical heterogeneity is low (or there are too few studies to actually incorporate statistical heterogeneity into the model), the difference between the two approaches becomes minimal.

Now for the second sub-question: did the researchers appropriately account for key confounding variables in their analysis? The previously discussed meta-analysis about carnitine and exercise recovery is a great example of this. When looking at recovery outcomes at several different timepoints after exercise, the analysis failed to account for time-related changes (for example, we should absolutely assume that outcomes like creatine kinase levels and muscle soreness are going to change during the four-day period following an exercise bout, regardless of supplementation strategies). When neglecting to account for the effect of time, this analysis yielded a disastrous funnel plot, which was very effectively rectified by adjusting the model to incorporate the effect of time (Figure 6).

Graphic by Kat Whitfield

One of this month’s Research Briefs in MASS provides another great example. The researchers were constructing meta-regression models, which are a special type of meta-analytic model. Meta-regression is, essentially, exactly what it sounds like. Rather than focusing on simple group comparisons (for example, supplement versus placebo), meta-regression pools data from multiple studies to form a regression model, which instead focuses on how a particular predictor variable (which can be continuous, such as age or daily protein intake) influences an outcome, such as hypertrophy or strength gains. In this month’s Research Brief about predictors of resistance training adaptations in adults with overweight or obesity, the researchers presented two different types of meta-regression models: univariable (unadjusted models looking at how one variable impacts one outcome), and multivariable (models looking at how one variables impacts one outcome, while adjusting the model to account for several other confounding variables).

There are, of course, some other ways to address confounding variables without incorporating them directly into the primary meta-analysis model. For example, researchers might do “moderator tests,” where they statistically test to determine if a particular confounding variable meaningfully influences the results. Alternatively, they might do “subgroup analyses” to split the data into groups based on a particular variable of interest (such as, for example, age or sex), then analyzes each group separately to make indirect inferences about the impact of the grouping variable. An even simpler approach would be to use inclusion and exclusion criteria to eliminate the issue entirely. For example, if researchers think that training status has potential to totally distort their analysis, they might decide to exclude all studies including untrained participants before they even begin extracting data. In summary, there are many ways to address confounding variables; there are pros and cons for each approach, but the most important thing is that key confounding variables aren’t ignored and left unaddressed.

8. Are there any implausibly large effects, implausibly narrow confidence intervals, or outliers?

When I open up a meta-analysis, the first thing I do is check the forest plot and funnel plot. I’m initially scanning for unusually large effect sizes, unusually narrow confidence intervals, and massive outliers. If I see anything that looks peculiar, I follow up by looking at the tables presented in the meta-analysis and the full text of the original study that’s contributing the unusual data point to see if there might be a calculation error, misinterpretation, or a downright sketchy study in the mix. When researchers identify an outlier or an implausibly large effect, they have several options for addressing it. For example, they might lean on any number of statistical procedures that quantify the impact of the outlier on the analysis. They can also present multiple versions of the analysis – one including the outlier, and one omitting the outlier. A related procedure, called a “leave-one-out” analysis, takes this even further by removing every single study, one at a time. The researchers re-analyze the entire data set several times, with each analysis missing exactly one study; this allows researchers to determine if the presence of any particular study has a meaningful impact on the overall results of the analysis. I should also mention that there are some instances in which an outlier value is totally legitimate, and the researchers can identify contextual factors that led to the inflated effect size. Some researchers may choose to simply highlight this observation and qualitatively explain these contextual factors, which may or may not warrant removal from the data set. In summary, there are many viable ways to deal with outliers, but researchers certainly shouldn’t ignore them entirely.

9. Does the funnel plot actually look like a symmetrical funnel?

As noted previously, funnel plots provide a visual representation of some key statistical characteristics of the literature being summarized in a meta-analysis. In ideal circumstances (that is, scenarios in which the data conform to some important assumptions that increase our confidence in the results of the analysis), the data should “fan out” in a symmetrical, funnel-shaped pattern. We often talk about funnel plots when discussing publication bias, which is one factor (but not the only factor) that can cause asymmetry in a funnel plot. Small studies tend to require less time and resources to complete, so researchers sometimes decide not to publish small studies with underwhelming results. When you see an asymmetrical funnel plot, you can see a bunch of small studies reporting positive effects, but their “mirror image” counterparts in the funnel (that is, the small studies reporting less positive effects) seem to be missing. Whenever this occurs, we have very good reason to believe that the pooled effect size is biased and unrepresentative of the “true” effect size, so we should interpret the meta-analysis very, very cautiously. Funnel plots also give us a great opportunity to identify outliers or noteworthy data patterns that threaten the robustness of the meta-analysis. For example, if the plot totally lacks a funnel-shaped structure, or you’ve got one data point way out in left field, or a large part of the funnel seems “shifted” to the side, or there appears to be two different funnel shapes forming within the same plot, you’ve got some data issues that require serious probing.

A visual scan of the funnel plot is always a great first step, but some researchers may choose to use statistical tests to determine if there is statistically significant funnel plot asymmetry. A couple of examples include Begg’s rank test and Egger’s regression test. While these are great tests, it’s important to note that they are often underpowered in exercise science meta-analyses; as a result, we can’t assume that a non-significant test results totally rules out the possibility of issues related to funnel plot asymmetry. Another common approach is to use the “trim and fill” method. This procedure examines the funnel plot, determines if there are “missing studies” (for example, an asymmetrical plot with too many studies on the right would be “missing” studies on the left), and retroactively fills in the missing studies that theoretically should exist (I’m using extremely simplistic terminology here, but you get the idea). In other words, it adjusts the analysis in a manner that tries to correct for the funnel plot asymmetry that is present. I’ve spoken to some pretty knowledgeable folks who argue that this procedure tends to undercorrect (meaning that in many cases, it doesn’t adjust quite enough to fully offset the observed funnel plot asymmetry), but it’s still a common and viable option that’s often better than nothing. I suspect that we’ll see more robust and more sophisticated solutions for dealing with funnel plot asymmetry over the next 10-20 years, but my aim in this section is to cover the most common approaches you’re likely to encounter in the literature. This is yet another scenario in which researchers have plenty of defensible options for adequately addressing the issue, but it would be ill-advised to ignore the issue altogether.

10. Is there a great deal of unexplained heterogeneity?

When discussing fixed and random effects models, I mentioned the concept of statistical heterogeneity. You’ll see researchers report heterogeneity in a variety of different ways, but the two common methods include the Cochrane Q statistic or the I2 value. We could get bogged down in a bunch of equations and Greek letters, but this concept is actually pretty intuitive when kept at the surface level. Imagine you’re interested in trying a supplement that’s supposed to increase strength performance, and you’ve got 12 very similar studies testing its effectiveness. The average effect size is d = 0.2, which isn’t too bad for a dietary supplement. However, there are a few studies (not just one or two flukes) reporting pretty large reductions in performance, and a few studies (not just one or two flukes) reporting remarkably large improvements in performance. Before you can have any confident expectation of how that supplement might impact your performance, you need to dig into those inconsistencies.

Perhaps all of the large effects (both negative and positive) tend to be the smaller studies with less precise effect estimates, and the funnel plot looks totally fine. If so, no big deal. Perhaps you can identify a specific study characteristic (such as the population sampled, the supplement dose, or the type of exercise being tested) that fully explains the inconsistencies. If so, you now know the specific scenarios in which you might expect performance improvements. Perhaps the funnel plot is a mess, but you are unable to identify any specific study characteristics that explain the inconsistencies. If so, that’s an issue – the pooled effect size of d = 0.2 doesn’t do much for us if we can’t predict the scenarios that yield large negative effects, large positive effects, or fairly neutral effects. Heterogeneity isn’t always a huge issue, but unexplained heterogeneity should lead us toward an extremely cautious interpretation of the findings.

Wrapping up

Admittedly, this is a long list of things to check as you’re working your way through a meta-analysis. To make things a little easier and more organized, I have arranged them in a comprehensive checklist (Table 1), which you can hold onto for safekeeping and future reference.

Graphic by Kat Whitfield Concluding remarks

This project falls under the umbrella of meta-science and error detection, which may sometimes seem like a bunch of know-it-alls trying to boost their own egos by nitpicking others in a condescending manner. I am very confident that this was not the motivation behind the presently reviewed paper, and it’s certainly not the intention of this MASS article. A primary purpose of research is to work incrementally toward robust answers to important questions; erroneous or suboptimal research methods detract from this purpose, and the people working to identify key errors and pragmatically suggest better methods are filling a critically important role in the scientific community. 

This type of work isn’t done by perfect researchers who are looking down on others with a perceived sense of superiority. Like many researchers I’ve talked to, I tend to cringe a little bit when I look back at some of my first research publications. Not because they are absolutely dreadful or fatally flawed, but because I’ve learned plenty of stuff over the years, so I know I’d do a better job if I re-did the project today. It’s like asking an experienced coach if they do anything differently in their 10th year of coaching when compared to their first year – I certainly hope the answer is yes. Frankly, if a researcher doesn’t look back at some of their earliest work and cringe a little bit, there are three potential reasons that come to mind: 1) they are an absolute savant (probably not), 2) they are in denial, or 3) they stopped learning and growing as a researcher. The presently reviewed paper aims to proactively address reasons #2 and #3 by shining a light on the high prevalence of errors (to help people overcome their denial), and by providing guidance to help researchers circumvent common errors in the future. While this can cause some growing pains, the short-term discomfort is certainly worth the long-term improvement of research quality. If we wish to have a thriving field built upon reliable research, scientists must maintain the humility required to constantly revisit their approaches and improve them when necessary. 

Acknowledgments

While this isn’t a standard section of MASS articles, I wanted to briefly acknowledge Dr. Charlie Poole. As a member of the Cochrane Collaboration statistical methods group, there are few people walking the planet who can match his expertise in the realm of meta-analysis. During graduate school, I had the great pleasure of taking his semester-long course on systematic reviews and meta-analyses. If I made any good points in this article, it’s because of him; if I made any bad points (hopefully not), it’s because I oversimplified his nuanced instruction in a heavy-handed manner. I was really lucky to have access to his course, and I hope this article relays a fraction of that experience to those who read it.

Next Steps

Now, we wait. Over the next few years, I expect the quality of meta-analyses in our field to improve dramatically. I’ve been keeping tabs on this issue for a few years now, and I’ve already observed substantial improvement (the bad metas are still pretty bad, but a rising tide seems to be lifting the quality of the metas that fall in the average-to-excellent range). In fact, the rate of improvements seems to be accelerating (as far as I can tell), and the really savvy meta-analysts are pushing their analyses to increasingly impressive levels of nuance and rigor. 

Application and Takeaways

Systematic reviews and meta-analyses are rightfully placed at the very top of the hierarchy of evidence, but that doesn’t mean we should let our guard down and uncritically accept their findings. When you’re reading a meta-analysis, it’s important to approach it with the same level of scrutiny you’d apply to any other piece of research. My hope is that the checklist provided in this article will facilitate your meta-analysis reading endeavors. After working through the information in this article, the Stronger By Science master list of systematic reviews and meta-analyses should now be more accessible than ever. In addition, if you’d like to take your knowledge of meta-analyses even further using some free resources, I suggest that you check out the Cochrane Collaboration handbook, the introductory textbook by Borenstein and colleagues, and the supporting documentation for the R package called “metafor,” which is my unequivocal recommendation for software if you’re planning to conduct a meta-analysis yourself. If you prefer to watch videos rather than reading text, there are also many helpful tutorials available on YouTube. 

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References Kadlec D, Sainani KL, Nimphius S. With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research. Sports Med. 2022 Oct 8; ePub ahead of print. Henderson J. The Plato of Praed Street: the Life and Times of Almroth  Wright. J R Soc Med. 2001 Jul;94(7):364–5. Pearson K. Report on Certain Enteric Fever Inoculation Statistics. Br Med J. 1904 Nov 5;2(2288):1243–6. Viribay A, Burgos J, Fernández-Landa J, Seco-Calvo J, Mielgo-Ayuso J. Effects of Arginine Supplementation on Athletic Performance Based on Energy Metabolism: A Systematic Review and Meta-Analysis. Nutrients. 2020 May 2;12(5). Yarizadh H, Shab-Bidar S, Zamani B, Vanani AN, Baharlooi H, Djafarian K. The Effect of L-Carnitine Supplementation on Exercise-Induced Muscle Damage: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. J Am Coll Nutr. 2020 Jul;39(5):457–68. Han Q, Li X, Tan Q, Shao J, Yi M. Effects of vitamin D3 supplementation on serum 25(OH)D concentration and strength in athletes: a systematic review and meta-analysis of randomized controlled trials. J Int Soc Sports Nutr. 2019 Nov 26;16(1):55. Hadzic M, Eckstein ML, Schugardt M. The Impact of Sodium Bicarbonate on Performance in Response to Exercise Duration in Athletes: A Systematic Review. J Sports Sci Med. 2019 Jun;18(2):271–81. 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. Lawrence JM, Meyerowitz-Katz G, Heathers JAJ, Brown NJL, Sheldrick KA. The lesson of ivermectin: meta-analyses based on summary data alone are inherently unreliable. Nat Med. 2021 Nov;27(11):1853–4. Gao X, Zhang H, Guo XF, Li K, Li S, Li D. Effect of Betaine on Reducing Body Fat-A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients. 2019 Oct 16;11(10). Trexler ET, Persky AM, Ryan ED, Schwartz TA, Stoner L, Smith-Ryan AE. Acute Effects of Citrulline Supplementation on High-Intensity Strength and Power Performance: A Systematic Review and Meta-Analysis. Sports Med. 2019 May;49(5):707–18. Williams TD, Tolusso DV, Fedewa MV, Esco MR. Comparison of Periodized and Non-Periodized Resistance Training on Maximal Strength: A Meta-Analysis. Sports Med. 2017 Oct;47(10):2083–100. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013 Nov 26;4:863.

