MASS
ER I C H E L M S
G REG N U C K O L S
M IC HAEL Z O U R D O S
ERIC T R E X L E R
M O NTHLY A PP LICATIONS IN S TRENGTH S PO R T
Everything There is to Know About High- Versus
Low-Load Training
Plus a new study that may have implications for long-term adherence with low-load
training. p.7
VOLU ME 6 , I SSU E 1 0
O C T OB E R 2022
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
7
BY MI CHAEL C. ZOUR DOS
Everything There is to Know About High- Versus Low-Load
Training
There has been a plethora of research comparing high- and low-load training over
the past decade. This article breaks down everything you could want to know about
those comparisons, while also tackling a new study that may have implications for
long-term adherence with low-load training.
33
BY ER I C T R EXL ER
Key Models and Theories for Effective Coaching
Coaching is not a program delivery service, but a highly interactive profession
requiring a comprehensive set of skills and perspectives. Every client is different, but
this article covers a few key theories and frameworks that set the stage for effective
coaching practices.
50
BY ER I C HEL MS
Bodybuilding Contest Recovery - What do we Know?
The recovery phase after a physique competition is challenging, both physically and
mentally. There is no contest in the near future to focus on, and the physiological
adaptations to chronic energy restriction and intense drive to eat persist for some time,
even after increasing calories. But is there research to inform best practices for this phase?
65
BY MI CHAEL C. ZOUR DOS
Does Benching First in a Workout Improve Lower Body
Performance?
Postactivation potentiation studies typically have individuals perform a few heavy squat
or bench reps to improve volume performance on the same exercise. A new study
examined if a few heavy bench reps could potentiate lower body performance. Did the
idea work? This article breaks it down.
78
BY ER I C T R EXL ER
Putting Metabolic Adaptation Into Perspective
It’s widely accepted that metabolic adaptation exists. But how impactful and
persistent is it, and how much does it vary from person to person? That’s precisely
what this article seeks to cover.
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.
144
BY MI CHAEL C. ZOUR DOS
VIDEO: Two-A-Days Part I
Training twice per day is pretty awesome, but is it necessary? This video evaluates
the evidence for splitting your training into two sessions per day to augment
hypertrophy and strength. The practice may have efficacy, but conditions apply.
146
BY ER I C HEL MS
VIDEO: Practical Long Muscle Length Training
As more research emerges, it becomes increasingly apparent that training which
puts a muscle in a more lengthened position seems to induce greater hypertrophy.
In this video, we’ll go over some of the research which supports this concept,
the different types of ways this has been shown in research, the potential
considerations of implementing long muscle length training, and then finally,
practical applications. Specifically, form modification, exercise selection, partial
range of motion training, and microcycle-level programming are discussed.
Letter From the Reviewers
V
olume 6, Issue 10 of MASS has arrived, and it is absolutely packed with
practical, evidence-based content. This month’s issue features thirteen study
reviews and two video lectures covering some of the most popular topics and
frequently asked questions in the fitness world.
In this month’s cover story, Dr. Zourdos takes on a frequently discussed (and debated)
topic: high-load versus low-load training. In the process of reviewing a brand new
study, Dr. Zourdos thoroughly summarizes the existing literature on high- and lowload training for strength and hypertrophy outcomes, explores the research related
to perceptual and affective responses to different loading paradigms, and provides
practical examples of implementing both high- and low-load training into a program.
Of course, the cover story isn’t the only full-length, training-focused study review this
month. Dr. Zourdos also reviews a new study on a somewhat atypical approach to
post-activation potentiation. While most studies on the topic assess localized effects,
this new study sought to determine if upper-body exercise could acutely enhance
performance in a subsequent test of lower-body strength and power performance.
On the nutrition side, Dr. Helms and Dr. Trexler both contributed full-length reviews
this month. In one such article, Dr. Trexler reviews a new study about metabolic
adaptation in former elite athletes. His article takes a close, evidence-based look at
who experiences metabolic adaptation, how long it seems to persist after active weight
loss, and how much it actually impacts successful attainment of body composition
goals. While metabolic adaptation is a familiar topic for anyone who has implemented
a particularly aggressive weight loss diet, it’s just as important to explore the process
of recovering after the diet. Fortunately, Dr. Helms covers a new scoping review
about recovery from aggressive diets, such as those implemented by physique athletes
during contest preparation. More specifically, his article addresses the short-term and
long-term factors that may facilitate, expedite, or delay recovery from this unique
physiological state.
Dr. Trexler’s other full-length article doesn’t quite fit in the training or nutrition
categories, as it addresses evidence-based coaching strategies. In this article, he
reviews a new study exploring the key elements of a person-centered approach to
coaching, while proposing a cohesive set of conceptual models to guide an effective
coaching practice. While this article directly focuses on coaching, the theories and
models discussed are relevant to other applications of leadership or management, and
are even helpful for people who “coach” themselves.
5
Greg and Dr. Trexler teamed up on this month’s Research Briefs, collectively bringing
you eight study reviews on a diverse selection of training and nutrition topics. This
month’s briefs cover protein quality, the relationship between meal energy density and
satiety regulation, the effects of vegan diets on bone health, artificial and non-nutritive
sweeteners, training for strength endurance, high-load versus low-load training during
energy deficits, optimal exercise selection for triceps hypertrophy, and the potential for
aerobic exercise to boost long-term hypertrophy responses to resistance training.
We also have two excellent video lectures this month. In the first, Dr. Zourdos
evaluates the evidence for splitting your training into two sessions per day to augment
hypertrophy and strength. In the second, Dr. Helms discusses how to effectively
incorporate exercises that emphasize training at long muscle lengths into your program.
As always, be sure to check out the audio roundtables and join us in the Facebook
group. Lastly, if you need some CEUs to maintain your current certifications, be sure to
take advantage of our continuing education opportunities for NSCA, ACSM, NASM,
and ACE.
We hope you have a great month, and we thank you for being a part of MASS.
Sincerely,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
6
COVER STORY
Studies Reviewed: Affective and Perceptual Responses During an 8-Week resistance
Training to Failure Intervention at Low vs. High Loads in Untrained Women. Anderson et
al. (2022) and Low-Load vs. High-Load Resistance Training to Failure on One Repetition
Maximum Strength and Body Composition in Untrained Women. Dinyer et al. (2019)
Everything There is to Know About
High- Versus Low-Load Training
BY MICHAEL C. ZOURDOS
There has been a plethora of research comparing high- and lowload training over the past decade. This article breaks down
everything you could want to know about those comparisons,
while also tackling a new study that may have implications for
long-term adherence with low-load training.
7
COVER STORY
KEY POINTS
1. Researchers split untrained men into two groups. Both groups performed leg
extensions, seated shoulder presses, leg curls, and lat pulldowns for eight weeks.
One group performed three sets to failure at 30% of 1RM (low-load group), and
the other performed three sets to failure at 80% of 1RM (high-load group).
2. The researchers assessed 1RM strength and body composition pre-, mid-, and
post-study. Effort-based rating of perceived exertion (RPE) was assessed after
each set and each session (sRPE). The affective response was assessed via
the feeling scale after each session, as was the intention to complete the same
exercise within the next week or month.
3. Both groups tended to improve strength but not body composition, and there
were no group differences for either measure. Further, there were no group
differences for either RPE measure or the affective response at any time point.
Additionally, feeling scale ratings were positively related to intention to train.
4. All measures were unaffected by the load. Importantly, since lifters similarly
enjoyed high- and low-load training, this might mean that lifters would not have
an issue adhering to low-load failure training over the long-term. However, the
findings from this study were not in lockstep with the literature. Overall, it seems
that higher loads are preferable for strength, but hypertrophy occurs mostly
independent of loading.
BA C KGRO UN D
Five years ago, an often-cited meta-analysis
from Schoenfeld et al (3) found that strength
gains were augmented with high- (>60% of
one-repetition maximum (1RM)) versus low(≤60% of 1RM) load training, but muscle
growth was similar between training loads.
Since then, more data (4, 5) seem to confirm those findings, suggesting that lifters
can choose their preferred loading paradigm
(high or low loads) and maximize hypertrophy. However, lifters must consider how
other factors, such as fatigue, may influence
long-term adherence with low loads. Ribeiro
et al. (6) found that trained men reported high
session rating of perceived exertion (RPE)
scores and greater ratings of “displeasure”
after low-load (25-30RM) than after highload (8-12RM) training to failure. This study
prompted me to write an article titled, “Most
People Find Low-Load Training to Failure
Miserable.” In this article, I questioned the
utility of using low loads as a sole method
of long-term training, since lifters seem to
enjoy it less, which may lead to decreased
adherence. However, in that article’s “Next
Steps,” I called for a longitudinal study to assess acute perceptual responses to see if enjoyment increases over time with low-load
8
COVER STORY
training. This article reviews two papers from
Anderson et al (1) and Dinyer et al (2), which
were part of the same study that attempted to
tackle my previous proposal.
The reviewed study (1, 2) split 23 untrained
women into high- (n = 12, 80% of 1RM)
and low- (n = 11, 30% of 1RM) load training groups for eight weeks. Both groups performed 2-3 sets of machine-based exercises
(leg extensions, seated shoulder presses, leg
curls, and lat pulldowns) to failure twice per
week. The researchers assessed body composition and 1RM strength on each exercise
before and after the training program. Effort-based RPE was assessed after each set,
and sRPE was assessed immediately after
each session. Feeling scale ratings (-5 – very
bad to +5 very good) and intention to exercise
within the next week and next month (0% no intention to 100% - strong intention) were
assessed immediately, 15 minutes, and 60
minutes after each training session. Findings
showed no significant differences between
groups for strength gains, body composition
changes, set RPE, sRPE, feeling scale ratings,
or intention to exercise at any time point. Set
RPE and sRPE scores tended to increase over
time across both groups. Feeling scale ratings
were positively related to intention to exercise
at various points throughout the study (i.e.,
more pleasure related to greater intention to
exercise again). These findings suggest that
untrained lifters can gain strength to a similar
degree with both high- and low-load training.
Further, these individuals had similar perceptual and affective responses to training,
suggesting that long-term training adherence
may not be compromised with low-load train-
ing. However, we should consider that some
individuals may prefer one type of training,
and that some exercises may be more tolerable with long-term low-load failure training.
Moreover, there is a large body of literature
comparing high and low-load training in recent years; thus, this article is also a good opportunity to thoroughly examine the topic as
a whole. Therefore, this article will:
1. Thoroughly review the existing literature
on high- and low-load training for strength
and hypertrophy outcomes.
2. Determine if the presently reviewed
study’s findings are in agreement with the
previous literature.
3. Examine the research related to the perceptual and affective response to different loading paradigms and determine how
these findings may influence long-term
adherence.
4. Provide practical examples of implementing both high- and low-load training into
a program.
Purpose and Hypotheses
Purpose
As an overarching note, this review covers
two different published papers (1, 2) that came
from data collected during a single study. The
researchers published the strength and body
composition data in one paper (2) and the perceptual and affective responses in another (1).
Therefore, going forward in this article, I will
refer to both papers together as “the study” or
“the presently reviewed study.”
9
The presently reviewed study compared longterm strength and body composition outcomes between high load (80% of 1RM) and
low load (30% of 1RM) in untrained women.
It also compared the perceptual and affective
responses and intention to exercise between
the training protocols.
Hypotheses The researchers hypothesized the following:
1. Strength gains would not be significantly
different between groups.
2. Body composition would improve to a
similar degree in both groups.
3. There would be no significant differences
between groups for perceptual or affective
responses.
4. There would be a positive relationship between affective responses and intention to
exercise.
Subjects and Methods
Subjects
23 untrained women between the ages of 1827 completed the study. Additional subject
details are presented in Table 1.
Study Protocol
This study was a parallel-groups design in
which the researchers split 23 untrained
women into high-load and low-load training
groups for 12 weeks. Subjects completed 2-3
sets to failure twice per week during weeks
2-4 and 6-11, while weeks 1, 5, and 12 served
as pre-, mid-, and post-study testing. Subjects
trained four machine-based exercises (leg extension, seated shoulder press, leg curl, and
lat pulldown) during each session. The highload group used 80% of 1RM, while the lowload group used 30% of 1RM, with the load
in each group being adjusted in weeks 6-11
based on the mid-study 1RM testing.
Outcome Measures
Longitudinal outcome measures included
1RM strength, bone- and fat-free mass, and
body fat percentage. The researchers also
compared volume load and time under tension. Further, the researchers assessed set
RPE, sRPE, feeling scale ratings (affective
response), and intention to perform the same
training session within the next week or the
next month. Further description and the time
points when each outcome measure was assessed can be seen in Table 2.
10
Findings
The only significant differences between
groups were for volume load and time under
tension. There were no significant group differences for strength gains, body composition changes, set RPE or sRPE, the affective
response, or intention to exercise.
Volume, Time Under Tension, Body Composition, and Strength
The low-load group performed significantly
more volume and had a greater time under
tension when the researchers averaged all
training sessions together (p < 0.05). In addition, strength increased significantly from
pre- to mid-study and from mid- to poststudy in both groups (p < 0.05), but with
no significant differences between groups.
Neither group significantly improved either metric of body composition (p > 0.05).
However, subjects in the low-load group
tended to increase bone- and fat-free mass
(+1.1 kg) more than subjects in the highload group (+0.1 kg) The findings for 1RM
strength can be seen in Figure 1.
11
12
Set and Session RPE
There were no significant group differences
for set RPE or sRPE. However, both RPE metrics tended to increase over time. For example,
when both groups were combined, set RPEs
were significantly greater during sessions 1
Similar to set RPE, there were also no between-group differences for sRPE, but when
both groups were combined and both sessions
per week were averaged, sRPE did tend to increase over time. The significant differences
are in Table 4.
and 2 of weeks 4 and 8 compared to the corre-
Affective Response
sponding sessions in week 1 (Table 3).
There were no significant group differences
13
for scores on the feeling scale. However, feeling scale scores tended to be lower (less pleasurable) immediately following training than
at 15 and 60 minutes post-training (Table 5).
Intention to Exercise
There were no significant group differences
for intention to exercise at any time point.
Further, when researchers combined all subjects and averaged the responses at all time
points throughout the study, subjects had an
intention of 81 ± 4% and 68 ± 5% to participate in resistance training to failure in the
next month and week, respectively.
Feeling scale ratings were also positively
related to intention to exercise when both
groups were combined at various points
throughout the study (i.e., more pleasure related to greater intention to exercise again).
Specifically, feeling scale scores immediately post-training in week 1 were significantly
related to intent to exercise within the next
month (r = 0.416, p = 0.049) and feeling scale
scores 15 minutes post-training during week
4 were significantly related to intent to exercise within the next week (r = 0.497, p =
0.016) and next month (r = 0.485, p = 0.019).
Finally, feeling scale scores at all time points
(immediately, 15 minutes, and 60 minutes
post-training) were significantly related to
the intention to exercise within the next week
and next month. The relationships between
feeling scale scores and intention to exercise
in week eight can be seen in Figure 2AB.
Findings from Anderson et al. (1). Dots represent feeling scale scores and y-axis represents intention to exercise within the next
week (Panel A) and within the next month
(Panel B). Squares = Individual feeling scale
scores for immediately post-training. Open
Circles = Individual feeling scale scores for
15 minutes post-training. Triangles = Individual feeling scale scores for 60 minutes
post-training.*Significant relationship.
Interpretation
The previous section presented findings from
two papers, Anderson et al (1) and Dinyer et al
(2), which reported different outcomes from the
same study. Overall, Anderson et al (1) found
that untrained women reported low-load, high
rep training to cause similar fatigue to highload, moderate rep training. Further, the researchers reported that the women had a similar
intent to train within the next week or month,
regardless of which protocol they performed.
Additionally, Dinyer et al found that strength
gains and body composition changes were not
significantly different between high- and lowload training. Together these findings suggest
that lifters can use high or low loads for strength
and potentially hypertrophy based upon preference. Further, similar sRPE and affective responses indicate that adherence to both loading
zones might be similar over time. Previously,
Ribeiro et al (6 - MASS Review) found that
men reported higher sRPE following low-load
versus high-load training; thus, the lack of group
differences in this study for the perceptual and
affective responses are intriguing.
Aside from the presently reviewed study,
there is a relatively large body of literature
comparing low- versus high-load training
over the past decade, especially within the
past 5-6 years. So, before getting back into
both facets (performance and perceptual/af-
14
fective) of the presently reviewed study, let’s
take a deep dive into the totality of the high
versus low-load literature. Therefore, this interpretation is split into four parts:
1. I will thoroughly review how muscle hypertrophy, strength, and endurance are
affected by high and low load training,
including how moderating factors (sex,
proximity to failure, and upper or lower
body) influence the responses.
2. I will review the present study’s performance findings to determine how they fit
with the total body of literature.
3. I’ll review the previous data on the perceptual and affective response to highand low-load training, followed by a discussion of how the reviewed study fits
with the literature.
4. I’ll provide practical examples of how to
incorporate high- and low-load training
into your training.
It’s going to be a long Interpretation, so let’s
get started.
Main Findings to Date on Strength and Hypertrophy
In the last six years, there have been five meta-analyses and a systematic review evaluating high- versus low-load training. Specifically, four of the meta-analyses examined
both strength and hypertrophy outcomes
(3, 4, 7, 8), one meta-analysis examined only
hypertrophy, including fiber type-specific outcomes (9), while the systematic review discussed both strength and hypertrophy (5). Additionally, three (10, 11, 12) narrative reviews
have covered high versus low-load training
within the last five years. Before continuing,
I would like to provide a working definition
of high- versus low-load training; however,
that’s difficult as meta-analyses used different
criteria to categorize low- and high-load training. Further, some looked at training load as
a continuum from 30-90% of 1RM (5), while
others categorized training load as low, moderate, or high (4, 8). Therefore, even though
the categorization can be much more specific, for simplicity, I’ll generally refer to highand low-load training as training at >60% of
1RM and ≤60% of 1RM (3) unless otherwise
stated. However, no matter the categorization,
the consensus in this literature is quite clear:
muscle hypertrophy can be maximized independently of training load, while higher loads
are needed to maximize strength gains. Table
6 summarizes all six meta-analyses/systematic
reviews and distinguishes how each paper categorized high-, low-, and in some cases, moderate-load training.
STRENGTH GAINS
ARE GREATER WITH
MODERATE- AND HIGHLOAD TRAINING, AND
HYPERTROPHY DOES
NOT APPEAR TO BE
SIGNIFICANTLY AFFECTED
BY TRAINING LOAD
15
16
The overarching theme of the meta-analyses
is that strength gains are greater with moderate- and high-load training, and hypertrophy
does not appear to be significantly affected by
training load. Although it’s well-known that
higher loads generally lead to greater strength
outcomes, it’s worth noting that the first meta-analysis on the topic (7) did not quite find
significance (p = 0.09) for strength. However, that meta-analysis included only 10 studies, and all were on untrained individuals.
Future meta-analyses that analyzed strength
had more total studies and included both
trained and untrained subjects. Therefore, it
seems that the lack of significant difference
(although close) for high loads to lead to
greater strength gains is due to the literature
being underdeveloped at the time of the first
meta-analysis. In other words, a meta-analysis is only as useful as the studies it includes,
and when the first one was conducted there
wasn’t nearly as much data to analyze.
On balance, the meta-analyses reveal that
hypertrophy seems to be unaffected by training load. I agree with that position, and it’s
the opinion I primarily espouse. However,
there’s enough ambiguity in the literature
that I think the hypertrophy findings warrant
a closer look. For example, the Grgic 2020
(9) meta-analysis found a small effect size
(0.30) and p-value that was close to significance (0.089), favoring high-load training
for hypertrophy of type II muscle fibers.
However, Grgic’s model only included five
studies (13, 14, 15, 16, 17), and only one of
them (17) found a significant difference (p =
0.039) between groups for type II fiber hypertrophy. Further, although Lopez et al (4)
reported no significant difference between
high, moderate, and low loads for hypertro-
17
phy the authors did state the following, “the
results of the consistency model indicate that
moderate-load (84.5%) and high-load resistance training (75.8%) are the best load for
muscle hypertrophy in overall and high-quality subgroup analyses, respectively.” In other
words, when comparing high- versus lowload (19 total comparisons) and moderateversus low-load (7 comparisons) it seemed
that high and moderate loads were, on average, more likely to lead to larger increases
in muscle growth than low-load training. A
forest plot from Lopez’s meta-analysis comparing high- and moderate-load training versus low-load training for hypertrophy can be
seen in Figure 3.
80% or 30% of 1RM training for six weeks.
Moreover, narrative reviews from Grgic and
Schoenfeld (10) and Fisher et al (12) suggest
that load does not seem to affect muscle hypertrophy, especially when volume is equated
between training protocols. Finally, the Carvalho meta-analysis (8), only included volume-equated studies and showed only high
p values for all of its hypertrophy comparisons (p values = 0.559 - 0.938). Therefore,
even though the Lopez et al (4) meta-analysis
suggests the possibility that high-load training may provide additional hypertrophic benefits, I think the most appropriate interpretation is that, on the group level, muscle growth
is maximized independently of training load.
To be clear, the plot from Lopez is not convincing that high-load training leads to greater muscle growth. However, Lopez’s (4) note
that higher loads tended to be better on average along with the Grgic meta (9) (albeit only
five studies) suggests that the data might lean
ever so slightly in the high load direction, but
not to a degree which we can be confident.
Moderating Factors
In reality, there’s evidence on both sides for
hypertrophy. Specifically, Schuenke et al (17)
found a 25.6% and 23.4% greater increase in
type I and type IIa fiber cross-sectional area,
respectively, among untrained women training with 6-10RM loads than those training
with 25-30RM loads. However, Franco et al
(18 - MASS Review) found that untrained
women gained more fat-free leg mass with
25-30 reps per set (+4.6%) than with 8-10
reps per set (+1.5%). Lastly, Stefanski et
al (19 - MASS Review) found a similar increase in biceps muscle thickness among untrained women completing sets with either
A few of the meta-analyses (4, 8) examined
if factors such as training sex, training status,
training upper or lower body training, and
proximity to failure moderated the findings.
For example, Lopez et al (4) reported that
untrained individuals tended to experience
more hypertrophy (p = 0.033) than trained
individuals with low-load training compared
to high-load training. Further, Lopez found
that men tended to gain more strength than
women with high-load training, but women
tended to gain more strength than men with
moderate-load training. In their sub-analyses, Carvalho et al found that when training
not to failure, strength gains (p = 0.049) and
hypertrophy (p = 0.002) were greater with
high-load than with moderate-load training. In other words, when training with high
loads, it seems to be preferable to train shy of
failure. Notably, the Carvalho meta-analysis
only included one study (20) which directly
18
compared failure versus non-failure training;
thus, Carvalho did not meta-analyze if training to failure was necessary with low loads.
Low Load Training To Failure
Despite the Carvalho meta-analysis (8) not
comparing failure versus non-failure training, a few studies provide insight into the
necessity of failure training with low loads
to maximize hypertrophy. If you’re familiar
with my MASS content on training to failure,
you may be rolling your eyes at this point and
thinking, “here comes Zourdos again telling
us to train 149 reps shy of failure.” If that’s
you, then you can breathe a sigh of relief as
I’m not here to do that this time.
There are four studies providing insight into
low-load non-failure training, and they include Lasevicius et al 2022 (20 - MASS
Review), Terada et al 2022 (21 - MASS Review), Ikezoe et al 2020 (22), and Kapsis et al
2022 (23 - MASS Review). Lasevicius used
a within-subjects design (one leg performed
each condition) and had subjects perform
leg extensions not to failure or to failure at
30% of 1RM for eight weeks. The researchers reported that quadriceps cross-sectional area increased by +7.7%% in the failure
condition but only by 2.6% in the non-failure condition. Terada et al (21) compared
pec and triceps hypertrophy over eight weeks
in untrained men bench pressing at 80% of
1RM (8 reps per set), training to failure at
40% of 1RM, or benching to a 20% velocity loss threshold at 40% of 1RM. The difference between groups wasn’t significant, but
the 80% (+4.4mm; +14.5%) and 40% to failure (+4.9mm; +16.8%) groups tended to increase triceps muscle thickness more than the
40% not to failure group (+2.4mm; +8.1%).
Therefore, based upon the findings of Lasevicius et al and Terada et al there seems to be
an added benefit to low-load training when
it’s performed to failure.
The Ikezoe et al (22) and Kapsis et al (23)
studies did not compare low-load non-failure
training to low-load failure training; rather,
they compared low- and high-load non-failure training. Ikezoe et al (22) found no significant differences in quadriceps muscle
thickness in healthy men after leg extension
training of either 12 (sets) × 8 (reps) at 30%
of 1RM versus 3 × 8 at 80% of 1RM for eight
weeks. While Ikezoe did not report significant group differences, it should be noted that
the low-load group performed nine more sets
than the high-load group. Of course, it cannot
be known if hypertrophy would have been
the same if sets were equated. However, it’s
possible that if training far from failure with
low loads then additional volume is needed
for muscle growth to be similar to high-load
training. Lastly, Kapsis et al (23) had both
women and men perform circuit training for
12 weeks. Each session consisted of four
rounds of five exercises. Each round consisted of one set performed for 30 seconds at either 30% or 70% of 1RM. The researchers reported that increases in lean body mass were
not significantly different between groups
(30%: +1.11 kg; 70%: +1.25kg). Overall, the
current body of evidence may lean toward
performing low-load training to failure, or
at least closer to failure, to maximize hypertrophic benefits. However, it’s premature to
make definitive conclusions on the necessity of failure training or how close to failure
19
one needs to train with low loads. The lack of
ability to draw conclusions is partly because
the majority of non-failure low load studies
have had subjects train really far (>7 RIR)
from failure; thus, we cannot know if failure
training would be necessary compared to a
more moderate number of RIR (i.e., 2-5 RIR).
Muscular Endurance
There isn’t a ton of data comparing high and
low loads for muscular endurance; however,
a new study from Fliss et al (24) was published just two days after the commencement
of this article. Therefore, to be comprehensive, I wanted to briefly touch on the Fliss
study. Fliss et al had untrained women perform unilateral dumbbell biceps preacher curl
training and unilateral leg extensions for 10
weeks. This study was a within-subjects design, so the women performed training with
one arm and one leg at a load corresponding
to 80% of 1RM (6-12 reps per set) while the
other side of the body performed sets with a
load corresponding to 30% of 1RM (20-30
reps). The researchers assessed both absolute and relative muscular endurance. Relative muscular endurance tests the number of
reps performed with a percentage of 1RM
that corresponds to that specific day’s max.
For example, if someone wants to test reps
performed with 60% of 1RM and on that
specific day their squat max is 100kg, then
they would use 60kg to test muscular endurance. However, if their squat max is 105kg a
week later, they would need to use 63kg to
test their relative muscular endurance. To test
absolute muscular endurance, this individual
would always use their starting load, which
was 60kg. Fliss had the women perform rel-
ative and absolute muscular endurance tests
at pre- and post-study with both heavy (80%
and 90% of 1RM) and light (30% and 60%
of 1RM) loads. The main findings were that
changes in leg extension muscular endurance
tended to be specific to the training protocol.
In other words, on average, the leg training at
80% of 1RM increased heavy-load absolute
muscular endurance significantly more than
the 30% leg; however, the 30% leg tended to
improve absolute muscular endurance more
with light-loads. One other note about lowload training and muscular endurance is that
Terada et al (21) found that absolute muscular endurance at 40% of 1RM improved to
a similar degree following bench press training to failure at 40% of 1RM and bench press
training to a 20% velocity loss at 40% of
1RM. Therefore, it seems that low-load training to failure or not to failure is effective at
producing improvements in muscular endurance at low loads. However, similar to low
loads being inferior to high loads for strength
gains, low loads also seem inadequate for
increasing absolute muscular endurance at
heavy loads, which, in part, demonstrates the
principle of specificity. For more on the principle of specificity as it relates to muscular
endurance, please see Greg’s research briefs
from this month.
Where Does The Presently Reviewed Study
Fit?
So now that we have thoroughly reviewed
the literature on performance outcomes with
high- and low-load training, does the presently reviewed study (1, 2) agree with the consensus positions? As a reminder, the reviewed
study was a non-volume-equated study com-
20
paring high- (n = 12, 80% of 1RM) and low(n = 11, 30% of 1RM) load training groups
for eight weeks in untrained women. Both
groups performed 2-3 sets of machine-based
exercises (leg extensions, seated shoulder
presses, leg curls, and lat pulldowns) to failure twice per week. Strength, on all exercises, was tested at pre-, mid-, and post-study as
were fat-free mass and body-fat percentage.
Both groups increased strength, but strength
gains were not significantly different between groups. Further, neither group improved measures of body composition. These
findings suggest that low loads are just as effective as high loads for increasing strength
in untrained women and that muscle growth
(although assessed indirectly) did not occur
in either group.
Further, fat-free mass increased by 1.1 kg in
the low-load group but only by 0.1 kg in the
high-load group. A 1 kg difference between
groups certainly could be meaningful; however, it’s hard to read too much into it since
the totality of literature suggests that muscle
growth is not different between high- and
low-load training.
First, the strength findings conflict with the
total body of literature. Of the six meta-analyses and systematic reviews, only the very
first one (7) did not find strength gains to be
significantly greater with high-load training,
but it was close (p = 0.09). That meta-analysis was officially published in 2016 but was
published online in 2014, and the study’s
search procedures ceased in 2013. In other
words, it came out too early to include some
data (25), which has shown strength to be increased more with high load training among
untrained women. It’s possible that there
were no group differences for strength gains
because the subjects were untrained; thus,
they could progress with low-loading.
Despite the plethora of research examining
long-term strength and hypertrophy outcomes following high- and low-load training,
there are far fewer studies comparing these
training paradigms for perceptual and affective responses. Therefore, before diving into
the existing literature on these topics, let’s
briefly explain the perceptual and affective
responses and why they are necessary measures to assess.
In the presently reviewed study, there was
no significant main time effect (change over
time collapsed across groups) for bone- and
fat-free mass, but it was close (p = 0.079).
Overall, I’m not sure that performance findings from the presently reviewed study add
much to the literature, and I’m comfortable
siding with the consensus of the meta-analyses that high and low loads lead to similar
hypertrophy. However, high loads are needed
to maximize strength gains.
Main Findings on Perceptual and Affective
Responses
The perceptual response to training is traditionally assessed via effort-based RPE, which
I’ve written about effort-based RPE before.
In brief, effort-based RPE is assessed either
after every set or after the entire session (e.g.,
sRPE) when used in resistance training. The
presently reviewed study (1) used the original Borg 6-20 scale (26); however, the Borg
0-10 scale (27) is also commonly used to
assess sRPE. Both scales are anchored on
21
the low end with a descriptor of “little to no
effort” and on the high end with “maximal
effort.” In general, if two protocols lead to
similar hypertrophy and strength outcomes,
but lifters deem that one of the protocols took
less effort, then the implication is that lifters
will recover more quickly from the lower effort protocol and possibly increase their longterm adherence to training.
Similar to the perceptual response, the affective response can also be assessed via a simple scale and has been suggested to have longterm adherence implications. In the presently
reviewed study, the affective response was
assessed via the -5 (very bad) to +5 (very
good) feeling scale. Negative ratings on the
scale are seen as “displeasure” while positive
ratings are viewed as a “pleasurable” experience. More broadly, feeling scale responses
may encompass a variety of feelings related
to mood, emotion, and someone’s general
psychological state (28, 29 – MASS Review).
It seems intuitive that feeling scale ratings of
greater pleasure (more positive) would be related to a greater intention to exercise, and
they were in the presently reviewed study.
However, Ekkekakis (28) indicated in a review paper that ratings of displeasure might
indicate a feeling of accomplishment and
pride; thus, we shouldn’t be so quick to classify negative feeling scale ratings as a sign
that the lifter wouldn’t want to continue with
the training program.
When comparing sRPE between high- and
low-load training, some research has shown
low-load training to elicit a greater sRPE
(6, 30, 31), and some research has indicated high-load training to elicit a greater sRPE
(32, 33, 34, 35). For example, Pritchett et al
(30) found that 20 recreationally trained men
reported a significantly higher sRPE following three sets on six exercises to failure at
60% than at 90% of 1RM. In agreement with
Prichett (30), Shimano et al (31) found that
both trained and untrained individuals reported higher sRPE following one set to failure at
60% of 1RM on the squat, bench press, and curl
compared to one set to failure at both 80% and
90% of 1RM. Further, Ribeiro et al (6 - MASS
Review) found that trained men reported higher sRPE, greater discomfort (on a 0-10 Likert
scale), and lower feeling scale ratings (more
displeasure) when following three sets to failure with a 25-30RM load on the bench press,
hack squat, and lat-pulldown versus three sets
to failure with an 8-12RM load. Based upon
the above, I previously questioned the utility
of using solely (more later on mixing high and
low loads) low-load training to maximize hypertrophy because I theorized adherence and
long-term enjoyment might be lower.
