V O L U ME 6 , ISS U E 4
AP RI L 2 0 2 2
MASS
M ONTHLY A PPL ICATIO N S IN
STRE N G TH SPO R T
E R I C H E LMS | G R E G N UCK O LS | MIC HAEL ZO URDO S | ERIC T REXL E R
The Reviewers
Eric Helms
Eric Helms is a coach, athlete, author, and educator. He is a coach for drug-free strength and physique
competitors at all levels as a part of team 3D Muscle Journey where he is also the Chief Science
Officer. Eric regularly publishes peer-reviewed articles in exercise science and nutrition journals on
physique and strength sport, in addition to contributing to the 3DMJ blog. He’s taught undergraduateand graduate-level nutrition and exercise science and speaks internationally at academic and
commercial conferences. He has a B.S. in fitness and wellness, an M.S. in exercise science, a second
Master’s in sports nutrition, a Ph.D. in strength and conditioning, and is a research fellow for the Sports
Performance Research Institute New Zealand at Auckland University of Technology. Eric earned pro
status as a natural bodybuilder with the PNBA in 2011 and competes in numerous strength sports.
Greg Nuckols
Greg Nuckols has over a decade of experience under the bar and a B.S. in exercise and sports
science. Greg earned his M.A. in exercise and sport science from the University of North Carolina
at Chapel Hill. He’s held three all-time world records in powerlifting in the 220lb and 242lb classes.
He’s trained hundreds of athletes and regular folks, both online and in-person. He’s written for many
of the major magazines and websites in the fitness industry, including Men’s Health, Men’s Fitness,
Muscle & Fitness, Bodybuilding.com, T-Nation, and Schwarzenegger.com. Furthermore, he’s had the
opportunity to work with and learn from numerous record holders, champion athletes, and collegiate
and professional strength and conditioning coaches through his previous job as Chief Content
Director for Juggernaut Training Systems and current full-time work on StrongerByScience.com.
Michael C. Zourdos
Michael (Mike) C. Zourdos, Ph.D., CSCS, has specializations in strength and conditioning and skeletal
muscle physiology. He earned his Ph.D. in exercise physiology from The Florida State University (FSU)
in 2012 under the guidance of Dr. Jeong-Su Kim. Prior to attending FSU, Mike received his B.S. in
exercise science from Marietta College and M.S. in applied health physiology from Salisbury University.
Mike served as the head powerlifting coach of FSU’s 2011 and 2012 state championship teams. He
also competes as a powerlifter in the USAPL, and among his best competition lifts is a 230kg (507lbs)
raw squat at a body weight of 76kg. Mike owns the company Training Revolution, LLC., where he has
coached more than 100 lifters, including a USAPL open division national champion.
Eric Trexler
Eric Trexler is a pro natural bodybuilder and a sports nutrition researcher. Eric has a PhD in Human
Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research
papers on various exercise and nutrition strategies for getting bigger, stronger, and leaner. In
addition, Eric has several years of University-level teaching experience, and has been involved in
coaching since 2009. Eric is the Director of Education at Stronger By Science.
Table of Contents
6
BY GR EG NUCKOL S
Can You Avoid Plateaus by Manipulating Relative Training
Intensity?
A recent six-month crossover study had subjects perform three months of low-load
training and three months of moderate-load training, in a randomized order. The
researchers found that lean mass gains were similar during the first and last three
months of training, in contrast with other research. Read on to learn if you can truly
avoid plateaus via wholesale shifts in relative training intensity every few months.
24
BY MI CHAEL C. ZOUR DOS
Everything You Need to Know About Rest Intervals
Over the past decade, longer rest intervals have prevailed over shorter rest intervals
in both research and practice for hypertrophy and strength. However, most of the
rest interval data are from male lifters. Thus, a rest interval study in female lifters is a
welcome addition to the literature.
39
BY ER I C HEL MS
Can Bodybuilders Change Their Intra-to-Extracellular
Water Ratio During Peak Week?
More research is steadily published examining the “peak week” methods
bodybuilders use in attempts to acutely enhance stage appearance. This study
assesses a potential mechanism behind many peaking methods: manipulating one’s
intra-to-extracellular water ratio.
50
BY ER I C T R EXL ER
Coaching Beyond the Spreadsheet: How (and Why) To
Promote Intrinsic Motivation
A lot of evidence-based coaches and fitness enthusiasts invest considerable time
and effort into optimizing the nuts and bolts of their programs. A new meta-analysis
on psychological aspects of coaching suggests that there are ample opportunities
(beyond the spreadsheet) to enhance a lifter’s long-term success and well-being.
63
BY GR EG NUCKOL S
“Activation Training” May Increase Glute EMG
A new study suggests that you can increase your glute activation during squats by
doing some light glute “activation training” twice per day. This article will discuss
some potential confounders in the study, and how confident we can be that glute
activation training will actually improve muscle growth and strength gains.
72
BY ER I C T R EXL ER
Should You Adjust Training Volume While Cutting?
How should you manipulate training volume while cutting? Due to a lack of direct
research, it’s one of the most commonly debated questions in evidence-based fitness.
A new systematic review adds fuel to the fire, but clarity still eludes us.
88
BY MI CHAEL C. ZOUR DOS
Should You Take A Few Days Off to Peak Strength?
I’ve previously contended that tapering is overrated. However, I haven’t touched much
on training cessation as a tapering strategy. Is training cessation the tapering strategy
that you need to peak strength? A new study examined this concept.
101
BY ER I C T R EXL ER
Research Briefs
In the Research Briefs section, Eric Trexler shares quick summaries of recent
studies. Briefs are short and sweet, skimmable, and focused on the need-to-know
information from each study.
123
BY MI CHAEL C. ZOUR DOS
VIDEO: Time-Efficient Programming Strategies Part 2
Part 1 of our time efficiency series provided examples of training strategies which
can be used to decrease training time. However, there are certain circumstances
which warrant training twice per day, in which further adjustments are needed
to accommodate two-a-days. Therefore, part 2 of this series provides practical
examples for how time-efficient programming strategies can be used to make twoa-days feasible.
125
BY ER I C HEL MS
VIDEO: Interpreting Failure and Non-Failure Data
You might have noticed the studies comparing failure versus non-failure for
hypertrophy outcomes often conflict. We’ve reviewed many of them in MASS, and
not only do they tend to conflict with one-another, but even within the MASS team,
we don’t always see eye-to-eye when interpreting the impact of failure. Why is
that? In this video, you’ll understand why interpreting these data is challenging.
Letter From the Reviewers
V
olume 6, Issue 4 of MASS has arrived, and it’s absolutely packed with content to help
you take your training, nutrition, and coaching to the next level.
On the training side, Greg’s got an excellent article related to programming and
periodization, which specifically examines whether or not you can avoid plateaus by switching
up your relative training intensity. This month had so many great papers to choose from that
Greg opted to write a second full-length review in lieu of his Research Briefs; in his second
article, Greg explores the concept of using “activation exercises” to promote more glute
activation while squatting.
Also in the Training Department, Dr. Zourdos has two fantastic articles this month. In the first,
he covers a new paper exploring the psychological, physiological, and performance changes
that accompany short-term cessation of training. In the second, he takes a deep dive into the
literature on optimal interset rest periods for promoting resistance training adaptations.
On the nutrition side, Dr. Helms covers a very interesting paper about “peak week” in
physique athletes, with results that appear to challenge some of the conventional wisdom
about attempts to manipulate intracellular and extracellular fluid balance. Dr. Trexler also
covers a topic near and dear to more physique-oriented lifters this month, with an article that
explores if (and how) lifters should be adjusting their lifting volume in order to maximize lean
mass retention when they’re in a caloric deficit.
Moving beyond the nutrition category, Dr. Trexler’s second article more broadly relates
to psychology and coaching. It covers a paper about how autonomy-supporting coaching
behaviors can maximize client success, while discussing a variety of ways you can use selfdetermination theory to support success for yourself or your clients. Finally, Dr. Trexler’s
Research Briefs take a quick and concise look at the 5:2 diet (a form of intermittent fasting) in
lifters, the use of continuous glucose monitors, the effects of morning versus evening workouts
on energy expenditure, and some of the potential risks faced by female physique athletes.
As for this month’s video content, Dr. Helms has a very insightful video about how to
interpret studies comparing “failure” and “non-failure” training interventions. In addition, Dr.
Zourdos is back with part 2 of his excellent (and very practical) video series on implementing
time-efficient programming strategies.
As always, we hope you enjoy this issue of MASS. 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.
Sincerely,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
5
Study Reviewed: Different Load Intensity Transition Schemes to Avoid Plateau and NoResponse in Lean Body Mass Gain in Postmenopausal Women. Carneiro et al. (2022)
Can You Avoid Plateaus by Manipulating
Relative Training Intensity?
BY GREG NUCKOLS
A recent six-month crossover study had subjects perform three
months of low-load training and three months of moderate-load
training, in a randomized order. The researchers found that lean
mass gains were similar during the first and last three months of
training, in contrast with other research. Read on to learn if you
can truly avoid plateaus via wholesale shifts in relative training
intensity every few months.
6
KEY POINTS
1. Healthy, postmenopausal women completed 24 weeks of lower-body resistance
training, consisting of 12 weeks of moderate-load training (sets of 8-12 reps), and 12
weeks of low-load training (sets of 27-31 reps), performed in a randomized order.
2. Both orders of training sequencing produced similar increases in lean soft tissue
mass (15). Furthermore, average rates of lean mass accrual were similar in the first
and second 12 weeks of training.
3. The present study employed a crossover design and reported individual subject
data. Such data allows us to probe questions related to optimizing relative training
intensity for individuals, and the reliability of classifying people as “high responders”
and “low responders” to resistance training.
W
hen you reach a plateau in your
training – you keep training hard,
but further muscle growth and
strength gains are hard to come by – how can
you break through that plateau? Potential options range from fun (dreamer bulk, baby),
to practical-but-boring (try to sleep more and
manage stress better), to illegal-in-many-jurisdictions (up the dose, lift the most). One
surprisingly controversial option, however, is
to make significant adjustments to your training program. Some will argue that changing
your training program is only logical, since
your current program isn’t producing the
results you want. Other people will accuse
you of “program hopping,” or say you have
“training ADHD,” and that you just need to
commit harder to your current training approach (especially if it previously produced
solid results for you).
So, what does the research say? Can you
get the gains rolling again by changing your
training approach, or is doing so a waste of
time? Surprisingly, there’s not a ton of research on the topic. However, a recent study
(1) purports to demonstrate that switching
to a completely different relative training
intensity (percentage of 1RM) can help you
avoid a plateau. In the present study, subjects
completed 24 weeks of training. Half of the
subjects did 12 weeks of low-load training
(starting with 30% of 1RM, and performing
sets of 27-31 reps), followed by 12 weeks of
moderate-load training (starting with 80%
of 1RM, and performing sets of 8-12 reps).
The other half did 12 weeks of moderate-load
training first, followed by 12 weeks of lowload training. The researchers found that, in
contrast with other research (which suggests
that hypertrophy slows down dramatically after about three months of training), lean mass
gains occurred at the same rate during both
12-week blocks of training. So, are wholesale
changes in training intensity the holy grail for
avoiding or breaking through plateaus? Read
on to find out. I’m not fully convinced, but
the design and thorough data reporting in the
present study allow us to probe several interesting and practical questions related to individual responses to different training styles.
7
Purpose and Hypotheses
Purpose
The primary purpose of this study was to
compare the effects of low-load training
followed by moderate-load training, versus
moderate-load training followed by low-load
training, for the purpose of supporting lean
mass accretion. The secondary purpose was
to see how changing training intensity would
affect responsiveness to resistance training.
Hypotheses
No hypotheses were directly stated.
Subjects and Methods
Subjects
24 postmenopausal women participated in
the present study. All subjects were at least
50 years old, had not menstruated in the preceding 12 months, did not use hormone re-
placement therapy, were free of significant
cardiometabolic or orthopedic issues, and
had not participated in regular physical activity more than once per week in the six months
preceding the study. Subject characteristics
can be seen in Table 1.
Experimental Design
This study took place over 28 weeks, including a 24-week training intervention. During
the first week, subjects performed three familiarization sessions to get acquainted with
the exercises used in the study (leg press,
knee extensions, leg curls, and calf raises).
During the second week, subjects completed
1RM tests to determine their initial training
loads, and researchers collected the subjects’
basic anthropometric data (height, weight,
and body fat), along with assessing thigh
lean soft tissue mass (via DXA). From weeks
3-14, subjects completed twelve weeks of resistance training. During week 15, research-
8
ers reassessed the subjects’ thigh lean soft
tissue mass and 1RM strength for all four
training exercises. From weeks 16-27, subjects completed 12 more weeks of resistance
training. Finally, thigh lean soft tissue mass
was reassessed in week 28. Post-training
1RM strength was not assessed (or, at minimum, it wasn’t reported).
All subjects completed two different resistance training programs. Both programs consisted of leg press, knee extensions, leg curls,
and calf raises, all performed for three sets
(2) with 90 seconds of rest between sets. All
sets were performed “until, or close to, voluntary concentric failure,” according to the
study (though, as far as I can tell, proximity
to failure wasn’t strictly controlled or directly
quantified). The two programs only differed
in terms of training load. The moderate-load
program started with subjects at 80% of their
1RM, with the aim of completing 8-12 reps
per set, while the low-load program started
with subjects at 30% of 1RM, with the aim of
completing 27-31 reps per set. When subjects
completed at least 12 reps during the first set
of an exercise on the moderate-load program,
or 31 reps during the first set of an exercise
on the low-load program, training loads were
increased by 5-10% for their next training
session. Half of the subjects were randomly
assigned to complete the moderate-load program first, while the other half completed
the low-load program first. After the first 12
weeks of training and the mid-study strength
and lean mass assessments, subjects switched
training programs for the second half of the
study. So, half of the subjects completed 12
weeks of moderate-load training followed
by 12 weeks of low-load training, and half
of the subjects completed 12 weeks of lowload training followed by 12 weeks of moderate-load training.
The researchers were primarily interested in
two outcomes: progression of training loads
(absolute training loads, total reps completed, and volume load), and accrual of thigh
lean soft tissue mass. The lean mass data was
also analyzed in two different ways. First,
the researchers compared the two training
approaches (low-load followed by moderate-load training, versus moderate-load followed by low-load training) to see if either
loading scheme produced better outcomes.
Second, the researchers split apart “high responders” and “low responders” to resistance
training following the first 12 weeks of training, using a median split (i.e., the 50% of subjects with the largest lean mass gains during
the first 12 weeks of training were deemed
to be high responders, and the 50% of subjects with the smallest lean mass gains were
deemed to be low responders). They were interested in assessing whether training responsiveness during the first 12 weeks of training
was predictive of responsiveness during the
last 12 weeks of training, and whether changing relative training intensities would improve overall training responsiveness in the
initial low responders.
Findings
Table 2 shows the training load progression observed following the two training approaches.
Overall, both of the programs worked, insofar as absolute load and volume load in-
9
creased over time. I’m not too interested in
the direct comparisons between the two programs, though. According to the results table, the low-load program looks like it was
more effective, but I think that’s an artifact
of the way initial training loads were assigned. Once people get a bit of training experience (i.e., once someone establishes decent strength endurance), they can generally
complete considerably more than 27-31 reps
at 30% of 1RM, whereas 8-12 reps at 80%
of 1RM is pretty challenging for most folks.
So, by using 30% of 1RM as the initial intensity for the low-load training protocol, I
think the researchers simply (inadvertently)
stacked the deck for low-load training to appear to allow for greater progression of training loads. Regardless, it’s clear that both programs effectively promoted improvements in
strength (demonstrated by absolute training
loads increasing in both programs) and work
capacity (demonstrated by total volume load
increasing in both programs).
10
Table 3 shows the changes in lean mass over
time.
Both protocols were similarly effective at
promoting increases in thigh lean soft tissue mass. In the group completing low-load
training followed by moderate-load training,
subjects gained an average of 0.4kg of lean
mass in the first 12 weeks of training, and
0.3kg of lean mass in the last 12 weeks of
training, for a net increase of 0.7kg. In the
group completing moderate-load training followed by low-load training, subjects gained
an average of 0.3kg of lean mass in the first
12 weeks of training, and 0.4kg of lean mass
in the last 12 weeks of training, also for a net
increase of 0.7kg.
However, an interesting pattern emerges
when we look at the thigh lean soft tissue
mass accrual of “low responders” versus the
“high responders.” During the first 12 weeks
of training, which were used to determine re-
sponsiveness, the low responders gained very
little thigh lean soft tissue mass (0.1kg), while
the high responders gained 0.6kg. However,
during the last 12 weeks of training, the low
responders actually gained (nominally) more
thigh lean soft tissue mass (0.4kg) than the
high responders (0.2kg; p = 0.16). The high
responders did still wind up gaining significantly more thigh lean soft tissue mass over
the entire 24 weeks of training (0.8kg versus
0.5kg; p = 0.044), but the difference between
high and low responders narrowed considerably. You can see individual subject data
illustrating the different time course of lean
mass gains in Figure 1.
Criticisms and Statistical
Musings
My only significant criticism of the present
study was the decision to analyze high versus
11
low responsiveness to training using a median split. Ultimately, if you’re interested in
analyzing whether training responses during
the first 12 weeks of training are predictive
of training responses during the last 12 weeks
of training, regression analysis is far more informative (3). With a median split, you flatten out differences between individuals, such
that the worst responder is treated the same as
a subject in the 49th percentile, and the best
responder is treated the same as a subject in
the 51st percentile. Similarly, subjects near
the median who experienced very similar responses during the first 12 weeks of training
are treated as if they’re completely different.
I extracted the data in Figure 1 using WebPlotDigitizer, and wanted to see the relationship
between relative increases in thigh lean soft
tissue mass in the first 12 weeks, versus increases in the last 12 weeks of training. You
can see the results in Figure 2. Each data point
represents a single subject; their x-axis coordinate tells you their relative increases in thigh
12
lean soft tissue mass during the first 12 weeks
of training, and their y-axis coordinate tells
you their relative increase in thigh lean soft tissue mass during the last 12 weeks of training.
As you can see, there wasn’t much of a relationship between hypertrophy during the first
phase of training and hypertrophy during the
second phase of training (r = -0.28; p = 0.18).
This scatterplot is more informative than the
group-level analysis comparing mean responses at the bottom of Table 3. And, while
Figure 1 displays all of the same information
seen in this scatterplot, it’s still harder to directly interpret since the low responders and
high responders are split out onto different
axes. However, when we treat training responsiveness as a continuous variable (rather
than a binary variable), we can more clearly
see that lean mass accretion during the first
phase of training was poorly predictive of
lean mass accretion during the second phase
of training.
Interpretation
This is a study I’ve wanted to see for a long
time. My anecdotal observation is that some
people simply seem to respond better to
heavier training, and some people simply
seem to respond better to lighter training.
However, the controlled evidence to back
up that observation was shaky at best. There
were two studies that could be used to make
that case, but both of them had some obvious issues. First, a study by Beaven and colleagues ran subjects through four different
training protocols (3 sets of 5 reps at 85% of
1RM, 4 sets of 10 reps at 70% of 1RM, 5 sets
of 15 reps at 55% of 1RM, and 4 sets of 5
13
reps at 40% of 1RM), and tested the subjects’
acute salivary testosterone and cortisol responses (4). Subjects completed three weeks
of training with the protocol that elicited the
largest increase in testosterone:cortisol ratio,
and three weeks with the protocol that elicited the smallest increase (or largest decrease)
in testosterone:cortisol ratio. The researchers
found that subjects experienced a larger increase in body mass during the three weeks
when they performed the protocol that elicited the largest increase in testosterone:cortisol
ratio. However, the limitations in that study
should be obvious. The training intervention was really short (just three weeks with
each protocol), and it’s a stretch to assume
that increases in body mass necessarily equal
increases in muscle mass. Another study by
Jones and colleagues assigned subjects to
training protocols that were supposed to be
compatible with their genetics or incompatible with their genetics, using a proprietary algorithm (5). The subjects completed strength
endurance-based training or power-based
training. Overall, subjects experienced larger improvements in several measures of performance when training in a manner that was
compatible with their genetics. However,
that study contained a clear conflict of interest – the algorithm used to assign subjects to
different groups isn’t publicly available, and
an employee of the company that developed
the algorithm was one of the authors of the
study. Now, studies with conflicts of interest
shouldn’t be discounted out of hand, but it
helps for results to be independently validated by other research groups that lack those
same conflicts of interest. To the best of my
14
knowledge, the results of the study by Jones
and colleagues haven’t been replicated.
Furthermore, as we’ve covered in MASS
before, there’s quite a bit of evidence that
many training variables have a much smaller influence on hypertrophy than innate
differences in trainability. For example, a
study by Hammarström and colleagues (reviewed in MASS; 6) investigated the effects
of training volume (total sets performed) on
quad growth. Subjects completed six weekly sets of quad work with one leg, and 18
weekly sets with another leg. The researchers found that higher training volumes tended to result in more muscle growth, but that
innate differences in trainability influenced
hypertrophy far more than training volume.
In other words, someone who experiences
a lot of muscle growth with high training
volumes will probably experience a lot of
muscle growth with lower training volumes,
and someone who doesn’t experience much
growth with low training volumes probably
won’t experience that much more growth
with higher training volumes. A study by
Damas and colleagues (7 – also reviewed
in MASS) had similar findings when testing the effect of keeping training variables
consistent session-to-session versus varying
load, rest intervals, the muscle actions performed, and training volume. Subjects who
experienced a lot of growth following varied
training were also likely to experience a lot
of growth with more consistent training, and
subjects who experienced little growth with
varied training were unlikely to experience
way more or way less growth with consistent training (Figure 3).
Thus far, I’ve painted a pretty bleak picture
for folks who want to optimize their training. Maybe manipulating training variables
can improve your results slightly, but the
vast majority of your results are merely dictated by your innate trainability. However,
there’s a notable exception to this rule. Another study by Damas and colleagues tested
the effects of different training frequencies
(frequencies of 2, 3, and 5 times per week),
using a within-subject unilateral design (8).
Each training session employed the same
volume, such that a frequency of five times
per week coincided with 2.5-times more volume than a frequency of 2 times per week.
In that study, innate trainability didn’t seem
to matter quite as much. Some subjects grew
a ton with higher volumes and frequencies
(and not very much with lower volumes and
frequencies), and some grew a ton with lower
volumes and frequencies (and not very much
with higher volumes and frequencies). So,
while volume itself seems to matter less than
innate trainability when training frequencies are the same (6), a single individual can
achieve dramatically different results – either
better results or worse results – when pairing
higher or lower training volumes with higher
or lower training frequencies (Figure 4).
I’ve long suspected that relative training intensity was another variable that can influence individual results. Some people really seem to respond well to heavier training,
while other people swear by low-load, highrep training. However, while some studies
employing within-subject unilateral designs
have tested the effects of different relative
training intensities (9 – MASS review; 10
15
– MASS review), they haven’t reported individual subject data, which would be necessary to see if individual results do, in fact,
significantly differ when training at different
relative intensities.
ilarly effective when it came before moderate-load training and when it followed moderate-load training (and the same was true
with moderate-load training, preceding or
following a period of low-load training).
The present study helps fill that gap (1).
While it didn’t employ a within-subject unilateral design, the subjects did all complete a
relatively long period of both low-load training and moderate-load training. It’s possible
that the crossover design may have introduced some sequencing effects (i.e., perhaps
subjects respond differently to one training
style after a period of training with a different
training style), but that seems unlikely, since
group-level results of both moderate-load
and low-load training seemed unaffected by
which training style was performed first. In
other words, low-load training seemed sim-
Referring back to Figure 2, it doesn’t just
show individual results during the first twelve
weeks of training versus the last twelve
weeks of training. Since subjects changed
training styles at the midpoint of the study, it
also shows individual results following moderate-load training versus low-load training.
Figure 5 displays the same data in a slightly
different format (similar to Figure 1), showing that individual hypertrophy responses to
one loading scheme have virtually no bearing
on individual hypertrophy responses to the
other loading scheme.
16
This is an exciting finding, because it presents us with another clear option to try when
“normal” training isn’t producing particularly notable results in a particular trainee.
Most hypertrophy-focused training employs
a moderate set volume (5-10 sets per muscle group per session), a moderate intensity
(loads that let you complete 6-15 reps per
set), and a low-to-moderate frequency (training most muscle groups 1-3 times per week).
For most people, that produces pretty good
results. However, if that doesn’t work particularly well for someone, there aren’t a ton of
great troubleshooting recommendations that
we know are likely to produce substantially
different outcomes. There are some things
that might, on average, produce slightly better results (i.e., increasing training volume;
6). There are also plenty of options for which
within-subject variability is unquantified.
For example, we know that training to failure
and stopping a few reps shy of failure tend
to produce similar muscle growth, on average, but we don’t know if they tend to produce substantially different results within a
single trainee. If stopping a few reps shy of
failure doesn’t result in much growth for you,
are you likely to achieve meaningfully different results if you start training to failure?
We don’t know for sure. Ideally, we’d have a
long list of training variables that are known
to result in large within-individual differences in muscle growth. However, until now, the
Damas study which tested the effects of different volumes and frequencies provided us
with the only known set of variables that produce large within-subject differences in muscle growth (8). So, if “normal” hypertrophy
training wasn’t working, you could advise
someone to double (or halve) their weekly training volume and per-muscle training
frequency, and be reasonably confident that
such a recommendation would produce substantially different results…but that’s a pretty
dramatic recommendation. The present study
suggests that a less dramatic recommendation
could also be prudent: if sets of 10 aren’t cutting it for you, give sets of 30 a shot instead.
Before wrapping up, I want to tie up a few
loose ends.
First, the authors of the present study suggest
that their findings demonstrate that shifting
from moderate- to low-intensity training or
from low- to moderate-intensity training prevents (or at least delays) plateaus, and preserves training responsiveness over a longer
period of time (1). Some evidence suggests
that hypertrophy tends to slow down quite
a bit after about 12 weeks of training (11),
but subjects in the present study grew just as
much during the last 12 weeks of training as
the first 12 weeks of training. However, I’m
not sure I agree with the authors’ interpretation of their results. It appears to me that
changing training intensities only preserved
the same average rate of progress because
it dramatically improved the results of the
subjects who experienced minimal muscle
growth during the first 12 weeks of training.
If anything, changing training intensities may
have hindered the growth of subjects who experienced substantial muscle growth during
the first 12 weeks of training. In other words,
average results were similar during both 12week blocks of training because most of the
subjects completed one block of training they
responded well to, and one block of training
17
they didn’t respond well to; for about half of
the subjects, the first block of training was the
effective block, and for the other half of the
subjects, the second block of training was the
effective block. I think a better takeaway is
that you shouldn’t necessarily change training intensity every so often for its own sake;
rather, you should change training intensity
if it becomes clear that a particular relative
training intensity isn’t producing the results
you want, but you should stick with a particular relative training intensity as long as it’s
still working for you.
Second, this study could be interpreted as a
study comparing linear periodization versus
reverse linear periodization. As we discussed
recently, it appears that linear and reverse
linear periodization result in similar muscle
growth, on average (12). The present study
adds to that body of literature.
Third, the present study beautifully illustrates
a pitfall that’s pretty common in the evidence-based fitness community. When you
consume a lot of scientific literature, it’s easy
to fixate on average results, because most
statistical techniques are focused on testing
for differences between means. However,
averages often cover up a lot of underlying
variability. For example, imagine someone
asks you, “should I give low-load training a
shot? I’ve been doing sets of 8-12 for the past
six months, and I haven’t seen any results.”
It might be tempting to answer, “don’t waste
your time. Low-load and moderate-load
training produce similar muscle growth, so
if moderate-load training isn’t working for
you, low-load training probably won’t either.” While that answer would be based on a
kernel of truth (13), it would still be a pretty
bad answer. “Thing A and Thing B produce
similar results, on average,” is not the same
as, “Thing A and Thing B produce similar
results for all (or even most) individuals.”
When fielding questions or evaluating anecdotes, it’s important to keep that distinction
in mind.