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- Milo Wolf and Greg Nuckols
Longer and Stronger: How Range of Motion and Muscle Lengths Affect Muscle Growth and Strength Gains

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

When you walk into a commercial gym, you’ll see folks executing reps with every technique under the sun. Some people lift every rep explosively, and other folks move the bar at a snail’s pace. Some folks pause and squeeze at the bottom and top of each rep, and other folks treat biceps curls as if they’re plyometrics. And, notably for our purposes here, some folks move every rep through a maximal range of motion, while other folks stick to partial reps.

When it comes to range of motion, who has it right? Do you need to train through a full range of motion? Are you leaving gains on the table when you do partial reps? If you do partials, does it matter what kind of partial reps you do?

This article will attempt to answer all of these questions and more.

Sports science is experiencing a bit of a renaissance around the topic of range of motion. Almost half of the research examining the impact of range of motion on strength gains and/or muscle growth was published within the past five years, and nearly all of it was published within the past decade. So, we’re finally reaching the point where we can start providing relatively solid answers about the impact of range of motion on strength and hypertrophy adaptations, with a reasonable degree of detail and nuance. 

With that in mind, my (Milo’s) coauthors and I recently completed a meta-analysis examining the effects of range of motion on strength, hypertrophy, power, body composition, and sport performance outcomes (1). We started with a systematic literature search to identify all of the studies meeting these three criteria:

The study needed to be a full-text peer-reviewed study, or a graduate-level thesis or dissertation available in English.The study needed to include a longitudinal training intervention with at least two groups or conditions that involved training through differing ranges of motion.The study needed to report at least one outcome related to strength, hypertrophy, power, body composition, or sport performance, with sufficient detail for statistical analysis.

Ultimately, 23 studies met these criteria and were included in our meta-analysis. Outcome data were extracted from each study, and effect sizes were calculated as Hedges’ g values, which are interpreted similarly to Cohen’s d values.

Our first set of analyses were “big picture” analyses. We wanted to see, in general, whether training through a full range of motion produces larger beneficial outcomes than training through a partial range of motion. We found that, when pooling all outcomes from all studies together, training through a full range of motion was generally preferable to training through a partial range of motion, though the overall difference between full and partial range of motion training was trivial in magnitude (g = 0.12; Figure 1).

Graphics by Kat Whitfield

When separating different outcomes (strength, hypertrophy, power, body composition, and sport performance), a similar picture emerges: training through a full range of motion generally produces larger adaptations than training through a partial range of motion, but the pooled effects are, once again, trivial in magnitude (Figure 2).

Graphics by Kat Whitfield

At this point, you may be underwhelmed. If range of motion only has a trivial impact on most adaptations, why worry about it in the first place?

As mentioned previously, this is a surprisingly nuanced subject. When we go one step deeper, the more interesting details begin to emerge.

Let’s start by looking at a secondary analysis of strength adaptations. We wanted to see whether the exercise used for testing strength influenced whether full or partial range of motion training proved superior for strength development. In other words, a study might have one group of subjects training half squats for three months and one group of subjects training full squats for three months. Before the start of the training intervention and at the end of the training intervention, all subjects in both groups test their 1RM half squat strength, 1RM full squat strength, and maximal isometric knee extension torque. In this scenario, the 1RM half squat is a test of strength biased in favor of the group training through a partial range of motion – the subjects in the half squat group performed a lot of half squats during the training intervention, so they should be more skilled half squatters by the end of the study than the subjects in the full squat group. Conversely, the 1RM full squat is a test of strength biased in favor of the full squat group. Finally, the test of maximal isometric knee extension torque is an unbiased strength test – neither group was specifically “training for the test.”

Unsurprisingly, we found that full range of motion training produced larger strength gains in strength tests biased in favor of full range of motion training. Conversely, partial range of motion training produced larger strength gains in strength tests biased in favor of partial range of motion training. However, partial range of motion training was only trivially better than full range of motion training (g = 0.11) for increasing partial range of motion strength, whereas full range of motion training was quite a bit more effective than partial range of motion training (g = 0.34) for increasing full range of motion strength. Finally, both full and partial range of motion training seemed to be similarly effective for increasing strength in unbiased strength tests (Figure 3).

Graphics by Kat Whitfield

In other words, full squat training might increase your full squat 1RM by 20% and your half squat 1RM by 15%, whereas half squat training might increase your half squat strength 1RM 20%, but only increase your full squat 1RM by 10%.

Overall, this should be a pretty intuitive finding. After all, the entire half squat range of motion is included within the full squat range of motion, so if you’re doing full squats, you are still training your knee and hip extensors through the joint angles associated with half squatting. So, it makes sense that improving your full range of motion strength is pretty effective at improving your partial range of motion strength. However, when you do partial range of motion training, there’s simply a range of muscle lengths and joint angles you aren’t stressing with each rep, thus making partial range of motion training considerably less effective at promoting gains in full range of motion training in most contexts.

Moving onto a secondary analysis of hypertrophy results, it’s worth asking whether it matters what type of partial range of motion training you do. After all, in most comparisons of full versus partial range of motion training, there isn’t just one difference between training protocols (range of motion). There are two differences: the total range of motion you train through, and the muscle lengths being trained. So, it’s worth considering which of those variables has the largest impact.

When people think about partial range of motion training, they generally think about partials that train the target muscle(s) through short muscle lengths. Examples include standard half squats and standard half-reps on bench press. With standard half squats, the muscle fibers of the quads don’t have to stretch very far at the bottom of each rep; with standard half-reps on bench press, the muscle fibers of the pecs and triceps don’t have to stretch very far at the bottom of each rep. So, these exercises only train the prime movers through short muscle lengths.

Conversely, you could also perform the opposite, less common form of partial range of motion training: partial range of motion training through long muscle lengths. For example, you could do half squats where you only perform the bottom half of each rep – starting the concentric phase at the very bottom of a deep squat, with the “top” of each rep being the typical “bottom” position for a standard half squat. Similarly, instead of half-repping bench press reps by not touching the bar to your chest, you could do half reps that involve starting with the bar on your chest, and only pressing the bar until it’s halfway to a “locked out” position. In both of these examples, instead of training the prime movers through short muscle lengths and neglecting long muscle lengths, you’re training the prime movers through long muscle lengths and neglecting short muscle lengths.

If range of motion is the most important variable for muscle growth, then training through a full range of motion should produce more muscle growth than training through a partial range of motion regardless of the muscle lengths being trained by the partials. However, if muscle length is the important variable, then we should expect partials through short muscle lengths and partials through long muscle lengths to produce different hypertrophy results.

Our analysis suggests that muscle lengths matter more than range of motion per se. Full range of motion training produced slightly more muscle growth than partials through short muscle lengths, whereas partials through long muscle lengths generally produced more muscle growth than full range of motion training (Figure 4).

Graphics by Kat Whitfield

Furthermore, it appears that training through longer muscle lengths specifically leads to more hypertrophy in more distal regions of muscles. For example, comparing the effects of full squats and half squats (through the top half of the range of motion), full squats will likely lead to slightly more muscle growth in the regions of the quads that are closer to the hips, but considerably more muscle growth in the regions of the quads that are closer to the knees. We didn’t specifically meta-analyze this precise consideration, but Table 1 and Figure 5 summarize the research investigating the impact of training at longer versus shorter muscle lengths on regional hypertrophy responses.

Graphics by Kat Whitfield Graphics by Kat Whitfield Further Nuance for Strength and Performance

Ultimately, meta-analytic data can only take us so far – it can tell us, in broad strokes, what general factors tend to influence particular outcomes, but there’s often considerable nuance lurking beneath the surface.

Before discussing some studies that shed further light on the influence of range of motion on strength and performance, it’s worth taking a step back to discuss a few reasons why range of motion should impact specific strength and performance outcomes.

Potential Mechanisms

First, training through a longer range of motion (or training through longer muscle lengths) seems to lead to more hypertrophy. Therefore, since hypertrophy contributes to strength development, you should expect that training through a longer range of motion will eventually lead to larger strength gains. After all, muscle size reflects the number of contractile proteins in parallel within a muscle – if you have more parallel contractile units, your muscles should be able to contract with more force, all else being equal (2). However, the current research presented in this meta-analysis suggests that differences in hypertrophy probably aren’t a major factor contributing to the aggregated strength and performance results in the extant research. Unbiased measurements of muscle strength tend to find that full-ROM training and partial-ROM training are similarly effective for strength development. And, when taking a step back, that makes a lot of sense. During the early phases of training, gains in muscle mass are only minimally predictive of changes in strength (3); early strength gains seem to be much more strongly influenced by neural adaptations. So, it’s possible that training at longer muscle lengths or through longer ranges of motion would eventually result in larger strength gains due to their ability to promote greater hypertrophy, but such an effect may not be detectable within the time frame of a typical resistance training study (i.e. over a span of weeks-to-months).

Second, the range of motion you train through likely influences the specific motor skills you’ll hone. If you consistently do full squats, you’ll become more skillful at doing full squats. If you consistently do half squats, you’ll become more skillful at doing half squats. This is a very simple point, but it likely explains most of the strength-related findings in the meta-analysis. Partial-ROM training tended to lead to larger strength gains in partial-ROM strength tests, and full-ROM training tended to lead to larger strength gains in full-ROM strength tests.

Third, resistance training with an eccentric component (i.e., most resistance training) can shift the length-tension relationship of muscles, and therefore affect the optimal joint angle for force output (4; Figure 6). For instance, your quads may be able to generate the most knee extension torque at 60° of knee flexion pre-training. However, after a few months of deep squatting, your quads may generate peak levels of torque at 75° of knee flexion. Since the hardest part of the squat occurs when the knees are quite flexed, this shift toward greater force production at longer muscle lengths would tend to independently increase deep squat performance. This could, in part, explain the meta-analysis’s findings related to biased strength tests. In other words, it could help explain why full-ROM training was considerably better than partial-ROM training for improving full-ROM strength, but partial-ROM training was only slightly better than full-ROM training for improving partial-ROM strength. 

Graphics by Kat Whitfield Potential Impacts of Training Status

A particular pair of very similar studies with very different results warrant a closer look.

A 2013 study by Bloomquist and colleagues had one group of subjects train deep squats (through 120° of knee flexion) and one group train shallow squats (though 60° of knee flexion) for 12 weeks (5). Squat jump and countermovement jump performance were assessed pre- and post-training. Deep squats led to larger improvements in jump performance than shallow squats (Figure 7).

Graphics by Kat Whitfield

A 2016 study by Rhea and colleagues had one group of subjects train full squats (through at least 110° of knee flexion), one group of subjects train half squats (through 85-95° of knee flexion), and one group of subjects train quarter squats (through 55-65° of knee flexion) for 16 weeks (6). Vertical jump performance was assessed pre- and post-training. Half squats and quarter squats led to larger improvements in jump performance than full squats (Figure 8).

Graphics by Kat Whitfield

The biggest differences between these two studies were the characteristics of the subjects. The Bloomquist study was performed on effectively untrained subjects. To quote the authors: “If [the subjects] had been squat training more than once weekly during the preceding 6 months, or if they were engaged in strength or power sports, they were excluded from the study.” On the other hand, the Rhea study was performed on collegiate athletes with at least two years of prior training experience, and a 1RM squat of at least 1.5-times body mass.

At least in a vacuum, one should anticipate that partial squats would be more effective for promoting gains in jump height, because the joint angles of a vertical jump more closely mimic the joint angles of a partial squat – not many people drop into a full squat position when attempting to jump as high as possible. As such, these findings suggest that novice lifters derive general benefits from full-ROM training that trump even the principle of specificity. In high-level athletes, on the other hand, the principle of specificity won out, as quarter squats (more closely mimicking the joint angles of jumping) produced larger gains in jump performance.

