V O L U ME 5 , ISS U E 12 DEC EMBER 2 0 2 1 MASS M ONTHLY A PPL ICATIO N S IN STRE N G TH SPO R T E R I C H E LMS | G R E G N UCK O LS | MIC HAEL ZO URDO S | ERIC T REXL E R The Reviewers Eric Helms Eric Helms is a coach, athlete, author, and educator. He is a coach for drug-free strength and physique competitors at all levels as a part of team 3D Muscle Journey where he is also the Chief Science Officer. Eric regularly publishes peer-reviewed articles in exercise science and nutrition journals on physique and strength sport, in addition to contributing to the 3DMJ blog. He’s taught undergraduateand graduate-level nutrition and exercise science and speaks internationally at academic and commercial conferences. He has a B.S. in fitness and wellness, an M.S. in exercise science, a second Master’s in sports nutrition, a Ph.D. in strength and conditioning, and is a research fellow for the Sports Performance Research Institute New Zealand at Auckland University of Technology. Eric earned pro status as a natural bodybuilder with the PNBA in 2011 and competes in numerous strength sports. Greg Nuckols Greg Nuckols has over a decade of experience under the bar and a B.S. in exercise and sports science. Greg earned his M.A. in exercise and sport science from the University of North Carolina at Chapel Hill. He’s held three all-time world records in powerlifting in the 220lb and 242lb classes. He’s trained hundreds of athletes and regular folks, both online and in-person. He’s written for many of the major magazines and websites in the fitness industry, including Men’s Health, Men’s Fitness, Muscle & Fitness, Bodybuilding.com, T-Nation, and Schwarzenegger.com. Furthermore, he’s had the opportunity to work with and learn from numerous record holders, champion athletes, and collegiate and professional strength and conditioning coaches through his previous job as Chief Content Director for Juggernaut Training Systems and current full-time work on StrongerByScience.com. Michael C. Zourdos Michael (Mike) C. Zourdos, Ph.D., CSCS, has specializations in strength and conditioning and skeletal muscle physiology. He earned his Ph.D. in exercise physiology from The Florida State University (FSU) in 2012 under the guidance of Dr. Jeong-Su Kim. Prior to attending FSU, Mike received his B.S. in exercise science from Marietta College and M.S. in applied health physiology from Salisbury University. Mike served as the head powerlifting coach of FSU’s 2011 and 2012 state championship teams. He also competes as a powerlifter in the USAPL, and among his best competition lifts is a 230kg (507lbs) raw squat at a body weight of 76kg. Mike owns the company Training Revolution, LLC., where he has coached more than 100 lifters, including a USAPL open division national champion. Eric Trexler Eric Trexler is a pro natural bodybuilder and a sports nutrition researcher. Eric has a PhD in Human Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research papers on various exercise and nutrition strategies for getting bigger, stronger, and leaner. In addition, Eric has several years of University-level teaching experience, and has been involved in coaching since 2009. Eric is the Director of Education at Stronger By Science. Table of Contents 6 BY GR EG NUCKOL S Training Sequencing Doesn’t Matter Much Over the Short-to-Medium Term Does a block of hypertrophy training really potentiate a subsequent block of strength-focused training? Over the span of a 12-week training cycle, probably not. 19 BY MI CHAEL C. ZOUR DOS Free Weights and Machines Provide Similar Benefits A new meta-analysis says that other than strength being specific to the modality trained, training with free weights and machines provides similar results. This article breaks down the good news for those that want to keep training as they prefer. 33 BY ER I C HEL MS The Link Between Overtraining and Low Energy Availability For decades, iron gamers have said, “There is no such thing as overtraining, only under-eating.” While this is an incorrect statement deserving of an eye-roll, it may carry an element of truth. 45 BY ER I C T R EXL ER Building Muscle in a Caloric Deficit: Context is Key Trainees with body composition goals often want to lose fat and build muscle. Unfortunately, these goals generally lead to contradictory recommendations for caloric intake. Read on to learn when and how both goals can be achieved simultaneously. 59 BY MI CHAEL C. ZOUR DOS Tapering Strategies are Goal-Dependent I contend that tapering is generally overrated and is most beneficial following an overreach. However, questions remain, such as, “What type of tapering is best for strength?” And, “How does tapering affect muscle growth?” A new study has some answers. 76 BY ER I C T R EXL ER Metabolic Phenotypes, Weight Regulation, and Reverse Dieting A new study clarifies distinctions between “thrifty” and “spendthrift” metabolic phenotypes, with results providing numerous implications related to weight management. Read on to learn more about who struggles with weight loss, who struggles with weight gain, and how this relates to reverse dieting. 90 BY GR EG NUCKOL S & ER IC TREX LER Research Briefs In the Research Briefs section, Greg Nuckols and Eric Trexler share quick summaries of recent studies. Briefs are short and sweet, skimmable, and focused on the need-to-know information from each study. 127 BY MI CHAEL C. ZOUR DOS VIDEO: Verbal and Visual Feedback Part 1 C’mon! Let’s go! Great Work! We’ve all had training partners yell these phrases at us during training and have also been the training partners yelling. But does this type of encouragement help? This video discusses if shifting to an external focus while lifting via verbal encouragement and verbal and visual velocity feedback enhances acute performance. 129 BY ER I C HEL MS VIDEO: Periodizing Singles in Powerlifting Training Heavy singles are often used in powerlifting, but equally as often they are misunderstood or misapplied. In this video, Dr. Helms discusses the feasibility, rationale, pros and cons, and utility of heavy singles. Then, he presents a model of how to periodize singles into powerlifting training as an example you can use to integrate into your training. Letter From the Reviewers T he final issue of MASS Volume 5 has arrived, and it brings a very exciting change with it. The “Research Briefs” section of MASS has been incredibly well received since Greg pioneered the concept, so we have expanded it. From this point forward, Greg and Dr. Trexler will team up on a supersized “Research Briefs” section each month, which will substantially increase the number of studies reviewed in each issue of MASS. Having said that, full-length articles and videos are still MASS’s bread and butter, and this issue brings plenty of both. On the training side, Greg’s got an excellent article related to periodization, which specifically examines whether or not a block of hypertrophy training really potentiates a subsequent block of strength-focused training. In addition, Dr. Zourdos has two extremely practical articles comparing free-weight exercises versus machine-based exercises and step tapers versus exponential tapers. On the nutrition side, Dr. Helms covers an insightful paper suggesting that much of the published literature related to overreaching and overtraining might actually be describing the negative effects of relative energy deficiency. Dr. Trexler also brings two very informative nutrition articles to the table this month. In the first, he discusses how an individual’s “metabolic phenotype” (thrifty versus spendthrift) can influence their ability to gain, lose, and maintain weight. In the second, he explores the impact of energy deficits on strength and hypertrophy, and discusses when (and how) lifters can successfully achieve simultaneous fat loss and muscle accretion. As for this month’s video content, Dr. Helms has a very practical video about how to integrate heavy singles into a periodized training plan. In addition, Dr. Zourdos is back with part 2 of his excellent video series on external feedback. In this installment, he examines the longitudinal data assessing the effects of velocity feedback and external encouragement on strength and velocity improvements. As always, we hope you enjoy this issue of MASS, and we hope you’ll continue to join us for Volume 6 and beyond. If you have any questions about the articles or videos in this month’s issue, or you’d just like to discuss them further, be sure to post your questions or comments in the MASS Facebook group. Thanks, The MASS Team Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler 5 Study Reviewed: Order of Resistance Training Cycles to Develop Strength and Muscle Thickness in Resistance-Trained Men: A Pilot Study. Camargo et al. (2021) Training Sequencing Doesn’t Matter Much Over the Short-to-Medium Term BY GREG NUCKOLS Does a block of hypertrophy training really potentiate a subsequent block of strength-focused training? Over the span of a 12-week training cycle, probably not. 6 KEY POINTS 1. 16 resistance-trained men completed a 12-week training program. In one group, a hypertrophy-focused block of training preceded a strength-focused block of training; in the other group, the strength-focused block preceded the hypertrophy-focused block. 2. Gains in squat 1RM, bench press 1RM, biceps thickness, triceps thickness, and vastus lateralis thickness didn’t significantly differ between groups. 3. The results of the present study add to the relatively sparse body of research suggesting that, over the course of a single normal-length training cycle (i.e., 12 weeks), blocks of training focused on a particular outcome don’t potentiate the effects of a subsequent training block focused on a different outcome. In this case, a hypertrophy-focused block didn’t potentiate subsequent strength gains, and a strength-focused block didn’t potentiate subsequent hypertrophy. T he concept of periodization is often discussed, but theoretical arguments for and against various periodization models often far exceed what can actually be supported with robust longitudinal data (2). One of the main ideas underlying traditional periodization models is the concept of phase potentiation. Phase potentiation is the idea that adaptations accrued during one block of training will improve the results of a subsequent block of training. In the context of strength training, the most common application of phase potentiation is using a hypertrophy-focused training block to increase strength gains during a subsequent strength-focused training block. The idea is simple, logical, and intuitive: if you complete a block of training focused on building muscle, you’ll then have more muscle upon which “neural adaptations” can act, resulting in larger strength gains than would have been possible without completing the hypertrophy-focused training block. However (I’m sure you knew a “however” was coming), the evidence in favor of this application of phase potentiation is lacking. While I do strongly believe that hypertrophy raises your “strength ceiling” over time (3), there’s not strong evidence to suggest that this effect shows up over the course of a single training cycle. In other words, you shouldn’t necessarily expect a strength-focused block of training to result in larger strength gains because it was directly preceded by a 6-8 week hypertrophy-focused block of training. In the present study (1), resistance-trained men were randomized into two groups. One group completed six weeks of hypertrophy-focused training, followed by six weeks of strength-focused training. The other group completed the same blocks of training in the opposite order. Over the entire 12-week training cycle, strength gains (bench press and squat 1RM) and hypertrophy (biceps, triceps, and vastus lateralis thickness) didn’t significantly differ between groups. Read on to learn more about how these findings should influence program design. 7 Purpose and Hypotheses Subjects and Methods Purpose Subjects The purpose of this study was to “verify if different orders of [resistance training] cycles with equated volume affect maximum strength, muscle thickness, and training load (external and internal) in recreationally resistance-trained men.” 22 subjects initially volunteered for the present study, but 6 dropped out (due to injuries not associated with the study or voluntary drop-outs), so 16 subjects completed the study. The subjects were required to have at least one year of resistance training experience, with a training frequency of at least three sessions per week. More details about the subjects can be seen in Table 1. Hypotheses The authors hypothesized that “the equalization of the total work performed would be more important than the cycle temporal sequence for strength and morphological improvement.” In other words, the authors thought that if the two different training sequences led to similar training volumes (in this case, volume loads), strength gains and hypertrophy would be similar. Experimental Design The present study employed a randomized, longitudinal design. Initially, subjects were pair-matched based on baseline strength, and each pair was randomized to one of two groups: a strength-first group or a hypertrophy-first group (4). Both groups completed the same two 8 six-week blocks of training (a strength block and a hypertrophy block), but the strength-first group started with the strength block, and the hypertrophy-first group started with the hypertrophy block. The strength block involved exclusively compound exercises, trained in the 2-4RM range. The hypertrophy block consisted of a mix of compound and single-joint exercises, trained in the 10-12RM range. For both blocks of training, loads were increased if a subject completed more reps in a set than the top end of the prescribed rep range (>4 reps during the strength block and >12 reps during the hypertrophy block). Furthermore, to standardize the sets, subjects were required to maintain a cadence of at least one rep per (approximately) three seconds. Subjects reported a reps in reserve-based (RIR) rating of perceived exertion (RPE) of 9.5-10 for all sets of all exercises. More details about the training programs can be seen in Tables 2 and 3. Before the start of the training intervention, after the first six-week block of training, and after the end of the training intervention, bench press and back squat 1RM were measured to assess strength gains (5), and muscle thicknesses of the biceps, triceps, and vastus lateralis were measured using ultrasound to assess hypertrophy. Furthermore, the re- 9 searchers recorded each subjects’ total volume load completed (sets × reps × weight), and each subject reported a session RPE after each training session; the researchers calculated subjects’ “internal training load” by multiplying time under tension and session RPE scores. Of note, the mid-study testing period also effectively served as a deload week – subjects completed squat and bench press strength assessments, but no actual training sessions. Findings Gains in bench press 1RM, squat 1RM, biceps thickness, triceps thickness, and vastus lateralis thickness didn’t significantly differ 10 between groups (p = 0.09-0.67 for all outcomes). However, non-significant differences favored the hypertrophy-first group for all outcome measures except for gains in bench press strength (which were effectively identical between groups). jects in the strength-first group had baseline squat 1RMs 21kg higher than subjects in the hypertrophy-first group, on average. Similarly, baseline muscle thicknesses – especially vastus lateralis thickness – tended to be greater in the strength-first group. Furthermore, total volume load and internal training load didn’t significantly (or meaningfully) differ between groups. It’s possible that, after accounting for dropouts, the groups differed enough to meaningfully affect the results of the present study. Now, I don’t necessarily buy the common argument that weaker and/or less muscular research subjects can be assumed to be less well-trained, and therefore more likely to experience large increases in strength and muscularity. The opposite scenario is also possible, when you’re dealing with trained subjects: If subjects are stronger and more muscular at baseline, that may simply suggest that they respond better to resistance training. However, in any discrete study, we don’t know which of these possibilities most accurately describes a particular cohort of subjects. Criticisms and Statistical Musings There was one major issue that jumped out to me when reading the present study (1): The pre-training strength numbers in Table 1 and Table 4 don’t match up. For example, Table 1 reports mean pre-training bench press 1RMs of 89kg and 82kg, while Table 4 reports pre-training 1RMs of 105kg and 95kg. I assume that Table 1 reports pre-training strength values for all subjects who enrolled in the study (all 22 subjects), while Table 4 reports strength values for all subjects who completed the study (16 subjects). That explanation conflicts with the group sizes reported in Table 1 (n = 7 for the strength-first group, and n = 9 for the hypertrophy-first group), but it’s the explanation that makes the most sense to me. If my assumption is correct, that suggests that a disproportionate amount of the weaker squatters dropped out in the strength-first group. The researchers pair-matched the subjects according to baseline strength, with the intent of both groups having similar baseline strength levels; however, after dropouts, sub- Interpreting the present study is even more difficult due to the small sample size. With small samples, larger differences between groups are required to attain statistical significance. In the present study, there were no significant differences between groups, but some of the non-significant differences in favor of the hypertrophy-first group may seem large enough to be practically meaningful. So, did the training programs lead to truly different outcomes that would have cleared the threshold for statistical significance if sample sizes were larger? Or did the hypertrophy-first group just wind up with a group of subjects with a relatively low training sta- 11 tus, who were therefore primed to respond well to any training configuration? If you approached this study with a strong bias in favor of traditionalist periodization paradigms (i.e., the assumption that a hypertrophy block of training “potentiates” a subsequent strength block), it would be easy to interpret these results – especially the squat 1RM results – in a way that supported your beliefs. However, I think the results were driven primarily by greater general training responsiveness in the hypertrophy-first group. My main reason for this supposition is that squat 1RMs increased to virtually the same extent in the hypertrophy block in the hypertrophy-first group, and the strength block in the strength-first group. If training at higher intensities leads to larger strength gains in the short-to-medium term (it does; 6), then training with 2-4RM loads should lead to larger strength gains over six weeks than training with 10-12RM loads. Since 1012RM training increased squat strength in the hypertrophy-first group to the same extent as 2-4RM training in the strength-first group, I do think that, in this instance, we can probably assume that the weaker, less muscular subjects in the hypertrophy-first group simply had a lower training status than the subjects in the strength-first group, and were therefore likely to experience larger strength gains and more hypertrophy, independent of training sequencing. With that in mind, the interpretation section of this article is going to lean pretty hard on assuming the null, since there weren’t any significant differences in the first place, and since the hypertrophy-first group seems like it wound up with subjects who were simply more likely to respond well to training in general. However, other interpretations of the present results could certainly be reasonable and justifiable. Interpretation I mostly wanted to review the present study (1) because, while it may seem like a pretty standard periodization study – a well-worn area of research – it actually gives us a pretty rare glimpse into the effects of training sequencing. Within the periodization literature, linear periodization – increasing loads and decreasing reps over time, like the strength-first group in the present study – is taken to be the “standard” periodization approach. Linear periodization relies on a simple assumption: training adaptations accrued in one phase of training LINEAR PERIODIZATION RELIES ON A SIMPLE ASSUMPTION: TRAINING ADAPTATIONS ACCRUED IN ONE PHASE OF TRAINING WILL IMPROVE OUTCOMES IN SUBSEQUENT PHASES OF TRAINING. 12 will improve outcomes in subsequent phases of training. The “full” linear periodization model starts with strength endurance training, followed by hypertrophy training, followed by strength-focused training, followed by power-focused training. It’s assumed that strength endurance adaptations will improve one’s ability to complete more reps and handle more volume during hypertrophy training, leading to greater hypertrophy. Then, it’s assumed that prior hypertrophy will potentiate larger strength gains during the strength-focused block. Then, it’s assumed that greater strength will potentiate larger increases in power during the power-focused block. That all seems very logical, but there’s a catch: most of those assumptions lack strong empirical support. First, the “full” model is completely untested, as far as I’m aware (at least, it hasn’t been directly compared to other methods of organizing training); the vast majority of periodization studies just test the effects of the middle two blocks (the hypertrophy and strength blocks). Furthermore, the underlying assumption undergirding the sequence of the middle two blocks – that hypertrophy training will potentiate strength gains in a subsequent strength block – is poorly tested. Most research on linear periodization either compares the effects of linearly periodized training versus non-periodized training, or linear periodization versus undulating periodization. However, neither of these comparisons directly tests the underlying assumption that a hypertrophy training block will potentiate strength gains during a subsequent strength-focused block. When comparing linear periodization versus non-periodized training, the non-periodized training almost always consists purely of hypertrophy-style training (so the results can primarily be chalked up to differences in peak training intensity; 7). Furthermore, undulating periodization approaches largely rest on assumptions that are similar to those of linear periodization: it’s still assumed that hypertrophy training will potentiate strength gains (but undulating approaches assume that hypertrophy and strength can be developed in tandem, rather than requiring distinct phases of training). Ultimately, studies comparing linear and reverse linear periodization offer us with the best evidence concerning whether phase sequencing matters. Reverse linear periodization is exactly what it sounds like – instead of increasing loads and decreasing reps over time, you decrease loads and increase reps over time (like the hypertrophy-first group in the present study). If the assumption underpinning linear periodization is correct (hypertrophy training potentiates strength gains during a subsequent strength-focused block of training), linear periodization should result in larger strength gains than reverse linear periodization. With reverse linear periodization, your initial strength block should be less efficient since it’s not following a hypertrophy-focused training block, and the subsequent hypertrophy block shouldn’t result in particularly large strength gains (since specificity is lower than it was in the preceding strength block). Thus, if linear periodization ultimately results in larger strength gains than reverse linear periodization, that would con- 13 stitute strong evidence that the assumption underlying linear periodization is correct (at least for the middle two blocks of the “full” linear periodization model). Unfortunately … the evidence doesn’t support the underlying assumption of linear periodization. There have been three studies comparing linear and reverse linear periodization; in all three, linear and reverse linear periodization produced comparable strength gains. In the present study (1), gains in squat and bench press strength didn’t significantly differ between groups. In a 2016 study by Eifler (8), linear periodization and reverse linear periodization led to virtually identical strength gains across eight different exercises (horizontal leg press, chest press, flyes, lat pull-downs, horizontal rows, dumbbell shoulder press, cable triceps push-downs, and dumbbell biceps curls). Finally, in a 2009 study by Prestes et al (9), linear and reverse linear periodization led to comparable THERE HAVE BEEN THREE STUDIES COMPARING LINEAR AND REVERSE LINEAR PERIODIZATION; IN ALL THREE, LINEAR AND REVERSE LINEAR PERIODIZATION PRODUCED COMPARABLE STRENGTH GAINS. gains in bench press (14.57% vs. 16.15%), pull-down (26.45% vs. 21.55%), biceps curl (15.67% vs. 17.07%), and knee extension (36.84% vs. 30.26%) 1RM strength. To be clear, I do still think hypertrophy matters for maximizing long-term strength development. However, the evidence suggests that any impact of sequencing training phases is unlikely to show up over the course of a single training program (both the present study and the Prestes study included 12 weeks of training in total). Over the short-to-medium term, I doubt trained lifters experience enough hypertrophy to really move the needle. In other words, the accrued effects of hypertrophy over a matter of years can absolutely improve strength development, but you shouldn’t necessarily expect a single block of strength-focused training to be meaningfully more pro- 14 ductive because it followed a single block of hypertrophy-focused training. Moving on, the present study also provides evidence related to the impact of training sequencing on hypertrophy. It’s been argued that strength-focused training potentiates the effects of subsequent hypertrophy-focused training. The underlying assumption is that strength gains will allow you to generate greater muscular tension during hypertrophy training, and since muscular tension is a major driver of hypertrophy, getting stronger will therefore make hypertrophy training more effective. Earlier this year, Dr. Zourdos critically reviewed a study purporting to show that a strength-focused block of training potentiated the effects of a subsequent hypertrophy-focused block of training (10). That study generated a lot of chatter, but the between-group differences were quite small, in both absolute and relative terms. However, the only directly com- THE PRESENTLY REVIEWED STUDY THROWS EVEN MORE COLD WATER ON THE IDEA THAT STRENGTHFOCUSED TRAINING WILL POTENTIATE SUBSEQUENT HYPERTROPHY TRAINING. parable study with hypertrophy-related measures was the aforementioned Prestes paper (9). The Prestes study had the opposite results (linear periodization produced larger increases in fat-free mass than reverse linear periodization, suggesting that strength-focused training didn’t potentiate subsequent hypertrophy-focused training), but it only assessed changes in fat-free mass, rather than direct measures of hypertrophy (like muscle thicknesses or cross-sectional areas). The presently reviewed study (1) throws even more cold water on the idea that strength-focused training will potentiate subsequent hypertrophy training, as hypertrophy outcomes didn’t differ between groups in the present study. I think the takeaway here is simple: Keep short-term goals in mind in the short term, and long-term goals in mind in the long term. If you’re aiming to maximize strength development, think about when your strength needs to be at its peak. If you have a meet coming up in six weeks, short-term considerations probably trump long-term considerations. High-intensity, lower-volume, highly specific training probably won’t cause an appreciable amount of muscle growth, but it should help you maximize your performance on the platform. However, if you don’t have an imminent competition, make sure your training volume is high enough to provide a robust hypertrophy stimulus. Your day-today rate of strength gains might be somewhat slower, but the accumulated effects over a longer time scale should raise your strength ceiling. However, a single 1-2 month block of hypertrophy training probably won’t move the needle very much on its own. 15 APPLICATION AND TAKEAWAYS Over the course of the single training program, there’s surprisingly little evidence that the sequencing of training blocks makes much of a difference for hypertrophy or strength outcomes: There’s not strong evidence that a hypertrophy-focused training block potentiates the effects of a subsequent strength-focused training block, or vice versa. When you have short-term goals in mind (i.e., peaking for a powerlifting meet in eight weeks), use greater specificity to meet those short-term goals. Otherwise, train with a long-term mindset, instead of expecting adaptations from a single six-week block of training to have a major impact on your next six-week block of training. Next Steps I’d love to see a similar study on a longer time scale. For example, instead of six-week blocks of training, a study with six-month blocks of training (six months of hypertrophy-focused training followed by six months of strength-focused training, and vice versa) would allow for plenty of hypertrophy to occur during the hypertrophy phase. If the hypertrophy-first group in such a study experienced larger strength gains than the strength-first group, that would suggest that the underlying assumption of linear periodization – hypertrophy potentiates subsequent strength gains – is correct, but that it primarily matters over longer time scales. 