Fourth, the present study helps illustrate one
of my problems with the concept of identifying high versus low responders to resistance
training. Ultimately, if someone is identified
as a “high responder,” that just means they
responded particularly well to one particular training program. Similarly, if someone
is identified as a “low responder,” that just
means they responded particularly poorly
to a particular training program. Over the
course of your training career, I’m sure you
can think of periods where you were making
rapid progress, and periods when your results were stagnating (or even regressing). If
a researcher took a snapshot of your training
at various points in your lifting career, they
might identify you as a high responder sometimes, and a low responder other times. Thus,
the label doesn’t seem particularly meaningful, because your innate ability to respond to
training didn’t change – you were the same biological organism the whole time (I assume).
In the present study, if the median split technique to identify high versus low responders
was repeated after the subjects completed all
24 weeks of training, a full third of the subjects would have switched groups (Table 4).
Four of the “high responders” (within the top
50% of responders) after 12 weeks of training would have been categorized as “low
18
responders” (within the bottom 50% of responders) after 24 weeks of training; similarly, four of the low responders after 12 weeks
of training would have been categorized as
high responders after 24 weeks of training.
After completing a single training program,
we really don’t know if a particular individual is a high or low responder to training in a
general sense. Furthermore, it’s worth noting
that virtually all subjects ended up accruing
a substantial amount of lean mass over the
course of the study. It’s not uncommon for
studies to report that approximately 1/3rd of
subjects are “nonresponders” – people who
either lose muscle, or experience gains that
fall below the limits of reliable detectability. In the present study, 7 out of 24 subjects
(29%) either had reductions in lean mass, or
increases smaller than 1.3% (the lower end
of reliable detectability for the DXA used for
lean mass measurements) after the first 12
weeks of training. However, after giving low
responders the opportunity to try a new training program for an additional 12 weeks, only
two subjects (8%) would have been classified as nonresponders after all 24 weeks of
training. With all of that in mind, I think most
classifications of “high responders,” “low responders,” and “nonresponders” are essentially bunk. At minimum, there’s not great
evidence that such a classification is particularly persistent or reliable, and the present
study provides evidence that such classifications are not persistent or reliable.
Finally, just to address a potential criticism
of this article, I’m sure some readers may
be concerned that the subjects of the present
19
study were untrained postmenopausal women. First, I don’t think that’s inherently a
limitation; postmenopausal women do exist,
after all, and some of them read MASS. So,
even if the results of this study don’t generalize beyond that population, the results are
certainly still valuable. Second, and more importantly, I do suspect that the results of the
present study will generalize to other populations. In studies on older adults, I’m primarily
concerned about three things when assessing
whether the results are likely to generalize:
1) are the subjects healthy enough to handle
a robust training stimulus, 2) are the results
broadly in line with studies on other populations, and 3) are the subjects so old that they
simply don’t experience a robust training response anymore. This study ticks all three
boxes. First, the subjects were completing 18
sets of quad training to failure (or near failure) per week, so they were clearly capable
of handling some pretty challenging training.
Second, it’s very well-established in a variety of populations (including both trained
and untrained lifters) that moderate-load and
low-load training produce similar hypertrophy, on average. That was the main point of
intersection between the present study and
the broader literature, and the results of the
present study were perfectly in line with the
broader literature. Third, the subjects clearly
experienced a robust hypertrophy response:
thigh lean soft tissue mass increased by an
average of 0.7kg (7.6%) over 24 weeks of
training. A 2020 meta-analysis found that
whole-body lean mass increases by about
1.5kg (about 2.5%) after about 10 weeks of
training, on average, in subjects between 18
and 40 years old (14). You can’t make a strict
apples-to-apples comparison between those
two figures – thigh lean soft tissue mass isn’t
identical to whole-body lean mass, and 24
weeks is much longer than 10 weeks – but
they’re certainly in the same general ballpark. Thus, while I’d certainly like to see
these results replicated in other populations,
I’m not too terribly concerned about a lack of
generalizability.
Next Steps
I’d love to see the present results replicated
in other populations. I’d also love to see this
same study design applied to other training
approaches. As just one of many potential
possibilities, I’d love to see a study investigating whether people truly get desensitized
to training volume over time. There’s a popular idea proposing that if your training volume creeps too high, you’ll be unable to continue building muscle if you try shifting to
a lower-volume approach. A crossover study
could investigate that claim. One group could
perform 12 weeks of high-volume training
(maybe 20 sets per muscle group per week),
followed by 12 weeks of lower-volume training (maybe 10 sets per muscle group per
week). Another group would complete the
same blocks of training in the opposite order (lower volume followed by higher volume). The study could investigate total muscle growth over the full 24 weeks of training,
and also examine whether the high-volumeto-low-volume group was unable to continue building muscle during the lower volume
block of training.
20
APPLICATION AND TAKEAWAYS
If moderate-load training isn’t helping you build muscle at the rate you’d like, lowload training might just help you get over your plateau. Even though low-load
and moderate-load training are similarly effective at building muscle, on average,
that doesn’t necessarily mean that your individual results will be similar with both
approaches. The present study demonstrates that how well you respond to training
at one intensity isn’t predictive of how well you’ll respond to training at a very
different intensity.
21
References
1. Carneiro MAS, de Oliveira Júnior GN, Sousa JFR, Martins FM, Santagnello SB, Souza
MVC, Orsatti FL. Different load intensity transition schemes to avoid plateau and noresponse in lean body mass gain in postmenopausal women. Sport Sci Health. 2022.
https://doi.org/10.1007/s11332-022-00907-2
2. During the very first week of training, subjects only completed one set per exercise. They
completed two sets per exercise in week 2. They completed three sets per exercise in the
subsequent 22 weeks of training.
3. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006
May 6;332(7549):1080. doi: 10.1136/bmj.332.7549.1080. PMID: 16675816; PMCID:
PMC1458573.
4. Beaven CM, Cook CJ, Gill ND. Significant strength gains observed in rugby players after
specific resistance exercise protocols based on individual salivary testosterone responses.
J Strength Cond Res. 2008 Mar;22(2):419-25. doi: 10.1519/JSC.0b013e31816357d4.
PMID: 18550956.
5. Jones N, Kiely J, Suraci B, Collins DJ, de Lorenzo D, Pickering C, Grimaldi KA.
A genetic-based algorithm for personalized resistance training. Biol Sport. 2016
Jun;33(2):117-26. doi: 10.5604/20831862.1198210. Epub 2016 Apr 1. PMID: 27274104;
PMCID: PMC4885623.
6. Hammarström D, Øfsteng S, Koll L, Hanestadhaugen M, Hollan I, Apró W, Whist JE,
Blomstrand E, Rønnestad BR, Ellefsen S. Benefits of higher resistance-training volume
are related to ribosome biogenesis. J Physiol. 2020 Feb;598(3):543-565. doi: 10.1113/
JP278455. Epub 2020 Jan 15. PMID: 31813190.
7. Damas F, Angleri V, Phillips SM, Witard OC, Ugrinowitsch C, Santanielo N, Soligon
SD, Costa LAR, Lixandrão ME, Conceição MS, Libardi CA. Myofibrillar protein
synthesis and muscle hypertrophy individualized responses to systematically changing
resistance training variables in trained young men. J Appl Physiol (1985). 2019 Sep
1;127(3):806-815. doi: 10.1152/japplphysiol.00350.2019. Epub 2019 Jul 3. PMID:
31268828.
8. Damas F, Barcelos C, Nóbrega SR, Ugrinowitsch C, Lixandrão ME, Santos LMED,
Conceição MS, Vechin FC, Libardi CA. Individual Muscle Hypertrophy and Strength
Responses to High vs. Low Resistance Training Frequencies. J Strength Cond Res. 2019
Apr;33(4):897-901. doi: 10.1519/JSC.0000000000002864. PMID: 30289872.
9. Lasevicius T, Ugrinowitsch C, Schoenfeld BJ, Roschel H, Tavares LD, De Souza EO,
Laurentino G, Tricoli V. Effects of different intensities of resistance training with equated
22
volume load on muscle strength and hypertrophy. Eur J Sport Sci. 2018 Jul;18(6):772780. doi: 10.1080/17461391.2018.1450898. Epub 2018 Mar 22. PMID: 29564973.
10. Nóbrega SR, Ugrinowitsch C, Pintanel L, Barcelos C, Libardi CA. Effect of Resistance
Training to Muscle Failure vs. Volitional Interruption at High- and Low-Intensities on
Muscle Mass and Strength. J Strength Cond Res. 2018 Jan;32(1):162-169. doi: 10.1519/
JSC.0000000000001787. PMID: 29189407.
11. Counts BR, Buckner SL, Mouser JG, Dankel SJ, Jessee MB, Mattocks KT, Loenneke JP.
Muscle growth: To infinity and beyond? Muscle Nerve. 2017 Dec;56(6):1022-1030. doi:
10.1002/mus.25696. Epub 2017 Jun 11. PMID: 28543604.
12. 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.
13. Schoenfeld BJ, Grgic J, Ogborn D, Krieger JW. Strength and Hypertrophy Adaptations
Between Low- vs. High-Load Resistance Training: A Systematic Review and
Meta-analysis. J Strength Cond Res. 2017 Dec;31(12):3508-3523. doi: 10.1519/
JSC.0000000000002200. PMID: 28834797.
14. Benito PJ, Cupeiro R, Ramos-Campo DJ, Alcaraz PE, Rubio-Arias JÁ. A Systematic
Review with Meta-Analysis of the Effect of Resistance Training on Whole-Body
Muscle Growth in Healthy Adult Males. Int J Environ Res Public Health. 2020 Feb
17;17(4):1285. doi: 10.3390/ijerph17041285. PMID: 32079265; PMCID: PMC7068252.
15. The actual term used in the study was “thigh free-bone LBM.” I’m pretty sure they meant
lean soft tissue mass of the thigh (all of the lean mass of the thigh, minus bone mass –
lean soft tissue mass is the most common term for this), but I’m not 100% positive.
█
23
Study Reviewed: The Effect of Rest Interval Length on Upper and Lower Body Exercise in
Resistance-Trained Females. Millender et al. (2022)
Everything You Need to Know
About Rest Intervals
BY MICHAEL C. ZOURDOS
Over the past decade, longer rest intervals have prevailed
over shorter rest intervals in both research and practice for
hypertrophy and strength. However, most of the rest interval data
are from male lifters. Thus, a rest interval study in female lifters is
a welcome addition to the literature.
24
KEY POINTS
1. In a crossover design, researchers examined the effects of rest interval length
on rep performance and blood lactate responses. The lifters performed four
sets to failure at 75% of their 10RM on the chest press and leg press with one
and three minutes of interset rest.
2. Findings showed that the lifters better maintained rep performance and, thus,
performed more total volume on the chest press (+33.01%) and leg press
(+33.60%) in the three-minute than in the one-minute rest condition. There was
no significant difference between conditions for the acute blood lactate response.
3. This study shows that longer interset rest leads to more volume than set-equated
short rest interval training to failure. Overall, longer rest intervals are preferable to
maximize short term volume and long term strength and hypertrophy.
F
or many years, short rest intervals (i.e.,
30-90 seconds) were recommended to
maximize muscle hypertrophy due to
the substantial increase in anabolic hormones
associated with short interset rest (i.e., the
hormone hypothesis). However, a 2009 study
from Buresh et al (2) showed no significant
difference in muscle growth with 2.5- versus 1-minute rest intervals, but increases in
arm cross-sectional area did tend to favor the
2.5-minute group. Nonetheless, the hormone
hypothesis persisted, but, in 2016, a study from
Schoenfeld et al (3) rocked hormone hypothesis defenders to their core. Schoenfeld et al
found that, over eight weeks, trained men who
rested three minutes between sets experienced
greater lower body muscle growth and strength
than men who rested for one minute between
sets. The primary mechanism by which longer
rest yields greater muscle growth is that longer
interset rest allows lifters to maintain training
loads better from set-to-set than shorter rest,
thereby enabling more volume when sets are
equated between short and long rest training.
Additionally, many acute, crossover-design
studies (4, 5, 6, 7, 8, 9) demonstrate that lifters can perform more volume with longer than
shorter rest intervals. Although the data seem
clear on this topic, all but one (10) of the acute
studies examining rest interval length and rep
performance in young individuals were conducted in male populations. Therefore, the
presently reviewed study from Millender et
al (1) on trained women is a welcome addition to the literature. Millender had 14 women perform four sets to failure at 75% of their
10-repetition maximum (RM) on the chest
press and leg press, while resting for either one
or three minutes between sets. The researchers assessed total volume performed and the
acute blood lactate response. Unsurprisingly,
the women performed significantly more volume on both the chest press (+33.01%) and
leg press (+33.60%) in the three-minute rest
condition; however, the blood lactate response
did not differ between conditions. These findings confirm that set-to-set rep performance is
better-maintained with longer rest intervals,
25
leading to more total volume when sets are
equated between short and long rest training.
Therefore, the simple interpretation is that lifters should use longer rest intervals of about
three minutes. Although that simple interpretation is generally good advice, it’s a bit simplistic. Therefore, this article will aim to:
compare volume performance and the acute
blood lactate response to four sets to failure with 75% of a 10RM load on the chest
press and leg press between short (one-minute) and long (three-minute) rest intervals in
trained women.
1. Review the data examining the acute studies on rest interval length and volume performance.
The researchers hypothesized that the rate
of decline in reps would be uniform from
set-to-set in both conditions, and total volume performance would be greater in the
three-minute rest condition. No hypothesis
was provided regarding the blood lactate response. However, gauging the introduction’s
tone, it seems that the researchers expected a
greater acute lactate response in the one-minute rest interval condition.
2. Review the longitudinal data on rest interval length, hypertrophy, and strength
outcomes.
3. Discuss how volume and long term outcomes play out when sets are equated between short and long rest interval training
to equate for total training session time.
4. Examine the rate of decline in reps from setto-set during both failure and non-failure
training with short and long rest intervals
and if this differs between men and women.
5. Determine appropriate rest intervals across
different exercises and programming strategies.
Purpose and Hypotheses
Purpose
The purpose of the reviewed study was to
Hypotheses Subjects and Methods
Subjects
14 women who had trained at least two times
per week for at least one year completed the
study. Additional subject details are in Table 1.
Study Protocol
All subjects completed this study over three
days. On day one, subjects performed chest
press and leg press 10RM testing. Subjects
returned to the laboratory to complete two
different experimental conditions; however,
26
the researchers did not specify the time between days in the study. In both conditions,
subjects completed four sets to failure at 75%
of 10RM (about 55% of 1RM) on the chest
press, then, 30 minutes later, completed four
sets to failure on the leg press also at 75% of
10RM. The only difference between conditions is that subjects rested three minutes between sets in one condition and one-minute
between sets in the other condition.
Outcome measures included total volume
(sets × reps × load), reps per set, and the
acute blood lactate response. Lastly, all reps
were performed with a two second eccentric
and a one second concentric.
Findings
Lifters performed more volume on both the
chest press (+33.01%) and leg press (+33.60%)
in the three-minute rest condition (Figure 2).
Further, lifters performed more reps on sets
two, three, and four in the three-minute condition than in the one-minute condition (Table
2). The blood lactate response was not significantly different between conditions. Lastly,
the researchers did not report means and standard deviations. Thus, the volume percentages
differences above and data in Table 2 are estimations extracted via WebPlotDigitizer from
graphs in the published paper.
27
Criticisms and Statistical
Musings
without reaching momentary failure, it’s possible that lifters held back a little bit on the
penultimate set to prepare for the last set.
This study was pretty straightforward, but I’d
like to bring up two minor points. First, there
was minimal decrease in the number of reps
performed from set three to set four in both
conditions for both exercises. In fact, in the
one-minute rest condition, there was a nominal increase of 1.33 reps, on average, from
set three to set four. This finding is surprising since it’s expected that there would be
a decrease in reps performed each set when
training is performed to failure, and the same
load is maintained. It’s hard to imagine performing sets to failure and performing slightly more reps on set four than on set three, especially if only resting one minute between
sets. One explanation for this could be related
to how the researchers defined failure: “failure was achieved if the subject voluntarily
terminated the set, could not complete the
concentric phase of the lift, broke their form
to complete the concentric phase of the lift,
or could not maintain the 2:1 cadence for two
consecutive repetitions.” Therefore, since
lifters could terminate sets voluntarily, even
Second, the researchers assessed effort-based
rating of perceived exertion after each set
using the OMNI scale (11). The researchers
noted that RPE after every set was between
8-10 (8 RPE was anchored to the descriptor
“hard” while 10 RPE was anchored to “hardest exertion possible”). Since some sets were
terminated at an 8 or 9 RPE, it is likely that
some sets weren’t performed to momentary
failure. Sets ending with an 8 RPE support
the first point above that some lifters may
have held back on the penultimate set. Additionally, the researchers did not statistically analyze the RPE values. Comparing the
RPE values would have determined if the
lifters perceived longer rest with more volume (three-minute condition) or shorter rest
with less volume (one-minute condition) to
be more difficult.
Interpretation
The reviewed study from Millender et al (1)
is important to discuss because most acute
28
and longitudinal data pertaining to rest interval length come from male lifters. Further,
although we have covered this topic before
in MASS (one, two) there are some updates
and nuances to discuss. This interpretation
will cover the acute and longitudinal data to
date and discuss how the literature shapes up
for both women and men. Additionally, since
most studies comparing rest interval lengths
use failure training we’ll examine the rate of
repetition decline from set-to-set across different exercises and loads in rest interval studies.
There is only one previous acute study, to my
knowledge, examining rest interval length
for rep performance in young women (10)
and one acute study for rep performance in
older women (12). Additionally, one longitudinal study examined rest interval length in a
mixed-sex cohort (13) and another longitudinal study solely in women (14). The present
study was in concert with the existing body
of literature on men (4, 5, 6, 7, 8, 9), showing that performance from set-to-set is better
maintained with longer rest intervals when
29
training to failure. Table 3 summarizes acute
crossover design interset rest studies comparing longer versus shorter rest intervals
for rep performance. A portion of Table 3
(4, 5, 6, 7, 8, 9) was originally printed in Volume 4 Issue 5, and it has been significantly
updated (1, 10, 12, 13) for this article.
Table 3 paints a clear picture. That is, in both
sexes, across different loads and reps ranges
(3RM to >20RM), and on various exercises
(squat, bench, chest press, leg press, flyes),
performance is better maintained with longer
interset rest. Therefore, if the number of sets
is equated between rest interval lengths, more
total volume will be performed with long rest
intervals. Although this notion holds in both
men and women, data from Ratamess (10)
suggests that rep performance may be better maintained in women than in men during
bench press sets at 75% of 1RM (Table 4).
In Table 4, it appears that women had barely any decline in rep performance with three
minutes rest, only lost one rep from sets 1-3
with two minutes rest, and two reps from
sets 1-3 with one minute rest. On the other
hand, male lifters from sets 1-3 lost ~2 reps,
~4 reps, and ~6 reps with three, two, and one
minute of interset rest. Importantly, participants were told to perform “up to” 10 reps per
set. Thus, the first set was likely not to failure for some participants, probably masking
a more exacerbated decline in the number of
reps performed from set-to-set. It’s also possible that women, on average, can perform
more reps at a given relative load than men
(at least at loads <70% of 1RM), which, in
MEN HAD A LARGER
DECREASE IN REPS
FROM SET-TO-SET THAN
WOMEN AT ALL DIFFERENT
REST INTERVALS.
30
part, accounts for the higher number of total
reps. However, any way you want to slice it,
men had a larger decrease in reps from set-toset than women at all different rest intervals.
The next set of findings from Ratamess was
published in a different paper (15) but was
part of the same study referenced in Table 4.
Ratamess also had the women and men perform three sets of “up to” 10 reps at 75% of
1RM on the barbell incline bench press, barbell shoulder press, and bent over row following the bench press. In all conditions, subjects
rested for two minutes between sets of assistance movements. The take home message for
the assistance work is that both men and women tended to perform fewer reps on the first
set of incline bench and shoulder press in the
one-minute than in the three-minute rest condition, presumably due to lingering fatigue.
However, the largest declines in rep performance from sets 1-3 occurred for women in
the three-minute condition on the incline bench
and for men in both the two- and three-minute
conditions on the shoulder press. Overall, the
data from Ratamess shows that women may
be more resistant to fatigue than men during
sets to failure (or close to failure). However,
when a primary compound movement (bench
press) is performed first, the rest interval for
that primary compound movement is unlikely
to affect total volume performance on subsequent assistance exercises.
In a previous study from Willardson and Burkett (6), trained men performed four sets to
failure with an 8RM load on both the squat
and bench press. The rate of decline in reps
from Williardson and Burkett can be seen in
Table 5.
In Willardson and Burkett, rep maintenance
was again time-dependent; both the absolute
(number of reps) and relative (%) decrease in
rep performance from set-to-set was greater
in the squat than the bench press at all interset
rest intervals. Here’s a summary of the rate
of decline in rep performance from set-to-set
with various rest intervals in men and women
based upon the data presented above:
31
1. Rep performance is better maintained on
the bench press than the squat when training to failure.
2. Women have slightly lower decreases in
reps from set-to-set than men across all rest
intervals with moderate reps (i.e., 8-10).
3. Men lose about 2-3 reps per set, with
1-minute of interset rest and 1-3 reps per
set with 2-3 minutes of interset rest with
moderate reps.
4. Women lose about 1-2 reps per set with
1-minute of interset rest at moderate loads.
5. Women lose, on average, <1 rep per set
when resting ≥ 2 minutes.
I should stress that the decline in rep performance is likely individual and exercise-dependent; thus, the above take homes are not
blanket statements to be applied across the
board. Also, the study from Ratamess likely
did not cause most women to train to failure
32
on the first set, and some likely did not reach
failure on the second set, meaning the rate of
rep decline is higher than observed.
I also don’t find the lack of group differences for blood lactate in the presently reviewed
study especially important. In Hill-Haas et
al (14), 20-second rest intervals did lead to
a greater acute blood lactate response than
80-second rest intervals. However, others
comparing the acute blood lactate response
between one and three minutes (16), and 30
seconds, one minute, and two minutes (17 MASS Review) of interset rest have shown
no significant difference in the blood lactate
response, despite subjects performing more
volume in the longer rest interval conditions.
Most importantly, the one-minute rest condition in the presently reviewed study still
performed fewer reps despite a lack of difference in the blood lactate response between
conditions.
Although some studies show no difference
between short and long rest intervals for hypertrophy (16, 18) and strength (13, 16, 18),
others do favor long rest intervals for hypertrophy (2, 3) or strength (14). Similar to the
acute literature, the data are mostly relegated
to men. Studies from Longo et al. (13 - MASS
Review) and Hill-Hass (14) enrolled women,
but these women were not resistance trained.
A summary of these longitudinal studies is in
Table 6.
With the exception of one study on older
men (19), Table 6 shows that the longitudinal data mostly tracks the acute data. In other
words, some studies favor longer rest intervals for strength and hypertrophy, and some
are neutral. Regarding studies in women,
Hill-Haas showed that longer rest intervals
led to greater strength gain. However, these
longer rest intervals in Hill-Haas were only
80 seconds compared to a shorter interval of
20 seconds. This comparison still shows the
concept of longer rest intervals allowing the
maintenance of performance from set-to-set
and better long term outcomes. The Longo
study used a mixed-sex cohort, and was the
first study to equate for volume between long
and short rest by adding sets to the short rest
interval protocol. In practice, the purpose of
taking short instead of long rest and performing more sets would be to save total training
time. However, as Greg previously illustrated, this strategy doesn’t save that much time.
Short rest intervals still have their place when
true time-efficient strategies, such as restpause training and agonist/antagonist supersets, are used. Further, not all exercises need
the same rest intervals. Three to five-minute,
or even longer, rest intervals may be used on
major movements (i.e., squat, bench press,
and deadlift), but are hardly ever necessary
on some assistance movements (i.e., biceps
curls, triceps extensions, lateral raises).
Lastly, while recommendations such as 3-5+
minutes of interset rest for major movements, and 2-3-minute rest intervals for most
assistance movements, are reasonable suggestions, I don’t think lifters need to have
their eyes glued to a stopwatch between sets.
We’ve reviewed studies in MASS showing
the efficacy of self-selected rest intervals.
Specifically, Ibbot et (20 - MASS Review)
observed that, when resting intuitively,
trained men selected, on average, 4-5-minute
33
rest intervals and completed nearly 100% of
the volume when prescribed 5 × 5 at a 5RM
on squats. Simao et al (21 - MASS Review)
found that trained men selected rest intervals
of ~90-133 seconds across various exercises to failure at 75% of 1RM over an 8-week
training study. Simao also had another group
rest exactly 75 seconds between all sets, and
found that the self-selected group performed
more reps throughout the training study. Additionally, de Salles et al (22) reported that,
when performing three sets to failure at 75%
of 1RM on the squat, bench press, leg press,
and biceps curl, subjects performed a similar number of total repetitions with two minutes of interset rest versus a self-selected rest
interval. The average self-selected rest intervals from de Salles were: 104.74 (squat),
117.07 seconds (leg press), 115.84 seconds
(bench press), and 102.67 seconds (biceps
curl). Overall, I think it’s a good idea to know
how long you’ve generally been resting, but
staring at a clock isn’t necessary, and you’ll
probably know when you’re good to go.
IT’S A GOOD IDEA TO
KNOW HOW LONG YOU’VE
GENERALLY BEEN RESTING,
BUT STARING AT A CLOCK
ISN’T NECESSARY, AND
YOU’LL PROBABLY KNOW
WHEN YOU’RE GOOD TO GO.
Next Steps
I’d like to see a study comparing shorter
(two-minute) versus longer (five-minute) rest
intervals with high volume non-failure training. For example, researchers could split lifters
into two groups using the same RIR prescription for 10 weeks (or another longitudinal time
frame) but different rest intervals. Specifically, both groups could train sets to a 1-3 RIR,
but one group could take two minutes rest, and
the other group could take five minutes of interset rest. Researchers could measure muscle
size and strength before and after the study.
The overarching purpose of the proposed
study would be to see if the longer rest interval
group would maintain a higher load and more
volume, and if that advantage led to enhanced
strength and hypertrophy.
Of course, other rest interval comparisons
could be made, such as one versus three minutes, or a wider comparison of one versus
five minutes. While I think either of these
comparisons (1 vs. 3 minutes or 1 vs. 5 minutes) would lead to greater volume and long
term adaptations with the longer rest intervals, I don’t think many trained lifters take
only one minute between sets on the squat or
bench press. Therefore, I’d opt for the more
ecologically valid comparison of two versus
five minutes.
34
APPLICATION AND TAKEAWAYS
1. Millender et al (1) found that lifters who performed four sets to failure with 75%
of their 10RM (~55% of 1RM) on the chest press and leg press better maintained
rep performance from set-to-set with three versus one minute of interset rest.
2. When the number of sets is equated, and sets are taken to failure, longer rest
intervals enhance volume performance, and women, on average, tend to maintain
rep performance from set-to-set better than men. Also, longitudinal studies
tend to show that longer rest intervals lead to greater hypertrophic and strength
outcomes than shorter rest intervals when the number of sets are equated.
3. When comparing two different rest interval lengths (i.e., three versus one minute),
choosing the longer interval for both acute rep performance and long-term
outcomes is preferable. However, there is a point of diminishing returns for
rest interval length. Further, short rest intervals still have their place in various
programming strategies, such as rest-pause and agonist-antagonist superset
training.
35
References
1. Millender DJ, Mang ZA, Beam JR, Realzola RA, Kravitz L. The Effect of Rest
Interval Length on Upper and Lower Body Exercises in Resistance-Trained Females.
International Journal of Exercise Science. 2021;14(7):1178-91.
2. Buresh R, Berg K, French J. The effect of resistive exercise rest interval on hormonal
response, strength, and hypertrophy with training. The Journal of Strength &
Conditioning Research. 2009 Jan 1;23(1):62-71.
3. Schoenfeld BJ, Pope ZK, Benik FM, Hester GM, Sellers J, Nooner JL, Schnaiter JA,
Bond-Williams KE, Carter AS, Ross CL, Just BL. Longer inter-set rest periods enhance
muscle strength and hypertrophy in resistance-trained men. Journal of strength and
conditioning research. 2016 Jul 1;30(7):1805-12.
4. Hernandez DJ, Healy S, Giacomini ML, Kwon YS. Effect of Rest Interval Duration on
the Volume Completed During a High-Intensity Bench Press Exercise. The Journal of
Strength and Conditioning Research. 2020 April.