Other research has found that higher loads
lead to a greater perceptual response than
lower loads; however, those findings are
likely a product of the higher load condition
training closer to failure. For example, in a
crossover design, Gearhart (32) had trained
men and women perform 1 × 5 at 90% of
1RM in one condition and 1 × 15 at 30% of
1RM in another condition on seven different
exercises. Subjects reported RPE after each
rep in the 90% condition and after every three
reps in the 30% condition. When all RPE
scores were averaged together on each exercise, the RPE was significantly higher in the
90% condition. However, 90% of 1RM for
22
five reps is far closer to failure or at failure
(or past failure on some exercises), while 15
reps at 30% might have left some individuals
with roughly 15 repetitions in reserve (RIR).
Other studies have also found higher RPE
following high- versus low-load training
(33, 34, 35), but all have had subjects train
closer to failure in the high-load condition.
Overall, load lifted may play some role in the
acute perceptual and affective response, but
at both high and low loads per set effort, independent of load, may be the determining
factor. For instance, the previously discussed
Lasevicius et al (20 – MASS Review) study
had some subjects perform unilateral leg extensions to failure at 80% of 1RM on one leg
and perform leg extensions shy of failure at
80% on the other leg. Another group of subjects performed unilateral leg extensions to
failure and non-failure at 30% of 1RM. Importantly, in each group, the researchers had the
non-failure leg perform more sets to equate
volume load with the failure leg. The researchers assessed sRPE 30 minutes after each session. The subjects reported significantly higher sRPEs in both failure conditions with no
difference between high-load failure and lowload failure conditions. These findings from
Lasevicius can be seen in Figure 4, which is
from a previous article written by Greg.
Where Does The Presently Reviewed Study
Fit?
Findings from the presently reviewed study
(1, 2) are, in part, at odds with the current
consensus. First, the researchers found that
both set and sRPE were not significantly
different between high- and low-load training. This lack of difference is despite both
groups training to failure and the low-load
group performing significantly more volume
load and spending more time under tension.
23
As previously noted, Ribeiro et al (6) found
that sRPE was significantly higher with low
load than with high-load training when lifers
performed both protocols to failure and sets
were equated. Further, Ribeiro reported that
subjects had feeling scale scores of displeasure after low-load failure training and scores
of pleasure after high-load training. Yet, the
presently reviewed study found similar scores
of pleasure (Table 5) after both protocols.
The other findings from Dinyer et al (2) were
that sRPE values tended to increase over the
study while feeling scale ratings tended to
decrease. Although the researchers did not
statistically analyze it, Figure 4 from Lasevicius et al (20) shows that sRPE values did not
seem to change, on average, from the beginning to the end of the study. Speculatively, the
increase in sRPE in Dinyer could be due to
accumulated fatigue since the subjects were
untrained, while Lasevicius’ subjects were
trained, but we cannot be sure. However, the
decline in feeling scale ratings over time (Table 5) along with the increase in sRPE (Table
4), makes sense. In other words, the women
tended to express lower ratings of pleasure
when they perceived more effort.
Perhaps the most critical finding of the presently reviewed study is that feeling scale
scores at all time points (immediately, 15
minutes, and 60 minutes post-training) were
positively related to the intention to exercise
within the next week and month (Figure 2AB).
My previous hesitation in recommending low
loads over the long term was due to a potential lack of adherence; however, the presently
reviewed study suggests that my position may
have been unfounded. Interestingly, previous
research has not always found feeling scale
scores to be predictive of intent to exercise in
the future. Specifically, Focht et al (36) observed trained women to record higher (more
pleasurable) feeling scale scores following
training at 40% of 1RM than at 70% of 1RM.
However, despite lower feeling scale scores,
subjects had a greater intention to exercise
following the 70% of 1RM condition in the
future. Importantly, when intent to exercise
is assessed, researchers ask how likely someone is to perform the same exercise session
again within the next week or month. So,
even though subjects did not find the moderate-load 70% of 1RM training as pleasurable
as the low-load 40% training, they indicated
a greater likelihood to repeat the training.
One explanation is that feeling scale scores
pick up on various factors related to mood,
emotion, physical fatigue, and a sense of accomplishment; thus, subjects may have been
more fatigued after the 70% condition. However, that fatigue did not deter them from
wanting to repeat the session. Additionally,
the subjects in Focht’s study were trained;
thus, it’s possible they knew that higher loads
were preferable for strength gains; thus, their
greater desire to continue performing the
70% training was partly based upon wanting
to maximize improvement.
Ultimately, the presently reviewed findings
are not in lockstep with previous literature;
however, there isn’t much data on the longterm affective response to high- and low-load
training. Therefore, I am not yet wholly convinced that using solely low loads over the
long-term is a viable strategy, especially if
low loads need to be performed to failure (or
24
at least closer to failure than when using high
loads) to maximize muscle growth, which is
still open for debate. Importantly, and as with
most training concepts, training with high
or low loads is not an all or none principle;
instead, it can be intertwined into the same
training program.
Practical Implementation
If one thing is clear from this article, both
high- (and moderate-) and low-load training
have merit. Sure, if you’re interested in maximizing your squat or bench press strength,
you must train heavy at some point. However,
even someone interested in top-end strength
could still use low load training for hypertrophy, especially on assistance work. Further,
if you’re a physique athlete or just interested
in generally growing muscle, then either low
or high loads should work just fine.
As noted earlier, a lifter doesn’t have to
make a binary choice between low or high
loads. I think we too often think training decisions are a binary choice. For example, research has debated if it’s better to prescribe
load with RIR or velocity; however, as I’ve
pointed out before, those concepts can be intertwined, and the specific situation might
dictate which autoregulation strategy is used.
Further, suppose one training strategy does
tend to work better than another. In that case,
we often become antagonistic toward the
inferior approach, but it’s important to remember that it might work to some degree.
Besides, research mostly looks at mean data,
and in most studies, at least a few individuals
respond better to the “inferior” protocol. In
the present context, high-load training leads
to better strength gains than low-load train-
JUST BECAUSE SOME ARE
USING LOW LOADS DOES
NOT MEAN THEY WON’T
GAIN ANY STRENGTH
ing, but in research low-load training groups
still get stronger. In fact, many of the strength
tests in research are 1RM strength, and low
loads are not specific to 1RM testing. For example, in the Fliss study (24), absolute muscular endurance (reps performed) improved
more with low-load training than with highload training. In other words, adaptations
tend to be specific to the training protocol.
Besides, if someone is just generally training
to gain muscle, then 1RM strength is not of
great importance to the person. Therefore,
just because some are using low loads does
not mean they won’t gain any strength.
The specific training phase or exercise may
also dictate whether high- or low-load training is used. For example, if a powerlifter is in
an intensity block close to a meet, the lifter
likely wouldn’t use low-load training since
it’s too unspecific to their current goal. However, if a powerlifter is in a volume block six
months out from a meet, they might include
some low-load training to accumulate volume. Specifically, a powerlifter may utilize
low loads on assistance movements like curls,
triceps extensions, or rows while training in a
more traditional 6-15 hypertrophy rep range
on the competition lifts. Another outside-of-
25
the-box example would be for a powerlifter
to work up to a heavy squat or bench press
single (e.g., 1 rep at 1 RIR) a couple of times
per week and then back off to 40% of 1RM
for their volume work. The bottom line is that
there are many ways to intertwine the different loading schemes.
Ultimately, suppose someone is training for
general purposes (e.g., hypertrophy, body
composition, general health, and fitness),
their training should meet the main tenets
of an appropriate program. In that case, the
details (i.e., high or low loads, periodization
type, programming strategies) can be filled
with what they enjoy and will sustain. For
example, if an individual likes the exhaustive
feeling of performing low loads to failure
but is worried that it will become too much
over time, they should include various loading schemes. For others who have a specific
goal (i.e., powerlifting, physique, etc.), the
programming details will need to be filled
with a strategy that will best prepare the lifer for that goal; however, this often includes
various loading paradigms. Besides, even if
you’re a powerlifter, performing sets of 20
reps on curls is still fun.
To finish up this monster of an article are tables showing examples of how to intertwine
high- and low-load training.
Table 7 demonstrates using heavy singles on
the main lifts and low loads on the back-off
sets. You’ll notice that the squat and bench
press frequency is twice per week and sessions on the same exercise are separated by
72 hours. I chose a frequency of twice per
26
week as opposed to three times per week and
to spread out the sessions in case there’s any
lingering fatigue for a couple of days from
the low-load training. Of course, lifters can
perform more assistance work after the main
lifts and on an off day, but this table is a simple example of isolating the concept of heavy
singles followed by performing volume with
low-load training.
Table 8 shows how to integrate high, moderate, and low loads throughout a week. This
table also shows that the main lifts (squat
and deadlift in this example) are trained with
moderate to high loads, and some assistance
work is programmed with low loads. When
intertwining multiple training strategies,
some nuanced details must be manipulated
to make everything work, and that is no exception here. For example, on Wednesday, I
did not include squats to consider that there
might still be some general fatigue from the
low-load, high rep failure training on Monday; thus, I utilized leg press as the main
lift. Further, Wednesday is largely devoid
of low-load training also to account for lingering fatigue from Monday. There is low
load non-failure training for one exercise
on Wednesday (seated row). Even though
non-failure low load training may be suboptimal for muscle growth, it still does something and is an easy way to add some volume
if fatigue lingers from Monday’s session. Friday’s heavy squats and deadlifts are placed
as far as possible (96 hours) away from the
low-load failure training to ensure the lifter
is fresh. For example, walking lunges don’t
specify that the 20 steps are to actual failure
because that’s extraordinarily difficult on
walking lunges. Further, low-load assistance
movements have a 10 rep range spread (20-30
RM) because, in practice, it may be difficult
to know your 20RM, 25RM, or 30RM load.
Further, a lifter may end up getting more or
fewer reps than predicted on some assistance
movements since lifters typically don’t keep
the movement pattern as strict on those movements (i.e., rows, curls, etc.) as they do on the
main lifts. Therefore, just generally aiming
for that rep range should be sufficient to perform low-load training to failure effectively.
Lastly, Table 8 is just a conceptual example,
and there are many ways to intertwine highand low-load training. Additionally, someone
could include many other exercises instead of
those chosen.
Next Steps
The last time I covered low- versus high-load
training, I called for a long-term study on
high- versus low-load training that assessed
the perceptual and affective response. Well,
we got that study, but I’m still unfulfilled. I
think the next step is replicating the presently reviewed study using trained individuals.
Further, in a dream world, I’d like to see two
replications in trained individuals, one that
uses a compound exercise that recruits a lot
of musculature like the squat and another that
uses single-joint exercises only (e.g., biceps
curls and triceps extensions).
27
APPLICATION AND TAKEAWAYS
1. Anderson et al (1) and Dinyer et al (2) found that long-term strength and body
composition changes were not different between groups of untrained women
performing low-load (30% of 1RM) and high-load (80% of 1RM) training to failure.
Further, this study found that the perceptual and affective responses to high and
low loads were not significantly different.
2. The totality of literature in this area suggests that high loads are needed to
maximize strength, but muscle growth can be maximized independently of load
as long as load is ≥30% of 1RM.
3. Overall, using high or low loads does not have to be a binary choice for coaches
and lifters. All loading schemes can and probably should be intertwined. For
example, a powerlifter could use high loads on the main lifts but low loads on
assistance work to accumulate volume and facilitate hypertrophy.
4. For general training purposes, if someone enjoys one style of training to another
and can adhere to that style over the long-term, I would encourage them
to train as they see fit. Training for general fitness or muscle growth allows
for considerable flexibility in programming; thus, programming based upon
preference is perfectly fine if the programming is sustainable.
28
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32
Study Reviewed: Characteristics Of A Person-Centred Coaching Approach. Garner et al.
(2022)
Key Models and Theories
for Effective Coaching
BY ERIC TREXLER
Coaching is not a program delivery service, but a highly
interactive profession requiring a comprehensive set of skills and
perspectives. Every client is different, but this article covers a
few key theories and frameworks that set the stage for effective
coaching practices.
33
KEY POINTS
1. In the present study, the researchers conducted “stimulated recall interviews”
with several alpine ski coaches to explore the role of humility in personcentered coaching.
2. Interview results confirmed the researchers’ hypothesis that humility plays
an important role in facilitating a person-centered approach to coaching, and
enabled the researchers to propose the “POWA” conceptual model of humility
(Perspective, Other-centered focus, Willingness to learn, and Accurate selfassessment).
3. Every client is different, but combining POWA with effective goal-setting
guidance, self-determination theory (SDT), and the COM-B (capability,
opportunity, motivation, behavior) model of behavior change provides a strong
and generalizable foundation for effective coaching (and general leadership)
practices.
M
any MASS readers are currently trainers or coaches, and others
may consider coaching at some
point in the future. If you’re a current coach
(or an aspiring coach, or a leader or manager
of any kind), it’s important to ask yourself a
critical question: who is this really about?
If we take an objective look at the fitness
industry, a lot of coaches see themselves as
the star of the coach-client relationship, and
make their coaching services mostly (or all)
about themselves. However, the concept of
person-centered or other-centered coaching
offers us a better path. Ideally, a coach understands that their role is to serve and support
their client, acts in the best interest of their
client, and acknowledges that their client is a
whole person, not just an athlete or competitor. That sounds nice from a conceptual perspective, but you might be wondering what
types of characteristics, actions, and viewpoints enable someone to effectively facilitate
a person-centered approach in their coaching.
The authors of the presently reviewed study
(1) were wondering the exact same thing.
In this study, the researchers recruited five alpine ski coaches (four males and one female)
who self-identified as being “person-centered” coaches. All five were very experienced, followed the same person-centered
training pathway, and were confirmed to
practice in a person-centered manner by their
employers and colleagues. Each coach took
a client through a typical coaching session
(three to six hours in duration), while the client wore a recording device (audio and video)
fixed to their chest. Within 48 hours of the
coaching session, the research team reviewed
the footage, extracted key clips, and conducted a 60-90 minute semi-structured interview
with each coach. After coding and reviewing
transcripts of the interviews, the researchers
found that humility was a key characteristic
underpinning a person-centered approach to
34
coaching. Further, they identified four key
themes in the practice of experienced, person-centered coaches. These themes enabled
the researchers to propose the POWA (Perspective, Other-centered focus, Willingness
to learn, and Accurate self-assessment) model of humility, which is specifically related to
effective coaching practices.
If you’re a coach, manager, or leader, you’ll
need a variety of complementary skills to
translate a great plan into effective action.
This article will explore the practical applications of the POWA model, while tying in other evidence-based theories and frameworks to
help coaches more effectively navigate communication, goal-setting, motivation support,
and behavior change.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was to explore, via structured interviews,
two primary research questions: 1) “What
causal mechanisms facilitate the manifestation of person-centered intent in coach behavior?” and 2) “To what extent does humility underpin a person-centered approach
to coaching?”
Hypotheses
The researchers hypothesized that humility
is “an integral component of idealized influence” that “provides a fitting framework
for person-centered coaching,” and that
“person-centered intentions are governed
by humility.”
Subjects and Methods
Subjects
Five alpine ski coaches (four male, one female, aged 33-50 years old) participated in
the presently reviewed study. The selection
of this sample was very unique compared
to the typical MASS study. In most studies
that we review, the researchers are aiming to
get as random a sample as they feasibly can;
recruitment efforts utilize very passive processes that aim to minimize any influence or
bias from the research team, with the intention of finding individuals who are representative of the broader population. In the present study, the researchers were specifically
seeking out experienced coaches who utilize
and emphasize a person-centered approach in
their coaching. As the researchers stated: “all
coaches followed the same coach education
pathway that is explicitly learner-centered,
and conversations with employers and professional colleagues reinforced the appropriateness of participant sampling.” In addition,
the lead researcher knew all of the participants for over 10 years, so the research team
was very certain that they had found coaches
who adopt a person-centered focus in their
practice.
These coaches were very experienced. All
held international-level credentials and had
16-30 years of coaching experience. In addition, four of the five had been working as
coach educators (i.e., training and educating
other coaches) for 18-25 years; the fifth had
been doing so for eight years, but was considered an expert by their peers and employer. In
summary, this study used a pretty intensive
35
recruitment and selection process for study
participants. In some contexts and scenarios,
this could be a massive study limitation, but
I don’t believe it’s a major issue for this particular study.
Methods
For this study, data collection occurred in the
French Alps. It was a qualitative study; rather than gathering numerical measurements
or questionnaire responses, the researchers
conducted long-form interviews with the
coaches to obtain a deeper understanding
of their thoughts, actions, and perspectives.
The researchers utilized a really interesting
approach to the interviews, which is called
“stimulated recall.” As the researchers put it:
“Stimulated recall interview is an introspective research procedure that uses audio and
video footage to assist the recall and interpretation of experience and is suggested as
an effective way to facilitate immersion and
specificity of recall.” To implement this approach, the researchers had each coach lead a
full coaching session (lasting between three
to six hours) with a client, and fixed a GoPro camera to the client’s chest. The cameras
were positioned with the intention of being
as unobtrusive as possible, with the goal of
blending in with the client’s typical ski apparel. By making the session feel as natural
as possible (rather than having a member of
the research team join the session or asking
the coach to wear extra recording gear), the
researchers hoped to minimize the risk of influencing or biasing the coaches’ actions.
The cameras captured audio and video footage of the entire coaching session, which
was then reviewed and edited by the research
team (in order to extract and focus on the
most important elements). These shortened
videos were used to guide semi-structured
interviews, which consisted of the researchers asking some questions and showing some
video clips to facilitate better recall of the
coaching session. The interviews were 60-90
minutes in duration, and took place within
48 hours of the coaching session. They were
recorded, which enables the research team
to revisit and analyze the responses without
relying on memory. The following questions
served as a starting point for the semi-structured interviews:
1. “Was there a reason for that particular behavior/exchange/gesture etc.?”
2. “Why did you behave in that way, was it a
conscious decision or just something you
do?”
3. “Can you tell me what was going through
your mind at that moment?”
After completion, the research team completed a “thematic analysis” of the interviews.
First, they transcribed the interviews verbatim and re-read the transcripts. Then, they
coded the responses and aimed to identify
overarching themes and subthemes to gain
a more structured and systematized understanding of the survey responses.
Findings
Here’s where we reach a fork in the road.
On one hand, we could adopt a very narrow
scope of focus for the current article; that
would involve getting deep into the weeds to
36
explore individual quotes from the interviews
in order to appraise and audit the researchers’ perspectives and conclusions pertaining
to the quotes provided. On the other hand,
we could adopt a broader scope of focus; that
would involve summarizing the key themes
and findings extracted from the interviews,
and allocating more focus toward practical
application and combining some key theories
and conceptual frameworks to guide coaching practices. I have elected to pursue the latter option in this article, and I suspect at least
98% of readers are happy to hear that.
Through careful and thorough analysis of interview responses, the researchers were able
to broadly confirm their hypothesis that humility was a key virtue or characteristic underpinning the practices of person-centered
coaches. Digging deeper, they identified four
major themes.
The first theme was other-centeredness. Put
simply, the coaches understood that they
weren’t the star of the show – their focus
was on making decisions that benefit the
client rather than themselves, and providing
instruction based on the unique and immediate needs of the client. Multiple subthemes
emerged within this topic. By facilitating
learning through structured autonomy, clients were placed at the center of their own
learning process, which allowed them to play
an active role in that process. By building
trust, coaches were acknowledging that understanding their clients and working to earn
their trust was a critical prerequisite for facilitating client success. By using humor, coaches were acknowledging that client comfort is
an important element of coach-client interac-
tions, and that humor is a tool that can ease
concerns, favorably influence the tone of a
conversation, and make certain instructions
or lessons more memorable (and, by extension, more effective for the client). Finally,
by implementing accurate social assessment,
the coaches were able to more effectively
guide their conversations and interactions
with clients. This concept is closely related
to emotional intelligence and empathic accuracy. The general idea is that coaches make
an effort to “read” the client, and to use that
assessment to dictate how they interact. I’m
sure we’ve all had experiences where we said
a joke at the wrong time, tried to cheer someone up before they were ready for it, or upset
someone by making light of a situation that
was important to them, while we (incorrectly) perceived it as fairly inconsequential. In
other words, someone who is truly focused
on their client will make an effort to “read the
room,” meet the client where they’re at, and
adjust the tone and direction of the conversation based on verbal and nonverbal feedback.
The second theme was accurate self-assessment. This involved two major subthemes:
accurate awareness of role, and accurate
awareness of abilities. A person-centered
coach understands that the role of coach and
client are inherently different, and the coach
has a duty to effectively serve the client. Ideally, a coach has sufficient self-awareness to
understand that they’re in a position of authority within the coach-client relationship,
while also having sufficient humility to understand that their job is to work to ensure
the best possible outcomes for the client. In
other words, a coach must have a sense of
37
self-value that encourages them to live up to
their role and duty as a coach, without having an inflated opinion of themself or serving
their own self-interests. Aside from a coach’s
awareness of their role, they must also have
accurate awareness of their abilities. If they
overstate their abilities (or bring attention to
their abilities at inappropriate times), it could
be perceived as arrogance. However, if they
convincingly understate their abilities, it
could undermine their credibility in the eyes
of the client. Ideally, a coach will truly understand their strengths and weaknesses, and
discuss them with transparency when it’s
necessary and relevant to do so.
The third theme was willingness to learn. This
one is pretty straightforward – an effective
coach has the humility to admit when they’ve
made a mistake, admit when they don’t know
an answer, or share relatable stories about
contextually relevant struggles of their own.
From a client’s perspective, it can be very
discouraging to believe that your coach has
never struggled with anything, while you’re
running into several challenges that feel insurmountable. A little bit of openness, transparency, and humility goes a long way in this
regard. A good coach is always willing to
entertain questions, explain thought processes, and consider new ideas when conversing
with their clients.
The fourth theme was perspective – more specifically, the ability to step back, look at the
big picture, and adopt a “wide-angled” perspective. This type of perspective allocates a
sufficient amount of focus to the long-term
development and interests of the client, rather than being entirely focused on short-term,
decontextualized objectives or the minutiae
of day-to-day coaching tasks. For example,
a coach with a short-term, self-focused perspective might simply give a client a generic
food plan when asked. A coach interested in
fostering autonomy, competence, and self-efficacy might instead suggest a collaborative
exercise that refines skills related to portion
size estimation, macronutrient tracking, food
preparation, and meal planning. The generic food plan is a quick fix, while the latter
strategy builds an important set of skills for
the future. A coach with a short-term, self-focused perspective might coach in a manner
that keeps clients dependent on them, such
that a client will feel a need to stick around
for a long time. In contrast, a coach interested in fostering long-term development will
coach in a way that makes them less necessary over time, such that the client acquires
the skills, knowledge, experience, and confidence to take matters into their own hands.
A good coach doesn’t deliver a scattered assortment of quick-fix directives, but explains
how something is done, why it’s done, and
how it fits within the context of long-term development. As the researchers explain: “Perspective of this kind allows the coach to help
the learner rationalize their progress, negotiate realistic goals, manage expectations, and
maintain motivation.”
After identifying these major themes, the researchers formulated the POWA model of
humility, which is summarized in Figure 1.
Very importantly, the researchers also highlighted the need for a balanced approach to
coaching. A coach should be other-centered
and focused on the client’s best interests, but
38
that doesn’t mean they should let clients walk
all over them – boundaries are important, and
sometimes a coach has to deliver the instruction or interventions that a client needs, rather
than the one the client wants. A coach should
achieve a balanced self-assessment; they
shouldn’t be arrogant or egotistical, but also
shouldn’t be so self-denigrating as to undermine their own credibility. A coach should be
willing to learn, but within reasonable limits –
they shouldn’t act as if they already know everything, but they also shouldn’t act as if they
know nothing at all. Finally, a coach should
be sure to maintain a perspective that keeps
the “big picture” and long-term development
in mind, but not to the extent that it detracts
from the importance of day-to-day tasks and
short-term objectives that foster incremental
development. This concept of “balance” in
the POWA model is depicted in Figure 2.
Interpretation
It’s difficult to deliver the results of a qualitative study without interpreting as you
go. So, rather than rehashing the Findings
section, I want to focus on tying some important topics together. As I read through
this paper, I noticed some pretty substantial
overlap with other important theories and
frameworks within the realm of coaching,
psychology, and behavior change. As a result, I intend to present a cohesive set of
39
models that support a solid, comprehensive
foundation for effective coaching. But before I get into the specifics, I want to address
a few limitations, and I also want to explain
why it’s valuable to use these types of models and theories in the first place.
Let’s begin with the limitations. Compared
to the typical study reviewed in MASS, the
methods and design of the present study require us to grant a few more assumptions than
we’re accustomed to. The researchers essentially hand-picked a sample of person-centered coaches for this study, and they carried
out the study under the implicit assumptions
that: 1) person-centered coaching is generally an advisable approach to coaching; 2) the
selected coaches are very good at what they
do; 3) the selected coaches represent good
examples of how the typical person-centered
coach operates; and 4) the selected coaches
did not meaningfully modify their behavior
based on the knowledge that their coaching
sessions were being recorded. This is an interesting scenario, because the selective,
“hands-on” nature of the recruitment and enrollment process would generally be viewed
as a negative thing for generalizability (i.e.,
it reduces the likelihood that this sample of
coaches is truly representative of the broader
population of coaches). However, it simultaneously strengthens our ability to grant a
few of the previously listed assumptions; by
actively recruiting from a group of coaches
that they personally know, the researchers
can more confidently assure readers that the
coaches are effective, well-respected by their
peers, and representative of the person-centered coaching approach. So, we should inter-
pret the findings in light of these limitations,
but I don’t believe they dramatically detract
from the internal validity of the study. Now,
moving on – why do we bother with all these
models and theoretical frameworks pertaining to psychology and behavior change in the
first place?
If you’re old enough to be reading MASS,
you’ve probably already figured out that people can be different. Very different. As a result, it’s impossible to construct the “perfect
coaching playbook,” or a nice little flowchart
that enables you to effectively navigate all
elements of coaching. There is no singular
approach that will work optimally for every
single client you’ll encounter, and all models pertaining to human psychology and
behavior are inherently limited. However,
these types of models and conceptual frameworks help us set a foundation for how we
approach our coaching – it’s a starting point
from which we invariably customize and adjust as we better understand each client and
their unique characteristics and circumstances. There is plenty of spirited debate across
multiple academic fields about which exact
models and frameworks are “most correct,”
and I’m in no place to unilaterally settle those
scores within this article. Rather, I’d like to
present and discuss some key evidence-based
models that form the basis of my coaching,
and might be helpful to MASS readers who
find themselves in coaching roles (or other
leadership positions).
Coaching is a profession requiring a multifaceted set of skills; a coach needs to construct
and deliver effective programs, communicate
well, and help a client identify goals, sustain
40
motivation, and change behaviors along the
way. When it comes to developing exercise
and nutrition programs, we won’t discuss that
here – we’ve got a few hundred MASS articles covering the ins and outs of training and
nutrition. So, let’s shift our focus to the other elements of effective coaching, and some
conceptual frameworks to work from.
Goal Setting
When you start working with a new client, it
can sometimes be tempting to speed through
the goal setting process. Coaches often specialize and focus on a particular training population, which leads to a lot of categorization
and oversimplification of client goals. For
example, your client might tell you in a very
straightforward manner that their goal is fat
loss. “Sounds great,” you say. “I get plenty
of folks with fat loss goals. You’re in good
hands.” Notice how this response violates the
spirit of person-centered coaching and the
POWA model – rather than focusing on the
perspective of the client, the answer focuses
on the coach’s experience and treats the client
like a category rather than a unique person.
For some coaches, the goal-setting process
ends there and is rarely (if ever) revisited.
For others, they might take it a step further
and set one or more SMART goals, as is
commonly taught to up-and-coming personal trainers. However, I would contend that
coaches do themselves a disservice when
they take this approach to goal setting. First,
as Dr. Helms covered in a previous MASS
article, the SMART goal framework leaves
a lot to be desired, in spite of its widespread
adoption and popularity. Second, I would
exercise great caution before sorting goals
into broad categories that lack nuance. For
example, fat loss goals take many forms.
Are you focusing on fat loss because of a
concerning visit to the doctor? Because
you’d like to feel more confident about your
physique? Because you want to win a bodybuilding competition? Because you want
to get into a different powerlifting weight
class? The basic nuts and bolts of a fat loss
plan might be similar across this wide range
of scenarios, but they certainly wouldn’t be
coached in an identical manner.
In the spirit of person-centered coaching and
the POWA model of humility, it’s valuable to
explore each client’s goal in a more nuanced
way. Doing so will help the client explore
their goal more deeply (which might be an illuminating experience for them), while also
building trust and rapport by demonstrating
that you value their goals and are interested in
understanding them as a person. This process
will also enable you to communicate with them
more effectively, tailor their programs in a
more individualized manner, and better understand their motivations and challenges along
the way. In lieu of the SMART goal framework, I would recommend working from the
goal hierarchy framework described by Höchli et al (2). I have described this framework in
more detail elsewhere, but the concept can be
summarized pretty concisely.
The general idea is that we should focus
on a structured hierarchy of interconnected
goals rather than focusing on an assortment
of seemingly independent goals. The goal
hierarchy is anchored by a superordinate
goal, which is a broad, overarching goal that
is vaguely reminiscent of a value. Ideally,
41
a superordinate goal should clearly reflect
what’s important to you, and should reflect
an idealized and aspirational version of
one’s self. In many cases, the superordinate
goal tells us why we’re even bothering with
all of this goal striving in the first place. Just
below the superordinate goal in the goal hierarchy, we’ll find a number of intermediate
goals. These are less abstract than superordinate goals, but still reflect relatively general
courses of action that lead someone toward
their superordinate goal. The lowest level
of the goal hierarchy consists of subordinate goals, which are more like the SMART
goals we’re used to encountering in the fitness world. They tend to be very specific,
and often include quantitative targets and
details about how and when certains tasks
will be completed in order to make progress
toward the intermediate goals they support.
Höchli et al provide an example of a goal
hierarchy, which is depicted in Figure 3.
Motivation
Goal hierarchies are great because they get
a client thinking deeply about self-actualization, or striving to become the best (from
their perspective) version of themselves. A
well-constructed and well-aligned goal hierarchy also sets the stage for suitable motivations levels; I believe a great goal hierarchy is
necessary, but not sufficient, for motivation
during goal pursuit. It’s hard to muster up a
high level of motivation for a goal that fails
to excite you or align with your values, but
even a great goal hierarchy will require some
additional thought and support when it comes
to sustaining motivation over time. When we
combine the concept of self-actualization
with the autonomy-supporting elements of
the POWA framework, all signs point to leveraging self-determination theory as a helpful framework for supporting sustained motivation over time.
We’ve talked about self-determination the-
42
ory several times before (one, two, three),
so I’ll keep it brief in this article. In short,
self-determination theory (3) argues that people have an inherent drive toward self-improvement and achievement of a more idealized version of themselves, but they will
struggle to aggressively pursue this process
when they lack the foundational conditions
necessary. The foundational conditions refer to three psychological needs, which have
been defined by Patrick et al (4): relatedness
(“the need to feel close to and understood by
important others”), autonomy (“the need to
feel choiceful and volitional, as the originator of one’s actions”), and competence (“the
need to feel capable of achieving desired outcomes”). When these psychological needs
are sufficiently met, individuals feel empowered, enthusiastic, and motivated to pursue
goals that take them closer to their perceived
version of their best self (Figure 4).
Self-determination theory also distinguishes between different types of motivation (5)
that exist on a spectrum (Figure 5). The lowest-quality form of motivation is amotivation,
or a complete absence of motivation. The
second lowest-quality form is extrinsic motivation, which might take many coaches by
surprise. Many coaches seem to operate under
the assumption that their job is to provide the
extrinsic motivation, while the client’s job is
to cultivate the intrinsic motivation. Self-determination theory offers us a better perspective to operate from; it suggests that a coach’s
job is to operate in a manner that facilitates a
transition from lower forms of motivation to
higher forms of motivation, ultimately leading
to sustained cultivation of intrinsic motivation.
Intrinsic motivation comes from within, but
it’s absolutely affected by external factors, and
the words and actions of a coach are among
the most impactful external factors.
43
One appealing aspect of self-determination
theory is that it aligns very well with the
POWA framework discussed in the present
article. As you go through different themes
and subthemes, you see clear connections to
key psychological needs. When a coach has
a willingness to learn and makes accurate
self-assessments of their role and abilities,
clients feel like their voice really matters.
They feel like they’re working with a coach
to solve problems rather than receiving a constant stream of one-sided directives, and that
fosters a sense of relatedness. The POWA
model also includes subthemes of humor and
trust-building, which further solidifies the
sense of relatedness. One critical subtheme
of other-centeredness in the POWA model is
supporting client learning through structured
autonomy which, as you might have guessed,
is directly supportive of client autonomy.