16 References 1. DE Camargo JBB, Brigatto FA, Braz TV, Germano MD, Nascimento GS, DA Conceição RM, Teixeira I, Sanches TC, Aoki MS, Lopes CR. Order of Resistance Training Cycles to Develop Strength and Muscle Thickness in Resistance-Trained Men: A Pilot Study. Int J Exerc Sci. 2021 Aug 1;14(4):644-656. PMID: 34567366; PMCID: PMC8439707. 2. Kiely J. Periodization paradigms in the 21st century: evidence-led or tradition-driven? Int J Sports Physiol Perform. 2012 Sep;7(3):242-50. doi: 10.1123/ijspp.7.3.242. Epub 2012 Feb 16. PMID: 22356774. 3. 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):993-997. doi: 10.1007/s40279-019-01107-8. PMID: 31016546. 4. In the paper, the authors refer to the hypertrophy training as “strength endurance” training, but it’s standard 10-12RM hypertrophy work, so I’m electing to refer to it as hypertrophy training in this article. 5. In the abstract of the paper, it’s stated that half-squat strength was assessed; everywhere else in the study, the squat is referred to as a parallel squat. I suspect the mention of halfsquats in the abstract was just a typo. 6. 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. Med Sci Sports Exerc. 2021 Jun 1;53(6):1206-1216. doi: 10.1249/MSS.0000000000002585. PMID: 33433148; PMCID: PMC8126497. 7. Antretter M, Färber S, Immler L, Perktold M, Posch D, Raschner C, Wachholz F, Burtscher M. The Hatfield-system versus the weekly undulating periodised resistance training in trained males. International Journal of Sports Science and Coaching. 2017 December. 8. Eifler C. Short-Term Effects of Different Loading Schemes in Fitness-Related Resistance Training. J Strength Cond Res. 2016 Jul;30(7):1880-9. doi: 10.1519/ JSC.0000000000001303. PMID: 26670986. 9. Prestes J, De Lima C, Frollini AB, Donatto FF, Conte M. Comparison of linear and reverse linear periodization effects on maximal strength and body composition. J Strength Cond Res. 2009 Jan;23(1):266-74. doi: 10.1519/JSC.0b013e3181874bf3. PMID: 19057409. 10. Carvalho L, Junior RM, Truffi G, Serra A, Sander R, De Souza EO, Barroso R. 17 Is stronger better? Influence of a strength phase followed by a hypertrophy phase on muscular adaptations in resistance-trained men. Res Sports Med. 2021 NovDec;29(6):536-546. doi: 10.1080/15438627.2020.1853546. Epub 2020 Nov 26. PMID: 33241958. █ 18 Study Reviewed: Machines and Free Weight Exercises: A Systematic Review and MetaAnalysis Comparing Changes in Muscle Size, Strength, and Power. Heidel et al. (2021) Free Weights and Machines Provide Similar Benefits BY MICHAEL C. ZOURDOS A new meta-analysis says that other than strength being specific to the modality trained, training with free weights and machines provides similar results. This article breaks down the good news for those that want to keep training as they prefer. 19 KEY POINTS 1. The presently reviewed meta-analysis sought to determine if free-weight or machine-based training is better for strength, hypertrophy, and power outcomes. 2. The researchers found that strength gains are best achieved when the modality tested is the modality trained. Further, researchers found that strength gains on a “neutral” modality (not trained in either group) were similar between free weight and machine training, as were muscle growth and power gains. 3. Overall, the findings of this meta-analysis are intuitive. You need to train with free weights if you want to maximize strength on a free-weight exercise, and vice versa for machines. Since findings showed similar rates of muscle growth between training modalities, bodybuilders and general trainees should use the type of training they prefer and can adhere to. I n the quest for size and strength, you must consistently work hard in the gym. If you’re a powerlifter, you must squat, bench press, and deadlift at some point in training to maximize performance of those lifts. If you’re a bodybuilder, you must diet for the stage. However, there aren’t many things that you “must” do (outside of the obvious) when it comes to training prescription. For example, squatting can certainly lead to big quads, but you can also get there using leg press, hack squats, and other exercises. Further, a powerlifter must do the competition lifts, but they can also effectively utilize machines for some assistance work. The reviewed meta-analysis from Heidel et al (1) examined the differences in hypertrophy, strength, and power gains when training with machines versus free weights. Overall, the findings were unsurprising. Free-weight training was better for increasing strength with free weights, and machine-based training was better for increasing strength with machines. Further, strength gains were sim- ilar when strength was tested using a modality not trained by either group. Changes in both power (effect size = 0.049) and muscle growth (effect size = 0.01) were similar between freeweight and machine-based training. Overall, these findings reinforce the principle of specificity regarding strength gains, and suggest that those primarily interested in muscle growth can choose a modality based upon preference. However, there are some nuances related to practical application that warrant a closer look. Therefore, this article will aim to: 1. Review the general findings of this meta-analysis and dive into individual studies to analyze the findings critically. 2. Consider if the present findings for muscle growth apply to all muscle groups or all regions of a muscle. 3. Discuss the role of machine-based exercise for assistance work in powerlifters. 4. Examine affective responses and injury rates with free-weights versus machines. 20 5. Propose that the general lifter does not have to make a dichotomous choice between machines and free weights. Purpose and Hypotheses Purpose The purpose of the presently reviewed meta-analysis was to compare the magnitude of change in muscle size, strength, and power between free-weight and machine-based resistance training. Hypotheses tabases for studies that met specific criteria, shown in Table 1. The analysis included 16 studies (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17), and within these 16 studies, the researchers made five different comparisons between freeweight and machine-based training (Table 2). Findings The findings are straightforward and intuitive. First, the principle of specificity held up – training with free weights was better As is customary in a meta-analysis, the researchers did not state a hypothesis. Methods As we know, a meta-analysis begins with a systematic search of the literature using preset criteria to collate all the studies on the specific research question, then pools their results for analysis. The researchers conducted their systematic search by searching the PubMed, Scopus, and Web of Science da- 21 for free-weight strength, and training with machines was better for machine-based strength. Otherwise, there were no significant differences between free weights and machine-based training for muscle growth, power, or strength tested on a neutral modali- ty or device. Table 3 provides effect sizes and their associated 95% confidence intervals for each of the five analyses. Figures 1, 2, and 3 are forest plots of the strength-specific findings, while Figure 4 displays the muscle growth findings. 22 Statistical Criticisms and Musings I have no criticisms; instead, I’d like to point out that the authors presented two effect sizes for each comparison. One of the effect size calculations used the standard deviation of the change score mean, and the other used the standard deviation of the pre-test mean. The values I reported in the findings section use the pre-test standard deviation, which we prefer in MASS. However, the values in Figures 1-3 use the change score. You’ll notice that all effect size values using the pre-test standard deviation are smaller than the values using the change score standard deviation. Here are the two generalized equations used to calculate effect sizes: 1) [(mean free-weight group change score – mean machine-based group change score) / pooled standard deviation of the change scores] or 2) [(mean free-weight group change score – mean machine-based group change score) / pooled standard deviation of the pre-test mean]. Using the standard deviation of the pre-test is preferable for calculating between-group effect size comparisons, which we’ve written about before, because when using the standard deviation of the change score, the vari- 23 ability of a characteristic in the population is no longer being used as a comparison. Instead, the change score is only looking at the variability of the change. Again, the presently reviewed meta-analysis presented the effect size using both methods; however, I wanted to explain why I chose the pre-test standard deviation method. Interpretation The findings of this meta-analysis from Heidel et al (1) are intuitive. Strength is specific to the exercise modality that you train. Since muscle growth is achieved via mechanical tension, it increases to the same degree regardless of modality (free weights or machines). These findings are good news for the general practitioner, as they offer flexibility when selecting a resistance training modality. However, as we work our way through interpreting each analysis, we’ll dig a bit deeper into individual studies, take note of the training status and specific exercise or muscle group tested, and discuss the nuanced, practical applications of these findings. One of the few things that someone must do is train the specific exercise they want to strengthen. So, a competitive powerlifter must train the squat, bench press, and deadlift. Further, the findings showed that the specificity of strength gains is especially important in trained individuals (13, 16). For example, a recent study from Schwanbeck et al (16) found that well-trained men and women who trained the free-weight squat increased their 1RM by 21.3% (122kg to 148kg) over eight weeks, while those using a Smith machine improved free-weight squat 1RM by 13.1% (122kg to 138kg). Schwanbeck also reported significantly greater increases in Smith machine bench press 1RM in the machine-based group than the free-weight group. So, although strength is specific, a noteworthy question remains: how much does a lifter need to practice their primary movement(s)? Of course, there are data demonstrating that squatting every day is a viable method to increase 1RM (18), but that doesn’t mean it’s necessary or optimal. A study from Rossi et al (13) found that men who hadn’t trained in six months improved squat 1RM by 31.5% when squatting twice per week (12 total sets) and 7.9% when leg pressing twice per week (12 total sets) for 10 weeks. However, Rossi also found that men who performed half the volume of each exercise, six sets of squats, and six sets of leg press twice per week split the difference and gained 19.8% on their squat 1RM. In contrast, Rossi also found that gains in leg press 1RM were similar between groups (squat training: +34%, leg press training: +34.2%; squat + leg press training: +31.1%). The presently reviewed meta-analysis shows that training with machines is better for machine-based training strength gains. However, the findings from Rossi suggest that for at least these specific exercises (squat and leg press), strength increases in the complex movement (squats) transferred quite well to the simple movement (leg press), but strength increases in the simple movement didn’t transfer as well to the complex movement in detrained individuals. Although Rossi’s study shows that squat strength improved less when squat volume was cut in half, free-weight strength can be 24 substantially improved while mixing in both modalities (machines and free weights). The Rossi study also had lifters perform all sets between 8-12RM, suggesting that the squat load was ~75-80% on any given day. Androulakis-Korakakis, who wrote a recent MASS article on minimal effect dose training (19), found that powerlifters who trained the squat, bench press, and deadlift with only 3-9 sets per week up to ~90% of 1RM for 10 weeks tended to gain strength (19 - MASS Review). While 3-9 weekly sets for the main lifts may not be sufficient over the long-term, it is likely a minimum effective training dose for a few training blocks. Further, additional volume could be performed with machine-based work when minimal effective volume is used on the main lifts. The first part of this interpretation has focused on the findings related to the specificity principle; however, most people who engage in resistance training don’t care about their 1RM squat, bench press, or deadlift, or at least not enough to take up powerlifting. Rather, many trainees are interested in general strength to improve their quality of life and facilitate activities of daily living. A common strength test used in clinical patients is carrying groceries from points A to B. The good news is that this meta-analysis supports the idea that both machines and free weights can improve strength on a neutral task, such as grocery carrying. Carrying more groceries to the car or carrying them into the house faster may seem trivial to a lot of us, but remember, these general benefits are more wide ranging than just improving strength for powerlifters. In Figure 3, the largest effect for neutral strength is in favor of machine-based training, even though the trivial effect size for the analysis leans toward free weights. That study from Fisher et al (5) compared a group that trained the Romanian deadlift to a group that performed an isotonic machine-based back extension exercise. The “neutral” strength test was isometric torque at various joint angles on the machine back extension. From data extraction, there was roughly 13% greater isometric torque in the machine back extension group. However, I wouldn’t really consider this test “neutral.” The machine-based group used this device for 10 consecutive weeks, while the Romanian deadlift group did not. Further, the back extension machine trained almost solely the lower back, while the Romanian deadlift also trained the hamstrings. I’m not sure the Fisher study should count in the neutral category, and without that study, the forest plot (Figure 3) seems to favor free-weights for neutral strength. Even still, for most looking to improve general strength, there is insufficient evidence to suggest that the use of free weights is mandatory. The machine-based groups still improved strength to a physiologically meaningful degree. As seen in Table 3 and Figure 4, the hypertrophy findings were based upon only three studies (five total effects), and the 95% confidence interval for the effect size was -0.52 – 0.55. In other words, it’s hard to take much from this data. Further, of the five total effects, two were based on fat-free mass measurements, and the other three were based on measurements of muscle thickness. The three 25 studies that comprised the hypertrophy analysis are summarized in Table 4. Although it’s difficult to say too much about the hypertrophy findings, the Schwanbeck study (16) does provide interesting results. First, it is the only muscle growth study in trained individuals and includes both women and men. When analyzing the data within each sex, there was also no significant difference between free-weight and machine groups. Further, Schwanbeck used modality-specific assistance movements (i.e., not just squat and bench press) in each group. So, in addition to confirming the null findings of Saeterbakken (15) for quadriceps muscle thickness, Schwanbeck also found a similar increase in biceps thickness between groups. The Schwanbeck study (16) also equated sets and reps between groups, with no attempt to control for total volume even though different absolute loads are often used between freeweight and machine exercises that train the same muscle group. I think this approach is an ecologically valid design since it equates training duration between groups. This approach doesn’t necessarily mean that you can replace five sets of free-weight squats with five sets of leg extensions as a 1:1 tradeoff, but five sets of Smith machine squats for five sets of free-weight squats seem to be an appropriate swap for quad growth. However, it’s worth considering that hypertrophy wouldn’t be the same between Smith and free-weight squats in all lower body muscle groups. Schwanbeck et al (20) conducted another study in 2009 which compared electromyography (EMG) activity among six trained individuals (3 women and 3 men) between Smith and free-weight squats. When performing a modality-specific 8RM, EMG activity was higher in the gastrocnemius (+34%), biceps femoris (+26%), vastus medialis (+49%), and vastus lateralis (+25%) during free-weight squatting. Overall, it seems that when multi-joint and 26 single-joint exercises, respectively, are equated for sets and reps, muscle growth is similar between machine-based and freeweight training. Therefore, general trainees and bodybuilders can train exercises that they prefer when muscle growth is the goal. However, more data are needed to determine if swapping in the machine-based version of an exercise (i.e., Smith machine for freeweight squats) produces similar growth in all engaged muscle groups. Overall, the findings of this meta-analysis suggest that if you want to improve strength for a specific exercise, you need to train that exercise. Otherwise, lifters are free to choose between machine or free-weight exercises for general strength and muscle growth. STEADY-STATE CARDIO WILL PROMOTE DIFFERENT NEUROMUSCULAR ADAPTATIONS AND INTRACELLULAR SIGNALING THAN OUR TYPICAL LIFTING PARADIGMS. However, it is also worth considering if free weights increase injury risk. Unfortunately, there aren’t direct data comparing injuries between machines and free weights. Kerr et al (21) reported that 90.4% of all US emergency room visits due to resistance training between 1990-2007 were related to using free weights. However, of those free-weight injuries, 95.5% of them were due to “dropping weights” on oneself, someone being “smashed between weights,” or “hit(ting) oneself.” Although Kerr’s analysis did not consider training status, I’d imagine that the incidence of dropping weights on oneself would be lower in well-trained individuals. So, free weights may account for a much greater percentage of total lifting-related injuries than machines, but this comparison is confounded by inadvertent injuries that are unrelated to proficient execution of the exercises in question. I think the safest statement we can make regarding injury incidence is that novice individuals should learn appropriate techniques and avoid loading too heavy, especially for skilled movements. I don’t find the power data in the meta-analysis too important for MASS readers. Overall, Smith machine squat jumps and bench press throws, although common in the scientific literature, aren’t common explosive movements for athletes. So the findings here were null and aren’t worth dwelling on, in my opinion. For the general population, adherence to exercise is a primary concern (dare I say it’s the foundation of the pyramid?) when choosing free weights or machines. Adherence usually stems from the affective response to training, which is the overall enjoyment (or lack of en- 27 joyment) that somebody gets from an activity. We have an article here with more details on the affective response to lifting. Carraro et al (22) investigated the affective response to free-weight and machine-based training in a crossover design with subjects recruited from fitness centers. Carraro found that even though the participants felt that freeweight training (equated for reps and sets), which included squat and bench press, was more strenuous, they also rated it as more enjoyable and indicated a more pleasant state following training than in the machine-based condition. The participants in this study had two years of training experience and were recruited from fitness centers, so it does not mean that all populations would prefer freeweight training. In fact, Cavaretta et al (23) investigated the affective response to freeweight and machine-based training in women (n = 21) and men (n = 7) who were novices (≤ 1 session/week for 1 year). Cavaretta observed that the novice trainees reported similar enjoyment between the two protocols, which were equated for sets. Therefore, it may be worthwhile for novice trainees to utilize machine-based movements in the early stages of training while practicing their skill at the free-weight movements, then gradually increase the frequency at which they use free-weights as their experience increases. Lastly, it’s necessary to reiterate that freeweight or machine-based training is not a binary choice or a one-size-fits-all decision. Powerlifters may perform a lot of heavy work or volume on the squat, bench press, and deadlift, but they can still perform the bulk of their assistance work on machines. This approach may be more suitable for powerlifters when aiming to build muscle far out from a meet. However, as they get closer to their meet, they may want to prioritize free-weight assistance movements that transfer more directly to the competition lifts (i.e., close grip bench press, pause deadlifts, etc.). Similarly, bodybuilders who like to perform the barbell compound exercises can still incorporate these free-weight exercises into their program, while also using machine-based assistance exercises to train all muscle regions. Next Steps I think the next steps in this area are to expand the body of hypertrophy literature. Specifically, a longitudinal study could compare free weights versus machines, and analyze hypertrophy in a muscle’s proximal, middle, and distal regions, along with hypertrophy in synergistic muscle groups. Further, the literature to date has used 1:1 replacements for comparison, such as a Smith machine squat versus a barbell squat. However, some lifters may prefer not to squat, so a comparison of free-weights squats versus leg press is warranted. If exercises are equated for sets, this comparison would determine if swapping out one exercise for the other and performing the same number of sets is sufficient. Additionally, a squat versus leg press comparison would also determine if other machine-based exercises are needed to promote hypertrophy in secondary muscle groups. 28 APPLICATION AND TAKEAWAYS 1. The reviewed meta-analysis from Heidel et al (1) found that strength gains are specific to the modality trained when comparing free-weight versus machine-based training. Hypertrophy and power gains occur similarly with both modalities. 2. Someone who wants to improve strength on a specific exercise needs to train that exercise; however, those interested in muscle growth and strength to enhance daily living tasks should feel free to choose whichever training modality they prefer. 3. As with most training prescription choices, selecting either free weights or machines doesn’t have to be a binary decision. A powerlifter can squat, bench press, and deadlift, while also performing machine-based assistance work. Similarly, a bodybuilder can utilize their preferred free-weight movements while selecting appropriate machine-based movements to ensure they are training all muscle groups and regions of a muscle. 29 References 1. Heidel KA, Novak ZJ, Dankel SJ. Machines and free weight exercises: a systematic review and meta-analysis comparing changes in muscle size, strength, and power. J Sports Med Phys Fitness. 2021 Oct 5. 2. Boyer BT. A Comparison of the Effects of Three Strength Training Programs on Women. The Journal of Strength & Conditioning Research. 1990;4(3). 3. Wirth K, Keiner M, Hartmann H, Sander A, Mickel C. Effect of 8 weeks of free-weight and machine-based strength training on strength and power performance. Journal of human kinetics. 2016;53(1):201-10. 4. Augustsson J, Esko A, Thomeé R, Svantesson U. Weight training of the thigh muscles using closed vs. open kinetic chain exercises: a comparison of performance enhancement. J Orthop Sports Phys Ther. 1998 Jan;27(1):3-8. 5. Fisher J, Bruce-Low S, Smith D. A randomized trial to consider the effect of Romanian deadlift exercise on the development of lumbar extension strength. Phys Ther Sport. 2013 Aug;14(3):139-45. 6. Frost DM, Bronson S, Cronin JB, Newton RU. Changes in Maximal Strength, Velocity, and Power After 8 Weeks of Training With Pneumatic or Free Weight Resistance. J Strength Cond Res. 2016 Apr;30(4):934-44. 7. Kim Y, Lee K, Moon J, Koo D, Park J, Kim K, et al. Effect of Resistance Training Maintaining the Joint Angle- torque Profile Using a Haptic-based Machine on Shoulder Internal and External Rotation. J Phys Ther Sci. 2014;26(4):525-8. 8. Langford GA, McCurdy KW, Ernest JM, Doscher MW, Walters SD. Specificity of machine, barbell, and water-filled log bench press resistance training on measures of strength. J Strength Cond Res. 2007 Nov;21(4):1061-6. 9. Lennon E, Mathis E, Ratermann A. Comparison of strength changes following resistance training using free weights and machine weights. Missouri Journal of Health, Physical Education, Recreation and Dance. 2010;20:29- 35. 10. Mayhew JL, Smith AE, Arabas JL, Roberts BS. Upper-body strength gains from different modes of resistance training in women who are underweight and women who are obese. J Strength Cond Res. 2010 Oct;24(10):2779-84. 11. Milton K, Wojcik JR, Boyd JM, Bowers CJ. Comparison of Strength Gains in Untrained College-Age Females Using Free Weights and FreeMotion Machines. Physical Educator. 2018;75(1):37-49. 30 12. Park J, Kim K, Hong D, Moon J, Koo D-H, Kang M, et al. The shoulder abduction exercise with a haptic- based resistance training machine. International Journal of Precision Engineering and Manufacturing. 2012 2012/12/01;13(12):2239-43. 13. Rossi FE, Schoenfeld BJ, Ocetnik S, Young J, Vigotsky A, Contreras B, et al. Strength, body composition, and functional outcomes in the squat versus leg press exercises. J Sports Med Phys Fitness. 2018 Mar;58(3):263-70. 14. Saeterbakken AH, Andersen V, Behm DG, Krohn-Hansen EK, Smaamo M, Fimland MS. Resistance-training exercises with different stability requirements: time course of task specificity. Eur J Appl Physiol. 2016 Dec;116(11- 12):2247-56. 15. Saeterbakken AH, Olsen A, Behm DG, Bardstu HB, Andersen V. The short- and longterm effects of resistance training with different stability requirements. PLoS One. 2019;14(4):e0214302. 16. Schwanbeck SR, Cornish SM, Barss T, Chilibeck PD. Effects of Training With Free Weights Versus Machines on Muscle Mass, Strength, Free Testosterone, and Free Cortisol Levels. J Strength Cond Res. 2020 Jul;34(7):1851-9. 17. Schwarz NA, Harper SP, Waldhelm A, McKinley-Barnard SK, Holden SL, Kovaleski JE. A Comparison of Machine versus Free-Weight Squats for the Enhancement of LowerBody Power, Speed, and Change-of-Direction Ability during an Initial Training Phase of Recreationally-Active Women. Sports (Basel). 2019 Sep 30;7(10). 18. Zourdos MC, Dolan C, Quiles JM, Klemp A, Jo E, Loenneke JP, Blanco R, Whitehurst M. Efficacy of daily one-repetition maximum training in well-trained powerlifters and weightlifters: a case series. Nutricion hospitalaria. 2016;33(2):437-43. 19. Androulakis-Korakakis P, Fisher J, Kolokotronis P, Gentil P, Steele J. Reduced Volume ‘Daily Max’Training Compared to Higher Volume Periodized Training in Powerlifters Preparing for Competition—A Pilot Study. Sports. 2018 Aug 29;6(3):86. 20. Schwanbeck S, Chilibeck PD, Binsted G. A comparison of free weight squat to Smith machine squat using electromyography. The Journal of Strength & Conditioning Research. 2009 Dec 1;23(9):2588-91. 21. Kerr ZY, Collins CL, Dawn Comstock R. Epidemiology of weight training-related injuries presenting to United States emergency departments, 1990 to 2007. The American Journal of Sports Medicine. 2010 Apr;38(4):765-71. 22. Carraro A, Paoli A, Gobbi E. Affective response to acute resistance exercise: a comparison among machines and free weights. Sport Sciences for Health. 2018 Aug;14(2):283-8. 31 23. Cavarretta DJ, Hall EE, Bixby WR. Affective responses from different modalities of resistance exercise: timing matters!. Frontiers in Sports and Active Living. 2019 Aug 2;1:5. █ 32 Study Reviewed: Overtraining Syndrome (OTS) and Relative Energy Deficiency in Sport (RED-S): Shared Pathways, Symptoms and Complexities. Stellingwerff et al. (2021) The Link Between Overtraining and Low Energy Availability BY ERIC HELMS For decades, iron gamers have said, “There is no such thing as overtraining, only under-eating.” While this is an incorrect statement deserving of an eye-roll, it may carry an element of truth. 