5. Senna GW, Figueiredo T, Scudese E, Baffi M, Carneiro F, Moraes E, Miranda H, Simão
R. Influence of Different Rest Interval Lengths in Multi-Joint and Single-Joint Exercises
on Repetition Performance, Perceived Exertion, and Blood Lactate. Journal of Exercise
Physiology Online. 2012 Oct 1;15(5).
6. Willardson JM, Burkett LN. A comparison of 3 different rest intervals on the exercise
volume completed during a workout. The Journal of Strength & Conditioning Research.
2005 Feb 1;19(1):23-6.
7. Tibana RA, Vieira DC, Tajra V, Bottaro M, Willardson JM, de Salles BF, Prestes
J. Effects of rest interval length on Smith machine bench press performance and
perceived exertion in trained men. Perceptual and motor skills. 2013 Dec;117(3):682-95.
8. Willardson JM, Burkett LN. The effect of rest interval length on the sustainability of
squat and bench press repetitions. Journal of Strength and Conditioning Research. 2006
May 1;20(2):400.
9. Scudese E, Willardson JM, Simão R, Senna G, de Salles BF, Miranda H. The effect of
rest interval length on repetition consistency and perceived exertion during near maximal
loaded bench press sets. The Journal of Strength & Conditioning Research. 2015 Nov
1;29(11):3079-83.
10. Ratamess NA, Chiarello CM, Sacco AJ, Hoffman JR, Faigenbaum AD, Ross RE, Kang
J. The effects of rest interval length on acute bench press performance: The influence of
gender and muscle strength. The Journal of Strength & Conditioning Research. 2012 Jul
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1;26(7):1817-26.
11. Robertson RJ, Goss FL, Rutkowski JA, Lenz BR, Dixon C, Timmer J, Frazee KR,
Dube J, Andreacci JO. Concurrent validation of the OMNI perceived exertion scale for
resistance exercise. Medicine and science in sports and exercise. 2003 Feb 1;35(2):33341.
12. Jambassi Filho JC, Gurjão AL, Prado AK, Gallo LH, Gobbi S. Acute effects of different
rest intervals between sets of resistance exercise on neuromuscular fatigue in trained
older women. The Journal of Strength & Conditioning Research. 2020 Aug 1;34(8):223540.
13. Longo AR, Silva-Batista C, Pedroso K, de Salles Painelli V, Lasevicius T, Schoenfeld
BJ, Aihara AY, de Almeida Peres B, Tricoli V, Teixeira EL. Volume load rather than
resting interval influences muscle hypertrophy during high-intensity resistance training. J
Strength Cond Res. 2020 Jun 10.
14. Hill-Haas S, Bishop D, Dawson B, Goodman C, Edge J. Effects of rest interval during
high-repetition resistance training on strength, aerobic fitness, and repeated-sprint ability.
Journal of sports sciences. 2007 Apr 1;25(6):619-28.
15. Ratamess NA, Chiarello CM, Sacco AJ, Hoffman JR, Faigenbaum AD, Ross RE, Kang J.
The effects of rest interval length manipulation of the first upper-body resistance exercise
in sequence on acute performance of subsequent exercises in men and women. The
Journal of Strength & Conditioning Research. 2012 Nov 1;26(11):2929-38.
16. Fink JE, Schoenfeld BJ, Kikuchi N, Nakazato K. Acute and long-term responses to
different rest intervals in low-load resistance training. International journal of sports
medicine. 2017 Feb;38(02):118-24.
17. Lopes CR, Crisp AH, Schoenfeld B, Ramos M, Germano MD, Verlengia R, da Mota GR,
Marchetti PH, Aoki MS. Effect of rest interval length between sets on total load lifted
and blood lactate response during total-body resistance exercise session. Asian Journal of
Sports Medicine. 2018 Jun 1;9(2).
18. Ahtiainen JP, Pakarinen A, Alen M, Kraemer WJ, Häkkinen K. Short vs. long rest
period between the sets in hypertrophic resistance training: influence on muscle strength,
size, and hormonal adaptations in trained men. The Journal of Strength & Conditioning
Research. 2005 Aug 1;19(3):572-82.
19. Villanueva MG, Lane CJ, Schroeder ET. Short rest interval lengths between sets
optimally enhance body composition and performance with 8 weeks of strength
resistance training in older men. European journal of applied physiology. 2015
Feb;115(2):295-308.
37
20. Ibbott P, Ball N, Welvaert M, Thompson KG. Variability and Impact of Self-Selected
Interset Rest Periods During Experienced Strength Training. Perceptual and motor skills.
2019 Jun;126(3):546-58.
21. Simão R, Polito M, de Salles BF, Marinho DA, Garrido ND, Junior ER, Willardson JM.
Acute and Long-Term Comparison of Fixed vs. Self-Selected Rest Interval Between Sets
on Upper-Body Strength. The Journal of Strength & Conditioning Research. 2022 Feb
1;36(2):540-4.
22. De Salles BF, Polito MD, Goessler KF, Mannarino P, Matta TT, Simão R. Effects of
fixed vs. self-suggested rest between sets in upper and lower body exercises p
█
38
Study Reviewed: Changes in Intra-to-Extra-Cellular Water Ratio and Bioelectrical Parameters
from Day-Before to Day-Of Competition in Bodybuilders: A Pilot Study. Nunes et al. (2022)
Can Bodybuilders Change Their
Intra-to-Extracellular Water Ratio
During Peak Week?
BY ERIC HELMS
More research is steadily published examining the “peak week”
methods bodybuilders use in attempts to acutely enhance stage
appearance. This study assesses a potential mechanism behind
many peaking methods: manipulating one’s intra-to-extracellular
water ratio.
39
KEY POINTS
1. Bodybuilders acutely manipulate nutrition in the days prior to and day of getting
on stage in an attempt to enhance appearance. One purported mechanism of
enhancing appearance is increasing the intra-to-extracellular water ratio.
2. The authors of this study evaluated this claim by measuring changes in intrato-extracellular water ratio with bioelectrical impedance as well as upper arm,
waist, and thigh circumference among 11 bodybuilders the day prior to, and
day of competition.
3. Intra-to-extracellular water ratio likely increased, as did upper arm
circumference, while waist circumference decreased. However, we can’t
attribute specific peaking practices to these outcomes nor directly confirm that
these changes enhanced appearance.
T
he present study (1) is the latest in
a series of recent publications investigating the methods physique athletes use in the days prior to and on the day
of competition to enhance appearance. I’ve
covered this topic multiple times in MASS.
In Volume 1, I made a video on the limited research and the physiological principles
that can be applied to peaking. In Volume
3, I reviewed a survey that comprehensively investigated the peaking methods of 81
natural physique athletes in the UK (2), and
in the next issue we were fortunate enough
to have the authors of that survey provide
their thoughts on the study’s findings as bonus content. Most recently, in Volume 4, I
reviewed a quasi-experimental study that
compared anthropometric differences and
differences in photo silhouette scores from
bodybuilding judges between competitors
who practiced carbohydrate loading, versus
those who did not (3). I’ve been intrigued as
this body of evidence moves from theoretical, to observational, to quasi-experimental,
and this latest study steps sideways to look
at a potential mechanism of action.
For background, many peaking strategies are
purported to increase the intra-to-extracellular
water ratio; supposedly reducing subcutaneous water and increasing muscle-cell hydration, theoretically enhancing appearance. To
investigate this claim, the present study evaluated changes in upper arm, thigh, and waist
circumference as well as the intra-to-extracellular water ratio with single-frequency bioelectrical impedance in 11 male bodybuilders
from the day prior to the day of competition.
Indeed, compared to the day prior, the bodybuilders experienced a significant decrease
in waist circumference, total body water, and
extracellular water, and an increase in upper
arm circumference and intracellular water,
resulting in an increased intra-to-extracellular water ratio on the day of competition.
In this review I’ll discuss the limitations of
these findings, what they do and don’t tell us,
and what the next steps should be to advance
peaking research.
40
Purpose and Research
Questions
Purpose
The authors of the present study “aimed to
evaluate changes in body water fractions
and BIA [bioelectrical impedance] parameters from the day-before to day-of competition in bodybuilders.”
Hypothesis
The authors “hypothesized that TBW [total
body water] and ECW [extracellular water] would decrease, ICW [intracellular water] would increase, and R and Xc [metrics
inversely related to TBW and representing
muscle cell membrane integrity, quality, and
density, respectively] would increase.”
Subjects and Methods
Subjects
Out of 50 male and female athletes invited to
participate in this study, 11 male bodybuilders
volunteered (more on the relevance of this in
the discussion). These bodybuilders had the
following demographic characteristics: age =
28.8 ± 4.1 years (range: 22–35); weight = 80.5
± 7.9 kg; height = 172.0 ± 7.2 cm; body mass
index = 27.2 ± 1.9 kg/m2; prior competitions =
6 ± 4. One bodybuilder competed in the junior
category (≤ 23 years-old, ≥ 75 kg) while the
remaining 10 competed in the open category
of both the men’s physique and bodybuilding
divisions across various weight classes (men’s
physique = 5; men’s bodybuilding = 5).
Study Design and Assessments
This study was conducted over two days, the
day prior to competition and the day of competition. The participants were amateur competitors at a state-level bodybuilding competition sanctioned by the International Federation
of Bodybuilding and Fitness (IFBB). The day
prior to competition, the participants reported
to the contest venue for their official weigh-ins,
and then the researchers measured their waist,
right-side upper-arm, and mid-thigh circumferences, and obtained a self-reported height.
Following anthropometry, single frequency
phase-sensitive bioelectrical impedance was
used to assess foot-to-hand body water metrics following standardized lab procedures to
ensure accuracy. These measurements were
then repeated the following day, the day of
competition, before the “warm-up” (I assume
the authors meant before pumping up) prior to
prejudging.
All 11 competitors agreed to take part in the
bioimpedance analysis, but only eight agreed
to have their circumferences measured. Notably, while the authors reported low variability using their standardized lab techniques
for bioimpedance, these measurements were
carried out after the athletes had fasted for a
minimum of 1.5 hours. Previously validated
equations were used to estimate total, intracellular, and extracellular body water (see the
Criticisms and Statistical Musings section for
details on the equations).
Findings
Given the relevance of the findings to the
focus of MASS, I’ll focus primarily on the
41
circumference measurements and the main
bioelectrical impedance findings, total body
water, intracellular water, and extracellular
water, rather than getting into the “raw” bioimpedance outcomes from which these data
were estimated.
Circumferences and Bodyweight
From the day prior to the day of the contest,
the competitors experienced significant decreases in waist circumference (79.3 ± 3.2
cm to 78.6 ± 2.8 cm; p = 0.036; effect size
= −0.21) and increases in upper-arm circumference (36.1 ± 1.5 cm to 36.8 ± 1.5 cm; p =
0.028; effect size = 0.40), and no significant
change in thigh circumference (56.6 ± 2.6
cm to 56.2 ± 2.0 cm; p = 0.468; effect size =
−0.14) or bodyweight (80.8 ± 7.9 kg to 80.2 ±
8.0 kg; p = 0.158; effect size = −0.07).
Body Water
As shown, there were significant decreases in
total body (51.4 ± 4.6 L to 50.3 ± 4.2 L; p =
0.028; effect size = −0.22) and extracellular
water (19.8 ± 1.8 L to 17.2 ± 1.4 L; p < 0.001;
effect size = −1.39) and a significant increases
in intracellular water (31.6 ± 2.9 L to 33.1 ±
2.8 L; p < 0.001; effect size = 0.50), resulting
in significant increases in both the intra-to-extracellular water ratio (1.60 ± 0.03 L to 1.92
42
± 0.01 L; p < 0.001) as well as the intracellular-to-total body water ratio (0.61 ± 0.01 L
to 0.66 ± 0.01 L; p < 0.001). Notably, all but
one participant experienced decreases in total
body water, while all 11 participants increased
intracellular and decreased extracellular water.
Criticisms and Statistical
Musings
What follows is one minor point that will
help you better understand its limitations –
but doesn’t fundamentally change the interpretation of this study – and one major point
on methodology that potentially does.
First, like almost all studies on physique
athletes, this study has a small sample size,
limiting our ability to generalize the findings
to the broader population of physique competitors. This is no fault of the authors, and
their paper hints at just how difficult it is to
recruit dedicated athletes in close proximity
to competition. Specifically, of the 50 competitors invited to participate, only 11 agreed.
Further, in the grand scheme of research, this
was an incredibly non-invasive study. The
researchers simply made observations, they
didn’t manipulate the participants’ nutrition
or training, and the measurements didn’t require blood draws, fatiguing exercise tests,
or long personal questionnaires about eating
behavior or body image. All the researchers
wanted was to take body circumference and
bioimpedance measurements on two consecutive days, which I suspect took, at most, 3045 minutes on each day. Further, the researchers didn’t require the participants to come to
the lab; rather, they were at the competition
venue. Despite that, only 11 agreed to participate, and of those 11, three opted out of
the circumference measurements. This is not
because bodybuilders are selfish or anti-science, as even I would have hesitated to participate in this study. The second measurements
were taken on the day of competition, before
the pump-up, immediately before getting on
stage. From a scientific perspective, it might
have been beneficial to select this time point
to avoid the acute effect of the pump-up routine on water balance. But, from a competitor’s perspective, I’d be really nervous that
I’d miss, or have to rush, my pump-up, or, at
worst, miss my stage time and get disqualified. Also, I can understand not wanting to
get circumference measurements; depending
on the type of stage color you use, the tape
measure could smudge it, and potentially impact your appearance. All this is to say, it’s
a challenge to get high quality data on physique athletes close to competition.
Moving on to the major limitation, the authors assessed body water in a dynamic state,
in a very unique population, using single
frequency bioelectrical impedance. As reviewed, peaking strategies differ between
athletes as to whether more or less carbs are
consumed, whether alcohol is or isn’t consumed on show day, whether water is restricted or not (or loaded then restricted), whether
diuretics are or aren’t used (herbal or potentially prescription in the case of the present
study’s non-tested competitors), and whether
other dehydration techniques are implemented or not. These differences also exist within athletes on different days as they progress
through their peaking plans. To increase the
43
validity and reliability of body composition
testing via bioimpedance, efforts to control
hydration status are recommended, such as
taking measurements after an overnight fast,
at the same time of day, after a similar prior day of eating and drinking, and at a similar proximity to the last training session (4).
These best practices, which couldn’t occur
in the present study, are encouraged due to
the limitations of bioelectrical impedance, as
changes in hydration can impact body composition measurements. Bioimpedance estimates body composition using assumptions
about the hydration of different body tissues, and by measuring the body’s resistance
to electrical current. However, this is all to
improve the validity and reliability of body
composition measurements, not measurements of body water. Certainly, you might
think that bioimpedance measurements of
body water are more robust to variation than
measurements of body composition, as the
typical view is that bioimpedance estimates
body composition by “measuring” body water. However, bioelectrical impedance estimates body water from electrical resistance,
then uses those (estimated) values to estimate
body composition. Thus, estimations occur at
all levels, even for the measurement of body
water. Thus, the efforts to control hydration
status and proximity to exercise that are important for body composition measurements
are equally important for body water measurements.
To improve upon these issues, instead of
single-frequency bioimpedance, researchers can utilize multi-frequency alternatives
like multi-frequency bioelectrical imped-
ance analysis or bioimpedance spectroscopy,
which use multiple frequencies ranging from
low (which don’t penetrate cell membranes,
and only pass through extracellular fluid volume) to high (which do penetrate cell membranes, and therefore pass through extra and
intracellular fluid volume). By utilizing a
combination of high and low frequency currents, multi-frequency alternatives allow the
tester to distinguish between intracellular and
extracellular fluid volume (5). In contrast, the
presently reviewed study used a single-frequency device, which does a poor job of precisely detecting changes in total body water
(6), and makes no direct distinction between
intracellular and extracellular fluid compartments. For this reason, the authors used previously published equations for estimating
fluid compartments in athletes (7). Moreso,
the researchers who developed these equations obtained their data under best-case-scenario conditions by testing after an overnight
fast lasting at least 12 hours, at least 15 hours
from the last exercise session, in a confirmed
euhydrated state. Even in such ideal conditions, “TBW could be over- or underestimated by ~5.6 kg, ECW could be underestimated
by ~3.6 kg or overestimated by ~4 kg, and
ICW could be over- or underestimated by ~6
kg (7).” These ideal conditions, through no
fault of the present study’s authors, simply
weren’t possible, as the aim was to observe
the effect of manipulating these variables.
The bodybuilders were definitely not euhydrated, may or may not have trained recently
(some physique competitors do a light session one-day out), and were only fasted for
“at least” 1.5 hours, likely due to carb loading. On top of these issues, the fact that the
44
participants were bodybuilders creates a
problem. The equation the authors used was
specifically based on a mixed-sex sample of
team sport, endurance, track and field (sprinters, hurdlers, and jumpers), and combat athletes. According to the creators of the equation, they developed it because “Athletes
differ from the general population specifically with sport-specific differences in body
geometry, that almost always are ignored by
researchers despite awareness that geometry
is a factor affecting bioimpedance measurements, and that BIA prediction equations are
sample-specific (7).” Likewise, you’d expect
bodybuilders’ body geometry to differ meaningfully from this cohort of athletes.
Ultimately, single frequency devices and
estimation equations are much less accurate
for estimating changes in body water when
dealing with abnormally altered hydration
states where interindividual differences in
lean tissue hydration are potentially too high
for the uniform estimation equations to accurately represent intracellular or extracellular water volumes (8). Just how abnormal
were the hydration states of the competitors
in this study? Well, the authors actually noted that the phase angle (the raw variable
representing an index of the ratio between
extracellular and intracellular water) of this
group of competitors was the highest ever
observed in the literature, and that the phase
angle of the participant on competition day
with the highest score was the highest individual value ever reported in the literature,
which was 4.4 standard deviations above
the reference value for lean male athletes. In
short, very abnormal.
With that said, I don’t think this completely invalidates the present study’s findings.
These bodybuilders probably did have high
intra-to-extracellular water ratios, higher than
most “normal” conditions, and this, in combination with the use of single-frequency bioelectrical impedance and unstandardized conditions, are the sources of this major limitation.
In such abnormal conditions, with extreme
body geometries, using equations that don’t
represent the studied population, we’re likely
to see variability and errors in measurement.
Thus, I disagree with the authors’ suggestion that bioelectrical impedance derived intra-to-extracellular water ratios can be used to
assess the effectiveness of a peaking strategy.
The variability from the aforementioned reasons causes too much error for within-individual day-to-day comparisons (in addition to the
fact that the intra-to-extracellular water ratio
may not be a proxy for appearance improvement, which I discuss in the interpretation). In
fact, in such a dynamic state, accurately measuring intra-to-extracellular water ratios may
only be possible with multiple, complementary assessment methods beyond impedance
testing to assess hydration status (9).
So, what’s the take home of this section? For
one, the specific values in the results may not
be that accurate based on the variability associated with the methods and population, but
I’d venture a guess that the directionality of
the changes is probably accurate. Meaning, I
think the bodybuilders might have increased
their intra-to-extracellular water ratio, but, to
know for sure, and to what degree, we need
follow up studies with more rigorous methods. Also, we have to take a step back from
45
the authors’ suggestion of using bioelectrical impedance as a tool to assess individual peaking success, at least with single-frequency bioelectrical impedance. Instead we
should just look at the present study as tentative proof that intra-to-extracellular water ratios can change in response to peaking, which
is primarily what this paper is about anyway.
Interpretation
A huge part of the vernacular of assessing
physiques in the bodybuilding world are comments on how “dry,” “separated,” “hard,” or
“grainy” athletes look. If you’re a scientist
and a bodybuilder like me, you end up with
a love-hate relationship with these terms.
Sure, they can be apt descriptions of what a
physique looks like, and I get excited when I
see physiques that exemplify these descriptions, but, at the same time, I can’t know if
the terms accurately describe the physiological state, nor the cause, associated with that
look. For example, many times I’ve seen extremely lean competitors who drank a ton of
water on show day get described as “dry,”
likely because they are lean and full enough
(presumably from carbohydrate loading,
which can enhance the appearance of muscularity [3]), and got a good pump, such that
there’s nothing but stretched skin to obscure
their muscularity, producing a hard, grainy,
separated, and “dry” look, despite the fact
that they are well-hydrated. Likewise, I’ve
seen competitors who restricted their water
intake get described as “watery,” potentially because they weren’t lean enough to look
hard, and/or potentially because their blood
pressure was too low, and they couldn’t get
a good pump, leaving them looking “watery”
even while dehydrated, because their water
balance was out of whack. But, ultimately, all
of this is supposition, and this supposition is a
continual source of contention between bodybuilders who put faith in traditional peaking
theory (which I discuss in depth HERE) and
science-minded bodybuilders, who are skeptical of some of these claims. That’s where
the present study comes in.
A common claim among bodybuilders is that
the traditional peaking process manipulates
fluid shifts which increase intracellular water
retention, while reducing extracellular water
retention, increasing the intra-to-extracellular water ratio, which has a beneficial effect
on appearance. A common sciency response
to this claim is that intra-to-extracellular fluid balance is tightly regulated, and can’t be
manipulated to any significant extent, and I’ll
admit it, I’ve said as much. However, I think
the present study might (see the criticisms
section) establish that this sciency response is
perhaps too sweeping, and that bodybuilders
may be able to manipulate their intra-to-extracellular water ratio in the way they intend.
In fact, every single participant in this study
BODYBUILDERS MAY BE
ABLE TO MANIPULATE THEIR
INTRA-TO-EXTRACELLULAR
WATER RATIO IN THE
WAY THEY INTEND.
46
decreased their extracellular water while simultaneously increasing their intracellular
water from the day prior to the day of the
competition. This finding may be an important step in understanding what is happening
physiologically during the peaking process.
However, we have to fight against the urge to
over extrapolate based on it.
First, it’s important to note that intracellular is
not synonymous with intramuscular, and that
extracellular is not synonymous with subcutaneous. This is an important distinction from
an appearance standpoint. For example, extracellular water retention within muscle, should
enhance appearance. Anecdotally, I look more
muscular in some muscle groups during the
days after I train, perhaps due to intramuscular
(but not necessarily intracellular) muscle damage-related edema. Indeed, the time course of
acute muscle swelling in response to resistance
training is roughly 48-72 hours (10), which is
why researchers typically wait this long before
assessing changes in muscle thickness, so they
aren’t erroneously characterizing swelling as
hypertrophy. Further, the vascular system is
extracellular, but, theoretically, you wouldn’t
want a fluid reduction in the vascular system
as this would make it harder for blood to be
delivered to your muscles while pumping up.
All in all, just because bodybuilders might be
able to increase their intra-to-extracellular water ratio, doesn’t mean it has a positive effect
on appearance in all cases, or that appearance
improves commensurate with this ratio.
The next point worth highlighting is that all
the competitors increased their intra-to-extracellular water ratio. I find this interesting, because there is a lot of variability in approaches
JUST BECAUSE
BODYBUILDERS MIGHT BE
ABLE TO INCREASE THEIR
INTRA-TO-EXTRACELLULAR
WATER RATIO, DOESN’T
MEAN IT HAS A POSITIVE
EFFECT ON APPEARANCE
IN ALL CASES.
to peaking. Not all competitors carb load (3),
and not all competitors manipulate hydration,
and, of the competitors who do, they don’t all
do it the same way or to the same magnitude
(2). Further, in my experience, some bodybuilders do indeed mess up their appearance
with their peaking strategies. So while it’s
possible the researchers recruited 11 competitors who improved their appearance, I
think it’s unlikely, and, considering they all
increased their intra-to-extracellular water
ratio, I question the notion that such an increase is a perfect proxy for an improvement
in appearance (assuming the measurements
were accurate). Ultimately, we don’t know
any details about the competitors’ peaking
strategies in the present study; we just know
they probably increased their intra-to-extracellular water ratio. Thus, unfortunately, we
simply can’t attribute any specific, individual
peaking practice to the body water changes
observed in this study. To conclude, body-
47
APPLICATION AND TAKEAWAYS
The peak week approaches that bodybuilders use might result in acute increases
in intra-to-extracellular water ratios that are higher on contest day than the day
prior. Thus, there may be some accuracy to the claims that some combination of
water, electrolyte, and carbohydrate manipulation meaningfully change these ratios.
However, the specific methods that increase these ratios, and the degree to which an
increase enhances appearance, if it does at all, is yet to be determined.
builders may be increasing their intra-to-extracellular water ratios, but whether this is
a good thing that scales with improvements
in appearance, or what specific practices increase this ratio, is unknown.
Next Steps
As I mentioned in the interpretation, this is a
cool mechanistic study that indicates bodybuilders are actually doing what they intend
BODYBUILDERS MAY BE
INCREASING THEIR INTRATO-EXTRACELLULAR WATER
RATIOS, BUT WHETHER
THIS IS A GOOD THING
THAT SCALES WITH
IMPROVEMENTS IN
APPEARANCE, OR WHAT
SPECIFIC PRACTICES INCREASE
THIS RATIO, IS UNKNOWN.
during peak week, physiologically speaking.
However, the missing link is whether this
physiological change commensurately impacts bodybuilders’ appearance the way they
think it does. To investigate this, the methods of the present study (but using multi-frequency bioelectrical impedance analysis or
bioimpedance spectroscopy, or even more
direct reference methods, such as deuterium
dilution and bromide dilution) could be combined with elements of the last study on carb
loading I reviewed (3), where the researchers had bodybuilding judges score photos of
the participants, while blinded to whether the
photos were taken before or after carb loading. If changes in appearance could be quantified, as well as changes to intra-to-extracellular water ratios, while the peaking methods
of the participants were documented, you
could actually see which methods resulted in
positive changes in appearance, and whether
they coincided with increases in intra-to-extracellular water ratios.
48
References
1. Nunes, J. P., Araújo, J., Ribeiro, A. S., Campa, F., Schoenfeld, B. J., Cyrino, E. S., et
al. (2022). Changes in Intra-to-Extra-Cellular Water Ratio and Bioelectrical Parameters
from Day-Before to Day-Of Competition in Bodybuilders: A Pilot Study. Sports (Basel,
Switzerland), 10(2), 23.
2. Chappell, A. J., & Simper, T. N. (2018). Nutritional Peak Week and Competition Day
Strategies of Competitive Natural Bodybuilders. Sports (Basel, Switzerland), 6(4), 126.
3. de Moraes, W., de Almeida, F. N., Dos Santos, L., Cavalcante, K., Santos, H. O.,
Navalta, J. W., et al. (2019). Carbohydrate Loading Practice in Bodybuilders: Effects on
Muscle Thickness, Photo Silhouette Scores, Mood States and Gastrointestinal Symptoms.
Journal of Sports Science & Medicine, 18(4), 772–779.
4. Androutsos, O., Gerasimidis, K., Karanikolou, A., Reilly, J. J., & Edwards, C. A. (2015).
Impact of eating and drinking on body composition measurements by bioelectrical
impedance. Journal of Human Nutrition and Dietetics, 28(2), 165–171.
5. Marra, M., Sammarco, R., De Lorenzo, A., Iellamo, F., Siervo, M., Pietrobelli, A., et
al. (2019). Assessment of Body Composition in Health and Disease Using Bioelectrical
Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical
Overview. Contrast Media & Molecular Imaging, 2019, 3548284.
6. Ugras S. (2020). Evaluating of altered hydration status on effectiveness of body
composition analysis using bioelectric impedance analysis. The Libyan Journal of
Medicine, 15(1), 1741904.
7. Matias, C. N., Santos, D. A., Júdice, P. B., Magalhães, J. P., Minderico, C. S., Fields,
D. A., et al. (2016). Estimation of total body water and extracellular water with
bioimpedance in athletes: A need for athlete-specific prediction models. Clinical
Nutrition, 35(2), 468–474.
8. Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Manuel Gómez,
J., et al. (2004). Bioelectrical impedance analysis-part II: utilization in clinical practice.
Clinical Nutrition, 23(6), 1430–1453.
9. Armstrong L. E. (2007). Assessing hydration status: the elusive gold standard. Journal of
the American College of Nutrition, 26(5 Suppl), 575S–584S.