Finally, by adopting a client-centered and
wide-angled perspective that emphasizes
long-term development and skill acquisition,
clients are more likely to build up the skills
they need to develop a sense of competence,
and less likely to be discouraged by shortterm struggles or setbacks. A second appealing aspect of self-determination theory is that
it seems to work quite well for coaches in
athletic settings, where it has been correlated
with a wide range of positive outcomes including satisfaction of psychological needs,
intrinsic motivation, general well-being, positive affect, life satisfaction, self-esteem, performance and achievement, effort, and athlete-coach relationship quality (6).
You might be sold on the general premise of
self-determination theory, while still feeling
uncertain about exactly how you would apply
it as a coach. Fortunately, we’ve got a whole
MASS article on how a coach might work to
support their athletes by leveraging self-determination theory. However, the shortened
version is quite simple: use the POWA model
to dictate your coaching perspective and interactions, make sure your client is pursuing
a goal hierarchy that leads to self-actualization, provide autonomy-supporting feedback
(7), seek to form trust and rapport with your
clients, and focus on helping them build a robust skill set that will set them up for successful long-term goal striving.
Behavior change
Having a great goal hierarchy and well-supported psychological needs can be viewed as
necessary, but not sufficient, for promoting
behavior change. There are other factors influencing behavior change, which is where the
COM-B model comes into play. COM-B is an
acronym that stands for capability, opportunity, motivation, and behavior. As defined by
Michie et al (8), capability refers to having the
skills, knowledge, psychological capacity, and
physical capacity to engage in a given activity. Opportunity refers to “all the factors that
lie outside the individual that make the behavior possible or prompt it.” For example, this
is where access to resources, a suitable environment, and social support would come into
play. Motivation refers to “all those brain processes that energize and direct behavior, not
just goals and conscious decision-making.” As
it is presently defined, the motivation component includes goals (which is compatible with
the concept of a well-aligned goal hierarchy),
but encompasses much more than goals alone,
44
which is compatible with self-determination
theory. The COM-B model posits that capability, opportunity, and motivation all directly
impact behavior. While opportunity and capability directly influence behavior, they also
indirectly influence behavior by directly influencing motivation (Figure 6). This mediating
relationship makes motivation the centerpiece
of the COM-B model, which is why it ties in
so well with self-determination theory.
As stated by Willmott et al (9): “According
to the COM-B model, for a given behavior to
occur, at a given moment, one must have the
capability and opportunity to engage in the behavior, and the strength of motivation to engage in the behavior must be greater than for
any other competing behavior.” In this sense,
modification of behavior can be prompted by
intentional modification of factors influencing
capability, opportunity, or motivation (10). A
well-crafted goal hierarchy identifies key behaviors that ought to be changed, while the
COM-B model helps us identify impactful
barriers when an individual is facing challenges during the behavior change process.
When an individual is struggling with behavior change (or engaging in behaviors that
are incompatible with their stated goals),
it’s helpful to work through the items independently. Are they lacking sufficient prerequisites pertaining to capability? Are they
lacking social or environmental support or
infrastructure necessary for sufficient opportunity? Have they slipped to a lower category
of the motivation spectrum? As you can see,
the COM-B model is a tool for identifying
where barriers or challenges exist, such that
one can devise a strategy to rectify or overcome the observed obstacle to support successful behavior change. Ideally, a coach
would use the POWA framework to dictate
how they approach and converse about the
subject, while adopting a wide-angled, longterm perspective and allowing clients to learn
through structured autonomy whenever possible. Finally, to assuage any concerns pertaining to the relevance of the COM-B model, it’s important to note that research directly
supports its application within the realm of
physical activity and eating behaviors (9).
Summary
To wrap up this section, I want to summarize how all of these pieces fit together into
a cohesive coaching framework. But before
45
I do that, I want to reiterate that every single
client is different. The art of coaching is the
art of individualization, which pertains to all
elements of the coaching process (not just the
nuts and bolts of program design). Some clients might be overwhelmed by too much autonomy, or might perceive that you’re being
lazy by outsourcing too many decisions to
them. Some clients might perceive strategies
to build trust and rapport as a bit invasive,
and would prefer a coach-client relationship
that’s more formal than friendly. Some clients might dislike collaborative goal-setting,
as it could prompt them to divulge thoughts or
feelings that they aren’t comfortable sharing,
or it might seem as if you’re guiding them to
a goal that serves your interests rather than
theirs. So, the following framework is merely
a generalized starting point; a great deal of
customization and individualization may be
required on a client-by-client basis.
First, coaches can lean on the POWA
framework to form the basis of their overall perspective toward coaching and communication. This solidifies a commitment
to person-centered coaching, working with
your client’s best interests in mind, and
maintaining the humility required to be an
effective and supportive coach. This carries
into the process of developing a goal hierarchy, which prompts clients to do some
introspection, while also allowing you to
build trust and rapport while learning more
about their personality and values. A wellaligned goal hierarchy identifies what an
individual’s idealized self looks like, while
also setting the stage for intrinsic motivation. That’s where self-determination theory
comes into play – by supporting key psychological needs (competence, relatedness,
and autonomy), a coach can help clients cultivate and sustain the intrinsic motivation
that prompts goal-striving behaviors (identified in the goal hierarchy) that bring them
46
incrementally closer to self-actualization.
The POWA framework provides excellent
guidance for acting and communicating in a
manner that lends support to the psychological needs outlined in self-determination theory, ultimately promoting development of
high-quality motivation. The COM-B model recognizes that motivation plays a pivotal
role in behavior change, but there are other
factors (namely capability and opportunity)
that influence both motivation and behavior.
With the POWA framework and goal hierarchy concept serving as backdrops to one’s
coaching approach, Figure 7 depicts how
coaches may choose to merge elements of
self-determination theory and the COM-B
model of behavior change into a cohesive
framework.
After a client gets rolling for a while, challenges are inevitable. This framework allows
a pretty effective procedure for troubleshooting when challenges emerge. First, one might
identify the specific behavior at the center of
the issue. From there, they can work to determine if there are specific issues related to
capability or opportunity, as described in the
COM-B model. For example, it’s possible
that the issue may be resolved with a fairly
simple environmental modification, utilization of a new tool, or refining a particular
skill. If not, it then makes sense to shift attention toward motivation.
When exploring motivation, it’s worthwhile
to consider whether or not the key psychological needs of self-determination theory
(competence, autonomy, and relatedness)
are being sufficiently supported. One should
also consider if they have been coaching in
accordance with the key themes identified
in the POWA framework; if not, they might
actually be detracting from psychological
needs support. If no apparent issues are identified with regards to the POWA framework
or psychological needs support, it’s quite
possible that the goal hierarchy is no longer
well-aligned. Peoples’ goals, desires, priorities, and perspectives change over time, and
goal hierarchies need to be updated to reflect
these changes. If a person is pursuing a goal
hierarchy that no longer aligns with their values and priorities, it will be hard to sustain
intrinsic motivation, even if all psychological needs are being supported pretty well. In
summary, this unified framework gives us a
great starting point for helping clients identify goals and get started with goal-striving
behavior change, while also providing a great
troubleshooting tool when challenges arise.
This is by no means the “correct” or “only”
way to approach coaching, but it’s a cohesive framework that is compatible with the
available evidence, and you could certainly
do worse.
Next Steps
Given that this study has introduced a brand
new conceptual framework (the POWA model of humility), the next steps are basically all
of the steps. The first step in this type of research line is to synthesize the ideas and put
the model together, which is what the presently reviewed study accomplished. The next
steps involve waiting to see if other researchers take enough of an interest in the idea to
follow up with more research. If so, the subsequent steps involve exploring whether or
47
APPLICATION AND TAKEAWAYS
As a coach, it’s important to orient your communications, actions, and perspectives
in a person-centered manner. To be maximally helpful, you should offer assistance
in the goal-setting process, and continuously work to support the key psychological
needs of your clients. This fosters the intrinsic motivation required for successful
goal-striving and behavior change, but you should also be mindful of external factors
that influence your clients’ opportunities and capabilities for behavior change,
providing guidance and support as needed. We can merge all of these factors into a
cohesive framework that draws upon the POWA model, the goal hierarchy concept,
self-determination theory, and the COM-B model of behavior change. This framework
serves as a starting point for your general approach to coaching; from there, you’ll
need to customize your approach to suit the unique needs of each individual client.
not this concept generalizes to other scenarios. For example, the present study included
a hand-picked sample of coaches who had
pre-existing relationships with the research
team; will these same observations hold true
in a less meticulously curated sample? Will
the framework translate to other sports or activities? Will it translate to scenarios where a
coach is working with several athletes at the
same time, rather than one-on-one coaching
contexts? Will it translate to more general
applications of leadership or management
outside of the world of sports and athletics?
Time will tell, but we’ll need a lot more research to carry this line of research forward
and elucidate the answers to these questions.
In the meantime, the POWA model appears
to provide an intuitive framework that should
help coaches reinforce a person-centered approach to their work, which is likely to carry plenty of upsides and minimal downsides
when applied in the balanced manner depicted in Figure 2.
48
References
1. Garner P, Roberts WM, Baker C, Côté J. Characteristics Of A Person-Centred Coaching
Approach. Int J Sports Sci Coach. 2022 Aug 1;17(4):722–33.
2. Höchli B, Brügger A, Messner C. How Focusing on Superordinate Goals Motivates
Broad, Long-Term Goal Pursuit: A Theoretical Perspective. Front Psychol. 2018 Oct
2;9:1879.
3. Ryan RM, Deci EL. Self-Determination Theory And The Facilitation Of Intrinsic
Motivation, Social Development, And Well-Being. Am Psychol. 2000 Jan;55(1):68–78.
4. Patrick H, Williams GC. Self-Determination Theory: Its Application To Health Behavior
And Complementarity With Motivational Interviewing. Int J Behav Nutr Phys Act.
2012;9:18.
5. Ryan RM, Deci EL, Vansteenkiste M, Soenens B. Building A Science Of Motivated
Persons: Self-Determination Theory’s Empirical Approach To Human Experience And
The Regulation Of Behavior. Motiv Sci. 2021;7:97–110.
6. Mossman LH, Slemp GR, Lewis KJ, Colla RH, O’Halloran P. Autonomy Support In
Sport And Exercise Settings: A Systematic Review And Meta-Analysis. Int Rev Sport
Exerc Psychol. 2022 Feb 2; ePub ahead of print.
7. Mouratidis A, Lens W, Vansteenkiste M. How You Provide Corrective Feedback Makes
A Difference: The Motivating Role Of Communicating In An Autonomy-Supporting
Way. J Sport Exerc Psychol. 2010 Oct;32(5):619–37.
8. Michie S, van Stralen MM, West R. The Behaviour Change Wheel: A New Method For
Characterising And Designing Behaviour Change Interventions. Implement Sci IS. 2011
Apr 23;6:42.
9. Willmott TJ, Pang B, Rundle-Thiele S. Capability, Opportunity, And Motivation: An
Across Contexts Empirical Examination Of The COM-B Model. BMC Public Health.
2021 May 29;21:1014.
10. Timlin D, McCormack JM, Simpson EE. Using The COM-B Model To Identify Barriers
And Facilitators Towards Adoption Of A Diet Associated With Cognitive Function
(MIND Diet). Public Health Nutr. 24(7):1657–70.
█
49
Study Reviewed: After the Spotlight: Are Evidence-Based Recommendations for Refeeding
Post-Contest Energy Restriction Available for Physique Athletes? A Scoping Review. ChicaLatorre et al. (2022)
Bodybuilding Contest Recovery
- What do we Know?
BY ERIC HELMS
The recovery phase after a physique competition is challenging,
both physically and mentally. There is no contest in the near
future to focus on, and the physiological adaptations to chronic
energy restriction and intense drive to eat persist for some time,
even after increasing calories. But is there research to inform best
practices for this phase?
50
KEY POINTS
1. Scoping reviews gauge the breadth of data on a topic, identify gaps, and
summarize findings. This scoping review targeted the post-physique contest
recovery strategies and recovery timelines of drug-free competitors.
2. The 12 studies included were all observational. Athletes in this review either
sharply increased energy intake and quickly gained weight or followed a
spectrum of “reverse diets” ranging from slower to faster controlled increases in
energy and weight.
3. While direct comparisons of strategies don’t exist yet, we know for recovery
to occur, fat (and lean) mass and energy availability need to increase. But,
overshooting baseline fat mass can create psychological stress and make
subsequent seasons more challenging.
T
he concept of reverse dieting gets a
lot of press online and there are a fair
number of case studies and case series
in which the post-show recovery phase of physique competitors is well-described. Based on
these two facts, you’d think we have pretty
solid evidence-based guidance on post-show
recovery nutrition strategies. Right? That’s
exactly the question that the authors of the
present scoping review (1) asked. To back up
a second, a scoping review is a specific type
of review that, like a systematic review, has
a meticulous search strategy that follows set
guidelines. However, unlike systematic reviews, which answer narrow research questions like “how much protein is beneficial for
strength gains?” and may include meta-analyses, scoping reviews attempt to answer broader questions related to the scope of a body of
evidence to identify gaps in knowledge, opportunities for future research, and to describe
its breadth. The authors of the present review
collated all the studies available on drug-free
physique competitors in which their dietary
and training approaches were tracked during
both the dieting and recovery phases, along
with physiological and psychological markers of recovery to answer the question in this
paper’s title: “are evidence-based recommendations for refeeding post-contest energy restriction available for physique athletes?” In
this article I describe how the authors conducted this review, what they found, their conclusions, recommendations, and, finally, my
take on what approach physique competitors
should take post-contest.
Purpose and Hypotheses
Purpose
The purpose of this scoping review was to
“summarize the strategies of post-physique
contest recovery and characterize the timeline
in which physiological restoration occurs.”
Hypotheses
As is customary for scoping reviews, the researchers did not directly state a hypothesis.
51
Methods
If you’ve been reading MASS for a while,
you have read our articles summarizing systematic reviews, meta-analyses, and even
some narrative reviews. However, this is the
first pure scoping review we’ve written an
article on (Mike actually reviewed a scoping review with a meta analysis, but the focus was more on the meta), and you might
not have come across a scoping review previously, as they are a little less common in
our field than other review types. Scoping
reviews have the purpose of identifying gaps
in knowledge, clarifying concepts, or investigating research practices. Essentially, they
do what they sound like they do: assess the
scope of a body of research (2). Similarly to
systematic reviews, they start by identifying a research question, then systematically
searching the literature to identify all of the
published studies pertinent to that question
using strategically selected keywords. Unlike systematic reviews, however, scoping
reviews typically answer more “open ended”
questions (like the present question “are evidence-based recommendations for post-contest recovery available?”) rather than questions with more definitive answers (e.g.,
“does training volume have a dose response
relationship with hypertrophy?”). Given that,
this review’s search terms were all related to
physique contest preparation, recovery, refeeding, metabolic adaptation, and the like.
Importantly, for a study to be included it
had to include one or more adult drug-free
physique competitors, be short or long term,
peer reviewed, and discuss post-contest recovery. Studies that did not include “dietary
measures, assessments, recommendations, or
nutrition-associated consequences related to
post-contest recovery” or were on enhanced
competitors were excluded. As shown in Figure 1, this search yielded 12 total studies.
Findings
Overview of studies
All 12 studies had observational designs
and included one or more physique athletes
across competitive phases. Six were single
participant case studies, five were case series
of small cohorts of 2-15 competitors, and one
was a large scale prospective cohort study
of 50 participants, 27 of whom were physique competitors, and the remaining were a
non-competing control group. A total of 70
52
(n = 26 male, n = 44 female) competitors between the ages of 20-50 years were observed
during the recovery period across these 12
studies. The post-contest observation phases
varied in duration, ranging from four days to
a year and a half post-contest.
Body composition
Body composition was assessed in 11 out of
the 12 studies, and in the single participant
case studies was measured with two to four
different assessment techniques (e.g., bioimpedance, DXA, skinfolds, Bod Pod, ultrasound, etc.). In the four studies (3, 4, 5, 6)
where the post-contest phase was observed for
less than or up to two months, a regain of fat
mass toward baseline was observed in all athletes. In six studies (7, 8, 9, 10, 11, 12) where
the post-contest phase was observed for nine
weeks or longer, the regain of fat mass reached
or exceeded baseline levels prior to dieting between nine weeks and six months post-contest.
Finally, in the remaining study which assessed
body composition changes (13), the last measurement occurred 8-10 weeks post-contest
and all competitors regained some amount of
fat mass with two of the seven reaching their
self-reported pre-diet body mass (pre-diet fat
mass was not collected).
Dietary approach to recovery
Dietary data was reported in all studies, primarily via food logs (n = 10) and also through
a combination of 24-hour dietary recalls and
food logs (n = 2). In six studies (3, 4, 5, 8, 10,
11), the participants had relatively aggressive
increases in caloric intake and there was no
mention of an attempt at a gradual reintroduction of calories or slow regain of body
mass, or what might be colloquially considered “reverse dieting”. However, in four case
studies (6, 7, 9, 12) a reverse diet was explicitly mentioned or described as the post-contest approach. Furthermore, in one case series
(13) three of the seven competitors followed
a reverse diet approach.
Finally, there was a case report of refeeding
syndrome in a bodybuilder (14), that retrospectively reported the bodybuilder’s dietary
approach, and then noted their clinical symptoms leading to the diagnosis of refeeding
syndrome and subsequent recovery during
their four day hospital stay. No assessment
of body composition, psychology, or endocrine status occurred. Refeeding syndrome
describes the potentially fatal fluid and electrolyte shifts that are rare, but can occur in
malnourished clinical patients (typically)
from artificial refeeding (15). I’m pretty sure
this study was added in peer review, as it only
appears in the tabulated summary of studies,
but is not in the reference list or discussed.
While refeeding syndrome is certainly of
grave clinical importance, this study probably shouldn’t have been included in the scoping review as it doesn’t help to answer the
question. Thus, like the authors, I won’t be
discussing it in depth. However, that’s not a
dismissal of refeeding syndrome’s potential
likelihood of occurrence. Extreme dieting
practices with very low energy intakes and
highly restricted food sources resulting in
extreme weight loss in short periods of time
followed by rapid reintroduction of food can,
in rare cases, lead to refeeding syndrome in
non-clinical populations, such as bodybuilders. For this reason, these practices should be
53
avoided and evidence-based contest preparation approaches should be followed.
Indicators of recovery and their timeline
Measurements of recovery included energy expenditure, metabolic and sex hormone
concentrations, menstrual function, and psychometric scores, which were measured and
observationally linked to body composition
or dietary changes during post-contest recovery. In single participant case studies, these
links were typically qualitative, as the authors
of these studies noted that certain markers of
recovery coincided with changes in variables
(e.g., a certain amount of time had passed
or amount of body mass was gained before
hormonal recovery was indicated). However,
in some of the studies with multiple participants, quantitative statistical associations between recovery markers and other variables
were reported.
When resting metabolic rate (RMR) was reported, it was typically assessed via indirect
calorimetry, and when RMR suppression was
observed during contest preparation, subsequent restoration during the recovery phase
occurred in as little as 4-6 weeks for some
competitors, while other athletes required ~6
months. A mixture of blood and salivary assessment of hormone concentrations was reported across studies, and not all studies tested the same hormones. However, speaking
broadly, cortisol, sex-hormone binding globulin, and insulin were the fastest hormones to
recover, generally getting back to baseline in
4-6 weeks. Other metabolic hormones, such
as ghrelin, leptin, and thyroid hormones and
other sex hormones, such as testosterone and
estradiol, took 4-6 months to recover. Men-
strual function was tracked across 33 female
competitors in four studies (7, 8, 12, 13), and
31 of these competitors experienced some degree of menstrual cycle irregularity. In Hulmi
and colleagues’ study of 27 female physique
competitors (8), seven were still experiencing amenorrhea up to four months post-contest. In a case series of seven competitors, the
athlete with the greatest increase in energy
intake (roughly doubling), body mass (22%
increase), and fat mass saw a return of her
menstrual cycle (and restoration of RMR) in
10 weeks (13). In another case study in which
the athlete followed a regimented, slow reverse diet, the athlete’s menstrual cycle did
not return until 71 weeks post-contest (12).
Finally, an array of psychometric testing batteries related to eating behavior, mood state,
body image, and stress and anxiety were implemented. Use of different tests prevents a
definitive statement about the time course of
specific aspects of psychological recovery;
however, anxiety, weight phobia, compulsive self-monitoring, body image concerns,
and mood disturbance scores peaked around
competition and within 1-2 months following competition. While there was variability
depending on the specific psychometric test,
psychological recovery generally occurred
within ~2-6 months.
As mentioned, a few studies with multiple
participants reported quantitative relationships between recovery markers and other
variables. Longstrom and colleagues reported
statistically significant relationships regarding post-contest recovery (13): notably, fat
mass was strongly associated with the change
in resting metabolic rate (τ = 0.90; p = 0.001)
54
and increases in body fat percentage were
strongly associated with increases in leptin (τ
= 0.88; p = 0.003). A study led by our very
own Dr. Trexler (4) reported that testosterone
levels among male competitors were inversely associated (p ≤ 0.05; r = -0.81--0.88) with
increases in body fat and weight. Notably,
testosterone still increased at the group level
as body fat and weight increased post contest,
so this may indicate that while weight and fat
gain coincide with hormonal recovery, very
rapid increases in fat mass might slow testosterone recovery in some individuals. It’s also
entirely possible that this finding was simply
a spurious correlation, or that it identified a
transient, short-term alteration in testosterone responses that’s fairly inconsequential in
the long run (this study only looked at a brief
time window in the immediate post-competition period). The broader literature quite
clearly suggests that full restoration of baseline testosterone levels occurs over long time
scales (multiple months), during a time span
in which fat regain appears to play a permissive role in overall recovery from the preceding diet. Trexler and colleagues also reported
55
that increases in RMR were associated (p ≤
0.05) with increases in body fat percentage
(r = 0.59) and post-contest protein intake (r
= 0.60).
Table 1 is a summary of all the reviewed
studies, with relevant information about the
athletes, the time course of observation, the
observed outcomes, and the conclusions and
recommendations of the authors.
Interpretation
Based on the reviewed research, the authors
wrote a conclusion with recommendations
for future research and guidance for post-contest recovery. Unsurprisingly, given that all
the studies reviewed were observational, the
authors recommended that randomized controlled trials be carried out that control and
manipulate energy intake to truly compare
various post-contest dietary strategies. While
I fully agree with this recommendation, I
feel obligated to point out that the lack of
intervention-based data on this topic is not
for lack of trying. If you click the reference
link to the case series by Longstrom and colleagues (13), and scroll down to the acknowledgements section, you’ll see that the authors
thanked “Eric Helms from Auckland University of Technology for his insightful discussion and input when designing this study.” I
don’t point this out solely due to my never
ending egotistical desire for recognition and
crippling need for external validation - that’s
only 90% of it - but also because the input I
provided on study design was on a controlled
trial. Unfortunately, adherence to a structured nutritional plan post-contest is incredibly challenging, especially when it involves
quelling the intense desire to eat everything
in sight, and instead following a gradual reverse dieting approach. Thus, what was initially planned as a controlled trial became
an observation of various degrees of adherence to less and more gradual approaches to
post-contest refeeding. Obviously, this inability to get even a modicum of adherence
has implications for practice. On that note,
let’s talk about what the authors recommended for post-contest recovery.
First the authors characterized what was
generally observed, that “a gradual return to
baseline measures occurs for body composition, RMR, and endocrine measures during
post-contest recovery, so long as the athlete
increases energy availability and body fat
mass” (emphasis mine). Further, they note
that full recovery occurs in most cases within
six months, but with a lot of inter-individual variability. They finish by providing three
potential strategies for post-contest recovery,
which, by their estimation, should all be explored as they could plausibly be effective
based on the limited available evidence:
1. “A structured gradual increase in dietary
intake aimed at reaching maintenance energy levels.”
2. “An acute ad libitum increase in dietary
intake immediately post-contest to facilitate continued dietary adherence, followed
by a structured gradual increase in intake
toward maintenance energy levels.”
3. “An immediate return to maintenance dietary intake.”
To be clear, the authors did a fantastic job
56
THE AUTHORS OBSERVED,
THAT “A GRADUAL RETURN TO
BASELINE MEASURES OCCURS
FOR BODY COMPOSITION,
RMR, AND ENDOCRINE
MEASURES DURING POSTCONTEST RECOVERY,
SO LONG AS THE ATHLETE
INCREASES ENERGY AVAILABILITY
AND BODY FAT MASS”
summarizing and reviewing the literature, and
this was a very well-written review. I found
myself agreeing with the vast majority of
what was written, with the exception of these
three recommendations. Each concludes with
“maintenance energy levels/dietary intake,”
which is confusing, because the authors specifically pointed out that recovery occurs as
long as fat mass increases. As written, the
only strategy which would accomplish an increase in fat mass is number two, as an ad libitum intake post contest always results in a
surplus energy intake and fat mass gain. The
other two strategies would not accomplish fat
mass gain. In fact, strategy one would result in
further weight loss until maintenance energy
levels were gradually attained. Now, to be fair,
maybe I’m not being charitable enough in my
interpretation of the authors’ intended meaning. I think it’s likely that the authors meant
“baseline maintenance energy levels/dietary
intake.” In other words, I think the authors
mean that all three strategies should eventually get you back to your normal, pre-contest
energy requirements (and therefore, eventually to a similar body and fat mass), and it’s
only the speed and pattern of the increase in
calories that differs between approaches. Certainly, this would comport with their recommendation that fat mass needs to increase for
recovery to occur. Assuming that is what the
authors meant, I fully agree that these are strategies worth exploring.
Now, for the rest of this interpretation, I
could delve into the specifics of each study
for pages and pages, given the detail in the
case studies and series, and my personal interest in this topic. Indeed, both Dr. Trexler
and myself have written about how contest
prep affects competitors generally, and the
specific effects of prep on female competitors multiple times (here, here, and here).
Additionally, Dr. Trexler has written articles
on how to deal with metabolic adaptation,
how metabolic phenotypes relate to reverse
dieting, and strategies to restore menstrual
function in exercising women, and I have a
two-part video series on post-contest recovery. However, rather than retread old ground,
I’m going to specifically address the three
strategies the authors propose (assuming they
mean baseline energy levels), and give you
my take on each.
A gradual increase to baseline maintenance
energy
This approach is probably closest to what
people colloquially describe as a “reverse
diet.” Notably, “gradual” can mean a lot of
57
things, so a reverse diet could operate on a
spectrum from being an extremely slow or
relatively quick process. It could start with a
competitor still in a deficit (albeit smaller),
or at estimated maintenance, or at varying
degrees of a surplus. Among the studies that
were reviewed, there are four examples with
adequate reporting along this spectrum, with
varying degrees of recovery success, seemingly based on the speed of the reverse.
For example, on the slower side, Longstrom
and colleagues (13) noted that three of their
participants specifically followed a reverse
dieting approach, resulting in minimal weight
gain at the 8-10 week post-show time point (2
to 5% increases in body mass), coinciding with
small decrements in dry fat free mass (-0.5 to
-1kg), and negligible changes in RMR (-6%
to +4%). Likewise, Pardue and colleagues (9)
also specifically described the slow reverse
dieting approach of a male bodybuilder who
added 10–30g of carbohydrate and/or 4–10g
of fat along with 5-10 minute reductions in
cardio on a weekly basis post-show, resulting
in a return to his pre-diet DXA-based body
fat after 5 months (13.8%). Recovery in this
athlete was decent, but incomplete, and took
the entirety of the 5 month time period. All
measurements were trending back to baseline and many were fully restored, but not
all. Notably, anaerobic power, RMR, testosterone, and thyroid hormone were not fully
restored. Similarly, Halliday and colleagues
(12) described the reverse dieting approach
(in all but name) of a figure competitor who
was “more cautious” than previously studied competitors in the post-show phase and
“slowly increased energy intake….in an ef-
fort to limit a rapid increase in fat mass….
by diligently increasing energy intake slowly
and limiting days ‘off’ the diet.” Like the athlete observed by Pardue, this figure competitor did not return to baseline levels of body
weight and fat until 5 months post-competition. Notably, her baseline, habitual levels of
body fat were also very low, at ~15%. Furthermore, her energy availability immediately dropped below recommended lower end
thresholds (16) of ~30kcal/kg of lean mass
at the initiation of prep, and stayed relatively
low, not getting over 35kcal/kg of lean mass
until week 10 of recovery. Subsequently, the
last menstrual cycle occurred in week 11 of
20 of the dieting phase, and menstrual function did not return until 71 weeks post-contest
(you read that right), when the athlete’s body
fat had been maintained for multiple months
at ~20%.
In contrast to these slower reverse dieting
approaches, Tinsley and colleagues (7) observed a more aggressive, but still graded
approach whereby a figure competitor increased their body mass ~1% per week for
9 weeks after a two-show, 25-week competition prep phase. This relatively fast weekly
increase in body mass occurred with a ~8-9%
increase in total fat mass stores per week as
well, and at the conclusion of the nine week
recovery period, the competitor was roughly
back at their baseline body mass. Importantly, they not only fully restored but actually
exceeded their RMR and fat free mass values compared to their values prior to their
last show. Additionally, the competitor had
only one menstrual cycle during the observation phase, but self-reported that 3 months
58
after their last show their menses returned.
Uncontrolled eating scores were highest 1
month after the final show (notably higher
than during the diet), but were trending down
to their baseline levels at the ninth week of
recovery (~15% higher than baseline). Grip
strength and anaerobic power were still suppressed, however.
Finally, Newmire and colleagues reported that one of the two bikini competitors in
their case series followed a reverse diet (6);
however, they did not track recovery markers during this period. To conclude, out of all
six competitors who followed a reverse diet-esque strategy across the four studies with
adequate reporting, the most successful recovery happened in the competitor with the
most aggressive strategy, reported by Tinsley
(7), where they were back at their pre-diet
weight within ~2 months. In the other examples where the competitors’ weight regain
occurred at half this rate or slower, recovery
was incomplete for longer, delaying the start
of a productive offseason.
An initial ad libitum period, followed by a
gradual increase to baseline maintenance
energy
There are plenty of studies of bodybuilders
consuming large amounts of calories in an ad
libitum fashion post-contest, and subsequently rapidly regaining body weight. But, I’m
not aware of any studies that observed this
being done for an intentionally limited time
period, followed by a more gradual approach
afterwards as a recovery strategy. With that
said, I like this approach (with some caveats),
and I have a fair amount of anecdotal experience with it.
For those who aren’t familiar with competitive bodybuilding, it’s customary to go out
to eat after your show and eat a lot. Truly,
the things I’ve seen (and done early in my
career) at post-show feeds would astound and
disgust the uninitiated, as it can truly get out
of control. On the other hand, some reverse
dieting approaches ask you to forgo these
celebratory meals completely, or to be extremely rigid about what you have. There is
certainly a middle ground in my experience.
At 3DMJ we typically take an approach we
call the “recovery diet” post-competition,
whereby we encourage our athletes to celebrate with their family and friends the night
of the show, but to make their best attempt
at imitating a regular person who isn’t starving. We typically provide some broad, qualitative guidance, such as having an appetizer
or a dessert, but not both, not eating off other
people’s plates, and only ordering a single
meal (I know, we’re fascists). We then typically provide the guidance of eating “three
square meals” the following day to close out
the weekend, without tracking, which can be
meals eaten out, with similar guidance of imitating a non-starving person (avoid midnight
snack runs, ordering multiple meals, eating
from other people’s plates, etc.). Then, starting on Monday after the show weekend (most
competitions happen on Saturdays), we take a
more regimented approach where some form
of tracking is recommended, and we encourage a more “normal” frequency of eating out,
but at a reasonably aggressive pace, as we
want them in a surplus and regaining weight.
In fact, we recommend a similar timeline as
observed in the study by Tinsley: getting to
~5-10% over stage weight ~1-2 months post
59
show (these ranges allow for a lot of individualization).
Again, while I have no empirical evidence to
suggest that this is the best approach, I can say
it certainly worked better for us compared to
half a decade ago where we tried more conservative, gradual, controlled, reverse dieting
approaches. Back then, stress was higher, adherence was lower, relationships with food
and body image were worse, and, ultimately,
body weight increased rapidly in many cases
anyway, but our competitors felt like failures
when it happened, rather than weight regain
being contextualized as a necessary and ultimately healthy process. I do, however, want
to emphasize the importance of providing
some guidance around the post-show meal
and initial days immediately after the contest.
Like I mentioned in the findings, while it’s
rare and only possible in extreme dieting and
post-contest binge eating scenarios, refeeding syndrome has happened in some cases.