33 KEY POINTS 1. Overload is necessary, but if recovery is inadequate, fatigue can suppress performance in the short (overreaching) or long (Overtraining Syndrome) term, possibly accompanied by higher injury risk, disrupted sleep, mood, and immune function, and more. These overtraining symptoms are also Relative Energy Deficiency in Sport (RED-S) symptoms, caused by low energy availability (eating too little energy relative to lean mass for one’s training). 2. Overtraining Syndrome diagnoses require isolating the cause to excessive training and excluding other causes, including low energy, macronutrient, or micronutrient intakes. If this isolation doesn’t occur, RED-S can be mistaken for Overtraining Syndrome. 3. Authors of this review (1) analyzed 21 studies that attempted to induce overreaching or Overtraining Syndrome while reporting energy intake. They found that 18 of 21 studies were potentially confounded by lower energy availability (14 studies) or carbohydrate availability (4 studies) for participants in the overtraining group or condition. Thus, symptoms of overtraining in some cases may be due to insufficient energy intake. I don’t know who first said “There is no such thing as overtraining, only under-eating,” but it’s repeated so often it’s now cemented as bro “wisdom.” To be clear, it’s an objectively false statement, but a recently published review (1) indicates there may be some element of truth to it. To understand where this element comes from, we must 1) define overtraining and 2) define under-eating. The most recent (2013) consensus definition from the European College of Sport Science and the American College of Sports Medicine is that overtraining syndrome is a decline in performance lasting months or longer, with or without secondary symptoms including hormonal, psychological, immune, or sleep dysregulation, and more (a related concept, overreaching, has the same definition as Overtraining Syndrome just delineated by a shorter time course and a potential rebound in performance; more details later in this ar- ticle) (2). Under-eating in a training context was defined in 2014 and updated in 2018 in the International Olympic Committee’s consensus statement as low energy availability: “A mismatch between an athlete’s energy intake (diet) and the energy expended in exercise, leaving inadequate energy to support the functions required by the body to maintain optimal health and performance” (3). Chronic low energy availability can lead to what’s called Relative Energy Deficiency in Sport (RED-S), a syndrome with nearly identical symptoms to Overtraining Syndrome. But, to diagnose an athlete with Overtraining Syndrome, other potential contributing factors, such as medical conditions or insufficient energy, macronutrient, or micronutrient intakes, must be ruled out to isolate the cause as excessive training stress (2). Further, the most recent overtraining consensus definition came out the year before RED-S was in- 34 troduced, and unlike RED-S, overtraining is not a new concept and its definition hasn’t changed that much over the years (4). Therefore, the authors of the present review wanted to explore if RED-S could be misdiagnosed as Overtraining Syndrome or overreaching. To do so, they reviewed 21 overtraining studies (in which the authors induced or attempted to induce overreaching or Overtraining Syndrome) where energy availability of the participants was calculable. In 14 studies, the participants in the overtraining group or condition consumed less energy or had lower energy availability than the comparator, and in four studies the participants didn’t consume less energy but had lower carbohydrate availability. Meaning, in total, 18 of the 21 overtraining studies (86%) might have been confounded by nutritional factors. In this article I’ll discuss the specific nuances of this review and the relevance and application of the findings to lifters. Purpose and Hypotheses Purpose The purpose of this review was to highlight that many negative outcomes of excessive training load – whether or not it becomes overreaching (to be defined later in this article) or full-blown Overtraining Syndrome – can be caused by under-eating, and to highlight that RED-S can be mistaken for overreaching or Overtraining Syndrome. Hypotheses The authors hypothesized “that many of the negative outcomes of training-overload (with, or without an OTS [Overtraining Syndrome], NFOR [non-functional overreaching] or FOR [functional overreaching] diagnosis) may primarily be due to misdiagnosed under-recovery from under-fueling (LEA [low energy availability] leading to RED-S).” Subjects and Methods Subjects In the present review, the authors assessed a few collections of studies for specific outcomes. First, the authors gathered studies where athletes experienced overreaching or Overtraining Syndrome symptoms (57 studies) and studies where athletes experienced RED-S symptoms (88 studies) to compare symptomatology. Interestingly, the RED-S literature is dominated by female participants (n = 7,400 females [78%]; n = 2,105 males [22%]), while overtraining studies are dominated by male participants (n = 210 females [19%]; n = 880 males [81%]). This is most likely because RED-S research began as female athlete triad research (the convergence of disordered eating, menstrual cycle disruption, and reductions in bone density), which is also caused by low energy availability and sits under the umbrella of RED-S. However, overtraining research, like most of sport science broadly, has had historically greater male representation (which is fortunately changing). Additionally, the authors located and reviewed 21 studies in which the researchers attempted to induce a state of Overtraining Syndrome or overreaching, while also reporting sufficient nutritional and body composition data for the authors to calculate energy availability. Of the 21 studies, 9 used a 35 within-group design (e.g., a crossover or time series analysis in a single cohort where one condition or period was an intended overtraining phase) and 12 used a between-group design (an overtraining group and a “normal training” comparator group). The participants in these studies primarily consisted of endurance athletes (cyclists, long distance runners, and triathletes), rowers, and swimmers, but there were also two studies that included middle distance runners, and one study on active men. Importantly, there were no studies on resistance training. Methods While this was not a formal meta-analysis with a systematic literature search, the authors did perform some analyses. For the 57 overtraining and 88 RED-S studies, they assessed what symptoms were reported for each condition and which symptoms overlapped. For the 21 studies in which energy availability was calculable in groups or conditions where researchers attempted to induce overreaching or Overtraining Syndrome, the researchers assessed energy and carbohydrate availability by estimating if these intakes increased commensurately as training energy expenditure increased (or if they actually decreased). Findings In Table 1, the symptoms observed in the RED-S/Female Athlete Triad research are compared to those observed in the “overtraining” research. As you can see, all symptoms except for bone health decrements overlap in the two lines of research. Notably, in the over- 36 training research, Overtraining Syndrome (or even overreaching) is not always successfully induced (more on this in the interpretation), as a true Overtraining Syndrome diagnosis requires a reduction in performance. However, the high-volume, high-intensity protocols used to induce overtraining often produce secondary symptoms even when performance does not decrease. The authors actually used the terminology “training-overload” studies for this reason; however, I’m comfortable with calling a study an “overtraining study” (or referring to the “overtraining” group or condition) if the researchers attempted to induce overtraining, successfully or not. In the analysis of energy and carbohydrate availability, the authors found that participants in 14 studies had lower energy availability in the overtraining group or condition, and that participants in four studies had lower carbohydrate intake, without lower energy availability. Thus, 18 out of 21 studies (86%) may have observed reductions in performance or secondary symptoms due to inadequate energy or carbohydrate intake, rather than excessive training load. The difference in energy availability between the overtraining and comparator groups or conditions in the 21 analyzed studies was ~10kcal/kg FFM/ day (range: 6–18kcal/kg FFM/day). Notably, prior research has shown RED-S symptoms can occur with reductions in energy availability of just 7kcal/kg FFM/day (5). If you haven’t read or don’t remember from our previous articles (refresher here), energy availability represents “left over” energy for physiological function after training energy expenditure is subtracted from energy intake. It is expressed relative to fat-free mass (FFM) and an example calculation is as follows: a 10% body fat, 100kg athlete (90kg of FFM) consuming 3,000kcal and expending 400kcal on average in training (2,600kcal “left over”) has an energy availability of ~29kcal/kg FFM/day (2,600kcal divided by 90kg). In all four studies where energy intake was similar between groups or conditions, but carbohydrate intake was lower, poorer performance was also observed. Further, all but one paper that reported lower energy availability in the overtraining condition or group also had a lower carbohydrate intake. These carbohydrate intake differences ranged from 1.4-6.0 g/kg/day, and at the high end, this difference amounted to as much as a two fold difference in total daily carbohydrate intake between groups or conditions (e.g., 4g/kg/ day versus 8g/kg/day). These data highlight the importance of maintaining sufficient carbohydrate for athletes with very high endurance training volumes. Interpretation This paper targeted endurance athletes, so the findings as reported are only partially relevant to the majority of MASS readers. Thus, I’ll briefly interpret them, then frame the interpretation through a lifter’s lens. The main findings were that in more than three-quarters of studies where researchers exposed athletes to excessive training loads, energy and carbohydrate intake didn’t commensurately increase with increased exercise energy expenditure. In some cases energy and carbohydrate intake actually decreased despite an increase in energy expenditure. These reduc- 37 tions in energy availability may have been the cause of observed decreases in performance or secondary symptoms rather than excessive training load. Thus, endurance athletes do not always increase ad libitum energy intake to match high training loads, and they may even fail to do so when intentionally trying to consume sufficiently high energy intakes, resulting in instances of inadvertent low energy or carbohydrate availability. Indeed, in addition to the logistical challenge of consuming very high volumes of food, some data suggest high-intensity training can acutely blunt appetite in a dose dependent manner (6). Further, the data on individuals taking up exercise programs broadly show there is often an initial loss of weight or fat before energy intakes compensate for increased expenditure (7), and this time-lag may be mirrored in athletes during training periods with particularly high energy expenditure. However, energy expenditure during traditional resistance training isn’t nearly as high as energy expenditure during high-volume, continuous, high-intensity endurance training. As such, the risk of inadvertently low energy or carbohydrate availability due to the energy cost of training is comparatively lower in lifters than endurance athletes. Nonetheless, this review is relevant to lifters for three reasons, which I’ll address: 1) misconceptions around the concept of overtraining, 2) misconceptions about the role of sufficient energy intake for recovery (i.e., the bro wisdom that kicked this article off), and 3) a high occurrence of low energy availability among lifters due to competitive or non-competitive reasons for dieting. Because people often talk about “overtraining” without actually referring to Overtraining Syndrome or overreaching, I need to spend a bit more time on terminology. Overtraining Syndrome exists on the far end of the spectrum of training overload, and like I stated in the introduction, it results in sustained decreases in performance for at least months, and may or may not accompany negative secondary physiological and psychological effects. On the opposite end of the spectrum is “normal” overload, which results in an acute performance impairment due to fatigue that resolves before the next training session (or the next session that trains similar qualities). There are also two points on the spectrum between normal overload and Overtraining Syndrome: functional and non-functional overreaching. The distinction between functional and non-functional overreaching is that functional overreaching results in a decline or stagnation in performance that lasts days to weeks and is followed by an increase in performance above baseline, while non-functional overreaching is not followed by an increase in performance, and lasts weeks to months. Like Overtraining Syndrome, either may be accompanied by negative secondary physiological or psychological effects (2). To make the terminology even more complex, authors of the present review made the distinction between overreaching and Overtraining Syndrome and “mechanical overtraining.” Mechanical overtraining specifically refers to sustained performance decrements due to mechanical forces, such as repeated collisions in contact sports, ground reaction forces in running, or high volumes 38 of repetitive motor patterns (e.g., rowers performing 30,000–40,000 strokes/week) that stress specific joints or soft tissue structures past their capacity, without necessarily rising to the level of an injury, or surpassing the theoretical amount of training stress an athlete can systemically tolerate (8). True Overtraining Syndrome is rare, even among endurance athletes. One study found only 15% of endurance athletes experiencing declines in performance, fatigue, and other secondary effects of excessive training actually met the diagnostic criteria for true Overtraining Syndrome (9). But as rare as these diagnoses are among endurance athletes, they are even rarer in lifters (as I discuss in this video). I’d go as far to say that they are almost never experienced by lifters. The volume of training that’s logistically feasible with traditional weight training is far less than the volume of training (measured in total contractions or training time not including rest periods) that team sport or endurance athletes can accumulate. Grandou and colleagues (10) illustrated this in a 2020 systematic review of overtraining research in resistance training. In 10 of the 22 studies where researchers attempted to induce overreaching or Overtraining Syndrome, they failed, as no reduction in performance was observed. Of the 12 studies where a decrease in performance was observed, four didn’t do a follow-up to see how long the decrease lasted (meaning the protocol may have just resulted in functional overreaching), and the remaining eight studies that did follow-ups were not long enough to detect Overtraining Syndrome (the longest was eight weeks). Thus, full blown Overtraining Syndrome has never been documented in lifters in peer-reviewed literature to my knowledge, despite some absolutely ludicrous protocols. For example, Fry and colleagues had participants test their 1RM on the squat, followed by 10 singles with their 1RM, or as close to it as possible, twice per week (11). Another example is the study by Margonis and colleagues, which had participants increase baseline training volume four-fold while also increasing frequency, load, and proximity to failure for three weeks, and then increase volume seven-fold from baseline while also increasing frequency and load again, and training even closer to failure, for yet another three weeks (12). This figure from the study shows the protocol. I’ve expressed a number of times on podcasts or at seminars over the years, that “I’ve only observed overtraining in contest prep competitors and occasionally in CrossFit.” This is where the present review comes in, as my anecdote likely misattributed many incidences of low energy availability to overtraining. In the case of contest prep, physique athletes maintain a similar training schedule, add cardio, and concurrently and progressively reduce energy intake. In the case of CrossFit (and to be fair, this is very dependent on the box), it’s not uncommon (but also not universal) to adopt the “Paleo Diet” (or similar) which can result in a decreased energy and/ or carbohydrate intake. Furthermore, CrossFit isn’t a pure strength sport. You perform aerobic and anaerobic training as well, and success in most events is dictated by how much volume you can perform in as short a time period rather than absolute strength (to 39 be clear, this is not an indictment of CrossFit; anyone doing high-volume concurrent training with insufficient energy or carbohydrate intake could experience RED-S). It’s also relatively easy to confuse symptoms of low energy availability for excessive training load, because of how they can come about and because they are both related to under-recovery. RED-S symptoms are caused by low energy availability, a mismatch between exercise energy expenditure and energy intake. As shown in Figure 1, someone who drastically increases training volume (and thus energy expenditure), but keeps energy intake the same, is now in a state of low energy availability and may develop RED-S. However, it could be that even if they had commensurately increased their energy intake, the training load might have been too severe, and they’d also have experienced overreaching, and eventually developed Overtraining Syndrome. In a case like this, sure, you could just eat more (as the bros would suggest) to increase energy availability, but reducing training volume would take care of both low energy availability and excessive training stress. If you think these findings only apply to endurance athletes, or imply that lifters should aim for their highest possible training volume with the intention of eating their way to satisfactory recovery, I’m here to set you straight. Just because you won’t experience full blown Overtraining Syndrome doesn’t mean there isn’t such a thing as doing too much, or that just because you can do more, that you should. As I mentioned in my article on progression frameworks for hypertrophy, building the work capacity to perform a very high-volume protocol or being able to recover performance session-to-session while following a very high-volume protocol doesn’t necessarily mean you are actually improving at a 40 JUST BECAUSE YOU WON’T EXPERIENCE FULL BLOWN OVERTRAINING SYNDROME DOESN’T MEAN THERE ISN’T SUCH A THING AS DOING TOO MUCH, OR THAT JUST BECAUSE YOU CAN DO MORE, THAT YOU SHOULD. faster rate because of the high-volume training protocol. Work capacity and recuperability are different qualities than strength or hypertrophy, and strength is one of the qualities that is last and least negatively impacted by excessive training load. In many of the studies reviewed by Grandou, secondary signs of overtraining cropped up even when performance hadn’t declined. Further, if a study participant experiences an injury or “mechanical overtraining,” they drop out of the study and aren’t included in the final analysis. In my anecdotal experiences, when lifters overdo their training dose, they stagnate (indicative of overreaching) and then either change their training because they get hurt (injury or mechanical overload) or because of a loss of motivation or mild depression (secondary symptoms of overtraining). So, no, doing far more volume than you’d benefit from, even while eating more, is probably not a good idea, even though you won’t technically overtrain or experience RED-S. We can also look at this from another angle. If you’re experiencing an extended plateau or decline in performance (and/or secondary symptoms), Overtraining Syndrome is an unlikely diagnosis, so energy availability might be the issue. Whether it’s the pressure to stay reasonably lean in the offseason so you aren’t too far from stage condition as a physique athlete, the pressure to keep your bodyweight close to your weight class cutoff as a strength athlete, or the pressure from your damn Instagram feed to look good naked, undereating is a common occurrence. If you’ve been changing programs, trying supplements, or considering medical treatments or drugs, and nothing is working, it might be time to assess whether you’re simply not eating enough. Next Steps I would love to see a very similar review on overtraining research using resistance train- DOING FAR MORE VOLUME THAN YOU’D BENEFIT FROM, EVEN WHILE EATING MORE, IS PROBABLY NOT A GOOD IDEA, EVEN THOUGH YOU WON’T TECHNICALLY OVERTRAIN OR EXPERIENCE RED-S. 41 APPLICATION AND TAKEAWAYS The old adage “there is no such thing as overtraining, only under-eating” isn’t true, as you can certainly put yourself through an excessively challenging training protocol that won’t get you faster gains, but might leave you depressed and injured, no matter how much you eat. However, it’s also true that if you chronically under-eat, you can experience reductions in performance and negative mental and physical health effects, just like you would if you were overtraining, which can be alleviated by increasing your energy intake. ing. With that said, I suspect it would be a very small review. In the present review, there were 57 studies on overtraining in non-resistance trained athletes, and only 21 of them (about a third) had sufficient information to calculate energy availability. The 2020 Grandou review on overtraining in resistance training only had 22 total studies in it, and I suspect an even smaller proportion reported data sufficient to calculate energy availability. Therefore, what might be a more realistic next step, would be to conduct observational research on lifters who are plateaued or experiencing a decline in performance, and then assess them for both Overtraining Syndrome as well as low energy availability. This would allow us to see how often these plateaus or performance declines might be related to insufficient energy availability. 42 References 1. Stellingwerff, T., Heikura, I. A., Meeusen, R., Bermon, S., Seiler, S., Mountjoy, M. L., et al. (2021). Overtraining Syndrome (OTS) and Relative Energy Deficiency in Sport (RED-S): Shared Pathways, Symptoms and Complexities. Sports medicine (Auckland, N.Z.), 51(11), 2251–2280. 2. Meeusen, R., Duclos, M., Foster, C., Fry, A., Gleeson, M., Nieman, D., et al. (2013). Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Medicine and science in sports and exercise, 45(1), 186–205. 3. Mountjoy, M., Sundgot-Borgen, J. K., Burke, L. M., Ackerman, K. E., Blauwet, C., Constantini, N., et al. (2018). IOC consensus statement on relative energy deficiency in sport (RED-S): 2018 update. British journal of sports medicine, 52(11), 687–697. 4. Budgett R. (1998). Fatigue and underperformance in athletes: the overtraining syndrome. British journal of sports medicine, 32(2), 107–110. 5. Schaal, K., Tiollier, E., Le Meur, Y., Casazza, G., & Hausswirth, C. (2017). Elite synchronized swimmers display decreased energy availability during intensified training. Scandinavian journal of medicine & science in sports, 27(9), 925–934. 6. Hazell, T. J., Islam, H., Townsend, L. K., Schmale, M. S., & Copeland, J. L. (2016). Effects of exercise intensity on plasma concentrations of appetite-regulating hormones: Potential mechanisms. Appetite, 98, 80–88. 7. Blundell, J. E., Stubbs, R. J., Hughes, D. A., Whybrow, S., & King, N. A. (2003). Cross talk between physical activity and appetite control: does physical activity stimulate appetite? The Proceedings of the Nutrition Society, 62(3), 651–661. 8. Edwards W. B. (2018). Modeling Overuse Injuries in Sport as a Mechanical Fatigue Phenomenon. Exercise and sport sciences reviews, 46(4), 224–231. 9. Buyse, L., Decroix, L., Timmermans, N., Barbé, K., Verrelst, R., & Meeusen, R. (2019). Improving the Diagnosis of Nonfunctional Overreaching and Overtraining Syndrome. Medicine and science in sports and exercise, 51(12), 2524–2530. 10. Grandou, C., Wallace, L., Impellizzeri, F. M., Allen, N. G., & Coutts, A. J. (2020). Overtraining in Resistance Exercise: An Exploratory Systematic Review and Methodological Appraisal of the Literature. Sports medicine (Auckland, N.Z.), 50(4), 815–828. 11. Fry, A. C., Kraemer, W. J., van Borselen, F., Lynch, J. M., Marsit, J. L., Roy, E. P., et 43 al. (1994). Performance decrements with high-intensity resistance exercise overtraining. Medicine and science in sports and exercise, 26(9), 1165–1173. Margonis, K., Fatouros, I. G., Jamurtas, A. Z., Nikolaidis, M. G., Douroudos, I., Chatzinikolaou, et al. (2007). Oxidative stress biomarkers responses to physical overtraining: implications for diagnosis. Free radical biology & medicine, 43(6), 901– 910. █ 44 Study Reviewed: Energy Deficiency Impairs Resistance Training Gains in Lean Mass but Not Strength: A Meta-Analysis and Meta-Regression. Murphy et al. (2021) Building Muscle in a Caloric Deficit: Context is Key BY ERIC TREXLER Trainees with body composition goals often want to lose fat and build muscle. Unfortunately, these goals generally lead to contradictory recommendations for caloric intake. Read on to learn when and how both goals can be achieved simultaneously. 45 KEY POINTS 1. The presently reviewed meta analysis (1) quantified the impact of an energy deficit on strength and lean mass gains in response to resistance training. 2. 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). 3. “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. T hree 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 be- came 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 46 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 pro- 47 grams. 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. 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 48 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 49 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 shortterm 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 exag- 50 gerate 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 partici- pants 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 gain- 51 ing 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.” HYPERTROPHY IS AN ENERGY-INTENSIVE PROCESS THAT IS OPTIMALLY SUPPORTED BY A STATE OF SUFFICIENT ENERGY AVAILABILITY. 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 overweightto-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. composition 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. Since we can’t treat every deficit below 500kcals/day as being functionally equivalent, a dieter with ambitions related to re- As reviewed by Barakat and colleagues (6), there are several published examples of resistance-trained individuals achieving simul- 52 taneous 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 53 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 ener- 54 gy 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. pense 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). 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 ex- 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. Ob- 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) 55 viously, 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). 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. For Fat Loss Goals (Cutting) Next Steps 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 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 56 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. 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. SIMULTANEOUS FAT LOSS AND MUSCLE GAIN IS INDEED POSSIBLE, ALTHOUGH IT BECOMES LESS FEASIBLE AS AN INDIVIDUAL’S BODYFAT LEVEL DECREASES AND TRAINING STATUS INCREASES. 57 References 1. 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. 2. Roberts BM, Helms ER, Trexler ET, Fitschen PJ. Nutritional Recommendations for Physique Athletes. J Hum Kinet. 2020 Jan;71:79–108. 3. 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. 4. Hall KD. What is the required energy deficit per unit weight loss? Int J Obes. 2008 Mar;32(3):573–6. 5. 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. 6. 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. 7. 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. 