In fact, a 2019 study by Martínez-Cava and colleagues further bolsters this point (7; MASS Review). Three groups of young men completed a 10-week bench press training intervention through different ranges of motion. One group performed full-ROM bench press reps (touching the bar to their chest at the bottom of each rep, and locking out their elbows at the top of each rep), one group performed 2/3rds-ROM reps at short muscle lengths (locking their elbows at the top of each rep, but only bringing the bar down 2/3rds of the way to their chest), and one group performed 1/3rd-ROM reps at short muscle lengths (only bringing the bar down 1/3rd of the way to their chest). Full-ROM, 2/3rd-ROM, and 1/3rd-ROM bench press 1RM were assessed pre- and post-training. Subjects in this study weren’t completely untrained, but they also weren’t particularly well-trained – they were only required to have six months of prior training experience, and the average bench press 1RM was less than the average body mass of the subjects (71.8kg at a body mass of 73.4kg). 

In keeping with the principle of specificity – and the results of the present meta-analysis (1) – you should expect the full-ROM group to experience the largest gains in full-ROM bench press strength, the 2/3rd-ROM group to experience the largest gains in 2/3rd-ROM bench press strength, and the 1/3rd-ROM group to experience the largest gains in 1/3rd-ROM bench press strength. However, that’s not what the researchers observed. Instead, the full-ROM group experienced larger strength gains than the other two groups through all three ranges of motion (Figure 9). 

Graphics by Kat Whitfield

While there’s not research to back up this point, this finding runs counter to the experiences of high-level bench pressers. Really strong bench pressers who want to build strength through the top part of the bench press range of motion (to maximize equipped bench press performance, for example) routinely turn to partial-ROM board presses or pin presses to build lockout strength. It’s certainly possible that they’re all wasting their time, but that seems very unlikely. Raw lifters who don’t do much partial-ROM training might find that their full-ROM 1RM is only a little lower than their partial-ROM 1RM (a two-board or three-board press, for example). However, equipped powerlifters who regularly perform a lot of partial-ROM training might be able to use >20% more weight for a partial-ROM bench press than a full-ROM bench press. It certainly seems that, for these high-level benchers, a heavy diet of partial-ROM training yields ROM-specific strength gains through a partial range of motion.

Finally, a study by Pallarés and colleagues (58) obtained results that largely mirrored the results of the Martínez-Cava study. The male subjects in this study had some degree of prior squatting experience, but they weren’t particularly well-trained – pretraining, their average 1RM full squat was just 87.3kg, at an average body mass of 76kg. Subjects trained either half squats, parallel squats, or full squats for 10 weeks. The group training full squats achieved the largest nominal gains in full squat, parallel squat, and half squat 1RM, and also experienced the largest improvements in Wingate test, 20m sprint, and countermovement jump performance.

Thus, there’s at least some indication that, while gains in strength and performance tend to be ROM-specific, the training status of the lifter might also be relevant. Namely, training through a full range of motion may be generally preferable for less experienced lifters, even when aiming to improve performance through a partial range of motion. However, the principle of specificity seems to be a more reliable guide for more experienced lifters – if you’re aiming to increase strength and performance through a partial range of motion, then partial-ROM training seems to provide you with the biggest return on investment.

The reason for this divergence isn’t entirely clear. It’s possible that the partial-ROM strength and performance improvements following full-ROM training in less experienced lifters are simply downstream of hypertrophy – if lifters haven’t established an adequate base of muscularity to build upon, simply building more muscle pays the largest dividends. It’s also possible that eccentric stress through a longer range of motion aids in connective tissue adaptations that are conducive for force transfer to the tendons (8, 9). Ultimately, more research is needed to fully explain (or potentially refute) these tentative observations.

Combining Ranges of Motion for Performance

Before wrapping up this discussion of the impact of range of motion on strength and performance, it’s worth noting that you don’t have to choose between exclusively doing full-ROM training or exclusively doing partial-ROM training. In fact, that’s at least some indication that combining different ranges of motion may produce synergistic (not just additive) effects.

To illustrate, here’s how it may look if a combination of full-ROM and partial-ROM training produced additive effects. Imagine that eight weeks of deep squat training, with six sets of deep squats per workout, increases full-ROM squat strength by 10%, but eight weeks of half squat training, with six sets of half squat training per workout, only increases full-ROM squat strength by 2%. If the effects of combining ranges of motion were purely additive, we might expect three sets of full squats and three sets of half squats to lead to a 6% [(10% + 2%) ÷ 2] increase in full-ROM squat strength.

However, if a combination of full-ROM and partial-ROM training produced synergistic effects, we might instead observe that a combination of full squats and half squats lead to larger strength gains than performing only full squats (even if the addition of half squats comes at the expense of full squat sets), even though full squats produce larger gains in full squat strength than half squats produce in isolation.

We don’t have many studies examining the strength and performance impact of combining ranges of motion, but the studies that do exist paint a cautiously optimistic picture.

For example, in a 2014 study by Bazyler and colleagues (10), one group of subjects performed six sets of full-ROM squats twice per week, and one group of subjects performed three sets of full-ROM squats and three sets of partial-ROM squats (through 80° of knee flexion) twice per week. Testing was performed before and after a seven-week training intervention.

Gains in full-ROM squat strength didn’t significantly differ between groups, but they tended to be larger in the group that subbed out half of their full squats for partial squats (8.2 ± 2.1%, versus 5.1 ± 4.5%). Gains in partial squat strength tell a similar story – nominally larger gains in the group performing both full and partial squats (14.9 ± 11.8%, versus 10.2 ± 11.7%).

A recent study by Gillingham and colleagues (MASS review) had similar results (11). Two groups of collegiate wrestlers completed a six-week pre-season training program. One group only trained the deadlift with a full-ROM (with each deadlift workout consisting of two sets of deadlifts), while the other group performed one full-ROM set of deadlifts, followed by three heavy, partial-ROM sets of a single rep. The partial-ROM sets were performed in a power rack; repetitions began with the bar resting on the safety pins, which were set ~1 inch above the lifters’ kneecaps.

The group only doing full-ROM training experienced a non-significant decrease in deadlift strength (-5.3 ± 13.4kg), while the group doing both full- and partial-ROM training experienced a non-significant increase in deadlift strength (4.5 ± 12.2kg). Furthermore, only the group doing both full- and partial-ROM training significantly increased its partial-ROM 1RM (45.2 ± 18.9kg, versus 11.6 ± 23.2kg).

Furthermore, a 2021 study by Pedrosa and colleagues (MASS review) compared the effects of full-ROM knee extension training, versus a combination of different partial ranges of motion (12). In this study, full-ROM was defined as the range spanning from 30° of knee flexion (a mechanical stop kept lifters from fully extending their knees at the top of each rep) to 100° of knee flexion. There were five total groups in this study, but only two are relevant for our purposes here. One group performed all of their sets through a full range of motion. Another group performed half of their sets through just the bottom half of the range of motion (from 100° to 65° of knee flexion), and half of their sets through just the top half of the range of motion (from 65° of knee flexion to 30° of knee flexion). Full- and partial-ROM knee extension strength were assessed before and after the 12-week training intervention.

The group doing a combination of long muscle length and short muscle length partial-ROM training gained more strength than the group doing full-ROM training. The difference was statistically significant for gains in strength through the bottom of the ROM (32% vs. 16%), with non-significant differences through a full range of motion (24.1% vs. 17.5%), and through the top of the ROM (13% vs. 20%). 

Finally, a 2020 study by Whaley and colleagues addressed this question from a slightly different angle (13). It compared the effects of full-ROM squat training versus progressive-ROM squat training. So, instead of varying ranges of motion within each training week, a progressive-ROM group varied ranges of motion over time. The full-ROM group trained full-ROM squats for seven weeks; the progressive-ROM group started with very partial-ROM squats in week one, and progressed toward deeper and deeper squats as the weeks progressed. Gains in 1RM full-ROM squat strength and vertical jump height didn’t significantly differ between groups, but nominal gains were larger in the progressive-ROM group (14.5 ± 9.5 versus 11.0 ± 7.6kg for squat 1RM, and 3.6 ± 4.3 versus 1.4 ± 4.3cm for vertical jump height).

When we take a step back, these four studies seem to be telling an interesting and consistent story. Even though partial-ROM training is less effective than full-ROM training for increasing full-ROM strength in a vacuum, we see that replacing some of your full-ROM training with partial-ROM training may actually lead to larger full-ROM strength gains than exclusively doing full-ROM training, at least in some contexts.

There isn’t an obvious explanation for this observation.

It could simply be a matter of improved motor learning – varied practice (practicing multiple similar, but not identical skills) tends to improve motor learning compared to only practicing one motor skill (14). For example, a study by Fonseca and colleagues found that training a variety of lower body exercises (including squats) led to larger gains in squat strength than just training squats (15); a similar principle may be at play here. If improved motor learning explains these findings, then exercise variety per se is the important factor, and training different ranges of motion is simply one avenue for adding exercise variety.

Similarly, it’s possible that overload training – performing exercises that allow you to use greater external loads than you’d be capable of moving through a full range of motion – has direct beneficial effects on strength development via some unique unexplored mechanism (for example, it’s been proposed that overload training could lead to outsized effects on connective tissue strength). Plenty of successful strength athletes swear by overload training, but there’s virtually no research examining the mechanisms by which it may have uniquely beneficial effects, so consider this a very tentative potential explanation.

Finally, it’s possible that these are just four flukey studies, and future research will fail to replicate these findings. None of these studies had particularly large sample sizes, and for comparisons of full-ROM strength gains, none of these four studies actually found statistically significant differences between groups – nominal results just leaned in favor of training through multiple ranges of motion (Table 2).

Graphics by Kat Whitfield

However, I don’t think this is a topic that can simply be ignored until there are a dozen studies investigating the impacts of combining full- and partial-ROM training. Most people who are interested in partial-ROM training still perform plenty of full-ROM training. So, while it’s nice to know about the effects of partial-ROM training in isolation, discussing the effects of combining full- and partial-ROM training is ultimately the more interesting and practically relevant issue. At least for now, the research suggests that replacing all of your full-ROM training with partial-ROM training (especially partial-ROM training at short muscle lengths) will lead to smaller full-ROM strength gains. But, it also tentatively suggests that replacing some of your full-ROM training with partial-ROM training may have a neutral-to-positive effect on full-ROM strength gains, at least in the short-to-medium term (over the longer term, it’s possible that doing too much partial-ROM training would result in less muscle growth, and therefore smaller strength gains).

Further Nuance for Hypertrophy Is Range of Motion Strictly Synonymous With Muscle Length?

To this point in the article, “range of motion” and “muscle length” have been used roughly synonymously. For most exercises, if you train the first half of the typical concentric phase, you’re generally training your prime movers at long muscle lengths, and if you train the second half of the typical concentric phase, you’re generally training your prime movers at short muscle lengths.

However, when you’re training biarticular muscles (muscles that cross two joints, such as the hamstrings, the rectus femoris, and the long head of the triceps), you can alter muscle lengths without altering ranges of motion.

Using the hamstrings as an example, three heads of the hamstrings (the semitendinosus, semimembranosus, and the long head of the biceps femoris) are biarticular, because they cross both the hip and the knee. Since the hamstrings are hip extensors and knee flexors, you can lengthen the hamstrings both by flexing the hips and by extending the knees.

With this in mind, Maeo and colleagues examined the impact of seated versus lying leg curls on hamstrings hypertrophy (16; MASS Review). Employing a within-subject unilateral design, each subject trained lying leg curls (with the hips extended) with one leg, and seated leg curls (with the hips flexed) with their other leg for 12 weeks. Both exercises employed the same range of motion at the knee (the subjects trained through 90° of knee flexion), but since the hips were in a flexed position for the seated leg curls, seated leg curls still involved training hamstrings at longer muscle lengths.

The researchers observed greater hypertrophy of all three biarticular heads of the hamstrings in the legs performing seated leg curls (Figure 10).

Furthermore, both exercises involved training the short head of the biceps femoris (the only monoarticular head of the hamstrings, which only crosses the knee) at identical muscle lengths – both exercises led to similar growth in the short head of the biceps femoris. Additionally, lying leg curls trained the sartorius at longer muscle lengths (since the sartorius is a knee flexor and a hip flexor), and lying leg curls led to greater sartorius growth than seated leg curls.

Graphics by Kat Whitfield

Overall, this study demonstrates that muscle lengths and ranges of motion don’t always have a perfect 1:1 correspondence, and it also strongly suggests that even when equating for range of motion, training at longer muscle lengths still tends to result in more hypertrophy.

A more recent study, also by Maeo and colleagues, confirms and extends these findings (17; MASS Review). Instead of manipulating the muscle length of the hamstrings by manipulating exercise technique, the researchers manipulated the muscle length of the long head of the triceps by manipulating exercise technique. Since the long head of the triceps is both a shoulder extensor and an elbow extensor, it’s more lengthened when your arm is overhead than when it’s relaxed by your side. So, the researchers compared the effects of pushdowns (with the arms by the subjects’ sides) versus overhead triceps extensions on triceps growth.