Thus, having some loose guard rails to avoid
this possibly fatal outcome is a good idea. On
a lighter note, it’s possible that avoiding very
rapid weight regain in a short period might
also result in better recovery of some hormonal markers (4), although I would like to see
this finding replicated before I take it as fact.
An immediate return to baseline maintenance energy
This is an interesting approach as well, because theoretically it would result in relatively rapid weight regain that would taper off
slowly as you approached your offseason
weight and body composition. This would be
a relatively large increase in calories from the
end of prep, and would be easier to adhere
to than more strict approaches. By definition,
however, this approach wouldn’t cause substantial overshooting of your baseline body
composition. Most importantly, it would
meet both recovery requirements of increasing fat mass and energy availability. In fact,
as I think about it, another similar strategy
that would address the latter requirement
directly would be jumping right to your estimated calories that would put your energy
availability at 35-45kcal/kg of fat free mass.
Unfortunately, like the previous strategy,
we don’t have empirical data that I’m aware
of on physique athletes basically jumping
right to their offseason calories (or offseason maintenance calories more specifically). However, I’ve met and talked to a number of competitors over the years who have
taken this approach. Generally, they put fat
back on quickly, feel good after a handful
of weeks, and are back to making progress
pretty quickly. Further, they seem to get the
food focus “out of their system” faster than
reverse dieters, but it’s hard to know if this is
cause or effect. Many of the folks I’ve known
to do this are simply more interested in getting back to making gains, so they switch to
“offseason mode” mentally right after their
show, as they aren’t that focused on how lean
they are. This is in stark contrast to the competitors who are typically attracted to (false)
reverse dieting marketing narratives about
making the next diet easier because of a “super charged” metabolism which emphasizes
staying pretty lean and getting to eat more.
These folks tend to be more attached to their
competition levels of leanness and, unfortunately, often suffer for it. But I don’t know if
60
your typical competitor who is really attracted to the concept of a reverse diet can just
easily shift their mindset to this approach.
To conclude, I agree with the authors. So
long as fat mass and energy availability are
restored, recovery will occur. Thus, each of
these three approaches which accomplish
this (once again assuming they mean baseline energy levels) is worthy of further study.
Furthermore, it is likely that each could be
more effective in different individuals, and at
different points in their careers. Most competitors experience a push and pull between
trying not to overshoot their baseline body fat
levels, making their next season even harder,
and not being too rigid and staying too lean,
causing mental stress and hampering their recovery, delaying the start of a productive offseason. Depending on where the individual
is currently at, they may benefit from more
aggressive or more conservative approaches.
Next Steps
MOST COMPETITORS
EXPERIENCE A PUSH AND PULL
BETWEEN TRYING NOT TO
OVERSHOOT THEIR BASELINE
BODY FAT LEVELS, MAKING
THEIR NEXT SEASON EVEN
HARDER, AND NOT BEING TOO
RIGID AND STAYING TOO LEAN,
CAUSING MENTAL STRESS AND
HAMPERING THEIR RECOVERY
over, based on how quickly they increased
energy intake and body and fat mass, they
could be grouped and the impact on recovery
markers could be compared.
As I alluded to at the end of the interpretation,
I agree that we need to evaluate the three strategies the authors identified. Likewise, I agree
that in a perfect world, this would occur via
multiple intervention trials in well-controlled
conditions. However, I simply don’t think
this is realistic given how challenging it is to
follow regimented eating patterns post contest for most competitors. Thus, probably the
best approach would be a quasi-experimental
design where a bunch of competitors were
recruited and observed during recovery who
stated a priori that they planned to follow one
of the strategies outlined by the authors for
recovery. Then, after the recovery phase was
61
APPLICATION AND TAKEAWAYS
While this review might seem like it didn’t provide definitive guidance, the authors
made the extremely important statement that for recovery to occur, both fat mass and
energy availability need to increase. This should be taken to heart by competitors,
especially when confronted with the narratives associated with more extreme
versions of reverse dieting, where very slow and gradual increases in calories are
recommended, with the aim of only minimally increasing body fat. Don’t be fooled,
these approaches will not result in recovery, but will delay the start of a productive
offseason, and that’s only if you can adhere to them, which most people can’t.
62
References
1. Chica-Latorre S, Buechel C, Pumpa K, Etxebarria N, Minehan M. After the spotlight: are
evidence-based recommendations for refeeding post-contest energy restriction available
for physique athletes? A scoping review. J Int Soc Sports Nutr. 2022 Aug 8;19(1):505528.
2. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic
review or scoping review? Guidance for authors when choosing between a systematic or
scoping review approach. BMC Med Res Methodol. 2018 Nov 19;18(1):143.
3. Chappell AJ, Simper TN, Trexler ET, Helms ER. Biopsychosocial Effects of Competition
Preparation in Natural Bodybuilders. J Hum Kinet. 2021 Jul 28;79:259-276.
4. Trexler ET, Hirsch KR, Campbell BI, Smith-Ryan AE. Physiological Changes Following
Competition in Male and Female Physique Athletes: A Pilot Study. Int J Sport Nutr Exerc
Metab. 2017 Oct;27(5):458-466.
5. Mitchell L, Slater G, Hackett D, Johnson N, O’connor H. Physiological implications
of preparing for a natural male bodybuilding competition. Eur J Sport Sci. 2018
Jun;18(5):619-629.
6. Newmire DE, Webb HE. The role of age in the physiological adaptations and
psychological responses in bikini-physique competitor contest preparation: a case series.
J Int Soc Sports Nutr. 2021 Jun 9;18(1):45.
7. Tinsley GM, Trexler ET, Smith-Ryan AE, Paoli A, Graybeal AJ, Campbell BI, et al.
Changes in Body Composition and Neuromuscular Performance Through Preparation,
2 Competitions, and a Recovery Period in an Experienced Female Physique Athlete. J
Strength Cond Res. 2019 Jul;33(7):1823-1839.
8. Hulmi JJ, Isola V, Suonpää M, Järvinen NJ, Kokkonen M, Wennerström A, et al. The
Effects of Intensive Weight Reduction on Body Composition and Serum Hormones in
Female Fitness Competitors. Front Physiol. 2017 Jan 10;7:689.
9. Pardue A, Trexler ET, Sprod LK. Case Study: Unfavorable But Transient Physiological
Changes During Contest Preparation in a Drug-Free Male Bodybuilder. Int J Sport Nutr
Exerc Metab. 2017 Dec;27(6):550-559.
10. Rossow LM, Fukuda DH, Fahs CA, Loenneke JP, Stout JR. Natural bodybuilding
competition preparation and recovery: a 12-month case study. Int J Sports Physiol
Perform. 2013 Sep;8(5):582-92.
11. Schoenfeld BJ, Alto A, Grgic J, Tinsley G, Haun CT, Campbell BI, et al. Alterations in
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Body Composition, Resting Metabolic Rate, Muscular Strength, and Eating Behavior in
Response to Natural Bodybuilding Competition Preparation: A Case Study. J Strength
Cond Res. 2020 Nov;34(11):3124-3138.
12. Halliday TM, Loenneke JP, Davy BM. Dietary Intake, Body Composition, and Menstrual
Cycle Changes during Competition Preparation and Recovery in a Drug-Free Figure
Competitor: A Case Study. Nutrients. 2016 Nov 20;8(11):740.
13. Longstrom JM, Colenso-Semple LM, Waddell BJ, Mastrofini G, Trexler ET, Campbell
BI. Physiological, Psychological and Performance-Related Changes Following Physique
Competition: A Case-Series. J Funct Morphol Kinesiol. 2020 Apr 25;5(2):27.
14. Lapinskienė I, Mikulevičienė G, Laubner G, Badaras R. Consequences of an extreme diet
in the professional sport: Refeeding syndrome to a bodybuilder. Clin Nutr ESPEN. 2018
Feb;23:253-255.
15. Mehanna HM, Moledina J, Travis J. Refeeding syndrome: what it is, and how to prevent
and treat it. BMJ. 2008 Jun 28;336(7659):1495-8.
16. Burke LM, Lundy B, Fahrenholtz IL, Melin AK. Pitfalls of Conducting and Interpreting
Estimates of Energy Availability in Free-Living Athletes. Int J Sport Nutr Exerc Metab.
2018 Jul 1;28(4):350-363.
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64
Study Reviewed: May a Nonlocalized Postactivation Performance Enhancement Exist Between
the Upper and Lower Body in Trained Men. Bartolomei et al. (2022)
Does Benching First in a
Workout Improve Lower Body
Performance?
BY MICHAEL C. ZOURDOS
Postactivation potentiation studies typically have individuals
perform a few heavy squat or bench reps to improve volume
performance on the same exercise. A new study examined if a
few heavy bench reps could potentiate lower body performance.
Did the idea work? This article breaks it down.
65
KEY POINTS
1. Researchers tested the effects of two upper body postactivation potentiation (PAP)
protocols on isometric leg press force, countermovement jump power, and EMG.
The PAP exercise protocols were: 1) 5 × 1 at 90% of 1RM on the bench press, 2) 5
× 1 at 30% of 1RM on the bench press, and 3) control condition (no-PAP).
2. Countermovement jump power (+1.6%) and vastus medialis EMG (+4.4%)
increased from pre- to post-PAP exercise only in the 90% of 1RM condition.
These findings suggest that high load, but not explosive, upper body PAP
exercise may provide a non-localized performance enhancement.
3. Although the high load PAP exercise improved countermovement jump
performance, the benefit was small. Additionally, the PAP exercise did not
enhance isometric leg press force. So far, there is not yet convincing evidence of
a non-localized benefit of PAP for strength or volume performance. Thus, I would
stick to localized PAP prescriptions such as heavy squats to potentiate squat
volume performance.
T
raditionally, postactivation potentiation (PAP) research has focused on
improving jumping and sprinting performance. Specifically, PAP research has typically explored the effect of performing heavy
contractions on a resistance training exercise
to increase acute explosive or strength performance. For example, Esformes and Bampouras (2) found that PAP exercise consisting
of three sets of squats with a three repetition
maximum (RM) load improved countermovement jump performance 10 minutes later in
male Rugby players. Recently, PAP research
has explored lifting performance. Specifically, Conrado de Freitas (3 - MASS Review)
found 1 (set) × 2 (reps) on squat at 90% of
1RM improved squat reps to failure at 70% of
1RM five minutes later. Additionally, Alves
et al (4 - MASS Review) found that PAP exercise of 1 × 3 at 90% of 1RM bench press
increased reps to failure at 75% of 1RM. The
concept of PAP is simple, time-efficient, and
practical, but until recently, only a localized
PAP effect had been investigated. However,
is it possible that PAP exercise performed on
one muscle group can improve performance
on an unrelated muscle group?
The presently reviewed study from Bartolomei et al (1) examined if upper body PAP
exercise could improve lower body performance, and if absolute strength and muscle
thickness (chest and quad) were related to a
performance enhancement. In a crossover design, 13 trained men performed three conditions. Two conditions consisted of PAP protocols, and the other was a control condition
(no-PAP). In all three conditions, subjects
were pre-tested on various outcome measures
(bench press throw power, countermovement
jump power, leg extension isometric force
and rate of force development, and vastus
lateralis and medialis EMG). Then, subjects
performed the PAP exercise or 15 minutes
66
of quiet-standing (no-PAP condition) and repeated all outcomes measures 8-18 minutes
following PAP or standing (post-testing). In
one PAP condition, subjects benched 5 × 1
at 90% of 1RM, and in the other PAP condition, subjects performed 5 × 1 at 30% of 1RM
on the bench press throw. The only statistically significant changes were increases in
countermovement jump power (+1.6%) and
vastus medialis EMG activity (+4.4%) from
pre- to post-testing in the 90% PAP condition
and a significant decrease (-8.2%; p = 0.020)
in vastus medialis EMG in the 30% of 1RM
PAP condition. Further, neither absolute
strength nor muscle thickness were significantly related to performance changes. These
findings suggest that heavy bench, but not explosive benching, can improve acute jumping
performance and lower body muscle activation, but not maximal strength performance.
However, the increase in jump performance
was minimal; thus, I don’t think lifters should
implement an upper body PAP exercise and
expect a lower body performance improvement. This article will aim to:
1. Review the present findings.
2. Discuss what mechanisms could potentially lead to upper body PAP exercise eliciting a lower body performance improvement and determine if future research in
this area is warranted.
3. Provide an up-to-date review of the PAP
and lifting literature.
4. Determine in what situations PAP may
be appropriate and how to implement the
practice.
Purpose and Hypotheses
Purpose
The presently reviewed study sought to determine if upper body (bench press) PAP
exercise performed with a high-load (90%)
or a low-load (30%) could elicit lower body
performance improvements 8-18 minutes later. Additionally, the researchers also examined if pectoralis (chest) and vastus lateralis
(quadriceps muscle) muscle thickness and
1RM strength influenced the effect of PAP
on performance.
Hypotheses The researchers hypothesized that both PAP
conditions would improve all outcome measures from pre- to post-testing. Further, the
researchers predicted that there would be a
positive relationship between muscle thickness and bench press 1RM with changes in
outcome measures. In other words, they expected PAP-induced performance enhancement to be greater in those that were bigger
and stronger.
Subjects and Methods
Subjects
The presently reviewed study was completed
by 13 trained men between the ages of 1835 years who could bench press at least their
body mass. Additional subject details are
presented in Table 1.
Study Protocol
The reviewed study was a counterbalanced
crossover design with three conditions separated by exactly one week. Subjects report-
67
ed to the laboratory for a total of four visits.
During the first visit, the researchers determined subjects’ 1RM bench press, anthropometrics, and pec and vastus lateralis muscle
thicknesses. In all three conditions, subjects
completed two trials of pre-testing for bench
press barbell throw power, countermovement
jump power, isometric leg extension force
and rate of force development, and EMG
of the vastus medialis and lateralis (tested
during the leg extension). Subjects then performed a 15-minute intervention consisting
of either PAP exercise in two conditions or
quiet-standing in the non-PAP condition.
Eight minutes following each 15-minute intervention, post-testing began. Researchers
tested bench press throw power at 8 and 10
minutes post-PAP, countermovement jump
power was tested at 12 and 14 minutes postPAP, and leg extension isometric force was
tested at 16 and 18 minutes post-PAP. The
three condition-specific 15-minute interventions were:
1. PAP exercise consisting of the free-weight
bench press for 5 × 1 at 90% of 1RM with
three minutes rest between sets.
2. PAP exercise consisting of the free-weight
bench press for 5 × 1 at 30% of 1RM with
three minutes rest between sets.
3. 15 minutes of quiet standing (no-PAP
condition).
The procedures during each condition can be
seen in Figure 1.
Outcome Measures
The researchers examined if outcome measures changed from pre- to post-intervention
and if there was a significant difference in
the magnitude of change between outcome
measures. Further, the researchers examined
whether there were significant correlations
between baseline 1RM bench press strength
and muscle thickness and pre- to post-changes in countermovement jump power.
68
Findings
There was a significant condition × time interaction for countermovement jump power
(p = 0.049) and vastus medialis EMG (p =
0.024), indicating that the change in these
metrics was significantly different when
comparing all three conditions. These differences were driven by significant increases
from pre- to post-testing in countermovement
jump power (+1.6%’ p = 0.024) and vastus
medialis EMG (+4.4%; p = 0.032) only in the
90% of 1RM PAP condition. The only other
significant change from pre- to post-testing
was a significant decrease (-8.2%; p = 0.020)
in vastus medialis EMG in the 30% of 1RM
PAP condition. There were no significant
relationships between bench press 1RM (r
= -0.24 to 0.25; p = 0.423 to 0.50) with the
change in any outcome measures. Further pec
muscle thickness measurement (r = 0.41; p =
0.24) and vastus lateralis muscle thickness (r
= 0.38; p = 0.27) were not significantly related to the change in countermovement jump
power. The mean values at pre- and post-testing for all outcome measures can be seen in
Table 2, and the percentage changes are in
Figure 2.
69
Interpretation
Overall, I like the concept of PAP due to its
simplicity and practicality. Specifically, data
have shown that heavy squat doubles (3) and
bench press triples (4) increased reps to failure at 60%-70% of 1RM on the same exercise performed 5-10 minutes later. Therefore,
a lifter could work up to a few heavy reps
(~85-90% at 2-3 RIR) at the beginning of
their training session, improving their volume performance 10 minutes later. The presently reviewed study (1) took a slightly different approach, examining if PAP exercise
improved performance on an unrelated muscle group (i.e., non-localized performance
enhancement). However, the findings of this
study were mixed at best. Nevertheless, if upper body PAP exercise does improve lower
body performance, there would be practical
programming implications. Therefore, this
interpretation will review the present study’s
findings, discuss a potential mechanism for
PAP-induced, non-localized performance enhancement, review the PAP and lifting literature to date, and discuss the implementation
of PAP.
Presently Reviewed Study’s Findings and
Potential Mechanism
The reviewed study from Bartolomei et al (1)
could be viewed in two ways. First, one could
suggest that subjects improved performance
in the 90% of 1RM PAP condition. After all,
there was a significant improvement in countermovement jump power and vastus medialis EMG from pre- to post-PAP, and a significant condition × time interaction. Therefore,
you could point to these findings and argue
that a few heavy bench press singles might
potentiate lower body performance. However, I’m inclined to interpret the findings
more conservatively. Notably, the actual lifting-related performance measures (isometric leg press force and rate of force development) did not significantly improve from
pre- to post-PAP in either condition. Further,
it’s questionable whether the significant improvements for countermovement jump power and EMG are meaningful. Specifically, the
increase in countermovement jump power
in the 90% condition was small (+1.6%),
and the condition × time interaction p-value
was barely significant (p = 0.049). In short,
most of the findings are non-significant, the
PAP exercise did not improve strength (albeit isometric), and the countermovement
jump power changes were small. The other
main outcome of this study was that baseline
strength and muscle thickness were not significantly related to changes in countermovement jump performance.
Investigating for a localized PAP effect seems
logical and intuitive. However, it’s not intuitive why the researchers were investigating
a non-localized PAP effect. The rationale for
this research question centers around catecholamine release from the PAP exercise.
Specifically, catecholamine (epinephrine,
norepinephrine, and dopamine) release is purported to enhance arousal and muscle force
production (6). These catecholamines are released from the adrenal glands, and in resistance training research, they are measured in
the periphery (i.e., in circulation). Further,
acute training-induced catecholamine increases are positively related to training volume
70
(5). When released into the bloodstream, these
catecholamines, which serve as neurotransmitters, bind to receptors on skeletal muscle
and facilitate the sodium-potassium pump. In
turn, the sodium-potassium pump plays a role
in facilitating the interaction of the contractile
filaments (actin and myosin), increasing force
production. Indeed, French et al (6) found that
in a group of 10 trained men, the five subjects who maintained force the best over 6 ×
10 on squats at 80% of 1RM also maintained
greater levels of catecholamines from set-toset than the five who saw the greatest reduction in force production from set-to-set. The
data from French et al (6) could signal that increasing catecholamine levels before training
might improve force production. Therefore,
it seems the researchers were attempting to
increase circulating levels of catecholamines
prior to the performance test, but to do so with
low volume exercise to avoid unnecessary
fatigue. However, perhaps the primary exercise-related benefit of an acute increase in catecholamines is to distribute blood flow to the
muscle (6). This distribution of blood flow to
muscle tissue is positive. However, it would
mostly help with endurance or volume-related
lifting (e.g., reps to failure), neither of which
were tested in the presently reviewed study.
Ultimately, acute catecholamine release may
be beneficial, but this release seems more likely to aid the maintenance of force production
over multiple contractions rather than directly
improving maximal isometric force.
Previous PAP and Lifting Data
Only two previous studies have examined a
non-localized effect of PAP exercise on lifting performance. Further, four studies that
are directly applicable have investigated a local effect. The two non-localized studies are
from Cuenca-Fernandez et al 2017 (7), and
the other is from Bartolomei et al 2022 (8).
Cuenca-Fernandez had 15 collegiate swimmers (7 women and 8 men) perform a baseline explosive pushup and squat jump test on
a force plate to test impulse. Then, the athletes performed four different conditions of
PAP exercise, followed by post-testing on
the pushup and squat jump at 5, 8, 12, and 20
minutes post-PAP exercise. The four conditions were:
1. 4 squat reps at 90% of 1RM
2. 4 bench press reps at 90% of 1RM
3. 4 squat and bench press reps at 90% of
1RM
4. Quiet-standing (no-PAP)
Cuenca-Fernandez reported no significant change in pushup impulse from pre- to
post-testing. However, squat jump impulse
significantly improved from pre- to post-testing in all four conditions with no condition
differences. These findings mean that subjects improved squat jump performance even
in the non-PAP condition, which consisted of
only four minutes of quiet-standing. Although
squat jump performance improved following
the bench press PAP exercise, subjects also
experienced a performance improvement in
the no-PAP condition. Thus, it cannot be concluded that a non-localized PAP effect exists.
A possible explanation for squat jump increasing in all four conditions is that the pre-testing
squat jump and pushups tests simply served as
a warm-up for the subjects.
71
The second study from Bartolomei et al 2022
(8) split 24 trained men into two groups (upper body and lower body), and there were
two conditions (high intensity and power)
within each group. In both conditions, each
group tested bench press barbell throw power
at 30% of 1RM and countermovement jump
power, then performed their group- and condition-specific PAP exercise. After the PAP
exercise, both groups retested bench press
throw and countermovement jump power.
The upper body group had PAP interventions
of 5 × 1 at 90% of 1RM (high intensity) on
the bench press and 5 × 4 at 30% of 1RM
(power) on the bench press barbell throw.
The lower body group had PAP interventions
consisting of 5 × 1 at 90% of 1RM (high intensity) on the squat and 5 × 4 jumps (power)
onto a 40 cm box. Interestingly, there was a
group × time interaction (p = 0.012) for countermovement jump power in the high intensity condition. This group × time interaction
was driven by a significant increase from
pre- to post-testing in the upper body group
(p = 0.025). In other words, this study found
that non-localized performance improvement
occurred due to PAP. However, similar to the
present study, the increase in countermovement jump power following the high intensity bench press condition was only 1.6%.
Overall, two out of the three studies on
non-localized PAP, both from Bartolomei
(1, 8), have shown that PAP exercise of 5
× 1 at 90% of 1RM on the bench press elicits a small (1.6%) improvement in countermovement jump power. In my opinion, this
is just enough evidence to warrant anoth-
72
er study in this area. However, as a coach,
I’m not clamoring to include non-localized
PAP in any athletes’ programs, because no
study has shown an increase in strength or
volume performance, and the positive findings are small and from the same researcher. To be clear, there’s nothing wrong with
the same researcher finding similar results in
two studies, but there are different conditions
between labs. Thus, replication from another
lab would be preferable.
The remainder of the directly relevant PAP
literature is related to localized performance
enhancement. This remaining literature
consists of four studies (3, 4, 9, 10). These
studies have examined reps performed on
the squat (3) or bench press (4, 9) following
heavy loading on the same exercise or examined isometric force following eccentric
overload (10). All four of those studies are
summarized in Table 3.
It’s not particularly useful to rehash each
study in Table 3 since we’ve reviewed three
(3, 9, 10), and have a video discussing them
all. Therefore, for my most up-to-date review of these studies, I would recommend
checking out this article and this video, but
I will provide a brief overview of these studies’ general findings. For a localized performance enhancement, the research suggests
that a lifter should perform 2-3 sets of submaximal doubles or triples between 85-90%
of 1RM on the squat or bench press. Then, a
lifter might enhance performance on reps to
failure sets at a moderate load (65-75%) on
the same exercise 5-15 minutes later. I say
“might” be enhanced because although Alves
et al (4) found that subjects added one rep per
set on the bench press following PAP exercise, Kryztofik et al (9) did not find that PAP
exercise improved bench press reps to failure. Conrado de Freitas et al (3) had the most
impressive results, finding PAP exercise added 6.5 reps, on average, to the subjects’ squat
reps to failure on only the first set during four
sets to failure at 70% of 1RM. Overall, I do
think PAP has merit, and as noted earlier, it’s
a practical and time-efficient strategy, but I
think Alves’ results are more realistic than
Conrado de Freitas’ findings.
The presently reviewed study (1) did not report significant relationships between baseline
strength levels or muscle thickness with improvements in countermovement jump power.
Previous research (11, 12) has indicated that
individuals with greater strength levels, specifically those with more than three years of
experience (13), benefit more from PAP exercise. However, none of the studies in Table
3 related to lifting performance examined if
baseline strength or muscle size were related
to a PAP-induced performance enhancement.
The presently reviewed study employed a
small sample size (13 subjects) of trained men
whose training experience was 5.2 ± 3.5 years,
and their 1RM bench press was 99.5 ± 10.5
kg. While there is some variation around the
mean in each of those values, it’s not particularly large. Those with higher strength levels may benefit more from PAP; however, the
initial step to answering that question would
be to compare a cohort of well-trained individuals (>5 years of experience) versus novice
trainees (<1 year of experience). With a large
sample size and a wide range of training experience between two cohorts, researchers would
73
be more likely to observe the effect of baseline
strength or muscle size on PAP-induced performance enhancement in resistance training
if it does exist.
Practical Implementation of PAP
Even though I don’t personally believe a lifter should expect a massive benefit from PAP,
I do think it’s worth a try for some, due to
its practicality. For example, if a lifter has a
bench press or squat volume day, they could
first do up to a couple singles or doubles at
85-90% of 1RM and then back off and perform their volume work. If the volume sets
are to failure, then the lifter might complete
an extra rep or so on each set. If the sets aren’t to failure, then the lifter might have more
RIR than normal. For instance, let’s say a lifter can typically squat 4 × 10 at 70% and with
RIRs on each set of 3, 2, 2, and 1. Then, if the
lifter performs PAP exercise before the same
workout, the lifter may find the 4 × 10 at 70%
easier and record RIRs of 4, 3, 3, and 2. In this
example of increasing RIR, it’s possible the
lifter could then complete an additional set.
In other words, PAP isn’t just for failure sets;
rather, I would think of it as a strategy that
can make your volume work feel easier, no
matter how you structure the volume work.
Additionally, even if someone doesn’t experience performance enhancement from PAP
exercise, they still may experience strength
gains from performing a set or two of heavy
singles or doubles once a week before their
volume work.
Suppose PAP exercise can result in a non-localized performance improvement. In that
case, PAP exercise should most likely be
heavy (i.e., a few singles or doubles at 85-90%
of 1RM), as both Bartolomei studies found
that the small but significant, non-local performance enhancement occurred following 90%
but not 30% of 1RM PAP protocols. As noted
earlier, I’m not sure I would implement this
just yet since no study has shown a non-localized positive effect on strength or volume
performance. Further, the benefits of PAP can
be individual, with some experiencing a performance improvement and some becoming
fatigued from the PAP exercise and experiencing a performance impairment (11).
In the above paragraph, I noted that some
people might be fatigued by PAP exercise
and therefore experience a performance decrement. This fatigue is possibly due to the
uniform training protocols or rest times used
in most research. For example, if a PAP exercise of 3 × 1 at 90% is prescribed, some lifters
might end each set at 2-3 RIR. However, others might be at a 1 RIR on the first and 0 RIR
on the last set. In that case, the lifters closer
to failure may be more fatigued and less likely to experience performance enhancement.
Therefore, I would recommend performing a
few sets of heavy singles or doubles to a 2-3
RIR and not worrying so much about the percentage of 1RM. Besides, if you’re not feeling great one day, then 90% of 1RM could be
a 0 RIR when it is usually a 2 RIR. In short,
it’s a good policy to autoregulate the load for
PAP exercise as the point is to improve subsequent performance, not fatigue yourself.
Additionally, it’s been well-established that
lifters experience performance improvements
at different times following the PAP exercise
(9, 13, 14). In other words, if performance
is tested at 5, 10, and 15 minutes following
74
APPLICATION AND TAKEAWAYS
1. Bartolomei et al (1) found that PAP exercise consisting of 5 × 1 bench press with
a high load (90% of 1RM), but not low load (30% of 1RM), resulted in small but
statistically significant improvements in countermovement jump power and vastus
lateralis EMG activity.
2. Despite the small benefits, the PAP exercise did not improve isometric leg
extension force, and no study to date has found PAP exercise to have a nonlocalized effect on maximal strength or volume performance.
3. Ultimately, there isn’t enough evidence in the literature to warrant chasing a nonlocalized PAP effect. However, working up to a few sets of 1-3 reps at a 2-3 RIR
(i.e., 3 × 2 at 85% of 1RM) on the squat or bench press may improve volume
performance on the same exercise about 10 minutes later. This prescription for a
localized PAP effect isn’t a make-or-break strategy, but it’s simple and practical
and might be worth a shot.
the PAP exercise, some may improve performance at one of those times, and others may
benefit at a different point. In practice, this
may take a little trial and error. So, just because you don’t feel great five minutes postPAP doesn’t mean 10 minutes post-PAP isn’t
a viable option.
Next Steps
Since there are only three studies on non-localized PAP exercise, more research is warranted. Importantly, none of those three
studies have examined volume performance.
As discussed earlier, the release of catecholamines increases blood flow to the muscle,
which would be most beneficial for repeated contractions (i.e., volume performance).
Therefore, if catecholamine release is an important mechanism of action for non-local
PAP, then examining volume performance
rather than max strength may be a better outcome measure. So, a non-local study simi-
lar to one of the lifting studies from Table 3
would be the immediate next step. Specifically, a crossover design study in which individuals performed a PAP exercise of 3 ×
1 at 90% of 1RM on the bench press in one
condition and no-PAP in the other condition,
followed by four sets to failure at 70% on the
squat would be a good start. Of course, this
study could also use squat as the PAP exercise and bench press as the performance test.
Lastly, future research should also aim to
include both well-trained and novice participants to investigate if baseline strength and
muscle size are related to a PAP-induced performance enhancement.
75
References
1. Bartolomei S, De Luca R, Marcora SM. May a Nonlocalized Postactivation Performance
Enhancement Exist Between the Upper and Lower Body in Trained Men?. The Journal of
Strength & Conditioning Research. 2022 May 9:10-519.
2. Esformes JI, Bampouras TM. Effect of back squat depth on lower-body postactivation
potentiation. The Journal of Strength & Conditioning Research. 2013 Nov 1;27(11):29973000.
3. De Freitas MC, Rossi FE, Colognesi LA, De Oliveira JV, Zanchi NE, Lira FS,
Cholewa JM, Gobbo LA. Postactivation potentiation improves acute resistance exercise
performance and muscular force in trained men. The Journal of Strength & Conditioning
Research. 2021 May 1;35(5):1357-63.
4. Alves RR, Viana RB, Silva MH, Guimarães TC, Vieira CA, de AT Santos D, Gentil
PR. Postactivation potentiation improves performance in a resistance training session in
trained men. The Journal of Strength & Conditioning Research. 2021 Dec 1;35(12):32969.
5. Pullinen T, Nicol C, MacDonald E, Komi PV. Plasma catecholamine responses to four
resistance exercise tests in men and women. European journal of applied physiology and
occupational physiology. 1999 Jun;80(2):125-31.
6. French DN, Kraemer WJ, Volek JS, Spiering BA, Judelson DA, Hoffman JR, Maresh
CM. Anticipatory responses of catecholamines on muscle force production. Journal of
Applied Physiology. 2007 Jan;102(1):94-102.
7. Cuenca-Fernández F, Smith IC, Jordan MJ, MacIntosh BR, López-Contreras G, Arellano
R, Herzog W. Nonlocalized postactivation performance enhancement (PAPE) effects
in trained athletes: a pilot study. Applied Physiology, Nutrition, and Metabolism.
2017;42(10):1122-5.
8. Bartolomei S, Lanzoni IM, Fantozzi S, Cortesi M. A Comparison between Non-Localized
Post-Activation Performance Enhancements Following Resistance Exercise for the Upper
and the Lower Body. Applied Sciences. 2022 Feb 4;12(3):1639.
9. Krzysztofik M, Wilk M, Filip A, Zmijewski P, Zajac A, Tufano JJ. Can post-activation
performance enhancement (PAPE) improve resistance training volume during the bench
press exercise?. International Journal of Environmental Research and Public Health. 2020
Apr;17(7):2554.
10. Beato M, Stiff A, Coratella G. Effects of postactivation potentiation after an eccentric
overload bout on countermovement jump and lower-limb muscle strength. The Journal of
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Strength & Conditioning Research. 2021 Jul 1;35(7):1825-32.
11. Seitz LB, de Villarreal ES, Haff GG. The temporal profile of postactivation potentiation
is related to strength level. The Journal of Strength & Conditioning Research. 2014 Mar
1;28(3):706-15.
12. Seitz LB, Haff GG. Factors modulating post-activation potentiation of jump, sprint,
throw, and upper-body ballistic performances: A systematic review with meta-analysis.
Sports medicine. 2016 Feb;46(2):231-40.
