MASS 2022 01-v2

V O L U ME 6 , ISS U E 1
JANUARY 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
Sex Differences in Recovery: The Details (May) Matter
A recent study purported to show that male and female lifters recover from training
at approximately the same rate when training to failure, but that female lifters recover
faster when not training to failure. However, a deeper analysis of the data suggests
that the differences may not be quite as large as they initially appear.
22
BY MI CHAEL C. ZOUR DOS
It’s Okay to Fail Sometimes
We often think of training to failure in a binary fashion. However, a new study
compares the time course of recovery between performing all bench press sets to
failure versus taking one set to failure with other sets shy of failure.
38
BY ER I C HEL MS
Time-Restricted Feeding Protocols Might Help You Stay
Lean, But Hinder Muscle Gain
A year-long study compared time-restricted eating (AKA “intermittent fasting”) to a
standard eating pattern in well-trained, healthy men consuming high protein. This study
gives us a clear look at the long-term effects of a 16-hour fasting window in lifters.
51
BY ER I C T R EXL ER
When Popular Topics Collide: The Constrained Total
Energy Expenditure Model Meets Metabolic Adaptation
Metabolic adaptation and the constrained total energy expenditure model are two of
the hottest topics in metabolism research and may be more related than you think.
Read on to find out what happens when these topics collide.
66
BY MI CHAEL C. ZOUR DOS
Prime Cuts: Being Strong Makes Priming Sessions More
Effective
Performing a priming session a day or two before testing your strength may boost
performance. However, a recent study suggests lifters might need a certain level of
baseline strength to reap the benefits.
79
BY ER I C T R EXL ER
Gluten Gains: Similar Effects of Wheat and Milk Protein on
Muscle Protein Synthesis
A new study reports that 30g of wheat protein increases muscle protein synthesis as
much as 30g of milk protein. Read on to contextualize this surprising finding within
our perpetually evolving understanding of dietary protein.
94
BY GR EG NUCKOL S & ER IC TREX LER
Research Briefs
In the Research Briefs section, Greg Nuckols and Eric Trexler share quick
summaries of recent studies. Briefs are short and sweet, skimmable, and focused
on the need-to-know information from each study.
135
BY MI CHAEL C. ZOUR DOS
Mental Fatigue Part 1
Sometimes we peruse social media sites on our phones and then go right to
the gym. If you’ve ever done this and felt tired or lacked focus when training, it’s
possible that mental fatigue from engaging on social media was the culprit. This
video breaks down the literature on how mental fatigue affects acute performance.
138
BY ER I C HEL MS
VIDEO: Body Comp Behaviors
Intentional dieting isn’t for everyone, and even for those it is for, eventually it ends
and you move to maintenance. Further, some people struggle to stick to a diet
with quantitative food tracking or even if they can, can’t stick to tracking when the
diet is over during maintenance. In this video I discuss habits you can work on
adopting that can potentially result in a deficit, or that can be implemented to aid
weight loss maintenance.
Letter From the Reviewers
W
elcome to 2022 and Volume 6 of MASS! As always, we have a great issue
lined up to kick off the new year.
Starting in the nutrition department, Dr. Helms covered the first long-term
study investigating the impact of 16:8 time-restricted feeding (i.e., intermittent fasting)
on strength, body composition, and hormonal outcomes in lifters. Dr. Trexler’s first
article this month discussed long-term metabolic adaptation, the constrained energy
expenditure model, and whether a low metabolic rate makes it harder to maintain
substantial weight loss. His second article examined a study comparing the effects of
milk and wheat protein on muscle protein synthesis; this article delves pretty deep into
the limitations of acute muscle protein synthesis data.
In the training department, Dr. Zourdos’s first article looked at the recovery impact of
training to failure during every set, versus only reaching failure during the final set of
an exercise. His second article revisits the topic of priming sessions; in it, he reviews
a study that suggests stronger lifters are more likely to benefit from priming sessions
than weaker lifters. Greg’s article digs into a study that investigated whether training to
failure has different effects on recovery in male versus female lifters.
In the video department, Dr. Helms discusses behaviors and habits that can make
weight loss (and weight loss maintenance) easier, and Dr. Zourdos kicks off a series
examining the impact of mental fatigue on training performance.
Finally, our research briefs this month cover studies related to attentional focus,
whether cold exposure affects fat loss, concurrent training, buffering supplements,
sarcoplasmic hypertrophy, fish oil supplementation for recovery, citrulline malate, and
sleep.
After a turbulent 2021, we’re grateful to everyone reading this. We’re hoping that
things can broadly return to normal in 2022, and we’ll be doing our best to keep
you up-to-date about strength, hypertrophy, and physique science throughout this
upcoming year.
Thanks,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
5
Study Reviewed: Impact of Training Protocols on Lifting Velocity Recovery in Resistance
Trained Males and Females. Amdi et al. (2021)
Sex Differences in Recovery: The
Details (May) Matter
BY GREG NUCKOLS
A recent study purported to show that male and female lifters
recover from training at approximately the same rate when training
to failure, but that female lifters recover faster when not training
to failure. However, a deeper analysis of the data suggests that the
differences may not be quite as large as they initially appear.
6
KEY POINTS
1. Trained male and female lifters completed two squat protocols. One protocol
consisted of 5 sets of 5 reps at 80% of 1RM (non-failure protocol). The second
protocol consisted of 5 sets of squats to failure, starting with a 4-6RM load.
Researchers measured mean concentric barbell velocity at 80% of 1RM pre-training
and 5 minutes, 24 hours, 48 hours, and 72 hours post-training to assess recovery.
2. Following the failure protocol, the time course of recovery was similar in both sexes.
However, velocity recovered to a greater extent in the female lifters at 5 minutes,
24 hours, and 48 hours post-training in the non-failure condition, suggesting that
female lifters recover faster than male lifters when not training to failure.
3. For reasons discussed in the Criticisms and Statistical Musings section, I’m not
sure the female lifters actually recovered faster than the male lifters following the
non-failure protocol. This ambiguity is (frustratingly) consistent with the rest of
the literature: you could make the case that female lifters recover from resistance
training faster than male lifters, but you could make a similarly strong case that
male and female lifters recover from resistance training at similar rates.
T
here are a few good reasons to suspect
that female lifters recover faster from
training than male lifters. For starters,
estrogen protects against muscle damage and
aids in muscle recovery (2). Furthermore,
females tend to have a higher proportion of
type I muscle fibers than males (3), and people with a greater proportion of type I fibers
may recover faster from strenuous exercise
(4). However, when you actually dig into the
studies directly investigating the impact of
sex on recovery from resistance training, the
picture gets a lot murkier. There are certainly some studies suggesting that female lifters
recover faster than male lifters (5, 6), but it’s
also not hard to find studies suggesting that
males and females recover from training at
similar rates (7, 8).
A recent study by Amdi and colleagues sought
to provide some clarity (1) by investigating
whether training to failure is a key explanatory variable: perhaps males and females recover at similar rates when training to failure,
but female lifters recover faster when training
isn’t performed to failure. To that end, trained
male and female lifters completed two squat
protocols. One protocol consisted of 5 sets of
5 reps at 80% of 1RM (non-failure protocol).
The second protocol consisted of 5 sets of
squats to failure, starting with a 4-6RM load.
Researchers measured mean concentric barbell velocity at 80% of 1RM pre-training and
5 minutes, 24 hours, 48 hours, and 72 hours
post-training to assess recovery. Relative to
pre-training values, male and female lifters
had similar time courses of velocity recovery
following the failure protocol, but the female
lifters recovered significantly faster than the
male lifters following the non-failure protocol. However, for reasons I’ll explain in this
article, I suspect that the recovery results fol-
7
lowing the non-failure protocol were driven
by some form of measurement error, rather
than actual differences between the male and
female lifters. Read on to learn what we can
actually take away from this study.
Purpose and Hypotheses
Purpose
The purpose of this study was to investigate
sex differences in recovery from squat training.
Specifically, the authors were interested in seeing if responses were different when training to
failure versus stopping shy of failure.
Hypotheses
The researchers hypothesized that female
lifters would recover faster than male lifters
when not training to failure, but that when
both sexes trained to failure, rates of recovery
would be similar between the sexes.
Subjects and Methods
Subjects
A total of 24 subjects (14 males and 10 females) participated in this study. All subjects
had at least six months of resistance training experience, and were required to have a
1RM squat greater than or equal to their body
mass. More details about the subjects can be
seen in Table 1.
Experimental Design
The present study was intended to be a crossover trial with a single group of 21 subjects
completing two different testing protocols,
with three weeks between protocols. However, COVID-19 shutdowns postponed the
study by six months after subjects had completed testing for the first protocol, leading to
13 drop-outs. When the study resumed, the
original 11 subjects who didn’t drop out com-
8
pleted testing for the other protocol, and an
additional three subjects enrolled, completing both protocols. As such, the design of the
present study blurs the distinction between a
crossover study and a parallel groups design
– of the 24 total subjects, five females and six
males completed both testing protocols, and
five females and eight males only completed
one of the protocols.
Both protocols took place over 6-7 days. The
first day was for baseline testing, which consisted of a body composition assessment via
bioelectrical impedance (BIA) and a 1RM
squat test. Squat depth throughout the study
was judged using the standard powerlifting
criterion – the crease of the hip needed to descend below the top of the knee.
Subjects completed one of two squat sessions
48-72 hours after baseline testing. One session involved 5 sets of 5 reps at 80% of 1RM,
with “at least five minutes of rest between
sets.” The other session involved 5 sets to
momentary concentric failure (i.e., until the
subject actually failed to complete a rep, requiring the assistance of spotters) with a load
that corresponded to a 4-6RM on the first set,
with “at least five minutes between sets.” It’s
unclear whether the rest intervals between sets
were actually standardized; if they weren’t, a
reason for this lack of standardization wasn’t
provided; however, I don’t think this is too
big of a drawback. A five minute rest interval
should be plenty of time to recover between
sets, so I doubt subjects gained any significant advantage if they were allowed to rest an
extra minute or two.
Before the squat protocols began, five minutes after the final set was completed, and 24,
48, and 72 hours post-training, mean concentric squat velocity at 80% of 1RM was assessed using the PUSH bandTM 2.0. For these
sets (and for all squats performed throughout
the study), subjects were instructed to complete the concentric phase of each rep as ex-
9
plosively as possible. Changes in mean concentric velocity from baseline (expressed as
a percentage change) were used to assess recovery from the squat protocols. A graphical
overview of the study can be seen in Figure 1.
Finally, it’s worth discussing how the 4-6RM
loads used in the failure protocol were determined. After completing the pre-training velocity assessment with 80% of 1RM, subjects
completed a set of squats with 85% of 1RM. If
they failed before completing seven reps, this
set was counted as the first of their five sets
to failure. If they completed seven reps, they
racked the bar, and the load was increased
by ~2.5%. If they failed before completing
seven reps with ~87.5% of 1RM, this set was
counted as the first of their five failure sets.
If they completed seven reps, the load was
increased by another ~2.5%. This process
was continued until each subject failed after
4-6 reps. The researchers recorded the total
weight lifted during these sets of seven reps,
referring to it as “pre-tonnage.”
Findings
All of the results can be seen Tables 2 and 3
and Figures 2 and 3.
The most important finding was that velocity
recovery profiles were very similar between
sexes when training to failure (Figure 3), but
that recovery profiles differed considerably
between sexes when subjects just performed
5 sets of 5 reps at 80% of 1RM (17), not to
failure (Figure 2).
Beyond that, there are a couple of little details
worth noting, which will be relevant for the
interpretation section. First, the average load
used during the failure protocol was a higher percentage of 1RM for the female subjects
than the male subjects – 89.7% versus 87.7%.
All of the male subjects used loads between
85-90% of 1RM, whereas the female subjects
used loads between 87-95% of 1RM. Second,
mean velocity during the highest-effort reps
(1RMs, and the final rep during sets to fail-
10
11
ure) was pretty comparable between the sexes. However, during lower-effort reps (80% of
1RM when fresh), rep speed tended to be faster for males than females; that difference was
especially notable for baseline velocity testing
during the non-failure condition (0.72m/s for
males, versus 0.58m/s for females).
Criticisms and Statistical
Musings
When I first skimmed this study (1), I deeply wanted to simply accept the results at face
value. My thesis research investigated sex
differences in acute fatigue during resistance training, and recovery from resistance
training (9). In my literature review, I found
that the research on acute fatigability painted a pretty consistent (though nuanced) picture (10), whereas the research on recovery
was all over the place. However, the present
study had the possibility of putting a nice,
neat bow on the issue: maybe failure is the
critical issue. Perhaps female lifters recover meaningfully faster than male lifters (on
average) when training isn’t performed to
failure, but the muscle damage and fatigue
associated with training to failure is sufficient to mitigate potential sex differences,
resulting in similar time courses of recovery
in both sexes.
That would be a parsimonious explanation,
and it’s an explanation supported by the researchers’ interpretation of their data. However, after digging a bit deeper into the results of the present study, I’m not sure the
data support this interpretation.
One thing that jumped out at me when ana-
lyzing the tables provided in the present study
was that the subjects’ pre-training velocity at
80% of 1RM differed a bit from the subjects’
fastest reps when completing the non-failure
squat protocol. That’s noteworthy, because
the non-failure protocol was also completed
with 80% of 1RM, and the subjects should
have been completely fresh for the first set.
Thus, if velocity was assessed accurately,
and the subjects were actually completing the
concentric portion of each rep with the greatest velocity possible, the fastest velocity recorded during the first set of 5 reps with 80%
of 1RM should have been very similar to the
velocity with 80% of 1RM recorded pre-training. For the female lifters, the two velocities
are pretty similar – 0.58m/s pre-training, versus 0.60m/s during the first set of the squat
protocol. For the males, the opposite pattern
was observed, and the difference between the
two measurements was quite a bit larger –
0.72m/s pre-training, versus 0.66m/s during
the first set of the squat protocol. Now, a difference of 0.06m/s may not seem particularly
large, but that’s actually a much larger difference than you’d expect to see when assessing
velocity multiple times with the same load,
in the same group lifters, under similar circumstances. On a group level, you’d expect
the mean concentric velocities to only differ
by 0.01-0.02m/s at most, as Dr. Zourdos has
covered previously (11); on an individual
level, you’d expect velocity measurements to
only vary by about 0.02-0.03m/s.
With that in mind, I decided to see how the
recovery profiles would look following the
non-failure session if I centered the results
around the highest velocities attained during
12
the first set of 5 with 80% of 1RM – in other
words, the highest velocities attained during
the first set (0.66m/s for males and 0.60m/s
for females) would be the benchmark “100%”
value, instead of the highest velocities attained pre-training (0.72m/s for males and
0.58m/s for females). To do this, I extracted
the relative velocity change data from Figure
2 using WebPlotDigitizer, calculated the average velocities for each group from the relative changes reported, and re-calculated the
changes in average velocity relative to the
highest velocities attained during the first set
of the non-failure squat protocol. You can see
the results in Figure 4.
As you can see, if we use first-set velocity
as our baseline, the resulting recovery differences aren’t nearly as dramatic as they appeared in Figure 2.
After making Figure 4, I noticed something
else that was a bit strange. For both sexes,
velocity decreased slightly over the course
of the non-failure squat protocol. The male
subjects’ fastest reps averaged 0.66m/s for
the first four sets, dipping slightly to 0.65m/s
for the fifth set. The female subjects’ fastest reps averaged 0.60m/s for the first set,
dipping to 0.57m/s for their two slowest
sets (sets 2 and 5). When velocity recovery was assessed 5 minutes post-training,
it was essentially unchanged for the male
subjects – it was approximately 0.66m/s.
For the female subjects, on the other hand,
mean concentric velocity was about 9%
greater than baseline, or about 0.63m/s.
That’s noteworthy, because the recovery
time between sets was the same as the recovery time between the final set and the
first post-training velocity assessment: 5
minutes. The female subjects’ squat velocity at 5 minutes post-training was ~0.03m/s
faster than their velocity during any of their
training sets, ~0.05m/s faster than their
pre-training velocity, and ~0.06m/s faster
than their fastest rep during their fifth set
of squats. To simplify things, the average
velocities for both sexes for just the first
day of testing (pre-training, the fastest velocities for all five non-failure sets, and the
velocity assessment that occurred at 5 minutes post-training) can be seen in Figure 5.
13
Unfortunately, I think the most parsimonious
interpretations of these results conflict with
the authors’ interpretation (and my biases).
The first interpretation relates to the lifters’
level of effort and comfort with velocity assessments. The male lifters may have just put
more effort into the pre-training velocity assessment than the first reps of any subsequent
sets. There’s no logical reason that velocity
during their first training set would differ so
much from their pre-training velocity with the
same load. Beyond the decrease in velocity
from pre-training to set 1, velocity is remarkably stable throughout the entire day of testing for the male lifters, suggesting that minimal fatigue occurred. Conversely, I think the
female subjects may have just gotten more
comfortable attempting to squat at a maximal
concentric velocity throughout the first day
of testing. Taken literally, the results would
suggest that, rather than fatiguing, the female lifters were getting stronger in real time
during the first day of testing – their mean
concentric velocity was higher after five sets
of squats (5 minutes post-training) than it
was pre-training. If we discount that possibil-
ity (as we should), it seems most likely that
either a) the female lifters simply needed five
sets of squats as a warm-up (unlikely), or b)
the female lifters were just more comfortable
squatting at a maximal intended concentric
velocity post-training than pre-training.
The second interpretation is much more mundane: there may have been some measurement
errors. The researchers assessed velocity in
the present study using the PUSH bandTM 2.0.
PUSH bands are perfectly fine for consumer-grade velocity-based training applications
– if nothing else, they’ll put you in the right
general ballpark for velocity assessments.
However, I’d personally be hesitant to use a
PUSH band for research purposes. Their reliability during free weight squats with pretty
heavy loads isn’t great (CV = 13.14% with
85% of 1RM), and it’s not terribly uncommon for PUSH bands to produce errors of
>0.1m/s (12, 16), relative to linear position
transducers (which are most commonly used
to assess barbell velocity for research purposes). To be clear, I’m not faulting the researchers for using a PUSH band if their research
budget was insufficient to afford a linear po-
14
sition transducer (since accelerometry-based
velocity devices, such as PUSH bands, tend
to be quite a bit cheaper than linear position
transducers). However, given some of the
confusing results during just the first day of
testing – squat velocity dropping by 0.06m/s
from pre-training testing to set one for males,
and post-training velocity being 0.05m/s faster than pre-training velocity and 0.06m/s faster than set five velocity for females – I don’t
think we can rule out the possibility that the
researchers did everything correctly, the subjects put their full effort into every set, and
the PUSH band used for velocity assessments
simply produced a few group-level measurement errors. Directional errors would just
wash out with a larger sample size, but with
just eight subjects per group, some screwy
PUSH band readings could have a marked
effect on the outcomes.
So, what do we do with all of this? Honestly,
I’m not entirely sure. You could still make an
argument that the female lifters recovered faster than the male lifters. After all, in Figure 4
above, it still appears that the female lifters’
velocity recovery may have been greater than
the male lifters’ at 5 minutes and 48 hours
post-training. However, a more skeptical interpretation is also entirely justifiable – the results
are simply inconclusive. I personally lean toward the more skeptical interpretation, but I do
think the authors’ interpretation (female lifters
recover faster than male lifters when not training to failure) is at least somewhat justifiable.
Interpretation
If you made it through the criticisms and statistical musings section, you’ll know I initial-
ly approached this study (1) with high hopes.
The research investigating sex differences in
recovery from resistance is difficult to parse
– some studies find female lifters recover
faster from training (5, 6), some studies find
no significant differences between the sexes
(7, 8), and one study found that male lifters
recovery faster (13), with no clear trends explaining these differences. If the key finding
of the present study (females recover quicker
when training isn’t performed to failure, with
no sex differences in recovery when training
is performed to failure) stood up to scrutiny,
that could provide some clarity in an otherwise murky body of literature.
Instead, it reinforces the broader trend: if you
want to approach this study and this body of
literature with the assumption that female lifters recover from resistance training faster than
male lifters, you can easily find outcomes that
support your supposition. However, if you approach this study and this body of literature
with the intent of firmly assuming the null (i.e.,
no sex differences in recovery from resistance
training) until it’s been thoroughly disproven,
I don’t think the evidence is yet sufficient to
shake you from that assumption.
For example, if you wanted to argue that the
present study shows that female lifters recover faster than male lifters (1), you’d have an
easy time arguing your case. You could take
the results of the non-failure squat protocol at
face value (faster recovery of barbell velocity
in female lifters), and contend that the results
of the failure protocol still favor the female
lifters. Even though velocity recovery didn’t
differ between the sexes, the female lifters
all completed at least one extra set of seven
15
reps prior to their first working set (with at
least one subject completing four extra sets),
whereas some of the male subjects didn’t
have to complete any extra sets, resulting in
the female subjects performing their sets at a
higher relative load. Furthermore, minimum
velocity during the failure sets tended to be
lower in the female subjects, suggesting they
were grinding their reps harder than the male
subjects. Thus, the female lifters recovered
just as fast as the male subjects from the failure protocol, in spite of doing extra sets and
possibly putting more effort into their sets.
Conversely, if you prefer to assume the null
until it’s thoroughly disproven, I made the case
for the recovery results during the non-failure protocol not differing much between the
sexes in the criticisms and statistical musings
section of this article. Furthermore, the results
from the failure protocol already support the
null – you can say whatever you want about
potential differences in minimum velocity or
“pre-tonnage,” but the actual recovery results
were similar between the sexes. Maybe the
results would have been different if the researchers employed a different process for
selecting the loads to be used for the failure
training protocol, but we shouldn’t make assumptions about what might have happened
if the study was designed differently. We can
only see what did happen: males and females
completed five sets of squats to failure, and
their squat velocity recovered at similar rates
from 5 minutes to 72 hours post-training.
This is a surprisingly common trend throughout the literature: regardless of whether you
expect female lifters to recover faster than
male lifters, or whether you expect there to
REGARDLESS OF WHETHER YOU
EXPECT FEMALE LIFTERS TO
RECOVER FASTER THAN MALE
LIFTERS, OR WHETHER YOU
EXPECT THERE TO BE NO SEX
DIFFERENCES IN RECOVERY,
IT’S HARD TO FIND STUDIES
THAT UNAMBIGUOUSLY
SUPPORT YOUR PERSPECTIVE.
be no sex differences in recovery, it’s hard to
find studies that unambiguously support your
perspective. For example, Judge and Burke
found that female lifters recovered faster than
male lifters following a bench press protocol
(5), and Häkkinen found that female lifters
recovered faster than male lifters following
a squat protocol (6). However, neither exercise protocol was particularly representative
of “normal” training. In the Judge study, the
exercise protocol was just a single set with a
5RM load, and there were only six subjects
of each sex. In the Häkkinen study, the exercise protocol consisted of 10 × 10RM squats,
and the difference in strength recovery was
only present at 2 and 24 hours post-training.
By 48 hours post-training, the strength recovery was comparable between the sexes (thus,
the difference in recovery was only relevant
for people who planned on doing more lower
body training the day after doing a 10 × 10RM
squat workout). In both of these studies, the
16
findings appear to support the position that
female lifters recover faster than male lifters, but it’s unclear whether those differences
would matter in the context of “normal” resistance training.
Conversely, on its face, the recovery findings (bar velocity and soreness) in my thesis
study would seem to support the null position
– recovery post-training didn’t significantly differ between sexes (9). However, much
like the recovery results following the failure
protocol in the present study, there’s a massive caveat: my female subjects completed
almost twice as many bench press sets as my
male subjects. Would the female lifters have
recovered faster if relative volume load was
equated between the sexes? I can’t say for
sure, because that’s not the study I ran. Similarly, in a 2015 study by Baggett (7), male
and female lifters completed an ecologically
valid training protocol consisting of 3 sets of
8-12 reps for three upper body and three lower body exercises on back-to-back days. Male
and female lifters had similar performance
decrements during the second training session, and reported similar amounts of muscular discomfort during the second training
session. However, the female lifters reported
less delayed onset muscle soreness than the
male lifters.
That more-or-less sums up the state of the literature at the moment: it’s just very unclear.
I personally think the balance of evidence
suggests that, in general, female lifters may
recover from lifting slightly faster than male
lifters. However, I’m not convinced that the
difference is large enough to be practically
meaningful. At the very least, we’re a long
way from understanding the circumstances in which the difference (if it exists) really matters. Furthermore, the interindividual
differences in recovery capacity within each
sex are almost certainly larger than the average difference between sexes. For now,
even though I do think that female lifters may
recover a bit faster than male lifters, I don’t
think it’s a difference that’s large enough or
consistent enough to warrant planning for
when designing training programs.
There’s one final thing I’d like to address before wrapping up. The authors of the present
study proposed that, within this body of literature, female lifters recover faster than male
lifters in studies where the male subjects are
relatively stronger than the female subjects,
FOR NOW, EVEN THOUGH
I DO THINK THAT FEMALE
LIFTERS MAY RECOVER A
BIT FASTER THAN MALE
LIFTERS, I DON’T THINK
IT’S A DIFFERENCE THAT’S
LARGE ENOUGH OR
CONSISTENT ENOUGH
TO WARRANT PLANNING
FOR WHEN DESIGNING
TRAINING PROGRAMS.
17
but that recovery is similar in studies where
strength levels are more comparable. Therefore, when differences in recovery have been
observed, they may have been due to differences in training status rather than sex differences. However, I don’t think I agree with
this interpretation. First, in general, you’d expect more well-trained lifters to recover from
a standardized exercise protocol faster than
less-trained lifters. Due to the repeated bout
effect, the muscles of people with more training experience should be better-equipped to
resist muscle damage (14). Furthermore, in
some of the studies purporting to show sex
differences in recovery, the relative strength
differences between the male and female subjects weren’t particularly large. For example, in the study by Judge and Burke (5), the
male lifters had an average 5RM bench press
of 144.5kg at a body mass of 110.5kg, and
the female lifters had an average 5RM bench
press of 73.5kg at 92kg. Thus, normalizing
strength to body mass using IPF points, the
average male 5RM would have been 63.12
IPF points, versus 53.78 IPF points for the
females. It’s a similar story for the presently
reviewed study (1): the average male squat
1RM would score 19.88 IPF points (using
the equation for scoring a powerlifting total,
since there’s not a separate equation specific
to squats), versus 17.54 IPF points for the females. In both cases, the male subjects were
slightly stronger, relatively speaking, but I’d
consider the strength levels in both sexes to
at least be comparable. Finally, the velocity
findings of the present study aren’t consistent
with the male lifters being more skilled lifters
than the female lifters. In general, velocity
during reps near failure and during 1RM at-
tempts are lower in lifters with a higher training status (15), due to an improved ability
to grind out tough reps. In the present study,
velocity during 1RM testing was comparable
between the sexes, and the female lifters actually had slightly slower bar velocities during
their final rep of each set when completing
the failure protocol. Thus, I don’t think differences in training status can explain why
female lifters seem to recover faster than
male lifters in some studies but not others.
Next Steps
This may sound trite, but … we just need
more research investigating sex differences
in recovery following resistance training. It’s
still a wide-open body of research; we don’t
know that differences actually exist, we certainly don’t know the magnitude of the difference (if it exists) with any meaningful degree of precision, and we don’t know what
variables influence the magnitude of the effect (again, if it even exists). For two concrete suggestions:
I’d like to see a replication of the presently
reviewed study (1) with three small tweaks.
First, the study would involve a more thorough familiarization period, with assessments
to ensure the lifters’ maximal velocity at 80%
of 1RM was sufficiently reliable. Second, the
study would assess velocity using a linear
position transducer, to ensure that the actual
velocity measurements were sufficiently reliable. Third, the failure protocol would simply consist of five sets to failure with 85%
of 1RM, to negate the question of whether
“pre-tonnage” induced enough fatigue to affect recovery rates.
18
APPLICATION AND TAKEAWAYS
For now, the research is still too unclear to make any firm recommendations. I do think
that female lifters recover from training slightly faster than male lifters, but I’m not
convinced that the difference is large enough to make a meaningful difference. The
best you can do is assess and monitor recovery on an individual basis.
I’d like to see a study directly assessing muscle damage in male and female lifters following a standardized session of isotonic resistance training. Several studies have assessed
sex differences in muscle damage via creatine kinase measurements, but post-training creatine kinase levels tend to be higher
in individuals that simply have more muscle
mass. Since male lifters tend to have considerably more muscle than female lifters,
I don’t think creatine kinase assessements
tell us much about sex differences in muscle damage. Instead, muscle biopsies could
be performed 24 and 48 hours post-training
to quantify the percentage of muscle fibers
with z-line streaming or sarcolemma disruption, as a more valid (and directly comparable) measure of muscle damage.
19
References
1. Amdi CH, Cleather DJ, Tallent J. Impact of Training Protocols on Lifting Velocity
Recovery in Resistance Trained Males and Females. Sports (Basel). 2021 Nov
19;9(11):157. doi: 10.3390/sports9110157. PMID: 34822356; PMCID: PMC8618037.
2. Enns DL, Tiidus PM. The influence of estrogen on skeletal muscle: sex matters. Sports
Med. 2010 Jan 1;40(1):41-58. doi: 10.2165/11319760-000000000-00000. PMID:
20020786.
3. Hunter SK. Sex differences in human fatigability: mechanisms and insight to
physiological responses. Acta Physiol (Oxf). 2014 Apr;210(4):768-89. doi: 10.1111/
apha.12234. Epub 2014 Feb 25. PMID: 24433272; PMCID: PMC4111134.
4. Lievens E, Klass M, Bex T, Derave W. Muscle fiber typology substantially influences
time to recover from high-intensity exercise. J Appl Physiol (1985). 2020 Mar
1;128(3):648-659. doi: 10.1152/japplphysiol.00636.2019. Epub 2020 Jan 30. PMID:
31999527.
5. Judge LW, Burke JR. The effect of recovery time on strength performance following a
high-intensity bench press workout in males and females. Int J Sports Physiol Perform.
2010 Jun;5(2):184-96. doi: 10.1123/ijspp.5.2.184. PMID: 20625191.
6. Häkkinen K. Neuromuscular fatigue in males and females during strenuous heavy
resistance loading. Electromyogr Clin Neurophysiol. 1994 Jun;34(4):205-14. PMID:
8082606.
7. Baggett, SA. Resistance training and recovery: Influence of dietary supplements,
combined treatment therapies, and gender. Dissertation, University of Alabama (2015).
8. Benini R, Nunes PRP, Orsatti CL, Portari GV, Orsatti FL. Influence of sex on cytokines,
heat shock protein and oxidative stress markers in response to an acute total body
resistance exercise protocol. J Exerc Sci Fit. 2015 Jun;13(1):1-7. doi: 10.1016/j.
jesf.2014.10.002. Epub 2015 Jan 30. PMID: 29541092; PMCID: PMC5812867.
9. Nuckols G. THE EFFECTS OF BIOLOGICAL SEX ON FATIGUE DURING AND
RECOVERY FROM RESISTANCE EXERCISE. Thesis, University of North Carolina at
Chapel Hill (2019).