10. Ogasawara, R., Thiebaud, R. S., Loenneke, J. P., Loftin, M., & Abe, T. (2012). Time
course for arm and chest muscle thickness changes following bench press training.
Interventional Medicine & Applied Science, 4(4), 217–220.
█
49
Study Reviewed: Autonomy Support in Sport and Exercise Settings: A Systematic Review
and Meta-Analysis. Mossman et al. (2022)
Coaching Beyond the Spreadsheet: How
(and Why) To Promote Intrinsic Motivation
BY ERIC TREXLER
A lot of evidence-based coaches and fitness enthusiasts invest
considerable time and effort into optimizing the nuts and bolts of
their programs. A new meta-analysis on psychological aspects of
coaching suggests that there are ample opportunities (beyond the
spreadsheet) to enhance a lifter’s long-term success and well-being.
50
KEY POINTS
1. The presently reviewed meta-analysis sought to quantify associations between
coach autonomy support and a wide range of outcomes related to their clients’ or
athletes’ experiences.
2. Autonomy support was correlated with a wide range of positive outcomes,
including satisfaction of psychological needs, intrinsic motivation, general
well-being, positive affect, life satisfaction, self-esteem, performance and
achievement, effort, and athlete-coach relationship quality.
3. Self-determination theory yields some very simple and practical strategies
that can facilitate the successful implementation of a great program, ultimately
supporting a lifter’s subjective experience, general well-being, and intrinsic
motivation, which can provide a solid foundation for long-term success.
A lot of MASS readers coach others in pursuit of goals related to fitness or athletics.
Conversely, many MASS readers are coached
by someone else. Even if you don’t formally
identify as a coach or a client (or both), you
inevitably take part in the process of coaching yourself. As such, knowledge about effective coaching practices has tremendous utility,
whether you’re using it to improve the coaching services you provide, evaluate the coaching
services you receive, or reassess the strategies
and tools you use when coaching yourself.
Self-determination theory is used in several
motivation-related applications of psychology, such as behavior change and athletic performance. It posits that people have a desire
for personal growth, and that repeated instances of self-improvement, goal attainment, and
skill mastery positively impact one’s sense
of self. It also posits that people have inherent psychological needs for autonomy, competence, and relatedness. When these needs
are satisfied, we are more likely to be driven
by intrinsic motivation and thrive in the goal
pursuit process, ultimately making decisions
and engaging in self-directed behaviors that
promote self-satisfaction, fulfillment, and enjoyment. In contrast, when these psychological needs are not sufficiently met, we might
find ourselves in a state of amotivation (lack
of motivation), or increasingly driven by extrinsic sources of motivation. This can result
in a goal striving process that is characterized
by a reduced sense of volition, enjoyment,
and gratification. Even when goal-supporting
tasks are being completed, it’s like crossing
items off of a to-do list that was prepared for
us (without our input), and we don’t cultivate
the same feelings of mastery and fulfillment.
The presently reviewed study (1) was a meta-analysis investigating the overall impact of
coach autonomy support, which refers to “an
assortment of coach or instructor-led behaviors that collectively yield a climate of support, care, and understanding within the sport
setting.” The researchers sought to broadly
51
quantify the relationship between coach autonomy support and a wide range of psychological outcomes, in addition to exploring
possible “moderators,” or additional variables
that might impact the relationship between
coach autonomy support and the outcomes of
interest. In short, results generally indicated
that coach autonomy support was positively
associated with intrinsic motivation, satisfaction of basic psychological needs (autonomy,
competence, and relatedness), well-being, and
positive athlete functioning, and negatively associated with athlete distress and need frustration. These relationships were not significantly moderated by cultural group, type of sport,
age, or competition level, which suggests that
the findings may be generalizable to a broad
variety of contexts.
Psychology is important in endeavors related
to fitness and athletics, but we only occasionally cover psychology papers in MASS. As a
result, it’s possible that you’d like some clarification regarding the definitions of some of
the terms used throughout this introduction
section, and a little more context regarding
how this theory relates to the typical fitness
enthusiast or coach. Rest assured, the Interpretation section of this article will dig into the
results of this study, with a deliberate focus on
providing clear definitions of key terms and
actionable examples for practical application.
Purpose and Hypotheses
Purpose
The primary purpose of this meta-analysis was “to provide a basic overview of the
[self-determination theory] research in this
literature, providing broad descriptive evidence of various correlates and potential
consequences of coach autonomy support,
as well as their strength of association with
important athlete outcomes.” The secondary
purpose was “to examine possible moderators that could affect correlation magnitudes
in this literature.”
Hypotheses
The researchers stated four hypotheses, which
are directly quoted (with some elaboration
removed in the interest of brevity) below:
• “H1: Autonomy support will exhibit meta-analytic associations consistent with the
internalization of motivation regulations
in athlete motivation. That is, it should be
most strongly and positively associated
with motivations that are fully internalized
and autonomous (i.e. intrinsic motivation),
less related to external regulation, and negatively associated with amotivation.
• H2: Autonomy support will exhibit main
effect associations consistent with [self-determination theory] propositions: positive
with basic needs, well-being, and negatively with ill-being and need frustration.
• H3: Correlations with basic needs and
internalized motivations will not vary as
a function of the national culture of the
study population.
• H4: Correlations will be moderated by
type of sport (e.g. team versus individual)
with individual sports showing stronger
associations.”
52
Methods
Seasoned MASS readers are quite familiar
with meta-analyses, but not quite in this exact format. We’ve covered numerous meta-analyses, which generally tend to follow
the same format. The researchers identify a
research question, such as “Does creatine increase maximal strength?” Then, the researchers gather up all of the comparable studies that
investigate this question, while ensuring that
they all conform to a set of pre-specified study
criteria. Included studies will generally compare a creatine group to a comparator group
(such as a control group or placebo group),
and measure some continuous variable related
to strength (such as bench press 1RM, squat
1RM, isokinetic peak torque, and so on). Due
to the variety of different strength variables assessed, the researchers tend to convert all of
these outcomes into standardized effect sizes
(such as Cohen’s d or Hedges’ g). Finally, the
researchers mathematically pool all of the individual study results together, and determine
the overall (pooled) effect of, in this example,
creatine supplementation on maximal strength.
The presently reviewed paper took a similar approach in general, but aimed to quantify a very different type of effect size. This
meta-analysis quantified correlations among
participants within the included studies, rather than quantifying differences among groups
within the included studies. Obviously the
equations required for pooling correlation results are quite different from those required
for pooling mean differences among groups,
but the approach is statistically valid and the
overall process of the meta-analysis is similar.
These researchers searched the major electronic research databases for studies investigating the topic at hand, and included studies as long as they “investigated autonomy
support in a sport or exercise setting distinct
from physical education settings,” “measured
self-rated, athlete-perceived or observer-rated autonomy support,” and “provided a
correlation between autonomy supportive
behavior and at least one relevant criterion
variable (e.g., indices of athlete motivation or
basic needs, well-being or distress, indicators
of athlete functioning or climate, and other
instructor or peer-related behaviors).”
The initial search returned 13,780 studies.
After screening and applying the exclusion
criteria, this huge list was whittled down to
120. They further trimmed the list to avoid
instances of duplicated results, leading to a
final list of 119 studies, which included 131
samples and 38,844 total participants. With
these studies, the researchers computed
pooled correlations between coach autonomy support and a wide range of relevant outcomes, which generally fell into a few categories: 1) athlete motivation and basic needs, 2)
athlete well-being, ill-being, and functioning,
3) sports climate and coach behaviors, and 4)
athlete demographics. The correlations were
presented in raw (uncorrected) form (Pearson’s r), and also presented as “true score
correlations” (ρ), which are corrected to account for sampling and measurement error.
The researchers also completed additional
analyses to determine if results were meaningfully influenced by potentially impactful
moderator variables, such as cultural group,
type of sport, age, or competition level.
53
54
Findings
Correlations between coach autonomy support and outcomes related to athlete motivation and basic needs are presented in Table
1. Correlations between coach autonomy
support and athlete well-being, ill-being, and
functioning are presented in Table 2. Correlations between coach autonomy support and
sports climate, coach behaviors, and athlete
demographics are presented in Table 3. It
would be quite unpleasant to read through a
wall of text detailing the results for each of
the dozens of individual outcome variables
contained within these tables, but fortunately
the general findings can be summarized concisely. As the researchers put it: “Our meta-analysis confirmed positive associations
with athlete basic need satisfaction, internal-
ized motivation, well-being, positive athlete
functioning, and negative associations with
indicators of athlete distress and need frustration across cultures.” Even if you’re all about
business and only interested in “the bottom
line,” Table 2 shows that coach autonomy
support was significantly associated with
“performance and achievement” (ρ = 0.21,
95% confidence interval: [0.13, 0.30]). In addition, relationships did not appear to be substantially altered by the potential moderator
variables investigated, which suggests that
these correlations may be applicable and generalizable across a broad range of contexts.
Interpretation
The results of this study are fairly straightforward, and the limitations are similarly
55
straightforward. As a result, this Interpretation section will be heavily focused on understanding what self-determination theory is,
how it applies to the typical fitness enthusiast
(or coach), and how to actually put the theory
into action.
As I briefly mentioned in the Introduction
section, self-determination theory assumes
that people naturally have a curiosity to try
new things, master new challenges, and to
“integrate these experiences with a core sense
of who they are” (2). Ideally, people operate
with a strong sense of self-determined motivation, meaning that they are making volitional choices and taking volitional actions
that are motivated by their own desires and
ambitions, and they truly feel like they have
some degree of control over dictating their
own path forward. Self-determination theory was developed by Ryan and Deci (3), and
it builds upon Maslow’s hierarchy of needs.
Maslow proposed that humans have an inherent drive toward self-actualization, or becoming the best possible version of themselves
(from their perspective). However, Maslow
acknowledged that humans need to satisfy more basic, foundational needs (such as
physiological needs and safety needs) before
they can cultivate motivation to pursue higher-level goals related to self-actualization.
Similarly, self-determination theory acknowledges that humans have an inherent
drive toward self-improvement, but they will
struggle to aggressively pursue this process
when they lack the foundational conditions
necessary. People are more likely to perceive
a high level of self-determination in their
choices and actions when they have a sense
of relatedness, autonomy, and competence.
However, when they are lacking one or more
of these foundational elements, their sense of
self-determination is impaired. Put concisely
(2), relatedness refers to “being meaningfully
connected with others,” autonomy refers to
“feeling a sense of authentic choice in what
one does,” and competence refers to “feeling
effective in what one does.” Other authors
define these terms with slightly different
terminology (4), suggesting that relatedness
refers to “the need to feel close to and understood by important others,” autonomy refers
to “the need to feel choiceful and volitional,
as the originator of one’s actions,” and competence refers to “the need to feel capable of
achieving desired outcomes.” When these
three needs are met, people tend to feel empowered to confidently and enthusiastically
pursue growth opportunities, which are perceived as being very rewarding and fulfilling.
Broadly speaking, motivation can be defined as “psychological energy directed at
a particular goal.” However, an important
aspect of self-determination theory is that it
focuses on different types of motivation (5),
with varying levels of quality among them.
When insufficient relatedness, autonomy, or
competence lead to a low sense of self-determination, it’s common to experience amotivation (a total lack of motivation). In this
situation, a person might not feel competent
enough to engage in goal striving behaviors,
or might fail to see how a particular choice
or behavior is valuable or relevant to their interests. Even if they are able to avoid a state
of complete amotivation, they might instead
find themselves relying heavily on extrinsic
56
motivation due to their low sense of self-determination. Self-determination theory lays
out four different subtypes of extrinsic motivation (5), ranging from lowest quality (least
internalized) to highest quality (most internalized), but intrinsic motivation is higher in
quality than all forms of extrinsic motivation
(4). When someone is acting due to extrinsic
motivation, they might complete a task to get
a reward from some authority figure, avoid
ridicule or punishment from some authority
figure, gain approval from peers, or avoid a
sense of guilt. In contrast, someone acting
due to intrinsic motivation is completing a
task out of genuine interest, enjoyment, or
the sense of satisfaction it brings.
As Dr. Helms described in a previous MASS
video, self-determination theory offers valuable insight for promoting sustainable intrinsic motivation. People feel more self-determined when their needs for relatedness,
autonomy, and competence are sufficiently
met. When people feel more self-determined,
they tend to thrive, seeking to achieve goals
that enhance their sense of self and enjoying the process along the way. They have a
greater degree of intrinsic motivation, which
leads them to complete goal-compatible behaviors with “greater effort, engagement,
persistence, and stability” (4). In fact, when
people are more self-determined, there is evidence to suggest that they perform better,
display more creativity, and experience more
happiness and satisfaction across contexts including marriage, academics, and weight loss
programs (2).
With this in mind, the presently reviewed
findings are very straightforward and simple
in nature, but with high potential for impact.
The researchers assessed studies on coach
autonomy support, which generally involves
“taking steps to (a) provide choices to the
athletes under one’s instruction, (b) provide
athletes with a rationale for tasks and set limits, (c) acknowledge the athletes’ feelings and
perspectives, (d) provide non-controlling,
competence-based feedback, (e) aspire to
prevent ego involvement, and (f) avoid overt
controls, such as the use of tangible punishments or rewards to prompt desired behaviors” (1). As detailed in Tables 1, 2, and 3,
autonomy support was positively associated
with a huge list of positive psychological
outcomes. It was positively correlated with
the foundational components and the most
important type of motivation involved in
self-determination theory (relatedness, autonomy, competence, and intrinsic motivation),
but also positively correlated with things
like general well-being, positive affect, life
satisfaction, self-esteem, performance and
achievement, effort, and athlete-coach relationship quality. You might be surprised to
see that coach autonomy support was associated with performance and achievement, but
Dr. Helms previously reviewed some qualitative research suggesting that psychological
attributes related to self-determination theory
support success among world champions in
a variety of sports. Coach autonomy support
was also inversely associated with several unfavorable outcomes, such as thwarting
and “need frustration” (absence of need satisfaction) of the foundational components of
self-determination theory (relatedness, autonomy, and competence), negative affect,
burnout, and amotivation.
57
Of course, we have to keep some noteworthy
limitations in mind when interpreting these
results. These studies are reporting observational correlations, so we can’t assume that
coach autonomy support necessarily caused
all of these positive outcomes. In addition,
the researchers reported fairly substantial
heterogeneity for a number of the reported
outcomes. So, while results didn’t seem to
substantially differ based on the moderator
variables they tested (such as cultural group,
type of sport, age, or competition level),
there was still considerable variation in results among individual studies. Despite those
limitations, the presently reviewed study provides some support for self-determination
theory, while simultaneously suggesting that
coaches have an opportunity to enhance their
clients’ and athletes’ sense of relatedness,
autonomy, and competence, while simultaneously promoting a long list of other positive
psychological outcomes.
This illustrates what is, in my mind, the most
important point of this article: a good coach is
not merely a spreadsheet generator. A coach’s
job is to create and maintain a program that
addresses the needs of the client, but also
to cultivate an environment that enables the
client to thrive. In this context, thriving refers not only to objectively successful goal
attainment, but also to a positive subjective
experience during the goal striving process.
Coaches should aim to help their clients enjoy the process of achieving their goals, and
to make the process a fulfilling and rewarding
endeavor that contributes to personal growth
and self-actualization. Now that we’ve established what self-determination theory is and
A GOOD COACH IS NOT
MERELY A SPREADSHEET
GENERATOR.
why it’s important, we can now discuss how
to implement it in the context of a fitness program.
Practical Application
We can start promoting a client’s sense of
self-determination before the first training
session actually occurs. There is ample evidence (reviewed here) to suggest that establishing a robust and comprehensive goal
hierarchy is a highly effective contributor to
sustained client success. If you and your client approach goal setting in a collaborative
manner, it opens up multiple opportunities to
promote key elements of self-determination
theory. The theory suggests that humans have
a natural tendency to seek out self-improvement and self-actualization, and establishing
a good superordinate goal at the top of the
goal hierarchy can help a client see the clear
connection between their goals, their values,
and their ideal self (6). The mere act of collaborating on the goal setting process will
foster a sense of relatedness, as this will ensure that you and your client are aligned and
simultaneously pushing in the same direction
together. Finally, as you begin filling in the
details of intermediate and subordinate goals,
there are an extremely wide range of options
58
to choose from. By seeking (and incorporating) feedback from your client and offering
them genuine options to pick from, you can
foster a heightened sense of autonomy. Collectively, this approach to goal setting should
promote a great deal of intrinsic motivation
for your client, which should be reasonably
sustainable during the ups and downs of the
subsequent goal striving process.
there are multiple studies showing that incorporating even small opportunities for clients to make low-stakes choices can enhance
performance. A client is most likely to thrive
on a program when they share ownership of
the decisions, they are being appropriately
challenged, they believe the program is fully
aligned with their goals, and they feel a sense
of collaborative support from their coach.
Similar methods can be applied during the
program design process. When creating a program, it’s important that your client is asked
to utilize skills and strategies that they are
fairly comfortable with. A highly advanced
program for a beginner client will directly
threaten their sense of competence, and make
the program far less enjoyable (and successful). Obviously, in the interest of self-actualization, your coaching will prompt the client
to develop new skills and new proficiencies
over time; as long as they are being challenged appropriately and you’re explaining
how these challenges are conducive to their
long-term success, you’ll be fostering a greater sense of intrinsic motivation. Just as with
goal setting, it’s important to solicit feedback
from your client during program design, and
to actually incorporate that feedback to the
extent possible. You can also build some
autonomy-reinforcing opportunities into the
program itself. For example, you can build in
options for your client to determine when to
take a diet break or a refeed, when to take a
deload, how to distribute their total training
volume throughout the week, which accessory lift to select, how many back-off sets to
complete, or when to terminate a set. As Dr.
Helms wrote back in Volume 1 of MASS,
When providing corrective feedback to clients, we have another fantastic opportunity to support the foundational elements of
self-determination theory. To support relatedness, all you need to do is show that you
are truly listening to your client and trying
to understand their perspective with empathy
and compassion. As described by Mouratidis
et al (7), we can also actively support autonomy when providing feedback. While some
coaches might induce guilt or shame, verbally
express disappointment, reactively withdraw
support, or utilize threats of punishment, a
coach aiming to support autonomy would
“try to provide a desired amount of options
and choice, coach from the players’ internal
frame of reference, and give a meaningful rationale in case choice is constrained” (7). We
can also promote a greater sense of competence when providing feedback, by actively
providing some positive reinforcement and
encouragement in combination with corrective feedback, and by carefully considering
the skill level of the client when providing
options for improving shortcomings or overcoming challenges. When we truly listen to a
client, communicate areas requiring improvement while acknowledging areas of proficiency, provide some potential solutions that
59
are appropriate for their skill level, clearly
explain the rationale behind these solutions,
allow them to play a role in selecting their
preferred option, and maintain a supportive
style of communication throughout the process, we have seized an opportunity to cultivate an environment that fully supports the
client’s well-being and promotes their longterm success.
Obviously, these practical examples are written under the assumption that you are coaching someone else. However, you can still
incorporate aspects of self-determination
theory into the process of coaching yourself.
A self-coached scenario implies a substantial degree of autonomy, but autonomy alone
isn’t sufficient for promoting a high sense
of self-determination. For example, freedom
to make choices can lead to an unfavorable
sense of self-doubt when you lack confidence
in your ability to make the right choices and
carry them out effectively. As a result, it’s important to pursue opportunities to boost your
sense of competence. You might achieve this
by attending courses or workshops to reinforce your practical skills, consuming more
educational materials, or seeking out tools
or equipment to facilitate your self-coaching
process. You might also want to pursue opportunities to boost your sense of relatedness,
as a completely independent fitness journey
can potentially lead to an unfavorable sense
of aimlessness or isolation. By doing something as simple as finding a training partner, regularly communicating with a peer or
group of peers on a similar fitness journey, or
joining a supportive online community with
similar goals and interests, your self-coached
process is no longer a completely isolating
process, and you have a support network to
boost your sense of relatedness.
Next Steps
While the correlations reported in this meta-analysis are intuitive and appear to be
generalizable across many different contexts
and scenarios, it’s important to recognize that
these findings are observational in nature. As
a result, it’s very hard to make causal inferences with this level of scientific evidence.
In order to claim that greater coach autonomy support actually causes the favorable
outcomes that it was correlated with in this
meta-analysis, we’ll need well-controlled,
longitudinal trials to build upon these findings. For example, it’d be really cool to see a
longitudinal trial in which participants were
randomly assigned to one of two coaching
COACHES CAN IMPROVE
THEIR CLIENT’S SUBJECTIVE
EXPERIENCE, WELLBEING, AND INTRINSIC
MOTIVATION, WHICH CAN
ULTIMATELY PROVIDE A
SOLID FOUNDATION FOR
LONG-TERM SUCCESS.
60
APPLICATION AND TAKEAWAYS
In the fitness world, coaches often spend a lot of time optimizing the quantitative
components of the training and nutrition programs they prepare for clients, and
rightfully so. Sets, reps, and loads are critically important, as are carbs, fats, and
proteins. However, coaching involves a lot more than spreadsheet generation. The
way your client experiences their program is important, and coaches influence
the subjective experience of the client, whether that influence is intentional or
unintentional. Coaches have an excellent opportunity to promote client autonomy
by using simple strategies, such as inviting input, providing meaningful choices,
explaining the rationale behind programming decisions, acknowledging the athlete’s
feelings and experiences, and seeking to understand the athlete’s perspective. In
doing so, there’s a high likelihood that coaches can improve their client’s subjective
experience, well-being, and intrinsic motivation, which can ultimately provide a solid
foundation for long-term success.
approaches, which differ primarily (or, ideally, exclusively) by the degree to which they
aim to provide autonomy support. Researchers could measure some of the outcomes
explored in this meta-analysis to determine
whether or not greater coach autonomy support actually causes some of the favorable
outcomes that appear to be correlated with
greater coach autonomy support.
61
References
1. Mossman LH, Slemp GR, Lewis KJ, Colla RH, O’Halloran P. Autonomy support in
sport and exercise settings: a systematic review and meta-analysis. Int Rev Sport Exerc
Psychol. 2022 Feb 2; ePub ahead of print.
2. Greenberg J, Schmader T, Arndt J, Landau M. Social Psychology: The Science of
Everyday Life (1st ed). Macmillan Higher Education; 2015:221-223.
3. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation,
social development, and well-being. Am Psychol. 2000 Jan;55(1):68–78.
4. Patrick H, Williams GC. Self-determination theory: its application to health behavior and
complementarity with motivational interviewing. Int J Behav Nutr Phys Act. 2012;9:18.
5. Ryan RM, Deci EL, Vansteenkiste M, Soenens B. Building a science of motivated
persons: Self-determination theory’s empirical approach to human experience and the
regulation of behavior. Motiv Sci. 2021;7(2):97–110.
6. Höchli B, Brügger A, Messner C. How Focusing on Superordinate Goals Motivates
Broad, Long-Term Goal Pursuit: A Theoretical Perspective. Front Psychol. 2018 Oct
2;9:1879.
7. Mouratidis A, Lens W, Vansteenkiste M. How you provide corrective feedback makes
a difference: the motivating role of communicating in an autonomy-supporting way. J
Sport Exerc Psychol. 2010 Oct;32(5):619–37.
█
62
Study Reviewed: Activation Training Facilitates Gluteus Maximus Recruitment During WeightBearing Strengthening Exercises. Cannon et al. (2022)
“Activation Training” May Increase
Glute EMG
BY GREG NUCKOLS
A new study suggests that you can increase your glute activation
during squats by doing some light glute “activation training” twice
per day. This article will discuss some potential confounders in the
study, and how confident we can be that glute activation training
will actually improve muscle growth and strength gains.
63
KEY POINTS
1. Subjects performed glute “activation training” twice per day for a week. The training
consisted of isometric clamshells, side-lying hip abduction, and fire hydrants.
2. Before and after the week of glute activation training, gluteus maximus EMG
was assessed during bilateral and unilateral bodyweight squats.
3. During both bilateral and unilateral bodyweight squats, glute EMG increased
by >50% on average. However, since these findings are very preliminary, we
don’t yet know that glute activation training will definitely lead to greater muscle
growth and strength gains.
If you have trouble feeling a target muscle
“firing” when lifting, a common recommendation is to give “activation training” a shot.
In essence, you’re advised to find simple exercises that force the target muscle to carry
the load, so that your nervous system can
learn how to use the target muscle. This process is supposed to help you better activate
the target muscle, so that you can utilize it
more effectively during other exercises.
While this recommendation sounds good in
theory, I haven’t come across much evidence
for its efficacy outside of a rehab setting.
However, absence of evidence doesn’t necessarily imply evidence of absence. Fortunately, a recent study helps shed some light on the
potential efficacy of “activation training.”
In the present study (1), subjects completed
one week of twice-daily activation training
for their glutes. Before and after the training
period, researchers assessed electrical activity in the subjects’ gluteus maximus during
unilateral (single-legged) and bilateral (double-legged) bodyweight squats via electromyography (EMG), which provides a proxy
measure for muscle activation (6). Following
the week of glute activation training, glute
EMG increased during bodyweight squatting
variations by >50%, on average. So, is this
definitive proof that “activation” training is
effective? Read on to find out.
Purpose and Hypotheses
Purpose
The purpose of this study was to investigate
whether a one-week glute activation program
can increase glute EMG during bodyweight
bilateral and unilateral squats.
Hypotheses
The researchers hypothesized that glute EMG
would increase following a one-week activation program.
Subjects and Methods
Subjects
12 subjects (5 females and 7 males) participated in this study. They were all physically active (exercising 3-4 days per week), but
not actively engaged in resistance training.
Of note, subjects were excluded if they had a
64
history of lower back and/or lower body pain,
or a history of lower back or lower extremity
injuries.
Experimental Design
This study employed a single cohort pre-post
design. In other words, all subjects completed
the same intervention, which means that we
can see changes over time within the group,
but there was no control group.
Subjects completed two testing sessions, separated by seven days. The testing sessions
began by assessing maximal isometric hip
extension strength using a dynamometer. Following the hip extension strength assessment,
subjects completed a submaximal reference
voluntary contraction (RVC), to be used for
subsequent EMG normalization (2). EMG
electrodes were placed on the gluteus maximus, and the subjects completed five-second
isometric hip extension contractions against
a load equal to 75% of their maximal hip extension torque (for each subject, the same absolute load was used for both the pre-training
and post-training reference voluntary contractions). Subjects were provided with a visual
guide for the reference contraction to ensure
they maintained the appropriate level of force
output. For both the maximal and reference
isometric contractions, subjects were positioned prone on the dynamometer with their
hips hanging off the edge of the seat, and the
pad positioned behind their knees. Their hips
were in 30 degrees of flexion, and their knees
were in 90 degrees of flexion. In essence, the
subjects were in a position mimicking a bentknee reverse hyperextension.
Following isometric testing, subjects were
outfitted with reflective markers so that the
researchers could assess joint kinematics using a camera system, and subjects completed three sets of three bilateral and unilateral
bodyweight squats on a force plate. Bilateral
squat stance was standardized (feet hip-width
apart, with toes pointed forward), and unilateral squats were performed with the nonweight-bearing leg in 90 degrees of knee flexion. A metronome was used to standardize
rep cadence for the bilateral squat (the eccentric and concentric were both 0.75 seconds),
while subjects were allowed to perform the
unilateral squat at a self-selected speed, so
that they could more easily maintain balance.
Glute EMG was assessed during both squat
styles, and glute EMG values for each subject
were normalized against the glute EMG values obtained during the reference isometric
contractions.
In the week between the two testing sessions,
subjects completed twice-daily glute activation training. Each training season consisted of three exercises: side-lying clamshells,
side-lying hip abduction, and quadruped fire
hydrants. Subjects completed three one-minute isometric holds for each exercise (such
that each activation session consisted of 9
total minutes of isometric holds). They were
also provided with resistance bands to make
the isometric holds more challenging; subjects
started with the lightest band, and increased
the band resistance as necessary. Subjects
completed most of the activation training on
their own, but at least four sessions were supervised by research assistants to ensure that
the subjects were using proper technique.
65
Findings
Hip joint kinetics and kinematics suggested
that subjects’ squat technique didn’t meaningfully change after one week of glute activation training (Table 1). That’s a good
thing, as it suggests that changes in glute
EMG from pre- to post-training are reflective of “true” changes in glute EMG, rather
than changes in glute EMG that may be reflective of changes in squat technique.