13. Wilson JM, Duncan NM, Marin PJ, Brown LE, Loenneke JP, Wilson SM, Jo E, Lowery
RP, Ugrinowitsch C. Meta-analysis of postactivation potentiation and power: effects of
conditioning activity, volume, gender, rest periods, and training status. The Journal of
Strength & Conditioning Research. 2013 Mar 1;27(3):854-9.
14. Gouvea AL, Fernandes IA, Cesar EP, Silva WA, Gomes PS. The effects of rest intervals
on jumping performance: A meta-analysis on post-activation potentiation studies. Journal
of sports sciences. 2013 Mar 1;31(5):459-67.
█
77
Study Reviewed: Effects Of A 4-Month Active Weight Loss Phase Followed By Weight Loss
Maintenance On Adaptive Thermogenesis In Resting Energy Expenditure In Former Elite
Athletes. Nunes et al. (2022)
Putting Metabolic Adaptation
Into Perspective
BY ERIC TREXLER
It’s widely accepted that metabolic adaptation exists. But how
impactful and persistent is it, and how much does it vary from
person to person? That’s precisely what this article seeks to cover.
78
KEY POINTS
1. Former elite athletes successfully completed a 4-month weight loss phase
with an 8-month maintenance period. Individuals with the “thrifty” metabolic
phenotype experienced less weight loss than “spendthrift” individuals, but they
still lost 3.5kg, kept it off, and achieved a lower mean body-fat percentage than
spendthrift participants.
2. Empirical data continue to confirm that many people experience physiologically
meaningful magnitudes of metabolic adaptation, but it’s a very heterogeneous
effect that varies considerably from person to person.
3. Most importantly, metabolic adaptation is not an insurmountable roadblock or a
strong predictor of weight regain. We can unburden ourselves from worries about
metabolic adaptation if we simply expect it and account for it, either by altering
our calorie intake, activity level, or rate of weight loss.
W
e’ve covered metabolic adaptation (and related topics, such
as low energy availability and
relative energy deficiency) several times in
MASS. Nonetheless, important questions
persist, and for good reason. You could argue
that this particular line of research stretches
back to (at least) the 1940s, with the publication of the Minnesota Starvation Experiment.
While metabolic adaptation is of considerable importance to people with goals pertaining to weight management, studies were
few and far between in the decades that followed, and generally employed very different approaches to measuring and quantifying
the most relevant variables. The metabolic
adaptation literature started picking up some
steam in the 1990s, but the pace of progress
has really spiked in the last few years – when
you check the references for this article,
you’ll see that much of the research I cite has
been published within the last 2-3 years. As
new research becomes available, we get new
opportunities to assess some of the biggest
questions surrounding metabolic adaptation:
who experiences it, how long does it persist,
and how much does it actually matter?
The presently reviewed study (1) implemented a comprehensive lifestyle intervention to induce weight loss in former
elite athletes with overweight or obesity.
The intervention involved a 4-month active
weight loss phase (aiming for a 300-500
kcal/day energy deficit), followed by an
8-month maintenance phase, for a total intervention duration of one year. Participants
successfully lost an average of 4.3kg, and
maintained the entirety of this weight loss.
Participants experienced a statistically significant, adaptive reduction in resting energy expenditure (mean ± standard error; -85
± 29 kcal/day), which remained significant
throughout the maintenance phase (-72 ± 31
kcal/day). Metabolic adaptation did not affect every participant equally; the researchers were able to stratify the groups by met-
79
abolic phenotype, with “thrifty” individuals
experiencing greater metabolic adaptation
and energy conservation than “spendthrift”
individuals. Interestingly, the thrifty participants had higher daily energy expenditure at
baseline, despite having very similar weight
values and lower body-fat percentage values in comparison to the spendthrift group.
These thrifty participants lost significantly less weight than spendthrift individuals,
but they still still lost 3.5kg, kept it off, and
achieved a lower mean body-fat percentage
than the spendthrift participants by the end
of the study.
As you can see, this study offers plenty of
useful information to dig into, and we’ve only
scratched the surface. Read on to explore who
experiences metabolic adaptation, how long
it seems to persist after active weight loss,
and how much it actually impacts successful
attainment of body composition goals.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed
study was to investigate: 1) “if [adaptive
thermogenesis] remains significant during
a [weight loss] maintenance period, i.e.,
under a neutral energy balance, comparing
with a control group,” and 2) “if the degree of energy conservation is related with
changes in body composition, weight-related hormones, or the percentage of energy
restriction.”
Hypotheses
The authors did not directly state hypotheses.
Subjects and Methods
Subjects
A total of 94 former elite athletes who had either represented Portugal in international-level
competition or competed in high-level professional soccer participated in the present study.
In order to participate, these former athletes
needed to be between the ages of 18-65, have
a BMI of at least 25 kg/m2, be willing to complete the intervention (which included diet
modification, physical activity modification,
and a number of in-person study visits), be
weight-stable for at least three months prior to
the study, and be “inactive.” The researchers
considered subjects to be too active for study
inclusion if they completed at least 20 min of
vigorous physical activity three or more days
per week, or completed at least 30 min of moderate physical activity five or more days per
week. At baseline, participants had an average
(mean ± standard deviation) age of 43.0 ± 9.4
years and an average BMI of 31.1 ± 4.3 kg/m2,
and the sample was 34% female.
Methods
The presently reviewed study was a secondary
analysis of previously collected data from the
Champ4life Study. A full overview of the study
design and methods can be found here, but the
basic overview is that this was a comprehensive,
year-long randomized controlled trial. Upon
enrollment, participants were randomly allocated to either the intervention group or the control
group. The intervention was an extremely thorough and well-rounded weight loss program
that was designed to induce weight loss in the
first four months, then facilitate weight maintenance in months five through twelve.
80
The intervention was largely guided by the
basic principles of self-determination theory,
and included several different components.
Participants received ongoing nutrition guidance (including multiple nutrition appointments) that aimed to facilitate a 300-500 kcal/
day caloric deficit during the active weight
loss, and neutral energy balance during the
weight maintenance period. On top of that,
there were numerous educational sessions
designed to provide participants with knowledge and skills related to successful weight
loss (nutrition, physical activity, self-monitoring, and behavior change), and ample
opportunities for social support. Participants
were provided with pedometers and encouraged to practice multiple forms of self-monitoring throughout the intervention (monitoring of body weight, step counts, physical
activity behaviors, and eating behaviors). The
control group completed all of the necessary
testing visits at baseline, four months, and
12 months, but generally maintained their
typical diet and exercise habits. In order to
incentivize continued participation, the control group was essentially a waiting list; participants assigned to this group were granted
access to the full 12-month program after the
actual study was complete.
Key variables including body composition,
energy expenditure, and hormone levels were
assessed at baseline, after month four (the
end of active weight loss), and after month
12 (the end of the maintenance period). Body
composition was measured via DXA. Resting metabolic rate was measured via indirect
calorimetry and physical activity energy expenditure was estimated using accelerome-
ters. Based on the assumption that the thermic effect of food accounts for 10% of total
energy expenditure, the researchers were
able to combine resting expenditure, physical activity expenditure, and the estimated
thermic effect of feeding to calculate total
daily energy expenditure. In addition, the
researchers calculated energy balance over
time by calculating the change in stored body
energy (based on body composition testing).
Using calculated values for energy expenditure and energy balance, the researchers were
therefore able to make inferences about energy intake without relying on the accuracy
of self-reported food logs, given that energy
balance is equal to energy intake minus energy expenditure. They also used calculations
to make inferences about dietary adherence,
based on changes in calculated energy intake
and the prescribed magnitude of caloric restriction. Key hormones of interest included
insulin, thyroid-stimulating hormone, thyroid
hormones (T3 and T4), and leptin.
As for the statistical analyses, a number of
comparisons were made. The researchers
sought to assess changes over time within
the intervention group, while also comparing them to the control group. The researchers also compared measured resting energy
expenditure to predicted resting energy expenditure, as a means of assessing the magnitude of adaptive thermogenesis (metabolic
adaptation). Finally, the researchers stratified
the intervention group based on metabolic
phenotype for some additional comparisons
between subgroups. In the present study, participants were categorized as having a thrifty
metabolic phenotype (n = 11) if their calcu-
81
lated value for adaptive thermogenesis (the
adjusted difference between their measured
and predicted resting metabolic rate, indicating greater conservation of energy and more
resistance to weight loss) was greater than
the typical error of 103 kcal/day (note: this
typical error value accounts for the technical error of measurement and within-subject
variability). Participants who had positive
values for adaptive thermogenesis (n = 25)
were categorized as having a spendthrift metabolic phenotype.
Findings
As you would expect, the moderate weight
loss intervention (aiming for a 300-500 kcal/
day energy deficit for four months) led to
moderate amounts of weight loss. Energy
balance during the weight loss phase was
calculated to be -270 kcal/day, on average,
in the intervention group, while it was +14
kcal/day in the control group. In addition, the
intervention was very effective for long-term
maintenance, with identical mean weight values at four months and twelve months in the
intervention group. Weight remained fairly
stable throughout the entire year within the
control group, although they did gain about a
kilogram over that time span. Body composition values are presented in Table 1.
Both groups had similar resting energy expenditure values at baseline, and measured
values were extremely close to predicted
values for both groups (within 5kcal). In the
intervention group, statistically significant
adaptive thermogenesis was observed at the
end of the active weight loss phase (mean ±
standard error; -85 ± 29 kcal/day). By the end
of the weight maintenance period, this adap-
82
tive thermogenesis had shrunk a tiny bit in
magnitude, but remained statistically significant at -72 ± 31 kcal/day. The control group
actually experienced a statistically significant positive value for adaptive thermogenesis (i.e., there appeared to be an adaptive increase in energy expenditure). This increase
of 81 ± 31 kcal/day is most likely reflective
of being in positive energy balance, given
that the control group gained a bit of weight
toward the end of the study period. Metabolic
rate data are presented in Table 2.
The researchers identified some interesting
observations when stratifying the intervention group based on metabolic phenotypes.
While the spendthrift group lost an average of
6.4kg during the weight loss phase, the thrifty
group lost 3.5kg. Furthermore, the spendthrift
group lost an additional 0.4kg during the
maintenance phase, whereas the thrifty group
maintained a stable body weight. While one
might assume that a thrifty phenotype is associated with lower energy expenditure and
higher body weight and body-fat percentage
at baseline, that’s actually not the case. The
thrifty individuals were only 31.4% body-fat
at a weight of 92.0kg at baseline, whereas the
spendthrift individuals were 35.5% body-fat
at a weight of 91.7kg. In addition, baseline total daily energy expenditure was significantly greater in the thrifty group, by an average
of 396 kcal/day (I think – see the “Criticisms
and Statistical Musings” section for more information). It’s uncertain how biological sex
breakdowns in each subgroup might have affected this, but these values are derived from
models that mathematically adjust for the influence of sex. As a result, it’s very hard to
make the claim that people with spendthrift
phenotypes were enjoying powerful benefits
that allowed them to passively stay leaner or
consume disproportionately higher calorie
83
intakes at baseline based on the data reported.
Body composition comparisons between the
thrifty and spendthrift subgroups are presented in Table 3.
served for thyroid-stimulating hormone, T3,
or T4. However, hormone values were not
meaningfully correlated with adaptive thermogenesis values.
Dietary adherence was 89%; the prescribed
caloric restriction value was 17.5%, and the
participants achieved 13.6% caloric restriction. The calculated caloric restriction value was negatively correlated with adaptive
thermogenesis (in kcal/day), indicating that
larger magnitudes of energy restriction led to
larger magnitudes of energy conservation (R
= -0.325, p = 0.036). When comparing thrifty
and spendthrift groups, there were no significant differences in dietary adherence or
the calculated caloric restriction value. The
intervention group experienced statistically
significant reductions in insulin and leptin
levels, while no significant changes were ob-
Criticisms and Statistical
Musings
I wouldn’t necessarily call these criticisms or
statistical musings, but I have one clarification and one important caveat to share in this
section.
The caveat is that this paper, like many others, frames adaptive thermogenesis as a drop
in resting energy expenditure. We commonly
break total daily energy expenditure down into
four components: resting energy expenditure,
exercise activity thermogenesis, non-exercise
activity thermogenesis, and the thermic effect
84
of feeding. While resting energy expenditure
is impacted by metabolic adaptation, non-exercise activity thermogenesis is the component that experiences the largest magnitude of
change (2). However, resting energy expenditure is substantially easier and more affordable
to measure than non-exercise activity thermogenesis or total daily energy expenditure, so
much of the metabolic adaptation literature focuses on resting energy expenditure by default.
It’d be great for all of these studies to provide
extremely precise values for all energy expenditure components, but it’s rarely feasible to do
so, and that’s no fault of these authors – some
stuff is just very challenging and expensive to
measure. I think resting energy expenditure, in
the context of this study, serves as a suitable
“proxy” that should correlate quite well with
overall metabolic adaptation (summed across
multiple energy expenditure components), but
this is a limitation to keep in mind. Also, it’s
important to recognize that the magnitudes of
adaptive thermogenesis reported in this paper
are almost certainly underestimating the total magnitude of energy conservation, as they
only capture the conservation occurring in the
resting component of energy expenditure.
during at least 3 months (inclusion criteria),
we considered an [energy balance] = 0, and
therefore [energy intake] = [energy expenditure].” However, that’s not all of the confusion
to address. If you read the results section of the
paper, they actually report that baseline energy
intake was lower in the thrifty group than the
spendthrift group, this time providing exact
values (-396 ± 174 kcal/day, p = 0.031). This
statement directly contradicts the statement in
the abstract, so I emailed the authors for clarification. They were extremely helpful and
responsive, and confirmed that the abstract
was correct. As a result, I’m assuming that the
magnitude is correct (396 kcal/day), but that
the direction of the relationship was mistakenly inverted in the results section. In summary, total daily energy expenditure was higher
in the thrifty group at baseline, and it seems
that we can comfortably assume that the average magnitude of the difference between
subgroups was 396 kcal/day. To be clear, this
was a small oversight and we shouldn’t hold
it against the authors or view their work more
negatively – this kind of thing happens all the
time, and I really appreciate their responsiveness and willingness to clarify.
The clarification refers to the reported observation that total daily energy expenditure was
higher in thrifty participants than spendthrift
participants at baseline, by an average of (I
think) 396 kcal/day. If you read the abstract,
you might be a bit confused because the researchers reported that the thrifty group had
higher energy intake at baseline, without mentioning expenditure. However, they clarify in
the methods section: “For the baseline [energy intake], as participants were weight-stable
Interpretation
In the introduction section, I alluded to three
of the most pertinent and commonly asked
questions related to metabolic adaptation:
who experiences it, how long does it persist,
and how much does it actually matter? In
keeping with this general structure, we’ll address each of these questions in order by integrating the presently reviewed findings with
the broader literature that came before it.
85
Who experiences metabolic adaptation?
A recent meta-analysis by Nunes et al points
out that the metabolic adaptation literature is
characterized by high levels of heterogeneity
(3). Some of the variation in results pertains to
methodological differences between studies,
but some of it also pertains to individual-level
differences from person to person within the
same study. The concept of metabolic phenotypes offers a helpful heuristic to categorize
individuals as thrifty or spendthrift based on
their metabolic responses to overfeeding and
underfeeding, which seems to serve as a predictive tool related to metabolic adaptation.
We’ve covered metabolic phenotypes previously in MASS; a paper by Hollstein et al
categorized thrifty and spendthrift individuals
based on their metabolic response to shortterm fasting, and found that this categorization predicted differential metabolic responses
to short-term overfeeding as well (4). Naturally, one might have concerns about whether or
not these acute measurements actually pertain
to long-term weight regulation. Results of a
study by Reinhardt et al suggest they do, as
individuals who experienced larger drops in
energy expenditure during short-term fasting
(and smaller increases during overfeeding)
also achieved less weight loss during a 6-week
diet intervention (5).
The results of the presently reviewed study
provide additional longitudinal data to help
us paint a clearer picture of metabolic phenotypes. Thrifty individuals experience greater reductions in energy expenditure during
short-term fasting and longitudinal weight
loss, but they tend to have relatively similar
weight and body composition characteristics
at baseline (in comparison to their spendthrift
counterparts). While it’s tempting to assume
that thrifty individuals have low energy expenditure all the time, the present study (1)
and the previous study by Hollstein (4) both
suggest that thrifty individuals actually have
higher energy expenditure than spendthrift
individuals at baseline.
Having a thrifty metabolic phenotype doesn’t
seem to necessarily lead you toward a higher
baseline body weight or body-fat percentage,
nor does it suggest that you’ll need to eat disproportionately fewer calories at your “natural” body weight in comparison to someone
with a spendthrift metabolic phenotype. Rather, metabolic phenotypes tell us about how
your body is likely to respond to underfeeding
and overfeeding. In other words, we can’t look
at a sample of people and assume that individuals with higher body-fat will have lower
energy expenditure or will necessarily have
a thrifty metabolic phenotype. In contrast, it
seems that we can predict that a person with
a thrifty metabolic phenotype will experience
a little more friction during intentional weight
loss, and will be less resistant to fat gain (and
experience smaller increases in energy expenditure) during overfeeding.
If we consider the dual-intervention point
model (Figure 1), as previously described
by Speakman et al (6) and reviewed by Dr.
Helms, we can imagine a thrifty individual
as having a larger gap between their baseline body weight and their upper intervention
point, but a smaller gap between their baseline body weight and their lower intervention
point. As a result, they can gain considerable
amounts of weight before any attenuation
86
mechanisms kick in, whereas weight loss efforts are quite promptly met by factors that
attenuate further weight reductions. However, there aren’t major weight and body composition differences between metabolic phenotypes at baseline, likely because people
are sitting around their “natural weight” that
rests comfortably between both intervention
points. As a side note, this observation casts
very serious doubts on most common applications of reverse dieting.
How long does metabolic adaptation persist?
This is another area of the metabolic adaptation literature in which considerable heterogeneity exists (3). Unfortunately, this heteroge-
neity is difficult to minimize because studies
are largely attempting to take a snapshot of
resting energy expenditure at a single point in
time, but resting energy expenditure is meaningfully influenced by acute (very short-term)
energy balance (7) and physical activity levels (8). Previous research has suggested that
transitioning from active weight loss (negative energy balance) to weight maintenance
(neutral energy balance) partially, but not
fully, reverses adaptive reductions in resting
metabolic rate (7). The present findings lean
in that general direction, although the magnitude of reversal was quite small in this study.
You could spend a full article digging into
87
this particular question, but Dulloo (9) provides a great model for metabolic adaptation
reversal that is very compatible with the current body of evidence. The model proposes
that there are two distinct components of metabolic adaptation, or adaptive thermogenesis:
a non-specific component, which is dictated
by the presence of a current energy deficit,
and an adipose-specific component, which is
dictated by a loss of fat mass from baseline.
The model proposes that metabolic adaptation will be partially attenuated by switching
from active fat loss to maintenance, but will
likely persist to some extent until a considerable amount of fat mass is regained (perhaps
all of it). If we merge this with the dual-intervention point model and observational
research on physique athletes (10), we can
further speculate that the adipose-specific
component of metabolic adaptation is particularly pronounced when people are absolutely shredded, and necessarily far below their
lower intervention point.
How much does metabolic adaptation actually matter?
As noted previously, being particularly susceptible to metabolic adaptation (i.e., having
a thrifty metabolic phenotype) doesn’t necessarily lead to maintaining a higher body
weight or body-fat percentage. Without intentional weight loss or weight gain efforts,
most people passively stay comfortably between their upper and lower intervention
points, and the exact location is largely dictated by behavioral and environmental factors.
One can reasonably assume that spendthrift
individuals have more “safeguards” in place
to attenuate weight gain when behavioral and
environmental factors favor more positive
energy balance, but metabolic phenotypes
don’t seem particularly predictive of baseline weight or body composition. In contrast,
when we shift our focus toward intentional
weight loss, phenotypic differences become
more apparent.
In the present study, it seems that having a
thrifty phenotype and experiencing a greater
magnitude of metabolic adaptation contributed to some extra weight loss difficulty. While
the spendthrift subgroup lost 6.4kg of weight
(5.2kg of fat), the thrifty group lost only 3.5kg
of weight (2.5kg of fat). However, there are a
few key things to keep in mind. First, metabolic adaptation didn’t make weight loss impossible; the thrifty group still lost weight and
fat mass, it just wasn’t as much as observed
in the spendthrift group. Second, baseline differences make it a little complicated to make
direct, “apples to apples” comparisons. The
spendthrift group experienced larger drops
in fat mass and body-fat percentage, but the
thrifty group ended the study with slightly
lower fat mass and higher fat-free mass values than the spendthrift group. As a result,
it’s hard to say that having a thrifty phenotype was a huge detriment for attainment of
the stated body composition goals in this particular study.
Finally, and most importantly, these subgroup data aren’t presented in a way that
enables us to obtain a thorough and detailed
understanding of the independent impact of
metabolic adaptation. The degree of metabolic adaptation was calculated as a continuous
variable (i.e., you might have experienced
90, 105, or 160 kcal/day of adaptation), but
88
it was collapsed into a dichotomous variable
(i.e., “high” [thrifty] or “low” [spendthrift]).
This allows us to make broad comparisons
of one phenotype versus the other, but we
lose some detail and granularity in determining how specific factors contributed to
extra weight loss friction. For example, the
researchers found that the degree of caloric
restriction was associated with magnitude of
metabolic adaptation in the full sample (more
caloric restriction was correlated with greater metabolic adaptation), but they found no
significant difference in the degree of caloric restriction when comparing the subgroups
experiencing higher versus lower magnitudes
of metabolic adaptation. The calculated magnitude of metabolic adaptation in this study
fails to explain the entire difference in fat loss
between groups, but the data aren’t reported
in a way that allows us to easily quantify the
independent impact of metabolic adaptation,
or identify which other factors contributed to
friction in the weight loss process. Fortunately, we can lean on some previous studies that
did aim to quantify the independent impact
of metabolic adaptation, using units that are
very straightforward for the purposes of practical application.
For example, a 2021 study (11) found that
metabolic adaptation was associated with less
weight loss during a diet intervention. Participants lost an average of 14 ± 4 kg (13 ± 3% of
initial body mass), and a statistically significant adaptation of resting energy expenditure
was observed at the group level (-92 ± 110
kcal/day, p < 0.001). However, the researchers found that as an individual’s magnitude
of metabolic adaptation increased by 50
kcal/day, observed weight loss only tended
to drop by 0.5kg. Based on the group-level
means and standard deviations, it appears
that even those experiencing pronounced
metabolic adaptation generally achieved successful weight loss outcomes, and the small
amount of friction added by metabolic adaptation should be easily overcome by making
some very modest calorie adjustments during
the weight loss phase.
A 2022 study (12) found that metabolic adaptation delayed the time required to reach
weight loss goals. Participants lost an average
of 12.5 ± 3.1 kg (16.1% ± 3.4% of initial body
mass) over 155.1 ± 49.2 days. Once again,
a statistically significant adaptation of resting energy expenditure was observed at the
group level (-46 ± 113 kcal/day, p = 0.002).
However, the researchers found that as an individual’s magnitude of metabolic adaptation
increased by 10 kcal/day, the time to reach
one’s weight loss goal only increased by a
single day. Again, based on the group-level
summary data, it appears that even those experiencing pronounced metabolic adaptation
generally achieved successful weight loss
outcomes, and the small amount of friction
added by metabolic adaptation should be easily overcome by extending the timeline of the
weight loss phase a little bit. So, metabolic
adaptation makes weight loss a bit more challenging, but we can overcome these challenges with very straightforward and modest adjustments.
The same research group published a study
in 2020 that focused on weight regain (13).
In the study, participants initially lost an average of 12 ± 2.6 kg, and a statistically sig-
89
nificant adaptation of resting energy expenditure was observed at the group level (-54 ±
105 kcal/day; p < 0.001). One year later they
had regained 52% ± 38% of the lost weight
on average, and by the 2-year mark they had
regained 89% ± 54% of the lost weight. In
terms of the weight loss phase, the amount of
fat that an individual lost was correlated with
the magnitude of metabolic adaptation they
experienced. However, metabolic adaptation
was not predictive of weight regain at the
1-year or 2-year time points. These findings
are consistent with the results of the Biggest
Loser Study, as reported by Fothergill et al
(14). While substantial metabolic adaptation
was observed after dramatic weight loss, the
magnitude of metabolic adaptation was not
predictive of weight regain six years later. In
fact, the most successful weight maintainers
were the individuals with the greatest magnitude of metabolic adaptation at the end of the
six-year follow-up period, suggesting that the
persistence of metabolic adaptation reflected
the persistence of fat mass reduction. Collectively, the available research suggests that
metabolic adaptation is not reliably predictive of future weight regain.
In summary, an individual’s baseline metabolic rate tells us extremely little about their
propensity for subsequent weight change (15),
and while a thrifty phenotype (and, by extension, greater metabolic adaptation) appears
to predict some extra friction in the weight
loss process, successful weight loss is still
observed. So, people who experience greater
metabolic adaptation experience weight loss
success, but may require larger caloric reductions or longer weight loss timelines to per-
fectly match the weight loss of their spendthrift counterparts. Furthermore, the fact that
someone experiences greater metabolic adaptation during weight loss doesn’t appear to
tell us much at all about their likelihood of
maintaining their weight loss over time. To
be clear, we know some of the things that appear to influence successful weight loss and
subsequent maintenance; they include adherence to lifestyle modifications (16), eating
behavior characteristics (17), satiety-promoting strategies (18), lean mass retention (19),
regular self-monitoring (20), and plenty of
physical activity (20). The literature suggests
that these factors are far, far more impactful
than metabolic adaptation, despite the fact
that metabolic adaptation gets so much attention and causes so much worry among dieters. So, rather than worrying about avoiding
or reversing metabolic adaptation, we should
focus on identifying, quantifying, and accounting for it.
RATHER THAN WORRYING
ABOUT AVOIDING OR
REVERSING METABOLIC
ADAPTATION, WE SHOULD
FOCUS ON IDENTIFYING,
QUANTIFYING, AND
ACCOUNTING FOR IT
90
APPLICATION AND TAKEAWAYS
Metabolic adaptation exists, varies considerably from person to person, and can impact
the magnitude and rate of weight loss achieved during a standardized weight loss
intervention. Metabolic phenotypes offer a helpful heuristic to categorize individuals as
thrifty or spendthrift based on their metabolic responses to overfeeding and underfeeding.
Research clearly demonstrates that thrifty individuals (who experience larger magnitudes
of metabolic adaptation) can make satisfactory progress toward weight loss, and they can
probably overcome the challenges of metabolic adaptation by making appropriate calorie
adjustments or extending their weight loss timeline. Metabolic adaptation adds friction
to the weight loss process, but is by no means an insurmountable roadblock. With this
in mind, we need not worry about avoiding or reversing metabolic adaptation; instead,
we should focus on identifying, quantifying, and accounting for metabolic adaptation by
making some very feasible and straightforward adjustments.
Next Steps
We have multiple studies indicating that metabolic adaptation can slow the rate of weight
loss, or lead to less cumulative weight loss,
over the course of a standardized weight loss
intervention. However, it appears that this can
be overcome in a very straightforward manner, by simply implementing appropriate calorie adjustments or extending the timeline of
the intervention. While this is a somewhat
unconventional approach to study design, I’d
love to see a large study that explores metabolic phenotypes using an ecologically valid
and sustainable approach to weight loss, while
standardizing the outcome rather than the intervention. For example, in tightly controlled
conditions, the researchers would guide all
study participants toward a standardized
weight loss target (for example, a percentage
of body weight loss). When thrifty individuals
run into challenges, they adjust calorie intake
and, if necessary, allow for some extra weeks
of dieting to get to the desired goal. Ideally,
the intervention would include high protein
intake (≥1.6 g/kg of body weight [in kg] per
day, or ≥2.0 g/kg of fat-free mass [in kg] per
day) and resistance training to attenuate the
loss of lean mass. Within this type of design,
I’d be very interested to see exactly how much
more aggressively interventions would need to
ramp up in order to usher thrifty individuals to
the same endpoints achieved by spendthrift individuals. I’d also like to see if thrifty individuals experience extra friction along the way,
such as disproportionate changes in hormone
levels, hunger levels, and subjective outcomes
pertaining to symptoms of relative energy deficiency. As a final step, it’d be very informative to observe weight regain after the intervention, while aiming to identify key variables
that correlate with successful weight maintenance. I suspect we’d find that the weight loss
process is a little more challenging and a little
less pleasant for thrifty individuals, but that
they can feasibly achieve the same outcomes
as spendthrift individuals. I also suspect that
weight regain would largely be predicted by
variables related to behavioral outcomes and
appetite regulation rather than metabolic rate.
91
References
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4-Month Active Weight Loss Phase Followed By Weight Loss Maintenance On Adaptive
Thermogenesis In Resting Energy Expenditure In Former Elite Athletes. Eur J Nutr. 2022
Jul 14; ePub ahead of print.
2. Rosenbaum M, Leibel RL. Adaptive Thermogenesis In Humans. Int J Obes 2005. 2010
Oct;34 Suppl 1:S47-55.
3. 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. 2022 Feb 14;127(3):451-469.
4. Hollstein T, Basolo A, Ando T, Krakoff J, Piaggi P. Reduced Adaptive Thermogenesis
During Acute Protein-Imbalanced Overfeeding Is A Metabolic Hallmark Of The Human
Thrifty Phenotype. Am J Clin Nutr. 2021 Oct 4;114(4):1396–407.
5. Reinhardt M, Thearle MS, Ibrahim M, Hohenadel MG, Bogardus C, Krakoff J, et al. A
Human Thrifty Phenotype Associated With Less Weight Loss During Caloric Restriction.
Diabetes. 2015 Aug;64(8):2859–67.
6. Speakman JR, Levitsky DA, Allison DB, Bray MS, de Castro JM, Clegg DJ, et al.
Set Points, Settling Points And Some Alternative Models: Theoretical Options To
Understand How Genes And Environments Combine To Regulate Body Adiposity. Dis
Model Mech. 2011 Nov;4(6):733–45.
7. 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):1212–8.
8. Hall KD. Energy Compensation And Metabolic Adaptation: “The Biggest Loser” Study
Reinterpreted. Obes. 2022 Jan;30(1):11-13.
9. Dulloo AG. Physiology Of Weight Regain: Lessons From The Classic Minnesota
Starvation Experiment On Human Body Composition Regulation. Obes Rev.
2021;22(S2):e13189.
10. Longstrom JM, Colenso-Semple LM, Waddell BJ, Mastrofini G, Trexler ET, Campbell
BI. Physiological, Psychological and Performance-Related Changes Following Physique
Competition: A Case-Series. J Funct Morphol Kinesiol. 2020 Apr 25;5(2):E27.
11. Martins C, Roekenes J, Gower BA, Hunter GR. Metabolic Adaptation Is Associated With
Less Weight And Fat Mass Loss In Response To Low-Energy Diets. Nutr Metab. 2021
Jun 11;18(1):60.
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12. Martins C, Gower BA, Hunter GR. Metabolic Adaptation Delays Time To Reach Weight
Loss Goals. Obes. 2022 Feb;30(2):400–6.
13. Martins C, Gower BA, Hill JO, Hunter GR. Metabolic Adaptation Is Not A Major Barrier
To Weight-Loss Maintenance. Am J Clin Nutr. 2020 Sep 1;112(3):558–65.
14. 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.
15. Rimbach R, Yamada Y, Sagayama H, Ainslie PN, Anderson LF, Anderson LJ, et al.
Total Energy Expenditure Is Repeatable In Adults But Not Associated With Short-Term
Changes In Body Composition. Nat Commun. 2022 Jan 10;13(1):99.
16. Chopra S, Malhotra A, Ranjan P, Vikram NK, Sarkar S, Siddhu A, et al. Predictors
Of Successful Weight Loss Outcomes Amongst Individuals With Obesity Undergoing
Lifestyle Interventions: A Systematic Review. Obes Rev. 2021 Mar;22(3):e13148.
17. Hansen TT, Hjorth MF, Sandby K, Andersen SV, Astrup A, Ritz C, et al. Predictors Of
Successful Weight Loss With Relative Maintenance Of Fat-Free Mass In Individuals
With Overweight And Obesity On An 8-Week Low-Energy Diet. Br J Nutr. 2019 Aug
28;122(4):468–79.
18. Drapeau V, King N, Hetherington M, Doucet E, Blundell J, Tremblay A. Appetite
Sensations And Satiety Quotient: Predictors Of Energy Intake And Weight Loss.
Appetite. 2007 Mar;48(2):159–66.