10. Acute fatigability is outside the scope of this article; pages 12-20 and 47-51 of my thesis
address that topic in-depth.
11. Banyard HG, Nosaka K, Vernon AD, Haff GG. The Reliability of Individualized LoadVelocity Profiles. Int J Sports Physiol Perform. 2018 Jul 1;13(6):763-769. doi: 10.1123/
ijspp.2017-0610. Epub 2018 Jul 10. PMID: 29140148.
20
12. Pérez-Castilla A, García-Ramos A, Gijón-Nieto LM, Marcos-Blanco A, García-Pinillos
F. Reliability and concurrent validity of the PUSH BandTM 2.0 to measure barbell
velocity during the free-weight and Smith machine squat exercises. Proceedings of
the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and
Technology. July 2021. doi:10.1177/17543371211024018
13. Davies RW, Carson BP, Jakeman PM. Sex Differences in the Temporal Recovery
of Neuromuscular Function Following Resistance Training in Resistance Trained
Men and Women 18 to 35 Years. Front Physiol. 2018 Oct 23;9:1480. doi: 10.3389/
fphys.2018.01480. PMID: 30405436; PMCID: PMC6206044.
14. McHugh MP, Connolly DA, Eston RG, Gleim GW. Exercise-induced muscle damage
and potential mechanisms for the repeated bout effect. Sports Med. 1999 Mar;27(3):15770. doi: 10.2165/00007256-199927030-00002. PMID: 10222539.
15. Weakley J, Mann B, Banyard H, McLaren S, Scott T, Garcia-Ramos A. Velocity-Based
Training: From Theory to Application. Strength and Conditioning Journal: April 2021 Volume 43 - Issue 2 - p 31-49 doi: 10.1519/SSC.0000000000000560
16. Lake J, Augustus S, Austin K, Comfort P, McMahon J, Mundy P, Haff GG. The
reliability and validity of the bar-mounted PUSH BandTM 2.0 during bench press
with moderate and heavy loads. J Sports Sci. 2019 Dec;37(23):2685-2690. doi:
10.1080/02640414.2019.1656703. Epub 2019 Aug 16. PMID: 31418312.
17. It’s worth noting that some of the female subjects missed a rep or two while completing
the non-failure squat protocol (as a group, they averaged 4.9 reps on set 4 and 4.8 reps on
set 5). It’s unclear why there were missed reps, though. It’s possible that some subjects
just badly misgrooved a rep, it’s possible someone legitimately failed a rep or two, it’s
possible that the research assistants just miscounted reps a couple times, or it’s possible
that a few reps didn’t count due to inadequate depth. Since an explanation for these
missed reps wasn’t provided, I decided not to dwell on them.
█
21
Study Reviewed: Effect of the Repetitions-In-Reserve Resistance Training Strategy on
Bench Press Performance. Perceived Effort, and Recovery in Trained Men. Mangine et al.
(2021)
It’s Okay to Fail Sometimes
BY MICHAEL C. ZOURDOS
We often think of training to failure in a binary fashion. However,
a new study compares the time course of recovery between
performing all bench press sets to failure versus taking one set to
failure with other sets shy of failure.
22
KEY POINTS
1. Researchers compared the time course of recovery from bench pressing in
trained men under two conditions. In one condition, the lifters performed five
sets of bench press to failure at 80% of 1RM. In the other condition, lifters
performed four sets shy of failure, then took the last set to failure.
2. Barbell velocity and rate of force development during three reps at 80% of
bench press 1RM recovered in similar time courses in both conditions. The
change in the biomarker creatine kinase was not statistically different between
conditions (p = 0.06). However, creatine kinase was 9.1%, 49.0%, and 33.0%
greater in the failure condition at 6, 24, and 48 hours post-training, suggesting
muscle damage may have been higher in the failure condition.
3. Overall, it’s probably a good idea to keep most of your training shy of failure to
avoid long recovery time courses, possibly compromising weekly volume and
frequency. However, future longitudinal studies should investigate if the time
course of recovery from failure training is attenuated over time.
M
ASS has reviewed multiple studies (one, two) that have compared
the time courses of recovery following squat and bench press training (2, 3).
These studies have compared failure training
versus non-failure training with ~5 repetitions
in reserve (RIR), during moderate-repetition
sets (that is, around 5-8 reps per set, give or
take). In general, taking all sets to failure has
elongated the recovery time course compared
to 5 RIR training. Elongated recovery periods could result in diminished training frequency and volume during a training week.
For example, performing five sets of squats
to failure may take 3-4 days to recover from,
compared to a recovery time of <48 hours
with non-failure training. Further, research
has shown that lifters tend to report higher
session rating of perceived exertion (sRPE)
values following failure training compared
to non-failure training (4). However, it is un-
known if recovery rates and sRPE values are
still impacted to the same degree when only
the final set is taken to failure. The presently
reviewed study from Mangine et al (1) had
well-trained men perform five sets of bench
press at 80% of 1RM in two different conditions. In one condition, lifters took all sets to
failure. In the other condition, subjects were
instructed to terminate the first four sets when
they had 1-3 RIR, and then perform the fifth
set to failure. Findings showed that recovery
of creatine kinase (a biomarker of muscle
damage) and barbell kinetics (average velocity and rate of force development) during three
reps at 80% of 1RM on the bench press were
not significantly different between conditions
at 6, 24, 48, and 72 post-training. However,
creatine kinase did increase by 49% and 33%
more at 24 and 48 hours, respectively, in the
1-3 RIR + failure condition than in the failure
condition. These findings suggest that, when
23
training at a moderate intensity, velocity recovery is similar when one set of bench press
is taken to failure plus four sets to 1-3 RIR,
versus taking all five sets to failure. However, creatine kinase increases may be greater
when taking all sets to failure. Importantly, it
cannot be known from this data if taking all
five sets to 1-3 RIR would have led to quicker recovery than including one set to failure.
This article will aim to:
1. Discuss what we can and cannot take
away from this study.
2. Examine the proximity to failure and recovery time course literature.
3. Review how to allocate volume appropriately throughout a week.
4. Discuss the rate of fatigue from set to set
when not training to failure.
Purpose and Hypotheses
Purpose
The purpose of the reviewed study was to
compare the time course of recovery following five sets of bench press to failure versus
four sets to 1-3 RIR plus one set to failure in
trained men.
Hypotheses The researchers hypothesized that recovery
would be faster when lifters performed only
one set to failure.
Subjects and Methods
Subjects
14 men participated in this study. All of the
subjects had at least three years of training
experience, and trained at least three times
per week for the preceding year. The available subject characteristics are provided in
Table 1.
Study Overview
The presently reviewed study employed a
crossover design with two conditions. In
both conditions, subjects performed bench
press 1RM and muscular endurance (reps to
failure at 80% of 1RM) tests, then began one
of the two conditions 3-7 days later. In each
condition, subjects underwent a baseline
blood draw before the training session, then
performed five sets of bench press at 80% of
1RM. Recovery was then assessed 6, 24, 48,
and 72 hours later.
During the bench press sets at 80% of 1RM,
subjects performed all sets to failure in one
condition (failure condition). In the other con-
24
dition (1-3 RIR + failure), subjects stopped
the first four sets when they believed that no
more than three more reps were possible, then
performed the fifth set to failure. During each
training session, the researchers assessed the
number of reps performed, average velocity of
the entire set, and what they called Borg RPE
(see Criticisms and Statistical Musings section
for more on RPE) after each set. For recovery,
the researchers assessed blood creatine kinase
levels (a biomarker of muscle damage) and
various performance measures at 6, 24, 28, and
72 hours post-training. At each recovery time
point, subjects performed 1 (set) × 3 (reps) at
80% of 1RM on the bench press to test average velocity, rate of force development, and
impulse (force × time). Performance metrics
during the recovery period were compared to
values obtained during the first three reps of
the first set during each condition’s training
session. The training sessions in each condition occurred exactly one week apart.
One other note is that the researchers provided all subjects with creatine monohydrate
throughout the entire study (both conditions).
This study did not examine the effect of creatine on outcomes; rather, the researchers noted, “Before enrollment, it was expected that
several potential subjects would indicate that
they regularly or periodically consumed creatine monohydrate as a dietary supplement.”
Essentially, the researchers decided they didn’t
want to exclude people who were taking creatine as that would decrease their potential
subject pool, nor did they want to deal with the
limitation of pre-study creatine supplementation potentially accelerating recovery for some
but not others. Thus, they leveled the playing
field by providing supplementation for everyone. Specifically, five days before the bench
press 1RM test, subjects were given 100g of
creatine monohydrate and instructed to take
5g four times per day (typical load phase) for
five days. From then on, subjects consumed
5g of creatine each day for the remainder of
the study. The complete study protocol can be
seen in Table 2.
25
Findings
Training Session Observations
Total reps performed in each training session
were not different between conditions (failure condition: 30.9 ± 6.8; 1-3 RIR + failure:
32.1 ± 6.3; p = 0.237). However, subjects performed significantly more reps in the failure
condition during set one (p < 0.001) and significantly more reps in the 1-3 RIR + failure
condition on sets four and five (p < 0.001).
The number of reps performed in the failure
condition decreased, on average, by 5.7 reps
(or 60%) from set one to set five. Time series
graphs of reps performed, RIR per set, and
RPE are shown in Figure 1ABC with exact
values for those metrics presented in Table 3.
Some values in Table 3 were presented in the
full text, while others were estimated using
WebPlotDigitizer.
Recovery Measures
There were no significant condition × time
interactions (p = 0.54 – 0.89) for any performance recovery metric. There was actually
a statistical increase for average velocity to
above baseline levels at 72 hours, but it never
dropped significantly below baseline at any
time point (Table 4). Although it was stated
in the methods that average velocity was assessed at 6, 24, 48, and 72 hours post-train-
26
Criticisms and Statistical
Musings
ing, the six-hour time point was not reported
in the results section.
Across all subjects (conditions combined),
creatine kinase was significantly increased
immediately post training (p = 0.009) and at
48 hours post training (p = 0.010). There was
no significant condition × time interaction,
but the p-value for this interaction was 0.06
(nearly statistically significant), with a larger
increase in creatine kinase observed in the failure condition. Specifically, creatine kinase increased 9.1%, 49.0%, and 33.0% more in the
failure condition than in the 1-3 RIR + failure
condition at 6, 24, and 48 hours post-training,
respectively. Figure 2 shows the percentage
change in creatine kinase at each time point.
I have musings related to this study’s methods. First, the researchers instructed the subjects to stop the first four sets in the RIR
condition when they thought “no more than
3 repetitions were possible (RIR = 3; RPE
= 7).” This instruction is not very specific
as it literally means to train to less than or
equal to 3 RIR. Some could have interpreted the instruction as “stop at a 3 RIR,” while
others may have taken it as a license to go
closer to failure. The researchers also stated,
“Estimation of the expected repetition count
for the first set of 3 RIR [3 RIR condition]
was facilitated by the subject’s performance
during the initial muscular endurance test.”
As noted at the beginning of the “Methods”
section, all subjects performed one set to failure at 80% of 1RM after testing 1RM. Thus,
it seems possible that during the first set of
the 1-3 RIR + failure condition, subjects were
told to perform the exact number of reps that
would have coincided with 3 RIR during that
muscular endurance test. While I cannot be
sure that occurred, I suggest this possibility because the average RIR on the first set
27
in the 1-3 RIR + failure condition was 3.0 ±
0.0 (Table 3). It’s certainly possible that all
subjects record a 3 RIR of their own volition
because that’s what they thought they should
rate the set, and because they had knowledge of how many reps they should perform
based on their performance in the preceding
muscular endurance test. However, regardless of whether the researchers stopped everybody on the first set at a predetermined
number of reps, or if subjects rated a 3 RIR
on their own, in either case, this means that
subjects performed sets 2-4 differently than
set one. Also, RIR decreased each set (Table 3), and the average number of reps performed remained nearly identical through set
four; thus, it’s possible that subjects simply
performed the exact same number of reps as
set one (because that coincided with 3 RIR
during the muscular endurance test). Yet,
RIR simply decreased each set due to fatigue.
Subjects could have also aimed for a lower
RIR on each set of their own volition, since
the set termination instructions were vague.
Overall, we can’t be sure from the original
paper’s wording how subjects interpreted the
set termination instructions, or if they were
provided any further information. Ultimately, the authors billed this study as a recovery
time course comparison between failure and
3 RIR training. However, the actual question
answered ended up being: Does recovery differ when taking five sets of bench press to
failure versus four sets to a 1-3 RIR and one
set to failure at 80% of 1RM?
Second, the authors stated that Borg (effort-based) RPE was collected after each set,
but I don’t believe that was the case. The
authors wrote, “The Borg category ratio (0–
10) scale was used to subjectively measure
perceived effort put forth during the set. Its
inverse, the RIR (0–10) scale, was used to
quantify the subject’s perceived number of
repetitions they could have completed had
they not stopped the set. Proper utilization of
these scales was described to subjects during
enrollment.” The authors then noted that they
instructed the subjects that a 3 RIR would be
a 7 RPE. The author’s representation of the
Borg 0-10 scale is not accurate – while the
Borg 0-10 scale may be inversely related to
RIR (i.e., high Borg RPE should generally
mean lower RIR), it is not the inverse of RIR.
In fact, on the Borg scale, an RPE of 7 is anchored to the descriptor “really hard.” The
RIR-based RPE scale is where RPE and RIR
can be used interchangeably. On the RIRbased RPE scale, a 9 RPE is anchored by the
descriptor 1 RIR, an 8 RPE is anchored by
2 RIR, and 3 RIR anchors a 7 RPE. Therefore, I don’t believe the investigators actually used the Borg scale; rather, subjects were
simply instructed to state the corresponding
RPE based upon RIR (see findings in Table
3). Further, Hackett et al (5) found that bodybuilders reported a Borg RPE value of less
than maximal effort (i.e., <10) when performing squat and bench press sets to failure at
70% of 1RM, yet every subject in this study
reported at 10 RPE on each set in the failure condition (Figure 1 and Table 3). Thus, it
seems that subjects were instructed to default
to a 10 RPE when 0 RIR was reached, or a 10
RPE was recorded for them. I do wish postset or post-session Borg RPE was collected,
as it would have provided an accurate perception of effort and global fatigue. For more
28
information, please see our previous article,
“RPE and RIR: The Complete Guide,” which
details both the usage and history of the 0-10
Borg scale and RIR-based RPE scale.
Interpretation
The researchers billed the presently reviewed
study (1) as a comparison between failure
training and 3 RIR training. Also, the researchers concluded that the time course of
recovery was similar between conditions.
However, this study did not compare failure
training and 3 RIR training, and I don’t think
strong conclusions can be made regarding the
recovery time courses. In actuality, this study
compared five sets of bench press training to
failure versus four sets to 1-3 RIR plus one
set to failure. Let’s say we agree with the
researchers on the second point (similar recovery time course between conditions). In
that case, what can we conclude about the
protocol? That question is hard to answer
since, in the RIR condition, one set was performed to failure, and the others were to 1-3
RIR. In other words, with one set to failure,
it cannot be known if recovery time courses
would have been similar between conditions
if all five sets were terminated shy of failure.
It’s possible the single failure set accounted
for most of the measurable fatigue in the 1-3
RIR + failure condition, resulting in similar
recovery time courses in both conditions. It’s
also possible that recovery was similar between conditions because total reps, workload, and number of sets performed were not
significantly different between conditions.
However, we could also disagree with the
researchers’ conclusions and assume that re-
covery occurred slightly faster in the 1-3 RIR
+ failure condition. Specifically, creatine kinase concentrations were still 33% higher in
the failure condition than the RIR condition
at 48 hours post-exercise. To be clear, there
was no condition × time interaction for creatine kinase, but the p-value (p = 0.06) was
very close to the typical threshold for “significance.” Ideally, this study would have
included a third condition, in which all sets
were stopped shy of failure. With the addition of that hypothetical condition, we would
have been able determine the recovery impact of taking only one set to failure.
In my opinion, two other study design choices also contribute to the lack of ability to
form definitive conclusions. Those study design choices are a) not assessing session RPE,
and b) not testing volume during the recovery
period. Session RPE is typically assessed 30
minutes post-exercise to measure global fatigue (6), although recent data suggest that
session RPE ratings immediately after training are stable for up to 30 minutes (7). This
metric gauges how hard someone perceived
they worked during the session and how exhausted they felt after it. To explain why this
is important, I’d like to recycle a quote from
my previous concept review, “RPE and RIR:
The Complete Guide.” That quote is: “Let’s
look at an example of the utility of this scale
[session RPE] in the context of resistance
training: If two programs produced the same
long-term hypertrophy and strength, but one
produced a lower session RPE, then it might
make sense to recommend the program that
caused less fatigue or even consider adding
volume or intensity to the less-fatiguing pro-
29
gram.” Essentially, if failure and non-failure
training lead to the same long-term outcomes,
but non-failure training takes less effort, then
non-failure training may be the more desirable option. Indeed, multiple studies have
shown that programs that cause lower session RPE leads to similar hypertrophy and
strength as programs with a higher session
RPE (4, 8 - MASS Review). Specifically, Lasevicius et al (4 – MASS review) found that
subjects reported lower session RPE when
performing non-failure leg extension training for eight weeks compared to performing
all sets to failure, yet experienced similar
strength gains and hypertrophy. Perhaps the
most comparable longitudinal study to the
presently reviewed study is from Sampson
et al (9). Sampson compared a group performing all four sets of biceps curls to failure
versus two other groups (fast and slow concentric groups) that stopped their four sets of
curls shy of failure, except for taking one set
to failure each week. In that study, subjects
trained three times per week. The groups performing one set to failure per week reported
significantly lower session RPE (p = 0.001)
than failure training, yet strength gains and
hypertrophy were similar between groups.
The second study design choice – which limits the ability to make definitive conclusions
– was the decision to not test recovery of volume performance. Specifically, the researchers tested velocity recovery during a 1 × 3 set
and found no decline in velocity at 6, 24, 48,
or 72 hours post-training. However, the lack
of decrease in velocity during a submaximal
set does not mean that volume performance
wouldn’t have been harmed during the recov-
ery period. To be clear, I’m also not saying
that volume performance would have been
harmed in either condition. I’m only saying
that we can’t make definitive conclusions
about volume from the velocity findings. In
defense of not assessing volume, it is more
difficult to test volume performance than a
submaximal velocity measure. Testing volume performance could have been done by
having subjects replicate the condition-specific training sessions 48 hours after the initial training session. For example, Paz et al
(10 - MASS Review) had trained men perform four sets of both bench press and incline
bench press to failure at 80% of 1RM during
an initial session (e.g., on a Monday) in three
different weeks. Then, to test the time course
of recovery, subjects performed the same
training session 24 hours following the initial session during one week, and 48 and 72
hours following the initial session during different weeks. Paz found that, on the group
level, volume performance was fully recovered by 48 hours. Similarly, Miranda et al (11
- MASS Review) reported that volume performance recovered within 48 hours following 12 sets of pressing to failure. However,
it’s noteworthy that Miranda’s study reported
that volume performance recovered for some
individuals by 24 hours, yet others still performed less volume at 72 hours post-training.
It does seem that lifters in the presently reviewed study were mostly recovered by 48
hours. Still, we cannot be sure if actual performance was recovered, nor can we be sure
about the individual recovery rate.
Overall, while the time course of recovery
depends upon training volume, proximity
30
to failure, and exercise selection in addition
to individual factors (i.e., limb lengths and
training status), most studies show recovery
from reasonable training volume within 72
hours. To demonstrate this recovery within
72 hours, a collection of studies examining
the topic is summarized in Table 5.
31
Table 5 has four studies (1, 2, 3, 16), including the presently reviewed one, that compared
the recovery time course between failure and
non-failure training. Two of those studies,
Moran-Navarro et al (2 – MASS Review)
and Pareja-Blanco et al (3 – MASS Review),
showed that velocity with a load corresponding to 1.0 m/s at baseline recovered by 24 hours
with non-failure training. However, velocity
performance took 48-72 hours to recover following failure training. Moran-Navarro also
showed that creatine kinase concentrations
recovered by 24 hours post-training following squat and bench press training to 5 RIR,
compared to 72 hours with volume-equated
training to failure. Pareja-Blanco showed that
squat velocity and vertical jump performance
did not fully recover in subjects performing
failure training at various rep ranges (sets of
4, 6, 8, 10, and 12 reps to failure) until 48
hours post-exercise. However, subjects in
all non-failure conditions (ranging from 2-6
RIR) recovered by 24 hours post-exercise.
Two other notes from Pareja-Blanco are that
there was no difference in recovery between
the various submaximal RIR conditions, and
subjects’ bench press velocity was recovered
by 24 hours post-training in the failure condition. For the submaximal RIR comparisons,
it’s difficult to draw conclusions because the
number of reps per set were different. For example, a set of six reps to 6 RIR and a set of
two reps to 2 RIR resulted in a similar time
course of recovery. However, that doesn’t
mean a set of 15 to 5 RIR would result in similar muscle damage as a two-rep set to 2 RIR.
Thus, all we can say from the Pareja-Blanco study is that recovery tends to be faster
with non-failure versus failure training, and it
may take longer to recover from squat failure
training than bench press failure training.
The fourth study in Table 5 comparing failure
and non-failure training for recovery comes
from Gonazalez-Badillo et al (16). Gonzalez-Badillo found faster recovery with 3 × 4
squat and bench press training at ~4 RIR than
three sets of failure with an 8RM load. While
that finding is consistent with failure training
elongating the time course of recovery, the
failure condition also performed more volume, which may have exacerbated fatigue.
Before returning to failure and non-failure
training recovery time courses, let’s quickly
expand on the impact of specific exercises on
recovery. The last paragraph noted that lifters might recover more rapidly from bench
press failure training than squat failure training. In support of recovery time courses being exercise-specific, de Camargo et al (15)
observed a faster recovery time course on
the leg extension versus the squat in trained
men, while Soares et al (16) reported faster
recovery of the biceps from seated rows than
preacher curls in trained men. In other words,
de Camargo found more rapid recovery with
a single-joint versus a multi-joint movement,
while Soares reported faster recovery with
multi-joint training. Therefore, when allocating volume throughout a week, we shouldn’t
lump all multi-joint movements and all single-joint movements together in terms of potential fatigue. Rather, when programming,
lifters should be careful to avoid performing
exercises that train through longer muscle
lengths (i.e., RDLs, flyes, preacher curls) 2448 hours before a heavy session of the same
muscle group. For a more in-depth review on
32
the impact of specific exercises on the time
course of recovery, please see a video from
Volume 5, Issue 3.
The implication of measuring recovery time
courses is to make recommendations regarding training frequency. In general, it seems
wise to structure training so that recovery
mostly occurs within 48 hours. Recovering
within 48 hours will make training a muscle
group two to three times per week feasible.
To accomplish this, it’s wise to follow the
general principles outlined above: to stay shy
of failure (or at least primarily shy of failure)
and to manage per session volume. Ultimately, if someone is attempting to increase their
squat frequency to twice per week, but currently performs eight sets to failure once per
week, it might be difficult to just add an additional day of squat training. However, if that
individual cuts their per session squatting to
four sets at a 2-3 RIR, they should be able
to add a second session with a similar training prescription. It might then be possible for
the lifter to move to a three-day-per-week
frequency eventually. There are, of course,
many other iterations of increasing frequency
and weekly volume by managing individual
session training prescription appropriately;
the above is just one generic example. I don’t
want to dwell on this point because we have
written about it before, and I recently developed a sample training configuration demonstrating how to allocate volume appropriately
when considering all of the aforementioned
variables (see Table 4 here). It’s even possible, though not always advisable, to train
a muscle group five or six days per week, as
seen here and here when allocating volume
appropriately.
Although I’m not interested in dwelling on
examples of allocating volume to manage recovery time courses, I do want to further ponder the design and findings from Mangine et
al (1). Although total reps performed were
not significantly different between conditions, subjects had better maintenance of rep
performance from set-to-set in the 1-3 RIR +
failure condition. Specifically, on average, the
failure condition performed 5.7 fewer reps on
their fifth set (3.9 ± 1.5) than on their first set
(9.6 ± 1.5 reps); however, subjects in the 1-3
RIR + failure condition performed almost the
same number of reps on each set. While session RPE was not assessed, I’d bet the house
that subjects felt less fatigued during the 1-3
RIR + failure condition than during the failure condition. If there was indeed lower fatigue in the 1-3 RIR + failure condition, then
it’s possible subjects would have been able
to easily add more quality sets in the 1-3 RIR
+ failure condition than the failure condition,
thereby increasing total volume.
Perhaps the most interesting findings of the
presently reviewed study aren’t directly relevant to the authors’ intended aims, but were
rather a byproduct of the protocol. The RIR
protocol allows us to see the change in RIR
from set-to-set when initially training with 3
RIR. As seen in Table 3, when performing the
same number of reps between sets 1-4, there
was a drop in RIR of 0.5 from set to set. In
other words, when performing the same number of reps with the same load, lifters reported
they could perform 0.5 fewer reps each set.
On set five, subjects should have been able
to perform about 7.5 repetitions based upon
33
WHEN PERFORMING
THE SAME NUMBER
OF REPS BETWEEN
SETS 1-4, THERE WAS
A DROP IN RIR OF 0.5
FROM SET-TO-SET.
the RIR in set four, but they only performed
6.9 reps, suggesting that the rate of fatigue
increases with more sets during non-failure
training. We can’t know if the rate of change
in RIR from set-to-set would be the same at
different proximities to failure or with more
reps in a set. However, these data do show
that when performing the same number of
reps, in a moderate rep range, there is roughly
a 0.5 decrease in RIR from set-to-set. Practically, if you aim to maintain an RIR range
such as 2-4 with the same load over four or
five sets, it’s advisable to start by choosing a
load that lands you at 4 RIR on the first set.
Next Steps
When considering all of the literature to
date, it seems that failure training elongates
the recovery period compared to non-failure
training. However, the main limitation of this
body of literature is that these studies only
examine recovery during a single week. Over
time, muscle damage and soreness are attenuated when training the same exercise or the
same muscle group, which is called the repeated bout effect (17). The repeated bout effect also seems to be related to the magnitude
of the original stimulus. In other words, if
the time course of recovery from bench press
training to failure is measured during week
five of a training block, someone who has
been training to failure may have a quicker
rate of recovery than someone who has been
training to 3 RIR up until that point. In other
words, over time, a lifter may adapt to failure
training so that they recover more quickly.
However, even if the time course of recovery
becomes shorter, session RPE may still be
higher with failure training. A higher session
RPE could also have implications for longterm training adherence and sustainability.
With that said, the next step is to examine if
the repeated bout effect eventually negates
the recovery difference between failure and
non-failure training.
WHEN PERFORMING
THE SAME NUMBER OF
REPS, IN A MODERATE
REP RANGE, THERE IS
ROUGHLY A 0.5 DECREASE
IN RIR FROM SET-TO-SET.
34
APPLICATION AND TAKEAWAYS
1. Mangine et al (1) reported similar recovery of average velocity and rate of force
development during submaximal bench press sets between performing five sets of
bench press to failure versus performing four sets to 1-3 RIR plus one set to failure.
2. However, this study also found that failure training increased creatine kinase 49.0%
and 33.0% more at 24 and 48 hours post-training than performing four submaximal
sets plus one set to failure.
3. Overall, it seems advisable to keep the bulk of your training shy of failure, especially
on exercises that train through long muscle lengths, to recover in a time frame
that enables you to achieve your desired weekly training volume and frequency.
However, future studies should examine if the disparity in recovery between failure
training and non-failure training narrows over time due to the repeated bout effect.
35
References
1. Mangine GT, Serafini PR, Stratton MT, Olmos AA, VanDusseldorp TA, Feito Y. Effect
of the Repetitions-In-Reserve Resistance Training Strategy on Bench Press Performance,
Perceived Effort, and Recovery in Trained Men. The Journal of Strength & Conditioning
Research. 2021 Nov 18.
2. Morán-Navarro R, Pérez CE, Mora-Rodríguez R, de la Cruz-Sánchez E, GonzálezBadillo JJ, Sanchez-Medina L, Pallarés JG. Time course of recovery following resistance
training leading or not to failure. European journal of applied physiology. 2017
Dec;117(12):2387-99.
3. Pareja-Blanco F, Rodríguez-Rosell D, Aagaard P, Sánchez-Medina L, Ribas-Serna J,
Mora-Custodio R, Otero-Esquina C, Yáñez-García JM, González-Badillo JJ. Time course
of recovery from resistance exercise with different set configurations. The Journal of
Strength & Conditioning Research. 2020 Oct 1;34(10):2867-76.
4. Lasevicius T, Schoenfeld BJ, Silva-Batista C, Barros TD, Aihara AY, Brendon H,
Longo AR, Tricoli V, Peres BA, Teixeira EL. Muscle Failure Promotes Greater Muscle
Hypertrophy in Low-Load but Not in High-Load Resistance Training. Journal of strength
and conditioning research. 2019 Dec 27.
5. Hackett DA, Johnson NA, Halaki M, Chow CM. A novel scale to assess resistanceexercise effort. Journal of sports sciences. 2012 Sep 1;30(13):1405-13.
6. Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P,
Dodge C. A new approach to monitoring exercise training. The Journal of Strength &
Conditioning Research. 2001 Feb 1;15(1):109-15.
7. Foster C, Boullosa D, McGuigan M, Fusco A, Cortis C, Arney BE, Orton B, Dodge
C, Jaime S, Radtke K, Van Erp T. 25 years of session rating of perceived exertion:
Historical perspective and development. International Journal of Sports Physiology and
Performance. 2021 Jan 28;16(5):612-21.
8. Lima BM, Amancio RS, Gonçalves DS, Koch AJ, Curty VM, Machado M. Planned
Load Reduction Versus Fixed Load: A Strategy to Reduce the Perception of Effort With
Similar Improvements in Hypertrophy and Strength. International journal of sports
physiology and performance. 2018 Oct 1;13(9):1164-8.
9. Sampson JA, Groeller H. Is repetition failure critical for the development of muscle
hypertrophy and strength?. Scandinavian journal of medicine & science in sports. 2016
Apr;26(4):375-83.
10. Paz GA, de Freitas Maia M, de Araújo Farias D, Miranda H, Willardson JM. Muscle
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activation and volume load performance of paired resistance training bouts with differing
inter-session recovery periods. Science & Sports. 2021 Apr 1;36(2):152-9.
11. Miranda H, Maia MF, Paz GA, de Souza JAAA, Simão R, Farias DA, Willardson
JM. Repetition Performance and Blood Lactate Responses Adopting Different Recovery
Periods Between Training Sessions in Trained Men. J Strength Cond Res. 2018
Dec;32(12):3340-3347.
12. Ferreira DV, Gentil P, Ferreira-Junior JB, Soares SR, Brown LE, Bottaro M. Dissociated
time course between peak torque and total work recovery following bench press training
in resistance trained men. Physiology & behavior. 2017 Oct 1;179:143-7.