This study also employed a within-subject unilateral design, and had subjects train through the same elbow range of motion (through 90° of elbow flexion) in both conditions. Much like the hamstrings study, this study found that overhead triceps extensions (which train the long head of the triceps at longer muscle lengths) resulted in more growth of the long head of the triceps than pushdowns, even when equating for range of motion.

However, unlike the previous study which found that both seated and lying hamstrings curls resulted in similar growth of the (monoarticular) short head of the biceps femoris, this study found that overhead triceps extensions also resulted in more growth of the monoarticular heads of the triceps. Enhanced growth of the monoarticular heads of the triceps can’t be explained by differences in the muscle lengths being trained, so there must be some other explanation for this finding. For now, just put a pin in this little mystery; we’ll circle back to it later.

When You Equate Muscle Lengths, Do You Still Benefit from Overloading the Muscle at Longer Muscle Lengths?

Since training at longer muscle lengths seems to be beneficial for hypertrophy, it’s also worth asking whether placing more tension on a muscle at longer muscle lengths is also beneficial for hypertrophy, even when equating for range of motion and muscle lengths.

To illustrate, consider the difference between a cable preacher curl and a barbell preacher curl, as shown in Figure 11. For any biceps curl, the elbow extension moment imposed by the load (and therefore the amount of elbow flexion torque your muscles need to produce to lift the load) will be at its peak when the force vector associated with the load is roughly perpendicular to the forearm. With a cable machine, that means that your biceps have to work the hardest when the cable is perpendicular to your forearm, since the resistance from the machine is being transmitted through the cable. With a barbell, the resistance is due to gravity, which pulls straight down toward the floor. So, your biceps have to produce the most force when your forearm is parallel to the floor. As a result, cable preacher curls are relatively easy at the bottom of the exercise (at long muscle lengths), and relatively hard to the top of the exercise (at short muscle lengths). Conversely, barbell preacher curls are harder at the bottom of the exercise (at long muscle lengths), and easier at the top (at short muscle lengths). As such, if more tension at longer muscle lengths necessarily leads to more growth, you should expect to see greater hypertrophy following barbell preacher curls than cable preacher curls.

Graphics by Kat Whitfield

As luck would have it, Nunes and colleagues published such a study in 2020 (18). One group of subjects completed 10 weeks of cable preacher curl training, and one group of subjects completed 10 weeks of barbell preacher curl training. The net result: no difference in hypertrophy. Biceps thickness increased by 2mm (about 8%) in both groups.

Similarly, a study by Diniz and colleagues manipulated tension at different muscle lengths by manipulating rep cadence (19; MASS Review). All else being equal, a faster concentric rep cadence increases concentric tension at long muscle lengths – the muscles need to produce more force at the start of the concentric to accelerate the load to a higher velocity. Conversely, all else being equal, a faster eccentric tempo increases eccentric tension at long muscle lengths – the muscles need to produce more force at the end of the eccentric to decelerate the load before the start of the concentric.

In this study, subjects trained knee extensions for 10 weeks, with either a) a fast concentric (1 second) and slow eccentric (5 seconds), b) a slow concentric and fast eccentric, or c) moderate-speed (3 seconds) concentrics and eccentrics. The fast concentric and fast eccentric conditions ultimately resulted in more tension at long muscle lengths than the moderate-speed condition. However, all three protocols led to similar quad hypertrophy. More generally, a recent systematic review found that rep cadence doesn’t have a particularly reliable effect on hypertrophy results (20).

Overall, these studies suggest that, while training through longer muscle lengths is important for maximizing hypertrophy, you probably don’t need to go out of your way to choose exercises that specifically maximize muscle tension at longer muscle lengths. As long as an exercise makes you exert a reasonable amount of effort when your prime movers are in a lengthened position, it’s probably fine.

Going beyond what we have direct research on, it’s certainly possible that an exercise could be too easy at long muscle lengths. For example, imagine a squat performed with no weight on the bar, but with 400 pounds of band tension. In this example, at the bottom of the squat (when your quads would be at long muscle lengths), the only resistance would be your body weight, but the resistance would increase linearly as you progress through the concentric phase. This might be a scenario where the quads would be insufficiently challenged at long muscle lengths, and therefore experience less hypertrophy. However, that doesn’t seem to be a major concern for most “normal” exercises you’d commonly perform for the purpose of hypertrophy training. The major exception may be delt raises; with dumbbells, the delts are effectively unloaded at relatively long muscle lengths (when the arms are hanging straight down); cross-body cable delt raises allow you to keep considerably more tension on the delts at long muscle lengths.

Is it Always Preferable to Train at Longer Muscle Lengths?

To this point in the article, it might sound like it’s always preferable to train at longer muscle lengths for promoting hypertrophy – the longer the better. After all, full-ROM training seems to produce more muscle growth than partial-ROM training at short muscle lengths, and partial-ROM training at long muscle lengths may result in even more hypertrophy than full-ROM training. However, a few studies suggest that, while training at relatively long muscle lengths is beneficial for hypertrophy, it may not be necessary to train at the longest possible muscle lengths.

First, here’s a (very) brief rundown of some of the studies examining the impact of training at different muscle lengths on quad growth:

Bloomquist et al: Squatting through 120° of knee flexion led to more quad growth than squatting through 60° of knee flexion (5).

McMahon et al: Lower body training (with a variety of different exercises) through 90° of knee flexion led to more quad growth than lower body training through 50° of knee flexion (21).

Valamatos et al: Knee extensions through 100° of knee flexion led to non-significantly more quad growth than knee extensions through 60° of knee flexion (+7.6% vs. +6.7%; 22).

Pedrosa et al: Knee extension training through (up to) 100° of knee flexion led to more quad growth than knee extension training through (up to) 65° of knee flexion, especially at measurement sites closer to the knee (12).

When we look at these studies, a clear pattern emerges: Your quads grow more when you train them through greater degrees of knee flexion (i.e. at longer muscle lengths). However, in all of these studies, the “long muscle length” conditions all involved training through 90-120° of knee flexion, and the “short muscle length” conditions all involved training through 50-65° of knee flexion. Furthermore, these studies don’t test the effects of training at three or more different muscle lengths, thus making it difficult to sketch out a full dose-response curve. For example, in the Bloomquist study (5), we can see that training through 120° of knee flexion led to more quad growth than training through 60° of knee flexion, but we don’t know whether training through 90° of knee flexion (which seemed to be sufficient for robust quad growth in the McMahon study; 21) would have produced less quad growth than training through 120° of knee flexion, nor do we know if training through 140° of knee flexion would have led to even more quad growth than training through 120° of knee flexion.

However, a 2019 study by Kubo and colleagues helps tentatively fill in this gap (23). Instead of comparing a typical “long muscle length” condition against a typical “short muscle length” condition, it compared two (relatively) “long muscle length” conditions head-to-head. Two groups of subjects trained squats for 10 weeks. One group squatted deep – through 140° of knee flexion. The other group squatted through 90° of knee flexion – the same range of motion achieved in the long-ROM condition in the McMahon study. While glute and adductor growth differed between groups, quad growth was virtually identical between groups in this study (4.9 ± 2.6% versus 4.6 ± 3.1%), including the growth observed in all four heads of the quads (Table 3).

Graphics by Kat Whitfield

More research is required to confirm this finding, but this study at least suggests that for quad training, you may not necessarily need to train the quads through a maximal range of motion to reap all the benefits of training at long muscle lengths. For healthy people, maximum knee flexion range of motion is around 150°, but it appears that you may be able to maximize quad growth by training through ~90-100° of knee flexion.

A pair of studies examining triceps growth further reinforce this point.

We’ve already mentioned the study by Maeo and colleagues which compared the effects of pushdowns and overhead triceps extensions (through 0-90° of elbow flexion) on hypertrophy of the long head of the triceps (17). In this study, overhead triceps extensions (which trained the long head of the triceps through longer muscle lengths) resulted in more growth.

However, a separate study by Stasinaki and colleagues also compared the effects of pushdowns and overhead triceps extensions on hypertrophy of the long head of the triceps (24). However, instead of having both arms train through 0-90° of elbow flexion, this study had subjects perform overhead triceps extensions through even longer muscle lengths.

Pushdowns were performed with an elbow range of motion spanning from 10° to 90° of elbow flexion, while overhead triceps extensions were performed with an elbow range of motion spanning from 150° to 70° of elbow flexion. In other words, the arms performing pushdowns trained at muscle lengths that were comparable to the pushdown arms in the Maeo study, but the arms performing overhead triceps extensions trained at even longer muscle lengths (for the long head of the triceps) than the arms performing overhead triceps extensions in the Maeo study.

Unlike the Maeo study, the study by Stasinaki and colleagues found that overall increases in cross-sectional area for the long head of the triceps was similar following both training interventions. Overhead triceps extensions were non-significantly better for growing the distal region of the long head of the triceps (closer to the elbow), while pushdowns were non-significantly better for growing the proximal region (closer to the shoulder).

Taken together, these studies suggest that you can maximize triceps growth by training at long-but-not-extreme muscle lengths (overhead position, with up to 90° of elbow flexion), but that you achieve less growth by training through shorter muscle lengths (arms at your side, with up to 90° of elbow flexion) and even longer muscle lengths (overhead position, with up to 150° of elbow flexion). 

So, at least for now, the research seems to suggest that training at longer muscle lengths tends to result in more muscle growth, but it also suggests that you shouldn’t necessarily go out of your way to train at the absolute longest muscle lengths possible. To be clear, it’s not that training at maximal muscle lengths is bad – squatting through 140° of knee flexion still caused just as much quad growth as squatting through 90° of knee flexion, and training the long head of the triceps at extreme muscle lengths did still cause as much hypertrophy as training at shorter muscle lengths – it’s just not always necessary, or even beneficial.

As a general heuristic, it’s probably not a bad idea to train through the longest ranges of motion (or through the longest muscle lengths) that are comfortable and feasible, but you also shouldn’t avoid exercises or feel like you’re wasting your time if you can’t train a particular lift through a joint’s full range of motion, or at the longest possible muscle lengths. It seems that training at pretty long muscle lengths, or through a pretty long range of motion, is probably just fine (and sometimes even preferable). 

Is There Anything Special About the Hypertrophy Caused by Training at Long Muscle Lengths?

The hypertrophy induced by long muscle length training differs from the hypertrophy induced by training at shorter muscle lengths in two important ways.

First, as discussed previously, training at longer muscle lengths generally causes more hypertrophy in middle and distal regions of the muscles being trained, whereas long muscle length and short muscle length training seem to lead to similar amounts of hypertrophy in more proximal regions of muscles.

More research is needed to investigate precisely why training at long muscle lengths is particularly beneficial for increasing hypertrophy in the distal regions of muscles. However, a line of research by Wakahara and colleagues hints at one potential explanation (25, 26, 27). This line of research (intuitively) suggests that variation in intramuscular regional hypertrophy is related to variation in intramuscular regional activation. In other words, if a particular exercise robustly activates the proximal part of a muscle, but not the distal part, you’re likely to experience more hypertrophy in the proximal parts of the muscle than the distal parts of the muscle. So, it’s possible that training at short muscle lengths preferentially activates the proximal parts of muscles (leading to plenty of proximal hypertrophy, but less distal hypertrophy), whereas training at long muscle lengths activates the entire length of the muscles being trained (leading to plenty of hypertrophy in the proximal and distal regions of the muscles).

Second, training at shorter muscle lengths seems to primarily lead to hypertrophy by increasing muscle cross-sectional area, whereas training at longer muscle lengths leads to both increases in cross-sectional area and increases in muscle length.

The evidence that training at longer muscle lengths increases muscle length comes from two different sources. First, as discussed previously, resistance training (with an eccentric component) through long muscle lengths can shift the length-tension curve of muscle (4), such that the optimal length of the muscle for force output corresponds with a longer muscle length. This shift suggests the addition of sarcomeres in series. Second, training with a longer range of motion seems to increase muscle fascicle length (21, 22). Both of these findings suggest that training through long muscle lengths increases muscle fiber length (via an increase in sarcomeres in series). For more on the topic of increasing muscle length, this is an excellent review paper (28).

Graphics by Kat Whitfield Further Evidence that Tension at Long Muscle Lengths is Particularly Beneficial for Hypertrophy

To this point, we’ve only discussed the impact of dynamic contractions through different muscle lengths or ranges of motion. However, one final line of evidence further supports the idea that training at long muscle lengths is particularly beneficial for muscle growth: research on isometric training.

A 2019 systematic review by Oranchuk and colleagues summarized the research examining the impact of isometric training on hypertrophy (29). The researchers found three studies comparing the hypertrophy effects of isometrics performed at long versus short muscle lengths. In their words, “All three studies found that isometric training at long muscle lengths (LMLs) was superior to equal volumes of training at short muscle lengths (SMLs) for increasing muscle size.”

Why Does Training at Longer Muscle Lengths Result in Greater Hypertrophy?