Glute EMG during both bilateral and unilateral squats significantly increased from
pre- to post-training (p < 0.01). For bilateral squats, glute EMG increased from 14 ±
6% RVC to 22 ± 11% RVC. For unilateral squats, glute EMG increased from 49 ±
21% RVC to 75 ± 34% RVC. In terms of
individual subject data, 10 out of 12 subjects
saw an increase in glute EMG during bilateral squats (with two subjects experiencing
a decrease), and 9 out of 12 subjects saw an
increase during unilateral squats (with one
subject seeing a notable decrease, and two
subjects experiencing no real change).
Interpretation
This article is a spiritual sequel to an article
from Volume 4, which discussed a study in-
66
vestigating whether targeted accessory work
could affect pec, triceps, and anterior deltoid
activation (6) in the bench press (3). The study
reviewed in that article found that targeted
accessory work did seem to successfully increase EMG amplitudes for “lagging” muscles, but my discussion of the results pointed
out several reasons why it may not be prudent
to accept the results at face value.
The present study shares one of the major
drawbacks of the prior study (1). In both studies, it would likely be clear to the subjects
that the purpose of the study had something
to do with assessing muscle activation of
the muscles being targeted by their assigned
training intervention. If you show up to a
lab, researchers put some electrodes on your
butt, and then you’re told to do glute exercises twice per day for a week, you’re probably
going to realize that the researchers are interested in assessing changes in glute EMG.
With that in mind, it’s entirely possible that
the post-training increases in glute EMG are
IF YOU SHOW UP TO
A LAB, RESEARCHERS
PUT SOME ELECTRODES
ON YOUR BUTT, AND
THEN YOU’RE TOLD TO
DO GLUTE EXERCISES
TWICE PER DAY FOR A
WEEK, YOU’RE PROBABLY
GOING TO REALIZE THAT
THE RESEARCHERS ARE
INTERESTED IN ASSESSING
CHANGES IN GLUTE EMG.
67
largely attributable to shifts in the subjects’
attentional focus. They may have simply
been more cognizant of utilizing their glutes
during their post-training squat assessments,
resulting in an internal attentional focus on
their glutes, thereby increasing glute EMG.
It’s also possible that the changes partially reflect learning effects, which we can’t isolate
due to the lack of a control group. Conversely, it’s also entirely possible that the observed
increases in glute EMG in the present study
are entirely attributable to the effects of one
week of “activation training.” However, we
don’t have a good way of disentangling these
two potential explanations.
With that said, I do think the results of the
present study are a bit stronger than those of
the prior study on bench pressing, for three
primary reasons.
First, in the prior study, subjects were assigned
to groups based on the presence of muscle
groups with “lagging” levels of activation, as
identified during baseline testing. Thus, it’s
possible that the results of that study were
influenced by simple regression toward the
mean – even if there were no “true” changes in muscle activation, you’d expect people
with low pec EMG at baseline to record higher pec EMG values at post-testing, and people with high pec EMG at baseline to record
lower pec EMG values at post-testing. Since
all subjects in the present study underwent
the same training intervention, and increases
in glute EMG were observed in subjects with
both low and high glute EMG amplitudes
at baseline, it’s clear that the results of the
present study weren’t driven by regression
toward the mean.
Second, the relative increases in glute EMG
in the present study (53-57%) were simply
much larger than the increases in pec (7.5%),
triceps (20.5%), and front delt (12%) EMG
observed during dynamic benching in the prior study. Changes in attentional focus can certainly impact muscle EMG, but the increases
in the present study were large enough that
they’re less likely to be explained solely by
shifts in attentional focus. I think these results are more strongly suggestive of a “true”
training effect.
Third, the present study builds upon research
that provides a clear mechanistic rationale
for the observed effects. Back in 2016, the
same research group found that a week of
activation training could improve corticomotor excitability of the glutes (4). Thus, while
the present study didn’t assess corticomotor
excitability, it’s not unreasonable to assume
that increases in corticomotor excitability
help explain the observed increases in glute
EMG, independent of potential shifts in attentional focus.
So, how can we apply these findings?
For starters, we certainly can’t claim that a
week of activation training will definitely increase glute EMG during bodyweight squatting (it might for ~80% of people). We also
can’t claim that increased glute EMG during
bodyweight squatting will definitely translate to increased glute EMG when performing
weighted lower body exercises (it very well
might, but an overall increase in exertion may
wash out the effect). We also can’t claim that
increased glute EMG when performing weighted lower body exercises would definitely re-
68
sult in increased glute growth or an increased
rate of strength gains (it very well might, but
EMG hasn’t been validated as a predictor of
longitudinal strength gains or hypertrophy; 5).
So, with all of that in mind, the recommendations I’m about to give should be interpreted
as super tentative recommendations.
If you want to grow your glutes, if you feel
like you struggle to fully integrate your glutes
into compound lower body exercises, or if you
feel like increased glute strength would pay
dividends in exercises like squats and deadlifts, I don’t think it hurts to give daily “activation training” a shot. While more research
is needed to know if glute activation training will truly aid in the pursuit of all of those
goals, it doesn’t cost anything, the required
time investment is pretty low, and the results
of the present study are promising enough for
me to think that daily glute activation training
is at least worth experimenting with. I don’t
know if the exercise protocol used in the
present study (minute-long isometrics, twice
per day) is the best possible glute activation
training, but it certainly seems to work. If I
were to give glute activation training a shot,
I’d probably just do 30-40 bodyweight glute bridges a couple times per day (largely
because I think I’d find the isometrics to be
boring, and I personally feel clamshells and
hip abduction exercises in my gluteus medius
more than my gluteus maximus). It would be
nice to be able to give glute activation training an unqualified endorsement, and to know
the ideal daily glute activation protocol, but
there are still a lot of unanswered questions.
For now, though, I definitely don’t think it
would hurt to give it a shot.
I DON’T THINK IT
HURTS TO GIVE
DAILY “ACTIVATION
TRAINING” A SHOT.
Next Steps
There are four follow-up studies I’d like to
see.
First, I’d like to see a replication of the present study, with the pre- and post-training assessments modified slightly. In both sessions,
subjects should perform squats with both an
internal attentional focus on squeezing their
glutes, and an external attentional focus on
squatting (well, jumping) explosively. This
would negate my concern related to shifts in
attentional focus potentially influencing the
results. If glute EMG increased with both
an internal and an external attentional focus
from pre- to post-training, that would provide stronger evidence that “true” increases
in glute activation (6) occurred.
Second, I’d like to see an extension of the
present study, using weighted exercises. It
would be nice to see if glute EMG only increases when performing bodyweight exercises, or whether those increases still occur
when performing squats and deadlifts with
70-85% of 1RM.
69
APPLICATION AND TAKEAWAYS
1. While longitudinal studies are needed to demonstrate that activation training can
actually improve strength and hypertrophy outcomes, the present study suggests
that activation training may actually increase activation of the target muscle
during compound exercises. Thus, while I can’t yet give activation training a fullthroated endorsement, it’s certainly worth a shot if you feel like you struggle to
integrate a particular lagging muscle into compound exercises.
Third, I’d like to see a study comparing isometric and dynamic glute activation protocols. Do the increases in glute EMG occur
only after isometric training, or would dynamic glute training lead to similar increases?
Fourth, and most importantly, I’d love to
see a longitudinal study on the subject. Two
groups would complete the same training
program, consisting of loaded deadlift and
hip thrust training. One group would only
perform the loaded barbell exercises, while
the other group would perform a daily session
of glute activation training. The study would
compare strength gains and glute hypertrophy in both groups. If there were concerns
that the activation training might directly and
independently contribute to hypertrophy and
strength gains (beyond potentially making
the deadlifts and hip thrusts more effective),
the group not performing activation training
could perhaps do an extra set of deadlifts and
hip thrusts in each workout to compensate.
70
References
1. Cannon J, Weithman BA, Powers CM. Activation training facilitates gluteus maximus
recruitment during weight-bearing strengthening exercises. J Electromyogr Kinesiol.
2022 Feb 9;63:102643. doi: 10.1016/j.jelekin.2022.102643. Epub ahead of print. PMID:
35189569.
2. Besomi M, Hodges PW, Clancy EA, Van Dieën J, Hug F, Lowery M, Merletti R,
Søgaard K, Wrigley T, Besier T, Carson RG, Disselhorst-Klug C, Enoka RM, Falla D,
Farina D, Gandevia S, Holobar A, Kiernan MC, McGill K, Perreault E, Rothwell JC,
Tucker K. Consensus for experimental design in electromyography (CEDE) project:
Amplitude normalization matrix. J Electromyogr Kinesiol. 2020 Aug;53:102438. doi:
10.1016/j.jelekin.2020.102438. Epub 2020 Jun 10. PMID: 32569878.
3. Stronska K, Golas A, Wilk M, Zajac A, Maszczyk A, Stastny P. The effect of
targeted resistance training on bench press performance and the alternation of
prime mover muscle activation patterns. Sports Biomech. 2020 May 28:1-15. doi:
10.1080/14763141.2020.1752790. Epub ahead of print. PMID: 32460639.
4. Fisher BE, Southam AC, Kuo YL, Lee YY, Powers CM. Evidence of altered
corticomotor excitability following targeted activation of gluteus maximus training
in healthy individuals. Neuroreport. 2016 Apr 13;27(6):415-21. doi: 10.1097/
WNR.0000000000000556. PMID: 26981714.
5. Vigotsky AD, Halperin I, Trajano GS, Vieira TM. Longing for a Longitudinal Proxy:
Acutely Measured Surface EMG Amplitude is not a Validated Predictor of Muscle
Hypertrophy. Sports Med. 2022 Feb;52(2):193-199. doi: 10.1007/s40279-021-01619-2.
Epub 2022 Jan 10. PMID: 35006527.
6. As a note, I’m using the term “activation” in this article because it’s the term used in the
study, but excitation may be a better term, for reasons explained here.
█
71
Study Reviewed: Lean Mass Sparing in Resistance-Trained Athletes During Caloric Restriction:
The Role of Resistance Training Volume. Roth et al. (2022)
Should You Adjust Training
Volume While Cutting?
BY ERIC TREXLER
How should you manipulate training volume while cutting? Due to
a lack of direct research, it’s one of the most commonly debated
questions in evidence-based fitness. A new systematic review adds
fuel to the fire, but clarity still eludes us.
72
KEY POINTS
1. This systematic review sought to determine if differing levels of absolute training
volume tend to correlate with better (or worse) lean mass changes among healthy,
drug-free lifters consuming a hypocaloric (daily energy deficit ≥200kcal) and highprotein (daily protein intake ≥2.0g/kg of fat-free mass) diet. The researchers were
also interested in evaluating relative changes in training volume (e.g., reducing or
increasing volume from baseline).
2. The researchers concluded that actively reducing training volume during energy
restriction may lead to greater losses of lean mass, whereas actively increasing
training volume may lead to better lean mass retention. They also reported that highvolume programs were particularly effective for female lifters, who lost comparatively
little lean mass.
3. Due to several confounding variables, I don’t believe this body of literature is suitable
for answering the research questions at hand. Multiple perspectives are justifiable,
but the question remains unanswered. Due to research on glycogen availability
and detraining responses, I lean toward dropping volume a bit during particularly
aggressive cutting phases, but the lack of direct research leaves plenty of room for
individualization and differing perspectives.
W
eight loss phases (colloquially
known as “cutting phases”) can
get tough. Of course, there are
the obvious challenges: hunger, lethargy,
and food cravings are inherently unpleasant.
However, fairly aggressive cutting phases
(that is, cutting phases that implement a
large daily energy deficit or result in very
low body-fat levels) can lead to challenges
in the gym as well. As cutting phases become increasingly aggressive, many lifters
will tend to notice lower energy levels and
impaired performance in the gym, along with
hindered recovery from session to session.
In light of these observations, many lifters
cut some training volume in order to make
their program more efficient, effective, and
sustainable. This drop in volume might take
the form of reducing accessory exercises,
reducing set volume, or erring toward lower
repetition ranges. But is this really an advisable strategy?
You might be surprised to learn that we have
shockingly little evidence to lean on when
answering this question, and none of it addresses the topic in a truly direct manner.
As Greg and I have previously discussed on
our podcast (one, two), this question is frequently asked (and subsequently debated) in
evidence-based fitness circles. However, we
don’t have studies that actually compare longitudinal body composition changes in multiple groups undergoing experimental manipulations in training volume within the context
of an intentional energy deficit. As a result,
the presently reviewed paper (1) sought to
address this question by leaning on indirect
73
research. They did a systematic literature
search to gather studies investigating changes in lean mass among healthy, drug-free
lifters consuming a hypocaloric (daily energy deficit ≥200kcal) and high-protein (daily protein intake ≥2.0g/kg of fat-free mass)
diet for at least four weeks in duration, with
the intention of determining if differing levels of training volume tend to correlate with
better (or worse) lean mass changes. They
were also interested in determining if relative changes in volume (that is, increasing
or decreasing volume throughout a program)
impacted lean mass changes. It would have
been virtually impossible to justify pooling
these data together for a formal meta-analysis, so the researchers instead opted to summarize the individual studies and evaluate
the body of research in a qualitative manner,
looking for general trends and patterns that
might provide insight about the influence of
training volume on lean mass changes during
energy restriction.
After reviewing the available literature, the
researchers concluded that during energy
restriction: 1) females on high-volume programs tended to retain more lean mass than
males; 2) there was insufficient evidence to
confidently conclude that high-volume programs spare more lean mass than low-volume programs (although the data seem to
lean in that direction, particularly for female
lifters); 3) actively reducing training volume
may lead to greater losses of lean mass; and
4) actively increasing training volume may
lead to better lean mass retention. However,
they also cautioned that their “conclusions
are based on correlational data, which pre-
cludes the ability to draw strong causal inferences.” Aside from that caveat, I respectfully disagree with the premise that this body
of research is suitable for answering the research questions at hand, and I believe there
are several major confounding factors that
are substantially influencing the researchers’
conclusions. As such, this article will discuss
how the researchers’ conclusions came to be,
which confounding variables might be driving those conclusions, and how other areas of
research might point us toward entirely opposite conclusions.
Purpose and Hypotheses
Purpose
The authors of this systematic review sought
to investigate “whether higher training volumes are more appropriate for sparing lean
mass during [caloric restriction].” While not
directly stated as a purpose, they also sought
to investigate the impact of relative changes
74
(from baseline) in training volume during energy restriction.
Hypotheses
As is common with systematic reviews, the
researchers did not explicitly commit to a hypothesis. However, they highlighted the fact
that resistance training volume “is a wellknown anabolic stimulus for muscle hypertrophy” in the introduction of the paper, and
hinted at a rationale by which higher training
volumes might be advantageous during caloric restriction.
Methods
I don’t think the researchers explicitly claimed
the label of “systematic review” within this
paper, but it was a systematic review nonetheless. The researchers conducted a systematic
search of the literature, with the intention of
aggregating all relevant studies published between 1990 and December of 2020. They were
specifically looking for studies that evaluated
the effects of various training volume levels on
lean mass changes during caloric restriction in
healthy, drug-free lifters. In order to be included in the review, studies needed to: “1) include
lean, healthy, drug-free resistance-trained individuals, 2) last at least 4 weeks, 3) investigate hypocaloric conditions (≥200 kcal deficit/day), 4) report pre-post data for changes in
lean mass, 5) employ a high-protein diet ≥2.0
g/kg [of fat-free mass], and 6) present information about [resistance training] variables
used.” Unless I missed it, I don’t believe they
provided specific criteria for how lean participants needed to be in order to satisfy these inclusion criteria.
Notably, their inclusion criteria didn’t provide particularly specific requirements for
how studies reported volume. The researchers stated that training volume “was preferentially expressed in total tonnage (number of
repetitions × number of sets × intensity load;
kg) or, when not applicable, in weekly sets/
muscle group. In the event that information
regarding [resistance training] variables was
missing, we quantified [resistance training]
volume as sets/exercise.” As for categorizing training volumes as “high” or “low,” the
researchers stated that “total sets per muscle
group per week were categorized as follows:
low (< 5), medium (5–9), or high (10 +).”
The researchers would go on to categorize
studies based on whether or not they used
“progressive overload over time” or involved
“reduced volume” (that is, a reduction in volume over the course of the program), but I
couldn’t find any details about how these
classifications were operationally defined.
Based on the lax inclusion criteria and lack
of clear definitions, there were multiple studies that “made the cut” to be included in the
systematic review, but didn’t meet any of
the classifications outlined – in other words,
they weren’t categorized as high volume or
low volume, they weren’t identified as using
progressive overload, and they weren’t identified as using volume reductions over time.
This presents a fairly substantial challenge
for making inferences about training volume,
as we’ll discuss later.
After conducting the search and applying the
inclusion and exclusion criteria, the researcher
summarized the included studies by constructing a table containing information about study
75
methods, study duration, body composition
changes, protein intake, resistance training
program variables, and the estimated energy
deficit. In conjunction with this summary table, the researchers also used a common scale
(the PEDro scale) to assess the quality of each
included study. This was not a meta-analysis,
so the researchers did not attempt to formally
pool the data from these studies to calculate
an overall effect size. Rather, the researchers assessed the body of research in a qualitative manner, looking for general trends and
patterns that might provide insight about the
influence of training volume on lean mass
changes during energy restriction.
Findings
The initial literature search yielded 2791 total
studies. After screening these studies and applying exclusion criteria, the researchers ended
up with a total of 15 studies, including 8 case
studies. While case studies are not compatible
with the PEDro scale, all other studies (except
for one) were categorized as being “moderate
quality” or higher. Absolute lean mass changes
(from pre-testing to post-testing) in the included studies are presented in Figure 1. The researchers also provided a figure documenting
the specific time course of lean mass changes
(Figure 2). When interpreting figures 1 and 2,
there are a couple of important things to keep
in mind: 1) the results are divided by sex (female-only versus “other,” which includes all
the male studies and a study by Campbell et al
[2] with a mixed-sex sample); 2) the study by
Stratton et al (3) was not included in these figures, as whole-body lean mass changes were
not reported.
In the absence of a quantitative meta-analysis,
the “results” of this study consisted of a set of
qualitative observations of trends and patterns
in the literature, which were presented over
the course of several paragraphs. In short, the
researchers concluded that during energy restriction: 1) females on high-volume programs
tend to retain more lean mass than males; 2)
there is insufficient evidence to confidently
conclude that high-volume programs spare
76
more lean mass than low-volume programs
(although the data seem to lean in that direction, particularly for female lifters); 3) actively
reducing training volume may lead to greater
losses of lean mass; and 4) actively increasing
training volume may lead to better lean mass
retention. As a reminder, the researchers did
not operationally define these categorizations
(“reducing training volume” or “increasing
training volume”) in quantifiable terms; it’s
unclear if they’re specifically talking about
changes in total volume load, changes in set
volume, changes in repetition ranges, changes within a training block, or changes from
the beginning to the end of a full program. To
comprehensively assess the impact of relative changes in volume, you’d (ideally) want
detailed information about how participants
were training prior to study initiation, but this
type of information was generally unavailable.
As a result, it’s hard to conclusively determine
which studies are actually observing a relative
increase or decrease in training volume, relative to baseline.
Interpretation
There’s a common misconception that meta-analyses are “better” than systematic reviews, or that they provide a more robust level of evidence. In reality, that’s not always the
case. A good meta-analysis will begin with
a process that looks virtually identical to the
systematic review process. However, after
all of the studies are gathered and filtered according to the exclusion criteria, researchers
who intend to conduct a meta-analysis have
an important question to wrestle with: should
we actually move forward with a quantitative
THERE ARE FAR TOO
MANY CONFOUNDING
FACTORS WITHIN THIS
BODY OF LITERATURE
TO ATTRIBUTE
DIFFERENCES IN
LEAN MASS CHANGES
TO DIFFERENCES IN
TRAINING VOLUME.
77
statistical analysis? Sometimes, researchers
will begin a project with the intention of conducting a meta-analysis, but will ultimately
choose to do a systematic review (with no
statistical analysis) instead. This is not a sign
of failure or cutting corners, but rather a sign
of prudent decision making and excellent
discernment. There are many cases where it
simply isn’t appropriate to mathematically
combine a group of studies – due to the population studied, the study design, the details of
the intervention, the outcomes measured, or
any number of factors, the studies are simply
too different from one another to lump them
together and crunch the numbers.
You might be wondering how this relates
to the presently reviewed paper, given that
there’s no indication that these researchers
intended to conduct a meta-analysis in the
first place. In my opinion, this paper finds
itself in a scenario that is analogous to an intended meta-analysis with data that are simply too heterogeneous to combine. The presently reviewed paper sought to determine if
alterations in training volume would meaningfully impact lean mass retention during
energy restriction. Those types of inferences
can only be made in a robust and generalizable manner if we believe that the included
studies are generally similar in nature, except for quantifiable differences in resistance
training volume from study to study. I respectfully reject that premise, and therefore
place limited confidence in the conclusions
proposed in the paper. In my opinion, there
are far too many confounding factors within
this body of literature to attribute differences
in lean mass changes to differences in train-
ing volume. These researchers spent an entire
paper arguing their case, so a point-by-point
rebuttal would require a document of similar
length, which isn’t particularly feasible for
an Interpretation section (or feasible for the
busy readership of MASS). However, I believe I can concisely convey my perspective
by identifying a few of the more glaring examples that highlight my skepticism.
First, let’s take a look at the studies featuring male-only and mixed-sex samples in figures 1 and 2. The studies by Schoenfeld et al
(4), Kistler et al (5), and Robinson et al (6)
report substantial loss of lean mass (4+ kg).
These are case studies on contest preparation
in male physique athletes; considerable loss
of lean mass is to be expected, as these athletes do everything in their power to optimize
muscularity in the offseason, then undergo
aggressive energy restriction to achieve an
extremely lean physique. The Schoenfeld
study is categorized as a high-volume study,
and insufficient training volume information
is provided by the Robinson and Kistler studies to allow for categorization. The only male
contest prep study that fails to report considerable loss of lean mass is by Pardue et al (7),
which is also categorized as a high-volume
study, but that’s only true if you ignore the
BodPod data. According to DEXA, the participant got to around 5.1% body-fat, but according to BodPod, they got to around 9.6%
body-fat. If we assume that the “true” value
is somewhere in-between (as a co-author of
the paper, I believe this to be accurate), the
lean mass losses look a lot more similar to
the other case reports in this area. So, in the
male studies, we’ve got four case reports;
78
two report insufficient volume information
to be categorized, one high-volume study
reported considerable lean mass losses, and
another high-volume study reported large
or small losses of lean mass, depending on
which measurement you put more faith in.
In summary, these case reports give us no
indication that differences in training volume, or longitudinal changes in training volume, meaningfully alter lean mass retention.
If we shift our focus to the other male studies in this systematic review, we see minimal
lean mass losses reported by Campbell et al
(2) and Dudgeon et al (8). This is fairly intuitive; while the case reports involved people
with high baseline muscularity accomplishing
large amounts of total weight loss in order to
achieve extremely lean physiques, these other samples weren’t particularly huge or lean
at baseline, they didn’t lose much weight, and
they weren’t trying to get shredded. Even if
we ignore those factors, the Campell study is
categorized as utilizing progresive overload
over time but also “reduced volume,” whereas the Dudgeon study is only categorized as
utilizing progresive overload over time. At
the surface level, this makes the effect of volume reduction seem equivocal, as both are
treated as lean mass retention “success stories,” compared to the other studies including
male participants. If we dig deeper than the
surface level, we see that the Campbell study
actually increased set volume over time; it
looks like it was categorized as a “volume
reduction” study because they included a deload halfway through the training program.
While this is technically a transient volume
reduction, it doesn’t really reflect the spirit
of the research question; incorporating de-
loads within a high-volume program is quite
a bit different from intentionally reducing the
overall volume of a program to accommodate
the challenges of energy restriction. It’s also
worth noting that participants could have utilized deloads in their usual training before the
study, so this may or may not represent a “reduction” relative to baseline training habits.
The lone example of the male studies that
seems to directly support increasing (or at
least maintaining) volume during a cut is the
study by Mitchell et al (9). Participants had
higher volume in the first 8 weeks than they
did in weeks 9-16, and lean mass changes were
more favorable in the first 8 weeks (+0.4kg)
than the latter weeks (-0.9kg). However, there
are several catches. This was an observational study in a small cohort of bodybuilders,
so we would expect a greater threat for lean
mass losses later in prep than earlier in prep,
regardless of training specifics. It is known
that some number of participants consumed
high-calorie, high-carbohydrate refeeds prior to the 8-week midtesting, but there aren’t
enough specifics to determine how that might
have impacted the lean mass readings at that
time point. It’s quite possible that some extra
glycogen repletion and water weight could
have inflated those lean mass values at week
8. In addition, the researchers did not control
the training programs of participants; they
merely calculated volume load (repetitions ×
sets × load) after the fact. As such, we can’t
really conclude that an intentional decision to
drop volume load led to lean mass losses. It’s
equally possible that performance dropped a
bit over the course of contest prep (it almost
always does in male bodybuilders), and that
79
the observed drop in volume load represents
an extremely common consequence of contest prep, rather than a strategic mistake causing the loss of lean mass.
As a side note, these bodybuilders got to an
average body-fat percentage of <7%, but lost
only 4.1kg of total body mass during their entire contest prep. Given the context, this is a
very modest magnitude of weight loss. They
also lost only 0.5kg of lean mass from the
start of prep to the end of prep. If you offered
me that deal at the beginning of prep, I’d take
it 10 times out of 10. Given all these caveats, it’s extremely hard to view the results of
Mitchell et al as a cautionary tale about the
perils of intentional volume reduction during
energy restriction. We don’t know if the volume reductions were intentional or a simple
consequence of performance impairment, if
the time course of contest prep was a major
confounding variable, and if the results for
lean mass retention were actually excellent.
In summary, the male-only and mixed-sample studies don’t really tell us much about the
research question at hand. Unfortunately, we
don’t get much from the female-only studies
either. Only two samples documented losses of lean mass. One was the ketogenic diet
group in a study (10) by Vargas-Molina et al
(previously reviewed here), which was categorized as utilizing progresive overload over
time but also incorporating volume reduction. As discussed many times in MASS (one,
two, three), this small reduction in lean mass
is consistently observed in keto studies (regardless of training volume), and may be related to the water weight loss associated with
severe carbohydrate restriction. In addition,
this was another instance where a study was
categorized as a “volume reduction” study,
despite the fact that volume progressively increased within each training block. Volume
increased from week-to-week as participants
transitioned through 1-week strength, hypertrophy, and muscular endurance microcycles,
which culminated in a deload in week 4 of
each training block. With this in mind, the
studies by Vargas et al and Campbell et al are
more relevant to research questions about incorporating deloads rather than strategically
reducing the volume of your actual training
sessions during a cut.
The other female study reporting a loss of
lean mass was by van der Ploeg et al (11).
This was an observational study of five female bodybuilders during the 12 weeks prior to competition. There aren’t many details
about their training programs, but they certainly don’t seem like low-volume programs
– participants lifted for an average of nearly
6 hours per week, with the majority of sets
in the 10-12 repetition range. This was categorized as a volume reduction study, but,
once again, we have no clear indication that
any of them were dropping volume as part
of an intentional training strategy. The paper
states that “None of the bodybuilders reported an increase in the volume of weight-training over the 12 [weeks], but four of them
[out of five] stated that either the resistance
or number of repetitions decreased as body
mass was lost.” This could merely reflect the
very common observation that your performance will likely drop throughout contest
prep, and if you maintain the same general
program structure, you’ll be unlikely to com-
80
plete the same number of repetitions with the
same absolute training loads as prep continues. For example, it’s possible for absolute
volume load (sets × reps × absolute load in
kg) to decrease, even if you’re maintaining
the same relative volume load (sets × reps ×
relative load in %1RM), purely due to performance decrements over time. A drop in absolute volume load could reflect an intentional
decision to cut volume, but it could also reflect impaired performance on the exact same
training program. It’s also worth noting that
the competitors, on average, doubled their
cardio volume during this time period, reaching nearly 10 hours per week of cardio.