13. González-Badillo JJ, Rodríguez-Rosell D, Sánchez-Medina L, Ribas J, López-López C,
Mora-Custodio R, Yañez-García JM, Pareja-Blanco F. Short-term recovery following
resistance exercise leading or not to failure. International journal of sports medicine. 2016
Apr;37(04):295-304.
14. Belcher DJ, Sousa CA, Carzoli JP, Johnson TK, Helms ER, Visavadiya NP, Zoeller RF,
Whitehurst M, Zourdos MC. Time course of recovery is similar for the back squat, bench
press, and deadlift in well-trained males. Applied Physiology, Nutrition, and Metabolism.
2019;44(10):1033-42.
15. de Camargo JB, Braz TV, Batista DR, Germano MD, Brigatto FA, Lopes
CR. Dissociated Time Course of Indirect Markers of Muscle Damage Recovery Between
Single-Joint and Multi-joint Exercises in Resistance-Trained Men. Journal of Strength
and Conditioning Research. 2020 Dec 24.
16. Soares S, Ferreira-Junior JB, Pereira MC, Cleto VA, Castanheira RP, Cadore EL, Brown
LE, Gentil P, Bemben MG, Bottaro M. Dissociated time course of muscle damage
recovery between single-and multi-joint exercises in highly resistance-trained men. The
Journal of Strength & Conditioning Research. 2015 Sep 1;29(9):2594-9.
17. Zourdos MC, Henning PC, Jo E, Khamoui AV, Lee SR, Park YM, Naimo M, Panton
LB, Nosaka K, Kim JS. Repeated bout effect in muscle-specific exercise variations. The
Journal of Strength & Conditioning Research. 2015 Aug 1;29(8):2270-6.
█
37
Study Reviewed: Twelve Months of Time-Restricted Eating and Resistance Training Improves
Inflammatory Markers and Cardiometabolic Risk Factors. Moro et al. (2021)
Time-Restricted Feeding Protocols
Might Help You Stay Lean, But Hinder
Muscle Gain
BY ERIC HELMS
A year-long study compared time-restricted eating (AKA
“intermittent fasting”) to a standard eating pattern in well-trained,
healthy men consuming high protein. This study gives us a clear
look at the long-term effects of a 16-hour fasting window in lifters.
38
KEY POINTS
1. This (1) is the first long-term (one year) study of time-restricted eating (TRE),
AKA “intermittent fasting” in a lifting population. Healthy, trained men were
assigned to a TRE pattern or a standard eating pattern. Both groups ate highprotein diets and trained 3x/week, drinking whey protein afterward.
2. Metabolic health improved following TRE compared to baseline and to the
standard diet. TRE experienced a reduction in IGF-1, testosterone, and fat
mass from baseline, and lost significantly more body mass than the standard
diet. Uniquely, TRE spontaneously reduced energy intake by ~7%. Only the
standard diet significantly increased fat-free mass, and biceps and quads cross
sectional area. Cross sectional areas decreased in TRE, resulting in significant
between-group hypertrophy differences favoring the standard diet.
3. The changes following TRE are consistent with the effects of a caloric deficit,
which the subjects in the TRE group spontaneously achieved. Without a
comparative energy restricted standard diet group, these changes can’t be
attributed directly to TRE per se. Thus, we don’t know if TRE is inherently better
for health or fat loss, or worse for muscle gain. However, TRE may induce
spontaneous decreases in energy intake, a potential negative for muscle gain or
positive for fat loss and weight maintenance.
D
r. Trexler previously reviewed TRE
(here), colloquially called “intermittent fasting” or IF, back in Volume 3. The most typical and popular form in
the lifting world is a 16:8 feeding pattern (16
hours of fasting with an 8-hour feeding window), which was the type researched in the
present study. This feeding pattern was also
used in the study (2) reviewed by Dr. Trexler, but it was only eight weeks long. Indeed,
most TRE studies have several important
limitations relevant to MASS readers. They
are either: 1) 12 weeks or shorter in duration,
and/or 2) conducted with clinical populations
or those with overweight/obesity, and/or 3)
don’t include a resistance-training protocol
and a high protein intake (3). However, the
present study (1) avoided these limitations, as
it specifically recruited well-trained healthy
men, and examined the effects of TRE compared to a standard eating pattern for a full
year. To summarize, TRE resulted in significant reductions in body mass, fasting glucose
and insulin, and inflammatory biomarkers,
while also leading to lipid panel improvements compared to baseline and to the standard diet. However, there were also significant differences in anabolic hormones; TRE
experienced reductions in testosterone and
IGF-1, both of which remained unchanged
following the standard diet, resulting in significantly lower IGF-1 in the TRE group
compared to the standard diet group. Further,
significant body composition differences
emerged as well. Fat mass decreased following TRE but remained unchanged after the
39
standard diet, while fat-free mass increased
following the standard diet, but remained unchanged following TRE. Most importantly,
cross sectional area changes in the biceps and
quadriceps were significantly different between groups, as these metrics increased following the standard diet, but decreased following TRE. Before we attribute causation to
the eating pattern directly, we must acknowledge that the TRE group spontaneously reduced their energy intake by ~7%, while the
standard diet group did not. In this article I’ll
discuss the nuances of the findings, what can
be attributed directly and indirectly to TRE,
and how the findings can be applied to lifters.
Purpose and Hypotheses
Purpose
The authors “aimed to examine the effects of
a long-term (12-month) TRE pattern on body
composition, metabolic, and cardiovascular
risk markers.”
Hypotheses
The authors “hypothesized that 12 months of
[a] TRE protocol would lead to lower body
mass and a reduction in inflammatory and
metabolic markers as compared with a normal dietary pattern.”
Subjects and Methods
Subjects
This study is a continuation of a previously
published study from a few years back (4),
in which 34 resistance-trained, healthy males
were assigned to either a 16:8 TRE diet or
a standard diet for eight weeks. After the
conclusion of the eight-week period, the researchers invited the participants to continue
into the present study for another 10 months
so they could assess the long-term effects
of TRE. Of the original 34 participants, 20
completed the additional 10 months, with 10
participants per group finishing the full study
(although one participant in the standard diet
group did not attend the body composition
testing session, so their data was not included for that specific analysis). To be included, participants had to be males with at least
five years of continuous resistance training
experience, and they were excluded if they
previously used anabolic steroids or had any
clinical condition. Baseline data from the 10
participants in each group is shown in Tables
1-3 in the Findings section.
Resistance Training and Diet Protocols
Resistance training was performed three days
per week in a fed state between 4-6pm, and
was standardized and supervised for the first
eight weeks, as outlined in the full text of the
previously published study. However, for the
10 subsequent months, training sessions “were
not directly supervised because of the high experience of subjects.” It is unclear from the
protocol whether participants were instructed
to maintain the same training structure or to
revert to entirely self-directed training. However, the authors did specify that the participants continued to train at the same time of day
in the fed state, and in a similar loading zone
(75-90% 1RM), performing alternating hypertrophy and strength blocks. As a researcher, I
get the impression the investigators probably
directed the participants to keep training the
same way, but only required they maintain the
40
same fed-state training window and overall
training structure. Asking trained participants
to follow the same, rigid training structure for
a full year would have resulted in more drop
outs, so I can’t fault the authors here.
To initially establish individualized diet
plans, participants completed a validated
seven-day food diary with dietitian instruction prior to beginning the study, and were
told to maintain their habitual dietary patterns during this seven-day period. Based on
the initial diaries, each participant was given
a personalized diet specific to their group assignment. Diaries were then completed again
at the two-month mark and at study conclusion to enable the extraction of energy and
macronutrient data at each time point. Figure
1 is a graphical representation of the eating
schedules of both groups.
Both groups ate three meals per day, and on
training days, the researchers provided the
subjects with 20g of whey protein post-training. The TRE group consumed 40% of their
calories at their 1pm breakfast, 25% at their
4pm lunch, and 35% at their 8pm dinner,
whereas the standard diet had a 12-hour feeding window and consumed 25% of their calories at breakfast, 40% at lunch, and 35% at
dinner. During the first eight weeks of the
protocol, a dietitian consulted with the participants weekly to enhance nutritional adherence. For the rest of the year, the frequency
of dietitian consultations dropped to once per
month. In these consultations, the dietitians
asked about the participants’ meal timing and
composition, appetite, and any difficulties
following the protocol. As needed, the dietitians provided guidance on meal preparation
and gave advice on ingredients to keep adherence as close to the intended protocol as
possible. Participants who could not maintain
their assigned meal timing for three consecutive days or longer, or who could not maintain it for more than 35 total days out of the
year (10% of the total time) were excluded.
Methods
This study was conducted in a single-blind
manner, such that the researchers conducting
41
the assessments at baseline, eight weeks, and
one year were blinded to the group allocation
of the participants. At each of these three assessment points, the participants came to the
lab on three consecutive days, having abstained from caffeine, alcohol, and vigorous
physical activity during the previous 24 hours.
In the first visit to the lab, blood draws were
taken from the participants, which were used
to analyze anabolic and metabolic hormone
levels, inflammatory markers, and markers of
metabolic health (fasting glucose, insulin, and
a lipid panel). Following the blood draws, indirect calorimetry was used to assess resting
energy expenditure and respiratory exchange
ratio. On the following day, body composition
was assessed using DXA, and cross sectional area of the biceps and quadriceps was estimated from limb circumference and skinfold
measurements using a technique “validated”
against magnetic resonance (more on this later; for specific validity and reliability information, see the methods of the initial eight-week
study here). The second day of testing concluded with a 1RM assessment of the leg press
or bench press. The third and final day of testing consisted of a 1RM assessment of whichever exercise was not tested the prior day (the
order was not reported).
Findings
As shown in Table 1, food diary assessments
revealed that at the 12-month mark, energy
intake had significantly decreased compared
to the 2-month mark in the TRE group by just
under 200kcal on average (a ~7% reduction).
This resulted in a significantly lower energy
intake compared to the standard diet group,
although the specific time point during this
10-month period in which energy intake decreased among the TRE participants is unknown. Further, it seems this reduction in
energy intake occurred due to reductions in
carbohydrate and fat, as these values also decreased relative to baseline, and carbohydrate
intake was significantly lower in the TRE
group compared to the standard diet group.
Importantly, protein intake did not decrease
in the TRE group, was not significantly dif-
42
ferent between groups, and was maintained at
1.8-1.9 g/kg/day in both groups.
As shown in Table 2, changes in strength
did not significantly differ between groups.
Both groups increased their bench press and
leg press 1RMs over time to similar degrees.
Resting energy expenditure decreased slightly in the TRE group, and the respiratory exchange ratio decreased slightly as well, indicating a slight increase in the proportion of fat
used for fuel, although the absolute change
was small and unlikely to have any real-world
impact. Further, both of these changes are
expected following a prolonged caloric deficit. Additionally, body mass significantly diverged between groups. It increased by ~3kg
in the standard diet group, while it decreased
by ~3kg in the TRE group. Further, the composition of this change differed significantly,
as fat-free mass significantly increased with-
out a change in fat mass in the standard diet
group, while fat mass significantly decreased
without a change in fat-free mass in the TRE
group. Finally, the cross sectional areas of
the biceps and quadriceps decreased by ~2
and ~4cm2 in the TRE group, but increased
by ~5 and ~8cm2 in the standard diet group,
both of which were significant differences
between groups.
As shown in Table 3, changes in various blood
markers also significantly differed between
groups. Adiponectin increased while leptin
decreased in the TRE group (both of which
are expected following a caloric deficit and fat
loss), while they remained unchanged in the
standard diet group, resulting in a significant
difference between groups. Testosterone and
IGF-1 decreased from baseline in the TRE
group, but remained unchanged in the standard diet group, resulting in significantly low-
43
er IGF-1 levels in the TRE group than the standard diet group at 2 months and 12 months.
Furthermore, while the absolute levels of testosterone didn’t differ between groups at any
time point, the changes over time significantly
differed between groups. Finally, fasting glucose, insulin, LDL, triglycerides, IL-6, IL-1β,
and TNF-ɑ all significantly decreased while
HDL significantly increased in the TRE group;
these values remained unchanged in the standard diet group, resulting in significant differences between groups for fasting glucose,
insulin, HDL, LDL, triglycerides, and IL-1β.
Again, these biomarker differences would all
be expected when comparing a group that lost
body fat following a period of energy restriction against a group at or just above energy
balance that gained primarily muscle.
Criticisms and Statistical
Musings
I don’t have any criticisms; rather, I just have
a few points of caution. As mentioned, this
is the first long-term TRE study that mimics
common practice in IF circles. It was a year
long, used a 16:8 feeding window, and the
participants were healthy and well-trained,
followed a 3x/week progressive resistance
training plan, and consumed a high protein
diet. The natural tendency when a study fills
a gap in the literature is to think, “well, now
that the gap is filled, we have THE answer!”
However, this is just one study, and while
it overcame many limitations, one it didn’t
overcome (like many other studies in our
field) is a small sample size.
44
The authors conducted a power analysis for
the first eight weeks of the study, indicating they needed 15 participants per group.
They achieved this sample size in their initial
study, which the present study is a continuation of (they had 17 per group in the first
eight weeks, but it dropped to 10 per group
for the whole year). Notably, the sample size
of 15 was what was needed to detect an effect
size of 0.8 (a large effect size) at the p < 0.05
level (5% chance of a Type I error: a false
positive), with only a 20% chance of making
a Type II error (a false negative). While it is
common practice to use these parameters for
calculating sample size, I’d argue it shouldn’t
be. Assuming a large effect for an eight-week
period is generally unreasonable in our field,
as most of the effects we observe over this
time period confirmed by meta-analyses (like
taking creatine, or performing more volume)
produce small to medium effects at best (~0.20.5) when compared to a “control group” that
is also resistance training. Realistically, if we
wanted results from a single study that we
could hold up as representative of what we’d
observe on average in the sampled population with any confidence, we’d need two to
three times the participants we typically have
in studies in our field.
How does this apply to the present study?
Well, the authors couldn’t really rely on effect sizes from previous literature to calculate the sample size required for the full year
study, as there are no other year-long studies to base it on. However, the sample needed would likely be far smaller than what’s
needed for an eight-week period, as more
time allows larger effects to accumulate, ef-
fectively increasing the signal to noise ratio.
So, I’m confident the observed effects are
real, insomuch as it’s unlikely they can be
attributed to measurement error or random
differences between groups that are unrelated
to the study protocol, but with just 10 participants per group (and for body composition
analyses, 9 participants in the standard diet
group due to a no-show on testing day), I’m
not confident they are representative. Due
to the nature of sampling variance, a small
group sampled from a population has a higher probability of being different enough from
the population average that we can’t be sure
their average response represents the average
response of the population. Also, with small
sample sizes and unsupervised (potentially
self-directed) training programs, all you need
are a couple ill-advised programs, lackluster
efforts, closer-to-genetic-ceiling lifters, or
poorer-than-average responders in a group
to throw a wrench in the results. Ultimately,
even though this study fills a gap, we need
more long-term studies to confirm that these
findings are indeed representative of the true
effects.
Interpretation
This is a pretty cool study. It’s a full-year, parallel-groups trial that mimics what commonly
occurs in IF circles. That’s rare in our field,
which is unfortunate, as long-term studies
are really needed to make useful inferences
for long-term application. As mentioned, this
study is a continuation of an eight-week initial
study (4), and notably, the initial study had
different results at eight weeks than the present study did at one year. Broadly, that initial
45
study found that TRE resulted in a reduction
in fat mass without changes in fat-free mass
or muscle cross sectional area, while the standard diet resulted in no significant changes in
any of these parameters. Looking at just the
eight-week study’s findings, it’s a clear win
for TRE: you hold onto all your muscle and
lose a bit of fat – great news! However, the
results after a full year tell a different story.
In the present study, while the loss of fat mass
reported only in the TRE group matched the
initial study, biceps and quadriceps cross sectional area decreased in TRE, and increased
in the standard diet group, and fat-free mass
increased in the standard diet group without a
change in the TRE group. With a longer period to observe, the findings indicate that the
TRE intervention is actually not neutral for
muscle gain, but might result in muscle loss
in the long run.
Further, the extended time period provides
insights as to the causes of these differences.
In actuality, we can’t attribute the findings
directly to the meal distribution pattern itself,
as the TRE protocol resulted in a spontaneous
caloric deficit which likely contributed to the
results. Indeed, all the changes in anabolic
and metabolic hormones, inflammatory biomarkers, energy expenditure, fuel utilization,
and most importantly, the body composition
changes, are exactly what you’d expect from
an energy deficit. To be fair, we can’t be sure
that the changes were only due to the energy
deficit though. Without a third group that followed the standard diet pattern while being in
a comparable deficit, we can’t know whether
there were independent effects of TRE in addition to the effects of the energy deficit, or
if all the observable effects were due to the
deficit alone.
The authors indicated that the energy deficit
was only apparent when comparing the 2- to
the 12-month diet diaries. Thus, in the initial
study, which ended at the 2-month mark, the
authors didn’t think a deficit had occurred.
They stated in their discussion “a decrease
of fat mass in individuals performing IF was
observed. Considering that the total amount
of kilocalories and the nutrient distribution
were not significantly different between the
two groups, the mechanism of greater fat loss
in [the] IF group cannot simply be explained
by changes in the quantity or quality of diet,
but rather by the different temporal meal distribution” (4). This is an understandable interpretation if you only determine the presence or absence of an energy deficit by the
diet diaries. However, self-reported dietary
WE CAN’T ATTRIBUTE THE
FINDINGS DIRECTLY TO
THE MEAL DISTRIBUTION
PATTERN ITSELF, AS THE
TRE PROTOCOL RESULTED
IN A SPONTANEOUS
CALORIC DEFICIT WHICH
LIKELY CONTRIBUTED
TO THE RESULTS.
46
data are susceptible to considerable error,
even when using validated seven-day food
diaries like those used in the initial and present study. Some research suggests that the
correlation between seven-day food diaries
and objective indicators of nutrient intake
are moderate at best, with r-scores between
0.36-0.49, meaning they only share ~13-24%
of their variance (5). Rather, I think the best
metrics to determine whether or not an energy deficit occurred in these types of studies
are the body composition data. In the initial
eight-week study, the TRE group lost ~1.6kg
of fat mass (a significant change), while fatfree mass increased non-significantly by
only ~0.6kg. Therefore, in the initial study,
the majority of the TRE participants were
likely in a small deficit, despite the food diaries indicating this wasn’t the case. However, in the TRE cohort of 10 that completed
the full year study, they were also in a deficit
based on the body composition data, but the
size of the deficit decreased after the initial
eight weeks at some point between the 2- and
12-month mark. This directly contradicts the
seven-day diet diary data, which show that
caloric intake decreased from baseline at the
two-month mark and stayed depressed for the
full year, which you’d think would increase
the size of the deficit. However, if you look
at Table 2, you can see the TRE group lost
~0.8kg of fat mass and 0.5kg of fat-free mass
in the first two months, but then only lost
~0.9kg of fat mass and 1.7kg of fat-free mass
in the 10 months that followed. If you math
it out, using values of ~900kcal/kg of muscle (without intramuscular fat tissue which
DXA should account for) and ~3500kcal/kg
of pure fat mass (as DXA should account for
the water and protein in adipose tissue) (6,
7), you get a deficit of ~1600kcal/month in
the first two months, and ~450kcal/month for
the subsequent 10 months. But, nonetheless,
there was a deficit (albeit a small one later
on) the entire time.
All of this is not to say that the spontaneous
deficit acts just as a confounding factor, and
that we can’t conclude anything about TRE
from this study. Quite the opposite: I think
this study demonstrates that TRE tends to
induce a small caloric deficit, and that’s at
least part of what’s driving the anecdotes of
body composition improvements associated
with IF. Given how difficult it is for people
to lose body fat and keep it off, I think it’s
quite notable when an intervention produces fat loss unintentionally, especially in the
long term. Also, to throw another bone to the
IF crowd, I’m only somewhat confident that
muscle loss occurred. Fat-free mass didn’t
significantly decrease, while cross sectional
area did. Normally, I’d say that cross sectional area is a better indicator of changes in skeletal muscle mass, as fat-free mass includes a
lot of tissue that isn’t muscle; however, that is
only the case when we are talking about validated measures of hypertrophy like magnetic
resonance imaging, ultrasound, or computed
tomography scans. In the present study, they
estimated cross sectional area from arm and
thigh circumferences and skinfold measurements. While they state that prior work validated the accuracy of the method they used
against magnetic resonance imagery (8), the
referenced study only reports an r2 score,
which while high at 0.88, is not enough to
truly validate a method against a criterion
47
gold standard (for details, check out the Criticisms and Statistical Musings of this prior
article where I discuss Bland-Altman plots).
To be clear, this limitation doesn’t invalidate
the findings. It’s just a limitation that gives
me a bit of pause before I have complete confidence in the finding. But at best, the TRE
protocol resulted in no hypertrophy after a
full year of progressive training. That’s not a
great selling point if your goal is muscle gain.
However, on the flip side, I’m reasonably
confident that a TRE approach is suitable for
a cut. It’s also possible that TRE could be
suitable for a bulking phase if you overcome
the tendency to decrease energy intake, but
given the muscle loss reported in this study,
I suspect a standard diet would be equivalent
or better. Technically, as I mentioned earlier,
we’d have needed a standard diet group with
a similar deficit to compare to the TRE group
to see if it produced similar body composition
changes, but if we simply compare the TRE
group’s body composition changes to all the
other studies out there on lifters dieting, I’d
say TRE fared decently. The TRE group lost
a significant amount of fat mass while losing
a non-significant amount of fat-free mass according to DXA, increased 1RM in the upper and lower body, and while they probably
experienced some small reductions in muscle
cross sectional area, that’s not too surprising
in a non-overweight, trained population after a full year, even with just a small deficit.
More importantly, the TRE group achieved
these outcomes without intentionally dieting.
So, I think it’s fair to conclude that using TRE
for a cut is totally tenable, and may make the
deficit easier to achieve. Also, TRE is a via-
THE TRE GROUP ACHIEVED
THESE OUTCOMES WITHOUT
INTENTIONALLY DIETING.
SO, USING TRE FOR A CUT
IS TOTALLY TENABLE, AND
MAY MAKE THE DEFICIT
EASIER TO ACHIEVE.
ble option if your goal is to simply maintain a
leaner physique, without attempting to put on
more muscle. However, the caveat in either
case is that I only have confidence in a TRE
approach if it’s a 16:8 feeding schedule, with
a high protein intake, while training in the fed
state, and getting in post-workout protein, as
that’s the data we have to go on.
Next Steps
I mentioned a few limitations. The first is the
small sample size. If we want a better idea of
whether this study is representative of the average effect of TRE, we’d need a larger sample. But, realistically, getting a larger sample
to participate in a year-long study is unlikely. So instead, I’d simply encourage other
researchers to conduct longer studies with
whatever sample is feasible; anything longer than 12 weeks would be a great addition
to the body of evidence and would clarify
whether the present study’s findings are representative. Speaking of representation, this
48
APPLICATION AND TAKEAWAYS
At least in healthy, young, trained males, a 16:8 TRE approach concomitant
with a high protein intake might result in a small, unintentional energy deficit.
Understandably, this would make putting on muscle harder. Also, as you’d expect,
such a deficit will produce improvements in metabolic health, inflammatory markers,
and of course, fat loss. In the short term, this will probably not result in any notable
losses in muscle mass, but might if you were to adopt such an approach over the
long term. So for those who are looking to do a cut, but don’t want it to feel like a cut
and are okay with a slower rate of fat loss, TRE is worth trying. However, for people
who are primarily looking to put on muscle, struggle with hunger in the morning, or
find a specified calorie budget to be less restrictive than a specified feeding window,
it might not be the best approach.
was a male-only cohort. While there are studies on female-only cohorts – Dr. Trexler reviewed one a few volumes back (2) – they are
also short term. So, it would be great to see
longer-term TRE studies on women specifically. Further, it would be great to isolate the
effects of TRE from the effects of a deficit.
This would be hard to do in a longer study,
as getting someone to be in an intentional
deficit for multiple months in the standard
diet group would be a challenge, but I think
a shorter study would suffice. Thus, I’d love
to see a standard meal distribution group at
maintenance and a standard group in a 5-10%
deficit, compared to two TRE groups, one in a
deficit, and one that was carefully monitored
and adjusted to ensure they were at maintenance. Finally, I’d love it if some other validated measures of hypertrophy were used, or
even if the same estimation method was used,
but with more complete validity and reliability data so we could be sure it was valid.
49
References
1. Moro, T., Tinsley, G., Pacelli, F. Q., Marcolin, G., Bianco, A., & Paoli, A. (2021).
Twelve Months of Time-restricted Eating and Resistance Training Improves
Inflammatory Markers and Cardiometabolic Risk Factors. Medicine and science in sports
and exercise, 53(12), 2577–2585.
2. Tinsley, G. M., Moore, M. L., Graybeal, A. J., Paoli, A., Kim, Y., Gonzales, J., et al.
(2019). Time-restricted feeding plus resistance training in active females: a randomized
trial. The American journal of clinical nutrition, 110(3), 628–640.
3. Schuppelius, B., Peters, B., Ottawa, A., & Pivovarova-Ramich, O. (2021). Time
Restricted Eating: A Dietary Strategy to Prevent and Treat Metabolic Disturbances.
Frontiers in endocrinology, 12, 683140.
4. Moro, T., Tinsley, G., Bianco, A., Marcolin, G., Pacelli, Q. F., Battaglia, G., et al (2016).
Effects of eight weeks of time-restricted feeding (16/8) on basal metabolism, maximal
strength, body composition, inflammation, and cardiovascular risk factors in resistancetrained males. Journal of translational medicine, 14(1), 290.
5. Day, N., McKeown, N., Wong, M., Welch, A., & Bingham, S. (2001). Epidemiological
assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire
using urinary markers of nitrogen, potassium and sodium. International journal of
epidemiology, 30(2), 309–317.
6. Slater, G. J., Dieter, B. P., Marsh, D. J., Helms, E. R., Shaw, G., & Iraki, J. (2019). Is an
Energy Surplus Required to Maximize Skeletal Muscle Hypertrophy Associated With
Resistance Training. Frontiers in nutrition, 6, 131.
7. Hall K. D. (2008). What is the required energy deficit per unit weight loss? International
journal of obesity (2005), 32(3), 573–576.
8. Paoli, A., Pacelli, Q. F., Neri, M., Toniolo, L., Cancellara, P., Canato, M., et
al (2015). Protein supplementation increases postexercise plasma myostatin
concentration after 8 weeks of resistance training in young physically active
subjects. Journal of medicinal food, 18(1), 137–143.
█
50
Study Reviewed: Energy Compensation and Metabolic Adaptation: “The Biggest Loser”
Study Reinterpreted. Hall. (2021)
When Popular Topics Collide: The
Constrained Total Energy Expenditure
Model Meets Metabolic Adaptation
BY ERIC TREXLER
Metabolic adaptation and the constrained total energy
expenditure model are two of the hottest topics in metabolism
research and may be more related than you think. Read on to
find out what happens when these topics collide.
51
KEY POINTS
1. Back around 2016, the “Biggest Loser Study” reported that metabolic rate
suppression persisted six years after substantial weight loss, metabolic
adaptation immediately after weight loss did not predict future weight regain,
and that more successful maintainers tended to do more daily exercise but have
larger magnitudes of persistent metabolic adaptation.
2. In this paper (1), Dr. Kevin Hall reinterpreted the Biggest Loser data in light of
emerging observations that high levels of activity energy expenditure may lead to
compensatory reductions in other components of energy expenditure.
3. Engaging in high levels of physical activity appears to be very helpful for weight
loss maintenance, but is also linked to persistent reductions in resting energy
expenditure. Importantly, these reductions are not severe enough to reliably
undermine attempts to lose weight or maintain weight loss.
M
etabolic adaptation and the constrained total energy expenditure
model are two of the hottest topics in metabolism research. On the one hand,
metabolic adaptation (also known as adaptive thermogenesis) refers to compensatory
reductions in energy expenditure that result
from weight loss (2). Metabolic adaptation
researchers typically focus on resting energy
expenditure and non-exercise activity thermogenesis, and generally view metabolic adaptation as a regulatory mechanism that preserves body energy and opposes continuous
weight loss in response to a large cumulative
energy deficit. On the other hand, the constrained total energy expenditure model refers to compensatory adjustments in energy
expenditure that result from a high level of
physical activity (3). The theory suggests that
total daily energy expenditure is kept within
a limited range, such that increases in energy
expenditure from physical activity will only
increase total daily energy expenditure to a
point. As physical activity energy expenditure is pushed higher and higher, compensatory adjustments in other components of
energy expenditure will increasingly serve to
constrain total daily energy expenditure. This
would effectively work as a regulatory mechanism that preserves body energy and opposes continuous weight loss in response to large
volumes of physical activity.
You probably noticed considerable overlap
in the descriptions of each concept; both metabolic adaptation and the constrained total
energy expenditure model relate to compensatory drops in non-exercise components of
energy expenditure that could theoretically
threaten successful weight loss or weight loss
maintenance. As such, the presently reviewed
study (1) is a fascinating reinterpretation of
the Biggest Loser Study results, performed
by the lead researcher of the original work.
Before this reinterpretation, papers stemming
from the Biggest Loser Study had reported
that metabolic rate suppression persisted six
52
years after substantial weight loss (4), metabolic adaptation immediately after weight
loss did not predict future weight regain (4),
and that more successful maintainers tended
to do more daily physical activity (5) but have
larger magnitudes of persistent metabolic adaptation (4). This reinterpretation (1) took
these observations one step further by showing that study participants with physical activity increases above the group median had
significantly larger magnitudes of persistent
metabolic adaptation six years after weight
loss than participants below the median (p =
0.04), and that increases in physical activity energy expenditure were correlated with
persistent metabolic adaptation (r = 0.53, p =
0.049). We often view metabolic adaptation
as a barrier to weight loss (or maintenance),
but it was more like a byproduct of success
in this cohort, and seemed to be closely related to physical activity habits. Of course, we
need to practice extra caution when interpreting exploratory, retrospective associations
between variables, so this article will dig into
the details to determine exactly how linked
metabolic adaptation and the constrained total energy expenditure model truly are, and
how we might use this information to facilitate successful weight management.
Purpose and Hypotheses
Purpose
The purpose of this retrospective reinterpretation was to explore the possibility that persistent metabolic adaptation observed in the
“Biggest Loser Study” might be related to
constrained total daily energy expenditure due
to high levels of sustained physical activity.
Hypotheses
No hypothesis was directly provided, but the
implied hypothesis was that high physical
activity levels might be contributing to metabolic adaptation, defined as the difference
between measured and predicted resting metabolic rate.
Subjects and Methods
Subjects
Sixteen participants (9 female, 7 male) in a
televised weight loss competition enrolled
in the original study. Medical clearance was
required prior to participation, and subjects
could not participate if they were pregnant or
nursing, had any orthopedic conditions that
would impair their ability to walk, or had previously undergone bariatric surgery. All 16
participants completed baseline testing and
follow-up testing at the 30-week time point,
but 14 participants (8 female, 6 male) completed the six-year follow-up visit. Given the
extremely long timeline for this study, it’s
pretty remarkable that they were able to obtain complete data from 87.5% of the participants. The presently reviewed analysis only
used data from the 14 participants who completed follow-up testing; their baseline characteristics (mean ± standard deviation) were
as follows: age, 34.9 ± 10.3 years; weight,
148.9 ± 40.5 kg; BMI, 49.5 ± 10.1 kg/m2;
body-fat percentage, 49.3 ± 5.2%.