8. Helms ER, Zinn C, Rowlands DS, Naidoo R, Cronin J. High-protein, low-fat, shortterm 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. █ 58 Study Reviewed: Skeletal Muscle Adaptations and Performance Outcomes Following a Step and Exponential Taper in Strength Athletes. Travis et al. (2021) Tapering Strategies are Goal-Dependent BY MICHAEL C. ZOURDOS I contend that tapering is generally overrated and is most beneficial following an overreach. However, questions remain, such as, “What type of tapering is best for strength?” And, “How does tapering affect muscle growth?” A new study has some answers. 59 KEY POINTS 1. Researchers compared changes in maximal strength and cross-sectional area in powerlifters following a training program that included overreaching and concluded with either a step taper or exponential taper. 2. Squat, bench press, and whole muscle vastus lateralis cross-sectional area changes were similar between the taper groups. However, deadlift strength gains favored the exponential taper group, while type IIa single-fiber hypertrophy tended to favor the step taper group. 3. Overall, it seems that the principle of specificity applies to tapering. Specifically, the three-week exponential taper may have allowed fatigue to dissipate to a greater extent following overreaching, leading to larger increases in deadlift strength. However, a step taper, which only consisted of one week of reduced volume, seemed superior for muscle growth. Therefore, the choice of tapering style is goal-dependent. T apers are commonly used to peak strength. In recent years, we have come to understand that effective tapers generally decrease volume and maintain intensity. While results vary from study to study, tapering may boost strength performance by about 3% on average (2, 3). A handful of MASS articles have covered tapering before (one, two, three, four); however, while tapering seems to be positive for peaking strength, there are still lingering questions about tapering, including 1) what type of taper works best? and 2) how does a taper impact muscle size and fiber type composition? The reviewed study from Travis et al (1) examined the effects of two different tapers (step taper versus exponential taper) in 16 powerlifters (14 men; 2 women) training three times per week for six weeks. Both training programs were the same in weeks one and two. The step group progressed linearly through week four, then completed an overreach in week five, followed by a taper in week six. The exponential group progressed linearly through week two, completed an overreach in week three, then had a three-week taper during weeks 4-6. The most pertinent outcomes were squat, bench press, and deadlift one-repetition maximum (1RM) strength, and muscle fiber hypertrophy assessed via biopsy. The researchers assessed outcomes before the first training week and one week after the tapers were completed. Both groups experienced statistically significant increases in squat and bench press 1RM from pre- to post-study; however, only the exponential taper group significantly improved deadlift 1RM from pre- to post-study. Furthermore, both groups increased vastus lateralis size, but the change in type IIa fiber area at the single-fiber level was significantly greater (p = 0.014) in the step taper group than the exponential group. These findings suggest that lifters can experience positive strength and muscle adaptations with step or exponential tapers after a planned overreach. 60 However, a sharp drop in volume (step taper) may be most beneficial for muscle growth, whereas a gradual volume decrease (exponential taper) might be a better option for strength. This article will aim to: rently, it is unclear which tapering model is most effective for peaking maximal strength and positively augmenting skeletal muscle.” 1. Discuss what we can and cannot take away from this study. Subjects 2. Review the types of tapers and state of the literature. 3. Examine why strength and hypertrophy may be optimized by different taping strategies. 4. Discuss when tapering is necessary. 5. Provide practical tapering examples. Purpose and Hypotheses Purpose The purpose of the reviewed study was to compare skeletal muscle adaptations and changes in performance in powerlifters who used either a step or exponential taper following overreaching. Hypotheses A hypothesis was not provided. Further, in the introduction, the researchers stated, “Cur- Subjects and Methods 16 powerlifters (14 men and 2 women) participated in the present study. They were all between 18-35 years old, with a 1RM squat and deadlift at least 1.5 times body mass and a bench press 1RM at least equal to their body mass. Subjects’ baseline strength levels are in Table 1. Study Overview The presently reviewed study was a parallel-groups design with 16 powerlifters split into two groups: a step taper group and an exponential taper group. Both groups trained three times per week for six weeks, with outcome measures tested one week before and one week after the study. Table 2 lists all outcome measures. Training Program Tables 3 and 4 show each group’s specific exercises (Table 3) and sets, reps, and load lifted (Table 4). In brief, both training programs were the same in weeks one and two. 61 The step group progressed linearly through week four, then completed an overreach in week five, followed by a taper in week six. The exponential group progressed linearly through week two, completed an overreach in week three, then had a three-week taper during weeks 4-6. The squat, bench press, and deadlift were considered main exercises, with all other exercises considered assistance exercises. In Table 4, if only one set and rep prescription is provided, then that set and rep scheme applied to all exercises for the day. Any set and rep prescription after a “+” refers to back-off sets on the day’s main lifts, and any sets and reps after a semicolon applied to only the assistance work on that day. For example, for day two of week four, the prescription “3 × 3 + 2 × 5; 3 × 5” indicates that on the main lift for that day, the subject first performed 3 × 3 at 80-85% of 1RM followed by back off sets of 2 × 5, finishing with 3 × 5 on the assistance movements. Findings Summary This study was a monster. The main takehome is that both tapers were beneficial, and that the exponential taper may have been more beneficial for strength, while the step taper may have been preferable for hypertrophy. Before moving onto the finer details, here’s a bullet point summary supporting those primary take-homes. 62 • Squat, bench press, and powerlifting total increased similarly in both groups. Training Volume, Load, Monotony, and Strain • Deadlift 1RM changes were not statistically different between groups; however, only the exponential taper group significantly increased deadlift 1RM. Although not statistically significant (p > 0.05), total volume was slightly higher in the step taper group (213,323 ± 50,066kg) versus the exponential group (203,568 ± 35,260kg) throughout the entire training period. By extracting data from webplotdigitizer, I can estimate that subjects in the step taper group decreased volume by -49.63% from their overreach (week 5) to the taper week (week 6). I also estimated that the exponential taper group decreased volume from their overreach (week 3) by -24.37% (week 4), -31.18% (week 5), and -49.16% (week 6). Thus, the total volume decrease was almost identical between groups. Figure 1AB displays the total volume and load in both groups throughout the study. • While changes in vastus lateralis cross-sectional area were similar between groups, the cross-sectional area of type IIa fibers tended to increase more in the step taper group. Body Composition Body mass, fat mass, and fat mass index tended to increase slightly more in the step taper group, but there were no statistically significant group differences (p > 0.05). 63 The trends of training monotony and strain mirrored volume. In other words, both monotony and strain tended to be high during overreaching weeks and lower during taper weeks. However, the average monotony and strain weren’t significantly different between groups during the study. Strength Squat, bench, powerlifting total, and Wilks score all increased significantly (p < 0.05) from pre- to post-study in each group without significant group differences (p > 0.05). Subjects in the exponential taper group significantly increased deadlift strength (+8.7%; p = 0.009) while subjects in the step taper group did not significantly improve deadlift strength (+1.5%; p > 0.05). Isometric squat strength did not significantly improve in either group (p > 0.05); however, similar to the deadlift results, squat jump force significantly increased in the exponential taper group 64 (+9.4%; p = 0.001), but not the step taper group (+5.4%; p > 0.05). Table 5 shows the 1RM strength and Wilks score findings in the exponential taper group. Figure 2 shows the change in myosin heavy chain content from pre- to post-study. Skeletal Muscle Adaptations MicroRNA Genes At the whole muscle muscle level, there was no difference between groups for hypertrophy. However, at the individual fiber level cross-sectional area of type IIa fibers increased to a significantly (p = 0.014) greater extent in the step taper group than the exponential taper group. Further, subjects in the step taper group experienced significant hypertrophy at the single fiber level when all fiber types were averaged together (p = 0.010), while the exponential group did not (p > 0.05). The number of fibers expressing myosin heavy chain type I content significantly decreased (p = 0.015) while the number of fibers expressing only type IIa content significantly increased (p = 0.033) in the step taper group; however, fiber type composition did not significantly change To simplify, the genes MyoD (p = 0.002) and MyoG significantly decreased (p = 0.037) from pre- to post-study, while mir-499a significantly increased (p = 0.033). There was no significant group × time interactions, but MyoD decreased in the step taper group (p = 0.002) but not in the exponential group (p > 0.05). The end of the interpretation provides a brief overview of some of these molecular markers. Statistical Criticisms and Musings The presently reviewed study’s statistical approach was sound, but musings are warranted. First, although I believe I can provide an 65 honest and objective analysis of this study I feel it necessary to disclose my relationship with the authors. I am close friends with the senior author, Dr. Caleb Bazyler who is also a previous guest contributor to MASS. Further, Dr. Bazyler, myself, and the lead author (Dr. Travis) have published a paper together. You can decide if this information is important, but I want to make sure I am disclosing anything that could be perceived as a potential conflict. Secondly, I do have criticism of the study design, which limits our interpretation. This study assessed outcome measures before and after the six-week training period, which is typical. Ideally, the researchers could have conducted some tests just after the overreaching week and again after the taper. Testing after the overreaching period could determine the magnitude of progress already made, and then the post-study test could determine if the tapering period further aided progress. As the study stands now, it’s difficult to say how much impact each taper actually had. The presence of a “no taper” group and a “no overreaching” group would have provided clarity. The hypothetical no taper group would have trained with equated volume and simply tested strength 48-72 hours before and after the six weeks of training. In this way, each taper group could be compared to what happened with no tapering. In my opinion, either of those designs would allow us to state the actual effects of these tapers more confidently. Another factor that makes it difficult to form definitive conclusions is that more than one variable is different between groups; thus, we cannot know what drives the different outcomes. Typically, if comparing training program A versus training program B in research, you would only change one thing. For example, Helms compared an autoregulated program to a percentage-based program for his Ph.D. thesis. The training programs were the same between groups except for how he prescribed the load; thus, any group differences could be attributed to the different load prescription methods. However, in the presently reviewed study, the tapering methods were different between groups, but so was the duration of the overreaching period and the duration of the “normal” training period before the overreach. Thus, the step taper group could have experienced greater single-fiber hypertrophy simply due to performing more volume closer to the post-study testing period rather than a direct result of the tapering protocol itself. The exponential taper group may have experienced greater increases in deadlift strength due to the overreach being farther from the post-study testing day than the step taper group. In other words, when more than one variable is different, it’s difficult to pinpoint which variable is driving the outcomes. However, despite my quibbles, it should also be stated the reviewed study is already a monster. The researchers put a ton of work into this study and recruited 16 well-trained powerlifters to participate, which is quite challenging. Therefore, adding another group (or two) would have required 8 or 16 more subjects, and would have been a substantial financial burden considering the muscle biopsy and immunohistochemical techniques. Also, if researchers tested outcomes after the overreach, invasive assessments would like- 66 ly not have been possible at that time point due to the aforementioned cost. Additionally, isotonic 1RM assessments are probably not a good idea after the overreach, which may add to pre-taper fatigue. Thus, post-overreaching testing may be relegated to isometric strength or using submaximal velocity to predict a 1RM (i.e., a linear regression forecast: video and downloadable calculator). As a researcher, the above suggestions are ideal, but I also understand why they didn’t happen and how difficult it would have been to pull off an even more ambitious study. In short, I know the authors well, and you can decide what to do with that information. Otherwise, I do think the lack of testing after the overreaching week limits our takeaways. Still, it would have been challenging to pull off that testing, and the authors should receive an enormous amount of credit for this study’s huge undertaking. Interpretation Historically, most tapering research has been on endurance athletes (4, 5) until various tapering studies on strength in team sport (6, 7) or strength athletes (8, 9, 10, 11) started popping up more recently. Although recent studies have examined tapering in the context of resistance training, two questions remain in the literature: 1) what type of taper works best? and 2) how does tapering affect muscle growth? Despite the minor quibble about the study design mentioned earlier, the reviewed study is a welcome addition to the literature as it tackles both questions. In general, this study found that both tapers were similarly effective for promoting muscle size and strength. However, the exponential taper was slightly better for strength in some respects, and the step taper led to more hypertrophy and the single fiber level. Therefore, this interpretation will aim to decipher the present results, review the structure of step and exponential tapers, determine in what situations tapering is necessary, and provide practical taper examples. In terms of strength, the increases in squat and bench press were pretty similar between groups (Table 5); however, deadlift strength increased by an additional 7.2% in the exponential taper group versus the step taper group (+8.7% vs. +1.5%). Due to the divergent results for squat and bench strength versus deadlift strength, we could interpret the findings to mean that the tapering strategy one employs doesn’t matter for strength, or we could conclude that exponential tapers are more effective for peaking strength. Also, as noted earlier, it’s difficult to deduce if any potentially meaningful group differences were directly due to the taper strategies. For example, if we argue that the exponential taper was better for deadlift strength, that cannot be stated with confidence. Rather, it may just be that the overreach in the step taper group was performed too close to 1RM testing and that tapering was not long enough for neuromuscular fatigue to recover. However, after the overreaching week, the exponential taper group had three weeks of tapering, allowing neuromuscular fatigue to dissipate. Although both groups had a ~49% decrease in training volume from overreaching week to the lowest-volume week of tapering, it’s possible that the longer duration taper in the exponen- 67 tial group led to superior strength gains. But, if an exponential taper is better for strength, why did it only affect deadlift 1RM? That’s a tough question to answer. A few years ago, we could have easily defaulted to the old adage that deadlifting is more fatiguing than squatting or benching; however, a recent study from Belcher et al (12) observed similar recovery time courses between the three powerlifts. However, that study did not assess recovery of lower back soreness, which may take longer with deadlifting. I also only assessed indirect markers of muscle damage, and not 1RM performance. Thus, it’s still possible that 1RM deadlift strength takes longer to recover from a damaging workout or, in the present context, an extended overreaching cycle than the other competition lifts, which suggests a longer reduction in volume (i.e., exponential taper) is warranted to peak deadlift strength. It appears that powerlifters also believe the deadlift should use a longer taper. Specifically, survey data from Grgic and Mikulic (9) found that national-level Croatian powerlifters performed their last heavy deadlift day, on average, eight days before competition. In comparison, lifters performed the final heavy squat and bench press seven and six days before competition. Squat jump peak power also statistically improved in the exponential taper group but not in the step taper group; thus, the findings for deadlift are not completely isolative as it relates to performance. However, it must be stated that the authors observed no significant group × time interactions for any performance measure. A statistically significant improvement in one group but not in the other is not equivalent to saying that there was a significant group × time interaction (i.e., the change in one group was greater than the change in the other group). Therefore, while you can interpret the present findings liberally to conclude that an exponential taper may be more beneficial than a step taper for deadlift strength, a strict statistical interpretation does not necessarily suggest that. A recent study from Seppanen and Hakkinen (13 – MASS Review) compared a two-week step taper versus a two-week exponential taper following three weeks of overreaching. Volume was reduced by 54% during both weeks of the step taper relative to the overreaching phase. With the exponential taper, volume was reduced by 38% in the first taper week and an additional 32% (70% total) in the second taper week for an average of 54% over the two weeks. The step taper group improved Smith machine squat 1RM by an average of 3.4% from before to after the taper period, while the exponential taper group increased squat 1RM by 1.7%. It’s hard to call that a win for the step taper group since data from only six subjects were available for analysis in each group. One could argue that there was a slightly greater increase in squat 1RM in the step taper group. However, I would encourage reading the Statistical Criticisms and Musings section from my previous review of this Seppanen and Hakkinen study. In short, I believe it is difficult to derive definitive takeaways from that study due to the small sample size and unclear analysis. Previously, I’ve mentioned that tapering may not be necessary without first overreaching. In other words, if the purpose of a taper is to allow fatigue to dissipate, then sufficient fa- 68 tigue must be present. In addition to the Seppanen and Hakkinen study (13) and the presently reviewed study, Aubry et al (14), Coutts et al (15), and Williams (16) used overreaching weeks prior to tapering and observed performance benefits. Table 6 provides details of those overreaching and tapering studies. In my opinion, the findings in Table 6 support the notion that tapering is beneficial following an overreach. However, excluding the presently reviewed study, two of the studies included are observational (15, 16), and another had a really small sample size (13), leaving us unable to make definitive conclusions. I do like the Aubry et al study (14), which shows that tapering isn’t that helpful without overreaching. However, the Aubry study was on endurance exercise, and the individuals in the overreaching group ended up being overtrained, and experienced performance decrements. Therefore, the Aubry study also highlights that it’s of paramount importance to individualize the volume increase if you are performing an overreach. Further, if you often find that increasing volume is not beneficial for you, then you might not be a good candidate for an overreach. Even though the tapering literature is limited, we can make theoretically sound contentions, but of course, “theoretically sound” is still my opinion. Before offering some practical examples, let’s consider the key principles of effective tapering: reducing volume (by 50-80%) and at least maintaining intensity. I 69 say “at least” maintaining intensity, because a recent study from Pritchard suggests slightly larger strength gains for a group that increased intensity by 8.5% during a one-week taper versus a group that decreased intensity by 8.5%, despite both groups reducing volume by 70%. However, Pritchard used a step taper and no overreach; thus, there may not have been a large amount of fatigue prior to the taper, and the slightly larger strength gains observed when increasing intensity during the taper may have simply been due to one group training at a higher intensity in the absence of accumulated fatigue. However, this finding fits with the principle of specificity: higher intensity is beneficial as one approaches a meet or test day. Therefore, my theoretical contention is that the longer the high-volume overreach, the longer a taper should be, whether it be an exponential taper or a step taper. First, a longer taper would allow for fatigue to dissipate more than a oneweek taper. Second, as the lifter feels more recovered during a three or four week taper, the lifter would be able to nudge intensity up each week and train heavier going into meet day or test day. Oftentimes, we think of tapering as rest, or as an easy week before a meet, but tapering can indeed be the last three or four weeks of a training block in which you are training heavier, because a taper is characterized by the workload you are doing compared to what you were doing previously, and not just the absolute workload. Therefore, a well-planned exponential taper allows for dissipating fatigue and an opportunity to work up to heavier loads gradually. Previously, I discussed this in a video and framed it as “training into a meet.” For example, if someone performs an overreach followed by a one-week step taper with a 50-80% volume reduction, the lifter may not fully recover for their strength test. Conversely, a three-week exponential taper which cuts volume by 20%, 20%, and 10% per week (to reach a total volume reduction of 50%) may be a better peaking option. Additionally, this exponential taper strategy would allow for some programming variables to be manipulated. First, a longer overreach could be performed with a longer taper, possibly allowing for greater strength (and size) gains that the lifter can ultimately realize. Secondly, with the longer taper (with a less steep volume reduction), the lifter can recover from the longer overreach, feeling better each week, and allowing for a gradual increase in heavy doubles and singles as the test or competition day approaches. Since a taper involves reducing volume and (at least) maintaining intensity (17), a 20%, 20%, and 10% progressive volume reduction per week (or something like that) with a concomitant increase in load checks all the boxes. Table 7 shows an example of how this could play out. Table 7 is just a conceptual example, and all standard caveats apply. There are, of course, many other ways to program a taper (i.e., you don’t have to use RPE or a three-day per week frequency, etc.), and the exact magnitude of set volume (or total volume) decrease may vary from person to person. In periodization studies, there’s usually no between-group difference (periodization versus non-periodization) for muscle growth). However, this may be because periodization studies are often designed to test strength, 70 not muscle growth, as the primary outcome. Similarly, the tapering literature has focused mainly on performance – strength or endurance – and not muscle growth. The findings in the reviewed study from Travis et al (1) show similar hypertrophy at the whole muscle level but greater hypertrophy among type IIa fibers at the single-fiber level in the step taper group (+11.3%) versus the exponential taper group (+0.33%). This study also found that there tended to be more type IIa fibers following the step taper than the exponential taper. Logically, these findings make sense as total training volume reached its highest point in the step taper group in week five, much closer to post-study testing than the week three volume peak in the exponential taper group. Another parallel to the periodization literature is the specificity aspect of tapering studies. In other words, one critique of periodization research is that periodized groups are often training with higher intensity than non-periodized groups closer to poststudy testing; thus, the body of literature is simply an argument for specificity. Therefore, if a physique athlete is looking to maximize muscle growth for the stage, their taper (if they even need one) shouldn’t be the same as for peaking strength. Going back to the point above, most tapering studies designed with strength as the primary outcome found muscle size to be stagnant or to slightly decrease following a taper. For example, the Seppanen and Hakkinen study (13) observed vastus lateralis cross-sectional area to increase by 5.4 % (step taper) and 11.2% (exponential taper) groups following eight weeks of training, but it reported only a <0.1cm2 change in cross-sectional area after a two-week taper. Further, the current study’s senior author, Dr. Bazyler, conducted a 16week study (6) on female collegiate volleyball players. Bazyler’s study utilized 11 weeks of training with volume decreasing throughout, and then a four-week exponential taper, and reported significant decreases (-3.3mm) in muscle thickness of the vastus lateralis from the start to the end of the four-week taper. 71 Since Bazyler’s study did not have a comparison group, conclusions are limited. However, it does suggest 1) that an exponential-type taper with four weeks of low volume is not ideal for muscle growth, or 2) tapering without an overreach may harm muscle growth (or both). Additionally, a study from Suarez et al (11 - MASS Review) found that collegiate weightlifters who increased vastus lateralis cross-sectional area following a training program then experienced a slight decrease in cross-sectional area following a one-week overreach a three-week taper. Similarly, a previous case series on two national-level weightlifters (one woman and one man), also from Travis et al (18 - MASS Review), found that vastus lateralis cross-sectional area decreased in both individuals following an overreach and a taper compared to baseline. Overall, it seems that traditional tapers are not ideal for muscle growth. The findings of the presently reviewed study show that a large spike in volume (overreach) followed by a short taper is better for muscle growth than a long taper. However, it’s challenging to make definitive conclusions since researchers didn’t assess cross-sectional area before the start of the taper. As noted earlier, if muscle cross-sectional area had been measured immediately following the overreach and then again after tapering, we could see just how effective each taper was. In that design, it would also be ideal to have a volume-equated non-tapering group for comparison. fiber phenotype. For example, Nachtigall et al (19) found that mir499a expression regulates the phenotype of type I muscle fibers. Further, in mice, SOX6 expression has been shown to blunt genes specific to type I fibers (20). Also, myoD (among other myogenic regulatory factors) serves to proliferate satellite cells (21). Going further into these details is beyond our scope, but some of these markers tended to increase, and others decreased. Thus, it’s difficult and perhaps not worthwhile in our context to speculate on whether changes in these markers impacted the practical outcomes. To conclude, the reviewed study from Travis et al (1) is a welcome addition to the literature. This study highlights that following an overreach, an exponential taper that gradually reduces volume over a few weeks may be more beneficial for strength than a step taper, while the opposite seems to be the case for muscle growth. However, it is imperative to reiterate that there were no between-group differences for strength. Further, since many variables were different between groups, it cannot be fully justified that the tapering strategy itself I don’t find it important to spend a lot of time on the present molecular experiments in this MASS article. In general, the genes examined are related to the regulation of muscle 72 APPLICATION AND TAKEAWAYS 1. Travis et al (1) found that a three-week exponential taper may be a better option than a one-week step taper to peak 1RM strength. However, the researchers also found that step tapers may be better for muscle growth. 