Another confounding variable favoring lean
mass losses in this study pertains to the competitive category of the participants. They
were competing in the “physique” division,
and appear to have been striving for a different on-stage look than has been seen in similar case reports of female competitors. Based
on DEXA values, these competitors began
the observation period with nearly 56kg of
fat-free mass, and finished prep at just under
10% body-fat. This combination of fat-free
mass and leanness represents a look that falls
more on the bodybuilding side of the competitive class spectrum than the bikini side,
and would intuitively increase the likelihood
of lean mass losses. You might push back
on this assertion to some extent, given that
head-to-head body composition comparisons
are challenging when there are different measurement techniques involved, but it’s a factor worth considering.
Overall, I don’t believe the empirical data
included in the presently reviewed paper
lend adequate support for the conclusions
about differences between high-volume and
low-volume approaches or longitudinal alterations in training volume. If we revisit the
research question and purpose of the systematic review, I simply don’t think this body
of research is well-suited for answering the
question at hand. Weekly sets per muscle
group were reported for three studies, and total volume load was available for only one
study; for the other 11, training volume was
quantified quite vaguely. In addition, “energy
restriction” took many forms, ranging from
recreational lifters losing a few pounds to experienced bodybuilders doing a full contest
prep. On top of that, “volume reduction” did
not specifically refer to the strategic reduction
of training session volume to accommodate
the unique challenges of energy restriction.
Rather, it appears to have included things like
incorporating deloads into a periodized training program, or experiencing very predictable performance decrements late in contest
prep.
However, that doesn’t mean I think the proposed conclusions are entirely indefensible.
The researchers did a great job laying out the
theoretical, mechanism-driven rationale by
which maintenance of volume (or even increases in volume) might be favorable during
energy restriction. They also provide some
valuable insights about female lifters tending to retain lean mass more effectively than
male lifters within these studies, and why that
might be the case (although that’s probably a
discussion for another day). However, I think
there is a major disconnect between what
these studies tell us and the question we’re
81
trying to answer. The authors were very transparent about the limitations of their paper and
the tentative nature of their conclusions, so I
am hopeful that people will practice caution
when reading it, and won’t mistakenly assume that it provides a robust and definitive
answer to this frequently debated question.
sue if set volume per session is high enough.
In other words, when a cut starts to get pretty
aggressive, you might have to reduce training
loads substantially if you insist on maintaining the exact same set and rep schemes, and
your “borderline junk volume” can quickly
become “absolute dumpster fire volume.”
Sometimes we have to form tentative conclusions before strong scientific evidence is
available. The topic at hand has not been directly studied, but, in the meantime, people
are still doing fat loss phases and making decisions about training volume. While I welcome more direct research to provide some
clarity, I personally lean toward keeping
training variables pretty stable during conservative or moderate cutting phases, but sacrificing a bit of training volume (out of necessity) during more intense cutting phases, and
my reasoning is two-fold. First, as discussed
in a previous MASS article, even modest glycogen reduction has the potential to threaten high-volume resistance exercise performance. This might not be a huge deal when
you’re in neutral or positive energy balance,
or even if you’re doing a very conservative
cut. However, if you’re cutting pretty aggressively (which generally involves a very large
daily energy deficit or a very low body-fat
level), glycogen repletion is a continuous uphill battle. Due to the persistent state of low
energy availability, the limited carbohydrate
you ingest is quickly and consistently put
to use, such that it can be hard to make it to
your workout with sufficient glycogen to fuel
a tough workout full of high-repetition sets.
Even with an approach that favors more lower-rep sets, glycogen can still become an is-
If you trim some of your set volume and implement more higher-load, lower-rep sets,
you can theoretically limit the degree to
which glycogen depletion impacts training
performance by shifting the bioenergetic
burden of your workouts toward other energy
systems that aren’t glycogen-dependent. By
cutting volume in this manner, we have an
opportunity to tailor the metabolic demands
of our workouts to the relative capacities of
our different energy systems, while reducing total training volume in a manner that
accommodates our transiently reduced energy level and recovery capacity from session
to session. Ultimately, the same stimuli that
promote hypertrophy should generally promote lean mass retention during a cut. What
changes is your recuperative capacity, since
short-term (energy balance) and long-term
(body fat) energy availability decrease as a
cut progresses. It’s possible that my perspective on this topic is shaped by my tendency
to err toward higher levels of volume during
positive and neutral energy balance, as I think
it’s valuable to explore the upper limits of
your “ideal volume range” when conditions
are ideal for muscle growth (that is, you have
adequate energy availability and optimized
recovery capacity). If you repeatedly observe
that increasing volume dramatically offsets
lean mass losses during a cut, this could pos-
82
SMALL AMOUNTS OF
VOLUME SEEM TO GO
A LONG WAY TOWARD
PRESERVING LEAN MASS
GAINS IN CONDITIONS THAT
FAVOR MUSCLE ATROPHY.
sibly suggest that you would have benefited from doing more volume in your gaining
phase that preceded the cut.
This whole glycogen rationale seems to imply
that it’s okay to drop volume as long as intensity is maintained, and there’s some research
to reinforce this idea. Bickel et al (12) studied how differing levels of volume reduction
would impact the maintenance of training
adaptations in cohorts of older and younger
adults. After a 16-week lower-body resistance
training program, participants spent the next
32 weeks doing one third of their initial training volume, one ninth of their initial training
volume, or no training at all. As shown in
Table 1, low-volume training was very effective at maintaining training-induced increases in lean mass, particularly in the younger
cohort. Of course, there is a major limitation
to acknowledge: the participants in this study
were not on an energy-restricted diet. In addition, these were untrained subjects, who (by
definition) aren’t challenged with the task of
maintaining the high-level muscular adaptations seen in advanced lifters. As a result, it
requires a leap of faith to apply these findings
to the topic of volume fluctuations while cutting in well-trained lifters. Nonetheless, the
results do suggest that even small amounts of
volume seem to go a long way toward preserving lean mass gains in conditions that
favor muscle atrophy. Personally, I am more
83
comfortable accepting these limitations when
compared to the numerous confounding variables influencing the presently reviewed systematic review by Roth et al (1), but that’s
admittedly a judgment call.
Finally, there’s one more wrinkle to consider in this volume discussion. Even when we
understand the typical effect of a particular
training or nutrition strategy, we still have to
be mindful of differences between individuals. An individual’s experience won’t always
mirror the average group-level response observed in studies. Given that we don’t even
have a firm understanding of the typical response to volume alterations during energy
restriction, we should be even more open to
tailoring approaches based on a given individual’s response. In the absence of an energy deficit, we know that there is considerable variation when determining the optimal
amount of training volume to promote muscle hypertrophy (13). When you add energy
restriction into the mix, things might get even
more complicated. When cutting, it’s very
possible that some lifters will find it particularly challenging to perform well and recover effectively on a high-volume program,
while others might have a very different experience. With this in mind, it’s important to
lean on past experience when determining if
you should trim some volume during a cut.
If you don’t have any experience to lean on,
my inclination is to assume that most people
will do a little bit better with some modest
volume reductions during aggressive cutting
phases. However, if you aren’t comfortable
with this level of speculation, you could always include a mixture of high-volume and
low-volume training sessions early in the
cutting phase, and continuously monitor and
compare performance and recovery responses
to each type of session. As the cut progresses,
you’ll quickly gain more clarity about whether you should be increasing, maintaining, or
reducing your training volume.
Next Steps
This is probably the most obvious “Next Steps”
section I’ve ever written – in short, somebody
needs to do us a huge favor and actually investigate this research question. A nice approach
would be to recruit a sample of participants
and have them complete an 8-week familiarization phase, which would entail getting
acclimated to a fairly standard high-volume,
hypertrophy-focused training program in neutral energy balance. After this familiarization
phase, participants would be randomly assigned to one of multiple groups; one group
would maintain a similar amount of training
volume, and another group would implement
an intentional reduction of volume. Of course,
we’d probably need multiple volume-reduction groups (or multiple studies) to determine
if it’s particularly beneficial to drop volume by
reducing repetition ranges or by trimming set
volume (or neither). Throughout the duration
of the training program, both groups would be
in negative energy balance, with a daily energy
deficit of around 250-500 kcal/day. Until this
type of study is published and subsequently
replicated, the debate about manipulating volume during aggressive weight loss phases will
rage on. In the meantime, we’re left to lean on
anecdote, theory, and indirect evidence, because it’s all we have.
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APPLICATION AND TAKEAWAYS
When it comes to the topic at hand, the only “wrong” answer is (in my opinion) an
extremely confident one. At this point in time, we simply don’t have the applied,
empirical evidence to confidently determine if lean mass retention is optimally
promoted by high-volume or low-volume programs, or to determine if lean mass
retention is optimally promoted by increasing, decreasing, or preserving volume,
during periods of intensive energy restriction. In lieu of proper experimental evidence,
we are left to form tentative conclusions based on anecdotal experience, mechanistic
speculation, and indirect evidence. Based on evidence related to the effects of
glycogen depletion on glycolytic exercise performance, and the effects of volume
reduction on the maintenance of resistance training adaptations, I generally lean in
favor of sacrificing volume to accommodate the unique demands of aggressive energy
restriction. However, due to the glaring lack of direct evidence, this conclusion is
extremely tentative in nature, and alternative conclusions are defensible. As such, this
debate is sure to rage on until direct evidence becomes available. In the meantime,
lifters and practitioners should lean on their experience and individualize their approach
as needed; some lifters might struggle to perform well and recover from high-volume
programs while cutting, whereas others might have entirely different experiences.
85
References
1. Roth C, Schoenfeld BJ, Behringer M. Lean mass sparing in resistance-trained athletes
during caloric restriction: the role of resistance training volume. Eur J Appl Physiol.
2022 Feb 11; ePub ahead of print.
2. Campbell BI, Aguilar D, Colenso-Semple LM, Hartke K, Fleming AR, Fox CD, et al.
Intermittent Energy Restriction Attenuates the Loss of Fat Free Mass in Resistance
Trained Individuals. A Randomized Controlled Trial. J Funct Morphol Kinesiol. 2020
Mar;5(1):19.
3. Stratton MT, Tinsley GM, Alesi MG, Hester GM, Olmos AA, Serafini PR, et al. Four
Weeks of Time-Restricted Feeding Combined with Resistance Training Does Not
Differentially Influence Measures of Body Composition, Muscle Performance, Resting Energy Expenditure, and Blood Biomarkers. Nutrients. 2020 Apr 17;12(4):E1126.
4. Schoenfeld BJ, Alto A, Grgic J, Tinsley G, Haun CT, Campbell BI, et al. Alterations
in Body Composition, Resting Metabolic Rate, Muscular Strength, and Eating Behavior in Response to Natural Bodybuilding Competition Preparation: A Case Study.
J Strength Cond Res. 2020 Nov;34(11):3124–38.
5. Kistler BM, Fitschen PJ, Ranadive SM, Fernhall B, Wilund KR. Case study: Natural
bodybuilding contest preparation. Int J Sport Nutr Exerc Metab. 2014 Dec;24(6):694–
700.
6. Robinson SL, Lambeth-Mansell A, Gillibrand G, Smith-Ryan A, Bannock L. A nutrition and conditioning intervention for natural bodybuilding contest preparation: case
study. J Int Soc Sports Nutr. 2015;12:20.
7. Pardue A, Trexler ET, Sprod LK. Case Study: Unfavorable But Transient Physiological Changes During Contest Preparation in a Drug-Free Male Bodybuilder. Int J
Sport Nutr Exerc Metab. 2017 Dec;27(6):550–9.
8. Dudgeon WD, Kelley EP, Scheett TP. Effect of Whey Protein in Conjunction With
a Caloric-Restricted Diet and Resistance Training. J Strength Cond Res. 2017
May;31(5):1353–61.
9. Mitchell L, Slater G, Hackett D, Johnson N, O’connor H. Physiological implications of preparing for a natural male bodybuilding competition. Eur J Sport Sci. 2018
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Jun;18(5):619–29.
10. Vargas-Molina S, Petro JL, Romance R, Kreider RB, Schoenfeld BJ, Bonilla DA, et
al. Effects of a ketogenic diet on body composition and strength in trained women. J
Int Soc Sports Nutr. 2020 Apr 10;17(1):19.
11. van der Ploeg GE, Brooks AG, Withers RT, Dollman J, Leaney F, Chatterton BE.
Body composition changes in female bodybuilders during preparation for competition. Eur J Clin Nutr. 2001 Apr;55(4):268–77.
12. Bickel CS, Cross JM, Bamman MM. Exercise dosing to retain resistance training adaptations in young and older adults. Med Sci Sports Exerc. 2011 Jul;43(7):1177–87.
13. Scarpelli MC, Nóbrega SR, Santanielo N, Alvarez IF, Otoboni GB, Ugrinowitsch
C, et al. Muscle Hypertrophy Response Is Affected by Previous Resistance Training
Volume in Trained Individuals. J Strength Cond Res. 2020 Feb 27; ePub ahead of
print.
█
87
Study Reviewed: The Effect of 3 vs. 5 Days of Training Cessation on Maximal Strength. Travis et
al. (2022)
Should You Take A Few Days Off
to Peak Strength?
BY MICHAEL C. ZOURDOS
I’ve previously contended that tapering is overrated. However, I
haven’t touched much on training cessation as a tapering strategy.
Is training cessation the tapering strategy that you need to peak
strength? A new study examined this concept.
88
KEY POINTS
1. Researchers had male and female powerlifters perform a strength-focused training
block for four weeks. After four weeks, isometric force, body composition, and
psychological scales were assessed, then subjects took either three or five days of
complete rest before having outcome measures retested.
2. There were no significant differences between groups for any outcome measure
from pre- to post-training cessation. There was a statistically significant decrease for
isometric bench press force in the five day group, but the change was small (-2.38%).
3. Overall, this study showed that neither three nor five days of training cessation were
beneficial for performance. The body of literature surrounding training cessation is
underwhelming. At this point, I wouldn’t expect a large boost from a traditional taper
or training cessation unless these strategies follow an overreaching phase, which
causes substantial fatigue.
I
’ve generally started MASS articles on
tapering by whining that the concept is
overrated. Then, I usually acknowledge
that tapering is still good practice despite
being overrated. Ultimately, these articles
conclude with a statement indicating that
maintaining load and decreasing volume are
the tenets of tapering, and that tapering is
most beneficial following a period of overreaching. However, an emerging strategy in
the tapering research is training cessation,
which Dr. Hayden Pritchard touched on in a
MASS guest article. The presently reviewed
study from Travis et al (1) had 19 lifters (16
men and 3 women) complete a four-week,
powerlifting-specific (i.e., strength-focused)
training block. Then, subjects took three days
off of training (n = 9) or five days off of training (n = 10). Squat, bench press, and deadlift
one-repetition maximum (1RM) were tested
before and after the first four weeks of training. The researchers assessed isometric squat
and bench strength, and various body com-
position metrics, immediately after the four
weeks (pre-training cessation) and immediately after training cessation. Strength increased during the four-week training block
in both groups, with no between-group differences. Training cessation did not have
any statistically significant effects on isometric squat strength; however, there was a
statistically significant (p < 0.001) but small
(-2.38%) decline in isometric bench press
strength from before to after five days of
training cessation. There were no significant
differences between groups for body composition changes during the training cessation
periods. However, the changes in various
measurements such as skeletal muscle mass
of the torso, right arm, and left arm had effect
sizes favoring the three day group. Overall,
these results suggest that lifters can maintain
1RM strength on the powerlifts following
three days of training cessation, but upper
body strength may slightly decrease after five
days of complete rest. This article will aim to:
89
1. Review the present findings.
2. Place the present findings in context with
the whole body of literature.
3. Examine if training cessation can increase
strength or only maintain it.
4. Discuss the popularity of training cessation to peak strength among lifters.
Purpose and Hypotheses
Purpose
The purpose of the reviewed study was to
compare changes in strength, body composition, and psychological state following three
and five days of training cessation after a
preceding four-week powerlifting training
block.
Hypotheses No hypotheses were provided.
Subjects and Methods
Subjects
19 powerlifters (16 men and 3 women) participated in the present study. They were all
well-trained and members of a University
powerlifting club. Some lifters had competed
in sanctioned powerlifting meets, while oth-
ers had not yet competed. Subjects’ characteristics and baseline relative strength levels
are in Table 1.
Study Overview
The presently reviewed study was a parallel-groups design with 19 powerlifters (16
men and 3 women) who were pair-matched by
Wilks Score into two groups (five and three
days of training cessation). For example, if two
lifters with Wilks Scores of 435 volunteered
for the study, one would have been placed in
the three day group and the other in the five
day group so that baseline Wilks Scores would
be as similar as possible between groups.
First, all 19 lifters performed the same four
weeks of powerlifting-focused (low rep and
high load) training for four weeks. Then they
did not train for either three or five days.
The researchers assessed squat, bench press,
and deadlift strength pre-study and after four
weeks (before training cessation). Isometric
strength, body composition, and psychological scales were assessed pre-study, after four
weeks (before training cessation), and after
training cessation. Table 2 lists more specifics
of all outcome measures.
Training Program
Table 3 shows the specifics of the training
program. In brief, subjects trained three times
90
per week for the four-week block. They performed 1 to 5 reps per set with 80-100% of
1RM on the back squat, bench press, close
grip bench press, and barbell row (days one
and two) or the back squat, bench press, and
deadlift (day three).
Findings
1RM Strength
Strength significantly increased (p < 0.05)
during the four-week block for most measures
and increased non-significantly for other measures (Table 4) with no group differences.
Isometric Force
During the four-week training block, iso-
metric bench press strength statistically increased (p < 0.017); however, isometric
squat strength did not significantly change (p
> 0.05). During the training cessation period, isometric squat force did not significantly
change (p > 0.05) in either group; however,
isometric bench press force significantly decreased (p < 0.001; -2.38%) only in the five
day training cessation period.
Body Composition
Body composition changes related to fat-free
mass, fat mass, body mass, and skeletal muscle mass tended to increase over the fourweek block. Further, there were no significant within- or between-group differences for
any body composition measure from pre- to
91
post-cessation. Importantly, Figure 1 shows
the individual response for changes in fat
mass and skeletal muscle mass, which were
small and variable.
Psychological Scales
Most measures did not change throughout the
study. The only change from before to after
the cessation period was that muscle soreness
decreased (p = 0.04). Overall, the psychological measures were uninteresting and did not
significantly impact the findings; thus, we
won’t harp on them.
Statistical Criticisms and Musings
Two minor points, and a third, which is a statistical note. First, I have previously reviewed a
study from this research group. In that article, I
said the following, which I would like to state
here as well in the interest of full disclosure:
“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….In
short, I know the authors well, and you can decide what to do with that information.”
92
Second, the previous study I reviewed from
this research group was also a tapering study.
In that article, I noted that outcome measures
were assessed pre-study and again after the
tapering period, but not immediately before
and after the tapering period. I suggested that
it would have been ideal for the researchers
to also assess outcome measures immediately
before the taper period to assess the true effects of the taper. Along with that suggestion,
I noted that it would be difficult to test 1RM
strength immediately before and after the
taper period as 1RM tests within 1-2 weeks
of each other would be time-consuming and
fatiguing, but isometric tests would be feasible. In the current study, the researchers performed isometric tests at the beginning and
end of the training cessation period, which
was excellent. The presently reviewed study
did not assess 1RM strength after the training cessation period, presumably due to the
aforementioned time and fatigue considerations. I cannot fault the authors for that
choice since that would have been admittedly
difficult to do. However, it is worth pointing
out that the decrease in isometric bench press
force during the five days of training cessation does not necessarily indicate a decrease
in dynamic bench press strength.
As I noted in the last tapering review, since
testing 1RM before and after the training cessation period would have been challenging,
the authors could have used submaximal velocity to predict 1RM (video and downloadable calculator) before and after the cessation
period. While this prediction does have a degree of error, it would have been feasible and
straightforward.
Third, although there were no significant
changes in body composition measures from
pre- to post-cessation, the authors did note
moderate to large effect sizes in favor of the
three-day group for changes in various measures. For example, the authors reported a
0.78 between-group effect size favoring the
three-day group for skeletal muscle mass.
Yet, the three-day group only increased
skeletal muscle mass by 0.9% (33.1 ± 4.9kg
to 33.4 ± 5.2kg), while the five-day group
had a 1.1% decrease (35.2 ± 8.1kg to 34.8
± 7.8kg). When using the equation [(mean
three-day group change score – mean fiveday group change score) / pooled standard
deviation of the pre-test mean], I get an effect size of 0.10. However, the authors calculated a Cohen’s dz value (then converted
it to hedges g, which accounts for sample
size). A Cohen’s dz uses a similar equation
to the one above, except it divides by the
pooled standard deviation of the change
scores instead of the pre-test mean, inflating
the effect size. Overall, it’s somewhat immaterial as the raw values are presented, and
the changes from pre- to post-cessation are
small. Nonetheless, if someone wanted to
argue that the difference in skeletal muscle
mass was meaningful, this could very well
be explained by changes in total body water.
In fact, total body water decreased by 1.4%
from pre- to post-cessation in the five-day
group but increased by 0.8% in the three-day
group. Thus, any muscle mass changes favoring the three-day group may be explained
by the decrease in body water. However, it
should be stressed that the changes in skeletal muscle mass and total body water are
small and unlikely to be meaningful.
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Interpretation
Technically, training cessation is a tapering strategy. As we’ve covered before, there
are various tapering strategies, including
step tapers and exponential tapers. As I already whined about, my general thoughts are
that tapering is largely overrated. Tapering
may yield a small (1-3%) benefit (2, 3), on
average, but there should be a reason to taper
(i.e., substantial fatigue is present). In other
words, if someone is training with low volume, a taper may not be warranted. However, if someone is overreaching, tapering (i.e.,
decreasing volume and maintaining load lifted) is likely beneficial for maximal strength.
Since we’ve covered tapering multiple times
in the past, I want to focus this interpretation
on the reviewed study’s strength findings,
and the training cessation literature.
The main take-home from Travis et al (1)
is a slight decrease in isometric bench press
performance after five days of training cessation. Otherwise, neither training cessation
duration affected performance. Further, neither training cessation duration significantly
affected skeletal muscle mass or fat mass,
but muscle mass tended to be higher, and fat
mass lower, following three days of cessation
compared to five days. One could argue that
strength and muscle mass can be maintained
after three to five days of rest, but I don’t see
much difference between the groups. First,
there was no group × time interaction, and a
significant decrease in isometric strength in
the five day group is not equivalent to saying
that strength decreased more than the three
day group. Also, skeletal muscle mass did
TAPERING MAY YIELD A
SMALL (1-3%) BENEFIT
(2, 3), ON AVERAGE,
BUT THERE SHOULD BE
A REASON TO TAPER
(I.E., SUBSTANTIAL
FATIGUE IS PRESENT).
not statistically change in either group during
the cessation period. Additionally, the individual changes (Figure 1) in both isometric
strength and muscle mass tended to be small
and variable. On the other hand, just because
a statistically significant change in isometric
bench press strength wasn’t observed after
three days of training cessation doesn’t mean
strength wasn’t decreasing. After three days
of training cessation, there was a 0.94% decrease in isometric bench strength. While I
normally wouldn’t even mention a 0.94%
change in most outcome measures, it is only
logical that strength will gradually decrease
when you take time off. For example, if there
were a 10 day training cessation group in this
study, it would be surprising if strength was
not lower after 10 days compared to five days
of cessation. Ultimately, what’s most important to state is that the small nominal difference in strength changes between groups is
94
likely not biologically meaningful, and we
do not know if these changes in isometric
strength were indicative of changes in dynamic strength.
Although the tapering literature has been historically endurance training-focused, more
resistance training and tapering data have
emerged in recent years. Similarly, there is
a large body of training cessation literature,
including a meta-analysis from Bosquet
et al 2013 (4), focused on endurance and
sport-specific outcomes. The meta-analysis
included 24 studies that examined strength
and power outcomes following a resistance
training program. However, most of these
studies were really detraining studies, in
which subjects didn’t train for more than a
week and, in some cases, over 200 days; thus,
they are not applicable in the present context.
The presently reviewed study makes it the
fifth (1, 5, 6, 7, 8) to assess the efficacy of
less than one week of resistance training cessation on maximal strength performance. All
five studies are summarized in Table 5.
Table 5 shows that the training cessation literature is underwhelming in both the number of well-designed studies and the positive
results. For starters, the Anderson and Cat-
95
THE TRAINING
CESSATION LITERATURE
IS UNDERWHELMING
IN BOTH THE NUMBER
OF WELL-DESIGNED
STUDIES AND THE
POSITIVE RESULTS.
tanach (8) study was only a conference abstract, while one of the Weiss studies (6) tested calf raise 1RM performance. This leaves
us with the presently reviewed study from
Travis et al (1), Pritchard et al (5), and Weiss
et al (7). Most importantly, none of these
three studies found that performance was significantly increased from pre- to post-cessation periods of two to seven days. One could
argue that training cessation is worthwhile,
as Pritchard showed percentage increases
for all performance measures following both
3.5 and 5.5 day cessations, leaning in favor
of 3.5 days. Weiss et al also showed nominal
increases in 1RM bench press, but all effect
sizes were trivial, and Travis et al observed
performance to decrease. To further highlight
these three studies, Table 6 shows the within-group percentage or effect size (whichever was available) changes in each study from
pre- to post-cessation period.
I think Table 6 further highlights the underwhelming nature of the training cessation literature.
It’s possible that, similar to a conventional
taper, training cessation is most effective following an overreaching phase. In other words,
tapering is purported to be effective because
it allows fatigue and soreness to dissipate (9).
However, as I’ve written about before, the tapering literature is also underwhelming, and
tapering is likely most effective following
overreaching. None of the studies in Table
5 or 6 used an overreaching phase prior to
training cessation; thus, it’s unlikely the lifters were fatigued enough to warrant 2-7 days
of training cessation.
In fact, I’d argue that, without the presence of
an overreaching phase, taking five days off is
more indicative of a detraining study. Hwang
et al demonstrated that lifters mostly maintained maximal strength following two weeks
of detraining (10 - MASS Review). However,
the present study (1) shows a slight decrease
in strength following five days of cessation.
96
Further, other tapering studies also show small
decreases in measurements of hypertrophy after one to two weeks of significant volume reduction (11, 12, 13). Therefore, while it may
take some time for a decrease in muscle performance to become statistically significant
following training cessation or traditional tapering, it does seem that muscle performance
tends to decline with five days of cessation.
Despite a lack of evidence supporting a significant benefit of training cessation on maximal strength, lifters do tend to use some form
of training cessation. Specifically, survey
data have found that strongmen and strongwomen (14) tend to cease training ~4 days
before competition, elite male and female
New Zealand powerlifters (15) reported ceasing training ~4 days before a meet, and male
and female Croatian powerlifters (16) ceased
training ~3-4 days before competition. The
survey data also reported that lifters performed their final heavy sessions 4-10 days
before competition. Therefore, based on the
training cessation literature, it doesn’t seem
that strength athletes are using optimal tapering techniques (unless those athletes were
overreaching). However, it’s important to reiterate that the tapering literature as a whole
isn’t especially overwhelming either. To
conclude, I won’t write an in-depth rehash of
previous articles, but I will briefly restate that
tapering is generally overrated, and it probably works best following an overreach. Since
tapering won’t net a huge benefit, the first
rule of “meet week” or “test week” should
be to do no harm. To do no harm, I’d follow
the general recommendations in Table 7. You
can find specific examples here and here.
Next Steps
First, there need to be more studies looking
at the effects of training cessation on freeweight barbell 1RM performance. So far, the
only study to test free-weight 1RM immediately before and after a cessation period was
Weiss et al (7). However, it is difficult to test
a 1RM, and then test it again just a few days
later, following a period of training cessation.
Therefore, it’d be great to see a future training
cessation study utilize submaximal velocity
predictions of 1RM pre- and post-cessation.
Additionally, taper training is most effective
following an overreach; future studies should
use an overreaching phase before training
cessation. There are many different ways an
overreach and training cessation study could
go, since no studies on the topic currently
exist. However, I’d start with a longitudinal
study consisting of four groups. All groups
would first perform the same six-week training block (or some other time frame), then
the group protocols would be:
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APPLICATION AND TAKEAWAYS
1. Travis et al (1) found that both three and five days of training cessation have little
effect on muscle performance following a four-week powerlifting-focused training
block.