Methods
This is one of the few scientific studies for
which you could actually review video of the
intervention, if you felt so inclined. “The Biggest Loser” is a television show that involves
53
dramatic weight loss over a 30-week period
of time. As described in the first paper related
to this study (6), the weight loss intervention
consisted of 90 minutes per day of supervised
circuit training or cardio six days per week.
On top of that, participants were encouraged
to complete additional exercise (up to three
hours per day). Dietary intake was not standardized or quantitatively monitored, but the
competitive nature of the show encouraged
participants to achieve a fairly large caloric
deficit. Every 7-10 days, a participant was
voted out of the competition, but these participants were encouraged to continue their
intervention independently until the end of
the 30-week period.
Baseline testing occurred about a week before the competition; several measurements
were taken, but the most relevant (for our
purposes) were those related to body composition and energy expenditure. Body composition was measured via DXA, resting metabolic rate was measured after an overnight
fast via indirect calorimetry with a metabolic
cart, and total daily energy expenditure was
measured via doubly labeled water. If you’re
unfamiliar with doubly labeled water, Dr.
Helms did an excellent job describing it here.
Metabolic adaptation was quantified as the
difference between predicted and measured
resting metabolic rate, using regression to
generate predicted values based on fat-free
mass, fat mass, age, and sex. Energy expenditure during physical activity was not directly
measured, but was calculated by subtracting
the measured resting energy expenditure value and the estimated thermic effect of food
from total daily energy expenditure (as measured via doubly labeled water). Testing was
54
repeated immediately after the weight loss
intervention (30 weeks), and at a six-year follow up visit.
Findings
In order to provide a comprehensive overview of the findings from the Biggest Loser
Study, I’ll be reporting a selection of related
findings from multiple different papers (1, 4,
5). As shown in Table 1, the participants lost
a lot of weight during the intervention. While
the participants did a pretty nice job maintaining fat-free mass, there were still fairly
sizable drops in total daily energy expenditure and resting energy expenditure after the
30-week intervention, and resting energy expenditure was significantly lower after the
intervention than would be predicted based
on demographic and anthropometric charac-
teristics of the subjects (-275 ± 207 kcal/d;
p = 0.0061). While the cohort did maintain a
clinically relevant amount of weight loss on
average (~12%), a lot of weight was regained
by the six-year follow-up, primarily in the
form of fat mass. Nonetheless, total daily energy expenditure remained significantly lower than baseline, and resting metabolic rate
was nearly 500kcal/day below the predicted
value (−499 ± 207).
In theory, metabolic adaptation should set
you up for weight regain. After all, lower energy expenditure will lead to a larger caloric surplus for a fixed level of energy intake.
However, as shown in Figure 1A, the magnitude of metabolic adaptation observed at 30
weeks was not remotely predictive of how
much weight would be regained by the sixyear follow-up visit. The magnitude of met-
55
abolic adaptation that was still present at the
six-year mark was significantly correlated
with the amount of weight regain, such that
the more successful weight loss maintainers
were the individuals with the largest magnitudes of persisting metabolic adaptation (Figure 1B).
As shown in Figure 2, a separate analysis indicated that weight regain at the six-year mark
was significantly associated with changes in
physical activity (from baseline), such that
individuals who sustained higher levels of
physical activity were more successful at
maintaining their weight loss.
Now, for the featured analysis in the presently reviewed paper. As shown in Figure 3, the
magnitude of metabolic adaptation persisting
at the six-year mark was significantly correlated with changes (from baseline) in energy
expenditure during physical activity. In other
words, the largest degree of metabolic adapta-
tion was observed in the individuals who sustained higher levels of physical activity.
Criticisms and Statistical
Musings
You’ve probably heard the phrase, “correlation does not imply causation.” In fact, it’s so
common that the Smart Compose feature in
Google Docs finished that sentence for me.
This phrase is sometimes used as a lazy way
to disregard inconvenient observational data
in lieu of an informed critique, but the phrase
is still true at its core, and in many cases there
are obvious reasons to be wary of putting too
much stock in an observed correlation. If you
were skeptical of this potential link between
metabolic adaptation and the constrained total energy expenditure model, it wouldn’t be
hard to explain away these correlations.
When interpreting a correlation, we have to
lean on our understanding of the subject matter
56
and consider the correlation from multiple perspectives. For example, we have mentioned in
previous MASS articles that physical activity
is a well-known predictor of successful weight
loss maintenance, and there’s strong evidence
that loss of fat mass is an independent driver of metabolic adaptation (7). In the Biggest
Loser Study, the magnitude of metabolic adaptation that was still present at the six-year
mark was significantly correlated with weight
regain. Rather than wondering how persistent
metabolic adaptation could possibly attenuate
weight regain, you’d simply flip the direction
of the relationship to arrive at the more likely
perspective: if you regain a bunch of weight,
there’s no reason for metabolic adaptation to
persist. In other words, you’d conclude that
metabolic adaptation persists because of successful weight loss maintenance, not the other
way around.
The Biggest Loser Study also found that
individuals who sustained higher levels of
physical activity were more successful at
maintaining their weight loss, but the largest
degree of metabolic adaptation was observed
in the individuals who sustained higher levels of physical activity. You might argue that
high physical activity levels directly contribute to resting metabolic rate suppression by
way of the constrained total energy expenditure model. Indeed, the fact that mean resting
metabolic rate slightly decreased from week
30 to the six-year follow-up, despite an average weight regain of over 40kg during that
time period, seems to suggest that exercise
was impacting resting energy expenditure
independently of fat mass and fat-free mass
levels. Alternatively, you might reject this as
a spurious association that is actually driven by fat mass – physical activity facilitates
successful weight loss maintenance, which
keeps fat mass lower, and reduced fat mass
drives sustained metabolic adaptation. We
have a tangled web of correlated factors in
this scenario, which is inherently challenging
to parse with any degree of confidence. The
parsing process ultimately requires us to lean
on related literature to look for clues, which
is exactly what we’ll do in the Interpretation
section.
Interpretation
When digging into the details of metabolic
adaptation (concept reviews here and here),
there has been fairly broad agreement regarding the major points. Total daily energy expenditure drops more than you would expect
during active weight loss, this drop is mostly
attributable to reductions in resting energy
expenditure and non-exercise activity thermogenesis, and the effects on resting energy
expenditure are largely attenuated when you
transition from an energy deficit to energy
balance. That’s all great, but there was one
particular finding that always stuck out like a
sore thumb: the Biggest Loser Study (4).
The persistent drop in resting energy expenditure was absolutely massive, even six years
after the active weight loss phase. When you
look at other research assessing resting energy expenditure during weight loss maintenance, it tells a different story entirely. For
example, a study by Liebel et al (8) previously
reported that resting energy expenditure was
about 5-7% below predicted values during
weight loss maintenance, when measured in
57
approximate energy balance. A meta-analysis of several studies reported a fairly similar
figure, with the pooled effect suggesting that
resting energy expenditure was reduced by
about 3-5% during weight loss maintenance
(9). The ~20% reduction reported in the Biggest Loser Study was staggering in comparison to previous findings.
Some people argued that the study participants were not truly in neutral energy balance at the time of measurement, which suppressed resting energy expenditure values.
This is probably true to an extent, but it still
felt like an insufficient explanation to account for the sheer magnitude of adaptation
observed. Others pointed out that the Biggest
Loser competition applies an extreme intervention in a very specific population, which
yields data that are inherently ungeneralizable
to the typical dieter or fitness enthusiast. This
may also be true, but seems too vague and
imprecise to consider it a comprehensive explanation. A better explanation was needed –
not just because the findings were perplexing
and out of step with the rest of the literature,
but because the findings were impactful. This
research documenting large and persistent
drops in resting energy expenditure fueled fatalistic narratives that all dieting attempts are
doomed to fail in the long run, as the odds are
too heavily stacked against the dieter (nevermind the fact that this sample maintained a
clinically relevant degree of weight loss over
the six-year observation period).
So, on the surface, this fresh perspective on
the Biggest Loser data provides a plausible
explanation that could bolster the apparent
consistency of findings in the metabolic ad-
aptation literature while presenting a less
pessimistic outlook for people seeking to lose
substantial amounts of weight. This series
of papers also reinforces two very important points. The first major point is that the
metabolic adaptation (in this case, defined as
disproportionate drops in resting energy expenditure) you experience during a diet does
not render weight loss impossible, nor does
it predict future weight regain. That is something we’ve covered previously in MASS,
so I won’t belabor the point too much. In
short, these study participants lost a ton of
weight despite substantial metabolic adaptation during the active weight loss phase, and
there are far more influential factors driving
weight regain, such as non-exercise activity thermogenesis, physical activity levels,
self-monitoring behaviors, neurophysiological regulation of food intake, behavioral and
psychological drivers of dietary habits, fatfree mass retention, and many more.
The Biggest Loser data also suggest that
while metabolic adaptation at the end of a
diet is not predictive of future weight regain,
persistent metabolic adaptation is predictive of continued weight loss maintenance.
In other words, dieters with more persistent
metabolic adaptation are the dieters who
more successfully maintain weight loss. Of
course, this isn’t surprising – if you regain
all of your weight, there would be no reason
for metabolic adaptation to persist. However,
this pair of findings is a refreshing reminder
that metabolic adaptation is more so a marker of success than an inauspicious harbinger
of impending weight regain. We’d obviously
like to attenuate metabolic adaptation to the
58
METABOLIC ADAPTATION
IS OFTEN A BYPRODUCT
OF SUCCESSFUL WEIGHT
LOSS, AND BY NO MEANS
AN INSURMOUNTABLE
ROADBLOCK.
extent possible, but it’s important to recognize that metabolic adaptation is often a byproduct of successful weight loss, and by no
means an insurmountable roadblock when
we happen to encounter it.
The second major point reinforced by the
presently reviewed paper is that partial compensation does occur when we increase energy expenditure via physical activity, but
physical activity is still “worth it.” As Helms
covered in a recent MASS article, the energy
deficit we create from exercise isn’t quite as
large as we would expect it to be. When we
do 100kcals worth of physical activity, we
may reduce other components of energy expenditure to compensate, such that 100kcals
of activity only ends up increasing our energy deficit by an average of 72kcals, according to some estimates (10). It also appears
that the magnitude of this compensation varies from person to person; for example, one
study found that people in the 90th BMI percentile compensated for 45.7% of the calories
burned during physical activity, while leaner
individuals in the 10th BMI percentile only
compensated for 29.7% (10).
As this concept has become more widely
known, many people have misinterpreted the
idea and erroneously concluded that physical activity is not an effective strategy for
increasing energy expenditure or promoting
weight loss (or weight loss maintenance).
That conclusion is simply incompatible with
the empirical evidence available. As long as
the compensation level is below 100%, physical activity is making some contribution to
the energy deficit, and observed compensation levels are far below 100% in the most
rigorous and generalizable research available. On top of that, physical activity seems to
be a fairly consistent predictor of successful
weight loss maintenance (11). Whether this
is related to the effects of physical activity on
energy expenditure, appetite regulation, both,
or something else, the preponderance of ev-
PARTIAL COMPENSATION
DOES OCCUR WHEN
WE INCREASE ENERGY
EXPENDITURE VIA
PHYSICAL ACTIVITY, BUT
PHYSICAL ACTIVITY IS
STILL “WORTH IT.”
59
idence suggests that physical activity can be
an important part of a well-rounded weight
loss (or weight maintenance) program, and
partial energy compensation (in the form of
reduced resting energy expenditure) doesn’t
nullify that.
The ideas put forward in the presently reviewed paper could potentially fill an important gap in the metabolic adaptation literature,
but we shouldn’t get too carried away with
these conclusions just yet. The proposed link
between metabolic adaptation and the constrained total energy expenditure model is
intriguing and plausible, but the actual analysis driving it remains speculative and exploratory in nature. We’ll need some convincing
prospective research in order to solidify the
idea with any degree of certainty. However, I
think this paper demonstrates why we probably ought to shift the way we think and speak
about metabolic adaptation moving forward.
When you read the peer-reviewed research
on metabolic adaptation, there’s a lot of time
and energy spent arguing about how to define metabolic adaptation, how to measure it,
and where to lay the blame. Does metabolic
adaptation refer to drops in total daily energy
expenditure, non-exercise activity thermogenesis, or resting metabolic rate? Are metabolic adaptation and adaptive thermogenesis
fully synonymous, or do they explain slightly distinct phenomena? How exactly should
we calculate “predicted” energy expenditure
components for comparison purposes? What
is the most valid (but feasible) way to ensure
that participants are in energy balance prior
to measurements? The resulting conversations that take place in the peer-reviewed lit-
erature are commonly misinterpreted by the
people applying these ideas in the real world.
For example, you’ll see papers arguing that
metabolic adaptation is an “illusion” (12), or
that metabolic adaptation estimates are inflated when you neglect to account for the
loss of specific organ tissues (13). The dieter
or practitioner may infer that metabolic adaptation is largely ignorable, but when you dig
deeper into these papers, you typically find
that the confusion can be linked to differing
definitions of metabolic adaptation and differing assumptions about the methods used to
quantify it.
Researchers are supposed to get bogged down
in these details to achieve a deeper and more
technical understanding of the topic, but the
people applying this information should probably adopt a simplified perspective that focuses more on the big picture. Total daily energy expenditure drops in response to weight
loss, and that impacts the rate of progress and
the type of adjustments that need to be made
along the way. We need to have some understanding about the potential magnitude of this
effect, the potential persistence of this effect
throughout the weight maintenance period,
and the factors that may drive (or exacerbate)
the effect. A pragmatic approach to metabolic
adaptation and energy compensation involves
shifting focus from technical mechanisms to
relevant endpoints and feasible strategies to
promote continued success. If your current
exercise and nutrition habits used to support
your goal rate of weight loss but progress is
starting to plateau, the path forward involves
determining how to manage modifiable exercise and nutrition variables to keep things
60
rolling. From this perspective, it doesn’t really matter if we consider exercise-related
energy expenditure to be “part of” metabolic
adaptation, or a parallel phenomenon that is
similar in nature; at the end of the day, we’ve
got compensatory processes impacting total
energy balance, and we need to plan ahead
for that and come up with effective strategies
to facilitate ongoing success.
So, here’s a rundown of some key factors
that have the potential to impact energy balance during a weight loss diet. When you start
a weight loss diet, you introduce an energy
deficit; this reduces resting energy expenditure to some degree, although the magnitude
varies from person to person and this effect is
largely reversed when neutral energy balance
is restored. Over time, you lose body mass.
The loss of metabolically active tissue reduces resting energy expenditure, and the loss of
overall mass reduces the energy cost of locomotion. As we can piece together from reviews by Dullo et al (7), Rosenbaum et al (2),
and Nunes et al (13), the loss of fat mass specifically drives adaptive reductions in non-exercise activity thermogenesis (which seems to
persist until fat mass is restored), and the loss
of lean mass (which includes the loss of organ
tissues with very high metabolic rates) drives
reductions in resting energy expenditure and
increases in hunger (which seem to persist until lean mass depots are restored).
If physical activity is a major cornerstone of
your weight loss approach, it’s important to
recognize that some degree of energy compensation will occur. This is particularly true if
you’re doing really large amounts of physical
activity or you’re inherently predisposed to a
high level of compensation. Finally, ambitious
diet attempts can be pretty draining in terms
of subjective energy level, which could lead
to reduced energy expenditure from volitional physical activity throughout the day. These
various effects will be accompanied by a hormonal milieu that generally promotes energy
conservation, catabolic processes, and hunger,
but non-pharmacological efforts to impact
these hormones individually are probably futile – these hormonal fluctuations are responses to hypothalamic integration of information
related to energy intake, energy expenditure,
and physical activity, and the hypothalamic response to these inputs is robust.
In conclusion, reduced total daily energy
expenditure is a reality of weight loss, and
energy compensation is a reality of physical
activity. We can try to keep score and tally
up which aspects are adaptive and which are
non-adaptive in nature, but that doesn’t really
impact the modifiable factors at play: energy
intake, macronutrient distribution, resistance
training, and other physical activity. So, rather than getting bogged down in overly technical aspects or catastrophizing about metabolic adaptation or energy compensation, a
more pragmatic approach is to plan for these
realities and focus on manipulating the factors that can be manipulated.
During active weight loss or weight loss maintenance, we can take proactive steps to counteract some of these compensatory adaptations
(or, at minimum, to avoid exacerbating them).
For example, we can try to attenuate the loss
of lean mass by avoiding excessively large energy deficits and utilizing well-designed resistance training programs. We can try to adopt
61
a sensible distribution of macronutrients, with
enough carbs to fuel high-quality training sessions, enough protein to support lean mass retention, and enough fat to avoid exacerbating
drops in sex hormones (although a drop will
often be inevitable with more extreme or ambitious weight loss attempts). We can counteract increases in hunger by utilizing strategies
that promote satiety, which include minimizing hyperpalatable meals, reframing hunger
with an acceptance-focused perspective, and
structuring meals with low energy density, lots
of unprocessed or minimally processed foods,
and plenty of protein, fiber, and water content.
Appetite regulation might also be impacted by
physical activity – as Dr. Helms has discussed
previously, increasing physical activity levels from low to moderate can enhance satiety
responses to meals, but very large amounts
of physical activity can increase hunger. A
moderate approach also seems ideal when it
comes to energy expenditure compensation;
increasing physical activity can be expected
to efficiently increase energy expenditure up
to a point, but excessive amounts of physical activity are likely to be met with more
substantial compensation. When considering
how extremely high levels of physical activity can impact appetite, energy compensation,
resistance training adaptations, recovery, and
subjective energy levels, it seems safe to conclude that a moderate amount of extra cardio or physical activity (outside of resistance
training) can have some noteworthy benefits
for weight management. However, if you
push it too high, you’ll get less bang for your
buck. Once you start to notice that a particular level of extra cardio or physical activity is
impacting your appetite, training adaptations,
recovery, or subjective energy level, you’ll
probably want to scale it back.
There is some evidence that refeeds or diet
breaks may be useful throughout this process,
although the research in this area is inconclusive, and there’s not yet a true consensus on
the matter. A deep dive into the topic is beyond the scope of this article, but Dr. Helms
has discussed it several times within MASS
(one, two, three), and I shared my most updated views on refeeds and diet breaks in a
recent podcast episode (here). In short, recent
studies have identified some benefits related
to training quality, dietary adherence, and
psychological aspects of dieting, and there are
some empirical and theoretical justifications
for the idea that physiological benefits related to metabolic adaptation might be possible.
However, evidence related to physiological
benefits is quite mixed, and these physiological benefits are only likely to be observed
in more pronounced instances of metabolic
WHEN IT COMES TO
METABOLIC ADAPTATION
AND ENERGY
COMPENSATION, THERE
IS NOTHING TO FEAR BUT
PLENTY TO PLAN FOR.
62
APPLICATION AND TAKEAWAYS
When we lose weight, we experience many changes that nudge us toward positive
energy balance. Some of these changes are adaptive while others are non-adaptive,
some are transient while others persist into weight maintenance, and these various
changes can impact multiple distinct components of total daily energy expenditure.
Nonetheless, metabolic adaptation is not an insurmountable hurdle for weight loss
(or maintenance), and partial energy compensation does not nullify physical activity’s
utility in the context of weight management. We can facilitate our own success by
planning ahead for this collection of compensatory changes and modifying exercise
and nutrition variables accordingly.
adaptation (such as very large magnitudes of
weight loss or very shredded physique athletes). Once you achieve your weight loss
goal and shift to maintenance, you’ll want
to get back to neutral or slightly positive energy balance, although a meticulous reverse
dieting strategy is unnecessary in most cases. In summary, when it comes to metabolic
adaptation and energy compensation, there is
nothing to fear but plenty to plan for.
Next Steps
once again have their energy expenditure assessed in a state of energy balance, but would
be regularly engaged in high volumes of daily physical activity. If the hypothesis driving
the presently reviewed paper holds true, then
the participants’ resting energy expenditure
should be within a few percentage points of
the predicted value when physical activity is
low, but there should be a marked reduction
in resting energy expenditure when physical
activity is high.
The presently reviewed paper proposed some
intriguing ideas based on retrospective analysis, but longitudinal research would provide
much stronger support. I’d love to see if the
proposed relationship between physical activity and resting energy expenditure can be
reproduced longitudinally in a crossover design with weight-reduced participants. In one
study condition, subjects who are maintaining
weight loss would have their energy expenditure assessed in a state of energy balance,
with minimal physical activity (other than
activities of daily living). In the other study
condition, those same participants would
63
References
1. Hall KD. Energy compensation and metabolic adaptation: “The Biggest Loser” study
reinterpreted. Obes. 2021 Nov 23; ePub ahead of print.
2. Rosenbaum M, Leibel RL. Adaptive thermogenesis in humans. Int J Obes. 2010 Oct;34(0
1):S47–55.
3. Pontzer H, Durazo-Arvizu R, Dugas L, Plange-Rhule J, Bovet P, Forrester TE, et al.
Constrained Total Energy Expenditure and Metabolic Adaptation to Physical Activity in
Adult Humans. Curr Biol. 2016 Feb 8;26(3):410.
4. Fothergill E, Guo J, Howard L, Kerns JC, Knuth ND, Brychta R, et al. Persistent
metabolic adaptation 6 years after “The Biggest Loser” competition. Obes. 2016
Aug;24(8):1612–9.
5. Kerns JC, Guo J, Fothergill E, Howard L, Knuth ND, Brychta R, et al. Increased physical
activity was associated with less weight regain six years after “The Biggest Loser”
competition. Obes. 2017 Nov;25(11):1838–43.
6. Johannsen DL, Knuth ND, Huizenga R, Rood JC, Ravussin E, Hall KD. Metabolic
slowing with massive weight loss despite preservation of fat-free mass. J Clin Endocrinol
Metab. 2012 Jul;97(7):2489–96.
7. Dulloo AG, Miles-Chan JL, Schutz Y. Collateral fattening in body composition
autoregulation: its determinants and significance for obesity predisposition. Eur J Clin
Nutr. 2018;72(5):657–64.
8. Leibel RL, Rosenbaum M, Hirsch J. Changes in energy expenditure resulting from
altered body weight. N Engl J Med. 1995 Mar 9;332(10):621–8.
9. Astrup A, Gøtzsche PC, van de Werken K, Ranneries C, Toubro S, Raben A, et al.
Meta-analysis of resting metabolic rate in formerly obese subjects. Am J Clin Nutr. 1999
Jun;69(6):1117–22.
10. Careau V, Halsey LG, Pontzer H, Ainslie PN, Andersen LF, Anderson LJ, et al. Energy
compensation and adiposity in humans. Curr Biol. 2021 Oct 25;31(20):4659-4666.e2.
11. Thomas JG, Bond DS, Phelan S, Hill JO, Wing RR. Weight-loss maintenance for 10
years in the National Weight Control Registry. Am J Prev Med. 2014 Jan;46(1):17–23.
12. Martins C, Roekenes J, Salamati S, Gower BA, Hunter GR. Metabolic adaptation is an
illusion, only present when participants are in negative energy balance. Am J Clin Nutr.
2020 Nov 11;112(5):1212-1218.
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13. Nunes CL, Casanova N, Francisco R, Bosy-Westphal A, Hopkins M, Sardinha LB, et al.
Does adaptive thermogenesis occur after weight loss in adults? A systematic review. Br J
Nutr. 2021 Mar 25;1–19. ePub ahead of print.
█
65
Study Reviewed: Influence of Strength Level on Performance Enhancement Using Resistance
Priming. Nishioka and Okada (2021)
Prime Cuts: Being Strong Makes
Priming Sessions More Effective
BY MICHAEL C. ZOURDOS
Performing a priming session a day or two before testing your
strength may boost performance. However, a recent study
suggests lifters might need a certain level of baseline strength to
reap the benefits.
66
KEY POINTS
1. In a crossover design, researchers examined the effects of a priming session
on jump performance 24 hours later. Subjects performed a priming session
consisting of 5 (sets) × 4 (reps) on the jump squat at 40% of 1RM in one
condition, or no priming session in the control condition.
2. Subjects were also split into “stronger” (average relative squat 1RM of 2.22 ±
0.23 kg) and “weaker” (average relative squat 1RM of 1.76 ± 0.16 kg) groups. The
main findings showed that only the stronger group significantly improved jump
performance from before the priming session to 24 hours later. Further, during
the priming condition, countermovement jump height, power, and force improved
significantly more in the stronger individuals than in the weaker individuals.
3. Overall, this adds to the growing body of literature suggesting that a priming
session can improve explosive performance. Additionally, this study indicates
that stronger individuals are more likely than weaker individuals to benefit from
a priming session.
T
his article is timely, as Dr. Trexler and
I recently had a spirited discussion on
the utility of resistance training priming sessions. Priming sessions are training
sessions performed for the purpose of potentiating or improving performance sometime
within the next 48 hours. We have previously
reviewed priming sessions twice (one, two).
One of the previously reviewed studies (2)
showed that explosive jump squat training increased rate of force development 24-48 hours
later. The other (3) found that priming sessions
consisting of squat sets to a 20% velocity loss
at 80% of one-repetition maximum (1RM) improved velocity at the same load six hours later. Importantly, multiple priming studies have
observed a benefit in trained individuals (2, 3,
4, 5, 6), but no study has investigated if training status influences the priming effect. The
reviewed study from Nishioka and Okada (1)
examined if a priming session consisting of 5
(sets) × 4 (reps) on the jump squat at 40% of
squat 1RM could potentiate jump performance
24 hours later, compared to a control condition in 20 trained men. The researchers tested
various jumping metrics, then had the subjects
perform the priming session in one condition
or no training in the control condition. Then,
the researchers retested the jumping metrics
24 hours later. Further, the researchers analyzed the results by splitting the 20 men into
two groups of 10, based on the subjects’ relative squat strength. The stronger group could
squat an average of 2.22 ± 0.23 times body
mass, and the weaker group could squat an
average of 1.76 ± 0.23 times body mass. The
groups stratified by strength levels seemed to
have divergent responses to the priming intervention. There were significant group × time
interactions for countermovement jump height
(p = 0.015), power (p = 0.009), and force (p =
0.009), suggesting that priming had an ergogenic effect for the stronger group, but not for
the weaker group. These findings are further
67
evidence that priming sessions may indeed enhance performance, and that a relatively high
strength level (>2 times body mass 1RM squat
in men) may be needed to benefit from a priming session. Additionally, while not analyzed
in the presently reviewed study, it’s also important to note that to date, no study has reported 1RM strength improvements from a
priming session. This article will aim to:
whether baseline strength levels influenced
the benefits of priming.
1. Review the present study’s findings.
Subjects
2. Examine the current state of the priming
session literature.
3. Discuss why priming sessions may benefit stronger individuals more than weaker
individuals.
4. Discuss how the volume and intensity of
a priming session may affect performance
in the coming days.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was to examine if a priming session could
enhance jump performance 24 hours later. In
addition, this study also aimed to investigate
Hypotheses The researchers hypothesized that priming
sessions would improve jump performance
in stronger, but not weaker lifters.
Subjects and Methods
20 trained men with an average of more than
four years of resistance training experience
and more than 11 years of sport participation
completed the study. The 20 men were split
into “stronger” and “weaker” groups based
on squat 1RM using the median split method.
In this study’s context, the median split method simply means that subjects with a squat
1RM above the median were placed in the
strong group, and subjects with a 1RM below
the median were placed in the weak group.
The available subject details are in Table 1.
Study Overview
The presently reviewed study employed a
crossover design with two conditions. In both
conditions, subjects were first familiarized
68
with the jump squat, countermovement jump,
and drop jump during an initial visit. Also,
during the initial visit, subjects performed
a back squat 1RM test, and the researchers
determined the optimal drop jump height
for each subject. Optimal drop jump height
was the height from which subjects dropped
(i.e., jumped down) that produced the greatest reactive strength index upon landing and
jumping up from the force platform. Reactive
strength index was defined as jump height ÷
ground contact time.
Next, 44-172 hours after the initial visit, subjects returned to the laboratory for the first
experimental condition. After a short warmup, subjects performed baseline testing on a
force plate for the jump squat, countermovement jump, and drop jump. Both jump squat
and countermovement jump testing were
performed at 0% (plastic pipe serving as the
barbell) and 40% of 1RM. Jump squats were
performed with a two-second pause at the
bottom of the squat to reduce the influence
of the stretch reflex, while countermovement
jump testing was performed with a fluid motion (i.e., no pause). For drop jumps, subjects began on a wooden box set at optimal
height (discussed earlier) and dropped down
to the force plate; then, subjects immediately
jumped as high as possible off of both feet as
soon as they landed on the force plate. Following baseline testing, subjects performed
either the priming condition consisting of 5
× 4 jump squats at 40% of 1RM or no training in the control condition. Jumping performance was then retested 24 hours later. The
outcome measures related to jump performance included jump height, force, veloci-
ty, and power for the jump squat and countermovement jump, in addition to reactive
strength index for the drop jump.
Findings
The researchers reported a lot of findings.
The streamlined version is that various metrics of jump performance significantly increased (p < 0.05) from baseline to 24 hours
in the stronger group during the priming condition, while no jump performance metrics
significantly improved in the weaker group
(p > 0.05) in the priming condition. There
were also group × time interactions for countermovement jump height (p = 0.015), power
(p = 0.009), and force (p = 0.009), suggesting greater priming-induced improvements
for the stronger group than the weaker group.
All specific values, significant changes from
baseline to 24 hours, and group × time interactions are noted in Table 2. Also, when including all 20 subjects in the analysis, there
was a significant and positive relationship (r
= 0.61; p <0.01) between relative squat 1RM
and percentage improvement in unloaded
countermovement jump height from baseline
to 24 hours in the priming condition.
Interpretation
The presently reviewed study from Nishioka and Okada (1) adds to a growing body
of literature showing that priming sessions
enhance explosive performance. This study
also observed that baseline strength influences the effectiveness of a priming session.
Despite the present data, it’s unclear exactly how strength athletes should use priming
69
long after the priming session performance
enhancement is maximized. Therefore, this
interpretation section will examine the existing literature to determine how strength athletes can implement priming sessions.
sessions because only one study has shown
a priming session to enhance strength performance (e.g., 3RM) (4). Further, the scientific
literature lacks uniformity, regarding both the
prescription of a priming session, and how
The single finding that best summarizes the
presently reviewed study’s results is the significant and positive relationship (r = 0.61;
p <0.01) between relative squat 1RM and
percentage improvement in unloaded countermovement jump height from baseline to
24 hours in the priming condition. In other
words, stronger lifters tended to benefit more
from priming than weaker lifters. Indeed, it
is pretty typical in the literature to observe
that explosive priming sessions enhance explosive performance in trained individuals.