Before wrapping up, it’s at least worth taking a stab at attempting to explain why training at longer muscle lengths results in greater hypertrophy. And, I say “attempt” because there are a lot of potential mechanisms with some degree of support, but no single mechanism has overwhelming empirical support, or the ability to explain all of the hypertrophy research on this topic. Finally, I’ll note that I’ll be focusing on mechanisms that could explain the observed changes in muscle thickness and cross-sectional area, rather than the observed increases in muscle length.

Regional Activation?

As mentioned previously, training at long muscle lengths might lead to more hypertrophy – specifically, more hypertrophy in the distal regions of muscles – due to differences in regional muscle activation (25, 26, 27). This would be a tidy explanation for all of these findings, but it has one rather major problem: the research linking differences in regional activation to differences in regional muscle hypertrophy wasn’t carried out in the context of comparing the effects of training at long versus short muscle lengths.

In other words, we know that regional differences in activation can lead to regional differences in hypertrophy in some contexts, but we don’t yet know if long- and short-muscle-length training do actually lead to differences in regional muscle activation. So, if future research found that training at long muscle lengths does lead to greater muscle activation in the middle and distal regions of muscles (compared to training at short muscle lengths), that would provide us with a tidy explanation for most of the research on this topic. However, for now, this potential explanation should be considered extremely tentative.

Stretching as an Independent Hypertrophy Stimulus?

A handful of human studies have found that sufficiently long, sufficiently intense static stretching interventions can independently cause muscle hypertrophy (30, 31, 32; MASS Review). Furthermore, a line of animal studies involving extreme stretch stimuli (i.e. weighted stretches for a period of weeks or months) reported some of the most extreme muscle growth ever observed in a research context (33). So, is it possible that the additive effects of a stretch stimulus could explain the hypertrophy results observed in these studies?

Unfortunately, that seems unlikely. The types of stretching interventions shown to directly cause hypertrophy in humans don’t particularly resemble the type of “stretch” you experience in a normal resistance training context. These studies used static stretching interventions, at the very end of the subjects’ ranges of motion, with each stretch being held for 5-60 minutes.

Conversely, the studies supporting the idea that training at longer muscle lengths results in more hypertrophy rarely take the subjects to the very end of their range of motion. Even if these studies did push subjects to the end of their range of motion, each set would involve less than 30 seconds of end-ROM “stretching” (just a brief instant at the bottom of each rep). Furthermore, as previously discussed, there’s direct evidence that you don’t need to go to the very end of your range of motion for training at longer muscle lengths to yield greater hypertrophy (for example, the Kubo study which found that squatting through 90° of knee flexion led to just as much quad growth as squatting through 140° of knee flexion; 23). Finally, the Stasinaki study is really the only study in this area that did involve training at the end-ROM for a particular muscle (overhead triceps extensions with up to 150° of elbow flexion, which would put the long head of the triceps in a deep stretch) – it was one of the few studies finding that training at long muscle lengths didn’t lead to more muscle growth than training at shorter muscle lengths (24).

Thus, it does appear that sufficiently intense stretches, held for a sufficiently long duration, can directly contribute to hypertrophy. However, it also appears that this mechanism can’t explain the finding that training at longer muscle lengths tends to cause more muscle growth than training at shorter muscle lengths.

Of note, the phrase “stretch-mediated hypertrophy” is often used to describe the observation that training at longer muscle lengths tends to result in more hypertrophy than training at shorter muscle lengths. However, I’m not sure if “stretch-mediated hypertrophy” is actually an appropriate term to describe this phenomenon, as it’s unclear whether the increased hypertrophy observed when training at long muscle lengths is actually mediated by stretch per se.

Muscle Deoxygenation and Hypoxic Stress?

On the front end, I’ll note that I don’t think that this is a primary factor explaining why training at long muscle lengths leads to greater hypertrophy, but I do think it may contribute in some contexts.

Incidentally, the evidence supporting this tentative mechanism comes from the only study that observed substantially more hypertrophy in a group training at shorter muscle lengths (at least in terms of the maximal muscle lengths trained in both groups).

In a study by Goto and colleagues (MASS Review), one group of subjects performed lying triceps extensions through a full range of motion: from 0° to 120° of elbow flexion. Another group trained through the middle of the range of motion: from 45° to 90° of elbow flexion (34). Despite the fact that the full-ROM group trained through longer muscle lengths at the bottom of each rep than the partial-ROM group, the partial-ROM group experienced considerably more triceps growth. Furthermore, the researchers found that the triceps hypertrophy achieved by the subjects in the partial-ROM group was significantly associated with the degree of oxygen desaturation their muscles experienced during training (Figure 12).

Graphics by Kat Whitfield

In some circumstances, this mechanism could potentially contribute to increased hypertrophy following partial-ROM training at long muscle lengths. For many exercises, when you lock out a rep, your muscles can relax a bit. When they relax, they put less pressure on the arteries carrying oxygenated blood to your muscles, and the veins removing deoxygenated blood from your muscles (35). So, for example, if you did dumbbell press through just the bottom half of the range of motion (i.e. “constant tension” training), your pecs and triceps would stay under load the entire time, potentially leading to greater muscle deoxygenation and greater hypertrophy. Furthermore, a similar mechanism may explain why subjects experienced more muscle growth in the monoarticular heads of the triceps following overhead triceps extensions (versus pushdowns) in the previously discussed study by Maeo and colleagues (17), despite the fact that overhead triceps extensions and pushdowns train the monoarticular heads of the triceps at the same muscle lengths: with the arms overhead, oxygen delivery to the active muscles would be somewhat compromised, since arterial bloodflow would be resisted by gravity (unlike when performing pushdowns). However, this tentative explanation can’t explain all (or even most) of the research findings supporting long muscle length training. Many of the studies used knee extensions, where the quads are still under load at the top of each rep (and thus, at short muscle lengths), and the muscles aren’t under load at the bottom of each rep (and thus, at long muscle lengths). Finally, I’ll also note that I’d like to see more studies verifying that muscle deoxygenation is truly a driver of hypertrophy before I’d feel particularly confident in its explanatory power.

Greater Total Muscle Tension?

Research suggests that mechanical tension is the primary initiator of muscle hypertrophy signaling (36), so it’s been (quite logically) posited that training at longer muscle lengths might lead to greater total mechanical tension than training at shorter muscle lengths, and thus lead to greater hypertrophy for completely “normal” reasons, with no additional mechanism required. In other words, if training at longer muscle lengths necessarily increases tension on the prime movers for a particular exercise, we may expect it to result in greater hypertrophy, simply due to the importance of muscular tension for hypertrophy.

In general, muscles produce the most active force when their fibers (or, more appropriately, the sarcomeres within those fibers) are at resting length (37). At resting length, the actin and myosin proteins within each sarcomere have the greatest amount of overlap, thus allowing for the largest number of force-generating crossbridges to be formed. When muscle fibers acutely shorten or lengthen, fewer actin/myosin crossbridges can form, leading to a reduction in active muscular tension (Figure 13).

Graphics by Kat Whitfield

However, muscles also have non-contractile elements that can contribute to passive muscular tension as the muscles lengthen. Namely, as fibers are stretched past their resting length, the passive tension can increase in the collagenous extracellular matrix of muscles (the endomysium, perimysium, and epimysium; 38), and in the giant titin proteins that run through each muscle fiber (39). So, while active mechanical tension decreases as muscle length increases, passive mechanical tension increases as muscle length increases past the fibers’ resting length (Figure 14).

Graphics by Kat Whitfield

So, does the evidence suggest that these increases in passive tension are sufficient to increase total muscular tension when training at long muscle lengths?

Probably not. Or, at minimum, not always.

This is a sticky subject, in part because there aren’t a ton of studies directly quantifying muscles’ length-tension relationships in vivo, and in part because it’s challenging to differentiate the active and passive forces present even when the length-tension relationship is quantified (40).

However, we can look at torque-angle curves to get a rough idea of the amount of total tension being developed by different muscles across their full range of motion. In general, we see that muscles typically create peak levels of torque near the middle of most joint ranges of motion. For example, we observe peak knee extension torque at around 50-80° of knee flexion (Figure 15), and peak elbow flexion and extension torque at around 70-90° of elbow flexion (41). Notably, those peak torque values don’t occur near the maximal muscle lengths of the quads, biceps, and triceps (respectively).   

Graphics by Kat Whitfield

These torque-angle curves suggest that less total tension is being developed at very long muscle lengths, but that tentative takeaway is complicated by the fact that muscle moment arms can change with changes in joint angles. For example, if joint torque is 20% lower at end-ROM than it was through the middle of the range of motion, but the muscle’s internal moment arm is 30% shorter, that would imply that total contractile forces were still higher at end-ROM, despite a decrease in torque.

If you wanted to fully model out the length-tension relationship of most major muscle groups to see if total tension does truly increase at long muscle lengths, you can be my guest. I think there’s probably enough data on torque-angle curves and muscle moment arms to keep you busy for quite a while. However, we don’t have to go quite that far to answer the question at hand.

The aforementioned study by Maeo and colleagues (17) investigating the impact of joint position on triceps hypertrophy can help us answer these questions: is total mechanical tension necessarily greater when training at longer muscle lengths? And, does training at long muscle lengths need to result in an increase in total mechanical tension in order to cause greater hypertrophy?

In this study, movement only occurred at the elbow, and both training conditions involved training the elbow through the same range of motion (0-90° of elbow flexion). Thus, this study negates concerns related to muscle moment arms, since the muscle moment arms of the triceps at the elbow would have been essentially the same in both training conditions. Furthermore, this study reported the training loads used in each condition, thus letting us know if the subjects generated more total muscular tension when training at longer or shorter muscle lengths (since both active and passive tension contribute to net tension, and thus joint torque, and thus the loads that could be used in training).

So, what did the study find? Subjects used more weight for pushdowns than overhead triceps extensions (Figure 16), but overhead triceps extensions resulted in more hypertrophy than pushdowns. Thus, total muscular tension during training was lower when training at longer muscle lengths, but training at longer muscle lengths still resulted in greater hypertrophy.

Graphics by Kat Whitfield

To be clear, I’m not claiming that training at longer muscle lengths never results in greater total muscular tension. For example, in the other study by Maeo and colleagues (looking at the effects of seated versus lying leg curls on hamstrings growth; 16), subjects were able to use heavier weights when training at long muscle lengths, thus suggesting that training at longer muscle lengths did result in greater total muscular tension. Furthermore, I’m also not claiming that increases in total mechanical tension aren’t beneficial for hypertrophy. However, I am claiming that training at longer muscle lengths can lead to greater hypertrophy, even when training at longer muscle lengths results in decreases in total mechanical tension.

Thus, potential increases in mechanical tension don’t seem to be the primary factor explaining the increases in hypertrophy observed when training at longer muscle lengths. Mechanical tension isn’t always higher when training at longer muscle lengths, and training at longer muscle lengths can still lead to more hypertrophy, even when training at longer muscle lengths results in decreased mechanical tension.

Additive Tension- and Fiber Length-Related Signaling Mechanisms?

As mentioned previously, mechanical tension has been identified as the primary contributor to muscle hypertrophy. However, it’s worth briefly discussing how mechanical tension eventually results in muscle growth.

In short, it’s currently believed that there are proteins on the surface of your muscle fibers (called costameres) that kick off the party (42). These proteins connect the muscle fibers to the surrounding matrix of connective tissue. When a muscle fiber contracts, the position of the fiber changes relative to the surrounding (noncontractile) connective tissue. When this occurs, the muscle fiber pulls against the connective tissue (and the connective tissue pulls against the muscle fiber) via these costameres that anchor the fiber to the connective tissue. This process creates a shearing force that these costamere proteins “feel.” In turn, costamere proteins kick off a signaling cascade that ultimately results in increased muscle protein synthesis. This whole process is referred to as “mechanotransduction.”

Thus, it’s believed that the primary pathway that turns mechanical tension into a stimulus for hypertrophy ultimately starts with proteins on the surface of the muscle fiber (36).

However, there are also proteins inside muscle fibers that are sensitive to tension and changes in fiber length. The most notable of these proteins is called titin (as a fun piece of trivia, titin is the largest protein in the human body). There’s growing evidence that titin also has mechanosensitive signaling functions (similar to those anchor proteins on the surface of muscle fibers) that are sensitive to increases in both fiber length and fiber tension – which sounds a lot like resistance training at long muscle lengths. The net result of this signaling cascade is thought to be both increases in fiber length and fiber cross-sectional area – which sounds a lot like the adaptations observed following resistance training at long muscle lengths (43, 44, 45).

In short, it’s possible that “normal” mechanical tension is primarily just sensed by costameres, resulting in elevated muscle protein synthesis and muscle hypertrophy, whereas mechanical tension at relatively long muscle lengths is sensed by both costameres and titin, resulting in even larger elevations in muscle protein synthesis and even greater hypertrophy.