2. Notably, both groups in the presently reviewed study performed an overreaching phase. Tapering is supposed to improve performance by allowing fatigue to dissipate, so tapering may not be necessary if sufficient fatigue is not present. 3. The main recommendations for tapering are to decrease volume and to maintain or increase intensity. If you implement those recommendations, then your taper is on solid ground. However, the exact magnitude of strength and muscle changes is individual and may take some trial and error depending upon your state of fatigue entering the taper. was responsible for the outcomes, if you take the liberal interpretation. I should also state that other forms of tapering exist, and for that, I would recommend checking in with previous MASS articles (one, two, three, four). To wrap things up, I leave you with my overall thoughts on the topic in Table 8. Next Steps An excellent next step would be to replicate the current study, but with outcome measures also tested at the end of the overreach. As noted earlier, we could truly gauge each taper’s benefits with the addition of that testing time point. Even more ideal would be to add a volume-equated non-tapering control group and a non-overreaching + taper group to the mix. 73 References 1. Travis SK, Zwetsloot KA, Mujika I, Stone MH, Bazyler CD. Skeletal Muscle Adaptations and Performance Outcomes Following a Step and Exponential Taper in Strength Athletes. Frontiers in Physiology. 2021:1766. 2. Pyne DB, Mujika I, Reilly T. Peaking for optimal performance: Research limitations and future directions. Journal of sports sciences. 2009 Feb 1;27(3):195-202. 3. Pritchard HJ, Tod DA, Barnes MJ, Keogh JW, McGuigan MR. Tapering practices of New Zealand’s elite raw powerlifters. The Journal of Strength & Conditioning Research. 2016 Jul 1;30(7):1796-804. 4. Le Meur Y, Hausswirth C, Mujika I. Tapering for competition: A review. Science & Sports. 2012 Apr 30;27(2):77-87. 5. Bosquet L, Montpetit J, Arvisais D, Mujika I. Effects of tapering on performance: a metaanalysis. Medicine & Science in Sports & Exercise. 2007 Aug 1;39(8):1358-65. 6. Bazyler CD, Mizuguchi S, Sole CJ, Suchomel TJ, Sato K, Kavanaugh AA, DeWeese BH, Stone MH. Jumping performance is preserved but not muscle thickness in collegiate volleyball players after a taper. The Journal of Strength & Conditioning Research. 2018 Apr 1;32(4):1020-8. 7. Zaras ND, Angeliki-nikoletta ES, Krase AA, Methenitis SK, Karampatsos GP, Georgiadis GV, Spengos KM, Terzis GD. Effects of tapering with light vs. heavy loads on track and field throwing performance. The Journal of Strength & Conditioning Research. 2014 Dec 1;28(12):3484-95. 8. Pritchard HJ, Tod DA, Barnes MJ, Keogh JW, McGuigan MR. Tapering practices of New Zealand’s elite raw powerlifters. Journal of strength and conditioning research. 2016 Jul 1;30(7):1796-804. 9. Grgic J, Mikulic P. Tapering practices of Croatian open-class powerlifting champions. The Journal of Strength & Conditioning Research. 2017 Sep 1;31(9):2371-8. 10. Pritchard HJ, Barnes MJ, Stewart RJ, Keogh JW, McGuigan MR. Short-term training cessation as a method of tapering to improve maximal strength. The Journal of Strength & Conditioning Research. 2018 Feb 1;32(2):458-65. 11. Suarez DG, Mizuguchi S, Hornsby WG, Cunanan AJ, Marsh DJ, Stone MH. Phasespecific changes in rate of force development and muscle morphology throughout a block periodized training cycle in weightlifters. Sports. 2019 Jun;7(6):129. 12. Belcher DJ, Sousa CA, Carzoli JP, Johnson TK, Helms ER, Visavadiya NP, Zoeller RF, 74 Whitehurst M, Zourdos MC. Time course of recovery is similar for the back squat, bench press, and deadlift in well-trained males. Applied Physiology, Nutrition, and Metabolism. 2019;44(10):1033-42. 13. Stina S, Keijo H. Step vs. Two-Phase Gradual Volume Reduction Tapering Protocols in Strength Training: Effects on Neuromuscular Performance and Serum Hormone Concentrations. Journal of Strength and Conditioning Research. 2020 Dec 16. 14. Aubry A, Hausswirth C, Julien L, Coutts AJ, Le Meur Y. Functional overreaching: the key to peak performance during the taper?. Medicine and science in sports and exercise. 2014;46(9):1769-77. 15. Coutts A, Reaburn P, Piva TJ, Murphy A. Changes in selected biochemical, muscular strength, power, and endurance measures during deliberate overreaching and tapering in rugby league players. International journal of sports medicine. 2007 Feb;28(02):116-24. 16. Williams TD. Monitoring changes in resistance training performance following overload and taper microcycles (Doctoral dissertation, University of Alabama Libraries) 17. Travis SK, Mujika I, Gentles JA, Stone MH, Bazyler CD. Tapering and peaking maximal strength for powerlifting performance: a review. Sports. 2020 Sep;8(9):125. 18. Tavis SK, Mizuguchi S, Stone MH, Sands WA, Bazyler CD. Preparing for a national weightlifting championship: A case series. The Journal of Strength & Conditioning Research. 2020 Jul 1;34(7):1842-50. 19. Nachtigall PG, Dias MC, Carvalho RF, Martins C, Pinhal D. MicroRNA-499 expression distinctively correlates to target genes sox6 and rod1 profiles to resolve the skeletal muscle phenotype in Nile tilapia. PLoS One. 2015 Mar 20;10(3):e0119804. 20. Hagiwara N, Yeh M, Liu A. Sox6 is required for normal fiber type differentiation of fetal skeletal muscle in mice. Developmental dynamics: an official publication of the American Association of Anatomists. 2007 Aug;236(8):2062-76. 21. Mokalled MH, Johnson AN, Creemers EE, Olson EN. MASTR directs MyoD-dependent satellite cell differentiation during skeletal muscle regeneration. Genes & development. 2012 Ja █ 75 Study Reviewed: Reduced Adaptive Thermogenesis During Acute Protein-Imbalanced Overfeeding is a Metabolic Hallmark of the Human Thrifty Phenotype. Hollstein et al. (2021) Metabolic Phenotypes, Weight Regulation, and Reverse Dieting BY ERIC TREXLER A new study clarifies distinctions between “thrifty” and “spendthrift” metabolic phenotypes, with results providing numerous implications related to weight management. Read on to learn more about who struggles with weight loss, who struggles with weight gain, and how this relates to reverse dieting. 76 KEY POINTS 1. Results of the presently reviewed study (1) reinforce the idea that individuals can be categorized as having “thrifty” or “spendthrift” phenotypes based on responses to short-term underfeeding and overfeeding. 2. Thrifty individuals have higher energy expenditure during neutral energy balance, but experience large energy expenditure drops during underfeeding and minimal increases during overfeeding. The opposite is true for spendthrift individuals. 3. Thrifty individuals are likely to experience more friction during intentional weight loss, while spendthrift individuals are likely to experience more friction during intentional weight gain. However, weight regulation is multifaceted, and that friction can be overcome. 4. The utility of reverse dieting is probably more limited than is commonly believed. I n the nutrition and metabolism literature, people are often discussed as fitting one of two metabolic phenotypes: thrifty or spendthrift. Conceptually, the idea suggests that someone with a thrifty metabolic phenotype is good at conserving energy; as a result, they are likely to resist weight loss in an energy deficit, and likely to readily store fat tissue in an energy surplus. In contrast, someone with a spendthrift phenotype is bad at conserving energy; they are likely to readily lose weight in an energy deficit, and likely to be resistant to fat gain in an energy surplus. In support of this concept, empirical data tend to reinforce the idea that there is considerable inter-individual variability in terms of how much people reduce TDEE in a prolonged energy deficit (2), and how much people increase TDEE in a prolonged energy surplus (3). This has generated interest in identifying predictive factors of thrifty versus spendthrift phenotypes and determining how metabolic phenotypes may (or may not) affect body composition. The presently reviewed study (1) aimed to determine if acute responses to low-protein overfeeding or high-protein overfeeding are particularly useful for determining if an individual has a thrifty or spendthrift metabolic phenotype. Results indicated that individuals with a thrifty phenotype (that is, people who experience larger energy expenditure drops during fasting) had smaller changes in total daily energy expenditure during low-protein overfeeding (+42 kcal/day versus +100 kcal/ day) and high-protein overfeeding (+237 kcal/day versus +302 kcal/day) when compared to individuals with a spendthrift phenotype. It might be tempting to conclude that having a thrifty phenotype would therefore lead to consistently lower energy expenditure and higher body-fat, but it’s interesting to note that the thrifty group had virtually identical body weight and body composition at baseline, and actually had significantly higher total daily energy expenditure during energy balance (2,070 kcal/day versus 1,960 kcal/day), along with non-significantly high- 77 er absolute values during both low-protein and high-protein overfeeding. So, what gives? If this thrifty metabolic phenotype is supposed to be indicative of energy conservation and a higher propensity for obesity, why did thrifty individuals have higher baseline energy expenditure and identical body composition? More importantly, what does this tell us about how metabolic phenotype influences weight gain, loss, and maintenance? In addition, what insights related to reverse dieting can be gleaned from these results? The short answer is that it’s complicated. The long answer is the rest of this article. Purpose and Hypotheses Purpose The purpose of the presently reviewed study was to determine if short-term energy expenditure changes in response to low-protein and high-protein overfeeding diets distinguish between thrifty and spendthrift metabolic phenotypes. Hypotheses The researchers hypothesized that energy expenditure “measured during overfeeding diets with low-protein (3%) or high-protein (30%) content would more clearly reveal the impairment in adaptive thermogenesis of thrifty participants compared with normal-protein (20%) diets.” Subjects and Methods Subjects 77 healthy participants completed the pres- ently reviewed study (age: 37.2 ± 10.2 years; height: 173 ± 8cm; weight: 78.9 ± 14.1kg; BMI: 26.4 ± 4.3kg/m2; body-fat percentage: 27.7 ± 9.4%). Of the 77 total participants, 63 were men and 14 were women. To participate in the study, subjects were required to be at least 18 years old, weight stable (<10% weight variation) in the six months preceding the study, less than 350 pounds, and in generally good health (as determined by a health history review, a physical examination, and a fasted blood draw). Age values in the sample ranged from 18 to 55 years old and BMI ranged from 17.8 to 44 kg/m2, so this was a pretty heterogeneous sample of participants, but I expect that some number of potential participants with obesity were unable to participate due to the presence of comorbid cardiometabolic risk factors like hypertension or impaired glucose tolerance. Methods This is an unregistered secondary analysis of a previous study whose original methods were pre-registered prior to data collection. The original study was pretty multifaceted in terms of its aims and scope, but the presently reviewed paper focused exclusively on acute responses to fasting, low-protein overfeeding, and high-protein overfeeding. Obviously, a key prerequisite for participant inclusion was to have sufficient data related to the research question, so participants from the original study could only be included in this secondary analysis if they had valid energy expenditure measurements taken during energy balance, during a 24-hour fast, and during at least one of the overfeeding conditions (low-protein or high-protein). The fast- 78 ing condition allowed for only non-caloric, non-caffeinated beverages; the low-protein diet provided 200% of energy needs with a macronutrient breakdown of 3% protein, 46% fat, and 51% carbohydrate; the high-protein diet provided 200% of energy needs with a macronutrient breakdown of 30% protein, 46% fat, and 26% carbohydrate. There were some other overfeeding conditions included in the study, but they were fairly unrelated to the primary research question at hand, so I won’t discuss them in this article. Meals were prepared in a metabolic kitchen, meal ingestion was supervised, and uneaten food was weighed after meals; data were excluded from analysis if the participant consumed less than 95% of the calories provided. During each condition, 24-hour energy expenditure was measured in a whole-room calorimeter. For each measurement period, participants individually spent 23.25 hours straight in a small apartment that is equipped to assess total daily energy expenditure based on expired gases and radar-tracked activity level. For the study results, energy expenditure was presented a few different ways. The researchers reported raw values for total energy expenditure, sleep energy expenditure, energy expenditure in the inactive awake state, and energy expenditure in the awake and fed state. They also reported adjusted versions of these outcomes, which were adjusted to account for age, sex, ethnicity, fat mass, fat-free mass, spontaneous physical activity, ambient temperature, and the specific calorimeter room that each participant was tested in. They did a ton of different analyses, which were all generally aimed at exploring relationships between continuous variables and comparing values between groups of participants with thrifty or spendthrift metabolic phenotypes. In order to form these groups, the researchers divided the sample based on how much their total energy expenditure dropped during the 79 24-hour fast. Participants whose energy expenditure drop during fasting was larger than the median value were considered to have the thrifty phenotype, while participants below the median were considered to have the spendthrift phenotype. Findings As you’d expect, both low-protein and high-protein overfeeding led to increases in energy expenditure. When eating around 200% of energy needs, low-protein overfeeding led to an average energy expenditure increase of 72 ± 80 kcal/day, whereas high-protein overfeeding led to an average increase of 272 ± 107 kcal/day. Responses to both overfeeding conditions for the full sample are presented in Table 1. Of course, the primary focus of this analysis was to compare the responses of thrifty and spendthrift participants. On average, total en- ergy expenditure during fasting dropped by 236 ± 44 kcal/day in thrifty participants, but only by 91 ± 59 kcal/day in spendthrift participants. This was statistically significant but virtually guaranteed by design, as this metric was the defining criterion for group membership. During low-protein overfeeding, total energy expenditure increased by 100 kcal/day (95% confidence interval: 77, 124) in spendthrift participants, but only increased by 42 kcal/day (95%CI: 12, 71) in thrifty participants. During high-protein overfeeding, total energy expenditure increased by 302 kcal/ day (95%CI: 259, 344) in spendthrift participants, but only increased by 237 kcal/day (95%CI: 199, 276) in thrifty participants. In both cases, differences between groups were statistically significant (Figure 1). Comparing the groups yielded some additional interesting observations. As shown in Table 2, both groups had virtually identical body weight and body composition at baseline. In 80 addition, the thrifty group had significantly higher total (24-hour) energy expenditure during energy balance (2070 kcal/day versus 1960 kcal/day; Figure 1 and Table 2), and non-significantly higher total energy expenditure during both low-protein and high-protein overfeeding (Figure 1 and Table 2). The researchers also reported a variety of analyses using continuous data (rather than comparing the metabolic phenotype groups), which generally confirmed the group-level findings. One noteworthy observation was the degree of variability in individual-level responses to fasting and overfeeding. In the 81 fasting condition, the range in energy expenditure responses spanned from a 376 kcal/day decrease to a 104 kcal/day increase. In the low-protein overfeeding condition, responses ranged from a 127 kcal/day decrease to a 270 kcal/day increase, and in the high-protein overfeeding condition, responses ranged from a 41 kcal/day increase to a 496 kcal/day increase. Correlations showed that participants with higher energy expenditure during energy balanced had significantly larger decreases in energy expenditure during fasting, and that participants with larger energy expenditure drops during fasting had significantly smaller increases in energy expenditure during overfeeding. Overall, their analyses demonstrated that people with a thrifty phenotype tend to have higher energy expenditure in conditions of energy balance, larger drops in energy expenditure during fasting, and smaller increases in energy expenditure during overfeeding. They also looked specifically at the results for the five “most thrifty” and the five “most spendthrift” participants, and found the same pattern to hold true. Criticisms and Statistical Musings Before we dig into the interpretation section, there are a couple of considerations related to methods and statistics to keep in mind. First, the presently reviewed paper presents unregistered secondary analyses from a larger project that has led to several previously published papers. When you commit to an analysis a priori, before any data has been seen or explored, this helps constrain the likelihood of false positive findings and leads to outcomes that are generally viewed as more robust. In contrast, these types of unregistered secondary analyses are generally driven by hypotheses and research questions that arise after the researchers have seen the data (to some extent) and gotten a feel for it. This does not discredit the work or diminish the value of insights gleaned from the analysis, but it opens the door for a higher rate of false positives and raises questions about the robustness and generalizability of the findings. For an oversimplified metaphor, consider a challenge that requires you to guess the 10th card in a deck of playing cards. That task looks a lot different if you’re allowed to see the first nine cards before locking your pick in, because you’ve already obtained information from the deck to guide your guess and inflate your chances of success. It’s not a perfect analogy, but creating hypotheses after seeing the data and publishing related analyses in other research papers results in a similar scenario where knowledge of the data set can inform, guide, and bias the types of questions that are asked after the fact. The second thing to keep in mind is that metabolic phenotypes were assigned to participants based on a median split technique; the half of the sample with the largest energy expenditure drop during fasting was considered thrifty, while the other half of the sample was considered spendthrift. To the researchers’ credit, they acknowledged this strategy as “arbitrary” in nature, but it’s worth restating the shortcomings of this approach. As we’ve mentioned in previous issues of MASS, you lose a lot of information and nuance when you condense a continuous variable into 82 groups. You can also run into counterintuitive situations where two very similar values on opposite sides of the median are considered fully different from one another because they fall in different groups, while the lowest value in the whole sample and a value just below the median can be vastly different, but are treated as functionally equivalent because they fall in the same group. Generally speaking, it’s more favorable to split groups based on a physiologically meaningful cutoff (for example, we might split blood pressure values based on previously established cutoffs for hypertension), and it’s even more favorable to simply leave variables continuous. Fortunately, the researchers who conducted the presently reviewed study also provided some analyses of continuous data that supported the group-level observations, so we should feel reasonably comfortable applying the group-level findings. Nonetheless, this consideration is always worth keeping in the back of your mind when you encounter a study that uses the common strategy of arbitrarily grouping participants based on a continuous outcome. Interpretation The presently reviewed study (1) provides further support for the concept of thrifty and spendthrift phenotypes, and confirms that acute responses to fasting and protein-imbalanced (atypically low- or high-protein) overfeeding offer effective diagnostic tools for phenotyping individuals. This same research team has also established that thrifty individuals gain more weight and fat mass than spendthrift individuals in response to six weeks of low-protein overfeeding (4), which suggests that these acute observations seem to translate fairly well to longitudinal applications. So, it may be tempting to conclude that having a thrifty phenotype is a one-way ticket to low energy expenditure and obesity, and can largely explain cross-sectional differences in body-fat levels among groups of individuals. However, if you look closely at Table 2, it’s hard to ignore a couple of key observations. First, the thrifty group actually had significantly higher energy expenditure during conditions of energy balance. Thrifty individuals experienced a larger drop when transitioning from energy balance to fasting, but their average energy expenditure value during fasting was not significantly lower than the average value for spendthrift individuals. Second, the thrifty group and the spendthrift group had virtually identical average values for weight, BMI, and body-fat percentage. These body composition similarities could partially relate to the fact that groups were arbitrarily formed by the “median split” technique, so the most spendthrift individuals in the thrifty group and the most thrifty individuals in the spendthrift group probably weren’t so different from one another. They could also relate, to some extent, to the fact that anyone with cardiometabolic risk factors like hypertension or impaired glycemic control would have been excluded from study participation. This could theoretically lead to a scenario in which the thrifty individuals most likely to have obesity would have been unable to participate, so the sampling process could have biased body composition values in the thrifty 83 group by selecting for thrifty individuals with atypically low adiposity. However, the highest observed BMI in the study was 44 kg/m2, and the average BMI within each group was squarely within the “overweight” category, so a wide range of BMIs were represented within the data set. Aside from these methodological considerations, a likely factor contributing to the observed body composition similarities between groups is the fact that weight regulation is complex, multifaceted, and influenced by numerous variables. As Speakman and colleagues describe in an open-access review paper (5), body weight appears to be controlled by a mixture of compensated (primarily physiological) and uncompensated (environmental, social, psychological, and dietary) factors. Body weight seems to be loosely “defended” within a working range, but the lower end, upper end, and the size of the gap between them seems to be quite variable from person to person. It’s possible that the upper end of this defended weight range may tend to be lower among individuals with a more spendthrift metabolic phenotype, but the impact of metabolic phenotype is just one factor contributing to body weight regulation. As they state in the paper: “It seems that many different genes are involved in food selection, food intake, absorption, metabolism and energy expenditure, including physical activity – we might be looking at a puzzle of well over 1000 pieces.” Notably, that quote only pertains to the genetic factors influencing weight control; as we start unraveling the environmental, social, and psychological factors that impact energy balance, the complexity only grows. While metabolic phenotype doesn’t seem to single-handedly dictate an individual’s baseline body composition characteristics, it does appear to have some predictive utility when it comes to intentional attempts to lose or gain weight. For example, there is evidence indicating that people with thrifty phenotypes tend to experience less fat loss during controlled weight loss interventions (4). Nonetheless, several other factors can influence how challenging or how successful a weight loss attempt will be. For a non-exhaustive list of examples, the relative ease with which someone will lose fat (and keep it off) can be impacted by differences in appetite regulation, neurophysiological responses to food intake, food selection, satiety responses to exercise, the magnitude of energy expenditure compensation in response to physical activity (6), aspects of metabolic adaptation that are linked to fat mass reserves rather than acute energy status, the loss of lean mass, and more specifically the loss of specific organ tissues with high rates of metabolic activity (7). So, the evidence suggests that metabolic phenotype probably has an independent impact on one’s propensity to develop (or resist) obesity, but certainly isn’t the sole determinant. The evidence also suggests that metabolic phenotype is particularly important in states of energy imbalance (deficits or surpluses), and is likely to have a noteworthy impact on intentional attempts to gain or lose weight. Thrifty individuals are more likely to encounter considerable friction during intentional weight loss, and spendthrift individuals are more likely to encounter considerable friction during in- 84 METABOLIC PHENOTYPE PROBABLY HAS AN INDEPENDENT IMPACT ON ONE’S PROPENSITY TO DEVELOP (OR RESIST) OBESITY, BUT CERTAINLY ISN’T THE SOLE DETERMINANT. tentional weight gain, but this friction can be overcome by manipulating key variables pertaining to diet and exercise. The presently reviewed study also allows for some interesting insights related to “reverse dieting.” If you’re unfamiliar with reverse dieting, the process often involves slowly increasing calorie intake while attempting to remain approximately weight-stable, generally with the intention of increasing total daily energy expenditure. Most commonly, people pursue this option because they perceive that their energy expenditure is anomalously low (which is usually attributed to a recent history of energy restriction), and they are hoping to restore it to a higher level. As a result, this topic goes hand in hand with the idea of metabolic adaptation; in many cases, metabolic adaptation is presented as a problem, and reverse dieting is presented as the solution. Along those lines, it’s often suggested that a preceding weight loss attempt was particularly challenging because it was initiated from a state of low energy expenditure, and calories had to be pushed progressively lower as weight loss continued. By extension, it’s not uncommon for people to suggest that reverse dieting will set them up for a more successful diet next time around, allowing them to eat more calories for the duration of their next fat loss attempt. However, when we view this concept within the context of metabolic phenotypes, it raises an interesting pair of questions: who actually stands to benefit from reverse dieting, and when? After a weight loss attempt, there will be some aspects of metabolic adaptation that are related to the loss of fat mass, some that are related to the loss of lean mass, and some that are purely related to acute energy status (that is, the state of being in an energy deficit). In theory, reverse dieting aims to modify the latter component in order to promote increases in total daily energy expenditure. For someone who falls more on the spendthrift end of the metabolic phenotype spectrum, this is largely a non-issue following weight loss; their energy expenditure doesn’t seem to be particularly responsive to acute energy deficits, so there isn’t much of an issue to reverse. So, while a spendthrift individual would be most likely to have a favorable response to reverse dieting (due to their intrinsic capacity to upregulate energy expenditure in response to increased calorie intake), they probably need it the least. In contrast, consider someone with a thrifty metabolic phenotype. Building up their maintenance level of energy expenditure 85 during non-dieting periods was never the issue, as the present study reports higher energy expenditure in thrifty individuals when compared to spendthrift individuals in weight-stable conditions with neutral energy balance. After weight loss, their energy expenditure will trend back toward baseline as they transition from an energy deficit to energy balance. This would occur whether they implemented a slow, methodical, intentional reverse diet or simply bumped their calories straight back to a suitable maintenance level. In this scenario, it might seem like reverse dieting is doing something special, but the observed outcome (increased energy expenditure) is an inevitable consequence of shifting to a state of neutral energy balance. The magic of reverse dieting should theoretically involve leveraging this process to push maintenance energy expenditure levels far higher than they used to be, but overfeeding data would suggest that this is highly unlikely to occur to thrifty individuals; rather than experiencing a robust energy expenditure boost in response to overfeeding, they’ll probably just store more fat mass. So, reverse dieting after weight loss might produce the illusion of a ramped up metabolic rate for thrifty individuals, but it’s probably just an unavoidable process of energy expenditure reverting back toward typical levels when the caloric deficit is removed. More importantly, we have no reason to believe that anything will be different for thrifty dieters the next time around. The thrifty phenotype is characterized by high energy expenditure during neutral energy balance (which does not adaptively increase in response to a surplus), but a large drop when a deficit is imposed. For someone with a thrifty phenotype, ramping calorie intake even higher between diet attempts, while unlikely to effectively increase energy expenditure in the first place, would also fail to address the root of the issue. Reverse dieting is often framed as a strategy to promote future dieting success by allowing you to enter the subsequent diet with a higher starting level of energy expenditure. Unfortunately, our current understanding of the thrifty phenotype suggests that energy expenditure would promptly drop again the next time a caloric deficit is reintroduced, and I’m not aware of any evidence to the contrary. So, it would seem that this popular application of reverse dieting is least likely to help the people who need it the most (thrifty individuals), and most likely to impact the people who need it the least (spendthrift individuals). The concept of using reverse dieting to “set up” for an easier fat loss attempt down the road doesn’t seem to hold merit, but this THE CONCEPT OF USING REVERSE DIETING TO “SET UP” FOR AN EASIER FAT LOSS ATTEMPT DOWN THE ROAD DOESN’T SEEM TO HOLD MERIT. 