19. Turicchi J, O’Driscoll R, Finlayson G, Duarte C, Hopkins M, Martins N, et al.
Associations Between The Proportion Of Fat-Free Mass Loss During Weight Loss,
Changes In Appetite, And Subsequent Weight Change: Results From A Randomized
2-Stage Dietary Intervention Trial. Am J Clin Nutr. 2020 Mar 1;111(3):536–44.
20. 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.
█
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Research Briefs
BY GREG NUCKOLS & ERIC TREXLER
In the Research Briefs section, Greg Nuckols and Eric
Trexler shares quick summaries of recent studies. Briefs
are short and sweet, skimmable, and focused on the needto-know information from each study.
95
102
107
115
119
126
Does High-Rep Training Actually Improve
Strength Endurance More Than Heavier
Training?
Do Raw Eggs and Cooked Eggs Have
Different Impacts on Muscle Protein Synthesis?
Is Heavier Training or Higher-Rep Training
Better in an Energy Deficit?
Adding Nuance to the Relationship Between
Energy Density and Calorie Intake
Can Cardio (Eventually) Make You Bigger?
Do Vegan Diets Negatively Impact Bone
Health?
130
Are Overhead Triceps Extensions Better than
Pushdowns for Hypertrophy?
138
Reassessing the Impact of Non-Nutritive
Sweeteners on Metabolic Health and the Gut
Microbiome
94
Study Reviewed: Influence of Total Repetitions Per Set on Local Muscular Endurance: A
Systematic Review with Meta-Analysis and Meta-Regression. Hackett et al. (2022)
Does High-Rep Training Actually Improve Strength
Endurance More Than Heavier Training?
BY GREG NUCKOLS
Within exercise science, the “principle of
specificity” goes mostly unquestioned, and
for good reason. In general, if you want to
improve a particular aspect of performance,
your training should closely resemble the
type of performance you’d like to improve
(and the evidence bears this out). If you want
to get stronger, lift heavy. If you want to improve velocity and power output, do training
that requires high velocity and power outputs.
There are at least a dozen other examples of
the principle of specificity in action.
The principle of specificity isn’t a completely unerring north star, but it’s an incredibly
useful heuristic. However, when a heuristic
works too well, too often, I think there’s a
tendency to become increasingly reliant on
the heuristic, in place of critical thinking
and careful data analysis. Notably, I think
over-reliance on the principle of specificity
has over-simplified the way that many people
think about training to improve strength endurance. If you ask 100 fitness professionals
how to improve strength endurance, you’ll
probably get at least 80-90 answers along
the lines of, “do lighter, higher rep training.”
That’s a perfectly logical answer, given the
principle of specificity – strength endurance
has been defined as, “the ability to maintain
submaximal muscle actions,” so it stands to
reason that improving muscular endurance
would involve training that requires you to
perform more submaximal muscle actions.
However, when you actually dig into the
data, you’ll find that the actual answer depends pretty heavily on how you define and
measure strength endurance, since “strength
endurance” is a surprisingly nuanced concept. It’s not clear-cut that high-rep training
is always best.
A recent meta-analysis by Hackett and colleagues (1) beautifully illustrates this nuance.
The researchers started by identifying all
studies meeting these inclusion criteria:
1. The studies needed to directly compare
the effects of two different rep ranges on
measures of strength endurance, following a dynamic resistance training intervention lasting at least four weeks
2. The studies needed to be peer-reviewed,
and use adult subjects with no known med-
95
ical conditions or musculoskeletal injuries
3. The studies needed to assess muscular endurance via a reps-to-failure test, using
either the same percentage of pre-training
1RM at pre- and post-training testing sessions, or using the same percentage of moment-in-time 1RM at both testing sessions.
Furthermore, studies would be excluded if
the intervention involved any supplement
besides protein, if the training intervention
involved training modalities other than resistance training, or if the details of the training
intervention didn’t allow groups to be neatly classified as the “higher-rep group” and
“lower-rep group” within each study.
The researchers performed two primary meta-analyses. The two meta-analyses assessed
the impact of lower- versus higher-rep training on strength endurance a) when pre- and
post-training assessments of strength endurance were both performed with the same per-
centage of the subjects’ pre-training 1RMs,
and b) when pre- and post-training assessments of strength endurance were performance with the same percentage of the subjects’ moment-in-time 1RMs. In other words,
if a subject trained for 8 weeks, increased their
squat from 125kg to 150kg, and performed
reps-to-failure tests with 70% of 125kg preand post-training, their data would be included in the first meta-analysis. For the rest of
this article, I’ll refer to this as an increase in
“absolute strength endurance.” If they performed a reps-to-failure test with 70% of
125kg at pre-training, and with 70% of 150kg
post-training, their data would be included in
the second meta-analysis. For the rest of this
article, I’ll refer to this as an increase in “relative strength endurance.” Six studies were included in the first meta-analysis on absolute
strength endurance, and nine studies were
included in the second meta-analysis on relative strength endurance.
96
The researchers found that higher- and
lower-rep training were similarly effective
for improving absolute strength endurance
(Figure 1).
Conversely, higher-rep training was considerably more effective than lower-rep training
for improving relative strength endurance
(Figure 2).
Furthermore, the researchers performed a meta-regression (with segmental linear regression) to assess the point at which performing
more reps in a set no longer led to further increases in relative strength endurance. They
found that the breakpoint was 24 reps per
set, with a 95% confidence interval spanning
from 11 to 39 reps per set (Figure 3).
Finally, the researchers performed moderator
analyses to see if any subject or training characteristics were predictive of larger or smaller
increases in absolute and relative strength endurance. The only noteworthy result was that
gains in maximal strength were predictive of
smaller gains in relative strength endurance
(or even losses in relative strength endurance; β = -0.89; p = 0.002), but that changes in maximal strength weren’t predictive of
changes in absolute strength endurance (β =
0.06; p = 0.76).
The basic finding of this study is that if you
want to be able to perform more reps with
a particular percentage of your 1RM, even
as your 1RM changes over time, it certainly
makes sense to train with higher reps. How-
97
ever, if you want to improve your strength
endurance performance with a particular absolute load, you don’t necessarily need to do
higher-rep “strength endurance” training.
I think an illustration will clarify the difference. Let’s assume you currently squat 300
pounds, and you want to be able to squat as
many reps as possible in a single set with
225 pounds. Right now, you might be able to
complete 10 reps.
If you do higher-rep, lower-load training
over a number of months, your squat 1RM
might only increase to 320 pounds, but the
local aerobic and anaerobic capacity of your
prime movers should increase considerably.
You should be expected to complete more
than 10 reps with 225, because 225 now represents a lower percentage of your momentin-time 1RM (70% now, versus 75% of your
pre-training 1RM), but without increases in
local aerobic and anaerobic capacity of your
prime movers, you might only be expected
to complete about 12 reps with 225. However, since your prime movers are more highly
conditioned now, you might be able to complete 18 reps with 225 – a large improvement
in strength endurance performance, relative
to your absolute strength levels.
Conversely, you might do lower-rep, higher-load training over a number of months,
and increase your 1RM to 400 pounds. With
this large increase in strength, 225 only represents 56% of your current 1RM, so you
might be able to crank out 18 reps, even if
the relative aerobic and aerobic conditioning
of your prime movers hasn’t meaningfully
changed. So, you might say that both approaches were similarly effective for improving your strength endurance.
98
However, if your goal was to maximize the
number of reps you can perform with 75% of
your 1RM, this little experiment would have
very different results. In both cases, you could
complete 10 reps with 75% of your 1RM at
baseline. Following lighter, high-rep training, you may be able to complete 15 reps with
75% of your current 1RM (240 pounds; 75%
of 320). Following heavier, low-rep training,
you may only be able to complete 8 reps with
75% of your current 1RM (300 pounds; 75%
of 400). With this metric of strength endurance, you’d conclude that higher-rep training
was considerably more effective for improving strength endurance.
If you disambiguate absolute and relative
strength endurance, the results are considerably clearer. With higher-rep training, you
experience a small increase in strength (+20
pounds), a larger increase in relative strength
endurance (+6 reps at the same percentage of
1RM), and an even larger increase in absolute strength endurance (+8 reps with a fixed
load). With lower-rep training, you experience
a large increase in strength (+100 pounds), a
small reduction in relative strength endurance
(-2 reps at the same percentage of 1RM), but
the same increase in absolute strength endurance that was observed with higher-rep training (+8 reps with a fixed load).
Ultimately, when evaluating the effects of a
particular approach to training on strength
endurance, it’s worth asking which metric(s) of strength endurance matter the most
for you. I’d argue that in most contexts, assessing absolute strength endurance – rep
performance with absolute load (or with a
fixed percentage of your pre-training 1RM)
– is more representative of the carryover you
can expect between the gym and competitive
or “real world” scenarios. For example, if a
strongman contest has a max reps event for
log press or deadlift, all competitors in each
weight class use the same load – you don’t
get penalized with a heavier load if you’re
stronger than your competitors. So, you can
gain an advantage by either having better
relative strength endurance than your peers,
or by simply being a lot stronger than your
peers. Similarly, the muscular fatigue you
experience in most athletic or “real world”
scenarios comes as a result of simply moving your body around for a prolonged period
of time. Assuming your body mass doesn’t
fluctuate wildly, your body will be less
challenging to move around for a prolonged
period of time if local aerobic and anaerobic capacity improves (increases in relative
strength endurance), but it will also be less
challenging to move around for a prolonged
period of time if your body mass represents
a much smaller percentage of your maximal
capacity to generate force. So, unless you
simply want to be able to perform a ton of
reps at 60% of your 1RM (as an example)
for its own sake, I’d argue that lighter, highrep training isn’t actually better than heavier,
lower-rep training in most contexts. In most
contexts, I’d contend that absolute strength
endurance is a more useful metric than relative strength endurance.
Before moving on to applications, I’d just
like to make two more quick notes about the
present meta-analysis. First, if you want to
do high-rep “strength endurance training” to
increase your reps-to-failure performance at
99
a given percentage of your moment-in-time
1RM, you don’t necessarily need to do ultra
high-rep training. The meta-regression found
that sets of ~20-30 reps are likely sufficient
to maximize gains in relative strength endurance. So, you could do sets of 50 reps if you
feel particularly masochistic, but sets of 2030 reps are likely sufficient.
Second, there probably is a point at which
low-rep training becomes too low-rep to be
particularly useful for improving absolute
strength endurance. If you look back at Figure 1, you can see that for seven strength
measures from five different studies, lower-rep and higher-rep training were similarly effective for improving absolute strength
endurance – the individual effect sizes are
all trivial-to-small, and the individual confidence intervals all crossed the “zero effect”
line. However, one measure from one study
sticks out like a sore thumb – changes in knee
extension strength endurance, from the study
by Mattocks and colleagues (2). In that study,
higher-rep training was considerably more
effective than lower-rep training for improving absolute strength endurance. This could
just be a fluke, but it’s noteworthy that the
Mattocks study was the only study included
in this meta-analysis where the low-rep group
was performing single-rep sets. Sets of 3 reps
were just as effective as sets of 10 reps in
the study by Schoenfeld and colleagues (3),
but single-rep sets were trounced by 10-rep
sets in the Mattocks study. So, while heavier, lower-rep training seems to be effective
for improving absolute strength endurance,
it’s certainly possible that absolute strength
endurance could be negatively affected when
training gets too heavy and the reps get too
low. However, it seems like that may only be
a concern if you’re living on a diet of exclusively single-rep sets.
Finally, let’s step away from the research and
into the real world. This present meta-analysis purposefully set up a dichotomy to examine the effects of lower-rep versus higher-rep
training on strength endurance. If you’re trying to isolate the effect of a particular variable,
that’s precisely how you need to conduct your
analysis. However, in the real world, you don’t
have to choose one or the other – if you want
to maximize gains in absolute strength endurance, I can’t think of a good reason why you
wouldn’t do both: some heavier, lower-rep
training to increase maximal strength, and
some lighter, higher-rep training to increase
relative strength endurance.
If you’re training for the goal of maximizing
strength endurance, I’d recommend structuring your training in one of three ways:
1. Use a reverse linear periodization approach,
starting with heavy training to increase
your 1RMs, followed by lighter, higher-rep
training to maintain strength while maximizing relative strength endurance.
2. Use an approach employing daily undulating periodization, with heavy, low-rep
training in one training session per week
(for each lift), and lighter, higher-rep
training in one training session per week
(for each lift).
3. Use a hybrid approach, with shades of
Westside or the Hatfield method. You
could either do some heavy training and
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some high-rep training for most exercises
within most training sessions, or you could
train your compound lifts heavy with sets
of 3-8 reps, but opt for 20-30 rep sets for
most accessory exercises.
Ultimately, if we consider a strength-endurance curve (Figure 4), training designed to
maximize absolute strength will pull up on the
left side of the curve, while training designed
to maximize relative strength endurance will
pull up on the right side of the curve. You
can’t maximize absolute strength endurance
without doing both.
References
1. Hackett DA, Ghayomzadeh M, Farrell
SN, Davies TB, Sabag A. Influence of
total repetitions per set on local muscular
endurance: A systematic review with metaanalysis and meta-regression. Science &
Sports. 2020 Sep-Oct; 37(5-6):405-420.
2. Mattocks KT, Buckner SL, Jessee
MB, Dankel SJ, Mouser JG, Loenneke
JP. Practicing the Test Produces
Strength Equivalent to Higher Volume
Training. Med Sci Sports Exerc. 2017
Sep;49(9):1945-1954. doi: 10.1249/
MSS.0000000000001300. PMID:
28463902.
3. Schoenfeld BJ, Contreras B, Vigotsky AD,
Peterson M. Differential Effects of Heavy
Versus Moderate Loads on Measures of
Strength and Hypertrophy in ResistanceTrained Men. J Sports Sci Med. 2016
Dec 1;15(4):715-722. PMID: 27928218;
PMCID: PMC5131226.
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Study Reviewed: Raw Eggs to Support Post-Exercise Recovery in Healthy Young Men: Did Rocky
Get It Right or Wrong? Fuchs et al. (2022)
Do Raw Eggs and Cooked Eggs Have Different
Impacts on Muscle Protein Synthesis?
BY ERIC TREXLER
I’m not sure how well this generalizes across
age groups and generations, but when people
my age (or older) see someone crack an egg
into a glass and drink it, some very specific
themes come to mind. This act connotes a laser-focused dedication to training, and brings
back memories of all our favorite training
montages from muscle-bound films of the
70s, 80s, and 90s. While raw egg consumption
isn’t a rarity in some cultures, it carries very
specific connotations in American culture,
such that the title of the presently reviewed
article directly acknowledges Rocky Balboa,
the fictitious boxer whose egg-related habits
reinforced his commitment and turned the
stomachs of some American viewers.
Interestingly enough, the cinematic connotations of raw egg consumption translated
to the real world to some extent, and many
people seem to implicitly accept that there
is some inherent advantage to raw egg consumption. Some view it as a major sacrifice
(in many cultures, cooked egg variations are
seen as far more palatable and safer than raw
egg), and assume that there must be some
payoff for this sacrifice. Researchers have
also taken an interest in the comparison of
raw versus cooked eggs, but their perspective is actually reversed; the common belief
is that raw eggs are meaningfully inferior to
cooked eggs, when viewed from a protein-focused perspective.
This brings us to the idea of protein quality.
Over the past several decades, many different scales and metrics have been proposed
for the purpose of quantifying the nutritional quality of specific protein sources, and
there are noteworthy differences between
each individual scoring system (2). However, protein quality scales generally consider
two high-priority factors: amino acid composition, and digestibility. Obviously a raw
egg and a cooked egg are both eggs, so amino acid composition is not a distinguishing
characteristic between the two options. When
it comes to digestibility, that’s where things
get interesting. A study by Evenepoel et al
found that the ileal digestibility (in humans)
of cooked egg protein was 90.9%, whereas
the digestibility of raw egg protein was only
51.3% (3). They concluded “that the assimilation of cooked egg protein is efficient, albeit
102
incomplete, and that the true ileal digestibility of egg protein is significantly enhanced
by heat-pretreatment.” In theory, this finding
would suggest that regardless of amino acid
composition, raw eggs would be a poorer
choice than cooked eggs for promoting muscle protein synthesis and muscle hypertrophy
over time, given that we can only utilize the
amino acids that are digested and absorbed
for muscle building purposes.
The presently reviewed study (1) put this
idea directly to the test. The researchers enrolled 45 healthy, resistance trained young
men between the ages of 18 and 35 in the
present study. After enrollment, the participants were randomly assigned to one of three
groups: raw eggs, boiled eggs, or a control
breakfast. Participants completed a full-body
resistance training session after an overnight
fast, followed by post-exercise ingestion of
their assigned meal. The raw egg group consumed five raw eggs (~30g of protein), the
boiled egg group consumed five boiled eggs
(~30g of protein), and the control breakfast
group consumed a buttered croissant with orange juice (~5g of protein). All three experimental meals had similar energy content, and
were consumed very shortly after the end of
the exercise bout. The resistance training session consisted of four sets of 8-10 repetitions
(using 80% of 1RM) for both the leg press
and leg extension exercise, followed by two
sets of 8-10 repetitions (using 80% of 1RM)
for upper-body exercises, which included
the chest press, horizontal row, vertical pulldown and shoulder press. Blood samples and
muscle biopsies (of the vastus lateralis) were
taken over the course of the five-hour obser-
vation period, which enabled the researchers
to observe changes in blood amino acid levels and muscle protein synthesis rates.
In terms of blood amino acid responses, the
researchers looked at several different outcomes (for example, peak values and area-under-curve values for leucine, branched-chain
amino acids, and essential amino acids). Results all generally pointed in the same direction: both egg meals led to larger and more
robust increases across the board (which isn’t
surprising, given the comparison of meals
containing 30g of protein to a control meal
containing only 5g of protein). Similarly, in
line with concerns about the digestibility of
raw egg protein, the boiled egg meal consistently outperformed the raw egg meal in
terms of amino acid responses; the boiled egg
meal led to significantly higher peak values
and area-under-curve values for these key
amino acid outcomes. Given that leucine
stimulates muscle protein synthesis, and that
essential amino acids are required for the actual synthesis of new muscle proteins, you
might intuitively assume that the boiled egg
group also experienced significantly greater
rates of muscle protein synthesis compared to
the raw egg group – but you’d be wrong.
All three groups had similar rates of muscle
protein synthesis at baseline. From 0-2 hours
after meal ingestion, all three groups experienced significant elevations, and there were
no differences between groups. From 2-5
hours after ingestion, muscle protein synthesis
rates were significantly higher in the raw and
boiled egg groups when compared to the control group. However, there were no significant
differences between the raw and boiled egg
103
groups. When combining the data across all
five hours, the researchers found that all three
groups experienced significant elevations
from baseline, with no significant differences between any of the three groups (although
there was a clear visual pattern by which values were higher in both egg groups than the
control group). Results for myofibrillar protein synthesis rates are presented in Figure 1.
This is a point I’ve covered before in MASS,
so I won’t belabor it too much. Nonetheless,
it bears repeating: we (the fitness world) have
a tendency to make very strong claims about
protein sources that rely on a chain of assumptions linking amino acid profile and digestibility to plasma amino acid responses, which
we link to muscle protein synthesis responses, which we link to hypertrophy responses
over time. If we didn’t intuitively start with
this chain of assumptions, we’d never arrive
at it based on the empirical evidence – it has
failed us time and time again, and an excellent review article by Witard et al (4) identifies several reasons for the breakdowns in
this chain of assumptions. Given the repeated
failures of this approach to protein optimization, it’s time to heavily revise it.
To be clear, I’m not suggesting that the elements forming this chain aren’t related; they
clearly are. However, the pervasive emphasis on optimization has led to erroneous applications of the relationship between these
elements. Many people seem to operate under the assumption that if we optimize amino
acid composition, and optimize digestibility,
and optimize absorption kinetics, and optimize muscle protein synthesis rates, then
we’ll optimize muscle hypertrophy. In this
104
framework, every little detail matters, and
planning a day of eating becomes as complex
as micromanaging an investment portfolio.
It encourages you to agonize over every detail, questioning the return-on-investment of
every calculated adjustment. However, as
I covered in a previous MASS article, the
empirical data tell us over and over that the
return on investment is negligible, or even
zero, in most contexts. Rather than focusing
on granular details pertaining to amino acid
composition, digestibility, absorption kinetics, or even muscle protein synthesis responses in acute feeding studies, we should
zoom out and shift our attention toward the
“big picture” factors.
We should be aiming to provide a robust
stimulus for muscle protein synthesis by following an effective resistance training program, and we should view protein intake as
a modifiable factor that plays a permissive
role in allowing maximal adaptive responses
to that training stimulus. Based on the totality of the protein literature (as discussed here,
here, here, here, here, here, and here), we can
maximize our return-on-investment by following some very simple principles, without
any need for (or meaningful benefit from)
micromanaging our protein feeding strategies. We should be aiming for sufficient daily
protein intake, which is typically in the range
of 1.6-2.2 g/kg of total body mass per day,
or around 2-2.75 g/kg of fat-free mass per
day. We should be dividing that into 3-6 daily servings that are relatively similar in size,
and spread relatively evenly throughout the
day. If you want to take a small leap of faith
in the direction of optimization, you could
make a case for narrowing this guideline
even further, and shooting for 4-5 servings
per day instead of 3-6. You should be selecting a mixture of dietary protein sources that,
on the whole, does not lead to insufficient intakes of any particular essential amino acid.
Unless you eat a vegan diet or get the vast
majority of your protein from vegan sources, this is probably an irrelevant guideline.
Even if your diet is totally vegan, this guideline can easily be achieved by incorporating
some higher-quality vegan proteins (like soy
and mycoprotein), and/or by making sure
you’ve got a decent mixture of complementary protein sources in your typical diet. As
for digestibility, you should be fine as long
as you’re following fairly standard cooking
and food preparation guidelines for whatever
protein sources you’re eating, and not inducing regular stomach discomfort and gastrointestinal symptoms that would be reflective of
major digestion issues.
If you enjoy micromanaging your protein
feeding strategies, I’m not here to deprive
you of a good time. I understand the draw,
so by all means, go for it. There’s nothing
wrong with having fun digging into the details of a topic you are interested in. However, the available research suggests that any
such endeavor is a high-effort, low-reward
strategy that’s unlikely to bear meaningfully more fruit than applying some very basic
approaches to managing one’s protein intake.
References
1. Fuchs CJ, Hermans WJH, Smeets JSJ,
Senden JM, van Kranenburg J, Gorissen
SHM, et al. Raw Eggs to Support Post-
105
Exercise Recovery in Healthy Young Men:
Did Rocky Get It Right or Wrong? J Nutr.
2022 Aug 9; ePub ahead of print.
2. Hoffman JR, Falvo MJ. Protein – Which
is Best? J Sports Sci Med. 2004 Sep
1;3(3):118–30.
3. Evenepoel P, Geypens B, Luypaerts
A, Hiele M, Ghoos Y, Rutgeerts P.
Digestibility of Cooked and Raw Egg
Protein in Humans as Assessed by Stable
Isotope Techniques. J Nutr. 1998 Oct
1;128(10):1716–22.
4. 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. 2022 Jan 1;32(1):49-61.
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Study Reviewed: The Effects of Training Load During Dietary Intervention Upon Fat Loss: A
Randomized Crossover Trial. Carlson et al. (2022)
Is Heavier Training or Higher-Rep Training Better in
an Energy Deficit?
BY GREG NUCKOLS
In the fitness community, there’s a lot of debate about the optimal approach to training
when you’re in an energy deficit. Some people argue that you should focus on maintaining training loads while potentially dialing
back training volume, while others contend
that you should focus on keeping training
volume high, even if exercise loading needs
to be reduced. Both “sides” of this debate
have logical arguments to support their position, but they also lack direct human research
to back up their suppositions. As surprising
as it may seem, until very recently, there simply weren’t any longitudinal studies testing
the effects of different resistance training
protocols on changes in body composition
during an energy deficit (that I’m aware of, at
least). However, a new study by Carlson and
colleagues (1) finally provides us with a bit
of data that’s relevant to this debate. Unfortunately, I don’t think it brings us any closer to
a definitive answer about ideal training practices in a deficit.
The study by Carlson and colleagues is a large
(130 subjects) trial, employing a randomized
crossover design. Middle-aged subjects with
at least six months of training experience
completed the study. You can see more information about the participants in Table 1.
The study employed a four-week period of
heavier (~80% of 1RM; <10 reps per set) resistance training and a four-week period of
lighter resistance training (~60% of 1RM;
<20 reps per set) resistance training, with an
eight-week washout period between the intervention periods. The order of heavier training
and lighter training was randomly assigned
for each subject. The periods of heavier and
lighter resistance training were identical, beyond the training loads employed. Subjects
performed the same exercises, for a single set
of each exercise, with each set taken slightly
past momentary concentric failure (subjects
performed two forced reps at the end of each
set). When subjects completed more than 10
reps in a set when training with heavier loads,
or more than 20 reps in a set when training
with lighter loads, training loads would be
increased by 5%. During the washout period, subjects returned to their customary resistance training program – all subjects were
recruited from the same gym chain, which
107
employs supervised, fairly standardized
training programs for all of its members. The
training employed during the washout period
was similar to the training performed during
the two experimental training periods (the
4 week periods of heavier and lighter training), except that subjects trained with loads
of approximately 70% of 1RM. During the
experimental training periods, subjects’ daily energy expenditure was estimated using
standard equations that consider body composition and activity levels, and subjects
were instructed to consume 20% fewer calories than their estimated maintenance needs,
while aiming for 1.5 grams of protein per kilo
of body mass. Subjects logged their meals in
MyFitnessPal, and trainers were available to
answer questions subjects might have about
how to comply with their nutritional targets.
At the start of the study, and following each
training phase, body composition was assessed via BodPod, and leg press, chest press,
and pulldown 1RM strength were estimated
from 7-10RM tests using the Epley equation
[1RM = weight lifted × (1 + (0.033 × number
of reps performed))]. The study design is illustrated in Figure 1.
Overall, both approaches to training produced nearly identical results. Subjects didn’t
lose a ton of fat or gain a ton of lean mass in
either condition, as you should expect, since
each training period was just four weeks. If
you wanted to squint really hard, it might
appear that heavier training was slightly better for fat loss (-0.67kg of fat mass, versus
-0.55kg of fat mass following lighter training), while lighter training was slightly better
for gaining/retaining lean mass (+0.22kg of
lean mass, versus +0.06kg of lean mass following heavier training), but between-group
differences were absolutely tiny in practical
terms (I don’t think anyone’s going to get
worked up about a difference of 0.12kg of
fat mass, or 0.16kg of lean mass, especially
when assessed via BodPod), and were no-
108
where close to statistical significance. Gains
in strength were also similar between groups.
Furthermore, there were virtually no changes in strength or body composition during
the eight-week washout period. You can see
these results in Tables 2 and 3, and Figures
2-4.
Overall, I thought this was a very well-done study (1). I suspect that some readers will
be disappointed that it didn’t have any big,
flashy findings, suggesting that one style of
training was vastly superior. However, null
results are just as valuable as statistically significant results showing large differences. If
two approaches to training produce different
results, we want to know, so we can recommend the generally superior approach. Con-
versely, if two approaches to training produce
similar results, we also want to know, so we
don’t limit our options when making training
decisions.
It’s worth noting that the present study illustrates one of the barriers to research with an intentional component of weight gain or weight
loss: it’s really hard to put subjects in a controlled energy deficit or surplus of a predictable magnitude. In a vacuum, you would have
expected subjects to lose about 1.5-2kg of
fat during each intervention period – humans
burn around 2500kcal per day, on average, so
a 20% reduction should work out to an energy
deficit of ~500kcal/day, which should result
in about 0.4-0.5kg of fat loss per week. However, equations used to estimate daily energy
109
expenditure have considerable capacity for estimation error (2), human subjects rarely perfectly adhere to dietary targets, and metabolic
adaptation implies that reducing energy intake
by 500kcal/day isn’t necessarily equivalent to
generating an energy deficit of 500kcal/day
(since changes in the thermic effect of feeding,
spontaneous activity levels, and basal metabolism can reduce energy expenditure when you
enter an energy deficit). As a result, the average reductions in fat mass were very modest,
and some subjects even gained weight.
You might be inclined to fault the researchers
for insufficient dietary control, but I’d caution
against that. If you want to perfectly control
subjects’ diets, that necessarily involves designing a study that would be prohibitively
expensive – you’d need subjects to live in a
metabolic ward (to ensure they weren’t eating outside food), and all meals for all subjects
would need to be prepared by research assistants over a period of months. Unless governments decide that optimizing resistance training in an energy deficit is of critical importance
to public health, there’s simply never going to
be enough funding to see a study like that completed (with a decent sample size in a young
and otherwise healthy population, at least).
110
Furthermore, enhanced control also involves
a decrease in ecological validity. When fitness professionals give people advice, they’re
giving advice to autonomous human beings
who will make decisions for themselves, and
who won’t robotically adhere to every detail
of their training and nutrition plan. In other
words, when you give people advice about
how to train in an energy deficit, the people
who listen to you will behave similarly to the
subjects in this study – in aggregate, their behaviors will likely be directionally consistent
with your advice, but they also won’t perfectly
adhere to all of your recommendations. In other words, the effect of a particular set of behaviors isn’t necessarily identical to the effect
111
of advising people to engage in a particular
set of behaviors. So, this study doesn’t necessarily tell us “here are the results of these two
styles of training when subjects are in exactly
a 20% energy deficit,” but it does tell us, “here
are the results of these two styles of training
when subjects are instructed to achieve a 20%
energy deficit.” Being able to make the first
type of statement is very useful when you’re
attempting to calculate super precise effect
estimates and delineate mechanisms, but the
second type of statement is far more informative about the “real world” results of training
and dietary recommendations.
While the present study suggests that heavier
and lighter training are similarly efficacious
in an energy deficit, I don’t expect this study
to dampen the debate around the topic. Proponents of lighter training in an energy deficit contend that reductions in muscle mass are
downstream of reductions in volume load that
occur as a diet progresses (5). Proponents of
heavier training suggest that training heavy
will help you better-maintain strength levels
during a diet, thus allowing you to continue
exposing the muscles to higher absolute levels of mechanical tension, resulting in smaller
losses in muscle mass. Ultimately, the subjects
in the present study didn’t lose a ton of fat,
and they slightly increased both lean mass and
strength performance. So, I suspect that proponents of heavier and lighter training will
simply argue that the nutritional intervention
in the present study wasn’t intense enough or
long enough for their preferred style of training to “win out.” However, I will note that the
present study does provide a bit of evidence
against one of the purported benefits of lighter
training in an energy deficit. With lighter training, you’re performing more mechanical work
per set, so the energy cost of training should
be a bit higher. Thus, the thinking goes, lighter
training will burn more calories, resulting in
a larger energy deficit and greater fat loss. In
a vacuum, that’s probably true, but remember
that advice isn’t always perfectly adhered to.
When people burn more calories via exercise,
they tend to consume a few extra calories and
experience a reduction in other components
of energy expenditure to partially compensate
for the additional exercise energy expenditure.
So, while energy expenditure should theoretically be a bit higher with lighter training, that
doesn’t necessarily mean that lighter training
will spontaneously generate a larger energy
deficit than heavier training.
When reading the present study, I was struck
by the fact that gains in strength and losses in
fat mass were at least nominally larger during
both four-week intervention periods than the
eight-week washout period, and gains in lean
mass were similar during the intervention periods and washout period (meaning per-week
gains in lean mass were larger during the intervention periods). Subjects weren’t aiming
to be in an energy deficit during the eightweek control period, and body composition
changes over time bear that out (Figure 3).
Furthermore, the training protocol during the
washout period was very similar to the training protocols during the washout period. So,
all else being equal, you should anticipate
that gains in strength and lean mass should
have been larger during the washout period
than the intervention periods; instead, gains
were nominally larger during the intervention
112
period than the washout periods. What could
explain those results?
One possibility is that subjects engaged in
some unquantified behavior modifications
during the intervention periods, because they
knew they were being studied. Of course,
they were also being studied during the washout period, but I can certainly imagine that
the subjects may have thought that the intervention periods simply mattered more, and
acted accordingly. In other words, there may
have been a Hawthorne effect in play. Another possibility is that novelty per se may have
contributed to the results. During the washout
period, subjects trained with loads equal to
~70% of 1RM, which shouldn’t produce results that are meaningfully worse than training at 60% or 80% of 1RM. However, before
the study, subjects had already been training
at 70% of 1RM for a number of months or
years, following the standardized program of
the gym chain. It may be that the subjects had
almost fully habituated to training at one particular intensity, such that slightly changing
the training stimulus was sufficient to cause
further gains in strength and lean mass. Ultimately, I don’t think there’s a single, tidy explanation for these findings, but they tickled
my brain enough to warrant a mention in this
research brief.