This would certainly be an elegant explanation to explain why training at longer muscle lengths generally results in more hypertrophy. And, ultimately, I think it’s the most likely primary explanation. However, there are still quite a few missing puzzle pieces that need to be filled in before I’d be confident that titin-associated signaling is truly the primary explanation for these findings.

First, we need studies that measure titin kinase phosphorylation following long- and short-muscle-length training. Second, we’d need follow-up studies showing that titin kinase phosphorylation was both a) associated with hypertrophy in general and b) associated with the presence of within-subject hypertrophy differences following long- and short-muscle length training. Finally, we’d need studies examining the muscle lengths at which titin kinase phosphorylation occurs.

“Other”

This section focused on potential mechanisms that fall into one of three categories:

Mechanisms that seem like the most plausible potential explanations for the observation that training at long muscle lengths results in more muscle growth (regional activation, titin-associated signaling).Mechanisms that may potentially explain some of the individual findings, but seem insufficient to adequately explain the broader phenomenon (hypoxic stress).Mechanisms that seem unlikely to explain the broader phenomenon, but that warranted mention due to how frequently they’re invoked as potential explanatory factors (increased total tension due to increased passive tension, and stretch per se as an independent contributor).

However, this section isn’t meant to be completely exhaustive. There’s another category of potential mechanisms that don’t warrant an in-depth discussion, because they’re not often invoked to explain the superiority of long muscle length training, and they either seem unlikely to be primary drivers of the phenomenon, or they lack sufficient research for a thorough evaluation.

For example, eccentric stress through longer muscle lengths generally causes more muscle damage than eccentric stress through shorter muscle lengths (46). The current balance of evidence suggests that muscle damage likely isn’t a primary mediator of hypertrophy (36), but it hasn’t been fully disproven as a potential secondary mechanism. So, I don’t think it’s worth fully exploring the links between range of motion and muscle damage, and between muscle damage and hypertrophy. However, it’s not totally inconceivable that muscle damage could partially explain the benefits of long muscle length training in some contexts.

Furthermore, the effects of exercise range of motion on blood lactate levels are largely unexplored, but it’s likely that training through a longer range of motion or at longer muscle lengths would lead to a larger lactate response (due to higher levels of mechanical work per rep or due to increased vascular occlusion). There’s some indication that intramuscular lactate enhances hypertrophy signaling at sufficient concentrations (though the evidence is stronger in mice than humans; 47). So, at the moment, potential effects on lactate (and potential effects of lactate) seem unlikely to explain the hypertrophy effects of training at longer muscle lengths, but lactate-related effects can’t be fully ruled out until there’s more human research on the topic.

Similarly, some hormonal factors may (in part) contribute to the effects of training at long muscle lengths. For example, training at longer muscle lengths seems to increase IGF-1 levels to a greater extent than training at shorter muscle lengths (48), and IGF-1 is an important regulator of muscle hypertrophy and atrophy (49). To be clear, I’m certainly not proposing that the observed effects on IGF-1 could independently explain the hypertrophy findings within the body of literature, but the impact of range of motion and training at long versus short muscle lengths on hormonal responses (particularly autocrine and paracrine hormonal responses) is almost entirely unexplored.

Finally, there are plenty of other factors related to hypertrophy – capillary density (50, 51), satellite cell responses and myonuclear accretion (52), androgen receptor density (53), ribosome biogenesis (54), and probably a dozen others. Currently, we simply don’t know whether training at longer muscle lengths influences or interacts with these other factors to influence muscle growth, because those interactions haven’t been studied.

Ultimately, this line of research is still just scratching the surface, and I’d be quite surprised if we found out that training through longer muscle lengths leads to more muscle growth due to precisely one causative mechanism.

This Stuff is Messy

Before discussing practical applications and wrapping up, it’s worth simply acknowledging that this body of research is very interesting if you either a) want to get jacked or b) enjoy muscle physiology. Part of the reason it’s so interesting is that it hints at new frontiers in our understanding of muscle hypertrophy. We have an intervention (training at longer muscle lengths) that leads to more hypertrophy and we’re not entirely sure why. Unsolved mysteries are inherently tantalizing, and solving this mystery could teach us interesting new things about how to make hypertrophy training even more effective.

With that in mind, we’d just like to push back against a common impulse that seems to be going around. On social media, and across various blogs, plenty of people are acting like this is already a fully solved problem: we already know why training at longer muscle lengths produces more muscle growth.

Their explanations tend to reduce everything to total mechanical tension. Training at “long muscle lengths” means performing an exercise through a range of motion that stretches your sarcomeres beyond resting length (i.e., through a range of motion that progresses past the plateau of the active length-tension curve, down onto the descending limb of the length-tension curve). When you do so, passive tension will increase, resulting in more total mechanical tension, and thus more muscle hypertrophy.

The problem with this explanation is that it’s simply wrong at worst, or incomplete at best. As discussed previously, training at longer muscle lengths can lead to greater hypertrophy even when training at longer muscle lengths results in lower total tension. 

Furthermore, we also have evidence that long(er) muscle length training can result in greater hypertrophy, even when the long(er) muscle length training occurs at muscle lengths that are on the ascending limb of the active length-tension curve. For example, it seems that your biceps really don’t extend much (if any) past “resting length” when your arms are at your side (55). In other words, when your arms are fully extended, the sarcomeres of your biceps are at or near the plateau region of the length-tension relationship. When you’re in a bit of shoulder flexion (as you would be when performing a preacher curl, for example), the biceps would be at even shorter relative muscle lengths. However, a 2021 study by Sato and colleagues (56; MASS Review) found that training the biceps through long(er) muscle lengths (preacher curls from 0° to 50° of elbow flexion) led to more muscle growth than training the biceps through short muscle lengths (preacher curls from 80° to 130° of elbow flexion). In fact, there was a ~2.6-fold difference in hypertrophy between conditions (8.9 ± 3.9% versus 3.4 ± 2.7%).

Going beyond general mechanistic explanations, muscle lengths and passive tension can vary considerably between muscles and between individuals to an extent that (I suspect) most people don’t realize. For example, it’s commonly assumed that passive tension only starts increasing when sarcomeres are stretched beyond their optimal length for active force generation. However, that’s not always true. Sometimes, passive tension starts increasing when sarcomeres are still on the ascending limb of the length-tension curve. Sometimes, passive tension doesn’t start increasing until sarcomeres are stretched far past optimal length (59). Furthermore, the optimal length of sarcomeres can occur at different joint angles for the same muscle between individuals. For example, a 2010 study by Winter and Challis analyzing the rectus femoris and gastrocnemius found that the joint angles corresponding to the ascending limb of the length-tension curve for some subjects corresponded to the descending limb of the length-tension curve for other subjects (57).

In short, this perspective (that the effects of training at long muscle lengths are explained solely by increases in total mechanical tension) seems to be predicated upon several assumptions that are not always true:

Passive tension cannot be generated when sarcomeres are on the ascending limb of the length/tension curve.Passive tension reliably starts increasing at the instant that sarcomeres enter the descending limb of the length/tension curve. Total tension (summed active and passive tension) is reliably higher all along the descending limb of the length/tension curve than the plateau region.Training at longer muscle lengths results in more muscle growth if and only if training at longer muscle lengths results in greater total mechanical tension. Training at longer muscle lengths results in greater hypertrophy if and only if those longer muscle lengths coincide with the descending limb of the sarcomere length/tension curve.

If someone looks at this body of research and thinks they have everything figured out, we’d simply encourage them to soften their biases and take another look. It’s very satisfying to feel like you can fully understand and explain a wide array of complex phenomena by understanding a single mechanism (in this case, positing that muscle hypertrophy can be fully reduced to a function of mechanical tension), but we’d caution against giving into the allure of reductionism. If muscle hypertrophy really was that simple, it would have been a fully solved problem ages ago. When hundreds of scientists are still searching for answers, it seems quite unlikely that there is a single, simple explanation that has eluded all of them. 

We know a lot more about how muscle length and range of motion impact hypertrophy than we knew a few years ago, but there’s still a lot left to learn. Every new answer leaves us with several new questions. However, pushing forward and learning more about this topic promises to teach us exciting new things about how muscles adapt to training, and more importantly, how we can train more effectively.

Practical Recommendations For People With Strength or Performance Goals

For most people, most of the time, the principle of specificity applies: primarily train using the range(s) of motion you’d most like to build strength through.

There are three potential exceptions to this general principle:

If you’re relatively new to resistance training, and your primary goal is to build strength through a partial range of motion, it may still behoove you to do most of your training through a full range of motion.If your primary goal is to build strength through a partial range of motion coinciding with short muscle lengths, but you would also benefit from adding more muscle to your frame, it may still be a good idea to do the bulk of your training through a full range of motion (at least when you don’t have a competition on the immediate horizon).If your primary goal is to build strength through a full range of motion, you may still benefit from mixing in some partial-ROM training. For People With Hypertrophy Goals

As a general heuristic, you should perform most exercises through the longest range of motion you (safely and comfortably) can.

If you want to take things one step further, you could specifically seek out exercises that let you train your target muscle(s) though the longest possible (safe and comfortable) muscle lengths. Make sure these exercises are still challenging when your muscles are in a lengthened position.

If you want to take things an additional step further, you could eschew the portion of the range of motion that trains your muscles at short muscle lengths, and give long-muscle-length partials a shot. Pausing reps when your muscles are in a lengthened position will ramp the challenge up further.

If you’re feeling really experimental, you could even give long-muscle-length isometrics a shot, holding each isometric rep for 20-60 seconds, at the longest muscle length that still allows for active force generation.

For Everyone

As a final note, you don’t need to get super obsessive or neurotic about range of motion or training at long muscle lengths. Plenty of people have gotten strong and jacked by training through all sorts of ranges of motion. And, as discussed previously, training at the longest possible muscle lengths may not always be superior to training at pretty-long-but-not-quite-maximal muscle lengths. When the resistance training community takes a particular interest in a particular training variable, there’s a tendency for some folks to take things a bit too far. There’s also a tendency for content creators to make more and more extreme content around the hot topic, because the most extreme views tend to garner the most attention.

A few years back, training frequency was hot. “It may be better to train muscles 2-3 times per week instead of just once per week” turned into “natural lifters can’t build muscle or get stronger training each muscle just once per week, and you should really be training every muscle at least four times per week if you’re serious about your gains.” More recently, autoregulation was hot. “It may not be the best idea to train to failure all the time, and it may be beneficial to roughly quantify your proximity to failure” turned into, “if you don’t master the RPE scale and always leave 2-4 reps in reserve, you’ll overtrain, burn out, and be small and weak forever. Also, it’s literally impossible to recover from training to failure.” Similar dynamics have played out with training volume, accommodating resistance, and probably a half dozen other factors.

Already, I’ve seen some incredibly spicy (and completely unjustifiable) takes related to range of motion and long-muscle-length training. For example, “bench press doesn’t build your pecs because it doesn’t actually train your pecs through the longest possible muscle lengths,” and, “split squats won’t build your quads or glutes, because they don’t allow you to train through a maximal knee or hip range of motion.” Do not use this article to support that type of position. If you do, that means you didn’t read to the end (i.e., you didn’t make it to this sentence), and you don’t actually understand this area of research. 

We do know that training at longer muscle lengths tends to build more muscle than training at shorter muscle lengths, but…

That doesn’t imply that the compound exercises people have been successfully using for decades are suddenly ineffective because they don’t load every muscle through the longest conceivable muscle length.That doesn’t imply that you can’t build muscle without access to fancy equipment that allows you to place maximal tension on a muscle in its most stretched position.That doesn’t necessarily imply that training through a longer range of motion or at longer muscle lengths is always superior (as discussed in detail previously).That doesn’t imply that you should remove every exercise from your training routine that doesn’t load your muscles through the longest possible muscle lengths.That doesn’t imply that you should perform exercises in ways that are dangerous or painful just so you can train at slightly longer muscle lengths (for example, if your knees or hips bother you when squatting ass-to-grass, it’s perfectly fine to squat to parallel; if it hurts your shoulders to do really deep pec flyes, it’s perfectly fine to not let the dumbbells or cable handles sink quite as deep).That absolutely doesn’t mean you can’t build muscle unless you train through the longest possible muscle lengths all the time for every muscle, nor does it imply that training through short muscle lengths doesn’t also build muscle.

So, if you’re aiming to maximize muscle growth, it’s probably not a bad idea to train through the longest muscle lengths you (safely and comfortably) can, most of the time. But don’t be an extremist about it, especially since there’s still so much left to learn.

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- Cameron Gill
Neck Strength Training: Are Deadlifts and Shrugs Enough?

If you walk into a commercial gym, strap on a head harness, and start performing a neck extension exercise, there is a decent chance that you will draw some looks and maybe some grins from the people around you. Direct neck training is uncommon for the general population, and it can easily look silly to someone who is unfamiliar with this type of exercise. However, unusual does not equate to ineffective, and no substitute exists for direct neck training if your objective is to grow and/or strengthen the neck musculature.