86 doesn’t mean that reverse dieting is useless in all potential applications. I think the most defensible use of reverse dieting relates to the fact that people seem to have a range of maintenance calories rather than a singular value. Most of us have observed this in one form or another; you slightly decrease your calories to begin a weight loss phase or you slightly increase your calories to begin a weight gain phase, yet body weight changes less than would be mathematically predicted. It seems reasonable to suggest that people with a more spendthrift metabolic phenotype have a positively-biased maintenance range, whereas people with a more thrifty metabolic phenotype have a negatively-biased maintenance range. In other words, consider two people whose “true” daily energy expenditure is 2,300 kcals/day; the thriftier of the two might find that their weight remains fairly stable when they consume 2100-2300 kcals/day, whereas the more spendthrift individual might find that their weight remains fairly stable when they consume 2300-2500 kcals/day (note that this is merely a simplified, illustrative example – the exact boundaries of this range will inherently vary from person to person). If a person of either phenotype is near the bottom of their “maintenance range” (most likely after a period of energy restriction), I think it’s plausible to suggest that they may be able to nudge their calories upward without inducing appreciable gains in fat mass. So, strategies that resemble “reverse dieting” may have some application in the post-diet window; not to supercharge your metabolism for your next fat loss phase down the road, but to tran- sition to a more comfortable long-term maintenance level of calorie intake. You don’t necessarily need to nudge calories upward using a series of small, incremental calorie increases, but doing so reduces the likelihood of overestimating your capacity to increase energy expenditure, which would lead to an unexpectedly large caloric surplus that could cause some fat gain if left uncorrected for an extended period of time. For some thrifty individuals who begin this process with a very large suppression of energy expenditure, and for some spendthrift individuals who have a very large capacity to increase energy expenditure in response to overfeeding, this application of reverse dieting might seem very impactful. However, for the majority of people who aren’t on the extreme ends of the metabolic phenotype spectrum, this “reverse dieting” application might only increase energy expenditure enough to allow for an extra banana or two (roughly 200-300 calories or so). This won’t have a meaningful impact on hunger, satiety, or the nutritional adequacy of their diet, but introduces a little extra wiggle room into their daily calorie target. Whether or not that juice is worth the squeeze is up to the individual. Finally, if you’re interested in how reverse dieting and other related (and potentially more effective) nutrition strategies apply to physique competitors, be sure to check out Dr. Helms’ two-part video series on post-season nutrition strategies (one, two). Next Steps I think there’s ample evidence to suggest that these metabolic phenotypes exist on a spec- 87 APPLICATION AND TAKEAWAYS Having a high metabolic rate during neutral energy balance doesn’t necessarily mean you’ll have an easy time with fat loss. How your energy expenditure adapts to calorie surpluses and deficits seems to be much more impactful; thrifty individuals will experience more friction while cutting, and spendthrift individuals will experience more friction while bulking, but both types of friction can be overcome. Attempts to implement weight-stable calorie increases (such as “reverse dieting”) may help some individuals drift toward the top end of their maintenance calorie range, but are unlikely to make their next weight loss attempt meaningfully easier or more successful. trum; some people tend to be more thrifty, whereas others tend to be more spendthrift, but the magnitude of changes in energy expenditure can vary substantially from one person to another, even if they fall in the same general category. As for next steps, I have three primary questions: 1) Can we use this information to make substantively different recommendations to thrifty people and spendthrift people with various body composition goals? 2) Can we do anything to make a thrifty person meaningfully less thrifty, or a spendthrift person meaningfully less spendthrift? 3) Does reverse dieting have any noteworthy utility, outside of allowing someone to maintain body weight while eating an extra banana or two per day? The presently reviewed study showed that acute fasting and protein-imbalanced overfeeding protocols are helpful for metabolic phenotyping; future studies can build upon these findings and investigate all three of my questions by using protein-imbalanced overfeeding to perform baseline metabolic phenotyping, then assessing the impact of various longitudinal interventions. 88 References 1. Hollstein T, Basolo A, Ando T, Krakoff J, Piaggi P. Reduced adaptive thermogenesis during acute protein-imbalanced overfeeding is a metabolic hallmark of the human thrifty phenotype. Am J Clin Nutr. 2021 Oct 4;114(4):1396–407. 2. Martins C, Roekenes J, Gower BA, Hunter GR. Metabolic adaptation is associated with less weight and fat mass loss in response to low-energy diets. Nutr Metab. 2021 Jun 11;18(1):60. 3. Levine JA, Eberhardt NL, Jensen MD. Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science. 1999 Jan 8;283(5399):212–4. 4. Hollstein T, Ando T, Basolo A, Krakoff J, Votruba SB, Piaggi P. Metabolic response to fasting predicts weight gain during low-protein overfeeding in lean men: further evidence for spendthrift and thrifty metabolic phenotypes. Am J Clin Nutr. 2019 01;110(3):593– 604. 5. 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. 6. Careau V, Halsey LG, Pontzer H, Ainslie PN, Andersen LF, Anderson LJ, et al. Energy compensation and adiposity in humans. Curr Biol CB. 2021 Oct 25;31(20):4659-4666.e2. 7. Bosy-Westphal A, Kossel E, Goele K, Later W, Hitze B, Settler U, et al. Contribution of individual organ mass loss to weight loss-associated decline in resting energy expenditure. Am J Clin Nutr. 2009 Oct;90(4):993–1001. █ 89 Research Briefs BY GREG NUCKOLS & ERIC TREXLER In the Research Briefs section, Greg Nuckols and Eric Trexler share quick summaries of recent studies. Briefs are short and sweet, skimmable, and focused on the need-toknow information from each study. 91 97 100 104 108 Pre-Performance Routines Boost Performance The Capsaicinoid Literature Advances, but It's a Slow Burn An Update on Sarcoplasmic Hypertrophy Another Look at Genes and Caffeine: Who Can Benefit? Trained Lifters are Primarily Stronger Due to Muscularity, Not Neural Adaptations 112 Dietary Nitrate is Powerful Stuff 116 Low-Load Squat Training With Blood Flow Restriction Causes as Much Hypertrophy as High-Load Training in Experienced Squatters 120 BCAAs Are Overrated. But Are They Useless? 124 Ginseng Is Yet Another Thing That Doesn’t Increase Testosterone 90 Study Reviewed: The Effectiveness of Pre-Performance Routines in Sports: A Meta-Analysis. Rupprecht et al. (2021) Pre-Performance Routines Boost Performance BY GREG NUCKOLS Athletes commonly go through a pre-performance routine before executing a motor skill under pressure. For example, a basketball player may always spin or bounce the ball the same way before attempting a free throw, or a soccer player may go through a fixed set of actions before attempting a penalty kick. However, in spite of the popularity of pre-performance routines (both in practice, and as a research topic), the presently reviewed study (1) was the first attempt at meta-analyzing the effects of pre-performance routines on performance. The researchers began by completing a systematic search for all studies investigating the effects of pre-performance routines, including unpublished studies (i.e. theses, dissertations, and preprints). From there, they split the studies based on study design: pre-post studies, experimental low-pressure studies, and experimental studies under pressure. Prepost studies are exactly what they sound like: researchers assess subjects’ performance for a particular motor skill, teach them a pre-performance routine, and then assess their performance again with the use of the pre-performance routine. Experimental studies involve a control group: one group’s performance is tested with the use of a pre-performance routine, and one group’s performance is tested without the use of a pre-performance routine. An experimental study would be classified as an “under pressure” study if a pressure manipulation was used (validated by a measured increase in pre-performance anxiety) or if performance was assessed during real sporting events. After grouping the studies, the researchers meta-analyzed the results using standard random-effects models. They also tested for the effects of moderators (i.e., are the effects of pre-performance routines different between sexes, in people of different age cohorts, in people of different skill levels, or between more or less extensive pre-performance routines?). 112 discrete effect sizes from a total of 33 articles were included in the meta-analysis. 51 effect sizes were from studies with pre-post designs, 42 effect sizes were from low-pressure experimental studies, and 19 effect sizes were from high-pressure experimental studies. 70% of the studies were conducted on experienced athletes from a total of 15 different sports. Pre-performance routines were effective for improving performance, regardless of study design. The standardized effect size was 0.31 91 (95% CI = 0.18-0.44) for studies with prepost designs, 0.64 (95% CI = 0.45-0.83) for low-pressure experimental studies, and 0.70 (95% CI = 0.24-1.16) for high-pressure experimental studies (Figure 1). Furthermore, no moderators significantly impacted the effect size for any of the experimental designs (Tables 1 and 2). However, there was a (very non-significant) tendency for pre-performance routines to be more effective for novice subjects than subelite or elite athletes (Table 2). Pre-performance routines are thought to improve performance by improving concentration, increasing self-efficacy and feelings of control, reducing anxiety and distractions, and facilitating action planning (2, 3). The most common pre-performance routines take the form of physical routines, motor imagery, self-talk, relaxation, and external cueing. Examples of physical routines include bouncing a basketball a certain number of times before attempting a free throw, or going through an elaborate set-up prior to 92 pulling a sumo deadlift. Taking a few deep breaths to settle your nerves is probably the most common example of a relaxation-based pre-performance routine. Motor imagery, self-talk, and external cueing have been previously discussed in MASS (one, two, three, four, five). Furthermore, left-hand dynamic handgrip (4) and “quiet eye” (5) have gained popularity as pre-performance routines within the sports psychology community in recent years. Left-hand dynamic handgrip is exactly what it sounds like: clenching the left first about twice per second for about 30 seconds prior to performing a motor task. “Quiet eye” is just jargon for “visual fixation at a target” (which is just jargon for “staring at something very intently”). For example, “quiet eye” for a baseball pitcher may involve staring intently at the catcher’s mitt before and throughout their windup and delivery. This meta-analysis didn’t specifically separate out different types of pre-performance routines for analysis, but it did find that pre-performance routines do seem to improve performance, on average. It also found that more 93 extensive pre-performance routines (routines that involve multiple components, such as a physical routine, plus motor imagery, plus a relaxation component) didn’t improve performance to a greater degree than stand-alone pre-performance routines (routines involving just one component). That leads me to believe that the specific type of pre-performance routine you utilize doesn’t matter too much on average. That’s bolstered by the finding that individualized pre-performance routines weren’t more effective than non-individualized pre-performance routines. Of course, averages don’t rule out the possibility that a specific routine might be particularly beneficial in certain circumstances – for example, if you really struggle with anxiety, a relaxation-based routine might prove particularly beneficial – but in general, it seems the benefits simply come from just having any pre-performance routine. I found it interesting that pre-performance routines were similarly effective for improving performance in high-pressure and low-pressure situations. That leads me to believe that, of the proposed mechanisms by which pre-performance routines might improve performance, improved attention, concentration, and action planning are probably larger factors than reducing distractions and anxiety. I was initially surprised that pre-performance routines were found to improve performance to a greater extent in experimental studies (routine group versus control group) than pre-post studies (performance in an individual group without and with pre-performance routines). However, the authors of the pres- ent meta-analysis offered a logical explanation for this finding (1): many of the pre-post studies initially assessed performance in a relaxed state, and re-tested performance under pressure. Thus, a “positive” finding would be that pre-performance routines allowed subjects to merely maintain performance under pressure (i.e., an effect size of 0). In that light, a small actual positive effect (effect size >0) is very impressive, suggesting that people performed better under pressure with a pre-performance routine, than under relaxed conditions without a pre-performance routine. Basically, due to differences in experimental design, one should expect effect sizes to be smaller in pre-post studies than in experimental studies, but that doesn’t necessarily suggest that pre-performance routines had a smaller positive effect in pre-post studies than experimental studies. I noted that pre-performance routines may be slightly more effective in novice performers than elite athletes. To be clear, I may just be over-reacting to the appearance of a numerical pattern with a bit of logic behind it, since the statistical tests for “skill level” as a moderating variable weren’t anywhere close to attaining statistical significance. However, I couldn’t help but notice that the mean effect sizes were larger in novice performers than subelite athletes, and in subelite athletes than elite athletes across all three types of studies. To me, that seems pretty intuitive for two reasons. First, elite athletes are very good at their sports, so there may be less room for them to improve on assessments. For example, if a particular pre-performance routine can help a novice basketball player to make 94 60% of their free throws instead of 40%, that would be a large improvement. However, if the same pre-performance routine helped an elite basketball player improve from being an 85% free throw shooter to a 92% free throw shooter, that would be a notable improvement in the context of the sport, but a relatively small improvement in raw quantitative terms. Second, elite athletes may already have high concentration (in the context of their sport), high self-efficacy (in the context of their sport), great action planning ability (in the context of their sports), etc. Thus, the potential for further beneficial psychological effects from pre-performance routines may be a bit smaller for elite athletes as well. In other words, there may be some ceiling effects in play. maxing out or performing low-RIR sets. Over time, motor imagery and positive selftalk may improve strength gains, but acutely, pre-performance routines probably primarily help with skill-based aspects of training and competition. Before wrapping this article up, I want to make something clear: none of the studies on pre-performance routines tested the effects of pre-performance routines in strength athletes. The summary table indicated that one study tested the effects of imagery on “strength training” performance, but when I followed up on the citation (6), I found that it’s a study on drop jumps … not what I was hoping for. Most of the studies were interested in motor skills, specifically: making free throws, serving accurately, shooting targets, performing gymnastics skills, etc. Thus, in the context of strength sports, I wouldn’t necessarily expect a pre-performance routine to acutely increase maximal strength. However, I would expect a pre-performance routine to improve your proportion of “good reps” in training (reps where everything feels locked in), and reduce the number of reps you misgroove when References To wrap things up, I think the takeaway is simple: pre-performance routines seem to improve motor performance and they’re free, quick, and easy to perform. Doing a bit of motor imagery, rehearsing an external cue, doing some left-hand dynamic hand gripping, or having a physical routine you perform before starting a set seems to come with notable upsides, and no obvious downsides. Find a pre-performance routine that works for you, and employ it during training or competition as often as you see fit. 1. Rupprecht AGO, Tran US, Gröpel P. The effectiveness of pre-performance routines in sports: a meta-analysis. International Review of Sport and Exercise Psychology. 2021. doi: 10.1080/1750984X.2021.1944271 2. Cotterill S. Pre-performance routines in sport: current understanding and future directions. International Review of Sport and Exercise Psychology. 2010 3(2):132153. doi: 10.1080/1750984X.2010.488269 3. Singer RN. Preperformance State, Routines, and Automaticity: What Does It Take to Realize Expertise in Self-Paced Events? Journal of Sport and Exercise Psychology. 2002 24(4):359-375. 4. Beckmann J, Gröpel P, Ehrlenspiel F. Preventing motor skill failure through hemisphere-specific priming: cases from 95 choking under pressure. J Exp Psychol Gen. 2013 Aug;142(3):679-91. doi: 10.1037/a0029852. Epub 2012 Sep 3. PMID: 22946898. 5. Moore LJ, Vine SJ, Cooke A, Ring C, Wilson MR. Quiet eye training expedites motor learning and aids performance under heightened anxiety: the roles of response programming and external attention. Psychophysiology. 2012 Jul;49(7):1005-15. doi: 10.1111/j.1469-8986.2012.01379.x. Epub 2012 May 7. PMID: 22564009. 6. Bergmann J, Kumpulainen S, Avela J, Gruber M. Acute Effects of Motor Imagery on Performance and Neuromuscular Control in Maximal Drop Jumps. Journal of Imagery Research in Sport and Physical Activity. 2013 8(1):45-53. doi: 10.1515/ jirspa-2013-0001 96 Study Reviewed: Effects of Capsiate Supplementation on Maximal Voluntary Contraction in Healthy Men. dos Santos Gomes et al. (2021) The Capsaicinoid Literature Advances, but It’s a Slow Burn BY ERIC TREXLER We have previously discussed capsaicinoids in two separate contexts: weight management and performance. On the weight management side, I wouldn’t expect too much from capsaicin or closely related capsaicinoids; there is evidence that they can have acute and modest impacts on desire to eat (2) and energy expenditure (3), but these are unlikely to have major impacts on body composition in the long run. When it comes to performance, we have covered studies reporting capsaicinoid supplementation to enhance training volume (4), strength endurance (5), and longitudinal increases in bench press and lean mass (6). As noted in a previous MASS article, capsaicinoids activate transient receptor potential vanilloid 1 (TRPV1) channels, which could potentially impact performance and training adaptations by increasing fatty acid oxidation and preserving glycogen, increasing hypertrophy due to activation of pathways related to muscle protein synthesis, attenuating the inflammatory response to exercise, increasing pain tolerance during exercise, increasing acetylcholine release, and increasing calcium release from the sarcoplasmic reticula. If the final three mechanisms in that list are truly impacted in a meaningful way by capsaicinoid supplementation, then one might expect that acute capsiate supplementation would increase force production during a maximal voluntary contraction test. Indeed, that’s exactly what the researchers sought to assess in the presently reviewed study (1). Thirteen resistance-trained men (age = 25.2 ± 3.2 yrs; body mass = 81.1 ± 13.8 kg; height = 179 ± 10 cm) completed this placebo-controlled, crossover study consisting of two testing visits separated by a one-week washout period. At one visit, they consumed 12mg of capsiate 45 minutes prior to exercise testing, while they consumed placebo capsules at the other visit. Performance was assessed with a protocol of five, 10-second isometric knee extensions with the knee and hip fixed at a 90° angle. Extensions were separated by 45 seconds of rest, and outcomes of interest included peak force, mean force, minimum force, fatigue index (the relative drop from peak force to minimum force, expressed as a percentage), and the area under the curve for force plotted over time. 97 As displayed in Figure 1, capsiate supplementation led to statistically significant improvements in peak force and area under the force curve (both p < 0.05). In addition, the improvement in mean force was directly on the threshold of statistical significance (p = 0.05). Capsiate led to a larger fatigue index (p = 0.041), which would generally be interpreted as a bad thing. However, minimum force was not significantly impacted by sup- plementation (p = 0.839), so the increase in fatigue index was essentially an artifact of achieving higher peak force values. At this point, we’ve reviewed capsaicinoid literature reporting greater peak force (1), greater strength endurance, greater training volume, and better training adaptations over time. If you’re wondering what the catch is, it hasn’t changed since we last discussed capsaicinoids: nearly all of the available evidence 98 comes from a single lab group. That might sound like I’m implying that this group’s work is inherently unreliable, but that’s not at all the case. Reserving judgment when a body of research is restricted to an insular research group with a singular participant population is part of the game when it comes to cautious research interpretation. Lest my hesitation appear to arbitrarily or unfairly single out this research group, there are several other instances of similar hesitation in the published literature. For example, this meta-analysis (7) evaluating the effects of saffron supplementation on depression and anxiety symptoms states, “Due to the large effect sizes, the publication bias detected for both outcomes, and the majority of trials being conducted in 1 region, many by the same research group (n = 13/23), there is a need to replicate these results within other populations and in large, well-powered, rigorous trials before clinical recommendations are justified.” Replication is a critical aspect of the scientific process, so confidence in a particular finding increases as it is successfully replicated across a broad range of scenarios and contexts. So, we’ve got some promising data for capsaicin, and we’ve got some plausible mechanisms, but what we still need is replication, far and wide. Until we get that, I can’t feel confident broadly recommending capsaicinoid supplementation, or boosting it up to the second tier of my supplement hierarchy. References 1. Gomes W dos S, Freitas MC de, Dutra YM, Rossi F, Estanislau TB, Gonçalves DC, et al. Effects of Capsiate Supplementation on Maximal Voluntary Contraction in Healthy Men. Int J Sports Med. 2021 Oct 19; ePub ahead of print. 2. Westerterp-Plantenga MS, Smeets A, Lejeune MPG. Sensory and gastrointestinal satiety effects of capsaicin on food intake. Int J Obes. 2005 Jun;29(6):682–8. 3. Hursel R, Westerterp-Plantenga MS. Thermogenic ingredients and body weight regulation. Int J Obes 2005. 2010 Apr;34(4):659–69. 4. Conrado de Freitas M, Cholewa JM, Freire RV, Carmo BA, Bottan J, Bratfich M, et al. Acute Capsaicin Supplementation Improves Resistance Training Performance in Trained Men. J Strength Cond Res. 2018 Aug;32(8):2227–32. 5. de Freitas MC, Cholewa JM, Panissa VLG, Toloi GG, Netto HC, Zanini de Freitas C, et al. Acute Capsaicin Supplementation Improved Resistance Exercise Performance Performed After a High-Intensity Intermittent Running in Resistance-Trained Men. J Strength Cond Res. 2019 Nov 28; ePub ahead of print. 6. de Moura E Silva VEL, Cholewa JM, Jäger R, Zanchi NE, de Freitas MC, de Moura RC, et al. Chronic capsiate supplementation increases fat-free mass and upper body strength but not the inflammatory response to resistance exercise in young untrained men: a randomized, placebo-controlled and double-blind study. J Int Soc Sports Nutr. 2021 Jun 21;18(1):50. 7. Marx W, Lane M, Rocks T, Ruusunen A, Loughman A, Lopresti A, et al. Effect of saffron supplementation on symptoms of depression and anxiety: a systematic review and meta-analysis. Nutr Rev. 2019 Aug 1;77(8):557–71. 