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MASS 2021 12-v2

V O L U ME 5 , ISS U E 12
DEC EMBER 2 0 2 1
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,, T-Nation, and 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
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
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.
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.
The Link Between Overtraining and Low Energy
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.
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.
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
Metabolic Phenotypes, Weight Regulation, and Reverse
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.
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.
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.
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
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.
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
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
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.
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.
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.
Purpose and Hypotheses
Subjects and Methods
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.
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
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-
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.
Gains in bench press 1RM, squat 1RM, biceps thickness, triceps thickness, and vastus
lateralis thickness didn’t significantly differ
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
Criticisms and Statistical
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-
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.
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
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
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-
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
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-
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-
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.
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.
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
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:
10. Carvalho L, Junior RM, Truffi G, Serra A, Sander R, De Souza EO, Barroso R.
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:
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
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.
1. The presently reviewed meta-analysis sought to determine if free-weight
or machine-based training is better for strength, hypertrophy, and power
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.
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.
5. Propose that the general lifter does not
have to make a dichotomous choice between machines and free weights.
Purpose and Hypotheses
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).
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.
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-
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.
Statistical Criticisms and
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-
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.
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
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
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
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.
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-
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.
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.
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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.
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and machine-based strength training on strength and power performance. Journal of
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4. Augustsson J, Esko A, Thomeé R, Svantesson U. Weight training of the thigh muscles
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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
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.
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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
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training using free weights and machine weights. Missouri Journal of Health, Physical
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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.
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.
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
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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
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
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
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.
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.
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-
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
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.
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
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
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.
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).
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-
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.
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-
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
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
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
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-
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.
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),
11. Fry, A. C., Kraemer, W. J., van Borselen, F., Lynch, J. M., Marsit, J. L., Roy, E. P., et
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–
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
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.
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.
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
The primary purpose of the presently reviewed meta-analysis (1) was “to quantify the
discrepancy in lean mass accretion between
interventions prescribing resistance training
in an energy deficit and interventions prescribing resistance training without an energy deficit.” The secondary purpose was to investigate the same question, but with a focus
on strength gains rather than lean mass gains.
The researchers also conducted additional
analyses to determine if effects were meaningfully impacted by potentially important
variables including age, sex, BMI, and study
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
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-
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.
In analysis A, energy deficits led to significantly smaller gains in lean mass when compared
to a control diet (effect size [ES] = -0.57, p =
0.02). Energy deficits also led to smaller gains
in strength, but the effect size was smaller, and
the effect was not statistically significant (ES
= -0.31, p = 0.28). Forest plots for both analyses are presented in Figure 1.
The waterfall plots for analysis B are presented in Figure 2. For studies involving an energy deficit, the pooled effect size for lean mass
was negative (ES = -0.11, p = 0.03), while it
was positive for studies that did not involve
an energy deficit (ES = 0.20, p < 0.001). For
strength gains, effect sizes were positive and
similar in magnitude whether studies did (ES
= 0.84, p <0.001) or did not (ES = 0.81, p <
0.001) involve an energy deficit.
As for the meta-regression component of
analysis B, the relationship between energy deficits and changes in lean mass (when
controlling for age, sex, study duration, and
BMI) is presented in Figure 3. The slope
of the line was -0.00031 (p = 0.02), which
means there was a statistically significant
negative relationship between the size of the
energy deficit and the magnitude of changes
in lean mass. As the energy deficit grew by
100kcals/day, the effect size for lean mass
tended to drop by 0.031 units. By extension,
a deficit of ~500kcals/day was predicted to
fully blunt lean mass gains (ES = 0), and estimated changes in lean mass became negative
for energy deficits beyond ~500kcals/day.
Criticisms and Statistical
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-
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
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-
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.”
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
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-
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
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-
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)
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
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.
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
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
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
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
Study Reviewed: Skeletal Muscle Adaptations and Performance Outcomes Following a Step
and Exponential Taper in Strength Athletes. Travis et al. (2021)
Tapering Strategies are
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.
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.
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.
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.
2. Review the types of tapers and state of the
3. Examine why strength and hypertrophy
may be optimized by different taping
4. Discuss when tapering is necessary.
5. Provide practical tapering examples.
Purpose and Hypotheses
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.
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.
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.
• Squat, bench press, and powerlifting total
increased similarly in both groups.
Training Volume, Load, Monotony, and
• 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
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).
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.
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
(+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
The presently reviewed study’s statistical approach was sound, but musings are warranted. First, although I believe I can provide an
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-
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.
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-
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-
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
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,
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.
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
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.
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,
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.
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.
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
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
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
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.
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.
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-
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
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
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
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.
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-
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
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.
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
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
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
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
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.
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
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-
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
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
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-
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.
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
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–
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
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.
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-toknow information from each study.
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
Dietary Nitrate is Powerful Stuff
Low-Load Squat Training With Blood Flow
Restriction Causes as Much Hypertrophy as
High-Load Training in Experienced Squatters
BCAAs Are Overrated. But Are They Useless?
Ginseng Is Yet Another Thing That Doesn’t
Increase Testosterone
Study Reviewed: The Effectiveness of Pre-Performance Routines in Sports: A Meta-Analysis.
Rupprecht et al. (2021)
Pre-Performance Routines Boost Performance
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
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
(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
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
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
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
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
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
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:
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
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/
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
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.
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
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.
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
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
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.
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
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
(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.
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
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.
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.
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?
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).
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,
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.
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.
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
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
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
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,
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-
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
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:
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
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
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.
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
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.
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
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
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
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
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.
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.
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:
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.
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?
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
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
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
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
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
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
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.
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
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
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
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-
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.
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.
VIDEO: Verbal and Visual
Feedback Part 2
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.
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.
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
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.
Click to watch Eric's presentation.
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.
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).
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
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
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
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
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
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