Neck Muscle Hypertrophy

Many athletes perform strength training to enhance sport performance and decrease injury risk, but plenty of other people primarily care about lifting to be more jacked and/or become all around stronger human beings. With regard to physical appearance, no regions of the human body are more frequently visible than the face and neck. Except for the masticatory (i.e., chewing) muscles, whose strength and strength endurance have been reported to increase after a 6-week isometric jaw clenching exercise intervention, training the face musculature is not feasible (64). 

Masticatory Muscles

However, you can develop a wide variety of neck muscles through direct neck exercises. Most people would not consider the tree trunk that connects Jeff King’s head to the rest of his body to be the pinnacle of aesthetics, but a proportionally thick neck can certainly contribute to a more powerful appearance that is evident in nearly all settings.  

Jeff King Mike Tyson

Some coaches and lifters have asserted that the neck muscles can hypertrophy from shrugs, rows, and deadlift variations, and that direct neck exercises are not necessary to achieve this increased muscle size. The trapezius and levator scapulae muscles constitute a sizable portion of the musculature on the back of the neck, with MRI measurements indicating that they comprise 34.6% and 8.3% of total neck muscle volume, respectively (38). Given that both muscles attach to the scapula, an exercise that does not load neck movement but does load scapular movement (e.g. shrugs) may have the potential to increase neck muscle size. 

Trapezius   Levator Scapulae

However, none of the other muscles that contribute to neck movement attach to the scapula, so they will be neglected if direct neck exercise is avoided. Thankfully, researchers conducted a study on this matter to investigate whether shrugs, rows, and deadlift variations are sufficient to increase neck muscle size. Conley et al assessed two groups of recreationally active subjects that performed four full body resistance training sessions per week for 12 weeks where each exercise was trained for 3-5 sets of 3-10 reps (17). The exercises included squats, push presses, bench presses, midthigh pulls (i.e., above the knee rack pulls), Romanian deadlifts, crunches, bent over rows, and shrugs. While one group exclusively performed this traditional resistance training protocol, the other group additionally performed a weighted neck extension exercise for 3 sets of 10 reps three times per week. Before and after the intervention, neck extension strength (i.e., max load for 3 sets of 10 reps) was tested, and MRI cross sectional area measurements were performed at nine neck muscle sites (reflecting 13 total muscles), including the trapezius and levator scapulae.  

Following the intervention, the group that incorporated the neck extension exercise experienced significant increases in neck extension strength and neck muscle size at six of the nine sites measured, while no increase in neck strength or muscle size at any site occurred in the other group. Two of the muscle sites consisted of neck flexor muscles, so it is unsurprising that they did not exhibit any change for either group. However, the third site that exhibited no change in area for either group was the trapezius, which is a rather unexpected finding given the exercise selection. The authors did not discuss potential reasons for the apparent lack of trapezius hypertrophy, and speculating on this matter is beyond the focus of this article, but this intriguing outcome is worth highlighting nonetheless. The finding that levator scapulae hypertrophy was only observed in the neck extension group is also noteworthy, given that its anatomical attachments should enable it to function as a scapular elevator and allow it to be trained by shrugs. 

Ultimately, my main takeaway from this study is that commonly performed back exercises (e.g. shrugs, rows, and rack pulls) may be insufficient to induce meaningful increases in neck muscle size and strength, but these adaptations can readily occur when direct neck exercise is included. Potentially, neck muscle hypertrophy may still occur in the absence of neck exercise after a more prolonged period of well-designed traditional training with appropriate nutrition that yields an appreciable increase in total body muscle. To my knowledge, however, the only other resistance training interventions that reported increases in neck muscle size to occur after training utilized direct neck exercise (14, 31, 65).  

Neck Muscle Anatomy

The cervical spine comprises the skeletal structure of the neck, and the joints formed among its seven vertebrae enable neck movement to occur in all three planes of motion (50). 

Cervical Vertebrae 

Neck motion can occur as flexion (i.e., tucking the chin down), extension (i.e., looking up), side bending (i.e., tilting to the right or left), or rotation (i.e., twisting to the right or left). 

Neck Flexion and Extension Neck Side Bending Neck Rotation

The upper and lower cervical regions can move independently from each other, so two opposite movements can simultaneously occur at different regions. As a result, retraction (i.e., pulling the head backward) can occur as the upper cervical flexes while the lower cervical extends, and protrusion (i.e., pushing the head forward) can occur as the upper cervical extends while the lower cervical flexes (49). 

Neck Retraction and Protraction

The anatomy of the muscles that generate neck movement is quite complicated in part because they are often situated in different layers, which makes it difficult or impossible to visibly identify many of them. 

Neck Muscles

Additionally, a muscle that can generate a particular movement in one cervical region may not perform the same function in the other region. Most people will not be interested in exploring the nuance of these different muscles, nor is this required to incorporate effective neck training. Consequently, I summarized the data from several different studies on the neck musculature about which muscles will primarily be trained by each of the four neck movements. While most of the findings are congruent with one another, differences do exist, so some judgment is required with respect to functional classifications based on the body of evidence as a whole. For instance, the ability of the trapezius to contribute to neck movement is presently unclear. Biomechanical research reports this muscle’s upper region to have meaningful leverage to generate neck extension, side bending, and rotation torque (1, 47, 62, 67). However, EMG research has failed to detect any appreciable activation of the upper trapezius when exercises are performed for these three movements (25). Moreover, functional MRI research which measures changes in muscular fluid concentrations, a useful proxy for muscle engagement during an exercise which lacks some of the limitations of EMG research, has indicated that this muscle is not effectively trained with neck exercise (16). In light of the fact that all of the muscles suggested by this functional MRI study to function as neck extensors were reported to hypertrophy after the previously discussed neck extension exercise intervention, I can’t confidently assert that any neck exercise will meaningfully train the trapezius (16, 17). 

Neck flexion is produced by the sternocleidomastoid, longus coli, longus capitis, and infrahyoid muscles (1, 12, 16, 47, 62, 67). Neck extension is produced by the levator scapulae, erector spinae, splenius muscles, semispinalis muscles, and suboccipital muscles (1, 16, 21, 47, 62, 67). Neck side bending is produced by the sternocleidomastoid, levator scapulae, erector spinae, and scalene muscles (1, 16, 47, 67). Neck rotation is produced by the sternocleidomastoid, splenius muscles, semispinalis muscles, and suboccipital muscles (1, 16, 67).

Neck Muscle Functions

The main takeaway from this information is that you can target the vast majority of the neck musculature by using just two exercises because of the overlapping muscle functions. A combination of two movements, such as neck extension and neck flexion, will train nearly all of the muscles that would be targeted by performing one exercise for each of the four different movements. Consequently, supersetting a neck extension and neck flexion exercise together can be an efficient strategy to develop all regions of the neck in a brief period of time. If someone wishes to achieve every ounce of possible neck gains, a side bending neck exercise can also be included to ensure that none of the scalene muscles are neglected. However, if you are already utilizing extension and flexion exercises, I suspect that the overall effect of also including a side bending exercise would be minimal.

Neck Exercise

The exercises that you can utilize for direct neck training are quite simple. You can perform neck extension, flexion, or side bending exercise while lying atop a bench as you train the specific motion with a weight plate held against your head to provide resistance. For extension, you would lie on your stomach with the plate held on the back of your head. For flexion, you would lie on your back with the plate held on the front of your head. For side bending, you would lie on your side with the plate held on the side of your head. Each of the exercises will be most comfortable when wearing a beanie. 

Neck Extension with Plate Neck Flexion with Plate Neck Side Bending with Plate

To ensure that the exercises are effectively targeting the muscles in the neck rather than those elsewhere in the body, make sure that you are merely using your arms to keep the weight in position rather than actively contributing to moving the weight. Nobody cares how much weight you neck curl, so keep your technique strict and leave your ego at the door. With regard to load selection, it is worth noting that neck flexion strength will be lower than extension strength (24, 47). If you have a head harness available, this fairly inexpensive tool can also be used to load neck extension exercise with an elastic band, cable machine, or free weights such as a kettlebell or weight plate. 

Neck Extension with Harness

Given gravity’s vertical line of pull, free weights are not a viable modality to train neck rotation, but you can load this movement with a sheet band (e.g. TheraBand) wrapped around the head to apply tension when turning to either direction.

Neck Rotation with Band

Alternatively, you can manually apply external resistance to load any neck movement. This strategy can allow you to perform neck training and achieve a neck pump in nearly every environment, whether you are riding a bus, waiting for a job interview, or in between acts of your favorite play at the theater. If you opt to utilize manual resistance, you can readily individualize the level of resistance by adjusting how much pressure you apply through your hands to accommodate a particular rep range. 

Neck Extension with Manual Resistance Neck Flexion with Manual Resistance Neck Side Bending with Manual Resistance Programming Recommendations

If you are unaccustomed to neck training, you can likely progress with only a minimal time investment given the potency of a novel stimulus. A 10-week machine neck extension training intervention, consisting of a single set of 8-12 reps to volitional failure once per week with healthy untrained subjects, increased mean isometric neck extension torque by 6.3% to 14.3% depending upon which joint angle was tested (37). While a single set per week can be sufficient to enhance neck strength, including a second set has unsurprisingly been found to be even more effective. The lead researchers who directed the aforementioned study also conducted a similarly designed 12-week machine neck extension training intervention that included an additional group performing one set twice per week. Relative to the group training once per week, isometric strength gains across the eight joint angles tested were on average twice as large (21.9% vs 10.0%) in the group performing a second set of 8-12 reps at least 48 hours following the first set (52). 

It is worth noting that neither of these two studies measured changes in the neck muscle size, so it is unclear how much hypertrophy may have contributed to the increases in strength induced by these very low volumes (37, 52). Nonetheless, two other 12-week resistance training interventions have measured increases in biceps size to occur after performing a single set of 8-12 elbow flexion reps to failure twice per week, so it is certainly plausible that this volume could be sufficient to yield neck muscle hypertrophy as well (9, 59). 

Based on the available evidence, performing one set each of neck extension and neck flexion exercise twice per week is a reasonable and time-efficient approach to adding direct neck training into your existing program. As with the pursuit of any other objective, the key to improving neck strength and muscularity is to commit to a plan that can be consistently followed, so it is generally wiser to begin ingraining the habit of neck training with very low volumes which facilitate adherence. A greater rate of initial gains may potentially be experienced if someone starts utilizing higher volumes, but the increased opportunity cost may interfere with neck training consistency for individuals who have a finite amount of training time, which could prompt them to abandon neck training altogether. Once you form the habit with several weeks of minimal-volume neck training, you can incrementally increase set volumes to determine if a higher volume approach enables greater progress.  

With respect to rep range selection, I generally encourage beginning with a light to moderately light level of resistance that allows you to perform approximately 15-25 quality reps per set as you refine your neck exercise technique. As employed in three of the discussed resistance training interventions, somewhat heavier 8-12 rep sets can be quite effective for neck training, but the subjects in these studies were directly supervised during their sessions (17, 37, 52). For someone who is independently learning the technique for these exercises without a coach to monitor form, I typically recommend transitioning to moderate loads only after you have consistently demonstrated technical proficiency with lighter resistance. 

When performing any of the neck exercise variations where you hold a weight plate against your head, incrementally progressing the load through the addition of smaller diameter light plates can be rather awkward due to how you grip the plates. Particularly when using multiple plates with different diameters, it is difficult to position them properly for neck exercises, so progressing from week to week by increasing reps with a given weight is generally more convenient until you are strong enough to use a heavier single plate for a fairly wide target rep range. For instance, if you start with a 25lb plate for 15-rep sets of neck extension exercise, you can progressively add reps each week with the same weight until you are strong enough to use a 35lb plate for your minimum rep target. This may entail progressing to the point that you can perform 30-rep sets with 25lb before you can utilize 35lb for 15-rep sets. Many lifters will find that this method of progression is more practical than adding 2.5lb and 5lb plates on top of the 25lb plate while using a narrower rep range.     

The Relationship Between Neck Strength and Injury

Because head movement occurs through motion at the cervical spine, the muscles which act on the cervical spine serve a pivotal role in stabilizing both the head and neck, which contain the brain and spinal cord comprising the central nervous system. When an external force is applied to the head, individuals with high neck strength tend to experience lower velocity head movement than those with lower neck strength (20). If an athlete’s capacity to stabilize his/her head during a collision is influenced by neck strength, so too may an athlete’s risk of sustaining a concussion be affected by neck strength.