In addition, previous data (7) have shown
that baseline strength may influence postactivation potentiation within minutes after the
priming exercise, so it’s not too surprising
70
that baseline strength is also a factor influencing performance improvements 24 hours
after priming. However, I initially found it
surprising that the “weaker” group, which
was still fairly strong (relative squat = 1.76
times body mass), did not benefit from the
priming session. With that said, I wanted to
investigate the exact strength levels used in
previous studies to see if I could identify a
baseline strength level needed for priming to
be effective. Therefore, a summary of nine
previous priming studies, including the sub-
71
jects’ baseline strength levels, are included in
Table 3.
We often lament strength studies that enroll subjects with relatively low baseline
strength. However, Table 3 shows that the
priming literature does not suffer from this
limitation. One explanation for the relatively
high strength levels in the priming literature
is that these researchers often recruited team
sport athletes (commonly rugby players),
who have been training for years under the
direct supervision of a strength coach. Of the
nine studies in Table 3, only two had subjects
with lower baseline strength than the 1.76
times body mass squat of the “weaker group”
in the present study. First, Gonzalez-Badillo et al (8) found that men who could squat
1.44 times body mass, on average, did not
improve vertical jump height or velocity at
a load corresponding to 1.0 m/s at 6, 24, or
48 hours following a priming session. In the
Gonzalez-Badillo study, the priming sessions
involved three sets to failure at an 8RM load,
or 3 × 4 at an 8RM load on the squat. So,
it’s possible that participants’ strength levels were too low for priming to be effective,
but it’s also possible that the priming session
was too fatiguing. Linamo et al (9) did not
report baseline strength, but the men and
women were untrained; thus, we can be confident their baseline strength was lower than
the “weaker” group in the presently reviewed
study. Linamo found that priming sessions
consisting of five sets of a 10RM to failure
on the leg extension and chest press, or 5 ×
10 at 40% of 10RM on both exercises, both
failed to improve peak force 1, 2, 24, and 48
hours later. Further, all four studies in Table 3
which included subjects who squatted ≥2.00
times body mass on average, found that priming enhanced lower body explosiveness.
Suchomel et al (7) reported that subjects with
a relative squat of 2.1 times body mass increased unloaded jump squat power 10 minutes after performing a single repetition at
90% of 1RM on the squat. However, subjects
with a squat 1RM of 1.6 times body mass
did not potentiate jump squat power. Overall, data seem to indicate the possibility that
a certain degree of baseline strength is required for someone to benefit from priming
sessions. A relative squat in men of 2.0 times
body mass is unlikely a strict strength cutoff. Still, on average, it may be a reasonable
suggestion as the baseline strength threshold
needed for priming sessions to be effective.
There is not yet sufficient data to determine
a potential strength threshold for women to
benefit from priming sessions.
We have established that baseline strength
may influence priming’s effectiveness; however, the exact prescription of a priming session almost certainly influences priming’s
effectiveness. The present study’s priming
session prescription of 5 × 4 on jump squat
at 40% of 1RM was the exact same as the
prescription used in a previously reviewed
study from Tsoukos et al (2). Both the present study and the Tsoukos study found that a
priming session improved explosive performance 24 hours later in subjects who could
squat more than two times their body mass.
However, Tsoukos found that the same prescription failed to improve maximal isometric leg press force. Therefore, explosive
priming may enhance explosive performance
72
but not strength performance. In fact, the
only study to report positive effects for priming on strength performance is from Cook
et al (4). Cook reported that a priming session consisting of a 3RM squat and bench
press increased strength during a 3RM retest
just six hours later. While it’s possible that
priming sessions need to use a high enough
load to enhance top-end strength, I’d need
to see replication of the Cook study before
recommending 3RM training as a priming
session, especially only six hours before another 3RM. Thus, until the findings of Cook
are confirmed, I don’t think someone should
expect their 3RM strength to improve by performing a 3RM six hours beforehand.
Even though Cook et al (4) is the only study
showing priming to directly improve strength,
a previously reviewed study from Gonzalez-Garcia (3 - MASS Review) did show
priming to improve squat velocity at 80% of
1RM. Specifically, Gonzalez-Garcia found
that a priming session consisting of two squat
sets to a 20% velocity loss at 80% of 1RM
led to faster squat velocity at the same load
and higher vertical jump height six hours later. However, Gonzalez-Garcia also reported
that subjects did not improve squat velocity
at 80% of 1RM or vertical jump when performing squat sets to a 20% velocity loss at
60% of 1RM. This data suggests that a similar
load should be used during the priming session when looking to potentiate performance
with a moderate to heavy load. Interestingly,
both the Gonzalez-Garcia and Cook studies
found that priming sessions of heavy squats
improved vertical jump performance later
the same day. Therefore, while improving
WHILE IMPROVING
PERFORMANCE WITH
HEAVY LOADS MAY
REQUIRE HEAVY LOADS IN
THE PRIMING SESSION.
performance with heavy loads may require
heavy loads in the priming session, studies
that have used moderate (3), heavy (4), and
light loads (1, 2) have all been effective at
increasing explosive performance.
In terms of priming’s application, I think it’s
important to distinguish between sport performance and strength performance. Many
sport performances (i.e., individual sprinting
or jumping, basketball, hockey, American
football, real football, etc.) involve explosive components; thus, an explosive priming
session performed 6-48 hours before competition is probably a good idea. The priming
prescription could be similar to the presently
reviewed study (1) and Tsoukos (2) (5 × 4 on
the jump squat at 40%). This does not mean
that the 5 × 4 jump squat protocol is the only
viable prescription; rather, it is just a conceptual example.
The optimal priming prescription to improve
strength performance is unknown. As noted
earlier, only one study, Cook et al (4), has directly assessed priming and dynamic strength
performance. That study suggests that the
73
load used should be heavy in the priming
session, the volume of the priming session
should be low, and the exercise used should
be specific to the performance test. However,
with only one positive study on priming and
strength, it’s hard to say if the priming prescription for strength should be of the explosive or heavy variety. Heavy strength priming may be something like 3 × 1 at 80% of
1RM or 1 × 1 at 90% of 1RM on one or more
of the competition lifts 24-48 hours before a
powerlifting meet. Explosive priming would
be similar to the present study’s protocol, and
using a competition lift or close variant (i.e.,
jump squat) probably wouldn’t make much
difference. Since there is minimal data supporting the idea that priming improves maximal strength performance, it’s possible that
some lifters won’t receive a priming benefit.
That being said, I don’t see much downside to
implementing priming, and I do see various
potential upsides. While priming sessions can
be used within 48 hours of a powerlifting meet
or test day, they could also be used mid-week
to improve performance on a heavy day. For
example, if a lifter trains the squat Monday,
Wednesday, and Friday, this individual could
make Wednesday a priming session to potentiate heavy work during Friday’s session. Of
course, it’s possible that using priming sessions this way is ineffective. Still, if the lifter
chose the heavier priming option (i.e., 3 × 1 at
80% or 1 × 1 at 90%), they might also receive
a strength benefit and more movement skill
practice from the heavy-ish singles. On the
other hand, a drawback could be the opportunity cost of using a priming session every
week, rather than completing a training session with more total volume. Thus, intensity
blocks are perhaps the best time for priming
sessions if you’re aiming to improve weekly
performance. For practical examples of how
to implement priming sessions into your taper to enhance competition or test day performance, and for an example of how to implement this strategy into your weekly training,
please see Table 5 here. I would also stress
the importance of trying out various priming
strategies in training before implementing a
priming strategy during a powerlifting competition week. Trial and error are okay here. I
have used both heavy and explosive priming
sessions in coaching, both with success. Of
course, that “success” may not be attributable to the priming session. Still, I do think
priming sessions, even if ineffective for the
intended purpose, have utility in training.
To conclude, let’s briefly return to the discussion of baseline strength influencing priming,
and the potential time course of benefits from
priming sessions. Sure, a lifter might not benefit from a priming session if their baseline
strength isn’t high enough, but if a lifter is a
beginner or in the early intermediate phase,
I DO THINK PRIMING
SESSIONS, EVEN IF
INEFFECTIVE FOR THE
INTENDED PURPOSE,
HAVE UTILITY IN TRAINING.
74
then why worry about it in the first place? Even
if a priming session boosts strength, it’s not
going to improve performance by an amount
that improves placing at a powerlifting meet
from fifteenth to first. Lastly, we should consider the timing of a priming session. The
majority of studies in Table 3 examined the
efficacy of a priming session 6-48 hours before the performance test. While performing a
priming session six hours before competition
may be feasible for collegiate or professional
athletes, it’s not practical for most strength
athletes. It’s also hard to distinguish whether
it’s better to perform a priming session 24 or
48 hours before competition, but I wouldn’t
get too caught up in that. Rather, I would try
both time points in training and use whatever
is most effective and convenient for you or
your client. I’d bet that many would be wary
of performing a priming session 24 hours before competition, thinking it may leave them
fatigued the next day. If someone is worried
about training 24 hours before a competition
or test day, then trying out the less-fatiguing
explosive priming option may be preferable
for peace of mind.
1RM), explosive priming session (5 × 4 jump
squat at 40% of 1RM), and a non-training
control session (no priming) on 1RM squat
performance 24 hours later. Of course, other
priming prescriptions should be investigated,
as well as other time courses (i.e., 48 hours),
but this design is a start. Additionally, with a
large enough sample size, the proposed study
could also analyze results in the “stronger”
and “weaker” lifters. Finally, a glaring omission in the priming literature is the lack of
studies using highly trained women, making
it difficult to determine a strength threshold
for females. Thus, the proposed study should
ideally use female subjects, or at least use a
mixed-sex sample.
Next Steps
At this point, it’s pretty well-established
that priming sessions can improve explosive
performance. Therefore, I’d like to see researchers turn their attention to priming for
strength performance. The next study I’d like
to see would employ a crossover design, using trained lifters, comparing the effects of a
heavy priming session (3 × 1 squat at 80% of
75
APPLICATION AND TAKEAWAYS
1. Nishioka and Okada et al (1) reported that a priming session consisting of 5 ×
4 jump squats at 40% of 1RM potentiated jump performance 24 hours later in
stronger individuals, but not weaker individuals.
2. Based on the currently reviewed study and previous literature, men who can
squat at least two times body mass are most likely to receive both priming and
postactivation potentiation benefits. There is not yet enough data on women to
provide a general strength threshold at which priming might be effective.
3. Despite the literature being generally positive for priming sessions, this optimism
is mostly relegated to explosive performance. Therefore, if aiming to enhance
strength performance with a priming session, I wouldn’t expect a huge benefit.
76
References
1. Nishioka T, Okada J. Influence of Strength Level on Performance Enhancement Using
Resistance Priming. Journal of Strength and Conditioning Research. 2021 Oct 27.
2. Tsoukos A, Veligekas P, Brown LE, Terzis G, Bogdanis GC. Delayed effects of a lowvolume, power-type resistance exercise session on explosive performance. The Journal of
Strength & Conditioning Research. 2018 Mar 1;32(3):643-50.
3. González-García J, Giráldez-Costas V, Ruiz-Moreno C, Gutiérrez-Hellín J, RomeroMoraleda B. Delayed potentiation effects on neuromuscular performance after optimal
load and high load resistance priming sessions using velocity loss. European Journal of
Sport Science. 2020 Nov 3:1-28.
4. Cook CJ, Kilduff LP, Crewther BT, Beaven M, West DJ. Morning based strength training
improves afternoon physical performance in rugby union players. Journal of Science and
Medicine in Sport. 2014 May 1;17(3):317-21.
5. Mason BR, Argus CK, Norcott B, Ball NB. Resistance training priming activity improves
upper-body power output in rugby players: implications for game day performance.
Journal of strength and conditioning research. 2017 Apr 1;31(4):913-20.
6. De Villarreal ES, González-Badillo JJ, Izquierdo M. Optimal warm-up stimuli of muscle
activation to enhance short and long-term acute jumping performance. European journal
of applied physiology. 2007 Jul;100(4):393-401.
7. Suchomel TJ, Sato K, DeWeese BH, Ebben WP, Stone MH. Potentiation following
ballistic and nonballistic complexes: The effect of strength level. Journal of strength and
conditioning research. 2016 Jul 1;30(7):1825-33.
8. González-Badillo JJ, Rodríguez-Rosell D, Sánchez-Medina L, Ribas J, López-López C,
Mora-Custodio R, Yañez-García JM, Pareja-Blanco F. Short-term recovery following
resistance exercise leading or not to failure. International journal of sports medicine. 2016
Apr;37(04):295-304.
9. Linnamo V, Häkkinen K, Komi PV. Neuromuscular fatigue and recovery in maximal
compared to explosive strength loading. European journal of applied physiology and
occupational physiology. 1997 Dec 1;77(1-2):176-81.
10. Ekstrand LG, Battaglini CL, McMurray RG, Shields EW. Assessing explosive power
production using the backward overhead shot throw and the effects of morning resistance
exercise on afternoon performance. The Journal of Strength & Conditioning Research.
2013 Jan 1;27(1):101-6.
77
11. De Villarreal ES, González-Badillo JJ, Izquierdo M. Optimal warm-up stimuli of muscle
activation to enhance short and long-term acute jumping performance. European journal
of applied physiology. 2007 Jul;100(4):393-401.
█
78
Study Reviewed: No Differences in Muscle Protein Synthesis Rates Following Ingestion of Wheat
Protein, Milk Protein, and Their Protein Blend in Healthy, Young Males. Pinckaers et al. (2021)
Gluten Gains: Similar Effects of
Wheat and Milk Protein on Muscle
Protein Synthesis
BY ERIC TREXLER
A new study reports that 30g of wheat protein increases muscle
protein synthesis as much as 30g of milk protein. Read on to
contextualize this surprising finding within our perpetually evolving
understanding of dietary protein.
79
KEY POINTS
1. The presently reviewed study (1) found that ingesting 30g of wheat protein, 30g of
milk protein, and a 30g mixture of both led to similar acute muscle protein synthesis
responses in young men.
2. There are some specific scenarios in which the quality of protein sources in a diet is
of heightened importance. However, healthy lifters who are eating enough protein,
distributing it effectively, and getting adequate essential amino acid coverage
probably don’t need to micromanage protein sources too aggressively.
3. Muscle protein synthesis measurements aren’t always predictive of future
hypertrophy outcomes. Research must be thoroughly scrutinized to determine
when a proxy measure can (or cannot) be used to make robust inferences about a
different outcome of interest.
A
s I recently transitioned from my
twenties to my thirties, I formally
entered the phase of life in which
change terrifies me to my core, and I categorically refuse to revisit or update my longheld beliefs. That’s what makes all this protein stuff so hard. Back in the day, it was all
so simple – animal-based protein sources
generally have more leucine, more essential
amino acids, a more comprehensive profile
of essential amino acids, and greater digestibility, and that was all we needed to conclude that they’re categorically better for
hypertrophy than plant-based proteins. However, we recently covered a paper suggesting
that high-protein omnivorous and vegan diets
had similar impacts on hypertrophy in a longitudinal resistance training study (2). Some
people probably discarded that finding as a
fluke, while others concluded that high permeal protein doses compensated for the fact
that animal-based proteins cause larger acute
increases in muscle protein synthesis than
equivalent doses of plant-based proteins.
Don’t they?
According to the presently reviewed study (1),
maybe not. These researchers split 36 male
participants into three groups to assess myofibrillar protein synthesis rates for five hours
following ingestion of milk protein (30g),
wheat protein (30g), or a combination of the
two (15g each). Compared to wheat protein,
the milk protein had more leucine, lysine,
methionine, and essential amino acids, and
milk protein led to significantly larger postmeal elevations of these amino acids in the
bloodstream. Nonetheless, all three protein
beverages led to similar increases in muscle
protein synthesis. We clearly need to dig into
these results to figure out how they might impact protein recommendations, but perhaps
an even bigger discussion is warranted. It has
become increasingly common to see acute
muscle protein synthesis measurements used
to draw conclusions about how long-term hypertrophy outcomes might be impacted by a
80
particular training or nutrition strategy, or by
demographic characteristics such as age or
adiposity. Are such conclusions actually accurate? Read on as we explore the nuanced
world of muscle protein synthesis.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was to compare postprandial (post-meal)
muscle protein synthesis responses to milk
protein (30g), wheat protein (30g), or a combination of the two (15g each) in young,
healthy men.
Hypotheses
The researchers hypothesized that 30g of
milk protein would lead to greater postprandial rates of muscle protein synthesis than
30g of wheat protein, but that this difference
would be eliminated by combining wheat
protein with milk protein in equal ratios (15g
of each).
Subjects and Methods
Subjects
Healthy male participants between the ages
of 18-35 years old, with BMI values between
18.5 and 27.5kg/m2, were recruited for the
presently reviewed study. 36 participants
were enrolled, with 12 participants randomly
assigned to each of the experimental groups
(milk, wheat, or wheat + milk). Baseline
characteristics were similar among all three
groups, with mean values for age ranging
from 22-26 years old, weight ranging from
70.5 to 72.8kg, BMI ranging from 21.7 to
23.0kg/m2, and body-fat percentage ranging
from 20.0 to 23.1%. Participants were reported to be “recreationally active,” generally
performing 2-4 exercise sessions per week,
but were not actively engaged in a structured
resistance training program at the time of
study participation.
Methods
This was a very straightforward study. Participants were randomly assigned to one of
three treatment groups – one group ingested
milk protein (30g), one group ingested wheat
protein (30g), and one group ingested a combination of wheat and milk protein (15g
each). All three protein doses were provided
in the form of 400mL beverages, and their respective amino acid profiles are provided in
Table 1.
For three days prior to the experimental visit,
participants refrained from alcohol consumption and strenuous exercise, while keeping a
food and activity record for the entire threeday period. The night before the trial, participants received a standardized meal (65%
carbohydrate, 20% fat, 15% protein), which
was followed by an overnight fast. The next
morning, participants reported to the laboratory in a fasted state, and the researchers
measured blood amino acid levels and muscle protein synthesis rates before and for five
hours after protein ingestion. Several blood
samples were acquired throughout the observation period, which allowed the researchers
to document the time course of changes in
blood concentrations of leucine, lysine, methionine, and total essential amino acids. It’s
important to highlight that this study did not
involve a resistance training component, so
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all responses were observed in the absence
of training-induced elevations in protein synthesis.
During the five-hour observation period after
protein ingestion, muscle protein synthesis
was measured using a method that involves
the extraction of multiple muscle biopsies (in
this case, taken just before, 120 minutes after,
and 300 minutes after protein ingestion) and
uses an amino acid tracer (labeled phenylalanine) to determine the rate at which amino
acids are being incorporated into the muscle
tissue. This method was used to calculate an
outcome called the myofibrillar “fractional
synthetic rate,” which is defined as “the fraction of the protein pool that is synthesized
per unit time” (3). In other words, a fractional synthetic rate of 0.06% per hour indicates
that 0.06% of muscle protein is synthesized
per hour, and we could convert this to an absolute rate of synthesis by multiplying by the
rate by the total amount of muscle protein. If
you’re intrigued about different methods to
82
measure muscle protein synthesis, I highly
recommend reading The Ultimate Guide to
Muscle Protein Synthesis by Dr. Jorn Trommelen. But if you’re primarily interested in
the highlights, all we need to know is that this
is a preferred and validated method of muscle
protein synthesis measurement.
Findings
As shown in Figure 1, the different protein
beverages led to significantly different plasma amino acid responses. For total essential
amino acids, leucine, lysine, and methionine,
a clear and consistent pattern was observed:
milk led to significantly larger plasma amino
acid elevations than wheat or wheat + milk,
and the wheat + milk beverage induced plasma amino acid elevations that were smaller
in magnitude than milk but larger in magnitude than wheat.
Nonetheless, as can be seen in Figure 2, this
did not translate to significant differences
in myofibrillar fractional synthetic rate. All
three protein beverages led to significant
protein synthesis elevations above baseline
values, but there were no significant differences between the three experimental groups.
In fact, differences between groups weren’t
even close to being statistically significant,
with reported between-group p-values of p =
0.41 and above.
Interpretation
If you fear change as much as I do, I have
some good news: these findings may have
83
taken us by surprise, but they don’t necessarily demand that we throw out everything we
thought we knew about protein. Rather, we
need to revisit, update, and contextualize our
understanding of how different protein sources might impact hypertrophy.
The first point to consider is that, despite the
common assumption that there is a huge body
of literature indicating that animal-based proteins stimulate acute muscle protein synthesis
to a markedly and consistently larger magnitude than plant-based proteins, there are
surprisingly few studies that involve a direct
comparison. There are a decent number of
studies assessing protein synthesis responses
to various animal-based proteins, but fewer
assessing responses to plant-based proteins,
and even fewer that directly compare responses to a plant-based and an animal-based
protein within the same study. A recent review (4) by Pinckaers et al (the same group
that conducted the presently reviewed study)
identified six such studies, which all came
from a total of two laboratories. The results
were not as simple or straightforward as animal sources being categorically and invariably better than plant sources.
If you sequentially and selectively cherry-picked findings, you could argue that casein outperforms wheat (5), wheat is similar
to milk (1), milk outperforms soy (6), and soy
is similar to whey (7). If you cherry-picked
in a different sequence, you could argue that
whey outperforms casein (8), casein outperforms wheat (5), wheat is similar to milk
(1), and milk outperforms soy (6). Now, for
a third sequence: soy is similar to whey (7),
whey outperforms casein (8), casein outper-
84
forms wheat (5), and wheat is similar to milk
(1). Just like that, we can construct hierarchies with soy and whey at the bottom, soy
and whey at the top, or whey at the top and
soy at the bottom, with all the others between
them. That’s a dumb way to interpret this literature, and I’m transparently cherry-picking
in this example to prove a point, but you get
the idea – at a superficial level of observation, this body of research has some apparent
inconsistencies, and we need to be very wary
of any protein recommendations that seem to
be leaning too heavily on any single, isolated
finding in this area.
I mentioned the “superficial” level of observation and used the term “apparent inconsistencies” because I believe those qualifiers
are necessary. When you talk about inconsistencies in a body of research, it often feels
like you’re suggesting that somebody messed
something up. That’s not what I intend to imply; rather, it’s important to recognize that
a deeper level of analysis requires us to acknowledge that some inconsistent findings
from study to study are inevitable when you
consider the wide range of variables at play.
When comparing across these studies, you
have to consider characteristics of the study
population (such as age and activity level),
the details of the study protocol (such as the
observation timeline and exercise protocol, if
applicable), the details of the measurement
methods, and the characteristics of the specific protein treatments (such as amino acid
profile, rate of absorption, and total dose provided). With so many elements free to vary,
you should expect plenty of variation in responses, and this expectation is empirically
reinforced by previous research (9).
In the presently reviewed study, the researchers specifically dedicated a lot of focus to the
age of their participants. This research team
had previously found pretty underwhelming
protein synthesis responses to wheat protein
in older individuals with a mean age of 71
years (5), but observed much more favorable responses in this study (1), which enrolled substantially younger participants.
As Dr. Helms recently covered in MASS,
aging and inactivity can have a tendency to
go hand in hand, and it’s quite common to
see blunted protein synthesis responses in
older, more sedentary individuals when compared to younger and more physically active
individuals. It’s quite possible that young,
healthy, active individuals have such robust
responses to dietary protein intake that even
fairly low-quality proteins like wheat can get
the job done when the dose is large enough
(in this case, 30g). That second part (“when
the dose is large enough”) is important, as a
2015 review paper suggested that potential
downsides of low-quality proteins can be
overcome by consuming larger doses, or by
implementing other strategies such as amino
acid fortification or complementary protein
matching (10).
The presently reviewed study did not involve
a resistance training component, but I would
speculate that a lifter can probably get away
with even more suboptimal approaches to
protein intake when compared to a non-lifter. While the anabolic response to protein ingestion seems to be supported by generally
staying active, resistance training provides a
robust, additive effect that facilitates height-
85
A HEALTHY, ACTIVE LIFTER
WITH A VARIED DIET
PROBABLY DOESN’T NEED
TO LOSE TOO MUCH
SLEEP OVER THE QUALITY
SCORE OF EVERY PROTEIN
SOURCE IN THEIR DIET.
ened anabolic responses to dietary protein
for 24-72 hours after a training session (11).
You’ve probably heard the phrase, “you can’t
out-train a bad diet.” That overly reductive
advice is usually offered in the context of fat
loss, but when it comes to protein synthesis, you probably can out-train a somewhat
low-quality protein source (to some extent)
by sensitizing your anabolic response to dietary protein. If you’re consuming adequate
amounts of total daily protein (≥1.6g/kg/
day, give or take), distributing your protein
sensibly throughout the day (≥3 large boluses, with each providing at least 20-40g of
protein), and getting your protein from a variety of sources with reasonably complementary amino acid profiles, then a healthy, active lifter with a varied diet probably doesn’t
need to lose too much sleep over the quality
score of every protein source in their diet. Of
course, consistently lacking a specific essential amino acid is going to hold you back, but
this scenario is exceedingly unlikely if you
consume an omnivorous or vegetarian diet
with a decent amount of animal-based protein, or a vegan diet with a reasonably varied
mix of plant-based protein sources.
It’s not uncommon to field questions about
whether or not low-quality plant proteins
should count toward an individual’s daily protein total, or if dieters should be setting a specific daily target for their intake of
protein from high-quality sources. From my
perspective, even fairly low-quality plant
proteins do count as protein, and setting distinct targets for high-quality and low-quality
proteins is unnecessary. Protein recommendations are heavily influenced by studies involving free-living individuals, who tend to
get a significant portion of their total daily
protein from plants, even when consuming
an omnivorous diet. For example, plant protein makes up 44% of total protein intake in
the Americas, and 58% of total protein intake
globally (10). So, you could argue that getting a decent percentage of your protein from
lower-quality plant sources is inherently
baked into general protein recommendations,
while it may be possible that protein recommendations would actually be lower if people
exclusively consumed very high-quality protein sources.
To be clear, I am not refuting the general concept of protein quality in this article, nor am
I claiming that we have no idea which proteins seem to support muscle remodeling and
hypertrophy with a high level of “efficiency” (gram-for-gram, or calorie-for-calorie).
Proteins with high digestibility, fairly rapid
digestion and absorption kinetics, plenty of
leucine, and all essential amino acids in suf-
86
ficient amounts efficiently support protein
synthesis, which is why whey protein tends
to do so well in head-to-head comparisons.
However, the relative importance of the
characteristics of individual protein sources
in the diet is a bit circumstantial. There are
some scenarios where it’s very difficult to
consume high amounts of total daily protein,
an individual is experiencing some degree of
anabolic resistance to protein ingestion, or a
clinical condition creates unique constraints
that limit total protein intake. In these scenarios, the quality of individual protein sources is of heightened importance. Similarly, a
person who favors a mostly plant-based eating pattern might increasingly find it hard to
achieve sufficient protein intake from whole
foods during a period of energy restriction,
and may therefore gravitate toward including
more protein supplements or higher-quality protein sources that can support protein
synthesis in a more calorie-efficient manner.
However, a healthy, active lifter with plenty
of calories to work with can enjoy a great deal
of flexibility when choosing protein sources,
as long as they’re getting enough total protein, distributing it sensibly, and achieving
adequate essential amino acid coverage.
The second part of this Interpretation section
pertains to the inconvenient observation that
muscle protein synthesis values aren’t always
what they seem. In lieu of longitudinal studies
documenting tangible changes in body composition, fitness enthusiasts have historically leaned on acute muscle protein synthesis
studies to make inferences about how various
interventions and demographic characteristics might impact hypertrophy over time.
However, when you get too comfortable with
the habit of uncritically assuming that acute
muscle protein synthesis findings are reliably
predictive of hypertrophy outcomes, you
inevitably arrive at some untenable conclusions.
For example, you can find studies suggesting
that muscle protein synthesis can be maximally stimulated by only 20g of protein in young
and healthy individuals (12), that muscle protein breakdown is of minimal importance in
studies evaluating anabolic properties of various diet and exercise interventions (13), and
that refractory periods dictate that we should
only be able to maximally stimulate muscle
protein synthesis around 4-5 times per day
(14). If we jam these puzzle pieces together
and take each of them at face value, it would
support a virtually universal protein recommendation of 80-100g/day to maximize hypertrophy, but that’s incompatible with the
longitudinal evidence that actually measures
hypertrophy over time. For another example,
people commonly cite a study by Beals et al
(15) to suggest that obesity blunts hypertrophy. However, if you wish to take those protein synthesis rates at face value and assume
that they forecast future hypertrophy outcomes, you’d have to sign up for the conclusion that a person with obesity grows more
muscle from not resistance training than from
resistance training (paradoxically, muscle
protein synthesis rates were slightly higher
in their untrained leg than their trained leg).
Once again, this extrapolated conclusion is
entirely incompatible with stronger and more
direct evidence.
If you scour the literature, you’ll find sever-
87
al examples of isolated findings that lead to
dubious conclusions if we assume that acute
muscle protein synthesis is reliably predictive of long-term hypertrophy outcomes. So,
in order to retain a somewhat cohesive and
internally consistent understanding of the
universe, I’ve simply let go of that assumption in many contexts. Fortunately, people
who are much more knowledgeable than me
about muscle protein synthesis appear to be
in the same boat.
Witard and colleagues recently published a
thorough review paper called, “Making Sense
of Muscle Protein Synthesis: A Focus on
Muscle Growth During Resistance Training”
(16). Needless to say, that title really resonated with me, and I think it’s a really valuable
and important paper. Most notably, it highlights the fact that we can’t always extrapolate acute muscle protein synthesis findings
to draw inferences about long-term hypertrophy outcomes. This observation is nothing
new – for example, Mitchell and colleagues
reported in a 2014 study (17) that acute muscle protein synthesis rates in the six hours
following a resistance training bout did not
significantly correlate with subsequent muscle hypertrophy over a 16-week resistance
training program. In fact, this study reported
an R-value of 0.10, which means that acute
protein synthesis responses explained only
1% (i.e., none) of the variance in hypertrophy
outcomes in this sample.
thesis response is mostly related to repairing
and remodeling existing proteins rather than
hypertrophy. Along these lines, acute muscle
damage leads to large spikes in muscle protein synthesis that are not indicative of muscle
hypertrophy. As an individual becomes more
trained, they experience changes in the magnitude and time course of protein synthesis
responses. Studies measuring acute muscle
protein synthesis responses generally assess
the rate of synthesis within a narrow time
frame (≤6 hours), while muscle is sensitized
to amino acid ingestion for up to 24-72 hours
after an exercise bout. As such, these studies
do not capture the cumulative effect of repeated protein synthesis elevations that would
contribute to longitudinal hypertrophy. Finally, we intuitively know that individuals differ
in their responses to resistance training and
protein feeding. Due to differences in translational capacity, training status, sleep quality, physical activity levels, nutritional habits,
and other lifestyle variables that are difficult
to control, tremendous inter-individual variability should be expected when assessing
acute protein synthesis responses. Due to the
cost and invasive nature of measuring muscle
protein synthesis, this understandably leads
to scenarios where outcomes with high variability are measured in small samples, which
contributes to a relatively high likelihood of
false positives, false negatives, and inconsistencies from study to study.