To wrap things up, let’s bring this article
full circle. I framed this article by mentioning that there’s a healthy, ongoing discussion
about the style of training that works best in
an energy deficit. This study certainly won’t
end that discussion (we need follow-up research using different populations, longer or
larger energy deficits, higher-volume train-
ing protocols, etc.), but I think the individual subject data in the present study may be
instructive. Referring back to Figure 2, you
can see the individual changes in body composition for each subject, as a result of both
training protocols. Some subjects simply did
much better with heavier training, and some
subjects simply did much better with lighter
training. Now, some of those intra-individual
differences are probably at least partially attributable to measurement error and random
noise, but that cuts both ways (i.e. some of
the intra-individual differences would have
been smaller than the data suggests as-reported, but some would have also been even
larger than the data suggests as-reported). I
wouldn’t be surprised if folks have observed
that they do better when training with either
higher or lower loads in a deficit, assumed
their experience was the universal experience, and then cobbled together some plausible-sounding explanations to explain their
personal anecdote. We should also remember
that “no difference on average” isn’t necessarily the same as “no difference for individuals.” I don’t think training intensity (i.e. the
rep ranges you train in and the percentage of
1RM you use) has much of an effect on body
composition outcomes in an energy deficit
since it doesn’t have much of an effect on hypertrophy outcomes when trainees aren’t in
a purposeful energy deficit (3), on average.
However, we also know that training intensity can have a large impact on the amount
of hypertrophy an individual experiences (4),
and I suspect that also applies to folks who
are aiming to preserve lean mass while losing fat in a calorie deficit. Ultimately, heavier
training or lighter training may be markedly
113
more beneficial for you in an energy deficit.
For now, good old fashion trial-and-error is
the only way to find out which approach will
give you better results.
Physiol. 2022 May;122(5):1129-1151.
doi: 10.1007/s00421-022-04896-5. Epub
2022 Feb 11. PMID: 35146569; PMCID:
PMC9012799.
References
1. Carlson L, Gschneidner D, Steele J,
Fisher JP. The Effects of Training Load
During Dietary Intervention Upon Fat
Loss: A Randomized Crossover Trial. Res
Q Exerc Sport. 2022 Aug 23:1-11. doi:
10.1080/02701367.2022.2097625. Epub
ahead of print. PMID: 35998256.
2. Amaro-Gahete FJ, Jurado-Fasoli L, Dela-O A, Gutierrez Á, Castillo MJ, Ruiz JR.
Accuracy and Validity of Resting Energy
Expenditure Predictive Equations in
Middle-Aged Adults. Nutrients. 2018 Nov
2;10(11):1635. doi: 10.3390/nu10111635.
PMID: 30400196; PMCID: PMC6266118.
3. Lopez P, Radaelli R, Taaffe DR, Newton
RU, Galvão DA, Trajano GS, Teodoro
JL, Kraemer WJ, Häkkinen K, Pinto RS.
Resistance Training Load Effects on
Muscle Hypertrophy and Strength Gain:
Systematic Review and Network Metaanalysis. Med Sci Sports Exerc. 2021
Jun 1;53(6):1206-1216. doi: 10.1249/
MSS.0000000000002585.
4. Carneiro MAS, de Oliveira Júnior GN,
Sousa JFR, Martins FM, Santagnello
SB, Souza MVC, Orsatti FL. Different
load intensity transition schemes to avoid
plateau and no-response in lean body mass
gain in postmenopausal women. Sport
Sci Health. 2022. https://doi.org/10.1007/
s11332-022-00907-2
5. Roth C, Schoenfeld BJ, Behringer M.
Lean mass sparing in resistance-trained
athletes during caloric restriction: the role
of resistance training volume. Eur J Appl
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Study Reviewed: Time To Revisit The Passive Overconsumption Hypothesis? Humans Show
Sensitivity To Calories In Energy-Rich Meals. Flynn et al. (2022)
Adding Nuance to the Relationship Between Energy
Density and Calorie Intake
BY ERIC TREXLER
If you’ve been a MASS subscriber for a
while, you’ve heard the term “energy density” many times before. Dr. Helms had an
excellent two-part video series on the topic
back in Volume 3 (one, two), and it comes
up just about every time he or I discuss satiety or hunger management (for example, here
and here). Energy density is a very straightforward metric: if you divide the energy content of a meal (in kilocalories) by the mass of
the meal (in grams), you get the energy density. Foods with low energy density tend to
have relatively higher content of water and
fiber, on average, when compared to foods
with higher energy density. Foods with low
energy density fill our plates (and our stomachs) without delivering a huge caloric load;
as a result, they tend to be associated with
comparatively greater satiety, better hunger
management, and lower energy intake. By
extension, dieters pursuing weight loss goals
are often advised to seek out foods with low
energy density, and to reduce consumption
of foods with higher energy density. The first
part of Dr. Helms’ video series provides several examples of the energy density of individual foods, but a few examples (in kcal/g
units) include lettuce (0.15), watermelon
(0.30), grapes (0.69), skinless chicken breast
(1.10), white bread (2.64), peanuts (6.00),
and olive oil (8.85).
Imagine a hypothetical scenario in which
you’re participating in a research study. In one
study condition, you show up to the lab several hours after your most recent meal, you’re
given a limitless amount of watermelon, and
you’re encouraged to eat until you’re satisfied. In a second study condition, the procedures are identical, but you receive a limitless
amount of cheesecake instead of watermelon.
The most basic application of energy density
is very simple: you’re probably going to consume more total calories during the cheesecake visit than the watermelon visit. But what
is actually dictating your consumption? It
doesn’t appear to be the mass or volume of
food alone – you’d probably eat a larger mass
and volume of watermelon than cheesecake,
despite the fact that the cheesecake provides
more calories. However, it also doesn’t appear to be calorie content alone – if that were
the case, you’d simply eat the same amount
of total energy at each visit, which would
115
require tremendous amounts of watermelon
intake to make the calories you consumed
during the cheesecake visit.
The presently reviewed study by Flynn and
colleagues (1) explored this concept via retrospective analysis of previously collected data. They first analyzed the data from a
paper by Hall et al (2), which explored the
effects of ultra-processed food intake on energy consumption and weight gain in tightly
controlled conditions. If that vaguely rings a
bell, it’s because our very own Dr. Helms reviewed that study when it was first published,
back in Volume 3 of MASS. They also analyzed data from the UK National Diet and
Nutrition Survey, specifically looking at the
2000-2001 data from participants between the
ages of 19-64 years old (3). This was an enormous survey intended to document the eating
habits and nutritional status of the UK population, which contributed data from 1,724 individuals for the presently reviewed analysis.
These two data sets allowed the researchers
to separately look at tightly controlled, labbased data and a huge sample of data from
free-living individuals, and their analysis
aimed to assess the relationship between the
energy density of a meal and the number of
total calories consumed at the meal.
Both data sets led to similar conclusions.
When the overall energy density of a meal is
low, food volume seems to be the predominant satiation signal dictating the amount of
food consumed. The physical amount of space
within a person’s stomach is inherently limited, and food intake is discontinued once a
certain volume of food intake is achieved in
order to avoid the discomfort associated with
being overly full. The body senses that this
point is reached based largely on the degree of
gastric distention, without much influence at
all from the total amount of energy consumed.
However, there is an inflection point (probably between an energy density value of 1.25
and 2.25, give or take) beyond which food
volume is no longer the predominant satiation
signal. As the overall energy density of the
meal gets higher, energy content becomes the
major satiation signal dictating the amount of
food consumed. In fact, meal size (in grams)
tends to go down as energy density becomes
very high. The researchers posit that the body
integrates biological signals identifying high
intakes of carbohydrate, fat, protein, and total
energy, and eventually signals the discontinuation of the meal in order to avoid discomfort
and “soporific” (drowsiness-inducing) effects
related to excessive acute energy intake. The
proposed two-component model of meal size
is depicted in Figure 1.
It’s important to recognize that this model
is not, and does not claim to be, the singular
model that explains the entirety of human
meal size selection and energy intake. As the
authors note, it makes no effort to directly account for macronutrient content, cravings, hedonic eating, or the many other complicating
factors that can influence eating behaviors.
Nonetheless, it fills an important gap in the
literature. There are many different paradigms
used to explore the effects of energy density
on meal size and energy intake. For example,
there are preload test meal studies (e.g., participants are fed a small snack shortly before
a meal to see how it impacts eating behavior),
short-term ad libitum studies (<10 days in du-
116
ration), and longer-term ad libitum studies.
Across these different study paradigms, the
relationship between energy density, energy
intake, and meal size seemed very inconsistent, as noted by the authors of the present
study. However, when viewing these different
bodies of literature through the prism of the
two-component model depicted in Figure 1,
the apparent discrepancies in previous studies
were largely resolved. Collectively, the literature suggests that food volume is the key satiation signal when meal energy density is below
~1.75 kcal/g, while calorie content is the key
satiation signal when meal energy density is
above ~1.75 kcal/g.
Aside from filling a gap in the literature, this
study also provides some guidance for practical application. When seeking out foods
with low energy density, it seems like 1.75
kcal/g is a decent number to aim below. This
isn’t a perfectly rigid threshold, of course,
but it serves as a decent guide. In addition, if
you’re trying to leverage low energy density
meals as a strategy to manage hunger during
a weight loss diet, it probably makes sense
117
to focus more on the overall energy density of the meal rather than individual foods.
For example, if swapping in one food with
low energy density drops the overall energy
density of a meal from 4.0 to 3.0, you’re still
in a situation where food volume is not the
predominant satiation signal, and the relative
impact of this strategy will probably be modest in magnitude. In contrast, a similar food
swap that brings the overall energy density of
a meal from 2.5 to 1.5, or from 2.0 to 1.0, is
likely to have a comparatively larger impact
as the predominant satiation signal switches
from calorie content to food volume.
This single strategy isn’t likely to single-handedly revolutionize the way you diet,
but it can probably be combined with other
satiety-related strategies to have a helpful cumulative impact. As stated previously, some
practical strategies that may facilitate better
hunger management during energy restriction involve adopting an acceptance-oriented
approach to hunger, avoiding hyperpalatable
meals, emphasizing unprocessed or minimally processed foods, incorporating some
harder food textures, eating more mindfully,
avoiding distractions while eating, and taking
time to savor the aromas and flavors of our
meal. Manipulating meal-level energy density is just one more tool to add to the toolbox.
References
1. Flynn AN, Hall KD, Courville AB, Rogers
PJ, Brunstrom JM. Time To Revisit The
Passive Overconsumption Hypothesis?
Humans Show Sensitivity To Calories In
Energy-Rich Meals. Am J Clin Nutr. 2022
Aug 1;116(2):581–8.
2. Hall KD, Ayuketah A, Brychta R, Cai
H, Cassimatis T, Chen KY, et al. UltraProcessed Diets Cause Excess Calorie
Intake and Weight Gain: An Inpatient
Randomized Controlled Trial of Ad
Libitum Food Intake. Cell Metab. 2019 Jul
2;30(1):67-77.e3.
3. Swan G. Findings From The Latest
National Diet And Nutrition Survey. Proc
Nutr Soc. 2004 Nov;63(4):505–12.
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Study Reviewed: Short-Term Aerobic Conditioning Prior to Resistance Training Augments
Muscle Hypertrophy and Satellite Cell Content in Healthy Young Men and Women. Thomas
et al. (2022)
Can Cardio (Eventually) Make You Bigger?
BY GREG NUCKOLS
When we discuss aerobic training in MASS,
we’re usually discussing how to best mitigate the interference effect (the finding that
a combination of resistance training and aerobic training may lead to less muscle growth
and smaller strength gains than resistance
training alone). However, a recent study
found that, under the right circumstances,
aerobic training might actually increase muscle growth. So, let’s dig into the study, and
examine whether cardio may actually be a
useful tool to improve hypertrophy results in
the long run.
The present study by Thomas and colleagues
compared the effects of aerobic training and
resistance training versus resistance training alone (1), much like a typical concurrent training study, but it didn’t involve a
concurrent training intervention. Rather,
one condition involved six weeks of aerobic
training followed by 10 weeks of resistance
training, and one condition only involved
the 10-week resistance training intervention. In a typical concurrent training study,
the resistance and aerobic training would be
performed at the same time.
Fourteen young, recreationally active subjects completed the present study. For each
subject, one leg was randomized into the
aerobic + resistance training intervention,
with the other leg completing just the resistance training portion of the intervention.
Thus, each subject could serve as their own
control. For the first six weeks of the study,
subjects performed moderate-intensity single-legged cycling three times per week, for
45 minutes per session, with their leg that
had been randomized into the intervention
with an aerobic training component. The resistance training-only legs did not undergo
any structured exercise intervention during
these first six weeks. Then, for the next 10
weeks, subjects completed a standard bilateral resistance training intervention, consisting of squats, leg press, knee extensions,
hamstring curls, and calf raises for sets of
10-12 reps, with the last set taken to failure. This isn’t explicitly spelled out in the
study, but training loads were presumably
increased when the subjects completed
more than 12 reps on their last set, in order to keep the subjects within the desired
rep range. Muscle biopsies to assess fiber
119
size, capillary density, satellite cell content,
and myonuclei content were performed at
the start of the study, following the aerobic
training portion of the intervention, and following the resistance training portion of the
intervention. Furthermore, following both
the aerobic training and resistance training
portions of the intervention, squat and leg
press 1RM were assessed, and leg fat-free
mass was assessed via DEXA. The design of
the study is depicted in Figure 1.
Overall, capillary density increased to a
greater extent in the legs performing six
weeks of aerobic training before the resistance training intervention. Capillary density was assessed two ways: capillaries per
fiber (C/Fi), and capillaries per unit of fiber perimeter (CFPE). is generally the more
informative measure, since it accounts for
not just the number of capillaries, but also
the size of the muscle fibers. The benefits
of aerobic training were more apparent for
measures of CFPE than C/Fi (Figure 2).
Increases in type I fiber cross-sectional area,
type II fiber cross-sectional area, and mean
fiber area were generally larger in the legs
performing aerobic training prior to the resistance training intervention (0.05 < p < 0.10
for all comparisons; Figure 3). The legs performing aerobic training before the resistance
training intervention tended to experience
larger increases in satellite cell and myonuclear content as well.
Overall, increases in leg fat-free mass didn’t
differ between conditions, and subjects experienced large increases in squat and leg press
1RM strength (Figure 4).
Finally, the researchers separated out “high
responders” and “low responders” (the 10
legs experiencing the largest change in particular outcomes, and the 10 legs experiencing the smallest change in particular outcomes) for a series of exploratory analyses.
They found that a) the legs experiencing the
largest changes in satellite cell content generally experienced more hypertrophy than
120
the legs experiencing the smallest changes in
satellite cell content, b) the legs experiencing
the most hypertrophy had greater capillary
density than the legs experiencing the least
hypertrophy, c) the legs with the greatest
capillary density prior to resistance training
experienced more hypertrophy than the legs
with the lowest capillary density prior to resistance training, and d) the legs experiencing
the most hypertrophy generally experienced
larger increases in satellite cell content than
the legs experiencing the least hypertrophy
(Figure 5).
Overall, this study suggests that a higher degree of local aerobic fitness pre-training may
augment skeletal muscle hypertrophy fol-
lowing a resistance training intervention. The
authors specifically suggest that increases in
capillary density are particularly important,
which comports with prior research. A 24week study by Snijders and colleagues found
that baseline levels of capillary density were
predictive of training responses in older men
(2). The men with greater capillary density at
baseline experienced larger increases in satellite cell content and fiber hypertrophy than
the men with lower capillary density. The
present study found that these results generalize to younger subjects, and the present
study also suggests that increases in capillary
density due to prior aerobic training can also
augment hypertrophy and satellite cell responses (i.e., it’s not simply a matter of peo-
121
122
ple with innately higher levels of capillary
density experiencing more hypertrophy).
For the past five years, the Snijders study has
been in the back of my mind. We focus a lot
on factors that can increase rates of muscle
growth, but I don’t think we adequately consider the factors that limit muscle growth.
Sooner or later, we all reach the point where
rates of muscle growth slow way, way down.
Why does this happen?
Local aerobic fitness and capillary density
have always seemed like logical answers to
me. At the most basic level, the size of organisms and individual cellular structures
within organisms are strongly associated
with energy requirements and rates of energy production (3). As muscle fibers grow,
all of the “stuff” (contractile proteins, organelles, metabolic machinery, etc.) inside of the
muscle fibers gets further away (on average)
from the capillaries providing oxygen, ener-
123
getic substrates, and signaling molecules to
the muscle fibers, and clearing waste products from the muscle fibers. So, to avoid an
intracellular energy crisis, it makes sense that
fibers either need to a) stop growing, or b)
experience an increase in capillary density to
allow for further growth.
Of course, both the Snijders study (2) and
the present study by Thomas and colleagues
(1) don’t fully validate my little pet theory.
They both examined rates of muscle growth
in individuals who weren’t already resistance-trained, after all; they weren’t focused
on increasing the hypertrophic potential for
lifters who’d already hit a wall. However, I
do think they suggest that capillary density
and local aerobic fitness are more important
for muscle growth than (I suspect) a lot of
people realize.
Furthermore, it’s worth acknowledging that
the results of the present study probably aren’t
fully attributable to the increases in capillary
density observed during the aerobic training
intervention. For example, a prior study by
Kazior and colleagues employed a standard
concurrent training intervention, and the concurrent training group experienced considerably more hypertrophy than the resistance
training-only group (4). However, the changes in capillary density observed in that study
were pretty underwhelming, suggesting that
some other aspect of aerobic fitness (or the
training intervention itself) contributed to the
findings. In any biological system, a particular observed outcome (in this case, muscle
hypertrophy) is the result of dozens of inputs
and competing cellular signaling pathways.
So, even if increases in capillary density are
a factor that could augment hypertrophy results, that certainly doesn’t imply that there
aren’t other adaptations to aerobic training
that can also improve hypertrophy outcomes.
The obvious question is, “what can we actually do with these findings?” We’re in an
awkward situation where the results of aerobic training seem to have a positive impact on
hypertrophy, but aerobic training itself generally has a neutral-to-negative impact on hypertrophy in resistance-trained individuals.
As I see it, there are two potential options:
1. You could try alternating periods of concurrent training and periods where you
only perform resistance training. During
the periods of concurrent training, it’s alright if your rate of muscle growth slows
down a bit, because the goal is to make
your next training period consisting solely of resistance training more effective.
Ideally, you should include aerobic training modalities that train both the lower
body (cycling, jogging, etc.) and the upper body (arm cycling, swimming, etc.) so
that most major muscle groups receive a
local aerobic training stimulus.
2. Work in more high-rep training for accessory exercises. Since high-rep training leads to disproportionate increases in
relative strength endurance (as I discuss
in one of my other briefs this month), it
presumably causes some of the same local aerobic adaptations as more traditional
forms of cardio. So, if you’re training for
hypertrophy, instead of doing sets of ~612 reps for most exercises, you could stick
to sets of ~6-12 reps for the primary com-
124
pound exercises you use to gauge progress, but opt for sets of ~25-30 reps for
your accessory exercises. Set-for-set, this
approach should be similarly effective for
hypertrophy in the short term (on average), and it may set the stage for greater
hypertrophy in the long run.
To be clear, I’m not positive that either of
these approaches to training will definitely
lead to greater muscle growth in the long run,
but they’re my best guesses for how trained
lifters might apply the findings of the present
study. More importantly, I’m really excited
about this line of research moving forward.
I think it has the potential to not only tell us
how to make training more effective in the
short term, but also to help us learn more
about why muscles eventually stop growing
in the long term.
3. Gillooly JF, Brown JH, West GB, Savage
VM, Charnov EL. Effects of size and
temperature on metabolic rate. Science.
2001 Sep 21;293(5538):2248-51. doi:
10.1126/science.1061967. Erratum in:
Science 2001 Nov 16;294(5546):1463.
PMID: 11567137.
4. Kazior Z, Willis SJ, Moberg M, Apró W,
Calbet JA, Holmberg HC, Blomstrand E.
Endurance Exercise Enhances the Effect
of Strength Training on Muscle Fiber Size
and Protein Expression of Akt and mTOR.
PLoS One. 2016 Feb 17;11(2):e0149082.
doi: 10.1371/journal.pone.0149082. PMID:
26885978; PMCID: PMC4757413.
References
1. Thomas ACQ, Brown A, Hatt AA, Manta
K, Costa-Parke A, Kamal M, Joanisse S,
McGlory C, Phillips SM, Kumbhare D,
Parise G. Short-term aerobic conditioning
prior to resistance training augments
muscle hypertrophy and satellite cell
content in healthy young men and women.
FASEB J. 2022 Sep;36(9):e22500.
doi: 10.1096/fj.202200398RR. PMID:
35971745.
2. Snijders T, Nederveen JP, Joanisse S,
Leenders M, Verdijk LB, van Loon LJ,
Parise G. Muscle fibre capillarization is a
critical factor in muscle fibre hypertrophy
during resistance exercise training in older
men. J Cachexia Sarcopenia Muscle.
2017 Apr;8(2):267-276. doi: 10.1002/
jcsm.12137. Epub 2016 Aug 4. PMID:
27897408; PMCID: PMC5377411.
125
Study Reviewed: Self-Reported Resistance Training Is Associated With Better HR-pQCT
Derived Bone Microarchitecture In Vegan People. Wakolbinger-Habel et al. (2022)
Do Vegan Diets Negatively Impact Bone Health?
BY ERIC TREXLER
When it comes to bones, the fitness world
is finally coming around. For a long time,
fitness-minded folks generally disregarded bone health until it became immediately
relevant to them at the individual level (for
example, after a fracture or osteoporosis diagnosis). Bones were widely regarded as inert structures that give our body shape, rather
than dynamic, adaptive tissues with multifaceted physiological functions and roles in
human health. There appears to be increasing
awareness of, or at least increasing emphasis on, three important facts: 1) bone health
is an important component of human health,
2) taking care of our bones can lead to substantial benefits as we age, and 3) bones are
adaptive tissues that respond to training and
nutritional stimuli. However, this begs the
question – are we doing the right things to
take care of our bones?
As Greg has covered previously, resistance
training is an important stimulus for bone
strength, density, and overall health. That’s
great, and there’s absolutely no question that
public-facing guidelines pertaining to bone
health should involve a major emphasis on
load-bearing exercise for those who are able
to participate in such activities, with a particular emphasis on resistance training. However,
you’ve probably heard plenty of guidance that
is very nutrition-focused, and tends to prioritize consumption of dairy foods or individual
nutrients that are commonly found in fortified
dairy foods (specifically, calcium and vitamin
D). For individuals who do not consume moderate or high amounts of dairy products, such
as individuals who have lactose intolerance or
adhere to a vegan diet, this may prompt concerns pertaining to bone health. Indeed, the International Osteoporosis Foundation has identified protein, vitamin A, vitamin B12, vitamin
B6, vitamin D, calcium, magnesium, and zinc
as key nutrients of interest for bone health,
and there are credible concerns about whether
or not vegan diets provide adequate amounts
of some of these nutrients in sufficiently bioavailable forms. These concerns are reinforced
by meta-analyses linking vegan diets to lower
bone mineral density (2) and increased fracture risk (3).
So, it seems like adherence to a vegan diet
could potentially be a risk factor for adverse
126
bone outcomes, while resistance training
could reduce risks related to adverse bone
outcomes. With these observations in mind,
you might be wondering, “how might resistance training alter risk in vegans?” That’s
exactly what the presently reviewed study
explored (1). This was an observational study
in which the researchers enrolled 43 healthy,
nonobese male (n = 21) and female (n = 22)
participants who had adhered to a vegan diet
for at least five years (the average was around
10 years), in addition to 45 healthy, nonobese
male (n = 22) and female (n = 23) participants
who had adhered to an omnivorous diet for at
least five years. Within each group, there was
a mixture of people who did and did not report participating in regular, progressive resistance training at least once a week. There
were 20 vegan lifters and 25 omnivorous
lifters in the sample. The primary goals of
the study were to explore differences in dietary intake of key nutrients related to bone
health, and to explore differences in bone microarchitecture using state-of-the-art imaging
techniques (high-resolution peripheral quantitative computed tomography; HR-pQCt).
The researchers reported a ton of outcomes
in the full text, but in line with the purpose
of the Research Briefs section, I’ll merely hit
the highlights here. Compared to omnivores,
vegans had significantly lower BMI values
at baseline, but significantly higher intakes
of total energy, cobalamin, folic acid, vitamin D, vitamin K, and magnesium. In contrast, vegans had significantly lower intakes
of protein (in grams per kilogram) and calcium. Biomarkers of bone turnover were within normal ranges for both groups, although
circulating calcium levels were significantly
higher in the omnivore group. Notably, there
is no question that supplementation habits influenced these outcomes, and vegans got considerable assistance with their vitamin B12
and vitamin D levels from supplementation
127
and fortified foods. There were 32 vegans
who reported the use of dietary supplements,
compared to only 12 omnivores.
A number of bone architecture variables were
assessed. Generally speaking, bone architecture was better (representing lower risk for
bone-related adverse outcomes) in omnivores
than vegans, and better in lifters than non-lifters. However, things got much more interesting when exploring specific combinations
of both factors. The gap between lifters and
non-lifters was larger in the vegan group, such
that lifting was associated with an even larger
positive effect on bone architecture for vegans
than for omnivores, relatively speaking. When
comparing non-lifters, omnivores had significantly better bone architecture than vegans.
However, these differences were substantially attenuated by resistance training, such that
bone architecture in vegan lifters was quite
similar to bone architecture in omnivorous
lifters. Bone architecture data, stratified by
subgroups, are presented in Table 1.
For this particular research brief, the conclusions are short and sweet. Vegan diets are a
totally viable alternative to omnivorous diets.
However, individuals on a vegan diet should
be particularly mindful of participating in
regular resistance training, if possible. This is
generally good advice for health and wellness
across all dietary categories, but as shown in
the presently reviewed study, it has particularly high importance in the context of the
typical vegan diet. Vegans who do not participate in resistance training appear to present with poorer bone architecture that may
increase risk for adverse bone-related health
outcomes. However, this disadvantage ap-
pears to be markedly attenuated by resistance
training. It’d be great to see this observational finding confirmed in a long-term prospective cohort study, but the present findings are
plausible, intuitive, and mechanistically supported. They also direct vegans toward an intervention that is advisable regardless of the
robustness of the present findings – mounting
evidence suggests that resistance training is a
helpful tool for supporting general health and
wellness (4), vegan or otherwise.
It’s clearly a good idea for people to engage in
some amount of resistance training if they’re
able to, and this appears to be of heightened
importance for vegans. In addition, you’ll
want to pay extra attention to certain nutrients of interest whenever you eliminate one
or more entire food groups from a diet. Dr.
Helms provides excellent guidance for plantbased diets in his two-part series (one, two),
which walks through the process of structuring effective plant-based diets for lifters. As
the present study shows, dietary supplements
and fortified foods can go a very long way in
filling any gaps that may be observed in a typical vegan diet. This doesn’t mean that a long
list of supplements is necessarily required, but
merely that supplementation and food fortification offer some very easy and convenient
pathways to incorporating nutrients that may
be harder to come by on vegan diets.
As discussed in a previous MASS article, individuals on vegan diets (or heavily plant-based
diets) should be mindful of iron, vitamin B12,
vitamin D, iodine, zinc, calcium, and protein
intake. In many cases, adequate intakes of
these key nutrients can be achieved on a vegan diet that contains a mixture of thoughtful-
128
ly selected conventional and fortified foods,
but supplementation is also a viable option. A
very simple multivitamin can go a very long
way in these scenarios, in addition to being
a little more mindful of achieving adequate
daily protein intakes. When adequate protein
intake is achieved, vegan diets can very effectively support muscle protein synthesis
and hypertrophy. Furthermore, when vegans
engage in resistance training and effectively
cover their micronutrient bases, it appears
they can enjoy their diet without inherently
accepting tradeoffs that necessarily threaten
their long-term bone health.
References
1. Wakolbinger-Habel R, Reinweber M,
König J, Pokan R, König D, Pietschmann
P, et al. Self-Reported Resistance Training
Is Associated With Better HR-pQCT
Derived Bone Microarchitecture In Vegan
People. J Clin Endocrinol Metab. 2022 Aug
4; ePub ahead of print.
2. Li T, Li Y, Wu S. Comparison Of Human
Bone Mineral Densities In Subjects On
Plant-Based And Omnivorous Diets: A
Systematic Review And Meta-Analysis.
Arch Osteoporos. 2021 Jun 18;16(1):95.
3. Iguacel I, Miguel-Berges ML, GómezBruton A, Moreno LA, Julián C.
Veganism, Vegetarianism, Bone Mineral
Density, And Fracture Risk: A Systematic
Review And Meta-Analysis. Nutr Rev.
2019 Jan 1;77(1):1–18.
4. Westcott WL. Resistance Training Is
Medicine: Effects Of Strength Training
On Health. Curr Sports Med Rep. 2012
Aug;11(4):209–16.
129
Study Reviewed: Triceps Brachii Hypertrophy is Substantially Greater After Elbow Extension
Training Performed in the Overhead Versus Neutral Arm Position. Maeo et al. (2022)
Are Overhead Triceps Extensions Better than
Pushdowns for Hypertrophy?
BY GREG NUCKOLS
We’ve written about the effects of range of
motion on muscle growth quite a few times in
MASS, tentatively concluding that the beneficial hypertrophy effects of training through
a full range of motion are primarily driven
by the impact of training at longer muscle
lengths. In other words, training through a
full range of motion is generally more effective than employing a partial range of motion
where the prime movers are only trained at
short muscle lengths, but employing a partial range of motion where the prime movers
are trained at long muscle lengths seems to
be just as effective as training through a full
range of motion (2).
If training at longer muscle lengths is the critical factor, that opens the door to a new question: are exercise variations that allow you to
train at longer muscle lengths inherently better
for hypertrophy than exercise variations that
force you to train at shorter muscle lengths?
A previous study by Maeo and colleagues (3)
seemed to answer this question with a resounding, “yes.” It compared the effects of seated
versus lying hamstring curls. Since there are
multiple biarticular muscles crossing the knee
and hip, manipulating hip angles changes the
muscle lengths of several knee flexors, without
affecting knee flexion range of motion. Namely, seated hamstring curls train three of the
four heads of the hamstrings at longer muscle
lengths than lying hamstring curls. In keeping
with the hypothesis that training at longer muscle lengths is better for hypertrophy, all three
biarticular heads of the hamstrings (the long
head of the biceps femoris, the semitendinosus, and the semimembranosus) experienced
more hypertrophy following seated hamstrings
curls than lying hamstring curls, whereas the
sole monoarticular head of the hamstrings (the
short head of the biceps femoris) experienced
roughly the same amount of hypertrophy following both seated and lying hamstrings curls.
A recent study by the same group of researchers aimed to see if this same principle would
generalize to another muscle group: the triceps (1). Employing a within-subject unilateral design, 21 healthy but untrained subjects
completed 12 weeks of triceps training – one
arm performed overhead triceps extensions
on a cable machine, and one arm performed
cable pushdowns. Both arms trained through
130
90 degrees of elbow flexion, and subjects
performed 5 sets of 10 reps for each exercise
with a controlled cadence (2-second eccentrics and 2-second concentrics), twice per
week. Training loads increased by 5% when
a subject could complete all five sets of 10
reps with a particular load. 1RM strength
was assessed pre- and post-training, and triceps muscle volume was assessed pre- and
post-training via MRI. The researchers hypothesized that overhead triceps extensions
would result in more growth of the long head
of the triceps since overhead triceps exten-
sions train the long head of the triceps at longer muscle lengths (Figure 1). Furthermore,
they hypothesized that both triceps exercises would produce similar growth for the
monoarticular heads of the triceps (the lateral
and middle heads), since shoulder position
shouldn’t influence the muscle lengths of the
two monoarticular heads.
Relative increases in training loads (Figure 2)
and 1RM strength were similar between arms,
though the arms performing pushdowns trained
with heavier absolute loads throughout the study.
131
In keeping with the researchers’ hypothesis, overhead triceps extensions led to larger increases in muscle volume for the long
head of the triceps (+28.5% versus +19.6%;
p <0.001). However, in conflict with the researchers’ hypothesis, overhead triceps extensions also led to larger increases in muscle volume for the lateral and middle heads
of the triceps (+14.6% versus 10.5%; p =
0.002). You can see these results in Figure 3.
Before digging into the results of the present
study, it’s worth mentioning a prior study in-
vestigating the effects of pushdowns versus
overhead triceps extensions. A 2018 study
by Stasinaki and colleagues assessed hypertrophy in only the long head of the triceps,
also employing a within-subject design (4).