99 Study Reviewed: Myofibril and Mitochondrial Area Changes in Type I and II Fibers Following 10 Weeks of Resistance Training in Previously Untrained Men. Ruple et al. (2021) An Update on Sarcoplasmic Hypertrophy BY GREG NUCKOLS Sarcoplasmic hypertrophy is an interesting topic where the perspective of the “evidence-based” fitness community largely seems to stand in opposition to the actual evidence on the topic. I still commonly see people argue that sarcoplasmic hypertrophy is just a broscience myth, when the actual evidence supporting the existence of sarcoplasmic hypertrophy is fairly strong and consistent, dating back at least 50 years (as I documented in my last MASS article on the topic, and in an older Stronger By Science article). Just to rewind a bit, it’s worth first operationally defining sarcoplasmic hypertrophy. When muscle fibers increase in size, that’s referred to as hypertrophy. Muscle fibers are filled with structures called myofibrils (which contain contractile proteins), and the myofibrils are surrounded by “other stuff.” That “other stuff” (intracellular fluid, organelles, etc.) is referred to as sarcoplasm. When fibers undergo hypertrophy, the absolute volume of the myofibrils increases (due to increases in size, number, or both), as does the absolute volume of sarcoplasm. When the myofibril-to-sarcoplasm ratio remains constant or increases, that’s referred to as myofibrillar hypertrophy; when the myofibril-to-sarcoplasm ratio decreases (i.e., sarcoplasmic volume increases to a relatively greater degree than myofibrillar volume), that’s referred to as sarcoplasmic hypertrophy. In this research brief, I’m not interested in relitigating the existence of sarcoplasmic hypertrophy; rather, my primary aim is to illustrate the range of myofibrillar and sarcoplasmic hypertrophy responses to training. In a recent study by Ruple and colleagues (1), 15 untrained men completed ten weeks of moderate-rep (sets of 6-10 reps) full-body resistance training. Before and after the training intervention, researchers took biopsies of the subjects’ vastus laterales to assess a variety of outcomes. For our purposes, the three most important outcomes were 1) changes in fiber cross-sectional area, 2) changes in the area of each cross-section composed of myofibrils, and 3) changes in the area of each cross-section composed of mitochondria. The top-line findings were pretty straightforward: significant fiber hypertrophy occurred (26.5 ± 32.0% increase; p = 0.013), significant increases in mitochondrial area occurred 100 (from 6% to 8% of intracellular space in type I fibers, and from 5% to 6% of intracellular space in type II fibers), and significant reductions in myofibrillar density did not occur (there was an average reduction of about 5%, but it wasn’t statistically significant), suggesting that sarcoplasmic hypertrophy didn’t occur on average. However, those primary findings weren’t the interesting part of this study to me. I was most interested in a series of scatterplots showing the relationship between changes in fiber cross-sectional area and myofibrillar density, and the relationship between changes in fiber cross-sectional area and changes in mitochondrial area. These scatter plots don’t just show the relationships between these outcomes – they also illustrate the range of individual responses. If you know me, you know I’m a sucker for illustrations of interindividual variability. Starting with myofibrillar density, individual subject responses spanned the range from ~20% decreases to ~20% increases, and these changes weren’t associated with overall increases in fiber cross-sectional areas. In other words, some subjects experienced considerable sarcoplasmic hypertrophy, and some subjects experienced substantial myofibrillar packing (increases in myofibrillar density), even when exposed to the same training stimulus. Individual changes in mitochondrial area also varied considerably, ranging from decreases of ~4% to increases of ~9%. More interestingly, changes in mitochondrial area were moderately negatively associated with changes in fiber cross-sectional areas (the difference wasn’t statistically significant for type I or type II fibers, but it was significant for all fibers). In other words, subjects that experienced larger increases in mitochondrial area also experienced less fiber hypertrophy, on average. 101 I found the interindividual variability in myofibrillar density changes quite interesting. I’m not sure what can actually be done with that information, but it’s a good thing to be aware of: when exposed to the same stimulus, some people may experience considerable myofibrillar packing, while other people experience considerable sarcoplasmic hypertrophy in a manner that’s independent of the total amount of hypertrophy that occurs (though, it should be noted that some degree of the variability is probably just the result of noise in the measurements). That suggests we have a lot more to learn about what factors influence these responses, and whether these responses can be manipulated with specific training or nutrition interventions on an individual level. I was also very intrigued by the inverse association between fiber hypertrophy and changes in mitochondrial area. One of my pet theories is that local muscular metabolic capacity influences hypertrophy (since this is a research brief, I can’t go into all of the reasons here, but 102 I discuss it a bit in this podcast episode), and the mitochondrial findings in the present study may support that theory. Since the subjects who experienced the most hypertrophy also experienced the smallest increases (or even small decreases) in mitochondrial area, that might suggest that their muscles were “ready to grow,” whereas the larger increases in mitochondrial area in subjects who experienced less hypertrophy may suggest that their muscle fibers needed to prepare themselves for the increased metabolic burden of both muscle growth and maintaining larger fiber sizes. Ultimately, there aren’t any obvious practical takeaways from this study for athletes or coaches. However, I think that simply learning more about muscle physiology and interindividual variability can be its own benefit. Reference 1. Ruple BA, Godwin JS, Mesquita PHC, Osburn SC, Sexton CL, Smith MA, Ogletree JC, Goodlett MD, Edison JL, Ferrando AA, Fruge AD, Kavazis AN, Young KC, Roberts MD. Myofibril and Mitochondrial Area Changes in Type I and II Fibers Following 10 Weeks of Resistance Training in Previously Untrained Men. Front Physiol. 2021 Sep 24;12:728683. doi: 10.3389/fphys.2021.728683. PMID: 34630147; PMCID: PMC8497692. 103 Study Reviewed: CYP1A2 Genotype Polymorphism Influences the Effect of Caffeine on Anaerobic Performance in Trained Males. Minaei et al. (2021) Another Look at Genes and Caffeine: Who Can Benefit? BY ERIC TREXLER Few topics have been covered in MASS more regularly than caffeine, and for good reason. It’s one of the more effective supplements on the market for strength, physique, and endurance athletes alike, it has beneficial effects on wakefulness and alertness that extend outside of physical performance, and it’s found in a wide range of common foods and beverages. The MASS team has reached a consensus that caffeine acutely enhances performance, and that chronic caffeine users can still enjoy performance benefits, although habituation may influence the magnitude of caffeine’s ergogenic effects to some degree. However, two topics remain unresolved: we’re still not entirely certain if caffeine meaningfully improves longitudinal training adaptations, and we’re still not entirely certain if an individual’s genotype meaningfully alters the performance-boosting effects of caffeine supplementation. The CYP1A2 gene codes for the key enzyme that dictates the rate of caffeine metabolism, whereas the ADORA2A gene codes for a key receptor responsible for caffeine’s ergogenic effects. The presently reviewed study (1) focused on the former gene (CYP1A2), and sought to determine if caffeine had differential effects on performance for “fast metabolizers” (individuals with two copies of the “A” allele, thus having the AA genotype) and “slow metabolizers” (individuals with at least one copy of the “C” allele, thus having the AC or CC genotype). Sixteen resistance-trained men (age: 21.6 ± 7.1 years; height: 179.7 ± 5.6 cm; body mass: 72.15 ± 6.8 kg; habitual caffeine intake: < 100mg/day) completed this study. Six participants were “fast metabolizers” (AA genotype), and ten participants were “slow metabolizers” (AC or CC genotype). Just like the previously reviewed capsiate study, it was a placebo-controlled, crossover study consisting of two testing visits separated by a one-week washout period. At one visit, they consumed 6mg/kg of caffeine 60 minutes prior to exercise testing, while they consumed placebo capsules at the other visit. Performance was assessed using a single Wingate test, which is a 30-second maximal sprint on a cycle ergometer with an added resistance of 7.5% of body mass. Outcomes of interest included peak power, mean power, minimum power, and fatigue index (the relative drop from peak power to minimum power, expressed as a percentage). 104 As shown in Figure 1, there was a significant group × treatment interaction effect for peak power (p = 0.041); caffeine significantly increased peak power in fast metabolizers (p = 0.039), but led to a nonsignificant reduction in slow metabolizers (p = 0.135). However, as shown in Figure 2, the group × treatment interaction effect for average power was not statistically significant (p = 0.473), and caffeine wasn’t particularly helpful for either genotype group. Along those lines, significant group × treatment interaction effects were not observed for minimum power (p = 0.839) or fatigue index (p = 0.239). The researchers concluded that the results “partially” supported their hypothesis, given that caffeine improved peak performance in a genotype-specific manner, while the other outcomes were unaffected by genotype. Given the relatively mixed findings and the very small sample size (only six fast metabolizers), in addition to the somewhat fickle na- ture of peak power measurements, it’s hard to view these results as unequivocal proof that caffeine is only ergogenic for fast metabolizers. While the peak power results lean toward that conclusion, the broader literature on this topic paints a much more ambiguous picture. Without question, there are other studies suggesting that fast metabolizers enjoy more pronounced ergogenic effects from caffeine than slow metabolizers. For instance, the first study (to my knowledge) investigating the relationship between CYP1A2 genotype and performance effects of caffeine (2) found that fast metabolizers improved time trial by 4.9% after caffeine supplementation, which was a significantly larger improvement than was observed in slow metabolizers (1.8%). More recently, Wong et al (3) reported less favorable strength results for slower metabolizers, but it wasn’t because the faster metabolizers had exceptional responses to caffeine. Rather, subjects with AA and AC genotypes enjoyed no significant performance benefit, 105 while 4mg/kg of caffeine actually reduced handgrip strength by 12.8% in individuals with the CC genotype. In fact, as reviewed in a recent position stand by the International Society of Sports Nutrition (4), there are quite a few examples of studies in which people with AC or CC genotypes exhibited less pronounced ergogenic effects (or even ergolytic effects) when compared to people with AA genotypes. Nonetheless, counterexamples in which CYP1A2 genotype did not appear to significantly influence the ergogenic impact of caffeine are plentiful (5). The most recent systematic review directly addressing this topic (5) reported that only 2 out of 11 studies fitting the inclusion criteria found that CYP1A2 genotype significantly impacted caffeine’s effect on aerobic performance outcomes. Both studies reporting a significant impact found an impairment of ergogenic effects in slower metabolizers (people with AC or CC genotypes), and both used fairly long duration exercise tasks (10-40km cycling time trials). Of the 8 studies looking at “high-intensity” exercise (strength, power, and sprint tests), 2 of them found that CYP1A2 genotype significantly impacted the ergogenic effect of caffeine (with results once again suggesting more favorable caffeine responses in faster metabolizers). However, the size of the difference was pretty small in one of the studies (one extra rep per set during resistance training), and the other study measured eight performance outcomes, and only found a significant impact of CYP1A2 genotype for 1 of the 8 outcomes. In light of fairly mixed and equivocal evidence, the most straightforward way to address this topic might involve reframing the question. You might be wondering if the magnitude of caffeine’s ergogenic effect differs among CYP1A2 genotypes, but that’s not the most useful or actionable question. You accepted your CYP1A2 genotype at birth, and that’s pretty much locked in at this point. A more pragmatic question is whether or not slow metabolizers can still obtain an ergogenic effect of some magnitude from acute caffeine supplementation. The systematic review by Grgic et al (5) displays effect sizes for fast and slow metabolizers across a wide range of studies, and there are simply too many instances of slow metabolizers enjoying ergogenic effects for me to believe that inheriting the AC or CC genotype automatically makes you a nonresponder to caffeine. So, my tentative conclusions are as follows: 1) it’s possible that fast metabolizers (AA) might have slightly greater ergogenic effects from caffeine than slow metabolizers (AC or CC), but studies report differences that tend to be small and inconsistent; 2) genotype differences might become more pronounced during long-duration aerobic activity and when the ingested caffeine dose is particularly high (≥ 6 mg/kg); 3) there is plenty of evidence to suggest that slow metabolizers can still enjoy ergogenic effects for a variety of exercise outcomes with caffeine doses between 2-6mg/kg. References 1. Minaei S, Jourkesh M, Kreider RB, Forbes SC, Souza-Junior TP, McAnulty SR, et al. CYP1A2 Genotype Polymorphism Influences the Effect of Caffeine on Anaerobic Performance in Trained Males. 106 Int J Sport Nutr Exerc Metab. 2021 Oct 5;1–6. ePub ahead of print. 2. Womack CJ, Saunders MJ, Bechtel MK, Bolton DJ, Martin M, Luden ND, et al. The influence of a CYP1A2 polymorphism on the ergogenic effects of caffeine. J Int Soc Sports Nutr. 2012 Mar 15;9(1):7. 3. Wong O, Marshall K, Sicova M, Guest NS, García-Bailo B, El-Sohemy A. CYP1A2 Genotype Modifies the Effects of Caffeine Compared With Placebo on Muscle Strength in Competitive Male Athletes. Int J Sport Nutr Exerc Metab. 2021 Jul 20;1–7. 4. Guest NS, VanDusseldorp TA, Nelson MT, Grgic J, Schoenfeld BJ, Jenkins NDM, et al. International society of sports nutrition position stand: caffeine and exercise performance. J Int Soc Sports Nutr. 2021 Jan 2;18(1):1. 5. Grgic J, Pickering C, Del Coso J, Schoenfeld BJ, Mikulic P. CYP1A2 genotype and acute ergogenic effects of caffeine intake on exercise performance: a systematic review. Eur J Nutr. 2021 Apr;60(3):1181-1195. 107 Study Reviewed: Behavior of Motor Units during Submaximal Isometric Contractions in Chronically Strength-Trained Individuals. Casolo et al. (2021) Trained Lifters are Primarily Stronger Due to Muscularity, Not Neural Adaptations BY GREG NUCKOLS We’ve written about the relationship between muscle growth and strength gains several times in MASS (one, two, three), because understanding that relationship has several important implications for planning and monitoring training for people with strength and physique goals. If you’re a powerlifter, will a focus on building muscle (perhaps at the expense of some highly specific heavy, low-rep training) actually pay dividends over time? If you’re a physique athlete, can you use strength progress as a proxy for muscle growth? However, much of the research investigating the relationship between hypertrophy and strength gains is hindered by (very understandable) limitations. For example, cross-sectional studies show that there’s a strong correlation between muscularity and strength, but a correlation doesn’t necessarily indicate a causal relationship. Longitudinal studies would be better for investigating the relationship between muscle growth and strength gains but, particularly in trained lifters, it may be challenging to induce enough growth within the feasible length of a study to tease out the relationship between hypertrophy and strength gains. Fundamentally, when we ask about the relationship between muscle growth and strength gains, we’re asking, “to what extent are strength gains influenced by ‘neural’ adaptations, and to what extent are strength gains influenced by structural (i.e., hypertrophic) adaptations?” However, most of the time when researchers investigate this question, strength is quantified, muscle size is quantified, and neural adaptations are merely assumed to “fill the gap.” In other words, motor unit behavior (and the relationship between motor unit behavior and strength) isn’t actually quantified; it’s just assumed that variance in strength left unexplained by hypertrophy must be explained by neural factors (and measurement error) instead. With that in mind, the presently reviewed study fills a major gap in our understanding of this topic (1). Researchers recruited 16 trained (average of 5.9 years of resistance training experience) and 14 untrained men. The subjects’ maximal isometric elbow flexion strength was assessed via dynamometry, 108 their biceps anatomical cross-sectional area was assessed via MRI, and their motor unit behavior during submaximal isometric contractions (from 15% to 70% of maximal isometric force) was assessed via high-density surface electromyography (HDsEMG). Before discussing the results of the study, it’s worth discussing HDsEMG a bit. HDsEMG is distinct from standard surface electromyography (sEMG). While standard sEMG merely provides a gross measure of the total electrical activity underneath the electrode (2), HDsEMG uses an array of electrodes and specialized algorithms to decompose an EMG signal in a manner that allows you to isolate and assess the behavior of individual motor units. As a result, HDsEMG allows you to assess motor unit recruitment thresholds and discharge rates. In other words, it can help tell us whether trained lifters are more adept at recruiting motor units, and whether their motor units discharge at a higher frequency (thus presenting muscles with a larger impetus to contract). Furthermore, assessing motor unit discharge rates allows you to assess the relationship between discharge rates and force output. If trained lifters could create more force (relative to maximal force output) with lower motor unit discharge rates, that would be suggestive of enhanced “neural efficiency” (perhaps resulting from greater intra-muscular coordination). The results of the study were pretty straightforward: the trained lifters were considerably stronger than the untrained lifters (64.8% greater maximal isometric elbow flexion force) and considerably more muscular than the untrained lifters (71.9% greater biceps anatomical cross-sectional area). However, motor unit behavior was similar between the groups. Recruitment thresholds were similar (relative to maximal force), motor unit dis- 109 110 charge rates were similar during each submaximal contraction, and the relationship between discharge rates and relative force output were all similar between groups. The last sentence of the abstract summarizes these findings quite well: “The greater absolute force-generating capacity of [strength trained individuals] for the same neural input, demonstrates that morphological, rather than neural, factors are the predominant mechanism for their enhanced force generation during submaximal efforts.” to the amount of muscle mass they have, I doubt it’s due to a preternatural ability to recruit more motor units, or the ability of their motor units to discharge rapidly; rather, it’s more about technique and general motor skills (and probably favorable structural factors as well). “Technique” and “motor skill” also have neural origins, of course (probably relating to adaptations in the motor cortex or cerebellum), but I don’t think those are the sorts of “neural adaptations” people have in mind when they use the phrase. Every time I revisit this subject, I become more and more convinced that for healthy people without neurological conditions, muscle size really is the primary determinant of muscle contractile force, with all other factors playing much smaller roles. When you remove any technique or skill-based components from the equation (i.e., when you just assess force isometrically), “neural” factors don’t seem to matter much: bigger muscles are stronger muscles. That equation gets a little murkier once you start dealing with more complex lifts used to assess strength, but even with more complex lifts, I think “neural adaptations” should more appropriately be referred to in less opaque terms, like “technique” and “motor skill.” In other words, if a pitcher in baseball improves their pitching mechanics over an offseason and starts controlling their pitches better, we’d probably say, “they improved their mechanics, so they’re doing a better job of hitting their spots.” We probably wouldn’t say, “they’re pitching better due to neural adaptations.” I think the same concept applies to strength: if someone can lift a ton of weight relative References 1. Casolo A, Del Vecchio A, Balshaw TG, Maeo S, Lanza MB, Felici F, Folland JP, Farina D. Behavior of motor units during submaximal isometric contractions in chronically strength-trained individuals. J Appl Physiol (1985). 2021 Nov 1;131(5):1584-1598. doi: 10.1152/ japplphysiol.00192.2021. Epub 2021 Oct 7. PMID: 34617822. 2. Vigotsky AD, Beardsley C, Contreras B, Steele J, Ogborn D, Phillips SM. Greater electromyographic responses do not imply greater motor unit recruitment and ‘hypertrophic potential’ cannot be inferred. J Strength Cond Res. 2017 Jan;31(1):e1-e4. doi: 10.1519/JSC.0000000000001249. Epub 2015 Dec 11. Erratum in: J Strength Cond Res. 2017 Feb;31(2):e66. PMID: 26670996. 111 Study Reviewed: Effect of Dietary Nitrate on Human Muscle Power: A Systematic Review and Individual Participant Data Meta-Analysis. Coggan et al. (2021) Dietary Nitrate Is Powerful Stuff BY ERIC TREXLER I know that title sounds like a brazen exaggeration, so please allow me to clarify: that title is a pun – this research brief is about the effects of dietary nitrate on power outcomes. While I’d rather protect my credibility than overstate the effects of dietary nitrate, it’s no secret that I am quite intrigued by nitrate. It was a major focus of my dissertation research, and I’ve written about it in MASS several times (one, two, three, four). Ingested dietary nitrate can be converted to nitrite, which can then be converted to nitric oxide. Nitric oxide is generally known for its ability to increase vasodilation and blood flow, but evidence also suggests that increasing nitric oxide availability can reduce the energy cost of exercise, enhance the contractile function of muscle, increase cellular glucose uptake, and attenuate muscle fatigue (2). When looking more closely at mechanisms related to muscle function, it appears that nitric oxide can primarily impact contractility by increasing ryanodine receptor nitrosylation and guanylyl cyclase activity (3). This should result in increased calcium release from the sarcoplasmic reticula and greater myofibril- lar calcium sensitivity, and should ultimately lead to increased force production, shortening velocity, and power output during explosive muscle actions. For the presently reviewed meta-analysis (1), the researchers systematically searched for randomized, double-blind, placebo-controlled, crossover studies to determine if acute (single-dose) or chronic (5-6 days) nitrate supplementation significantly impacts maximal power outcomes. However, this wasn’t just any old meta-analysis. When performing a meta-analysis on “within-subject” data (such as studies with a pre-test and a post-test, or crossover studies in which each participant completes both the experimental condition and the placebo condition), it’s very advantageous to mathematically account for the within-subject correlation. It’s rarely feasible to account for this correlation for every single study within a meta-analysis, because the within-subject correlation is rarely reported directly in studies, and many studies report their results in a way that makes it impossible to back-calculate this value. In this scenario, the meta-analyst has 112 three options: ignore the correlation, assume its value, or track down the authors to either obtain the value of the correlation or the raw, participant-level data. These researchers chose the latter option, and only included studies where they could calculate the actual within-participant correlation or obtain the raw data for all study participants. This falls under the umbrella of individual participant data meta-analyses; they’re unequivocally better than traditional, summary data meta-analyses (4), and aggregation of participant-level data can lead to some very cool insights, but they’re uncommon because they’re logistically hard to complete. 113 The analysis included data from 268 participants (218 men, 50 women) across 19 studies. Nitrate doses ranged from 6.4mmol (~400mg) to 15.9mmol (~1000mg), and were generally consumed in the form of concentrated beetroot juice. Effect sizes naturally varied from study to study, and there were plenty of non-significant findings in the mix, but it’s interesting to note that all 19 studies reported a positive effect size (favoring nitrate over placebo). Acute nitrate supplementation led to a statistically significant increase in maximal power production (effect size [ES] = 0.54, p < 0.0001; Figure 1), and chronic supplementation did as well (ES = 0.22, p = 0.004; Figure 2). Combining them together, this results in an overall effect size of ES = 0.42 (if you prefer the fixed-effect model) or ES = 0.45 (if you prefer the random-effects model), with a statistically significant average improvement of around 5%. Statistical heterogeneity was low, despite the fact that the participant pool ranged from heart failure patients to older adults to Olympic athletes. Nonetheless, two of the three smallest effect sizes came from studies sampling highly trained sprinters, and previous reviews have noted that effect sizes tend to be a bit smaller (but still generally positive) in participants of advanced training status (5). Subgroup analyses found that results were not significantly impacted by age, sex, or test modality (muscle actions of small versus large muscle groups), but the effect size for chronic dosing was significantly smaller than the effect size for acute dosing (p = 0.021). This contradicts other nitrate research, which generally indicates that chronic dosing pro- tocols lead to effects that are similar or better than acute protocols (5). I wouldn’t get too carried away with this particular finding in the current meta-analysis, as only four studies with chronic dosing strategies met inclusion criteria, and the largest of the four (which carries the most weight for the analysis) included several participants of very advanced training status. At this point, the preponderance of the evidence across varied exercise outcomes suggests that chronic supplementation yields effects that are as good, if not better, than single-dose nitrate supplementation strategies. Overall, this meta-analysis was conducted quite rigorously, and indicates that nitrate increases maximal power output by around 5% on average, which can be practically meaningful depending on the context. Nitrate is not a complete game changer, but there’s now some pretty good evidence that dietary nitrate can modestly enhance strength, strength endurance, and power output, so it’s a nutrient of interest for lifters. I still have reservations about quality control among commercially available nitrate and beetroot supplements (6), and the few beetroot supplements that repeatedly stand up to third-party verification of nitrate content tend to be pretty costly (in many contexts, costly enough to meaningfully impact the individualized cost-benefit analysis that should always precede supplementation decisions). As such, I think the best way to seek out the potential ergogenic effects of nitrate is to aim for a daily intake of 400-800mg from nitrate-rich fruits and vegetables (such as celery, beets, spinach, rocket [arugula], pomegranates, and more). If you 114 wish to add some supplementation into the mix, there’s reason to believe that consuming 4-6g of citrulline (or 6-9g of citrulline malate) about an hour before workouts would complement this strategy quite nicely. References 1. Coggan AR, Baranauskas MN, Hinrichs RJ, Liu Z, Carter SJ. Effect of dietary nitrate on human muscle power: a systematic review and individual participant data meta-analysis. J Int Soc Sports Nutr. 2021 Oct 9;18(1):66. 2. Bailey SJ, Vanhatalo A, Winyard PG, Jones AM. The nitrate-nitrite-nitric oxide pathway: Its role in human exercise physiology. Eur J Sport Sci. 2012 Jul 1;12(4):309–20. 3. Coggan AR, Peterson LR. Dietary Nitrate Enhances the Contractile Properties of Human Skeletal Muscle. Exerc Sport Sci Rev. 2018 Oct;46(4):254–61. 4. Lawrence JM, Meyerowitz-Katz G, Heathers JAJ, Brown NJL, Sheldrick KA. The lesson of ivermectin: metaanalyses based on summary data alone are inherently unreliable. Nat Med. 2021 Sep 22; ePub ahead of print. 5. Jones AM, Thompson C, Wylie LJ, Vanhatalo A. Dietary Nitrate and Physical Performance. Annu Rev Nutr. 2018 Aug 21;38:303–28. 6. Gallardo EJ, Coggan AR. What’s in Your Beet Juice? Nitrate and Nitrite Content of Beet Juice Products Marketed to Athletes. Int J Sport Nutr Exerc Metab. 2019 01;29(4):345–9. 