The most notable data on this matter was provided by researchers who measured neck strength and neck circumferences for over 6,000 high school athletes who played soccer, basketball, and lacrosse, while tracking who suffered concussions over the subsequent two years (15). Overall, athletes who later sustained a concussion exhibited significantly lower neck strength and neck circumferences than those who did not experience a concussion during the observation period. These findings suggest that increasing neck muscle strength and size through training may provide a protective effect against concussion risk, although firm conclusions cannot be drawn without longitudinal training interventions. While forceful collisions can occur during soccer, basketball, and lacrosse, I suspect that the overall effect would be even more pronounced in other sports such as hockey, football, boxing, and mixed martial arts where collisions are even more routine. A smaller study examining 27 professional rugby players over the course of two years also sought to assess how neck strength training may affect the risk of suffering a cervical spine injury (45). Relative to the first season when no neck exercise was performed, significantly fewer cervical spine injuries occurred during matches in the second season where the rugby players incorporated a neck strength training program. Despite this favorable finding, the limited sample size restricts the scope of conclusions which may be drawn. To my knowledge, this is the only research that has investigated the potential for neck resistance training by itself to influence a healthy individual’s probability of sustaining an injury. However, rugby players have also experienced a reduced risk of sustaining a concussion or neck injury after participating in other interventions that included neck training along with a variety of different exercises (5,30).

Additionally, individuals suffering from chronic neck pain have experienced significant reductions in pain and disability following neck resistance training interventions (4,8,13). When the available body of evidence is comprehensively analyzed, it is also clear that individuals who experience chronic neck pain typically exhibit lower neck strength than otherwise similar individuals who do not suffer from neck pain (42). Correlation does not equate to causation, so this finding cannot indicate whether neck weakness contributed to the development of neck pain or resulted from pre-existing neck pain. Nonetheless, low neck extensor muscular endurance has been prospectively identified as a risk factor for developing new onset chronic neck pain in office workers with high computer use, so enhancing this quality through training may be beneficial for certain populations (56). While distinct from maximal strength (which was not found to be a risk factor in that study), absolute muscular endurance can be significantly improved through resistance training with a wide loading range, whether that be moderately heavy (e.g., 6-8 RM), moderately light (e.g., 15-20 RM), or light (e.g., 30-40 RM) (60).  

Beyond affecting muscle size and strength, direct neck strength training may also influence posture for some individuals. Specifically, neck retraction training interventions have helped alleviate forward head posture, which results from habitually maintaining an excessive degree of upper cervical extension and lower cervical flexion (4,35). It remains to be empirically demonstrated if forward head posture can serve as a contributing factor in the onset of neck pain, but many perceive this posture to be unaesthetic. With its ability to be impacted by a variety of factors including our environment and behavioral habits, posture is a complex phenomenon. Consequently, enhancing the strength and endurance of the neck musculature may not necessarily be sufficient to modify resting head position; however doing so can reduce potential barriers from creating new postural habits.  

Neck Retraction with Manual Resistance Sleep Apnea

Some individuals may be hesitant to perform direct neck exercises out of concern for developing obstructive sleep apnea, which is characterized by repeated bouts of partial or complete restriction of the upper airway while sleeping (3). This highly prevalent disorder, which can reduce blood oxygen concentrations and meaningfully impair sleep quality, is associated with an elevated risk of experiencing multiple diseases (28, 55). Relative to people without obstructive sleep apnea, individuals with this disorder often have greater neck circumferences, and neck circumference is positively correlated with its severity (2, 11, 18, 32, 34, 48). Given this relationship, some lifters may be apprehensive to train their necks with exercises that can increase neck circumference through muscle hypertrophy (63). While neck muscle thickness influences neck circumference, correlation does not equate to causation, and other variables are much more likely to serve a causal role in the development of obstructive sleep apnea.

Neck circumference, waist circumference, body mass index (i.e., weight to height ratio), and body fat percentage all positively correlate with each other (6, 7, 19, 22, 27, 29, 41, 48, 53, 68, 69). While not without exceptions, people in the general population who have rather thick necks will likely be overweight in the same way that people with large waists and high body mass indexes often have high levels of body fat. Neck circumference, waist circumference, and body mass index do not distinguish between lean mass and fat mass, but lifters who have added appreciable amounts of muscle mass are not representative of the average person assessed by these studies. When any of these three variables increase for the typical adult, fat gain is usually the primary reason rather than muscle gain. Like neck circumference, body mass index positively correlates with having obstructive sleep apnea, so it is reasonable to think that obesity may be the root issue rather than neck circumference (11, 51). Nonetheless, neck circumference is associated with obstructive sleep apnea even when controlling for body mass index, suggesting that the location of mass is relevant (34, 61).

Largely due to genetic differences, regional fat distribution varies among individuals, so two individuals with the same level of total body fat may have rather dissimilar fat concentrations at the same anatomical site (10, 40). When lying down to sleep, having a high amount of subcutaneous (i.e., underneath the skin) neck fat may have the potential to compress the upper airway and consequently contribute to obstructive sleep apnea. However, deep fat deposits, which can contribute to a narrowing of the upper airway, are much more likely to be the culprit. Compared to people without obstructive sleep apnea, individuals with this disorder have significantly larger tongues and greater volumes of tongue fat particularly at its base (33, 39, 43, 54). Notably, these differences are present even when adjusting for body mass index and neck circumference (33, 43). Tongue volume positively correlates with body mass index and neck circumference to an even greater degree, so the association that these two variables have with obstructive sleep apnea may in large part be mediated by their relationship with tongue size (26, 36, 46, 57). Of any particular structure, the tongue may be the most relevant with respect to obstructive sleep apnea, but it is not the only contributing variable. Other internal tissues comprising the upper airway are also enlarged with more voluminous fat deposits in individuals with this condition compared to healthy controls with similar body mass indexes (44, 54). 

Tongue Anatomy

The importance of tongue fat to obstructive sleep apnea is further demonstrated by Wang et al, who examined 67 obese adults with this disorder before and after they completed a weight loss intervention (70). In addition to tracking changes in obstructive sleep apnea severity and bodyweight, the researchers measured a host of anatomical variables including 10 factors relating to upper airway size and 12 soft tissue volume measurements. Congruent with previous findings, the magnitude of weight loss correlated with the degree to which disease severity reduced (23, 58, 66). Intriguingly, the magnitude by which tongue fat volume decreased similarly correlated with reduced obstructive sleep apnea severity and weight loss. When adjusting for participants’ percentage of weight loss, the change in tongue fat volume was the sole variable that significantly correlated with the degree to which obstructive sleep apnea severity decreased. In light of these results, Wang et al concluded that decreases in tongue fat volume serve a primary role in mediating reductions in obstructive sleep apnea severity after weight loss (70).

After reviewing the available body of research, I have yet to see any evidence demonstrating that neck muscle size specifically affects someone’s risk of developing obstructive sleep apnea. This absence of evidence does not necessarily exclude neck muscle size from possibly being a contributing factor for some individuals, but its potential to do so would merely be speculative. Internal fat deposits, particularly those localized at the tongue, are considerably more likely to have a direct impact. 

Conclusion

If you wish to increase neck strength for a particular sport or neck muscle size for an aesthetic goal, common back exercises like shrugs, rows, and deadlifts are likely inadequate by themselves. However, you may be able to experience meaningful neck muscle hypertrophy and strength increases through the addition of direct neck exercise into your existing program.

Image Sources

The trapezius, levator scapulae, and cervical vertebrae anatomy images were published by “BodyParts3D, © The Database Center for Life Science,” are licensed as Creative Commons works, and can be found here.

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

The tongue anatomy image was published by “Encyclopædia Britannica, Inc.” and can be found here.

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The post Neck Strength Training: Are Deadlifts and Shrugs Enough? appeared first on Stronger by Science.

- 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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This article was the cover story for the October 2022 issue of MASS Research Review. If you’d like to read the full, 150-page October issue (and dive into the MASS archives), you can subscribe to MASS here.

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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. Effects of Resistance Training Performed with Different Loads in Untrained and Trained Male Adult Individuals on Maximal Strength and Muscle Hypertrophy: A Systematic Review. International journal of environmental research and public health. 2021 Oct 26;18(21):11237.Ribeiro AS, Dos Santos ED, Nunes JP, Schoenfeld BJ. Acute effects of different training loads on affective responses in resistance-trained men. International journal of sports medicine. 2019 Dec;40(13):850-5.Schoenfeld BJ, Wilson JM, Lowery RP, Krieger JW. Muscular adaptations in low-versus high-load resistance training: A meta-analysis. European journal of sport science. 2016 Jan 2;16(1):1-0.Carvalho L, Junior RM, Barreira J, Schoenfeld BJ, Orazem J, Barroso R. Muscle hypertrophy and strength gains after resistance training with different volume-matched loads: a systematic review and meta-analysis. Applied Physiology, Nutrition, and Metabolism. 2022;47(4):357-68.Grgic J. The effects of low-load vs. high-load resistance training on muscle fiber hypertrophy: A meta-analysis. Journal of Human Kinetics. 2020 Aug 31;74(1):51-8.Grgic J, Schoenfeld BJ. Are the hypertrophic adaptations to high and low-load resistance training muscle fiber type specific?. Frontiers in physiology. 2018 Apr 18;9:402.Schoenfeld BJ, Grgic J, Van Every DW, Plotkin DL. Loading recommendations for muscle strength, hypertrophy, and local endurance: a re-examination of the repetition continuum. Sports. 2021 Feb 22;9(2):32.Fisher J, Steele J, Smith D. High-and low-load resistance training: interpretation and practical application of current research findings. Sports Medicine. 2017 Mar;47(3):393-400.Campos GE, Luecke TJ, Wendeln HK, Toma K, Hagerman FC, Murray TF, Ragg KE, Ratamess NA, Kraemer WJ, Staron RS. Muscular adaptations in response to three different resistance-training regimens: specificity of repetition maximum training zones. European journal of applied physiology. 2002 Nov;88(1):50-60.Lim CH, Kim HJ, Morton RW, Harris R, Philips SM, Jeong TS, Kim CK. Resistance exercise-induced changes in muscle metabolism are load-dependent. Med Sci Sports Exerc. 2019 Oct 9;51(12):2578-85.Mitchell CJ, Churchward-Venne TA, West DW, Burd NA, Breen L, Baker SK, Phillips SM. Resistance exercise load does not determine training-mediated hypertrophic gains in young men. Journal of applied physiology. 2012 Jul 1;113(1):71-7.Morton RW, Oikawa SY, Wavell CG, Mazara N, McGlory C, Quadrilatero J, Baechler BL, Baker SK, Phillips SM. Neither load nor systemic hormones determine resistance training-mediated hypertrophy or strength gains in resistance-trained young men. Journal of applied physiology. 2016 Jul 1;121(1):129-38.Schuenke MD, Herman JR, Gliders RM, Hagerman FC, Hikida RS, Rana SR, Ragg KE, Staron RS. Early-phase muscular adaptations in response to slow-speed versus traditional resistance-training regimens. 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

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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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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.

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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.

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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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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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33.  Starkey, DB, Pollock, ML, Ishida, Y, Welsch, MA, Brechue, WF, Graves, JE, et al. Effect of resistance training volume on strength and muscle thickness. Med Sci Sports Exerc 28: 1311–1320, 1996.Available from: https://pubmed.ncbi.nlm.nih.gov/8897390/

34.  Vaarbakken, K, Steen, H, Samuelsen, G, Dahl, HA, Leergaard, TB, Nordsletten, L, et al. Lengths of the external hip rotators in mobilized cadavers indicate the quadriceps coxa as a primary abductor and extensor of the flexed hip. Clin Biomech (Bristol, Avon) 29: 794–802, 2014.Available from: https://pubmed.ncbi.nlm.nih.gov/24961540/

35.  Vecchio, AD, Casolo, A, Negro, F, Scorcelletti, M, Bazzucchi, I, Enoka, R, et al. The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment and rate coding. The Journal of Physiology 597: 1873–1887, 2019.Available from: https://physoc.onlinelibrary.wiley.com/doi/abs/10.1113/JP277250

36.  Veeger, HE, Van der Helm, FC, Van der Woude, LH, Pronk, GM, and Rozendal, RH. Inertia and muscle contraction parameters for musculoskeletal modelling of the shoulder mechanism. J Biomech 24: 615–629, 1991.Available from: https://pubmed.ncbi.nlm.nih.gov/1880145/

37.  Ward, SR, Winters, TM, and Blemker, SS. The Architectural Design of the Gluteal Muscle Group: Implications for Movement and Rehabilitation. J Orthop Sports Phys Ther 40: 95–102, 2010.Available from: https://www.jospt.org/doi/full/10.2519/jospt.2010.3302

38.  Welsh, P, Howitt, S, and Howarth, SJ. THE INFLUENCE OF HIP JOINT ANGLE ON THE RATIO BETWEEN ADDUCTION AND ABDUCTION TORQUE IN EXPERIENCED, RECREATIONAL MALE ICE HOCKEY PLAYERS. Int J Sports Phys Ther 15: 22–33, 2020.Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015023/

39.  Yoo, S, Dedova, I, and Pather, N. An appraisal of the short lateral rotators of the hip joint. Clinical Anatomy 28: 800–812, 2015.Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/ca.22568

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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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.

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- 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.