One arm performed pushdowns with an elbow range of motion spanning from 10 to 90
degrees of elbow flexion, and the other arm
performed overhead triceps extensions with a
elbow range of motion spanning from 150 to
70 degrees of elbow flexion. In other words,
the arms performing pushdowns trained at
muscle lengths that were comparable to the
132
long head of the triceps (closer to the elbow),
while pushdowns were non-significantly better for growing the proximal region (closer to
the shoulder). I think there are three factors
that could explain why the results differed
between these two studies.
pushdown arms in the present study, but the
arms performing overhead triceps extensions
trained at even longer muscle lengths (for
the long head of the triceps) than the arms
performing overhead triceps extensions in
the present study. Unlike the present study,
the study by Stasinaki and colleagues found
that overall increases in cross-sectional area
for the long head of the triceps was similar
following both training interventions. Overhead triceps extensions were non-significantly better for growing the distal region of the
First, there may have just been some issues
with measurement error in the study by Stasinaki and colleagues (4). The present study
by Maeo and colleagues (1) measured changes in triceps size using MRI, which produces
crisp, clear images. The Stasinaki study used
B-mode ultrasound, which certainly can produce clear images, but image quality varies
between ultrasound devices. Figure 1 in the
Stasinaki study shows a representative scan
with the ultrasound device used in the study,
and it’s pretty clear that some judgment calls
would need to be made for determining the
boundaries of the long head of the triceps.
Second, in the Stasinaki study, subjects always completed all sets of pushdowns before
they performed overhead triceps extensions.
In the present study by Maeo and colleagues,
subjects alternated which arm was trained
first. So, it’s possible that subjects in the Stasinaki study simply trained a bit harder when
performing pushdowns, since they always
trained pushdowns when they were fresh.
Third, and most importantly, it’s possible that
the overhead triceps extensions in the Stasinaki study were performed at too long of muscle lengths. When muscle fibers are stretched
to more than about 150% of resting length,
there are too few actin-myosin crossbridges to create much active tension. The present study by Maeo and colleagues presents
some muscle modeling results, suggesting
133
that the long head of the triceps is effectively
“tapped out” once you reach 90 degrees of elbow flexion in an overhead triceps extension
(Figure 1). Since subjects trained overhead
triceps extensions from 150 to 70 degrees
of elbow flexion in the Stasinaki study, it’s
likely that the long head of the triceps was
barely producing any active force throughout
most of the range of motion being trained. In
other words, instead of increasing the training stress on the long head of the triceps, the
overhead triceps extensions performed in the
Stasinaki study may have reduced the training stress on the long head of the triceps, by
putting it in an over-stretched position.
Turning our attention to the study at hand, I’m
not going to say much more about the finding
that overhead triceps extensions were more
effective for promoting hypertrophy in the
long head of the triceps. That comports with
prior research, finding that training at longer
muscle lengths is generally superior for hypertrophy. However, I do want to discuss the
finding that overhead triceps extensions also
led to more muscle growth in the lateral and
middle heads of the triceps, because it’s both
a very strong and a very surprising finding.
First, it’s worth explaining why it’s a strong
finding. Since exercise selection didn’t affect
the muscle lengths of the lateral and middle
head of the triceps, a lot of people (including
the authors) expected that overhead triceps
extensions and pushdowns would be similarly effective for growing the monoarticular
heads of the triceps. So, I’ve seen quite a few
people on social media writing this finding
off as a fluke that we shouldn’t put much
stock in. However, purely from a scientific
and statistical perspective, this finding has
a lot going for it. First, the study design itself is great – with a within-subject unilateral
design, each subject can serve as their own
control, so your results won’t be impacted by
things like a failure of your randomization
protocol. Basically, both of your “groups”
are guaranteed to have the same lifestyles,
genetics, nutrition, etc. (i.e., your right arm
doesn’t sleep more or eat better than your
left arm), which isn’t necessarily guaranteed
with a parallel-groups design. Second, hypertrophy was assessed via MRI, which is the
gold standard for assessing changes in whole
muscle size in vivo. Third, the p-value for the
comparison of monoarticular triceps hypertrophy was very low (p = 0.002), implying
that overhead triceps extensions didn’t just
lead to more growth of the middle and lateral
heads of the triceps on average – overhead
triceps extensions predictably led to more
growth for the vast majority of individuals.
In absolute terms, the volume of the middle
and lateral heads of the triceps increased by
42.1 ± 33.4cm3 in the arms doing overhead
triceps extensions, and by 30.4 ± 26.9cm3
in the arms doing pushdowns. If this study
employed a parallel-groups design, that difference between groups wouldn’t be statistically significant with 21 subjects per group.
If you run an unpaired t-test on those change
scores, you’ll come up with a p-value of 0.22.
However, since the p-value for this comparison was very low (p = 0.002), that means that
overhead triceps extensions consistently produced superior results in this study. For more
on why consistency matters when you’re
dealing with correlated, paired data, see the
134
“criticisms and statistical musings” section of
this prior MASS article. But, in short, both
the magnitude and consistency of a finding
matter when evaluating how durable the finding is likely to be. If an intervention produces
40% more muscle growth, on average, but it
only produces more muscle growth for 60%
of individuals, you might not be dealing with
a particularly reliable and generalizable finding. However, if another intervention produces 40% more muscle growth, on average, but
it produces more muscle growth for 90% of
individuals, there’s a very good chance that it
is truly a generalizably superior intervention.
Now, let’s turn our attention to potential explanations for this finding. The middle and
lateral heads of the triceps were trained at the
same muscle lengths by both exercises, and
the general resistance curves would be been
similar for both exercises (hardest at the start
of the concentric when the arm is parallel to
the floor, and easiest at lockout), but overhead
triceps extensions reliably produced more
muscle growth in the monoarticular heads of
the triceps. What could explain these results?
The authors of the study put forth two potential explanations, and I’d like to add a third
(which, admittedly, may be a bit of a stretch).
Their first explanation is purely mechanical:
overhead triceps extensions put the long head
of the triceps in a very lengthened position,
where it isn’t capable of producing much active force. Therefore, the middle and lateral
heads of the triceps would have needed to
generate more force (especially at the start
of the concentric) during overhead triceps
extensions to “make up for” the diminished
contributions of the long head. This explanation initially makes intuitive sense, but the
more I’ve thought about it, the less plausible it seems. Quite simply, the nervous system doesn’t generally have issues recruiting
monoarticular muscles for single-joint exercises. The subjects were training to failure or
near failure on all of their sets, so the monoarticular heads of the triceps would have already
been producing as much force as they were
capable of (given the per-set rep targets) with
both exercises. Since the long head of the triceps couldn’t produce as much force during
overhead triceps extensions, the subjects just
performed overhead triceps extensions with
a lower load (Figure 2). Basically, reducing
the force output of the long head of the triceps didn’t make the monoarticular heads of
the triceps produce even more force; it just
reduced total force output.
The authors’ second explanation is that overhead triceps extensions may have increased
hypoxic stress for all heads of the triceps. Way
back in Volume 1 of MASS, I wrote about a
study by Goto and colleagues which suggested that “constant tension” training may lead
to greater muscle growth by increasing hypoxic stress during training (5). There’s also
some evidence suggesting that training in
low-oxygen environments may lead to greater muscle growth due to a similar mechanism
(6). The precise ways in which hypoxia increases muscle hypertrophy isn’t fully elucidated, but I do think this hypothesis has some
legs. When your arms are overhead, they
receive less arterial blood flow (because the
flow of blood is being counteracted by gravity; when your arms are to your side, gravi-
135
ty instead aids in arterial blood flow), which
could lead to greater hypoxia. So, via this
potential mechanism, overhead triceps extensions may lead to greater muscle growth of
the monoarticular heads of the triceps for a
reason that’s completely unrelated to training
at longer versus shorter muscle lengths.
My third tentative explanation is that changing the length of the long head of the triceps may have actually affected tension in
the monoarticular heads of the triceps, since
all three heads of the triceps share the same
distal tendon. Instead of individually inserting directly at the elbow, all three heads of
the triceps insert on a wide, flat tendon (an
aponeurosis), which then inserts at the elbow. The long head of the triceps inserts on
the medial side of this tendon, so putting a
stretch on the long head of the triceps might
medially displace the aponeurosis slightly,
which would effectively place the fibers of
the lateral heads of the triceps in a position
where fiber lengths would be slightly longer
with the same degree of elbow flexion. Alternatively, if passive force on the triceps tendon increases, activation of the monoarticular heads of the triceps may increase slightly
due to tendon-associated reflex arcs. With
that said, I do think the hypoxia explanation
is a far more plausible primary explanation
for these findings.
Ultimately, I think this study is a useful lesson
in not getting too seduced by reductionism
and single-factor thinking. The skepticism
I’ve seen toward this study’s results essentially boils down to “nothing was done to put
more tension on the monoarticular heads of
the triceps, and the monoarticular heads of
the triceps were trained at the same muscle
lengths with both exercises, so the results
must be wrong.” In other words, if you assume that only one or two factors could possibly influence muscle growth, and a study
result conflicts with those assumptions, the
result shouldn’t be trusted. However, I think
it’s far more helpful to instead assume that
your assumptions may have been faulty –
maybe there are simply other factors that influence hypertrophy. Only time will tell, but
for now, if you have lagging triceps, I think
it’s probably worth giving overhead triceps
extensions a shot (if they’re not already in
your program).
References
1. Maeo S, Wu Y, Huang M, Sakurai H,
Kusagawa Y, Sugiyama T, Kanehisa
H, Isaka T. Triceps brachii hypertrophy
is substantially greater after elbow
extension training performed in the
overhead versus neutral arm position.
Eur J Sport Sci. 2022 Aug 11:1-11. doi:
10.1080/17461391.2022.2100279. Epub
ahead of print. PMID: 35819335.
2. Pedrosa GF, Lima FV, Schoenfeld
BJ, Lacerda LT, Simões MG, Pereira
MR, Diniz RCR, Chagas MH. Partial
range of motion training elicits
favorable improvements in muscular
adaptations when carried out at long
muscle lengths. Eur J Sport Sci.
2022 Aug;22(8):1250-1260. doi:
10.1080/17461391.2021.1927199. Epub
2021 May 23. PMID: 33977835.
3. Maeo S, Huang M, Wu Y, Sakurai H,
Kusagawa Y, Sugiyama T, Kanehisa
H, Isaka T. Greater Hamstrings Muscle
Hypertrophy but Similar Damage
136
Protection after Training at Long versus
Short Muscle Lengths. Med Sci Sports
Exerc. 2021 Apr 1;53(4):825-837. doi:
10.1249/MSS.0000000000002523. PMID:
33009197; PMCID: PMC7969179.
4. Stasinaki A-N, Zaras N, Methenitis
S, Tsitkanou S, Krase A, Kavvoura
A, Terzis G. Triceps Brachii Muscle
Strength and Architectural Adaptations
with Resistance Training Exercises at
Short or Long Fascicle Length. Journal of
Functional Morphology and Kinesiology.
2018; 3(2):28. https://doi.org/10.3390/
jfmk3020028
5. Goto M, Maeda C, Hirayama T, Terada
S, Nirengi S, Kurosawa Y, Nagano A,
Hamaoka T. Partial Range of Motion
Exercise Is Effective for Facilitating
Muscle Hypertrophy and Function Through
Sustained Intramuscular Hypoxia in
Young Trained Men. J Strength Cond
Res. 2019 May;33(5):1286-1294. doi:
10.1519/JSC.0000000000002051. PMID:
31034463.
6. Ramos-Campo DJ, Scott BR, Alcaraz
PE, Rubio-Arias JA. The efficacy
of resistance training in hypoxia to
enhance strength and muscle growth: A
systematic review and meta-analysis. Eur
J Sport Sci. 2018 Feb;18(1):92-103. doi:
10.1080/17461391.2017.1388850. Epub
2017 Oct 18. PMID: 29045191.
137
Study Reviewed: Personalized Microbiome-Driven Effects Of Non-Nutritive Sweeteners On
Human Glucose Tolerance. Suez et al. (2022)
Reassessing the Impact of Non-Nutritive Sweeteners
on Metabolic Health and the Gut Microbiome
BY ERIC TREXLER
Skepticism about non-nutritive sweeteners,
often discussed as “artificial sweeteners,” is
a tradition as old as time (or, more accurately, as old as artificial sweeteners). There are
multiple intuitive factors contributing to this
widespread and pervasive skepticism. First,
the term “artificial” is in the name; if you’re
at all susceptible to the “appeal to nature”
logical fallacy, skepticism is virtually automatic. Second, artificial sweeteners seem like
a bit of a cheat code. While some taste better
than others, and some artificially sweetened
alternatives fall short of the sugar-sweetened
foods or beverages they aim to imitate, there
are many cases where artificial sweeteners
seem too good to be true. Dieting is generally viewed as difficult and unpleasant, so
it feels very unnatural to enjoy an effective
swap or substitution that doesn’t involve a
major sacrifice or downside. I suspect a lot of
people who switch to diet beverages in place
of sugar-sweetened beverages are pleasantly
surprised by the swap, but inherently assume
that there must be some associated cost that
they’re either 1) unknowingly paying for
now, or 2) bound to pay for later.
What exactly are those costs, you might ask?
For a while, cancer was the most notable and
widespread concern pertaining to artificial
sweeteners among the general population.
There’s no question that plenty of folks still
harbor this concern, but the data have largely assuaged those concerns in recent decades
(2). Another classic concern pertains to food
cravings; it was commonly theorized that the
sweet taste of artificial sweeteners would
cause the body to expect sugar, which it obviously wasn’t receiving. The theory postulates that this fuels cravings for sugar, such
that the body senses the “trick” you tried to
pull and seeks out the sugary foods and beverages it’s owed. Again, plenty of folks still
subscribe to this theory, but it’s pretty incompatible with the most recent and most reliable
evidence available (3), and contradicted by
some direct experimental evidence that Dr.
Helms previously covered in MASS.
The newest wave of concerns focuses primarily on two hot topics: the gut microbiome, and glycemic control. The gut microbiome has received considerable interest in
recent years, as researchers are just beginning
138
to scratch the surface of the many complexities of this poorly understood microbial environment. It’s clear that the gut microbiome
is very fickle and ever-changing, but the clinical ramifications of these constant fluctuations are relatively unknown. This leads to
a pretty chaotic atmosphere surrounding the
gut microbiome research, and more specifically how it’s conveyed to the general public.
The gut microbiome seems to respond (via
some sort of change) to just about everything, and in fact, it even responds to “nothing.” Research examining typical day-to-day
variation, within the same individual, in the
absence of any intentional intervention, indicates that this is a remarkably volatile environment characterized by frequent, transient,
and enormous changes (4). As a result, it’s
very easy to find a study indicating that just
about any intervention is associated with a
change in the gut microbiome, and there’s
plenty of wiggle room to spin that change as
a positive, negative, or neutral thing.
Variables quantifying glycemic control certainly aren’t as fickle as gut microbiome
outcomes, but they are certainly subject to
considerable variability. Furthermore, as
discussed in a previous MASS article, these
outcomes are currently in the crosshairs of
many biohackers intent on “optimizing” even
the most granular aspects of glycemic control. As a result, there is growing interest
in whether or not artificial sweeteners may
modulate acute and chronic insulin and glucose responses to feeding. For example, some
research has hinted that the sweet taste of artificial sweeteners can acutely induce a cephalic phase insulin response in the absence
of glucose, but the evidence is currently a bit
mixed (5). Some have speculated that regular
non-nutritive sweeteners may have chronic,
deleterious effects on glycemic control by a
variety of mechanisms, including alteration
of the cephalic phase insulin response (5) or
alteration of the gut microbiome (6). These
concerns are amplified by observational research linking artificial sweetener consumption to obesity and diabetes, but reverse causality is a potential confounding variable,
given that people with obesity or diabetes are
more likely to pursue artificial sweeteners as
part of a program to lose weight or reduce
sugar intake (5).
The presently reviewed study (1) was a large,
comprehensive project that investigated the
impact of non-nutritive sweetener intake on
gut microbiome composition and glycemic
control. The researchers enrolled 120 healthy
adults who did not previously consume
non-nutritive sweeteners to participate in the
study. Participants were randomly assigned
to ingest an experimental treatment daily for
two weeks: saccharin + glucose vehicle, sucralose + glucose vehicle, aspartame + glucose vehicle, stevia + glucose vehicle, glucose vehicle only (placebo), or no treatment
(control). If you’re wondering what a “glucose vehicle” is, you’ll notice that artificial
sweetener packets usually contain a small
amount of carbohydrate to add bulk and texture to the product; that’s the glucose vehicle,
and the amount (5g) was standardized for all
treatment groups (except for the no-supplement control group, obviously). Every day
during the supplementation period, participants consumed their assigned treatment,
139
which was dosed to be lower than the acceptable daily intake. Before, during, and after
supplementation, they examined glycemic
control via self-administered oral glucose tolerance tests (at home, unsupervised), which
was made possible via continuous glucose
monitors. The researchers also took stool
samples to examine microbiome changes, in
addition to a long list of additional outcomes.
They also did stool transplants, by which fecal samples (containing the gut microbes of
human participants) were transplanted into
mice, in order to see if those gut microbes
would alter glycemic control in the rodents
receiving transplants.
In short, the researchers found that sucralose
and saccharin impaired glycemic responses,
as measured via self-administered oral glucose tolerance tests. This result is depicted
in Figure 1. The researchers observed that all
of the non-nutritive sweeteners tested induce
changes to the microbiome, and they found
that fecal transplants allowed them to repro-
140
duce the human glycemic alterations in the
rodent transplant recipients. As you might
expect, this study generated a lot of buzz in
the fitness community, and had some regular non-nutritive sweetener users rethinking
their dietary choices. However, I don’t think
we should overreact to this study.
This is a large, ambitious, and well-conducted
study, and the researchers rightfully deserve
credit for that. However, these sucralose findings contradict a previously reviewed study
reporting non-significant impacts on glycemic control and gut microbiome composition
(7), so it might be advisable to wait for confirmation of these findings (via successful replication) before drawing firm conclusions. In
addition, there are some important limitations
to highlight. First, this study was conducted
in a very specialized population: people who
don’t consume any non-nutritive sweeteners.
You might be skeptical of that statement; after all, what percentage of the population actually consumes diet beverages and artificially sweetened dietary supplements? In reality,
non-nutritive sweeteners have become pervasive in the modern food supply, and most
people don’t even know that they regularly
consume non-nutritive sweeteners. For the
present study, 120 participants were enrolled
from 2018-2020, but the researchers had to
screen 1,375 individuals in order to enroll this
sample. They noted that “the vast majority of
ineligible candidates were found to consume
non-nutritive sweeteners, in many cases unknowingly.” This suggests that the researchers recruited a group of individuals with fairly unique dietary habits, which might not be
representative of the general population.
In addition, the oral glucose tolerance tests
were self-administered using continuous
glucose monitors. As discussed in a previous MASS article, these continuous glucose
monitors aren’t exactly “gold standard” measurement tools, even when used correctly.
Furthermore, administering an oral glucose
tolerance test is a pretty nuanced procedure.
They can be influenced by insufficient fasting time beforehand, excessive fasting time
beforehand, physical activity or nutrient intake in close proximity to the test, or even
being super stressed out about consuming an
artificial sweetener you’ve been intentionally avoiding (it’s possible that some of these
non-nutritive sweetener users were actually
non-nutritive sweetener avoiders, and the researchers noted that treatment blinding was
not feasible due to taste differences). Of the
1080 glucose tolerance tests that were expected, nearly 100 were missing or discarded
for a variety of reasons related to insufficient
test quality. Furthermore, it probably goes
without saying that glucose tolerance tests
are not dichotomously “perfect” or “so bad
they must be thrown out entirely” – given the
unsupervised nature of these tests, we have to
interpret them with some degree of caution.
Aside from those details, there is a far more
important limitation: from my perspective,
the reported differences in glucose responses
(taken at face value) don’t appear to be clinically relevant. There are subtle differences in
the glucose curves, but we aren’t seeing catastrophic metabolic derangement in these data.
At worst, you could look at these data and
say, “sure, this isn’t so bad after two weeks
of ingestion, but imagine what would happen
141
over the long term.” But we don’t have to
imagine – those longitudinal data exist, and
they don’t tell a particularly scary story.
If the reported alterations in oral glucose tolerance and gut microbiome composition are
clinically relevant, we should get some hints
of that from the longitudinal trials investigating non-nutritive sweetener intake. For
example, a 2022 meta-analysis by McGlynn
et al (3) pooled the results from 17 randomized controlled trials (RCTs) in adult men
and women. For interventions lasting 3-52
weeks in duration, beverages with non-nutritive sweeteners were associated with reduced
weight, BMI, body-fat percentage, and liver
fat when replacing sugar-sweetened beverages. The observed benefits of beverages with
non-nutritive sweeteners were similar to water consumption, and impacts on glycemic
control were negligible. A 2020 meta-analysis restricted its focus to studies on patients
with type 1 or type 2 diabetes (8). While fewer studies were available, nine RCTs spanning 4-10 months in duration failed to provide any convincing evidence of substantial
harms or benefits for patients with diabetes.
In both meta-analyses, adverse event reports
related to the actual treatments were effectively negligible in terms of quantity and severity. Similarly, a 2020 review by Pang et
al (9) noted that: “Meta-analyses of RCTs or
RCTs and prospective cohort studies suggest
that artificial sweeteners may have a neutral
effect on body weight and glycemic control,
respectively, or may have a beneficial effect
on long-term body weight regulation.” If
non-nutritive sweeteners were causing clinically relevant perturbations in glycemic con-
trol, I’d expect to see far more troubling results in the longitudinal trials to date.
To be clear, I’m certainly not suggesting that
we know everything there is to know about
all non-nutritive sweeteners, and that they are
physiologically inert substances that will never impact any clinical outcome at any level of
intake. Indeed, each individual non-nutritive
sweetener needs to be rigorously investigated
in relation to a wide range of clinical outcomes,
and there’s a somewhat surprising lack of data
for many of these sweeteners. We also need to
learn a lot about the gut microbiome before we
can purport to make strong claims about the
clinical relevance of transient shifts in composition. Based on the available literature, the
ratio of favorable to unfavorable findings (in
terms of frequency and magnitude of reported effects) has not convinced me to place any
strict limitations on my personal consumption of any particular non-nutritive sweetener.
However, risk tolerance is a personal matter;
if you prefer to avoid non-nutritive sweeteners
until more research unequivocally convinces
you of their safety, that’s a totally justifiable
stance to take.
References
1. Suez J, Cohen Y, Valdés-Mas R, Mor
U, Dori-Bachash M, Federici S, et al.
Personalized Microbiome-Driven Effects
Of Non-Nutritive Sweeteners On Human
Glucose Tolerance. Cell. 2022 Sep
1;185(18):3307-3328.e19.
2. Marinovich M, Galli CL, Bosetti C, Gallus
S, La Vecchia C. Aspartame, Low-Calorie
Sweeteners And Disease: Regulatory
Safety And Epidemiological Issues. Food
Chem Toxicol. 2013 Oct;60:109–15.
142
3. McGlynn ND, Khan TA, Wang L, Zhang R,
Chiavaroli L, Au-Yeung F, et al. Association
of Low- and No-Calorie Sweetened
Beverages as a Replacement for SugarSweetened Beverages With Body Weight
and Cardiometabolic Risk: A Systematic
Review and Meta-analysis. JAMA Netw
Open. 2022 Mar 14;5(3):e222092.
4. Vandeputte D, De Commer L, Tito RY,
Kathagen G, Sabino J, Vermeire S, et
al. Temporal Variability In Quantitative
Human Gut Microbiome Profiles And
Implications For Clinical Research. Nat
Commun. 2021 Nov 18;12(1):6740.
5. Wilk K, Korytek W, Pelczyńska M,
Moszak M, Bogdański P. The Effect
of Artificial Sweeteners Use on Sweet
Taste Perception and Weight Loss
Efficacy: A Review. Nutrients. 2022 Mar
16;14(6):1261.
6. Suez J, Korem T, Zeevi D, ZilbermanSchapira G, Thaiss CA, Maza O,
et al. Artificial Sweeteners Induce
Glucose Intolerance By Altering The
Gut Microbiota. Nature. 2014 Oct
9;514(7521):181–6.
7. Thomson P, Santibañez R, Aguirre C,
Galgani JE, Garrido D. Short-Term
Impact Of Sucralose Consumption On The
Metabolic Response And Gut Microbiome
Of Healthy Adults. Br J Nutr. 2019 Oct
28;122(8):856–62.
8. Lohner S, Kuellenberg de Gaudry D,
Toews I, Ferenci T, Meerpohl JJ. Non‐
nutritive Sweeteners For Diabetes Mellitus.
Cochrane Database Syst Rev. 2020 May
25;5(5):CD012885.
9. Pang MD, Goossens GH, Blaak EE. The
Impact of Artificial Sweeteners on Body
Weight Control and Glucose Homeostasis.
Front Nutr. 2021 Jan 7;7:598340.
143
VIDEO: Two-A-Days Part I
BY MICHAEL C. ZOURDOS
Training twice per day is pretty awesome, but is it necessary? This video
evaluates the evidence for splitting your training into two sessions per day
to augment hypertrophy and strength. The practice may have efficacy, but
conditions apply.
Click to watch Michael's presentation.
144
Relevant MASS Videos and Articles
1. Is It Better to Split Your Workout Into Multiple Daily Sessions. Volume 1 Issue 6.
2. How do Twice-Daily Training Sessions Affect Hypertrophy and Strength Gains? 5 Issue 4
3. Is Training Twice A Day For You? Volume 6 Issue 3.
References
1. Bartolomei S, Malagoli Lanzoni I, Di Michele R. Two vs. One Resistance Exercise Sessions
in One Day: Acute Effects on Recovery and Performance. Research Quarterly for Exercise and
Sport. 2022 Jan 15:1-6.
2. Storey A, Wong S, Smith HK, Marshall P. Divergent muscle functional and architectural
responses to two successive high intensity resistance exercise sessions in competitive
weightlifters and resistance trained adults. European journal of applied physiology. 2012
Oct;112(10):3629-39.
3. Häkkinen K, Pakarinen A. Serum hormones in male strength athletes during intensive short
term strength training. Eur J Appl Physiol Occup Physiol. 1991;63(3-4):194-9. doi: 10.1007/
BF00233847. PMID: 1761007.
4. Häkkinen K, Kallinen M. Distribution of strength training volume into one or two daily sessions
and neuromuscular adaptations in female athletes. Electromyogr Clin Neurophysiol. 1994
Mar;34(2):117-24. PMID: 8187678.
5. Hartman MJ, Clark B, Bembens DA, Kilgore JL, Bemben MG. Comparisons between twicedaily and once-daily training sessions in male weight lifters. Int J Sports Physiol Perform. 2007
Jun;2(2):159-69. doi: 10.1123/ijspp.2.2.159. PMID: 19124903.
6. Shiau K, Tsao TH, Yang CB. Effects of Single Versus Multiple Bouts of Resistance Training
on Maximal Strength and Anaerobic Performance. J Hum Kinet. 2018 Jun 13;62:231-240. doi:
10.1515/hukin-2017-0122. PMID: 29922394; PMCID: PMC6006538.
7. Corrêa DA, Brigatto FA, Braz TV, DE Carmargo JB, Aoki MS, Marchetti PH, Lopes CR.
Twice-daily sessions result in a greater muscle strength and a similar muscle hypertrophy
compared to once-daily session in resistance-trained men. J Sports Med Phys Fitness. 2022
Mar;62(3):324-336. PMID: 33634677.
█
145
VIDEO: Practical Long Muscle
Length Training
BY ERIC HELMS
As more research emerges, it becomes increasingly apparent that training
which puts a muscle in a more lengthened position seems to induce greater
hypertrophy. In this video, we’ll go over some of the research which supports this
concept, the different types of ways this has been shown in research, the potential
considerations of implementing long muscle length training, and then finally,
practical applications. Specifically, form modification, exercise selection, partial
range of motion training, and microcycle-level programming are discussed.
Click to watch Eric's presentation.
146
Relevant MASS Videos and Articles
.
1. The Effects of Range of Motion on Muscle Growth: The Current State of the Literature. Volume
4, Issue 3.
2. Matching Resistance Curves and Strength Curves: Great in Theory, but Iffy in Practice. Volume
4, Issue 4.
3. Seated Hamstrings Curls Cause More Growth Than Lying Hamstrings Curls. Volume 4, Issue 12.
4. Partial Range of Motion Training Might Increase Muscle Growth (If You Do the Right Type of
Partials). Volume 5, Issue 7.
5. Partial Range of Motion Training Can Work ... With the Right Kind of Partials. Volume 5, Issue 11.
6. Can Stretching Directly Cause Muscle Growth? Volume 6, Issue 7.
References
1. Sato S, Yoshida R, Kiyono R, Yahata K, Yasaka K, Nunes JP, et al. Elbow Joint Angles in
Elbow Flexor Unilateral Resistance Exercise Training Determine Its Effects on Muscle Strength
and Thickness of Trained and Non-trained Arms. Front Physiol. 2021 Sep 16;12:734509.
2. Pettitt RW, Symons DJ, Eisenman PA, Taylor JE, White AT. Eccentric strain at long muscle
length evokes the repeated bout effect. J Strength Cond Res. 2005 Nov;19(4):918-24.
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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, here are 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. de Paiva et al. Effects Of Probiotic Supplementation On Performance Of Resistance And
Aerobic Exercises: A Systematic Review
2. van der Heijden et al. Alternative Dietary Protein Sources To Support Healthy And Active
Skeletal Muscle Aging
3. Hidayat et al. Is Replacing Red Meat With Other Protein Sources Associated With Lower
Risks Of Coronary Heart Disease And All-Cause Mortality? A Meta-Analysis Of Prospective
Studies
4. Pinckaers et al. Potato Protein Ingestion Increases Muscle Protein Synthesis Rates At
Rest And During Recovery From Exercise In Humans
5. Moore et al. Walking Or Body Weight Squat ‘activity Snacks’ Increase Dietary Amino Acid
Utilization For Myofibrillar Protein Synthesis During Prolonged Sitting
6. Leduc et al. The Effect Of Acute Sleep Extension Vs Active Recovery On Post Exercise
Recovery Kinetics In Rugby Union Players
7. Wardenaar et al. Accuracy And Reliability Of College Athletes’ Scoring Of Artificial Urine
Color Samples To Determine Hydration Status
8. González-Valero et al. Could The Complying With WHO Physical Activity Recommendations
Improve Stress, Burnout Syndrome, And Resilience? A Cross-Sectional Study With
Physical Education Teachers
9. Lin et al. The Effect Of Sleep Restriction, With Or Without Exercise, On Skeletal Muscle
Transcriptomic Profiles In Healthy Young Males
10. Wirnitzer et al. Dietary Intake Of Vegan And Non-Vegan Endurance Runners—Results
From The NURMI Study (Step 2)
11. Cheng et al. Healthy Eating Index Diet Quality In Randomized Weight Loss Trials: A
Systematic Review
12. Sun et al. Meal Skipping And Shorter Meal Intervals Are Associated With Increased Risk
Of All-Cause And Cardiovascular Disease Mortality Among U.S. Adults
13. Wilhelmsen et al. Acute Effects Of Prior Dietary Fat Ingestion On Postprandial Metabolic
Responses To Protein And Carbohydrate Co-Ingestion In Overweight And Obese Men: A
Randomised Crossover Trial
14. Moesgaard et al. Myonuclear Addition Is Associated With Sex-Specific Fiber Hypertrophy
And Occurs In Relation To Fiber Perimeter Not Cross-Sectional Area
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15. Rappelt et al. Similar Strength Gains At Lower Perceived Efforts Via Cluster Set Versus
Traditional Home-Based Online Training: A Six Weeks Randomized Controlled Trial
16. Gorzi et al. Deceptive Intensities: An Exploratory Strategy For Overcoming Early Central
Fatigue In Resistance Training
17. Lim et al. An Evidence-Based Narrative Review Of Mechanisms Of Resistance Exercise–
Induced Human Skeletal Muscle Hypertrophy
18. Davies et al. Effect Of Set-Structure On Upper-Body Muscular Hypertrophy And Performance
In Recreationally-Trained Male And Female
19. Ruple et al. Comparisons Between Skeletal Muscle Imaging Techniques And Histology In
Tracking Midthigh Hypertrophic Adaptations Following 10 Wk Of Resistance Training
20. Handford et al. The Need For Eccentric Speed: A Narrative Review Of The Effects Of
Accelerated Eccentric Actions During Resistance-Based Training
21. Bastos et al. Set To Fail: Affective Dynamics In A Resistance Training Program Designed To
Reach Muscle Concentric Failure
22. Ben-Zeev et al. The Effect Of Exercise On Neurogenesis In The Brain
23. Macaulay et al. Effects Of A 12-Week Periodized Resistance Training Program On Resting
Brain Activity And Cerebrovascular Function: A Nonrandomized Pilot Trial
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reading MASS.
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subscribers on November 1, 2022.
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Graphics and layout by Kat Whitfield
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