115 Study Reviewed: Acute Cellular and Molecular Responses and Chronic Adaptations to Low-Load Blood Flow Restriction and High-Load Resistance Exercise in Trained Individuals. Davids et al. (2021) Low-Load Squat Training with Blood Flow Restriction Causes as Much Hypertrophy as High-Load Training in Experienced Squatters BY GREG NUCKOLS A recent study compared the effects of lowload training with blood flow restriction (BFR) and high-load training without BFR on squat strength, quad growth, and various molecular outcomes over nine weeks in trained subjects of both sexes (1). All subjects did lower body training three days per week, with a routine consisting of squats, leg press, and knee extensions days one and three, and Bulgarian split squats and knee extensions on day two. The group doing highload training started with ~75% 1RM loads, and did sets of eight with two minutes between sets. The group doing low-load training with BFR started with ~30% 1RM loads, and used the standard 30-15-15-15 protocol with 45 seconds between sets. Reps in reserve were assessed after each set. Loads were progressed in the high-load group if subjects had more than two reps in reserve after consecutive sets, and loads were progressed in the low-load BFR group if subjects had more than four reps in reserve after consecutive sets. The researchers assessed a lot of outcomes, but I’m most interested in the maximal strength, whole-muscle hypertrophy, and muscle fiber hypertrophy results. Unsurprisingly, squat 1RM increased to a significantly greater extent in the high-load group, but it did significantly increase in both groups (+9kg in the low-load BFR group, and +19kg in the high-load group). Furthermore, quadriceps cross-sectional area and type II fiber cross-sectional area (from the vastus lateralis) increased to a similar degree in both groups. Type I fiber cross-sectional area didn’t significantly increase in either group. I mostly wanted to review this study because it relates to two topics we’ve previously discussed in MASS. First, a prior study found that low-load training with BFR caused significant fiber type-specific hypertrophy in powerlifters, leading to disproportionate type I fiber growth (2). The present study did not corroborate those findings (1), suggesting that disproportionate type I fiber growth following low-load BFR training may not be a generalizable phenomenon; it may have just been a quirk observed in powerlifters who habitually do a lot of low-rep training. 116 117 Second, this study is informative about the relationship between proximity to failure and hypertrophy. It’s been argued (3) that low-load training only results in hypertrophy that’s equivalent to high-load training if low-load sets are taken to failure (to be clear, that’s never been my own position). In the present study, subjects didn’t intentionally train to failure (though I’m sure they probably inadvertently reached failure occasionally), and the low-load BFR group likely trained further from failure than the highload group, on average. Their training loads only increased if they completed consecutive sets with more than four reps in reserve, and their training loads increased from 30.5% to 56.3% of pre-training 1RM; in other words, the subjects in the low-load BFR group did a lot of sets with more than four reps in reserve. However, they still experienced quad growth that was comparable to that of the high-load group (+7.4 ± 4.3% for the low-load BFR group, and +4.6 ± 2.9% for the high-load group; p = 0.37), which likely trained a bit closer to failure. One could contend that the results of the present study may not extend to low-load training without BFR, but I personally haven’t seen any research which would lead me to such an assumption. Overall, the study provides further evidence that low-load training (with BFR, in this case) leads to hypertrophy responses that are comparable to those of high-load training. Furthermore, while high-load training is better for strength gains (since it’s a more specific stimulus), low-load training should be sufficient to at least maintain strength – that’s good news for lifters who resort to low-load training when they can’t train with heavier loads for a period of time (when training around an injury, for example). Finally, this study provides us with evidence that low-load training can cause a robust growth response even when it’s not performed to failure. 118 References 1. Davids CJ, Næss TC, Moen M, Cumming KT, Horwath O, Psilander N, Ekblom B, Coombes JS, Peake JM, Raastad T, Roberts LA. Acute cellular and molecular responses and chronic adaptations to low-load blood flow restriction and high-load resistance exercise in trained individuals. J Appl Physiol (1985). 2021 Sep 23. doi: 10.1152/ japplphysiol.00464.2021. Epub ahead of print. PMID: 34554017. 2. Bjørnsen T, Wernbom M, Kirketeig A, Paulsen G, Samnøy L, Bækken L, Cameron-Smith D, Berntsen S, Raastad T. Type 1 Muscle Fiber Hypertrophy after Blood Flow-restricted Training in Powerlifters. Med Sci Sports Exerc. 2019 Feb;51(2):288-298. doi: 10.1249/ MSS.0000000000001775. PMID: 30188363. 3. Lasevicius T, Schoenfeld BJ, Silva-Batista C, Barros TS, Aihara AY, Brendon H, Longo AR, Tricoli V, Peres BA, Teixeira EL. Muscle Failure Promotes Greater Muscle Hypertrophy in Low-Load but Not in High-Load Resistance Training. J Strength Cond Res. 2019 Dec 27. doi: 10.1519/JSC.0000000000003454. Epub ahead of print. PMID: 31895290. 119 Study Reviewed: The Use of BCAA to Decrease Delayed-Onset Muscle Soreness After a Single Bout of Exercise: A Systematic Review and Meta-Analysis. Weber et al. (2021) BCAAs Are Overrated. But Are They Useless? BY ERIC TREXLER If you were tapped into the bodybuilding world about 10-15 years ago, you would’ve been surprised to learn that water, in its naturally occurring form, was actually clear. At that time, every oversized water jug you encountered would be red, blue, orange, purple, you name it – anything but clear. In virtually all cases, the technicolor jugs were attributable to the widespread popularity of branched chain amino acid (BCAA) supplementation. It had become quite common to sip on BCAAs all day long for a variety of purported benefits related to promoting muscle protein synthesis or attenuating muscle protein breakdown, all within the context of diets with more than enough protein. As more research became available (and disseminated to the fitness world), this application of BCAA supplementation was found to be ineffective and unnecessary, which caused a lot of people to feel some residual resentment about the overpriced water they’d been drinking. However, we shouldn’t automatically discard all possible applications of BCAA supplementation just because one application received unwarranted interest and widespread adoption. The presently reviewed meta-analysis (1) sought to evaluate the effects of BCAA supplementation on delayed-onset muscle soreness in comparison to placebo. The literature search yielded a pool of 160 participants (146 male, 14 female) across 10 studies. Six studies enrolled sedentary or untrained participants, while the other four enrolled participants who regularly trained. On one hand, the researchers did an excellent job with most aspects of the meta-analysis, including their tool of choice for their risk of bias assessment and their application of GRADE (Grading of Recommendations, Assessment, Development, and Evaluation of scientific evidence) criteria to assess the certainty level of the evidence available. On the other hand, they could have modeled their analysis in a more nuanced manner that accounts for multiple effect sizes per study and longitudinal within-study correlations, as I’ve previously described in more detail. Nonetheless, results indicated that BCAA supplementation (with dosages ranging from 5.5 to 20g) led to statistically significant soreness attenuation at 24 and 72 hours post-exercise, while effects 120 were much smaller and non-significant at 48 and 96 hours post-exercise (Figure 1). The overall effect, averaged across time points, was statistically significant in favor of BCAA supplementation. This is not the first meta-analysis to report beneficial effects of BCAA supplementation for outcomes like delayed-onset muscle soreness (2) or biomarkers of post-exercise muscle damage (3). However, I think the oscillating 121 pattern of significance is pretty telling; one could propose some kind of complicated theory related to a quasi-circadian explanation for soreness attenuation on alternating days, but with different studies reporting effects at different time points, the more parsimonious conclusion is that the soreness-mitigating effects of BCAAs are highly dependent upon the context in which they’re observed. These researchers did some subgroup analyses to parse out the details, but I’m skeptical that such an approach is feasible with such a small number of studies. For example, imagine that larger effects were typically observed in studies recruiting well-trained participants, which could hypothetically use more arduous exercise protocols to induce muscle damage but find lower soreness levels due to the participants’ advanced training status. You might separately conclude that BCAAs are more effective for trained participants than untrained, for more arduous exercise bouts than more manageable bouts, and for modest soreness than severe soreness, but it’d be difficult to determine exactly which factor was driving the results in a tiny number of studies. It seems very intuitive to assume that the most defensible application of BCAAs to modify the time course of muscle soreness would be in scenarios where noteworthy soreness would be anticipated, due to any combination of low training status, very unaccustomed activity, particularly arduous activity, or insufficient protein intake. There are two important considerations that may limit the applicability of these findings. First, the meta-analyses on this topic compare BCAAs to a placebo. This makes sense as a general practice, but doesn’t help us determine if we could’ve obtained similar benefits by simply eating more protein, which many lifters would view as a more convenient and cost effective strategy. Second, as I’ve mentioned previously, these meta-analyses examine studies that assess recovery from acute bouts of unaccustomed exercise. That can be informative, but doesn’t tell us much about recovery from day-to-day training. As Greg has previously written, the repeated bout effect is “the term used to refer to the collective set of adaptations that make your muscles more resistant to damage when they’re repeatedly exposed to a given stressor that initially caused muscle damage.” In the context of typical training, the purported recovery benefits of BCAA supplementation may become entirely redundant due to proper training load management and ongoing protection conferred by the repeated bout effect. I don’t supplement with BCAAs, nor have I ever found an occasion to recommend BCAA supplementation to a client. However, to broadly categorize BCAAs as useless in all contexts would be, in my opinion, an oversimplification. As we’ve already discussed, there might be some specific scenarios in which BCAA could be used to facilitate recovery from certain types of exercise. There’s also some evidence to suggest that BCAAs could modestly reduce centrally mediated aspects of fatigue during very prolonged exercise, particularly when coingested with ingredients that facilitate ammonia clearance (4), although the practical utility of this strategy is still up for debate. The mechanism underlying this application is related 122 to the impact of competitive tryptophan and BCAA transport on brain serotonin synthesis, which is why we’ve previously recommended avoiding BCAA supplementation in close proximity to bedtime. The vast majority of fitness-oriented individuals will not meaningfully benefit from BCAA supplementation, but you can construct hypothetical scenarios in which BCAA supplementation could be defensible. For example, if doing a very prolonged exercise task, you could incorporate BCAAs into your fueling strategy to attenuate centrally mediated aspects of fatigue. If engaging in some very intense and unaccustomed exercise and unable to stomach higher intake of whole proteins, you might go for a broad-spectrum approach to muscle damage attenuation and recovery facilitation that could (for example) include some combination of BCAA (or essential amino acid) supplementation, tart cherry or antioxidant supplementation, fish oil, massage, and cold water immersion. So, BCAAs fall in an interesting spot: not strictly useless, but not something I’ve ever found an occasion to actually use (or recommend). For most people, you have to work fairly hard to construct scenarios in which BCAA supplementation would be advised, and even harder to construct scenarios in which a little extra protein wouldn’t do the trick. and meta-analysis. Amino Acids. 2021 Nov;53(11):1663-1678. 2. Fedewa MV, Spencer SO, Williams TD, Becker ZE, Fuqua CA. Effect of branchedChain Amino Acid Supplementation on Muscle Soreness following Exercise: A Meta-Analysis. Int J Vitam Nutr Res. 2019 Nov;89(5–6):348–56. 3. Rahimi MH, Shab-Bidar S, Mollahosseini M, Djafarian K. Branched-chain amino acid supplementation and exercise-induced muscle damage in exercise recovery: A meta-analysis of randomized clinical trials. Nutrition. 2017 Oct;42:30–6. 4. Chen I-F, Wu H-J, Chen C-Y, Chou K-M, Chang C-K. Branched-chain amino acids, arginine, citrulline alleviate central fatigue after 3 simulated matches in taekwondo athletes: a randomized controlled trial. J Int Soc Sports Nutr. 2016 Jul 13;13:28. References 1. Weber MG, Dias SS, de Angelis TR, Fernandes EV, Bernardes AG, Milanez VF, et al. The use of BCAA to decrease delayed-onset muscle soreness after a single bout of exercise: a systematic review 123 Study Reviewed: The Effect of Ginseng Supplementation on Anabolic Index, Muscle Strength, Body Composition, and Testosterone and Cortisol Response to Acute Resistance Exercise in Male Bodybuilders. Azizi et al. (2021) Ginseng Is Yet Another Thing That Doesn’t Increase Testosterone BY ERIC TREXLER It’s possible that you’ve asked yourself if ginseng supplementation could increase your testosterone. It’s more likely that you saw the title of this research brief and thought, “Wait… Why would it?” Well, a recent exploratory study (2) with 24 participants reported that ingesting 75mg/day of red Korean ginseng for a week significantly increased salivary testosterone (76.3 ± 16.6 to 98.4 ± 21.1 pg/mL; p < 0.01) and dehydroepiandrosterone (DHEA; 1.53 ± 0.63 to 1.98 ± 0.89 ng/ mL; p = 0.02) in the younger subgroup of women (20-32 years old). In the older subgroup (38-50 years old), the testosterone increase was not statistically significant (61.2 ± 16.9 to 68.1 ± 11.5 pg/mL; p = 0.132), but the increase in DHEA was (0.91 ± 0.32 to 1.62 ± 0.49 ng/mL; p = 0.014). The exact mechanism underlying these observations was unclear; the researchers suggested that bioactive components of ginseng called ginsenosides might reduce the conversion of testosterone to testosterone glucuronide and the sulfonation of DHEA to DHEAS, but I’ve seen some other publications suggest that ginseng can increase the release of luteinizing hormone, or can more vaguely “regulate” the hypotha- lamic–pituitary–gonadal and hypothalamic– pituitary–adrenal axes. To be totally fair to the researchers that conducted this exploratory study (2), they reiterated on several occasions that their results were preliminary in nature and required further replication and verification. That’s where the presently reviewed study comes into play (1). Twenty male drug-free bodybuilders were randomly assigned to supplement with ginseng (two 250mg capsules ingested twice daily) or a placebo for six weeks. Throughout the study, participants continued eating their typical diet and completed a pretty standard bodybuilding-focused training program, with workouts supervised by a bodybuilding coach and a member of the research team. Before and after supplementation, the researchers measured BMI, waist-to-hip ratio, resting concentrations of testosterone and cortisol, and estimated maximal strength for bench press and leg press. Estimated strength values were calculated based on an equation that predicts 1RM based on the number of reps completed during a multiple-repetition test to failure. As shown in Table 1, there were no 124 significant group × time interactions for body composition, strength, or resting hormone concentrations. The only significant interaction indicated that ginseng supplementation led to divergent effects on acute post-exercise testosterone fluctuations (a 13% decrease in the ginseng group and a 19.5% increase in the placebo group), which are inconsequential to chronic training adaptations (3). As a MASS reader, you’ve seen this play out before. When a study first reported that D-aspartic acid dramatically increased testosterone levels (4), it took the fitness world by storm. But as the good Dr. Helms said in his MASS article on the topic, sometimes it’s good to be a late adopter. Subsequent studies would fail to replicate those eye-catching findings, and the hype surrounding D-aspartic acid as a testosterone booster fizzled out. As I mentioned in a fairly recent MASS article, this has been the path that many purported testosterone boosters have taken, which is why I expressed so much hesitation about the preliminary positive findings for Dioscorea esculenta. It’s important to cover the boring replications, and not just the splashy preliminary results for the newest test booster on the block, so I felt it was prudent to briefly discuss this study in MASS. I had seen a tiny bit of chatter about the first study that reported increased testosterone in response to ginseng, so I wanted to make it known that this replication attempt, with a resistance training component in well-trained subjects and a longer duration of supplementation, found no benefits related to strength, body composition, or testosterone levels. In general, the test booster literature is difficult to navigate for a variety of reasons. For example, the sample sizes tend to be small, which makes this literature quite susceptible to sampling error and publication bias. Testosterone also tends to be a bit fickle and can fluctuate a fair amount between pre-testing and post-testing, even if the fluctuations have nothing to do with the supplement being studied. In addition, this is an area of research where potentially noteworthy conflicts of in- 125 terest appear to be fairly common. Despite these multifaceted challenges, there are a number of practical conclusions we can draw when it comes to supporting testosterone levels within the reference range. The causes of low testosterone can be multifactorial. In some cases, testosterone levels can be favorably impacted by lifestyle modifications such as getting more sleep, managing stress more effectively, managing recovery more proactively, or getting to a more optimal body-fat level (which could involve either gaining or losing weight). If testosterone levels are being impacted by low energy availability, excessive fat or carbohydrate restriction, or insufficiency of micronutrients (such as zinc, magnesium, or vitamin D), this might be correctable via dietary modification or supplementation. There are also some herbal ingredients that might be able to increase testosterone levels enough to impact libido or other symptoms of mild hypogonadism, but I’m not aware of any that can reliably increase testosterone enough to meaningfully impact strength or hypertrophy. And of course, if you suspect your testosterone is low because of an underlying medical condition requiring treatment (or you are uncertain and want to leave the troubleshooting to a qualified clinician), a visit to the doctor is your best bet. References 1. Azizi E, Moradi F. The effect of ginseng supplementation on anabolic index, muscle strength, body composition, and testosterone and cortisol response to acute resistance exercise in male bodybuilders. Sci Sports. 2021 Oct 1;36(5):383–9. 2. Al-Dujaili EAS, Abu Hajleh MN, Chalmers R. Effects of Ginseng Ingestion on Salivary Testosterone and DHEA Levels in Healthy Females: An Exploratory Study. Nutrients. 2020 May 28;12(6):E1582. 3. West DWD, Burd NA, Tang JE, Moore DR, Staples AW, Holwerda AM, et al. Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors. J Appl Physiol. 2010 Jan;108(1):60. 4. Topo E, Soricelli A, D’Aniello A, Ronsini S, D’Aniello G. The role and molecular mechanism of D-aspartic acid in the release and synthesis of LH and testosterone in humans and rats. Reprod Biol Endocrinol. 2009;7:120. 126 VIDEO: Verbal and Visual Feedback Part 2 BY MICHAEL C. ZOURDOS Last month we learned that external feedback benefits acute barbell velocity and power. However, do those acute benefits transfer to long-term strength outcomes? This video breaks down the longitudinal data related to verbal and visual velocity feedback. Click to watch Michael's presentation. 127 Relevant MASS Videos and Articles 1. How to Maximize Results with Velocity-Based Training. Volume 2 Issue 4. 2. Seeing is Believing: The Inherent Benefit of Viewing Intraset Velocity. Volume 2 Issue 6. 3. The Inherent Benefit of Tracking Velocity. Volume 4 Issue 9. 4. VIDEO: External Feedback: Part 1. Volume 5 Issue 11. References 1. Randell AD, Cronin JB, Keogh JW, Gill ND, Pedersen MC. Effect of instantaneous performance feedback during 6 weeks of velocity-based resistance training on sport-specific performance tests. The Journal of Strength & Conditioning Research. 2011 Jan 1;25(1):87-93. 2. Nagata A, Doma K, Yamashita D, Hasegawa H, Mori S. The effect of augmented feedback type and frequency on velocity-based training-induced adaptation and retention. The Journal of Strength & Conditioning Research. 2020 Nov 1;34(11):3110-7. 3. Weakley J, Till K, Sampson J, Banyard H, Leduc C, Wilson K, Roe G, Jones B. The effects of augmented feedback on sprint, jump, and strength adaptations in rugby union players after a 4-week training program. International journal of sports physiology and performance. 2019 Oct 1;14(9):1205-11. █ 128 VIDEO: Periodizing Singles in Powerlifting Training BY ERIC HELMS Heavy singles are often used in powerlifting, but equally as often they are misunderstood or misapplied. In this video, Dr. Helms discusses the feasibility, rationale, pros and cons, and utility of heavy singles. Then, he presents a model of how to periodize singles into powerlifting training as an example you can use to integrate into your training. Click to watch Eric's presentation. 129 Relevant MASS Videos and Articles 1. VIDEO: Daily 1RM Training. Volume 1, Issue 8. 2. VIDEO: Postactivation Potentiation. Volume 4, Issue 3. 3. What is Postactivation Potentiation, and Does it Work for Lifting? Volume 3, Issue 1. 4. Simplified Strength Tests Reveal the True Importance of Muscle Size for Force Output. Volume 4, Issue 2. 5. You Want to Get Better at Something? Do it First. Volume 4, Issue 4. 6. New Postactivation Potentiation Data is Less Promising. Volume 4, Issue 6. 7. A New Strategy for Postactivation Potentiation. Volume 5, Issue 9. 8. What’s the Least a Powerlifter Can Do and Still Get Meaningfully Stronger? Volume 5, Issue 9. References 1. Banister EW, Calvert TW, Savage MV, Bach T. A systems model of training for athletic performance. Aust J Sports Med. 1975 May;7(3):57-61. 2. Chiu LZ, Barnes JL. The Fitness-Fatigue Model Revisited: Implications for Planning Short-and Long-Term Training. Strength & Conditioning Journal. 2003 Dec 1;25(6):42-51. 3. Bartholomew JB, Stults-Kolehmainen MA, Elrod CC, Todd JS. Strength gains after resistance training: the effect of stressful, negative life events. The Journal of Strength & Conditioning Research. 2008 Jul 1;22(4):1215-21. 4. Strömbäck, E., Aasa, U., Gilenstam, K., & Berglund, L. (2018). Prevalence and Consequences of Injuries in Powerlifting: A Cross-sectional Study. Orthopaedic journal of sports medicine, 6(5), 2325967118771016. 5. Ralston, G. W., Kilgore, L., Wyatt, F. B., Buchan, D., & Baker, J. S. (2018). Weekly Training Frequency Effects on Strength Gain: A Meta-Analysis. Sports medicine - open, 4(1), 36. 6. Schoenfeld, B. J., Ogborn, D., & Krieger, J. W. (2017). Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and metaanalysis. Journal of sports sciences, 35(11), 1073–1082. 7. Halperin, I., Malleron, T., Har-Nir, I., Androulakis-Korakakis, P., Wolf, M., Fisher, J., & Steele, J. (2021). Accuracy in Predicting Repetitions to Task Failure in Resistance Exercise: A Scoping Review and Exploratory Meta-analysis. Sports medicine (Auckland, N.Z.), 10.1007/s40279021-01559-x. Advance online publication. 8. Sousa CA. Assessment of Accuracy of Intra-set Rating of Perceived Exertion in the Squat, Bench Press, and Deadlift (Masters Thesis, Florida Atlantic University). █ 130 Just Missed the Cut Every month, we consider hundreds of new papers, and they can’t all be included in MASS. Therefore, we’re happy to share a few pieces of research that just missed the cut. It’s our hope that with the knowledge gained from reading MASS, along with our interpreting research guide, you’ll be able to tackle these on your own. If you want to peruse our full journal sweep, you can find it here, and you can find our historical archive here. 1. Ruiz-Castellano et al. Achieving an Optimal Fat Loss Phase in Resistance-Trained Athletes: A Narrative Review 2. Fick et al. Acute and Chronic Citrulline Malate Supplementation on Muscle Contractile Properties and Fatigue Rate of the Quadriceps 3. Sargent et al. An Individualized Intervention Increases Sleep Duration in Professional Athletes 4. Castro et al. Association Between Changes in Serum and Skeletal Muscle Metabolomics Profile With Maximum Power Output Gains in Response to Different Aerobic Training Programs: The Times Study 5. Santos Junior et al. Classification and Determination Model of Resistance Training Status 6. Goulart et al. Cytokine response to resistance training sessions performed after different recovery intervals 7. Næess. Determining the optimal blood flow restriction protocol for maximising muscle hypertrophy and strength, pressure and cuff width: A mini-review 8. Anousaki et al. Effects of a 25-Week Periodized Training Macrocycle on Muscle Strength, Power, Muscle Architecture, and Performance in Well-Trained Track and Field Throwers 9. Valenzuela et al. Effects of Combining a Ketogenic Diet with Resistance Training on Body Composition, Strength, and Mechanical Power in Trained Individuals: A Narrative Review 10. Jakobsson et al. Effects of Different Types of Lower Body Resistance Exercise on Upperbody Strength in Men and Women, with Special Reference to Anabolic Hormones 11. Đurić et al. Effects of resistance training with constant, inertial, and combined loads on muscle power and strength output 12. Zhao et al. Exercise May Promote Skeletal Muscle Hypertrophy via Enhancing LeucineSensing: Preliminary Evidence 13. Mendonça et al. Force production and muscle activation during partial vs. full range of motion in Paralympic Powerlifting 14. Chennaoui et al. How does sleep help recovery from exercise-induced muscle injuries? 15. Cabral et al. Non-uniform excitation of the pectoralis major muscle during flat and inclined bench press exercises 16. Kruse et al. Stimuli for Adaptations in Muscle Length and the Length Range of Active 131 Force Exertion—A Narrative Review 17. Studer et al. The Effect of an Acute Farmers Walk Exercise Bout on Muscle Damage and Recovery in Recreationally Trained Adults 18. Santos et al. The Effect of Whole Egg Intake on Muscle Mass: Are the Yolk and Its Nutrients Important? 19. Holwerda et al. The impact of collagen protein ingestion on musculoskeletal connective tissue remodeling: a narrative review 20. Nederveen et al. The Importance of Muscle Capillarization for Optimizing Satellite Cell Plasticity 21. Yazdanpanah et al. Does exercise affect bone mineral density and content when added to a calorie-restricted diet? A systematic review and meta-analysis of controlled clinical trials 22. Pito et al. Effects of Concurrent Training on 1RM and VO2 in Adults: Systematic Review with Meta-analysis 23. Weber et al. Effects of instagram sports posts on the athletic motivation of female elite athletes: Do they inspire or backfire? 24. Smith et al. Examining the effects of calorie restriction on testosterone concentrations in men: a systematic review and meta-analysis 25. Kataoka et al. Is there Evidence for the Suggestion that Fatigue Accumulates Following Resistance Exercise? 26. Halson et al. Sleep Quality in Elite Athletes: Normative Values, Reliability and Understanding Contributors to Poor Sleep 27. Zheng et al. The Association of Muscle Dysmorphia, Social Physique Anxiety, and Body Checking Behavior in Male College Students With Weight Exercise 28. Murdoch et al. The effectiveness of stress regulation interventions with athletes: A systematic review and multilevel meta-analysis of randomised controlled trials 29. Forbes et al. Timing of creatine supplementation does not influence gains in unilateral muscle hypertrophy or strength from resistance training in young adults: a within-subject design 132 Thanks for reading MASS. The next issue will be released to subscribers on January 1, 2022. Graphics and layout by Kat Whitfield 133