2. Although there wasn’t a large effect on performance, it does seem that upper
body maximal isometric force was trending downwards after five days of training
cessation.
3. Ultimately, I’m skeptical that training cessation, or any traditional tapering
strategy, will lead to a large boost in strength following a low volume training
block. I’m of the opinion that tapering, of any kind, is best utilized following an
overreaching phase that causes substantial fatigue.
1. A two-week overreach + three days of
training cessation
2. A two-week overreach + five days of
training cessation
3. A one-week overreach + one week step
taper, then three days of training cessation
4. Two weeks of regular training followed
by three days of training cessation
The above design is not perfect, but we have
to start somewhere.
98
References
1. Travis SK, Mujika I, Zwetsloot KA, Gentles JA, Stone MH, Bazyler CD. The Effects
of 3 vs. 5 Days of Training Cessation on Maximal Strength. Journal of Strength and
Conditioning Research. 2022 Mar 1;36(3):633-40.
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. Bosquet L, Berryman N, Dupuy O, Mekary S, Arvisais D, Bherer L, Mujika I. Effect of
training cessation on muscular performance: A meta‐analysis. Scandinavian journal of
medicine & science in sports. 2013 Jun;23(3):e140-9.
5. Pritchard HJ, Barnes MJ, Stewart RJC, Keogh JWL, and McGuigan MR. Short term
training cessation as a method of tapering to improve maximal strength. Journal of
Strength & Conditioning Research 32: 458-465, 2018.
6. Weiss LW, Coney HD, and Clark FC. Optimal post-training abstinence for maximal
strength expression. Research in Sports Medicine 11: 145-155, 2003.
7. Weiss LW, Wood LE, Fry AC, Kreider RB, Relyea GE, Bullen DB, and Grindstaff
PD. Strength/power augmentation subsequent to short-term training abstinence. Journal
of Strength & Conditioning Research 18: 765-770, 2004.
8. Anderson T and Cattanach D. Effects of three different rest periods on expression of
developed strength [abstract]. Journal of Strength & Conditioning Research 7: 185, 1993.
9. Pritchard H, Keogh J, Barnes M, McGuigan M. Effects and mechanisms of tapering in
maximizing muscular strength. Strength & Conditioning Journal. 2015 Apr 1;37(2):72-83.
10. Hwang PS, Andre TL, McKinley-Barnard SK, Morales Marroquín FE, Gann JJ, Song
JJ, Willoughby DS. Resistance training–induced elevations in muscular strength in
trained men are maintained after 2 weeks of detraining and not differentially affected by
whey protein supplementation. Journal of strength and conditioning research. 2017 Apr
1;31(4):869-81.
11. 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.
99
12. 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.
13. Travis 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.
14. Winwood PW, Dudson MK, Wilson D, Mclaren-Harrison JK, Redjkins V, Pritchard
HJ, Keogh JW. Tapering practices of strongman athletes. The Journal of Strength &
Conditioning Research. 2018 May 1;32(5):1181-96.
15. 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.
16. 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.
█
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Research Briefs
BY ERIC TREXLER
In the Research Briefs section, Eric Trexler shares quick
summaries of recent studies. Briefs are short and sweet,
skimmable, and focused on the need-to-know information
from each study.
102
107
Is the 5:2 Diet a Viable Option for Lifters?
Continuous Glucose Monitors: What Are They
Good For?
113
Understanding the Risks That Accompany the
Rewards for Female Physique Athletes
119
Comparing the Effects of Morning Versus
Evening Workouts on Total Daily Energy
Expenditure
101
Study Reviewed: Intermittent Fasting and Continuous Energy Restriction Result in Similar
Changes in Body Composition and Muscle Strength When Combined With a 12 Week
Resistance Training Program. Keenan et al. (2022)
Is the 5:2 Diet a Viable Option for Lifters?
BY ERIC TREXLER
“Intermittent fasting” is an umbrella term. In
the evidence-based fitness world, we often
think of intermittent fasting as time-restricted feeding, which involves implementing a
daily fasting period (typically between 16-23
hours long), but allows for daily energy consumption within the specified feeding window. However, many other strategies can be
considered forms of intermittent fasting (2).
For example, there is alternate-day fasting,
in which dieters alternate between normal
eating days and complete fasting days (that
is, virtually zero energy intake for the full
24-hour period). There’s also modified alternate-day fasting, which alternates between
normal eating days and very, very low-calorie days (typically ≤40% of an individual’s
maintenance calories, or ≤600 kcal/day).
Another option is the “5:2 diet.” This consists
of five normal eating days per week, along
with two “fasting” days. These fasting days
can be consecutive or nonconsecutive, and
typically allow for ≤40% of an individual’s
maintenance calories, or ≤600 kcal/day.
We’ve covered time-restricted feeding many
times in MASS (one, two, three), but I’m not
aware of any studies investigating the 5:2
diet within the context of resistance training.
Until now, that is. The presently reviewed
study by Keenan et al (1) sought to compare
the effects of 5:2 intermittent fasting and regular, continuous energy restriction on changes in strength and body composition over
12 weeks. This study was completed by 17
weight-stable, healthy, untrained adults between 18-45 years of age (8-9 males and 8-9
females in each group). In order to participate, males needed to have a body-fat percentage above 18%, and females needed to
have a body-fat percentage above 25%, as
measured via DXA. Training consisted of
two supervised full-body resistance training
sessions per week, in addition to one unsupervised training session that consisted of
bodyweight calisthenics.
The continuous diet group consumed a daily energy deficit of around 20% (that is,
they consumed 80% of their estimated energy needs), while aiming for a daily protein intake of about 1.4g/kg. The 5:2 group
consumed 100% of their estimated energy
needs five days per week, and implemented
102
two modified, nonconsecutive fasting days
per week (on non-training days). I call them
“modified” fasting days because they allowed
for some energy consumption, rather than requiring a complete absence of energy intake.
On these fasting days, participants were instructed to consume only 30% of their energy
needs (e.g., a 70% deficit), and to consume all
of their calories between noon and 6pm. The
5:2 group was instructed to consume 1.5g/kg
of protein on non-fasting days, and 1.1-1.2g/
kg on fasting days. As a result, the diets were
approximately isocaloric and isonitrogenous,
with both groups averaging a 20% energy
deficit across the week and consuming an average of about 1.4g/kg/day of protein.
In short, the performance results in this study
weren’t particularly exciting or informative.
Strength and strength-endurance were assessed by determining bench press and leg
press 3RM values, in addition to total volume completed (load × reps) during a single
set to failure with 70% of the individual’s
estimated 1RM load for bench press and leg
press. The performance results weren’t particularly exciting, because there were no significant interaction effects to speak of; these
untrained participants generally got stronger
in response to 12 weeks of training, and the
males generally lifted greater loads than the
females. Beyond that, the performance results
were, in my opinion, bound to be fairly uninformative anyway. When untrained individuals complete 12 weeks of resistance training, their initial strength gains will be very
large and driven primarily by factors related
to motor learning and skill acquisition. In this
context, you’re unlikely to observe a statis-
tically significant effect of a nutrition intervention on strength outcomes, even in a very
well-controlled study. In addition, participants in the presently reviewed study didn’t
actually train the leg press or bench press
exercises throughout the 12-week training
program. This makes us even less likely to
observe a statistically significant effect, given that we’re looking at residual adaptations
that are carrying over to the bench press and
leg press tests from entirely different training
exercises. As a result, that’s just about all I’m
going to say about the performance outcomes
from this study.
As we move to body composition results,
things start to get more interesting. You might
have noticed that the beginning of this study’s
title gives away the ending: “Intermittent fasting and continuous energy restriction result in
similar changes in body composition.” That
statement isn’t untrue, but it tells only a portion of the whole story, and from a lifter’s
perspective, it tells the least informative part
(in my opinion). As that title suggests, DXA
results indicated that participants increased
lean mass while losing weight and fat mass,
with no significant differences between the
groups. However, in addition to measuring
whole-body lean mass via DXA, the researchers also measured changes in muscle size via
ultrasound and CT (computed tomography)
scans. These more direct measures suggested
that the continuous energy restriction diet led
to significantly larger increases in muscle surface area, and led to more favorable changes in
ultrasound measurements of muscle cross-sectional area and thickness, with two of the three
ultrasound outcomes reporting p-values near
103
the statistical significance threshold (p = 0.05
and p = 0.07). A selection of body composition and muscle size outcomes are presented
in Table 1.
When reviewing research, we always have
to keep important limitations in mind. When
it comes to hypertrophy studies, there are
two glaring and fairly ubiquitous limitations
that we encounter frequently: limited muscle
growth can occur over a timespan of only
8-12 weeks, and most common measurement
techniques for muscle hypertrophy lack precision. As a result, it would be very shortsighted to confidently conclude that two dietary
approaches are “equivalent,” just because
changes in whole-body lean mass, measured
via DXA, were not significantly different between groups. As a side note, this would also
be incorrect, as we cannot say that things are
“equivalent” just because they aren’t significantly different; you can find more information about this distinction here and here.
The first implication of these limitations is
that, whenever possible, we should place
more confidence in more direct measures of
hypertrophy than less direct measures of hypertrophy. In some of my recent work on protein sources, I’ve cautioned that blood amino
104
acid responses are not perfect proxies for muscle protein synthesis, and that muscle protein
synthesis is not a perfect proxy for changes in
lean mass. We can take that one step further
by acknowledging that the change in wholebody lean mass is not a perfect proxy for muscle hypertrophy. Lean mass changes derived
from DXA (and other common body composition techniques) are informative, but they
refer to all non-fat tissues of the body rather
than muscle tissue specifically. When compared to direct measures of hypertrophy (such
as changes in volume or area, as measured
via ultrasound, MRI, CT scan, or muscle tissue biopsies), these whole-body measures of
lean mass are less ideal because they are influenced by non-muscle lean tissues, and they
are less able to provide a precise quantification of changes in muscle size. In a study like
this, where the DXA-derived lean mass values
suggest no significant difference, but the more
direct measures (ultrasound and CT scan) lean
toward a less favorable change in the 5:2 diet
group, I’m inclined to place more confidence
in the ultrasound and CT scan results.
It might seem that I’m placing too much confidence in these results, given that only one
of the ultrasound and CT scan outcomes in
Table 1 actually cleared the bar for statistical significance. That’s a defensible perspective, but there are some scenarios in which
“almost significant” findings warrant some
extra attention. In this case, the CT and ultrasound results all generally lean in the
same direction, and we’re talking about hypertrophy measurements over a short 12week time frame. As we recently saw in a
review by Dr. Helms, it’s very possible that
we only get subtle clues about unfavorable
hypertrophy responses in short-term studies
(3) on intermittent fasting strategies (such as
time-restricted feeding), which start to materialize into larger and more noteworthy discrepancies over longer periods of time (4).
In addition, the 5:2 diet intervention in this
study was structured in a way that defies our
current understanding regarding “best practices” of total daily protein intake and protein distribution. The fasting days utilized a
protein target (1.1-1.2g/kg) that is well below the standard “optimal” range of 1.6-2.2g/
kg (5), and the meal distribution guidelines
constrained the feeding window in a manner that would reduce the likelihood of getting at least three large protein doses spread
throughout the day, which seems to be favorable (6). So, we’ve got a short study timeline,
some “almost significant” results leaning in
the same direction, and they’re leaning in a
direction that makes perfect sense based on
our current understanding of protein intake
and distribution. In this type of scenario, I’m
inclined to pay a little more attention to the
ultrasound and CT scan results, even if there
aren’t statistically significant findings across
the board.
In conclusion, these findings make it hard to
suggest that the 5:2 diet is an “optimal” approach for lifters. However, it’s also worth
noting that this dietary strategy did not have
an absolutely catastrophic impact on various
indices of hypertrophy. Collectively, the data
suggest that continuous energy restriction led
to more favorable changes in muscle size,
but the 5:2 approach might still have some
utility if your top priority is fat loss, you’re
105
comfortable with a slightly suboptimal effect
on muscle mass, and it’s the energy restriction method that most effectively suits your
preferences and reinforces your consistency.
Weight loss can be extremely challenging,
and the method that most effectively supports
your consistent adherence may not necessarily be the approach that is fully optimized for
skeletal muscle growth or retention. If you do
find the 5:2 approach to be an attractive option, I suspect you can minimize some of the
downsides by following a few simple guidelines for your fasting days: make sure they’re
scheduled on non-training days, try to aim
for a protein target of (at least) 1.4-1.6g/kg,
and try to get at least 3 large protein feedings,
distributed fairly evenly throughout a feeding
window of at least 8 hours in duration. This
approach still might not lead to a totally optimal situation for muscle growth (or maintenance), but it’s probably the best way to
structure the 5:2 diet for lifters.
References
1. Keenan SJ, Cooke MB, Hassan EB,
Chen WS, Sullivan J, Wu SX, et al.
Intermittent fasting and continuous energy
restriction result in similar changes in
body composition and muscle strength
when combined with a 12 week resistance
training program. Eur J Nutr. 2022 Jan 27;
ePub ahead of print.
3. Moro T, Tinsley G, Bianco A, Marcolin
G, Pacelli QF, Battaglia G, et al. Effects
of eight weeks of time-restricted feeding
(16/8) on basal metabolism, maximal
strength, body composition, inflammation,
and cardiovascular risk factors in
resistance-trained males. J Transl Med.
2016 13;14(1):290.
4. Moro T, Tinsley G, Pacelli FQ, Marcolin
G, Bianco A, Paoli A. Twelve Months
of Time-restricted Eating and Resistance
Training Improves Inflammatory Markers
and Cardiometabolic Risk Factors. Med Sci
Sports Exerc. 2021 Dec 1;53(12):2577–85.
5. Morton RW, Murphy KT, McKellar SR,
Schoenfeld BJ, Henselmans M, Helms E,
et al. A systematic review, meta-analysis
and meta-regression of the effect of protein
supplementation on resistance traininginduced gains in muscle mass and strength
in healthy adults. Br J Sports Med. 2018
Mar;52(6):376–84.
6. Yasuda J, Tomita T, Arimitsu T, Fujita
S. Evenly Distributed Protein Intake
over 3 Meals Augments Resistance
Exercise-Induced Muscle Hypertrophy
in Healthy Young Men. J Nutr. 2020 Jul
1;150(7):1845-1851.
2. Patikorn C, Roubal K, Veettil SK,
Chandran V, Pham T, Lee YY, et al.
Intermittent Fasting and Obesity-Related
Health Outcomes: An Umbrella Review
of Meta-analyses of Randomized Clinical
Trials. JAMA Netw Open. 2021 Dec
1;4(12):e2139558.
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Study Reviewed: Validity of Continuous Glucose Monitoring for Categorizing Glycemic
Responses to Diet: Implications for Use in Personalized Nutrition. Merino et al. (2022)
Continuous Glucose Monitors: What Are They Good
For?
BY ERIC TREXLER
Diabetes is a serious medical condition requiring ongoing management. In Type 1 diabetes,
which accounts for less than 10% of diabetes
cases in the United States (2), an autoimmune
attack damages the beta cells of the pancreas,
which are responsible for producing and secreting insulin when blood glucose rises. As
the beta cell population becomes depleted,
an individual with type 1 diabetes begins to
display symptoms of insulin deficiency and
impaired blood glucose regulation, which is
rectified by the use of exogenous insulin. In
people who are progressing toward type 2 diabetes, peripheral cells outside of the pancreas can become less responsive to insulin over
time, largely influenced by factors including
genetic predisposition, lack of physical activity, aging, and excess adiposity.
At first, the problem isn’t particularly severe;
peripheral cells become less responsive to insulin, but our pancreatic beta cells can simply
produce more insulin to induce the intended
cellular response. This is known as insulin resistance, which can be characterized by high
blood insulin levels, but fairly normal blood
glucose levels. However, if the condition
progresses, the body may become less able to
effectively regulate blood glucose levels over
time, leading to chronic hyperglycemia (high
blood sugar) and a progressive impairment of
beta cell function (2). As insulin resistance
progresses into advanced type 2 diabetes, we
increasingly see higher blood glucose levels
and lower blood insulin levels, which indicate impaired glycemic control and progressive damage to beta cells.
When you have diabetes, careful management of blood glucose levels is critically important. Acute hypoglycemic episodes (low
blood glucose) can induce confusion, fainting, seizures, comas, and even death (if severe and untreated). In addition, it may adversely affect brain development in children,
and may cause strokes or heart attacks in older adults. On the other end of the spectrum,
chronic hyperglycemia can promote progressive beta cell deterioration, atherosclerosis,
cardiovascular disease, stroke, retinopathy
(eye issues), nephropathy (kidney issues),
and neuropathy (nerve issues). Clearly, the
stakes for effective blood glucose management are high.
107
People with diabetes can use many tools and
assessments to monitor their blood glucose
regulation. For example, they may use fasted blood glucose tests, blood tests that assess
their blood glucose response to a standardized meal, or blood HbA1c tests, which give
a decent assessment of average blood glucose
levels over the preceding 2-3 months. Another option is to use a continuous glucose
monitor, which involves implanting a sensor
under your skin (usually on your arm or abdomen), which continuously monitors glucose levels in your interstitial fluid (the fluid found between cells). This makes all the
sense in the world for diabetes management,
but you might be surprised to hear that fitness
enthusiasts are increasingly using continuous
glucose monitors, in hopes that they’ll provide actionable feedback for ultra-personalized nutrition strategies. Of course, in order
to enable such nutrition strategies, the devices have to give glucose feedback that is both
valid (accurate) and reliable (consistent).
The presently reviewed study by Merino et
al (1) was very straightforward: the researchers analyzed data from a large study to assess
the repeatability of continuous glucose monitor readings by looking at the level of within-device agreement (for the the Abbott Freestyle Libre Pro), and looking at the level of
agreement between two different devices (the
Abbott Freestyle Libre Pro and the Dexcom
G6). In order to assess within-device agreement, 360 participants wore an Abbott Freestyle device on each arm. In order to assess
between-device agreement, 34 participants
wore the Abbott device on one arm, and the
Dexcom device on the other. The available
data documented blood glucose readings for
394 healthy participants, who utilized both
devices over the span of 14 days in a free-living setting. Over the course of 14 days, they
consumed a total of 4,457 standardized meals
and 5,738 ad libitum meals.
In short, this study reported pretty solid within-device repeatability, although the agreement between devices wasn’t quite as great.
For example, when looking at the coefficient
of variation for 2-hour glucose area-under-curve values after standardized meals,
it was 3.7% within-devices and 12.5% between-devices. In the context of ad libitum
meals, values increased to 4.1% within-devices and 16.6% between-devices. Figure 1
presents the agreement between measurements from the same device model (A) and
measurements from two different device
models (B), along with coefficient of variation statistics (C).
If it sounds like the science is settled and we
should all be ordering some continuous glucose monitors, it’s not quite that simple. First,
there are somewhat contradictory findings to
consider; while the presently reviewed study
reported pretty good agreement, that’s not the
unanimous consensus within the literature.
Howard et al (3) reported repeatability statistics that were considerably less impressive.
For example, both papers compared within-subject meal rankings (in terms of glucose
responses) using the different devices. While
the presently reviewed paper reported a high
degree of between-device agreement (tau =
0.68), Howard et al reported considerably
poorer agreement (tau = 0.43). Howard et al
also reported a significant bias by which one
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device provided higher glucose readings than
the other, but the magnitude of this bias was
significantly impacted by body-fat, such that
discrepancies for leaner individuals were different than those for individuals with higher
body-fat levels. For a visual representation
of the poor agreement reported by Howard
et al, Figure 2 shows how the two devices simultaneously reported very different glucose
responses to the same exact meals, eaten by
the same participant.
Discrepancies between the results of Howard
et al and the presently reviewed study could
be caused by several factors. For instance,
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Howard et al followed the placement recommendations provided by the device manufacturers, whereas the presently reviewed study
standardized monitor placement in a manner
that defied the Dexcom manufacturer instructions. While both compared an Abbott device
to a Dexcom device, the specific models were
different. As such, it’s possible that the newer
device models used in the presently reviewed
study simply perform substantially better
than the older models from each company.
However, even if we run with that justification, and trust that the newer models perform
better, all that tells us is that these devices
might help people monitor their blood glucose levels. That’s a far cry from justifying
any claims about facilitating health or performance in individuals without diabetes.
I’m not fond of leaning too hard on anecdotes,
but they provide great insight about the subjective experience of a particular intervention,
and this particular conversation (continuous
glucose monitors for health and performance
in people without diabetes) is completely bereft of our scientific fundamentals in the first
place. Unless I’m missing something, the
purported benefits seem to be hypothetical
and asserted without evidence, which means
you’d be well within your “scientific rights”
to reject them without evidence. However,
this article from Men’s Health managed to
highlight just about all of my practical concerns about continuous glucose monitors.
The article contains a lot of catastrophizing
about normal daily fluctuations in blood glucose, which appears to be causing unwarranted distress (“Watching my glucose level rise
and fall through the day told a different, and
more alarming, story than the static reading
at my doctor’s office”) and orthorexic tendencies (“My [continuous glucose monitor]
seemed to be okay with things like eggs and
bacon in the morning, steak or grilled salmon
at dinner, salads and leafy vegetables, nuts
instead of chips at snack time, and pretty
much anything involving avocados”).
It also led to a very predictable trend in which
the devices seemed to blatantly penalize carbohydrate consumption, nudging people toward lower and lower carbohydrate intakes
(“I’ve always loved carbs and have never
been a fan of any kind of labeled diet, but my
CGM was carb-shaming me”). If the goal is
just to keep glycemic control within standard,
non-diabetic ranges, then there’s no need for
a continuous monitor. If, however, you use a
monitor and phone app to “gamify” stability
of blood glucose responses, then a ketogenic
diet is a cheat code to win the game. I guess
that’s fine, as low-carb and ketogenic diets
are a viable dietary option, but it’s hard to
frame a device as promoting hyper-individualized diet optimization for health and performance if all roads lead to carb restriction;
that is explicitly not individualized, and that
doesn’t jive with the evidence on high-intensity exercise performance (4) or mortality (5).
In addition, the same meal eaten on multiple
occasions led to very different results while
using the same device (“One thing I did notice is that the various CGM sensors that I
was wearing almost never agreed with one
another”), and different devices provided
very different feedback for the same exact
meal when used simultaneously (“My glucose did not react much at all, and Levels
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rated the croissant a 6/10. A few days later,
however, the exact same croissant from the
same bakery at the same time jacked my glucose up”). This begs the question: what actionable feedback are these devices actually
providing?
In the context of diabetes, it’s quite clear: my
glucose is way below my intended range, so
I’ll eat some carbs; my glucose is way above
my intended range, so I need a larger insulin dose. If we can’t trust that our glycemic
response to a meal on Monday will predict
our response to the same meal ingested at
the same time on Wednesday, then I reject
the premise that these devices facilitate better individualized meal planning. If a glucose monitor tells us we had an unexpectedly
large glucose response after a meal, we can’t
confidently leverage that information for data-driven diet optimization, as we can’t be
certain that our monitor will give us the same
reading the next time we consume that meal.
With that in mind, it seems like these devices
(in their current form, when used by people
without diabetes) are just quantitatively reaffirming what we already know about how
glycemic responses are generally impacted
by fiber, fat, glycemic load, and proximity
to exericse, with some degree of variability
and measurement error. I’ve seen some people suggest that they facilitate proper maintenance of carbohydrate availability during
exercise, but lifters don’t have to work very
hard to ensure adequate carb availability, and
“bonking” due to low carb availability during
endurance exercise is an easily identifiable
and unmistakable sensation (no sensors or
phone apps needed).
Biohacking and physiological data monitoring are all the rage right now, so I totally
understand why people are so interested in
continuous glucose monitors. If that’s your
hobby and it brings you joy, then go for it
– trying to minimize post-meal glucose excursions will do no physiological harm, and
I have no reason to rain on your parade.
However, I would respectfully push back at
anyone who confidently asserts that this intervention is entirely harmless, or carries no
risks of any kind. When we consider psychological aspects of diet and exercise habits,
there is potential harm in convincing people
that normal post-meal increases in blood glucose are to be feared. There is potential harm
in baselessly reinforcing the idea that carbs
are inherently deleterious. There is potential
harm in convincing people to manage a very
scary problem they didn’t know they had, and
that the best way to manage it is by adopting
an unnecessarily and unsustainably rigid approach to food selection.
So, if continuous glucose monitors excite
you, I’m not here to deprive you of a fun experiment. However, if I were concerned about
my insulin sensitivity, I’d personally focus on
increasing my physical activity level, losing
some fat mass, and adjusting my daily fiber,
protein, and calorie targets as needed. If I was
having hypoglycemic episodes or suspected
that I was trending toward pre-diabetes or diabetes, I’d simply pay a visit to a qualified
medical professional to discuss treatment
options. In that scenario, it’s possible that a
continuous glucose monitor would be recommended. However, for non-clinical applications related to general health or performance
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in people without glycemic control issues,
I’m not seeing much evidence to support a
beneficial effect, and there are some potential
downsides when we burden ourselves with
the task of micromanaging glucose fluctuations that are well within normal ranges.
References
1. Merino J, Linenberg I, Bermingham KM,
Ganesh S, Bakker E, Delahanty LM, et al.
Validity of continuous glucose monitoring
for categorizing glycemic responses to
diet: implications for use in personalized
nutrition. Am J Clin Nutr. 2022 Feb 4;
ePub ahead of print.
2. Ferrier DR. Biochemistry (6th ed).
Philadelphia: Wolters Kluwer Health/
Lippincott Williams & Wilkins; 2014:338346.
3. Howard R, Guo J, Hall KD. Imprecision
nutrition? Different simultaneous
continuous glucose monitors provide
discordant meal rankings for incremental
postprandial glucose in subjects without
diabetes. Am J Clin Nutr. 2020 Oct
1;112(4):1114–9.
4. Burke LM. Ketogenic low-CHO, high-fat
diet: the future of elite endurance sport? J
Physiol. 2021;599(3):819–43.
5. Seidelmann SB, Claggett B, Cheng S,
Henglin M, Shah A, Steffen LM, et al.
Dietary carbohydrate intake and mortality:
a prospective cohort study and metaanalysis. Lancet Public Health. 2018
Sep;3(9):e419–28.
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Study Reviewed: Weight Loss Practices and Eating Behaviours Among Female Physique
Athletes: Acquiring the Optimal Body Composition for Competition. Alwan et al. (2022)
Understanding the Risks That Accompany the
Rewards for Female Physique Athletes
BY ERIC TREXLER
Physique competitions take many forms,
with a variety of competitive categories, including bodybuilding, physique, figure, bikini, and several others. Regardless of your
chosen category, the process of preparing for
and competing in a physique sport can be a
very rewarding experience. However, those
rewards are accompanied by some non-negligible risks. In a previous MASS article,
we discussed the multifaceted physiological
ramifications faced by female physique athletes. Contest preparation generally induces
a state of low energy availability. Depending
on the severity and duration of this state, it
can lead to unfavorable downstream effects
on the athlete’s hormone profile, total daily
energy expenditure, satiety regulation, menstrual cycle, and bone health (2). This article
also touched on disordered eating behaviors,
but only briefly.
That’s where the presently reviewed study
(1) picks things up. The primary purpose of
this study was to “investigate the weight loss
history, practices and influential sources of
dieting during the pre-competition period in
a large cohort of [female physique athletes],”
and the secondary purpose was to “determine
the extent of [disordered eating] symptoms
among [female physique athletes], in order to
identify whether these athletes were at risk of
developing an [eating disorder].” To achieve
these aims, the researchers administered a
survey to 191 female physique athletes who
were between the ages of 18-65 years of age,
had competed within the previous 12 months,
had never been diagnosed with an eating disorder, and had never used banned substances. Of the 191 survey recipients, 178 of them
completed it, and 20 of them were excluded
based on failure to meet the inclusion criteria. The 158 included respondents competed in the bikini fitness (n = 107), figure (n =
42), women’s fitness (n = 6), and physique
(n = 3) divisions. The survey consisted of the
Rapid Weight Loss Questionnaire and the
Eating Attitudes Test (EAT-26), which provide plenty of insight about the weight loss
practices and disordered eating symptoms of
respondents.