I highly recommend reading the full review
by Witard and colleagues, but here I will
summarize the most pertinent highlights of
their paper. After a single bout of unaccustomed exercise, the acute muscle protein syn-
While Witard and colleagues explore the topic in much greater detail than I’ve presented
here, there are a few straightforward takeaways that relate to the topic at hand. Acute
protein synthesis studies that don’t involve
88
resistance training might have somewhat limited applicability to lifters, as lifting induces
a pronounced and extended modification of
muscle protein synthesis responses. Acute
protein synthesis responses that do involve
resistance training are most likely to correlate
with longitudinal hypertrophy when participants are well-trained, the training stimulus
is familiar to the participants, and protein
synthesis measurements capture the full response over a longer time course. In addition,
demographic characteristics of a study sample are important, inter-individual variability must be expected (even within a generally homogenous sample of participants), and
magnitudes of effects should not be quantitatively generalized in a literal manner (that is,
an intervention that is 15% better for muscle
protein synthesis cannot be assumed to be
15% better for hypertrophy).
Looking back at the studies comparing whey,
casein, milk, wheat, and soy, they generally
involve a fairly small group of untrained individuals completing a single session of unaccustomed resistance training, or no training stimulus at all, followed by ≤6 hours of
muscle protein synthesis observation. There
are very understandable logistical constraints
that make this an attractive approach, and
these studies should give us a good glimpse
at which protein feeding strategies facilitate
the early process of muscle remodeling after
an acute bout of unaccustomed exercise, or
the acute response to protein feeding in people who are not exercising. They also serve to
generate informed hypotheses for future longitudinal studies. Nonetheless, we shouldn’t
expect these studies to have perfect agree-
ment from sample to sample, or to perfectly
predict longitudinal hypertrophy responses
in people who are lifting consistently. When
you compare plant-based proteins to animal-based proteins based purely on amino
acid profiles, digestibility, and blood amino
acid responses, the implied differences are
huge. When you compare based on acute protein synthesis responses, they’re still notable.
However, when you start looking at the literature assessing longitudinal training adaptations over a time course of several weeks, the
differences become smaller, less clear-cut,
and more contextually dependent (2, 18, 19).
So, the point is not that acute muscle protein
synthesis studies are useless – they’re just
complicated, and they don’t always generalize to the circumstances we want them to
generalize to. Along those lines, the presently reviewed study doesn’t suggest that all the
previous studies showing milk-derived proteins to stimulate muscle protein synthesis
more effectively than plant-based proteins
were wrong, or that the concept of protein
quality lacks validity, or that wheat protein
is a particularly potent and robust stimulator
of muscle protein synthesis. In my opinion, a
more prudent conclusion based on the totality of the evidence is that several contextual
factors influence the generalizability of acute
muscle protein synthesis findings, and that
several contextual factors also influence the
relative importance of protein quality within
a diet.
There are innumerable situations where we
need to rely on proxy measures rather than
directly measuring the true outcome of interest in experimental research. The outcome of
89
interest may require unacceptably invasive
or unfeasibly expensive measurement techniques, develop too slowly to reliably observe
it within a realistic study timeline, or simply
be too deleterious to allow its development
or progression without an ethical obligation
to intervene. For example, we see this in literature related to cardiovascular health; researchers are often interested in reducing the
occurrence of severe cardiovascular events,
but they’re likely to assess proxy measures
like changes in blood lipid profiles or vascular function instead of waiting forty years
to see if one group has more heart attacks
or strokes than the other. When it comes to
hypertrophy and body composition, longitudinal trials do require considerable time and
resources, but they are logistically, ethically,
and financially feasible nonetheless.
There are many excellent scientists doing
valuable and important research involving
proxy measures, and we certainly owe them
a debt of gratitude for their efforts and contributions. However, we should be mindful that
mechanistic data and proxy measures sometimes fail to generalize to the real-world scenarios we wish to apply them to. A particular
finding might have been observed in a unique
population, or under a unique set of circumstances. Or, perhaps we promptly adapt to an
acute effect, such that its impact over time
is inconsequential. We could also observe
an acute effect that is nullified or counteracted by a parallel, simultaneous effect that
we didn’t measure (or didn’t even know existed). All of that is to say, we should value
and appreciate experimental studies that establish mechanisms of action or utilize proxy
MECHANISTIC DATA
AND PROXY MEASURES
SOMETIMES FAIL TO
GENERALIZE TO THE REALWORLD SCENARIOS WE
WISH TO APPLY THEM TO.
measures, but we should lean more heavily
on longitudinal data that directly measures
the outcome of interest whenever possible.
When someone cites protein synthesis data
for a statement about hypertrophy, it’s important to recognize that the citation may or
may not be applicable.
Next Steps
We have some pretty solid longitudinal evidence suggesting that vegan diets can effectively support hypertrophy and strength gains
when combined with resistance training (2),
despite their relative lack of high-quality animal-based protein sources. However, this
study involved total daily protein intakes that
would generally be considered quite high in
the non-lifting world (≥1.6g/kg/day), fairly optimized protein distribution (≥3 large
daily boluses), and a large percentage of daily protein intake coming from soy protein,
which has an atypically high quality score for
a plant-based protein source. I’d love to see
a series of longitudinal studies that explore
90
APPLICATION AND TAKEAWAYS
There are some scenarios in which the quality of protein sources in a diet is of
heightened importance. However, lifters can typically enjoy a great deal of flexibility
when choosing protein sources, as long as they’re getting enough total protein,
distributing it sensibly, and achieving adequate essential amino acid coverage. In
addition, we should always practice caution when extrapolating proxy measures (such
as acute muscle protein synthesis) to longitudinal outcomes (such as hypertrophy).
Such extrapolations must be examined and interpreted on a case-by-case basis, with
a thorough understanding of the conditions in which the proxy measure can (or cannot)
be used to confidently draw conclusions about the outcome of interest.
exactly how many of these optimizations are
truly required to maximize gains on diets
that mostly or exclusively utilize plant-based
protein sources, which tend to have low or
moderate quality scores. For example, would
results be similar for vegan diets that are not
supplemented with large amounts of soy protein? Can you get away with less optimal
protein distribution? Is 1.2g/kg or 1.4g/kg
meaningfully worse than 1.6g/kg? Until we
get those answers, lifters on vegan (or nearly vegan) diets who wish to maximize their
gains should probably optimize these parameters to the extent that is feasible for them.
91
References
1. Pinckaers PJM, Kouw IWK, Hendriks FK, van Kranenburg JMX, de Groot LCPGM,
Verdijk LB, et al. No differences in muscle protein synthesis rates following ingestion of
wheat protein, milk protein, and their protein blend in healthy, young males. Br J Nutr.
2021 Dec 28;126(12):1832–42.
2. Hevia-Larraín V, Gualano B, Longobardi I, Gil S, Fernandes AL, Costa LAR, et al.
High-Protein Plant-Based Diet Versus a Protein-Matched Omnivorous Diet to Support
Resistance Training Adaptations: A Comparison Between Habitual Vegans and
Omnivores. Sports Med. 2021 Jun;51(6):1317-1330.
3. Wolfe RR. Skeletal Muscle Protein Metabolism and Resistance Exercise. J Nutr. 2006
Feb 1;136(2):525S-528S.
4. Pinckaers PJM, Trommelen J, Snijders T, Loon LJC van. The Anabolic Response to
Plant-Based Protein Ingestion. Sports Med. 2021;51(Suppl 1):59.
5. Gorissen SH, Horstman AM, Franssen R, Crombag JJ, Langer H, Bierau J, et al.
Ingestion of Wheat Protein Increases In Vivo Muscle Protein Synthesis Rates in Healthy
Older Men in a Randomized Trial. J Nutr. 2016 Sep;146(9):1651–9.
6. Wilkinson SB, Tarnopolsky MA, Macdonald MJ, Macdonald JR, Armstrong D, Phillips
SM. Consumption of fluid skim milk promotes greater muscle protein accretion after
resistance exercise than does consumption of an isonitrogenous and isoenergetic soyprotein beverage. Am J Clin Nutr. 2007 Apr;85(4):1031–40.
7. Churchward-Venne TA, Pinckaers PJM, Smeets JSJ, Peeters WM, Zorenc AH,
Schierbeek H, et al. Myofibrillar and Mitochondrial Protein Synthesis Rates Do Not
Differ in Young Men Following the Ingestion of Carbohydrate with Whey, Soy, or
Leucine-Enriched Soy Protein after Concurrent Resistance- and Endurance-Type
Exercise. J Nutr. 2019 Feb 1;149(2):210–20.
8. Tang JE, Moore DR, Kujbida GW, Tarnopolsky MA, Phillips SM. Ingestion of
whey hydrolysate, casein, or soy protein isolate: effects on mixed muscle protein
synthesis at rest and following resistance exercise in young men. J Appl Physiol. 2009
Sep;107(3):987–92.
9. Moore DR. Maximizing Post-exercise Anabolism: The Case for Relative Protein Intakes.
Front Nutr. 2019;6:147.
10. van Vliet S, Burd NA, van Loon LJC. The Skeletal Muscle Anabolic Response to Plantversus Animal-Based Protein Consumption. J Nutr. 2015 Sep;145(9):1981–91.
92
11. Jäger R, Kerksick CM, Campbell BI, Cribb PJ, Wells SD, Skwiat TM, et al. International
Society of Sports Nutrition Position Stand: protein and exercise. J Int Soc Sports Nutr.
2017;14:20.
12. Witard OC, Jackman SR, Breen L, Smith K, Selby A, Tipton KD. Myofibrillar muscle
protein synthesis rates subsequent to a meal in response to increasing doses of whey
protein at rest and after resistance exercise. Am J Clin Nutr. 2014 Jan;99(1):86–95.
13. Trommelen J, Betz MW, van Loon LJC. The Muscle Protein Synthetic Response to Meal
Ingestion Following Resistance-Type Exercise. Sports Med. 2019 Feb;49(2):185–97.
14. Mitchell WK, Phillips BE, Hill I, Greenhaff P, Lund JN, Williams JP, et al. Human
skeletal muscle is refractory to the anabolic effects of leucine during the postprandial
muscle-full period in older men. Clin Sci. 2017 Oct 27;131(21):2643–53.
15. Beals JW, Skinner SK, McKenna CF, Poozhikunnel EG, Farooqi SA, van Vliet S, et al.
Altered anabolic signalling and reduced stimulation of myofibrillar protein synthesis
after feeding and resistance exercise in people with obesity. J Physiol. 2018 Nov
1;596(21):5119–33.
16. Witard OC, Bannock L, Tipton KD. Making Sense of Muscle Protein Synthesis: A Focus
on Muscle Growth During Resistance Training. Int J Sport Nutr Exerc Metab. 2021 Oct
25;1–13.
17. Mitchell CJ, Churchward-Venne TA, Parise G, Bellamy L, Baker SK, Smith K, et al.
Acute post-exercise myofibrillar protein synthesis is not correlated with resistance
training-induced muscle hypertrophy in young men. PloS One. 2014;9(2):e89431.
18. Lim MT, Pan BJ, Toh DWK, Sutanto CN, Kim JE. Animal Protein versus Plant Protein
in Supporting Lean Mass and Muscle Strength: A Systematic Review and Meta-Analysis
of Randomized Controlled Trials. Nutrients. 2021 Feb;13(2):661.
19. Morgan PT, Harris DO, Marshall RN, Quinlan JI, Edwards SJ, Allen SL, et al. Protein
Source and Quality for Skeletal Muscle Anabolism in Young and Older Adults: A
Systematic Review and Meta-Analysis. J Nutr. 2021 Jul 1;151(7):1901–20.
█
93
Research Briefs
BY GREG NUCKOLS & ERIC TREXLER
In the Research Briefs section, Greg Nuckols and Eric
Trexler share quick summaries of recent studies. Briefs are
short and sweet, skimmable, and focused on the need-toknow information from each study.
95
98
102
108
112
Attentional Focus May Influence Strength
Development
Cold Exposure For Fat Loss: Physiology Can
Be “Cool” Without Being Useful
The Interference Effect is Getting Less Scary
by the Day
Do Buffering Supplements Make Sense for
Lifters?
Is Sarcoplasmic Hypertrophy Caused by
Frequently Manipulating Training Variables?
117
Revisiting Fish Oil Supplementation for
Recovery
121
The Progressive Effects of Sleep Restriction
and Extension
126
Citrulline Is Promising, But We Have a Lot to
Learn
131
If You Don’t Snooze, You Lose
94
Study Reviewed: Acute and Long-Term Effects of Attentional Focus Strategies on Muscular
Strength: A Meta-Analysis. Grgic et al. (2021)
Attentional Focus May Influence Strength
Development
W
BY GREG NUCKOLS
e’ve written about attentional focus a few times in MASS, discussing how an external focus
(focusing on the outcome of a task) can lead
to improved acute performance, and an internal focus (focusing on bodily movements
or sensations) may improve muscle growth
(one, two, three). However, when it comes
to strength development, we can’t necessarily assume that promising acute measures (increased strength performance when adopting
an external attentional focus) will necessarily
lead to improved long-term outcomes (greater
strength development). With that in mind, the
present meta-analysis (1) sought to analyze the
research investigating the impact of attentional focus on both acute strength performance
and longitudinal strength development.
The researchers identified seven studies investigating the impact of attentional focus
on acute strength measures and three studies investigating the impact of attentional
focus on longitudinal strength development
that met their inclusion criteria. The study
needed to 1) be written in English, 2) investigate the acute or longitudinal impact of an
internal versus an external attentional focus
on strength, 3) employ a crossover design or
between-groups design, and 4) report enough
statistical information for the results to be
useable in a meta-analysis. Details about the
included studies can be seen in Table 1.
The meta-analysis on acute strength measures found that an external attentional focus
significantly enhanced strength performance
(Figure 1A; Standardized Mean Difference
[SMD] = 0.34; p < 0.001). Furthermore, it’s
worth noting that the effect wasn’t solely
driven by the studies assessing the effect of
attentional focus on strength measures like
handgrip strength or index finger flexion
strength (2, 3). Studies assessing strength using isometric mid-thigh pull (4) or squat and
deadlift (5) also found positive effects in favor of an external attentional focus.
For longitudinal strength development, there
was an overall positive effect in favor of an
external attentional focus. This effect size
was quite similar to the effect size for acute
strength performance, but it failed to meet the
traditional standard for statistical significance
(SMD = 0.32; p = 0.11). However, the authors
95
also performed a sub-analysis on measures of
lower body strength development, finding a
small significant effect in favor of an external
attentional focus (SMD = 0.47; p = 0.02).
This meta-analysis was useful for two reasons. First, while it’s well-established that
an external attentional focus improves acute
motor learning and performance in a general
sense (6), most of the research in the area
has focused on tasks that are more dependent on coordination (for example, throwing darts or tossing bean bags) than force
output. I was aware that several individual
studies had identified positive impacts of an
external focus on strength performance, but
it’s good to have those findings confirmed
by a meta-analysis.
Second, and more importantly, this meta-analysis provides some evidence that an
external attentional focus during training
can improve longitudinal strength development. I’m not particularly concerned that the
general analysis of strength measures failed
to find a significant effect, and I’m also not
particularly impressed that the subanalysis of
lower body strength measures did find a significant effect. In both cases, the meta-analyses on longitudinal outcomes only included
three studies, so it’s still far too early to make
any definitive statements. However, the re-
96
References
1. Grgic J, Mikulic I, Mikulic P. Acute
and Long-Term Effects of Attentional
Focus Strategies on Muscular Strength: A
Meta-Analysis. Sports (Basel). 2021 Nov
12;9(11):153. doi: 10.3390/sports9110153.
PMID: 34822352; PMCID: PMC8622562.
2. Bredin SS, Dickson DB, Warburton
DE. Effects of varying attentional
focus on health-related physical fitness
performance. Appl Physiol Nutr Metab.
2013 Feb;38(2):161-8. doi: 10.1139/apnm2012-0182. Epub 2013 Feb 14. PMID:
23438227.
3. Kuhn YA, Keller M, Lauber B, Taube
W. Surround inhibition can instantly be
modulated by changing the attentional
focus. Sci Rep. 2018 Jan 18;8(1):1085.
doi: 10.1038/s41598-017-19077-0. PMID:
29348536; PMCID: PMC5773585.
4. Halperin I, Williams KJ, Martin DT,
Chapman DW. The Effects of Attentional
Focusing Instructions on Force Production
During the Isometric Midthigh Pull. J
Strength Cond Res. 2016 Apr;30(4):91923. doi: 10.1519/JSC.0000000000001194.
PMID: 27003451.
sults were generally positive, suggesting that
adopting an external attentional focus during
resistance training may not only enhance
strength performance acutely, but may also
improve rates of strength development over
time. Of course, more research is needed to
confirm these initial tentative findings.
5. Nadzalan AM, Lee JLF, Azzfar MS,
Muhammad NS, Shukri EWMC, Mohamad
NI. The effects of resistance training with
different focus attention on muscular
strength: Application to teaching methods
in physical conditioning class. IJITEE
2019, 8, 16–19.
6. Wulf G. Attentional focus and motor
learning: a review of 15 years. International
Review of Sport and Exercise Psychology
2013 6:1, 77-104
97
Study Reviewed: Altered Brown Fat Thermoregulation and Enhanced Cold-Induced
Thermogenesis in Young, Healthy, Winter-Swimming Men. Søberg et al. (2021)
Cold Exposure For Fat Loss: Physiology Can Be
“Cool” Without Being Useful
BY ERIC TREXLER
You’re currently reading MASS Research
Review, so I assume we can generally agree
that science is pretty cool. A positive aspect
of this perspective is that we can embrace
rigorous scientific principles and use them
to optimize our habits and practices related
to training, nutrition, and other health behaviors. There is, however, a potential drawback
of this perspective. If we get a little too enthusiastic about how cool science is, we can
sometimes get too enamored with fascinating
aspects of human physiology. If we aren’t
careful, we can get sucked into overhyped
concepts that encourage us to put the cart before the horse (i.e., apply an intriguing intervention before we have evidence to suggest
that it’s actually applicable). Not everything
that is fascinating is actionable, and not everything that is actionable is fascinating.
That brings us to the concept of cold exposure. I’m not talking about cold water immersion to reduce inflammation following
exercise or acute tissue injury, but simply exposing your body to cold conditions (usually
in the form of water immersion, since water
facilitates heat transfer so efficiently) to ob-
tain a long list of other extremely speculative
benefits. One of the purported outcomes most
frequently discussed is increased energy expenditure and fat oxidation, which has caused
some fairly notable figures to suggest that
frequent cold exposure is an effective way to
facilitate fat loss.
The idea is that cold exposure will acutely increase sympathetic nervous system activity,
shivering, and brown adipose tissue activation. Sympathetic nervous system activation
induces a neuroendocrine response that increases energy expenditure and fat oxidation,
and shivering increases energy expenditure
via increased muscle activity. You may be
less familiar with brown adipose tissue because, until fairly recently, the prevailing
belief was that adult humans typically had a
negligible amount of this tissue. However, research over the last 10-20 years has indicated
that adult humans do have clusters of brown
adipose tissue, and that this tissue is stimulated by cold exposure (2). Upon stimulation,
brown adipose cells (which are rich in mitochondria) ramp up their metabolic rate for the
purpose of generating heat. So, energy expen-
98
diture and fat oxidation are acutely increased
due to these impacts on the sympathetic nervous system, shivering, and brown adipose
tissue activation, but chronic effects are also
likely. For example, there is some evidence
that individuals who spend a lot of time in
cold environments have upregulated brown
adipose tissue activity, and that some of their
white adipose tissue (i.e., “normal” subcutaneous fat) starts looking and behaving more
like brown adipose tissue. This semi-converted fat is often referred to as “beige” adipose
tissue, and the potential to intentionally induce this conversion has spurred interest in
studying chronic cold exposure.
That’s where the presently reviewed study
(1) comes into play. Briefly, the researchers
were interested in comparing a huge list of
physiological characteristics and responses in
“winter-swimming men” and a control group
matched based on age, gender, BMI, and
physical activity level. Two to three times per
week, participants engaged in a form of winter swimming that involved a combination of
brief immersion in very cold water and hot
sauna bathing, which appears to be popular in
some Scandinavian countries. The researchers measured a ton of different outcomes, but
the most relevant (for our purposes) relate
to energy expenditure and brown fat activity. In short, the researchers measured resting energy expenditure in a thermal comfort
state (comfortable ambient temperature) and
during a 30-minute cooling condition (which
aimed to keep participants just slightly above
the shivering threshold).
In the interest of staying true to the “brief”
aspect of the research briefs section, I’ll skip
right to the point. As shown in Table 1, the
group of winter swimmers was significantly leaner than controls (12.0 versus 18.2%
body-fat), had similar resting energy expenditure in thermal comfort (2,038 versus 2,005
kcal/day), but had significantly higher resting energy expenditure during cold exposure
(3,044 versus 2,560 kcal/day).
It seems that a recent resurgence of interest
in cold-water immersion for fat loss pur-
99
poses has been fueled, to a large extent, by
a prominent podcast that sometimes covers
fitness-related topics. In the linked episode,
the presenter suggests that cold water immersion is a viable fat loss tool, but that you
should be mindful not to adapt to it because
of anecdotes involving cold-water swimmers
with high body-fat levels. This anecdotal evidence was perceived to indicate that adapting to cold-water immersion would diminish the effects related to energy expenditure
and fat loss. The presently reviewed study
directly contradicts these recommendations;
winter swimmers tended to be leaner than
well-matched controls, and had more robust
thermogenic responses to cold exposure.
More importantly, I’d like to focus on the
most practical aspect of this topic: whether
or not cold exposure is a viable fat loss target. The presently reviewed study reported
cold-induced increases in energy expenditure of nearly 50% (relative to thermoneutral energy expenditure), but this is far from
the norm. Other studies often report values
around the 15% range (2, 3), with a high degree of variability from person to person. In
addition, it’s critical to recognize that this is
the elevation observed during cold exposure;
if you increase your resting metabolic rate
by 250 kcal/day, but you only engage in one
hour of cold exposure, you’re talking about
an absolute increase of less than 11kcal (in
other words, an entirely negligible amount).
You could argue that this magnitude underestimates the true value of cold water immersion, because these studies use temperatures
just above the shivering threshold, which is
intended to exclusively quantify the impact
of brown adipose tissue activity in the absence of shivering-induced energy expenditure. However, this implies that in order for
the intervention to have any hope of producing a meaningful effect, it must be applied in
a manner that is impractical and tremendously uncomfortable.
My skepticism is comprehensively echoed in a
recent review paper by Marlatt and colleagues
(4). The highlights of their paper include the
observations that “studies in humans do not
support the hypothesis that induction and activation of [brown adipose tissue] may be an
effective strategy for body weight control,”
cold-induced increases in energy expenditure
likely lead to compensatory increases in appetite, and there is no evidence of seasonal body
composition changes that would link colder
conditions to reductions in body weight or fat
mass. In short, there is little reason to believe
that any practical and tolerable implementation of cold exposure will lead to meaningful
body composition changes. In addition, unaccustomed cold water exposure can lead to
severe adverse cardiovascular complications,
so these types of interventions should be approached with extreme caution. We must always be skeptical of “sciency” interventions
that are driven by mechanisms, anecdote, or
intuition. Learning about science is always encouraged, but remember: not everything that
is fascinating is actionable, and not everything
that is actionable is fascinating. We’ve got
plenty of boring strategies to effectively support body composition goals; physiological
responses to acute and chronic cold exposure
are really cool, but their relevance to fat loss is
dubious at best.
100
References
1. Søberg S, Löfgren J, Philipsen FE, Jensen
M, Hansen AE, Ahrens E, et al. Altered
brown fat thermoregulation and enhanced
cold-induced thermogenesis in young,
healthy, winter-swimming men. Cell Rep
Med. 2021 Oct 19;2(10):100408.
2. Trexler ET, McCallister D, Smith-Ryan
AE, Branca RT. Incidental finding
of low brown adipose tissue activity
in endurance-trained individuals:
Methodological considerations for positron
emission tomography. J Nat Sci. 2017
Mar;3(3):e335.
3. van Marken Lichtenbelt WD, Schrauwen P.
Implications of nonshivering thermogenesis
for energy balance regulation in humans.
Am J Physiol Regul Integr Comp Physiol.
2011 Aug;301(2):R285-296.
4. Marlatt KL, Chen KY, Ravussin E. Is
activation of human brown adipose tissue a
viable target for weight management? Am J
Physiol - Regul Integr Comp Physiol. 2018
Sep 1;315(3):R479.
101
Study Reviewed: Compatibility of Concurrent Aerobic and Strength Training for Skeletal
Muscle Size and Function: An Updated Systematic Review and Meta-Analysis. Schumann et
al. (2021)
The Interference Effect is Getting Less Scary by the
Day
BY GREG NUCKOLS
Long-time readers of MASS will know that
concurrent training (performing both resistance and endurance training) is a popular
topic around these parts; you can find all of
our articles on concurrent training here. With
concurrent training, you’re always trying to
balance and manipulate your strength training
and endurance training to mitigate the impact
of the dreaded interference effect (a reduction
in the rate of strength gains, power/velocity
gains, and hypertrophy observed when adding endurance training to a resistance training
program). However, the research regarding
concurrent training and the interference effect has been shifting over time.
The first study in the area by Hickson in
1980 (2) found that concurrent training led
to considerably smaller strength gains than
resistance training alone. By 2012, there was
enough research to warrant a meta-analysis
(3); this meta-analysis suggested that concurrent training led to smaller strength gains, less
muscle growth, and smaller improvements in
power output and explosive strength than resistance training alone. More recently, a 2021
meta-analysis (reviewed here) broke things
down further, separating studies by the training status of the subjects and the timing of
the resistance training and endurance training
sessions (trained versus untrained subjects,
and studies where endurance and resistance
training were performed in the same training
session versus different training sessions).
That meta-analysis suggested that, at least for
strength development, there’s no significant
interference effect for untrained subjects, nor
is there any interference effect when trained
subjects split their endurance and resistance
training into separate training sessions (4).
Thus, in the intervening years since 1980, the
balance of evidence has shifted considerably
– we used to be concerned that the interference effect would have a fairly large, fairly
consistent negative effect for virtually anyone who wanted to gain strength and build
muscle while also doing some endurance
training. Now, it appears that the interference effect should only be a small concern
for some people, some of the time (and only
in situations where they have to perform their
endurance and resistance training in the same
session). But will this trend continue?
102
Well, if you’re a fan of concurrent training,
a brand new meta-analysis should give you
even less reason to be concerned about the
interference effect (1). Schumann and colleagues started by identifying all of the studies that met the following inclusion criteria:
1. The studies needed to include a training
intervention lasting at least four weeks.
2. The studies needed to include groups completing identical resistance training programs, with one group performing only
resistance training, and at least one group
performing additional aerobic training.
3. The studies needed to include measures
of maximal strength, explosive strength,
and/or muscle hypertrophy.
4. The exercises used to assess performance
needed to be specific to the resistance
training the subjects performed.
The researchers identified 43 studies with a
total of 1,090 subjects that met their inclu-
103
104
sion criteria, including 37 studies measuring
maximal strength, 18 studies measuring explosive strength, and 15 studies assessing hypertrophy.
They found that concurrent training did not
lead to significantly smaller strength gains
than resistance training alone (Figure 1; Standardized Mean Difference [SMD] = -0.06; p
= 0.45), nor did it lead to significantly less
hypertrophy (Figure 3; SMD = -0.01; p =
0.92). However, concurrent training did lead
to significantly smaller improvements in explosive strength than resistance training alone
(Figure 3; SMD = -0.28; p = 0.007).
The researchers also performed a series of
subanalyses that can be found here. For
strength, they found that the modality of endurance training (running versus cycling),
the weekly frequency of endurance training,
the training status of the subjects (“active”
versus untrained; they didn’t run a subgroup
analysis on specifically resistance-trained
subjects), the age of the subjects (18-40 years
old versus >40 years old), and the timing of
resistance and endurance training sessions
(performing both in the same training session
versus different sessions) all failed to significantly modify the effect. Of note, however,
the researchers didn’t run a subanalysis investigating the impact of total endurance training duration. For hypertrophy, it’s a similar
story: endurance training frequency, training status, and the timing of resistance and
endurance training sessions all failed to significantly modify the effect. In other words,
this meta-analysis suggests that the interference effect doesn’t really exist in any generalizable sense for strength and hypertrophy
outcomes – it’s only “real” and noteworthy
for measures of power output and explosive
strength.
Overall, this meta-analysis doesn’t necessarily affect my recommendations regarding
concurrent training to any large extent, but I
do think it recontextualizes this body of research. Previously, the default assumption
was that the interference effect generally mattered quite a bit, and that it was the goal of a
coach to find the exact right mix of training
variables to mitigate the interference effect to
the greatest extent possible. However, I think
the overall balance of evidence now suggests
that the interference effect isn’t that big of a
deal, and you probably don’t need to be that
concerned about it most of the time.
To be clear, I don’t necessarily endorse the
position that would be implied by a literal and
expansive interpretation of this study’s findings: I absolutely think that if your endurance
training volume, frequency, and/or intensity
is high enough, it can have a negative impact
on your muscle growth and strength development. It’s always important to keep context
in mind when research findings seem to contradict common sense. Most concurrent training studies don’t involve resistance training
protocols that push subjects to their absolute
limits in an effort to maximize rates of hypertrophy and strength gains, nor do they put
subjects through an endurance training protocol that might be typical of a runner attempting to qualify for the Boston marathon. Your
capacity to recover from training is finite, so
the introduction of a non-trivial amount of endurance training will necessitate some level
of resistance training volume below the max-
105
imal amount you could theoretically tolerate
(and possibly/probably below the amount
of training volume that would theoretically
maximize your rate of muscle growth and/
or strength gains). However, I also think that,
in general, “we” (referring to myself and the
“evidence-based” fitness community in general) may have previously been a bit too concerned about the interference effect.
As more and more research on the subject is
published, I’m becoming more and more convinced that the interference effect shouldn’t
be a major concern for most people, most of
the time. However, there are a few groups of
people who probably need to be a bit more
careful:
1. If your capacity to recover from training
is significantly diminished (due to poor
sleep, high levels of psychogenic stress,
or a large calorie deficit), you may not
be able to handle a substantial amount of
simultaneous endurance and resistance
training.
2. If you’re already stressing your capacity
to recover from a given volume of endurance training, you may struggle to add in
a significant amount of resistance training
(and benefit from it).
3. If you’re already stressing your capacity to recover from a given volume of resistance training, you should be careful
about adding in a large amount of endurance training, or ramping up endurance
training volume too quickly.
4. Most importantly, if you have major goals
related to explosive strength or power out-
put (for example, improving your jumping ability), endurance training will likely
reduce your rate of progress.
Now, I realize that a lot of MASS readers
probably fall into the third group above.
However, I also suspect that >80% of people
who do some sort of endurance or resistance
training can combine both without compromising their strength and hypertrophy results.
And that’s really my main point: Rather than
framing the interference effect (for strength
and hypertrophy) as the likely outcome of
concurrent training that is challenging to mitigate, it may be more appropriate to frame it
as a relatively uncommon phenomenon that
is unlikely to impact training outcomes unless someone is already really pushing their
limits (or attempting to push their limits) in
multiple capacities at once.
Finally, I’d just like to acknowledge that most
of this article has been written with strength
and hypertrophy-related goals in mind (since
this is MASS, after all). However, it’s worth
reiterating that endurance training does seem
to consistently and significantly affect explosive performance. So, for example, a powerlifter may not notice any negative effects
from jogging a few times per week, but a
thrower or high jumper probably would. Or,
in the context of team sports, intensive conditioning work could reduce the explosiveness
and agility of athletes. If your main goal is to
maximize physical capacities related to power output, speed, or explosiveness, it wouldn’t
be a bad idea to limit endurance training to
whatever extent is feasible.
106
References
1. Schumann M, Feuerbacher JF, Sünkeler
M, Freitag N, Rønnestad BR, Doma K,
Lundberg TR. Compatibility of Concurrent
Aerobic and Strength Training for Skeletal
Muscle Size and Function: An Updated
Systematic Review and Meta-Analysis.
Sports Med. 2021 Nov 10. doi: 10.1007/
s40279-021-01587-7. Epub ahead of print.
PMID: 34757594.
2. Hickson RC. Interference of strength
development by simultaneously training for
strength and endurance. Eur J Appl Physiol
Occup Physiol. 1980;45(2-3):255-63. doi:
10.1007/BF00421333. PMID: 7193134.
3. Wilson JM, Marin PJ, Rhea MR,
Wilson SM, Loenneke JP, Anderson
JC. Concurrent training: a meta-analysis
examining interference of aerobic and
resistance exercises. J Strength Cond Res.
2012 Aug;26(8):2293-307. doi: 10.1519/
JSC.0b013e31823a3e2d. PMID: 22002517.
4. Petré H, Hemmingsson E, Rosdahl H,
Psilander N. Development of Maximal
Dynamic Strength During Concurrent
Resistance and Endurance Training in
Untrained, Moderately Trained, and
Trained Individuals: A Systematic Review
and Meta-analysis. Sports Med. 2021
May;51(5):991-1010. doi: 10.1007/s40279021-01426-9. Epub 2021 Mar 22. PMID:
33751469; PMCID: PMC8053170.
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Study Reviewed: Extracellular Buffering Supplements to Improve Exercise Capacity and
Performance: A Comprehensive Systematic Review and Meta-analysis. de Oliveira et al.
(2021)
Do Buffering Supplements Make Sense for Lifters?
BY ERIC TREXLER
Beta-alanine is an intracellular buffering supplement, which serves to attenuate reductions
in pH by accepting hydrogen ions produced
during high-intensity exercise (2). However,
there are also extracellular buffering supplements, which include sodium bicarbonate,
sodium citrate, sodium lactate, and calcium
lactate. Much like beta-alanine, these supplements aim to attenuate pH drops related
to the accumulation of hydrogen ions during
exercise. From this list of extracellular buffer supplements, sodium bicarbonate is by
far the most commonly used and commonly
studied. As seasoned MASS readers might
recall, Dr. Zourdos has previously mentioned
the fact that the effects of sodium bicarbonate supplementation are somewhat equivocal
in the context of resistance training. As such,
meta-analyses that seek to quantify the typical effects of extracellular buffering supplements and identify factors that moderate the
magnitude of these effects can be quite valuable and informative.
Fortunately, that’s exactly what the presently
reviewed study (1) sought to accomplish. The
researchers gathered up 189 studies evaluat-
ing the acute and chronic impact of sodium
bicarbonate, sodium citrate, sodium lactate,
and calcium lactate on exercise performance.
They also evaluated the impact of several potential moderating characteristics that might
influence the effects of these supplements,
including the supplement type, supplement
dose, duration of supplementation, type of
exercise test, duration of exercise test, training status of participants, and more. This was
a Bayesian analysis (rather than the more
common “frequentist” approach), so results
are reported as the pooled median effect size
(ES) and the associated 95% credible interval
(95%CrI). A thorough comparison of confidence intervals and credible intervals would
be beyond the scope of this research brief,
but the following open access paper describes
the distinction in as much detail as you could
possibly want (3). In short, the median effect
size reflects the “typical” effect size estimate
within the pooled research in this analysis,
and the width of the credible interval reflects
the precision of that estimate (wider intervals
reflect lower precision, and narrower intervals reflect higher precision).
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Supplementation led to a significant increase in blood bicarbonate levels (+5.2
mmol/L, 95%CrI: 4.7–5.7 mmol/L) and a
significant exercise performance improvement (ES = 0.17, 95%CrI: 0.12–0.21). There
weren’t enough sodium lactate and calcium
lactate studies to conduct informative comparisons, but sodium bicarbonate appeared
to be more effective than sodium citrate (ES
= 0.10, 95%CrI: -0.02–0.22). Unsurprisingly, effects were most pronounced when
blood bicarbonate levels were increased by
≥4mmol/L when compared to <4mmol/L,
and in exercise bouts lasting 0.5–10 minutes [ES = 0.18, 95%CrI: 0.13–0.24) and
>10 minutes [ES = 0.22, 95%CrI: 0.10–0.33)
when compared to exercise bouts lasting <30
seconds. Of course, these were not the only
noteworthy moderating factors; Figure 1 displays the median effect size estimates across
a broad range of potential moderating variables. Just to clarify, you’ll see categories
called “Exercise Duration I” and “Exercise
Duration II” in the figure. The purpose of the
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“Exercise Duration II” category was to further explore effect sizes for exercise bouts
within the 0.5–10 minute category, since a
30-second task imposes markedly different
metabolic demands than a 10-minute exercise task.
Overall, I wouldn’t say this meta-analysis
provides any truly earth-shattering results
that would surprise people who have been
following the sodium bicarbonate literature.
However, meta-analyses are a fantastic way
to summarize a large body of literature rather
than manually piecing together conclusions
from individual studies, and I’ve always felt
that a great figure is invaluable. Figure 1 is a
concise but informative representation of the
potential applications of extracellular buffer
supplementation, which effectively summarizes and contextualizes the findings from
nearly 200 studies on the topic. It also provides some very practical guidance regarding
the specific contexts and scenarios in which
extracellular buffering supplements may be
more (or less) effective.
Despite the large number of sodium bicarbonate studies in this literature, it would be inaccurate to suggest that we have all the answers.
Indeed, important questions remain. As Dr.
Zourdos has pointed out, a pretty small percentage of sodium bicarbonate studies focus
on resistance training, so we don’t have a ton
of empirical evidence showing meaningful
benefits for lifters (or determining the exact
types of resistance training approaches that
stand to benefit from supplementation). In
addition, the presently reviewed meta-analysis provides a little bit of weak evidence
suggesting that chronic supplementation is
more effective than acute supplementation,
but some direct evidence casts doubt on the
idea that regular use of sodium bicarbonate
translates to better resistance training adaptations over time. In addition, Greg posed
an intriguing question in a previous MASS
article: could lifters potentially get all the
buffering help that they need from strategic
hyperventilation? If so, the hyperventilation
approach would be free, easy to implement,
and devoid of the potential gastrointestinal
side effects that are commonly observed with
sodium bicarbonate supplementation.
Despite these lingering questions, a recent
meta-analysis (4) with about a dozen studies
found that sodium bicarbonate supplementation significantly improved muscular endurance (ES = 0.37, p = 0.001), which is directly
relevant to many lifters. When considering the
totality of the evidence (5), it’s fair to suggest
that some lifters may enjoy small (but arguably meaningful, depending on the context)
performance benefits from sodium bicarbonate supplementation. More specifically, small
benefits are likely for lifters whose training
programs directly stress the glycolytic energy
system. For example, if you do a lot of circuit training, CrossFit training, strongman/
strongwoman training, repeated sprint training, or you just do a lot of sets with moderate-to-high rep ranges and short rest periods
between sets, sodium bicarbonate might be a
defensible supplementation strategy. If you’re
concerned about the potential gastrointestinal
distress that is often observed, a recent open
access paper details a dosing strategy that delivers an ergogenic dose while minimizing the
likelihood of gastrointestinal symptoms (6).
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References
1. de Oliveira LF, Dolan E, Swinton PA,
Durkalec-Michalski K, Artioli GG,
McNaughton LR, et al. Extracellular
Buffering Supplements to Improve
Exercise Capacity and Performance: A
Comprehensive Systematic Review and
Meta-analysis. Sports Med. 2021 Oct 23,
ePub ahead of print.
2. Trexler ET, Smith-Ryan AE, Stout JR,
Hoffman JR, Wilborn CD, Sale C, et al.
International society of sports nutrition
position stand: Beta-Alanine. J Int Soc
Sports Nutr. 2015 Jul 15;12(1):30.
3. Hespanhol L, Vallio CS, Costa LM,
Saragiotto BT. Understanding and
interpreting confidence and credible
intervals around effect estimates. Braz J
Phys Ther. 2019 Aug;23(4):290.
4. Grgic J, Rodriguez RF, Garofolini A,
Saunders B, Bishop DJ, Schoenfeld BJ,
et al. Effects of Sodium Bicarbonate
Supplementation on Muscular Strength
and Endurance: A Systematic Review
and Meta-analysis. Sports Med. 2020
Jul;50(7):1361–75.
5. Grgic J, Pedisic Z, Saunders B, Artioli
GG, Schoenfeld BJ, McKenna MJ, et al.
International Society of Sports Nutrition
position stand: sodium bicarbonate and
exercise performance. J Int Soc Sports
Nutr. 2021 Sep 9;18(1):61.
6. Marcus A, Rossi A, Cornwell A, Hawkins
SA, Khodiguian N. The effects of a novel
bicarbonate loading protocol on serum
bicarbonate concentration: a randomized
controlled trial. J Int Soc Sports Nutr. 2019
Sep 18;16(1):41.
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Study Reviewed: Frequent Manipulation of Resistance Training Variables Promotes
Myofibrillar Spacing Changes in Resistance-Trained Individuals. Fox et al. (2021)
Is Sarcoplasmic Hypertrophy Caused by Frequently
Manipulating Training Variables?
BY GREG NUCKOLS
Sarcoplasmic hypertrophy is a topic we’ve
discussed a couple of times in MASS, including in a research brief from last month (2).
I’m revisiting the subject again this month
for a couple of reasons. First, I simply find
it to be a fascinating topic – it’s something
people have discussed for years, and there
have been a handful of studies on the subject
over the past few decades, but we’ve finally
reached the point where new research on the
subject is published semi-frequently. Second,
the study I’m discussing this month (1) is the
first to actually investigate whether specific
training manipulations impact sarcoplasmic
hypertrophy. For years, people have proposed that heavier, lower-rep training causes
myofibrillar hypertrophy, and lighter, higher-rep training causes sarcoplasmic hypertrophy. However, as far as I can tell, that little
nugget of gym wisdom was never based on
any direct evidence.
Just to rewind a bit, it’s worth first operationally defining sarcoplasmic hypertrophy.
When muscle fibers increase in size, that’s
referred to as hypertrophy. Muscle fibers are
filled with structures called myofibrils (which
contain contractile proteins – actin and myosin), and the myofibrils are surrounded by
“other stuff.” That “other stuff” (intracellular
fluid, organelles, etc.) is referred to as sarcoplasm. When fibers undergo hypertrophy, the
absolute volume of the myofibrils generally
increases (due to increases in size, number,
or both), as does the absolute volume of sarcoplasm. When the myofibril-to-sarcoplasm
ratio remains constant or increases, that’s referred to as myofibrillar hypertrophy; when
the myofibril-to-sarcoplasm ratio decreases
(i.e., sarcoplasmic volume increases to a relatively greater degree than myofibrillar volume), that’s referred to as sarcoplasmic hypertrophy.
The present study by Fox and colleagues
didn’t investigate the standard “high reps
cause sarcoplasmic hypertrophy and low reps
cause myofibrillar hypertrophy” claim, but it
did investigate the impact of manipulating
training variables on sarcoplasmic hypertrophy (1). Using a within-subject unilateral design, a group of 20 trained subjects (averaging
2.5 years of training experience) completed
an eight-week training intervention. One leg
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performed 8 sets of 9-12 reps of quad training (4 sets of unilateral leg press and 4 sets
of unilateral knee extensions) each session,
with two minutes of rest between sets. This
was considered the “consistent training” condition. For the other leg (in the “varied training” condition), one training variable was altered in each training session relative to the
“consistent training” protocol, including reps
and load (sets of 25-30 reps instead of 9-12
reps), rest intervals (4 minute rest intervals
instead of 2 minute rest intervals), muscle action (overloaded eccentric-only reps instead
of standard reps consisting of both eccentric
and concentric muscle actions), and set volume (12 total sets instead of 8). Of note, the
presently reviewed study is a secondary analysis of data collected for a study we previously reviewed in MASS (3); the training protocol is discussed further in that MASS article.
Muscle biopsies of the vastus lateralis were
performed 48 hours before the start of the
training period, and 96 hours after the final
training session. The researchers assessed
more outcomes than I could discuss in a research brief (though the paper is open-access
if you’d like to read it), but there are three
sets of outcomes that primarily interest me:
changes in myofibril and non-myofibril area
within the muscle fibers, relative changes in
myofibril and non-myofibril areas, and the
relationship between pre-training myofibril
area and training-induced changes in myofibril area.
Both training protocols produced similar hypertrophy: changes in vastus lateralis
cross-sectional area were similar between
protocols (as discussed in the prior MASS
article), and changes in fiber cross-section
area were also similar between protocols
(+14.6% for the consistent training protocol
and +13.9% for the varied training protocol). However, following consistent training,
the relative myofibril area and non-myofibril area within the muscle fibers were unchanged, whereas the relative myofibril area
decreased and the non-myofibril area increased following varied training (i.e., sarco-
113
ing training (r = -0.714; p = 0.006). In other
words, subjects with a greater myofibrillar
density pre-training experienced a larger decrease in myofibrillar density following varied training.
plasmic hypertrophy occurred). Direct comparisons between the protocols tell a similar
story: the relative changes in myofibril and
non-myofibril area tended to be different between protocols (p = 0.051), and the ratio of
myofibril area/non-myofibril area changes
significantly differed between protocols (p
= 0.048). Finally, within the varied training
protocol, there was a pretty strong negative
association between pre-training myofibril
area and the change in myofibril area follow-
This was a very interesting study, but we need
to interpret the results tentatively. For starters, the between-protocol differences were
small and teetered right around the edge of
statistical significance (p = 0.04-0.051). Furthermore, as I discussed in my last research
brief on sarcoplasmic hypertrophy, there was
tremendous inter-individual variability, so
we certainly can’t chalk all of the changes in
myofibrillar density up to differences in the
training protocols. However, this is the first
study to suggest that differences in training
protocols may influence the likelihood and
magnitude of sarcoplasmic hypertrophy,
which opens the door to an obvious follow-up
question: what sorts of training protocols
have the largest impact on sarcoplasmic versus myofibrillar hypertrophy? Will we find
that the bros were right all along, and that
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high-rep training predominantly causes sarcoplasmic hypertrophy? Only time will tell. I
hope we see some research on the topic soon.
Moving on, I found the negative association between pre-training myofibril area and
changes in myofibiril area (following varied
training) to be pretty intuitive, but still interesting (4). Human muscle fibers tend to contain around 70% myofibrils by volume, with a
typical range spanning from about 60%-80%
of fiber volume. Following a training protocol that resulted in some degree of sarcoplasmic hypertrophy on average, the changes in
myofibrillar density tended to push subjects
toward the low end of the typical range of myofibrillar density. In other words, if a subject
already had relatively low myofibrillar density
(~60% of muscle volume), they tended to not
experience much of a change in myofibrillar
density; however, if a subject had relatively
high myofibrillar density pre-training (>70%
of muscle volume), they tended to experience
a decrease in myofibrillar density that would
bring them closer to a myofibrillar density of
60-70% of muscle fiber volume. That leads
me to suspect that sarcoplasmic hypertrophy (and its inverse: myofibrillar packing)
might be a constrained phenomenon. In other
words, there may be regulatory mechanisms
that keep myofibrillar density from getting
too far below 60% of muscle volume; I’d also
speculate that there may be mechanisms that
keep myofibrillar density from getting too far
above 80% of muscle volume. I suspect that
those mechanisms would be related to bioenergetics (if myofibrils are too spaced out, that
could make oxygen delivery less efficient; if
myofibrils are too densely packed, the rate at
which they could burn through ATP might
outstrip the capacity for ATP replenishment
to too large of an extent, since the density of
mitochondria and proteins involved in anaerobic metabolism would likely be lower), but
we’d need more research to know for sure.
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References
1. Fox CD, Mesquita PHC, Godwin JS,
Angleri V, Damas F, Ruple BA, Sexton
CL, Brown MD, Kavazis AN, Young
KC, Ugrinowitsch C, Libardi CA and
Roberts MD. Frequent Manipulation of
Resistance Training Variables Promotes
Myofibrillar Spacing Changes in
Resistance-Trained Individuals. Front.
Physiol. 2021 12:773995. doi: 10.3389/
fphys.2021.773995
2. Ruple BA, Godwin JS, Mesquita PHC,
Osburn SC, Sexton CL, Smith MA,
Ogletree JC, Goodlett MD, Edison JL,
Ferrando AA, Fruge AD, Kavazis AN,
Young KC, Roberts MD. Myofibril and
Mitochondrial Area Changes in Type I and
II Fibers Following 10 Weeks of Resistance
Training in Previously Untrained Men.
Front Physiol. 2021 Sep 24;12:728683.
doi: 10.3389/fphys.2021.728683. PMID:
34630147; PMCID: PMC8497692.
3. 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.
4. In the interest of full disclosure, I also
suspect that some portion of the association
may simply be due to regression to the
mean. However, it’s hard to know whether
my suspicion is correct, and if it is, it’s hard
to know the degree to which regression
to the mean influenced the strength of the
association.
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Study Reviewed: Impact of Varying Doses of Omega-3 Supplementation on Muscle Damage
and Recovery After Eccentric Resistance Exercise. Visconti et al. (2021)
Revisiting Fish Oil Supplementation for Recovery
BY ERIC TREXLER
Back in Volume 4 of MASS, I covered a
study by VanDusseldorp and colleagues that
investigated the impact of fish oil supplementation on indices of recovery from eccentric exercise (2). After 7.5 weeks of supplementation with 2g, 4g, or 6g/day of fish
oil (providing 1400, 2800, and 4200 mg of
combined EPA + DHA, which are the key
fatty acids in fish oil), the group consuming
6g/day generally had more favorable recovery than the other groups in terms of vertical jump, soreness, and blood biomarkers of
muscle damage. It might have been tempting
to conclude that there’s obviously a dose-related threshold, and that fish oil “works” for
recovery as long as you’re consuming >4g/
day. However, when comparing the results
to other literature in the area, this proposed
threshold just didn’t seem to hold up. Findings were generally quite mixed and dosage
just didn’t seem to be particularly predictive
of study outcomes, but the study designs
were so different from one another that direct
comparisons were challenging to interpret.
Even looking within the study itself, results
weren’t particularly consistent from outcome
to outcome, and comparisons between 0g/
day, 2g/day, and 4g/day groups did not reveal
much of a dose-response pattern. I tentatively
concluded that, while there are more than a
few studies linking fish oil to enhanced recovery, we’d need more research to confirm
fish oil’s effectiveness and determine optimal
dosing strategies for the purpose of facilitating recovery.
Fortunately, the presently reviewed study (1)
sought to investigate this potential dose-response relationship between fish oil and recovery from eccentric exercise, with a particular emphasis on high-dose supplementation.
Young (23 ± 4 years old), resistance-trained
males supplemented with 0g/day (placebo;
n = 9), 6g/day (n = 10), or 8g/day (n = 7)
of fish oil for 33 days. Each capsule provided 1g of fish oil, which included 200mg of
EPA and 100mg of DHA. On day 30 of supplementation, participants completed a muscle-damaging eccentric resistance exercise
bout consisting of 10 sets of 8 Smith machine
squats using 70% of 1RM with 4-second eccentrics, followed by 5 sets of 20 bodyweight
split jump-squats (10 per leg in each set).
Outcomes of interest included vertical jump
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height, perceived muscle soreness, hip and
knee range of motion, serum creatine kinase
levels, and Smith machine squat repetitions
to fatigue at 70% of 1RM. These outcomes
were measured before the muscle-damaging exercise bout and repeated one, two, and
three days after, with one exception: due to
the fatiguing nature of squat reps to fatigue
testing, squat performance was only assessed
during a familiarization session (about a week
before the eccentric exercise protocol) and
three days after the eccentric exercise protocol. Supplementation continued throughout
the entire three-day recovery period.
In short, the design of this study was extremely similar to the previously reviewed study
demonstrating some recovery benefits from
6g/day of fish oil supplementation. However,
the results were quite different. The eccentric
exercise bout led to significant changes in
vertical jump height, perceived muscle soreness, and serum creatine kinase levels, but
neither 6g/day nor 8g/day of fish oil supplementation significantly impacted any mea-
sured outcome when compared to a placebo.
Vertical jump height and squat reps to fatigue
results are presented in Figure 1 and Figure
2, respectively. You could potentially argue
that the pattern of mean values for vertical
jump height appears to support a beneficial
effect of supplementation; unfortunately, the
exact p-value for the group × time interaction
effect was not presented, so it’s hard to say
how close this finding was to the statistical
significance threshold. Nonetheless, vertical
jump height was the only outcome with this
type of visual pattern. For all others, figures
did not suggest that fish oil was doing anything to get excited about.
When we look at this paper and the previously reviewed paper, we appear to have contradictory results. However, it’s not quite that
simple. The paper by VanDusseldorp and
colleagues used a similar battery of tests and
outcomes, but had a longer duration of supplementation (7.5 weeks versus 33 days). In
addition, the two studies used different fish
oil products with different concentrations of
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the active ingredients (EPA and DHA); while
6g of the product in the previously reviewed
study yielded 4200mg of combined EPA +
DHA, the 6g dose in the presently reviewed
study yielded only 2400mg. If you recall my
previous article on this topic, this is the point
in the interpretation where I would normally begin floundering, grasping at hypotheses,
and trying to make sense of this body of literature. However, I’m older and wiser now.
And fortunately for me, it seems I was not
alone in my inability to draw clear and concise conclusions from this research.
In a recent systematic review by Anthony et
al (3), the authors state: “The initial purpose
of this review was to investigate the role of
[long-chain omega-3 polyunsaturated fatty acids] on eccentric exercise induced [delayed-onset muscle soreness] and markers
of inflammation” (to be clear, when they say
long-chain omega-3 polyunsaturated fatty
acids, they’re referring to EPA and DHA).
The key word in that quote is initial. After
getting acquainted with the available literature, the systematic review took a hard pivot,
and they focused more on the inconsistencies
and methodological shortcomings in this area
of research. To be clear, the purpose was not
to be overly critical of the researchers who
have laid the groundwork for this topic, or to
be disrespectful or unappreciative of their efforts and contributions. However, as research
progresses and we start to see patterns in a
body of literature, we can sometimes retroactively identify some blind spots or oversights
that ought to be addressed in future studies.
In their systematic review (3), which can be
downloaded here, Anthony et al highlight-
ed several noteworthy considerations about
the research assessing fish oil’s impacts on
delayed-onset muscle soreness, which are
equally applicable to other aspects of acute
recovery. The research in this area generally
scores poorly on risk of bias and study quality
assessments, has a great deal of variability in
terms of supplementation protocols (such as
duration of supplementation and EPA/DHA
content of fish oil formulations), and has tremendous variability in terms of the exercise
protocols utilized to induce muscle damage.
In addition, many studies fail to exclude participants who consume large amounts of fish
containing EPA and DHA, fail to exclude
participants with high tissue concentrations
of EPA and DHA at baseline, fail to provide
supplements for a long enough time frame to
meaningfully impact tissue EPA and DHA
levels, and fail to quantify the change (from
baseline to post-testing) in tissue EPA and
DHA levels. So, several studies have reported positive effects of fish oil on acute recovery from muscle-damaging exercise (4), but
findings are somewhat mixed, and we simply don’t have the type of data required to
make confident recommendations related to
the context-dependent effect sizes we should
realistically expect, or the optimal dose required to facilitate recovery.
So, we’ve learned more about this subject
since the last time we covered it, but my
general conclusions remain effectively unchanged. Getting at least 0.3-0.5g/day of
combined EPA + DHA appears to be a positive thing for many different outcomes related to health and wellness. It’s still unclear if
higher doses will lead to tangible benefits re-
119
lated to lifting, and if they do, we don’t have
strong evidence for establishing exact dosing recommendations at this time. However,
1-3g/day of combined EPA + DHA seems to
be pretty sufficient for most purposes, and
still falls within ranges that even the most
conservative guidelines would consider safe
for a healthy adult.
References
1. Visconti LM, Cotter JA, Schick EE,
Daniels N, Viray FE, Purcell CA, et al.
Impact of varying doses of omega-3
supplementation on muscle damage and
recovery after eccentric resistance exercise.
Metab Open. 2021 Dec;12:100133.
2. VanDusseldorp TA, Escobar KA, Johnson
KE, Stratton MT, Moriarty T, Kerksick
CM, et al. Impact of Varying Dosages
of Fish Oil on Recovery and Soreness
Following Eccentric Exercise. Nutrients.
2020 Jul 27;12(8).
3. Anthony R, Macartney MJ, Peoples GE.
The Influence of Long-Chain Omega-3
Fatty Acids on Eccentric Exercise-Induced
Delayed Muscle Soreness: Reported
Outcomes Are Compromised by Study
Design Issues. Int J Sport Nutr Exerc
Metab. 2021 Jan 20;31(2):143–53.
4. Lewis NA, Daniels D, Calder PC, Castell
LM, Pedlar CR. Are There Benefits
from the Use of Fish Oil Supplements in
Athletes? A Systematic Review. Adv Nutr.
2020 Sep;11(5):1300.
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Study Reviewed: Extended Sleep Maintains Endurance Performance Better than Normal or
Restricted Sleep. Roberts et al. (2019)
The Progressive Effects of Sleep Restriction and
Extension
BY GREG NUCKOLS
We’ve discussed sleep research several times
in MASS, but a lot of the literature about the
impact of sleep duration on physical performance is plagued by a few common issues.
First, controlled studies on the effects of
sleep duration tend to use interventions lasting a single night – in other words, they generally just look at how one night of reduced
sleep affects performance. Second, studies
with longer durations tend to be observational in nature. In other words, they examine
differences between individuals who typically sleep more versus individuals who sleep
less, but we can’t be sure that sleep-related
associations are caused by sleep differences
– people who sleep less may simply systematically differ from people who sleep more in
a variety of ways. Third, much of the sleep
duration research focuses on the negative impacts of sleeping less, but very little research
investigates the potential benefits of sleeping more than normal. A systematic review
(which we covered in MASS) has suggested
that sleep extension might be the most effective sleep-related intervention for athletes,
but the body of sleep extension literature is
still quite small (2).
With that in mind, a 2019 study by Roberts
and colleagues offers valuable insight into
the effects of sleep restriction and extension
on performance (1). Research briefs generally cover studies that are hot off the press, but
I’m covering a 2019 study for a good reason.
I intended to cover a new study from the same
research group (3), but I noticed that it presented secondary analyses of an already-published experiment. The original study (which
I’m reviewing here) was more focused on
performance outcomes, so I decided that it
made more sense to center the discussion on
the 2019 paper.
In this study, nine endurance-trained athletes (average VO2max = 63 ± 6 ml/kg/min)
completed a crossover study consisting of
three conditions – a normal sleep condition,
a sleep restriction condition, and a sleep extension condition. In all conditions, subjects
completed a cycling time trial every day for
four days. The individualized workload completed during the time trials was the equivalent of cycling for one hour at each subject’s
anaerobic threshold. Before each time trial,
subjects completed a psychomotor vigilance
121
task (which assessed reaction times to a visual stimulus) and filled out a Profile of Mood
States questionnaire.
Before the start of the intervention, subjects’
habitual sleep habits were monitored for four
nights to establish a baseline. During the normal sleep condition, subjects were instructed to spend a typical amount of time in bed.
During the sleep restriction and sleep extension conditions, subjects were instructed
to decrease or increase their time in bed by
30%. Subjects wore accelerometers to verify that they were spending the appropriate
amount of time in bed during each condition.
The researchers also used accelerometry data
to calculate total sleep time (i.e. how long the
subjects actually slept, independent of how
long they spent in bed) and sleep efficiency
(the proportion of time in bed spent sleeping). Subjects also reported their subjective
sleep quality each day using a 5-point Likert
scale (1 = very good sleep and 5 = very poor
sleep). An overview of the study protocol can
be seen in Table 1.
As intended, subjects slept the most during
the sleep extension condition (averaging 8.28.6 hours per night), followed by the normal
sleep condition (averaging about 7 hours per
night), followed by the sleep restriction condition (averaging 4.7-4.9 hours per night).
Furthermore, while sleep efficiency was a bit
lower at some time points in the sleep extension condition (indicating that subjects spent
a slightly higher proportion of their night
awake in bed), differences between conditions weren’t particularly large. Reported
sleep quality also didn’t substantially differ
between conditions.
Time trial performance didn’t change much
across the four consecutive testing days
during the normal sleep and sleep extension
conditions. However, time trial performance
got progressively worse in the sleep restriction condition. During the first day of testing in the sleep restriction condition, subjects
completed the time trial in 57.6 minutes on
average; by the fourth day of testing (i.e. following three nights of reduced sleep), it took
122
them an average of 62.0 minutes to complete the same workload. Furthermore, by the
fourth day of testing, subjects completed the
time trial significantly faster in the sleep extension condition than either the normal sleep
condition or the sleep restriction condition.
Data related to mood disturbances and psychomotor vigilance both suggest that subjects were becoming progressively fatigued
over time in the normal sleep and sleep restriction conditions, even though time trial
performance wasn’t negatively affected in
the normal sleep condition. Total mood disturbance tended to increase (though the effect
wasn’t statistically significant in the normal
sleep condition), vigor tended to decrease
(again, not statistically significant in the normal sleep condition), reported fatigue significantly increased in both conditions, and
mean response time during the psychomotor
vigilance task significantly increased in both
conditions. Conversely, in the sleep extension
condition, total mood disturbance and vigor
didn’t meaningfully change, the increase in
fatigue tended to be smaller (2 points, versus
5 points in the normal sleep condition and 10
points in the sleep restriction condition), and
mean response time during the psychomotor
vigilance task significantly decreased (which
is a positive outcome).
Overall, these results paint a positive picture
for sleep extension. Most of the very positive
results in favor of sleep extension come from
studies on high-level collegiate athletes (4,
5). I’ve been somewhat concerned that those
results wouldn’t generalize to other populations, for a couple of reasons. First, I was
concerned that sleep extension might only
123
have a positive effect in athletes who were
actively engaged in super strenuous training.