These researchers administered comprehensive surveys and reported their data thoroughly, so there are a huge number of out-
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comes presented in this paper. In the interest
of brevity, this research brief will focus on
only the most pertinent outcomes. One of the
most eye-catching findings was that 37% of
the sample had a score of ≥20 on the EAT26 questionnaire. When you get a score at or
above 20, that’s indicative of a “high level of
concern about dieting, body weight or problematic eating behaviors.” As a result, you’re
generally encouraged to seek an evaluation
from a qualified medical professional to determine if you meet the criteria for an eating
disorder diagnosis.
I suppose we shouldn’t be entirely surprised
by this prevalence of high-risk EAT-26
scores, as we’ve seen concerning statistics
in previous female physique athlete literature. For example, Walberg et al (3) administered a series of questionnaires related to
disordered eating to 103 female lifters, 12 of
whom had previously competed in a bodybuilding competition. In the 12 respondents
with bodybuilding experience, the researchers observed high prevalence statistics for
binge eating (50%), feeling miserable after
binging (58%), getting uncontrollable urges
to eat (50%), fear that one can’t stop eating
(42%), obsession with food (58%), feeling
fat despite others telling them they’re thin
(50%), being terrified of becoming fat (67%),
and reporting a history of anorexia (42%) or
bulimia (8%). Having said all that, Walberg
et al submitted this paper 3 years before I was
born, and evidence-based fitness has come a
long way since then. They also had only 12
respondents with bodybuilding experience,
so prevalence statistics should be taken with
a grain of salt.
Unfortunately, they weren’t alone in presenting some concerning data related to
disordered eating habits in female physique
athletes. In a survey of 26 female bodybuilders published in 1998, Andersen et al
(4) reported that 46% of respondents were
“often or always preoccupied with food,”
60% had reported that they “lost control
of their eating in the past,” 38% reported a
history of post-competition binge eating episodes, and 60% reported being “very unsatisfied” with their physique, with another 8%
being “unsatisfied.” Mostly due to wishful
thinking, I held on to some hopes that these
previous studies were presenting an ungeneralizable view of eating attitudes among female physique athletes, given that they were
published decades ago and had very small
sample sizes. Unfortunately, the presently
reviewed results reported by Alwan et al
(from a large, contemporary sample) seem
to reinforce the fact that disordered eating
habits are a prevalent concern among female
physique athletes. In fact, the presently reviewed results might underestimate the true
severity of the issue, given that Alwan and
colleagues excluded competitors with a previously diagnosed eating disorder.
While the number of EAT-26 scores above
20 is concerning, it’s also fair to point out that
some eating disorder questionnaires have
survey items that don’t really make sense
in the context of bodybuilding or physique
sports. For example, your EAT-26 is unfavorably impacted if you are “aware of the
calorie content of foods” that you eat, “think
about burning up calories when [you] exercise,” “eat diet foods,” “display self-control
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around food,” or “engage in dieting behavior.” I wish we could pretend the presently
reviewed results are simply a matter of ungeneralizable survey selection, but unfortunately that’s not the case. The participants
in this study reported engaging in some really deleterious dieting behaviors, regardless
of context. As shown in Figure 1, around
35% of participants reported “never” having
binge eating episodes, around 75% reported
“never” relying on self-induced vomiting to
lose weight, and around 20% reported “never” relying on laxatives, diet pills, or diuretics to lose weight. Almost 80% of respondents reported using laxatives, diet pills, or
diuretics at least once a month, nearly 65%
of respondents reported binge eating at least
once a month, and almost 25% of respondents reported self-induced vomiting at least
once a month.
These numbers raise an interesting question:
does physique sport participation cause eating disorders, or do people with elevated eating disorder risk gravitate toward physique
sports? I suspect that the answer is quite
similar to a recent discussion pertaining to
the relationship between diet tracking and
disordered eating. In that MASS review, I
mentioned that individuals with high risk of
eating disorder development are more likely
to do quantitative diet tracking than people
with low risk of eating disorder development.
I also discussed the evidence suggesting that
the combination of elevated eating disorder
predisposition, perfectionist concerns, excessively restrictive guidelines, and rigid restraint (5) might lead to a scenario in which
diet tracking exacerbates issues related to
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disordered eating. In contrast, diet tracking
appeared to be quite benign in the absence of
these contextual factors.
I suspect a similar relationship between physique sport participation and eating disorders.
Evidence suggests that people with high eating disorder risk are more likely to gravitate
toward physique sports than people with low
eating disorder risk (5), and the presently reviewed study found that novice figure competitors had higher EAT-26 scores (indicating higher eating disorder risk) than more
experienced figure competitors and competitors in divisions that require less muscle
mass and allow for slightly higher body-fat
levels in judging criteria. Thus, it seems that
physique sports tend to attract individuals
with elevated eating disorder risk, and the
intensity of competition can exacerbate these
tendencies, especially for competitors who
have ambitious goals (e.g., competitors in
divisions with more extreme judging criteria
for leanness and muscularity) and have a lot
of ground to cover (e.g., novice competitors
who aren’t starting their prep within “striking
distance” of their on-stage physique).
As such, I don’t suspect that physique competition is independently and robustly causative
of eating disorders, but I very much suspect
that physique competition can reveal and exacerbate the tendency to engage in disordered
eating behaviors and deleterious weight loss
behaviors among individuals with a predisposition to eating disorder development. It’s
also very possible that those tendencies would
not have materialized, or would have materialized in less extreme or deleterious ways, if
the individual did not choose to participate in
a physique sport. To lean on a very clichéd
analogy, we can think of watering a seed.
You can water soil all you want, but it won’t
produce a plant unless a seed is present. Similarly, a seed may lay dormant in the soil, but
won’t give rise to a plant without sufficient
water. When an individual has an underlying
predisposition to disordered eating habits, a
seed is present. When they turn up the intensity by engaging in physique-focused competition, and combine this competition ambition with excessively restrictive guidelines,
rigid restraint, and the selection of a division
with judging criteria that virtually insist upon
perfectionist concerns, that provides a whole
lot of water for that seed to yield a plant. I
should also note that, up to this point, we’ve
exclusively focused on female physique athletes. However, as discussed previously in
MASS, this topic is extremely applicable to
male physique athletes as well, who self-report higher rates of eating disorders and body
image disorders than the general population
(6).
I assume that very few MASS readers are
clinically trained practitioners whose scope
of practice includes eating disorder diagnosis or treatment, so you might be wondering
what this has to do with you. As it turns out,
this might actually have a lot to do with you.
Survey respondents were asked to identify
the individuals who influence their dieting
practices, and the results might surprise you.
52.2% of respondents stated that nutritionists/dietitians were “not influential” to them,
while a whopping 82.2% said that their doctor was not influential. This means that, aside
from doctors, only parents were described as
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“not influential” more frequently than nutritionists/dietitians, with more favorable numbers reported for coaches, friends, romantic
partners, training partners, other physique
athletes, and “internet.” The biggest influences seemed to be coaches (by a wide margin) and other physique athletes, although
a non-negligible percentage of respondents
acknowledged being influenced by their romantic partner, training partner, and people
on the internet. This mirrors the results of
another recent study, which found that physique athletes primarily get their information
and guidance from websites, internet forums,
coaches, and successful physique athletes (7).
It would be intuitive to assume that doctors,
nutritionists, and dietitians have the largest
impact on the diet practices of female physique athletes. Given the high prevalence of
disordered eating symptoms in this population, that’d be ideal, as these individuals are
most equipped to diagnose eating disorders,
oversee treatment, and skillfully advise a dieter who is at elevated risk of developing an
eating disorder. These practitioners are also
best equipped to navigate the physiological
ramifications that may result from aggressive
dieting. Nonetheless, it’s quite possible that
you, as a coach, fellow competitor, experienced fitness enthusiast, or “person who engages in fitness conversations on the internet,”
have a larger impact on a physique athlete’s
dieting strategies than a doctor or dietitian. So,
what can you do to have a helpful influence,
or at least avoid having a harmful influence?
If you’re a coach, always stay within your
scope of practice. Promote and implement
strategies that emphasize flexible dietary re-
straint (5). Help your clients with their goal
setting, calibration of expectations, and mental approach to dieting and competing. Keep
your eyes open for signs of eating disorder
predisposition, elevated eating disorder risk,
or disordered eating behaviors, and refer to
the appropriate clinicians when necessary. If
you’re a competitor or fitness enthusiast who
frequently participates in online (or real-life)
fitness discussions, set a positive example
by promoting sustainable dieting practices
and flexible dietary restraint, and avoid reinforcing strategies or perspectives that may
broadly fuel disordered eating habits, perfectionist concerns, or body image concerns. In
addition, speaking with kindness and empathy can go a long way, whereas toxic online
fitness environments can fuel some of the
psychopathological traits and perspectives
that promote greater frequency and severity of disordered eating habits and body image issues. Finally, when you observe others
displaying concerning behaviors or signs of
disordered eating habits, you might privately express this concern to the individual (depending on the nature of your relationship
with them), while being mindful of respecting personal boundaries and relevant scope
of practice boundaries.
In short, eating disorders and deleterious
weight loss behaviors are a prevalent concern among physique athletes. This doesn’t
suggest that no one should ever compete in
physique sports, but individuals who suspect they have an elevated risk or predisposition for eating disorder development should
practice extreme caution when considering
if, why, and how they might participate in a
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physique-focused sport. Finally, it’s important to recognize that coaches, athletes, fitness enthusiasts, and members of online fitness communities have an outsized influence
on how physique athletes approach dieting,
whether they like it or not. That responsibility
shouldn’t be taken lightly, and coaches, competitors, fitness enthusiasts, and members of
online fitness communities should carefully
consider how their words and actions might
influence high-risk dieters.
6. Lenzi JL, Teixeira EL, de Jesus G,
Schoenfeld BJ, de Salles Painelli V.
Dietary Strategies of Modern Bodybuilders
During Different Phases of the Competitive
Cycle. J Strength Cond Res. 2021 Sep
1;35(9):2546–51.
7. Mitchell L, Hackett D, Gifford J,
Estermann F, O’Connor H. Do
Bodybuilders Use Evidence-Based
Nutrition Strategies to Manipulate
Physique? Sports. 2017 Sep 29;5(4):76.
References
1. Alwan N, Moss SL, Davies IG, Elliott-Sale
KJ, Enright K. Weight loss practices and
eating behaviours among female physique
athletes: Acquiring the optimal body
composition for competition. PloS One.
2022;17(1):e0262514.
2. Alwan N, Moss SL, Elliott-Sale KJ,
Davies IG, Enright K. A Narrative
Review on Female Physique Athletes:
The Physiological and Psychological
Implications of Weight Management
Practices. Int J Sport Nutr Exerc Metab.
2019 Nov 1;29(6):682-689.
3. Walberg JL, Johnston CS. Menstrual
function and eating behavior in female
recreational weight lifters and competitive
body builders. Med Sci Sports Exerc. 1991
Jan;23(1):30–6.
4. Andersen RE, Brownell KD, Morgan GD,
Bartlett SJ. Weight loss, psychological,
and nutritional patterns in competitive
female bodybuilders. Eat Disord. 1998 Jun
1;6(2):159–67.
5. Helms ER, Prnjak K, Linardon J. Towards
a Sustainable Nutrition Paradigm in
Physique Sport: A Narrative Review.
Sports. 2019 Jul 16;7(7):172.
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Study Reviewed: Effect of Morning and Evening Exercise on Energy Balance: A Pilot Study.
Creasy et al. (2022)
Comparing the Effects of Morning Versus Evening
Workouts on Total Daily Energy Expenditure
BY ERIC TREXLER
In recent years, there has been increasing interest in the intersection between circadian biology and nutrition. For example, we’ve written
MASS reviews about whether the thermic effect of feeding is greater in the morning than in
the evening, and whether the thermic effect of
feeding is impacted by irregular meal schedules. Due to our circadian rhythm, we experience physiological changes impacting energy
expenditure and macronutrient metabolism
throughout the day, so it’s intuitive to wonder
if our meal timing and distribution might have
a noteworthy impact on energy expenditure
patterns and, by extension, total daily energy
expenditure. Shifting the focus from energy
expenditure to training adaptations, the MASS
team has also addressed (on three separate occasions) whether or not there’s a universally
optimal time of day to train (one, two, three).
The presently reviewed study (1) merges these
two separate lines of inquiry, as it investigates
whether or not morning and evening cardio
workouts have divergent effects on energy expenditure and body composition changes.
This was a pilot study; as a result, the researchers recruited a fairly small sample of
participants, and were primarily interested in
assessing the feasibility and tolerability of the
training intervention, while gathering some
preliminary data to fuel future hypotheses. A
total of 33 weight-stable participants between
the ages of 18-56 years with overweight or
obesity were randomly assigned to complete
morning cardio workouts (n = 18) or evening
cardio workouts (n = 15). Morning workouts
occurred between 6:00-10:00, and evening
workouts occurred between 15:00-19:00. The
exercise intervention was 15 weeks long, and
incrementally progressed from moderate to
vigorous intensity (70% of maximum heart
rate to 80%) and from 187.5 kcal/session (750
kcal/week) to 500 kcal/session (2000 kcal/
week) by the 11th week of the program. They
prescribed exercise based on energy expenditure at 70%, 75%, and 80% of maximum heart
rate, which was measured during a maximal
baseline test. They provided an example as
follows: if a participant burned 9.3 kcal/min
at 80% of their max heart rate, they’d need to
exercise for 54 minutes at this particular intensity in order to burn 500kcals in the exercise
session (500 kcal divided by 9.3 kcal/min results in an exercise time of 54 minutes).
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Body composition was measured via DXA
before and after the training program. At the
beginning and end of the intervention, the researchers also measured total daily energy expenditure over 14-day periods using doubly
labeled water. In addition, participants wore
an accelerometer and another wearable device
to track sleep habits. On top of that, resting
energy expenditure was measured via indirect
calorimetry in a subset of participants (n = 23).
Finally, average energy intake was estimated
by combining the doubly labeled water data
with estimated changes in total body energy
based on changes in fat mass and fat-free mass.
Since this was a pilot study aimed at gathering
preliminary data (rather than forming generalizable conclusions), the researchers reported
their results fully, but opted not to perform inferential statistical tests.
Based on retention and adherence statistics,
it looks like both approaches (morning and
evening exercise) were feasible and tolerable
for participants. Compared to baseline, the
morning exercise group experienced a drop
in sleep duration (-10.2 ± 42.6 min/day),
whereas the evening exercise group experienced an increase (+26.6 ± 73.0 min/day).
The morning group started sleeping earlier
at night but waking up earlier for exercise,
whereas the evening group started sleeping
earlier at night but slept in a little bit later.
When it comes to energy expenditure, the
researchers reported that morning and evening exercise yielded divergent effects for
various components of energy expenditure
(Table 1). For example, the morning exercise
group experienced a larger increase in total
daily energy expenditure, a smaller drop in
non-exercise energy expenditure, and a larger increase in resting energy expenditure, but
also a larger increase in energy intake. Note
that these are merely directional statements,
and do not imply statistically significant effects (as no statistical tests were conducted).
Having said all of that, both groups had fairly similar changes in body composition, with
small reductions in body weight and fat mass
(~1-1.5kg), and negligible changes in fat-free
mass. You could argue that there was a very
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slight advantage leaning in favor of greater
weight loss in the evening exercise group, but
differences were small.
While I think the researchers did an excellent
job with this study, I don’t find the energy expenditure differences to be particularly compelling. First of all, we’re looking at a very
small sample of participants and some pretty
small differences between the two groups.
Beyond that, we’re looking at incomplete
data; for example, non-exercise energy expenditure was calculated based on the resting
energy expenditure values (which were only
measured in a subsample of participants) and
an assumed (not measured) value of energy
burned during exercise sessions. Similarly,
energy intake was calculated based on losses
of lean mass and fat mass (and their estimated
energy densities), and DXA estimates come
with some unavoidable measurement error.
However, the most important reason that I’m
not getting worked up about the energy expenditure results (yet) is that they aren’t corroborated by the body composition changes.
In many cases, we’re primarily interested in
energy expenditure outcomes because of their
assumed connection to long-term changes in
body composition. These energy expenditure
data would imply that more favorable energy expenditure changes were occurring in the
morning exercise group, but the weight loss
and fat loss results lean slightly in favor of the
evening exercise group. When looking at other studies on the topic, it seems that the data
are truly mixed, with some research reporting more favorable weight loss from morning
exercise (2), some research reporting more
favorable weight loss from evening exercise
(3), and other research showing no significant
difference (4). So, these energy expenditure
results are certainly interesting, but I’m going
to wait for replication in a larger study before I
place too much confidence in them.
Based on the totality of the available evidence,
I don’t think we should be deciding between
morning and evening workouts based on the
expectation of differential effects on energy
expenditure or fat loss. As mentioned previously, the MASS team has thrice discussed
whether resistance training sessions completed in the morning or evening might lead to
differential training adaptations (one, two,
three). When Dr. Zourdos most recently addressed the topic, he did a fantastic job summarizing the current state of the evidence, so
I’ll merely restate his conclusions rather than
trying to reinvent the wheel. As he explains
more fully in his Volume 4 article, the current evidence suggests that lifters can make
very similar long-term strength and hypertrophy gains whether they choose to train
in the morning or the evening. However, if
you’re accustomed to training in the evening, you might experience a transient 5-10%
strength reduction when you first switch to
morning workouts, but this should only last a
few weeks. In addition, there might be some
modest advantages (namely increased motivation and lower session RPE) when you opt
to train at your preferred training time. Finally, as noted in the presently reviewed study,
it doesn’t seem that nighttime workouts have
a particularly negative impact on sleep duration or quality, as long as you aren’t completing very intense exercise within an hour or
so of bedtime (5). In conclusion, there don’t
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seem to be major, persistent physiological
costs or benefits associated with completing
your workouts in the morning or the evening.
With this in mind, you should feel empowered to train at whatever time suits your preferences and fits your schedule.
References
1. Creasy SA, Wayland L, Panter SL, Purcell
SA, Rosenberg R, Willis EA, et al. Effect
of Morning and Evening Exercise on
Energy Balance: A Pilot Study. Nutrients.
2022 Jan;14(4):816.
2. Willis EA, Creasy SA, Honas JJ, Melanson
EL, Donnelly JE. The effects of exercise
session timing on weight loss and
components of energy balance: midwest
exercise trial 2. Int J Obes 2005. 2020
Jan;44(1):114–24.
3. Di Blasio A, Di Donato F, Mastrodicasa
M, Fabrizio N, Di Renzo D, Napolitano G,
et al. Effects of the time of day of walking
on dietary behaviour, body composition
and aerobic fitness in post-menopausal
women. J Sports Med Phys Fitness. 2010
Jun;50(2):196–201.
4. Teo SYM, Kanaley JA, Guelfi KJ,
Dimmock JA, Jackson B, Fairchild
TJ. Effects of diurnal exercise timing
on appetite, energy intake and body
composition: A parallel randomized trial.
Appetite. 2021 Dec 1;167:105600.
5. Stutz J, Eiholzer R, Spengler CM. Effects
of Evening Exercise on Sleep in Healthy
Participants: A Systematic Review
and Meta-Analysis. Sports Med. 2019
Feb;49(2):269–87.
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VIDEO: Time-Efficient
Programming Strategies Part II
BY MICHAEL C. ZOURDOS
Part 1 of our time efficiency series provided examples of training strategies
which can be used to decrease training time. However, there are certain
circumstances which warrant training twice per day, in which further
adjustments are needed to accommodate two-a-days. Therefore, part 2 of
this series provides practical examples for how time-efficient programming
strategies can be used to make two-a-days feasible.
Click to watch Michael's presentation.
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Relevant MASS Videos and Articles
1. Rest-Pause Training is a Viable Strategy to Maximize Hypertrophy. Volume 1 Issue 3.
2. Back-to-Back Champs: The Agonist-Antagonist Superset. Volume 4 Issue 3.
3. Minimum Effective Dose Training Part 2. Volume 5 Issue 9.
4. Time-Efficient Programming Strategies Part 1. Volume 6 Issue 3.
5. Is Training Twice A Day For You? Volume 6 Issue 3.
References
1. Iversen VM, Norum M, Schoenfeld BJ, Fimland MS. No time to lift? Designing time-efficient
training programs for strength and hypertrophy: a narrative review. Sports Medicine. 2021
Oct;51(10):2079-95.
2. Storey A, Wong S, Smith HK, Marshall P. Divergent muscle functional and architectural
responses to two successive high intensity resistance exercise sessions in competitive
weightlifters and resistance trained adults. European journal of applied physiology. 2012
Oct;112(10):3629-39.
3. Bartolomei S, Malagoli Lanzoni I, Di Michele R. Two vs. One Resistance Exercise Sessions
in One Day: Acute Effects on Recovery and Performance. Research Quarterly for Exercise and
Sport. 2021 Dec 19:1-6.
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Interpreting Failure and Non-Failure
Data
BY ERIC HELMS
You might have noticed the studies comparing failure versus non-failure for
hypertrophy outcomes often conflict. We’ve reviewed many of them in MASS,
and not only do they tend to conflict with one-another, but even within the
MASS team, we don’t always see eye-to-eye when interpreting the impact of
failure. Why is that? In this video, you’ll understand why interpreting these
data is challenging.
Click to watch Eric's presentation.
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Relevant MASS Videos and Articles
1. Bonus: Reps in Reserve and Hypertrophy. Volume 4, Issue 3.
2. VIDEO: Emerging Concepts in Velocity-Based Training. Volume 4, Issue 8.
3. Velocity Loss is Conceptually Flawed. Volume 4, Issue 12.
4. The Most Comprehensive Look at Proximity to Failure Yet. Volume 5, Issue 8.
References
1. Karsten, B., Fu, Y. L., Larumbe-Zabala, E., Seijo, M., & Naclerio, F. (2021). Impact of Two
High-Volume Set Configuration Workouts on Resistance Training Outcomes in Recreationally
Trained Men. Journal of Strength and Conditioning Research, 35(Suppl 1), S136–S143.
2. Martorelli, S., Cadore, E. L., Izquierdo, M., Celes, R., Martorelli, A., Cleto, V. A., et al. (2017).
Strength Training with Repetitions to Failure does not Provide Additional Strength and Muscle
Hypertrophy Gains in Young Women. European Journal of Translational Myology, 27(2), 6339.
3. Santanielo, N., Nóbrega, S. R., Scarpelli, M. C., Alvarez, I. F., Otoboni, G. B., Pintanel, L., et
al. (2020). Effect of resistance training to muscle failure vs non-failure on strength, hypertrophy
and muscle architecture in trained individuals. Biology of Sport, 37(4), 333–341.
4. Pareja-Blanco, F., Rodríguez-Rosell, D., Sánchez-Medina, L., Sanchis-Moysi, J., Dorado, C.,
Mora-Custodio, R., et al. (2017). Effects of velocity loss during resistance training on athletic
performance, strength gains and muscle adaptations. Scandinavian Journal of Medicine &
Science in Sports, 27(7), 724–735.
5. Vieira, A. F., Umpierre, D., Teodoro, J. L., Lisboa, S. C., Baroni, B. M., Izquierdo, et al. (2021).
Effects of Resistance Training Performed to Failure or Not to Failure on Muscle Strength,
Hypertrophy, and Power Output: A Systematic Review With Meta-Analysis. Journal of Strength
and Conditioning Research, 35(4), 1165–1175.
6. Grgic, J., Schoenfeld, B. J., Orazem, J., & Sabol, F. (2021). Effects of resistance training
performed to repetition failure or non-failure on muscular strength and hypertrophy:
A systematic review and meta-analysis. Journal of Sport and Health Science, S20952546(21)00007-7.
7. Hackett, D. A., Johnson, N. A., & Chow, C. M. (2013). Training practices and ergogenic aids
used by male bodybuilders. Journal of Strength and Conditioning Research, 27(6), 1609–1617.
8. Vieira, J. G., Sardeli, A. V., Dias, M. R., Filho, J. E., Campos, Y., Sant’Ana, L., et al. (2021).
Effects of Resistance Training to Muscle Failure on Acute Fatigue: A Systematic Review and
Meta-Analysis. Sports Medicine, 10.1007/s40279-021-01602-x.
9. Pelland, J. C., Robinson, Z. P., Remmert, J. F., Cerminaro, R. M., Benitez, B., John, T. A., et
al. (2022). Methods for Controlling and Reporting Resistance Training Proximity to Failure:
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Current Issues and Future Directions. Sports Medicine, 10.1007/s40279-022-01667-2.
10. Zourdos, M. C., Goldsmith, J. A., Helms, E. R., Trepeck, C., Halle, J. L., Mendez, K. M., et
al. (2021). Proximity to Failure and Total Repetitions Performed in a Set Influences Accuracy
of Intraset Repetitions in Reserve-Based Rating of Perceived Exertion. Journal of Strength and
Conditioning Research, 35(Suppl 1), S158–S165.
11. Halperin, I., Malleron, T., Har-Nir, I., Androulakis-Korakakis, P., Wolf, M., Fisher, J., et al.
(2022). Accuracy in Predicting Repetitions to Task Failure in Resistance Exercise: A Scoping
Review and Exploratory Meta-analysis. Sports Medicine, 52(2), 377–390.
12. Beck, M., Varner, W., LeVault, L., Boring, J., & Fahs, C. A. (2020). Decline in Unintentional
Lifting Velocity Is Both Load and Exercise Specific. Journal of Strength and Conditioning
Research, 34(10), 2709–2714.
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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. Ma et al. A scoping review of interventions to improve strength training participation
2. Halperin et al. Accuracy in Predicting Repetitions to Task Failure in Resistance Exercise:
A Scoping Review and Exploratory Meta-analysis
3. Hirschberg. Challenging Aspects of Research on the Influence of the Menstrual Cycle and
Oral Contraceptives on Physical Performance
4. Wehrstein et al. Eccentric Overload during Resistance Exercise: A Stimulus for Enhanced
Satellite Cell Activation
5. Hu et al. Effects of Transcranial Direct Current Stimulation on Upper Limb Muscle Strength
and Endurance in Healthy Individuals: A Systematic Review and Meta-Analysis
6. Skattebo et al. Increased Mass-Specific Maximal Fat Oxidation Rate with Small versus
Large Muscle Mass Exercise
7. Yacyshyn and McNeil. Intrinsic Neuromuscular Fatigability in Humans: The Critical Role
of Stimulus Frequency
8. Nóbrega et al. Muscle Hypertrophy Is Affected by Volume Load Progression Models
9. Kassiano et al. Partial range of motion and muscle hypertrophy: not all ROMs lead to
Rome
10. Lochbaum et al. Sport psychology and performance meta-analyses: A systematic review
of the literature
11. Nugent et al. The Effects of High-Repetition Strength Training on Performance in
Competitive Endurance Athletes: A Systematic Review and Meta-Analysis
12. Baena-Marín et al. Velocity-Based Resistance Training on 1-RM, Jump and Sprint
Performance: A Systematic Review of Clinical Trials
13. Mang et al. Aerobic Adaptations to Resistance Training: The Role of Time under Tension
14. Lim et al. Does a Higher Protein Diet Promote Satiety and Weight Loss Independent of
Carbohydrate Content? An 8-Week Low-Energy Diet (LED) Intervention
15. Potter et al. Effects of Exercise Training on Resting Testosterone Concentrations in
Insufficiently Active Men: A Systematic Review and Meta-Analysis
16. Grgic. Effects of post-exercise cold-water immersion on resistance training-induced gains
in muscular strength: a meta-analysis
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17. Campos et al. Importance of Carbohydrate Quality: What Does It Mean and How to Measure
It?
18. Kwan and Helms. Prevalence, Magnitude, and Methods of Weight Cutting Used by World
Class Powerlifters
19. Wachsmuth et al. The Impact of a High-Carbohydrate/Low Fat vs. Low-Carbohydrate Diet
on Performance and Body Composition in Physically Active Adults: A Cross-Over Controlled
Trial
20. Gebel and Ding. Using Commercially Available Measurement Devices for the Intake-Balance
Method to Estimate Energy Intake: Work in Progress
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Thanks for
reading MASS.
The next issue will be released to
subscribers on May 1, 2022.
Copy editing by Lauren Colenso-Semple
Graphics and layout by Kat Whitfield
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