I’m certainly not saying that an hour-long
time trial is easy, but I imagine it’s a much
smaller workload than Division I collegiate
swimmers are dealing with. Second, I’ve
been somewhat concerned that the effects of
sleep extension might only be present in athletes who are quite young. The subjects in the
present study weren’t old by any means (30
± 6 years old), but sleep duration and quality tend to decrease throughout the lifespan
(6), so I wondered if attempts at sleep extension for people who weren’t quite young (i.e.,
collegiate athletes or younger) would simply
result in steep declines in sleep efficiency,
negligible changes in total sleep time, and
no net ergogenic effect. So, this study reassures me that the benefits of sleep extension
for athletes are probably fairly generalizable.
With that being said, the performance-related
effects in the present study were smaller than
those observed in prior sleep extension studies. More research is needed to better understand who can benefit from sleep extension
(at least for performance-related outcomes),
and the degree to which sleep extension is
likely to improve their performance.
It’s also worth noting that the largest differences between the normal sleep and sleep extension conditions were related to psychomotor vigilance and mood states. In other words,
sleep extension may have only had a small
positive effect on maintaining (or improving)
performance, but it had larger positive effects on reaction times (and therefore general
mental acuity, I suspect) and how the athletes
generally felt. For my money, as someone
who’s not a professional athlete, those benefits would probably be the ones I cared about
124
the most in day-to-day life. If you spend a bit
more time in bed, you’ll probably perform a
bit better, but you’ll probably feel noticeably
better, even if you’re already getting the recommended seven hours of sleep per night.
References
1. Roberts SSH, Teo WP, Aisbett B,
Warmington SA. Extended Sleep
Maintains Endurance Performance Better
than Normal or Restricted Sleep. Med Sci
Sports Exerc. 2019 Dec;51(12):2516-2523.
doi: 10.1249/MSS.0000000000002071.
PMID: 31246714.
2. Bonnar D, Bartel K, Kakoschke N, Lang C.
Sleep Interventions Designed to Improve
Athletic Performance and Recovery: A
Systematic Review of Current Approaches.
Sports Med. 2018 Mar;48(3):683-703.
doi: 10.1007/s40279-017-0832-x. PMID:
29352373.
3. Roberts SSH, Aisbett B, Teo WP,
Warmington S. Monitoring Effects of Sleep
Extension and Restriction on Endurance
Performance Using Heart Rate Indices.
J Strength Cond Res. 2021 Oct 27. doi:
10.1519/JSC.0000000000004157. Epub
ahead of print. PMID: 34711770.
4. Mah CD, Mah KE, Kezirian EJ, Dement
WC. The effects of sleep extension on
the athletic performance of collegiate
basketball players. Sleep. 2011 Jul
1;34(7):943-50. doi: 10.5665/SLEEP.1132.
PMID: 21731144; PMCID: PMC3119836.
5. Schwartz J, Simon RD Jr. Sleep extension
improves serving accuracy: A study with
college varsity tennis players. Physiol
Behav. 2015 Nov 1;151:541-4. doi:
10.1016/j.physbeh.2015.08.035. Epub 2015
Sep 1. PMID: 26325012.
6. Li J, Vitiello MV, Gooneratne NS.
Sleep in Normal Aging. Sleep Med Clin.
2018 Mar;13(1):1-11. doi: 10.1016/j.
jsmc.2017.09.001. Epub 2017 Nov 21.
PMID: 29412976; PMCID: PMC5841578.
125
Study Reviewed: Acute and Chronic Citrulline Malate Supplementation on Muscle
Contractile Properties and Fatigue Rate of the Quadriceps. Fick et al. (2021)
Citrulline Is Promising, But We Have a Lot to Learn
BY ERIC TREXLER
As a primary focal point of my dissertation
research, citrulline supplementation truly
captures my interest. There aren’t that many
dietary supplements that are safe, affordable,
and effective for lifters, and citrulline is one
of the newer supplements with the potential
to check all three boxes. Back in 2019, I published a meta-analysis of studies evaluating
the impact of acute citrulline supplementation on strength and power outcomes (2).
At that time, I wasn’t yet part of the MASS
team, so Greg actually reviewed the paper in
MASS. In short, the analysis indicated that
citrulline had a small but statistically significant positive effect on strength and power
outcomes, which seemed to be largely driven by improvements in strength endurance.
I was pleased to see that these results were
more or less replicated in a well-conducted
meta-analysis by Vårvik et al in 2021 (3).
With confirmation from two independent
meta-analyses, you might be inclined to conclude that citrulline unequivocally works, and
that the case is fully settled. However, the meta-analysis by Vårvik et al sought to answer
a slightly different question and used a dis-
tinct analytical approach, but largely utilized
the same underlying literature. It did indicate
that the conclusions from my paper “held
up” when a different analytical approach was
taken, which is nice, but we can’t have double-confidence in this literature just because it
was analyzed twice. In my meta-analysis (2),
I was very adamant about reinforcing the preliminary nature of the results, and I included
the following cautionary note: “The current
analysis identified a statistically significant
effect favoring citrulline supplementation
over placebo, but it should be noted that the
95% confidence interval of this [effect size]
ranges from 0.01 to 0.39. As more literature
becomes available, this point estimate may
change in magnitude and precision, and even
a small shift toward the null could reverse the
statistical decision to reject the null hypothesis.” After all, the body of citrulline research
is pretty small, and the first studies on a given topic often tend to report inflated effect
sizes for reasons that are not intentional or
nefarious (4). As such, it wouldn’t be unusual
to see the pooled effect size trend lower over
time, until ultimately settling near what could
be considered a “true” estimate (5). Without
126
question, more literature was needed to solidify our understanding of citrulline supplementation, and more literature was certainly
on the way.
The presently reviewed study (1) examined
the impact of acute (single-dose) and chronic
(seven days) citrulline malate supplementation on contractile properties of the quadriceps muscles. This was a crossover trial, in
which 18 resistance trained males completed both acute and chronic supplementation
with citrulline malate (8g/day) prior to exercise testing. The testing protocol involved a
30-minute cycling bout at 50-65% of maxi-
mal power output achieved during an initial
graded cycling test, followed by the Thorstensson isokinetic leg extension test. This
leg extension test involves completing a set
of 50 isokinetic leg extensions at a fixed angular velocity of 180 degrees per second.
Ultimately, supplementation did not significantly impact fatigue rate, heart rate, peak
power (Figure 1), or peak torque (Figure 2)
during the leg extension test.
Some might accuse me of being excessively
optimistic about citrulline, but I don’t really
see it that way. I’ve published experimental
data showing null effects (6), and cautioned
127
against excessive enthusiasm in my meta-analysis that identified statistically significant benefits (2). Nonetheless, I might catch
some flak for even leaving the door open for
an optimistic assessment – assuming a default
position of extreme skepticism (bordering on
pessimism) is understandable and defensible
when you’re talking about dietary supplements, as the optimistic early-adopter gets
burned way, way more often than they get
vindicated. If you’re a big citrulline skeptic,
you might see these findings as justification
for the conclusion that citrulline is overhyped
and doesn’t do anything. I personally find it
hard to get on board with that perspective,
given that a couple of meta-analyses have reported small but statistically significant benefits for strength endurance outcomes. Alternatively, you might concede that citrulline does
something, sometimes, but that the effect size
isn’t large enough or consistent enough to
capture your interest or inspire enthusiasm. I
don’t adopt that particular perspective, given
that the citrulline literature doesn’t seem to
have an extreme degree of heterogeneity or
evidence of small-study effects (including,
but not limited to, publication bias).
At the risk of sounding too defensive of citrulline, I do want to highlight a couple of
methodological considerations that could
have potentially influenced the outcomes
of the presently reviewed study. First of all,
much of the citrulline research documenting
attenuated fatigue and enhanced strength endurance has used a testing approach involving
multiple strenuous sets of moderate-to-high
volume resistance exercise, which is reminiscent of how many lifters train (particularly
for lifters who train for hypertrophy or general fitness rather than sport-specific training
for powerlifting or weightlifting). Fatigue is
complex and multifactorial, and Dr. Zourdos
has previously discussed the fact that doing
a cardio session immediately before a lifting session can acutely impair lifting performance by inducing neuromuscular fatigue.
So, the results of this study might differ from
previous citrulline studies because it induced
a distinct type of fatigue through the inclusion of preceding cardio and the use of only
a single, prolonged set of exercise. Observations from the dietary nitrate research also
suggest that nitric oxide precursors may be
impacting performance through direct effects
on the contractile function of muscle (7), but
these effects appear to be most pronounced in
exercise tasks that involve very high contraction velocities, unconstrained contraction velocities, or fatiguing protocols that are more
reminiscent of high-volume resistance training than cardio.
So, wrapping things up – where are we at
with citrulline?
At this point, there is a small amount of literature that, in totality, points toward small
but positive effects on strength and power
outcomes (2), particularly in the context of
strength endurance (3). For this reason, I still
consider it a “second tier” supplement for
lifters – generally supported by evidence,
but not on par with a universally embraced
supplement like creatine. From a mechanistic
perspective, my null hypothesis is that chronic supplementation should be as effective (or
more effective) than acute supplementation
(8), but more evidence is needed to corrobo-
128
rate that speculation. As this body of research
grows, we may see that initial effect size estimates (around 0.2) were pretty accurate,
and citrulline does indeed have a small but
significant effect for lifters. Alternatively, we
may find that the effect size settles down to
an even smaller but still non-zero effect size,
which may lead to a slightly more complicated cost-benefit analysis for consumers. It’s
also possible that the effect size will drift further and further toward zero, with the eventual conclusion that the juice simply isn’t worth
the squeeze.
develops, we should resist the urge to let our
conclusions swing too wildly from study to
study. A mixture of null findings and positive
findings should be expected for any dietary
supplement – even our beloved creatine.
Of those three options, I suspect that #2 is
most likely; my educated guess is that we’ll
settle on the eventual conclusion that citrulline does something, but I would predict that
a meta-analysis about 10-15 years from now
will report a slightly smaller effect size than
initial estimates. If a supplement has a pooled
effect size between 0.1-0.2, the utility of the
supplement is totally open for debate. Some
might view it as worthwhile relative to the
cost and potential downsides, while others
will feel the opposite. Digging deeper, we
may find that the effects of citrulline are
more pronounced in very particular circumstances based on the characteristics of an individual’s training style, which will lead to
a more nuanced and individualized cost-benefit analysis. For now, I still view citrulline
as a defensible supplement with low cost
(relative to other popular supplements), minimal downsides, and potential for modest but
practically meaningful upsides, depending
on the way you train. It’s tempting to bail on
a supplement when the newest study shows
no clear benefit, but as any body of literature
129
References
1. Fick AN, Kowalsky RJ, Stone MS, Hearon
CM, Farney TM. Acute and Chronic
Citrulline Malate Supplementation on
Muscle Contractile Properties and Fatigue
Rate of the Quadriceps. Int J Sport Nutr
Exerc Metab. 2021 Sep 1;31(6):490–6.
2. Trexler ET, Persky AM, Ryan ED,
Schwartz TA, Stoner L, SmithRyan AE. Acute Effects of Citrulline
Supplementation on High-Intensity
Strength and Power Performance: A
Systematic Review and Meta-Analysis.
Sports Med. 2019 May;49(5):707–18.
7. Coggan AR, Peterson LR. Dietary Nitrate
Enhances the Contractile Properties of
Human Skeletal Muscle. Exerc Sport Sci
Rev. 2018 Oct;46(4):254–61.
8. Gonzalez AM, Trexler ET. Effects of
Citrulline Supplementation on Exercise
Performance in Humans: A Review of the
Current Literature. J Strength Cond Res.
2020 May;34(5):1480–95.
3. Vårvik FT, Bjørnsen T, Gonzalez AM.
Acute Effect of Citrulline Malate on
Repetition Performance During Strength
Training: A Systematic Review and MetaAnalysis. Int J Sport Nutr Exerc Metab.
2021 May 19;31(4):350–8.
4. van Zwet EW, Cator EA. The significance
filter, the winner’s curse and the need to
shrink. Stat Neerlandica. 2021;75(4):437–
52.
5. Molendijk ML, Bus BAA, Spinhoven
P, Kaimatzoglou A, Oude Voshaar RC,
Penninx BWJH, et al. A systematic
review and meta-analysis on the
association between BDNF val(66)met
and hippocampal volume--a genuine
effect or a winners curse? Am J Med
Genet Part B Neuropsychiatr Genet. 2012
Sep;159B(6):731–40.
6. Trexler ET, Keith DS, Schwartz TA, Ryan
ED, Stoner L, Persky AM, et al. Effects
of Citrulline Malate and Beetroot Juice
Supplementation on Blood Flow, Energy
Metabolism, and Performance During
Maximum Effort Leg Extension Exercise. J
Strength Cond Res. 2019 Sep;33(9):2321–
9.
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Study Reviewed: Sleep Interventions for Performance, Mood and Sleep Outcomes in
Athletes: A Systematic Review and Meta-Analysis. Gwyther et al. (2021)
If You Don’t Snooze, You Lose
BY ERIC TREXLER
Sleep is a very popular topic in MASS, which
makes plenty of sense – we all sleep, we all
establish and reinforce our own sleep-related
habits and behaviors (intentionally or unintentionally), and we’re all regularly making
decisions about when we should go to sleep
and when we should wake up. In other words,
it’s a daily health behavior that is directly under our control and inherently modifiable.
In addition, it seems to have a pretty big impact on life outside of the gym. When we’re
underslept, it tends to negatively impact our
mood, our ability to perform cognitive tasks,
and our subjective quality of life. Given the
dramatic impact that sleep deprivation has
outside of the gym, it’s natural to intuitively assume that sleep also has a major impact
on outcomes that are directly related to our
fitness goals. Nonetheless, intuition has led
humans astray before, so it’s always good
to verify our assumptions with scientific research.
In previous MASS articles, we’ve established that acute sleep deprivation can impair performance, particularly in the context of high-skill or high-volume work, and
that the cumulative effects of chronic sleep
insufficiency are generally more impactful
than a single night of bad sleep. We’ve also
seen that there’s a little bit of research indicating that sleep extension interventions can
promote sport performance, that longer sleep
duration is associated with better athletic performance, and that napping can acutely improve physical performance. Most recently,
Greg wrote a research brief about a study
indicating that getting at least eight hours of
sleep was associated with better outcomes
related to mood ratings, sleep quality, perceived energy levels, muscle soreness, and
perceived training quality in college athletes.
The presently reviewed meta-analysis (1) expands upon these observations by evaluating
the effects of actually implementing sleep interventions in competitive athletes.
The researchers systematically searched the
literature for studies that reported changes
in performance, sleep, and mood outcomes
in competitive athletes completing sleep interventions. For some additional context,
the types of interventions generally included things like sleep hygiene education, sleep
131
extension, assisted sleep (using specialized
garments or technologies to facilitate sleep),
sleep recovery (that is, recovery-focused
strategies involving things like cryotherapy or
compression garments), or a combination of
interventions. It’s also important to point out
that the studies generally were not intended
to target athletes with sleep problems or disorders. In other words, the studies were about
trying to increase sleep quality and quantity in
athletes, but not about rectifying severe sleep
issues. The researchers ultimately found 27
articles that met their inclusion criteria. They
provided a qualitative, narrative-style assessment of this literature, and followed that with
a quantitative meta-analysis. However, in order to jam a bunch of different studies into a
single statistical analysis, the studies have to
meet a number of pretty strict criteria – otherwise, you’d be mathematically combining
results from very dissimilar studies with very
dissimilar data, which would be inadvisable.
So, while the qualitative review included 27
studies, the quantitative meta-analysis contained only 12. Results were reported as the
pooled effect size (ES; in this case, Hedges’
g, which is similar to Cohen’s d) and 95%
confidence interval (95%CI).
Looking at the quantitative results first, the
researchers found that sleep interventions
improved subjective sleep quality (g = 0.62,
95%CI [0.21, 1.02]) while reducing sleepiness
(g = 0.81, 95%CI [0.32, 1.30]) and negative
affect (g = 0.63, 95% CI[0.27, 0.98]). However, interventions didn’t have a statistically sig-
132
nificant impact on subjective sleep duration,
objective sleep measures, or outcomes related
to aerobic performance (Table 1) or anaerobic
performance (Table 2). Having said that, null
findings should be taken with a grain of salt;
this was a fairly small meta-analysis that included some very different types of interventions, so statistical significance was going to
be an uphill battle from the start. Looking at
Tables 1 and 2, it’s interesting to note that all
interventions had a neutral or positive effect
on performance, with pooled effect sizes in the
0.146-0.228 range. In other words, these effect
sizes were on par with some of the most efficacious dietary supplements out there, despite the
lack of statistical significance. In terms of the
qualitative review with an expanded pool of
studies, the researchers concluded that “sleep
hygiene, assisted sleep, and sleep extension interventions may be associated with improved
sleep, performance, and mood outcomes.”
In summary, this study largely confirms what
many of us likely expected: increasing your
emphasis on high-quality sleep habits generally makes you feel a little better, and generally has a modest but positive impact on
exercise performance. Nonetheless, I wanted
to highlight this research in the Briefs section
because it relates to a fairly common question. I often hear from people who indicate
that their sleep is “fine” (which usually just
means they don’t get woken up throughout
the night on a regular basis or suspect that they
have a clinically relevant sleep disorder), but
still wonder if they might benefit from an increased emphasis on their sleep habits. There
are definitely plenty of gaps in this area of
research (longitudinal sleep research is very
hard to do), but the totality of the evidence
seems to suggest that going from terribly insufficient sleep to adequate sleep has a pretty big impact on a wide range of important
outcomes, and that small benefits are still observed when people with “fine” sleep aim to
get slightly more or slightly better sleep.
Intuitively, one would assume that the magnitude of benefits begins to diminish as greater
amounts of high-quality sleep are achieved,
and there’s probably a practical limit whereby
further sleep is hard to achieve for physiological reasons (surely we’ve all unsuccessfully tried to sleep when we simply aren’t tired
enough to do it) or practical reasons (few humans have a hibernation-friendly schedule).
Nonetheless, there are plausible mechanisms
by which sleep could facilitate recovery (2),
observational studies linking better sleep to
better performance (and quality of life) outcomes (3), and studies linking sleep-related
interventions to positive outcomes in athletes
(1, 4) and non-athletes (5) without substantial
sleep issues at baseline. As such, it seems that
fitness enthusiasts who overlook their capacity to improve their sleep quality or quantity
are probably missing out on a nice opportunity to facilitate their fitness goals (including
both performance and body composition)
while having a positive impact on their mood
and cognitive function.
I try not to make assumptions, but I’ll make
an educated guess: you probably don’t want
to hear a childless, self-employed person
who works from home telling you that staying in bed for a few more hours is a practical,
feasible intervention with absolutely no challenges or downsides. In many cases, there are
133
elements of our sleep environment or sleep
schedule that are simply outside of our control. Fortunately, the sleep-related toolbox is
pretty deep, and you can select the specific
strategies that are most feasible for your circumstances. If you have the luxury of exerting a tremendous amount of control of your
sleep habits, a comprehensive sleep hygiene
protocol in conjunction with a dedicated effort to spend at least 8-9 hours in bed each
night (with a consistent daily sleep time and
wake time) would go a pretty long way. If
that’s not feasible, even taking a few items
from the sleep hygiene checklist would probably be helpful, and napping could be utilized
if your schedule doesn’t permit nighttime
sleep extension.
When considering the relative cost, numerous
upsides, and feasibility of various sleep interventions, there are some great opportunities
to obtain meaningful benefits with minimal
downsides. In a fitness industry with a tendency to generate and reinforce massive hype
about fairly speculative and high-cost dietary
supplements, effective sleep interventions
with minimal cost (or no cost) seem to be flying under the radar, relatively speaking. Of
course, the “sleep versus supplements” comparison presents a false dichotomy, but the
underlying message provides a useful perspective. As someone who writes favorably
about some supplements from time to time, I
believe a huge number of lifters would benefit far more by tuning up their sleep habits
than putting together an elaborate stack of
supplements.
References
1. Gwyther K, Rice S, Purcell R, Pilkington
V, Santesteban-Echarri O, Bailey A, et
al. Sleep interventions for performance,
mood and sleep outcomes in athletes:
A systematic review and metaanalysis. Psychol Sport Exerc. 2022 Jan
1;58:102094.
2. Chennaoui M, Vanneau T, Trignol A,
Arnal P, Gomez-Merino D, Baudot C, et
al. How does sleep help recovery from
exercise-induced muscle injuries? J Sci
Med Sport. 2021 Oct 1;24(10):982–7.
3. Hamlin MJ, Deuchrass RW, Olsen PD,
Choukri MA, Marshall HC, Lizamore
CA, et al. The Effect of Sleep Quality and
Quantity on Athlete’s Health and Perceived
Training Quality. Front Sports Act Living.
2021 Sep 10;3:705650.
4. Kirschen GW, Jones JJ, Hale L. The
Impact of Sleep Duration on Performance
Among Competitive Athletes: A
Systematic Literature Review. Clin J Sport
Med. 2020 Sep;30(5):503–12.
5. Murawski B, Wade L, Plotnikoff RC,
Lubans DR, Duncan MJ. A systematic
review and meta-analysis of cognitive and
behavioral interventions to improve sleep
health in adults without sleep disorders.
Sleep Med Rev. 2018 Aug;40:160–9.
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VIDEO: Mental Fatigue Part 1
BY MICHAEL C. ZOURDOS
Sometimes we peruse social media sites on our phones and then go right to
the gym. If you’ve ever done this and felt tired or lacked focus when training,
it’s possible that mental fatigue from engaging on social media was the culprit.
This video breaks down the literature on how mental fatigue affects acute
performance.
Click to watch Michael's presentation.
135
Relevant MASS Videos and Articles
1. Is Your Brain Getting in the Way. Volume 4 Issue 12.
2. Put That Phone Down, Now! Volume 5 Issue 7.
References
3. Stroop JR. Studies of interference in serial verbal reactions. Journal of experimental psychology.
1935 Dec;18(6):643.
4. Gantois P, Lima-Júnior DD, Fortes LD, Batista GR, Nakamura FY, Fonseca FD. Mental Fatigue
From Smartphone Use Reduces Volume-Load in Resistance Training: A Randomized, SingleBlinded Cross-Over Study. Perceptual and Motor Skills. 2021 May 17:00315125211016233.
5. Queiros VS, Dantas M, Fortes LD, Silva LF, Silva GM, Dantas PM, Cabral BG. Mental
Fatigue Reduces Training Volume in Resistance Exercise: A Cross-Over and Randomized
Study. Perceptual and Motor Skills. 2021 Feb;128(1):409-23.
6. Lima-Junior DD, Fortes LS, Ferreira ME, Gantois P, Barbosa BT, Albuquerque MR, Fonseca
FS. Effects of smartphone use before resistance exercise on inhibitory control, heart rate
variability, and countermovement jump. Applied Neuropsychology: Adult. 2021 Nov 5:1-8.
7. Fortes LS, Lima Júnior D, Costa YP, Albuquerque MR, Nakamura FY, Fonseca FS. Effects
of social media on smartphone use before and during velocity-based resistance exercise
on cognitive interference control and physiological measures in trained adults. Applied
Neuropsychology: Adult. 2020 Dec 16:1-0.
8. Fortes LS, Lima-Junior D, Nascimento-Júnior JR, Costa EC, Matta MO, Ferreira
ME. Effect of exposure time to smartphone apps on passing decision-making in male soccer
athletes. Psychology of Sport and Exercise. 2019 Sep 1;44:35-41.
9. Fortes LS, Lima-Júnior DD, Gantois P, Nasicmento-Júnior JR, Fonseca FS. Smartphone use
among high level swimmers is associated with mental fatigue and slower 100-and 200-but not
50-meter freestyle racing. Perceptual and Motor Skills. 2021 Feb;128(1):390-408.
10. Fortes LS, Fonseca FS, Nakamura FY, Barbosa BT, Gantois P, de Lima-Júnior D, Ferreira
ME. Effects of Mental Fatigue Induced by Social Media Use on Volleyball Decision-Making,
Endurance, and Countermovement Jump Performance. Perceptual and motor skills. 2021 Aug
17:00315125211040596.
11. Fortes LS, Gantois P, de Lima-Júnior D, Barbosa BT, Ferreira ME, Nakamura FY, Albuquerque
MR, Fonseca FS. Playing videogames or using social media applications on smartphones
causes mental fatigue and impairs decision-making performance in amateur boxers. Applied
Neuropsychology: Adult. 2021 May 29:1-2.
12. Budini F, Lowery M, Durbaba R, De Vito G. Effect of mental fatigue on induced tremor in
human knee extensors. Journal of Electromyography and Kinesiology. 2014 Jun 1;24(3):412-8.
136
13. Le Mansec Y, Pageaux B, Nordez A, Dorel S, Jubeau M. Mental fatigue alters the speed and the
accuracy of the ball in table tennis. Journal of sports sciences. 2018 Dec 2;36(23):2751-9.
14. Pageaux B, Marcora S, Lepers R. Prolonged mental exertion does not alter neuromuscular
function of the knee extensors. Medicine & Science in Sports & Exercise. 2013 May 21.
15. Pageaux B, Marcora SM, Rozand V, Lepers R. Mental fatigue induced by prolonged selfregulation does not exacerbate central fatigue during subsequent whole-body endurance
exercise. Frontiers in human neuroscience. 2015 Feb 25;9:67.
16. Russell S, Jenkins D, Smith M, Halson S, Kelly V. The application of mental fatigue research to
elite team sport performance: New perspectives. Journal of science and medicine in sport. 2019
Jun 1;22(6):723-8.
█
137
VIDEO: Body Comp Behaviors
BY ERIC HELMS
Intentional dieting isn’t for everyone, and even for those it is for, eventually
it ends and you move to maintenance. Further, some people struggle to
stick to a diet with quantitative food tracking or even if they can, can’t stick
to tracking when the diet is over during maintenance. In this video I discuss
habits you can work on adopting that can potentially result in a deficit, or that
can be implemented to aid weight loss maintenance.
Click to watch Eric's presentation.
138
Relevant MASS Videos and Articles
1. VIDEO: Energy Density: A Forgotten Component, Part 1. Volume 3, Issue 8.
2. VIDEO: Energy Density: A Forgotten Component, Part 2. Volume 3, Issue 9.
3. The Role of Physical Activity in Appetite and Weight Control. Volume 2, Issue 3.
4. The Poptart Problem: Processed Foods and Overeating. Volume 3, Issue 9.
5. How the Brain Controls Eating Behavior. Volume 3, Issue 12.
6. Unfortunately, “Clean Eating” Lives On. Volume 4, Issue 8.
7. Protein Is Satiating, But It’s Not That Simple. Volume 4, Issue 10.
8. Weight Loss Maintenance is More Than Math. Volume 5, Issue 5.
9. Does Hibiscus Tea Increase Satiety Or Energy Expenditure (And Would It Actually Matter)?
Volume 5, Issue 10.
10. Diet Tracking and Disordered Eating: Which Comes First? Volume 5, Issue 10.
References
1. Westenhoefer, J., von Falck, B., Stellfeldt, A., & Fintelmann, S. (2004). Behavioural correlates
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middle-aged adults. The American journal of clinical nutrition, 85(4), 981–988.
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7. Parretti, H. M., Aveyard, P., Blannin, A., Clifford, S. J., Coleman, S. J., Roalfe, A., et al. (2015).
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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. Shukla and Heath. A Single Bout of Exercise Provides a Persistent Benefit to Cognitive
Flexibility
2. João et al. Acute Behavior of Oxygen Consumption, Lactate Concentrations, and Energy
Expenditure During Resistance Training: Comparisons Among Three Intensities
3. Cardoso et al. Acute effects of different rest intervals between agonist-antagonist pairedsets in the neuromuscular system performance of young adults
4. da Silva et al. Airflow restriction mask induces greater central fatigue after a non-exhaustive
high-intensity interval exercise
5. dos Santos et al. Analysis of Grip Amplitude on Velocity in Paralympic Powerlifting
6. de Souza et al. Associated Determinants Between Evidence of Burnout, Physical Activity,
and Health Behaviors of University Students
7. Luk et al. Differential Autophagy Response in Men and Women After Muscle Damage
8. Chung et al. Do exercise-associated genes explain phenotypic variance in the three
components of fitness? a systematic review & meta-analysis
9. de Moura et al. Dose Response of Acute ATP Supplementation on Strength Training
Performance
10. Wehrstein et al. Eccentric Overload during Resistance Exercise: A Stimulus for Enhanced
Satellite Cell Activation
11. Metz et al. Effect of oral contraceptives on energy balance in women: A review of current
knowledge and potential cellular mechanisms
12. Moreno-Villanueva et al. Effect of Repetition Duration—Total and in Different Muscle
Actions—On the Development of Strength, Power, and Muscle Hypertrophy: A Systematic
Review
13. Løken et al. Effects of bouncing the barbell in bench press on throwing velocity and
strength among handball players
14. Kreipke et al. Effects of Concurrent Training and a Multi-Ingredient Performance
Supplement Containing Rhodiola rosea and Cordyceps sinensis on Body Composition,
Performance, and Health in Active Men
15. Sexton et al. Effects of Peanut Protein Supplementation on Resistance Training Adaptations
in Younger Adults
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16. Lacio et al. Effects of Resistance Training Performed with Different Loads in Untrained and
Trained Male Adult Individuals on Maximal Strength and Muscle Hypertrophy: A Systematic
Review
17. Grgic et al. Effects of sodium bicarbonate supplementation on exercise performance: an
umbrella review
18. Liao et al. Effects of velocity based training vs. traditional 1RM percentage-based training
on improving strength, jump, linear sprint and change of direction speed performance: A
Systematic review with meta-analysis
19. dos Santos et al. Efficacy of Creatine Supplementation Combined with Resistance Training
on Muscle Strength and Muscle Mass in Older Females: A Systematic Review and MetaAnalysis
20. Oranchuk et al. Improved power clean performance with the hook-grip is not due to altered
force-time or horizontal bar-path characteristics
21. Vasudevan and Ford. Motivational Factors and Barriers Towards Initiating and Maintaining
Strength Training in Women: a Systematic Review and Meta-synthesis
22. Kim et al. Relationships between physical characteristics and biomechanics of lower
extremity during the squat
23. Steele et al. Slow and Steady, or Hard and Fast? A Systematic Review and Meta-Analysis
of Studies Comparing Body Composition Changes between Interval Training and Moderate
Intensity Continuous Training
24. Wangdi et al. Tart Cherry Supplement Enhances Skeletal Muscle Glutathione Peroxidase
Expression and Functional Recovery after Muscle Damage
25. Hindle et al. The biomechanical characteristics of the strongman atlas stone lift
26. Pohl et al. The Impact of Vegan and Vegetarian Diets on Physical Performance and Molecular
Signaling in Skeletal Muscle
27. Patterson et al. The influence of hip extensor and lumbar spine extensor strength on lumbar
spine loading during a squat lift
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reading MASS.
The next issue will be released to
subscribers on February 1, 2022.
Graphics and layout by Kat Whitfield
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