V O L U ME 5 , ISS U E 10
OC T O BE R 2 0 2 1
MASS
M ONTHLY A PPL ICATIO N S IN
STRE N G TH SPO R T
E R I C H E LMS | G R E G N UCK O LS | MIC HAEL ZO URDO S | ERIC T REXL E R
The Reviewers
Eric Helms
Eric Helms is a coach, athlete, author, and educator. He is a coach for drug-free strength and physique
competitors at all levels as a part of team 3D Muscle Journey where he is also the Chief Science
Officer. Eric regularly publishes peer-reviewed articles in exercise science and nutrition journals on
physique and strength sport, in addition to contributing to the 3DMJ blog. He’s taught undergraduateand graduate-level nutrition and exercise science and speaks internationally at academic and
commercial conferences. He has a B.S. in fitness and wellness, an M.S. in exercise science, a second
Master’s in sports nutrition, a Ph.D. in strength and conditioning, and is a research fellow for the Sports
Performance Research Institute New Zealand at Auckland University of Technology. Eric earned pro
status as a natural bodybuilder with the PNBA in 2011 and competes in numerous strength sports.
Greg Nuckols
Greg Nuckols has over a decade of experience under the bar and a B.S. in exercise and sports
science. Greg earned his M.A. in exercise and sport science from the University of North Carolina
at Chapel Hill. He’s held three all-time world records in powerlifting in the 220lb and 242lb classes.
He’s trained hundreds of athletes and regular folks, both online and in-person. He’s written for many
of the major magazines and websites in the fitness industry, including Men’s Health, Men’s Fitness,
Muscle & Fitness, Bodybuilding.com, T-Nation, and Schwarzenegger.com. Furthermore, he’s had the
opportunity to work with and learn from numerous record holders, champion athletes, and collegiate
and professional strength and conditioning coaches through his previous job as Chief Content
Director for Juggernaut Training Systems and current full-time work on StrongerByScience.com.
Michael C. Zourdos
Michael (Mike) C. Zourdos, Ph.D., CSCS, has specializations in strength and conditioning and skeletal
muscle physiology. He earned his Ph.D. in exercise physiology from The Florida State University (FSU)
in 2012 under the guidance of Dr. Jeong-Su Kim. Prior to attending FSU, Mike received his B.S. in
exercise science from Marietta College and M.S. in applied health physiology from Salisbury University.
Mike served as the head powerlifting coach of FSU’s 2011 and 2012 state championship teams. He
also competes as a powerlifter in the USAPL, and among his best competition lifts is a 230kg (507lbs)
raw squat at a body weight of 76kg. Mike owns the company Training Revolution, LLC., where he has
coached more than 100 lifters, including a USAPL open division national champion.
Eric Trexler
Eric Trexler is a pro natural bodybuilder and a sports nutrition researcher. Eric has a PhD in Human
Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research
papers on various exercise and nutrition strategies for getting bigger, stronger, and leaner. In
addition, Eric has several years of University-level teaching experience, and has been involved in
coaching since 2009. Eric is the Director of Education at Stronger By Science.
Table of Contents
6
BY GR EG NUCKOL S
Weighted Inter-Set Stretching: Time To Temper the Hype?
We previously reviewed a study suggesting that stretching between sets may
improve hypertrophy, but a recent study failed to replicate those findings. So, where
does that leave us?
16
BY MI CHAEL C. ZOUR DOS
How Much Ya Bench? Honestly, I Don’t Know.
If you had your training partners load the bar for you, you could lift without knowing
the load. Naturally, this practice would affect your attentional focus. But, would it be
positive or negative? A new study finds out if the approach of load-blinding improves
bench press 1RM.
27
BY ER I C HEL MS
The Energy Expenditure Exposé
Discussions on age and sex as they relate to weight loss often feature statements
about “my metabolism.” But, what we think we know about energy expenditure is
not always grounded in reality. In this article I review the latest and largest study to
date on human energy expenditure.
40
BY ER I C T R EXL ER
Diet Tracking and Disordered Eating: Which Comes First?
A common concern is that quantitatively tracking dietary intake may give rise to
disordered eating. A new randomized controlled trial casts doubt on this idea,
fueling optimism for people who want to more actively manage their diet without
unintended consequences.
54
BY GR EG NUCKOL S
Are Knee Flexion or Hip Extension Exercises Better for
Hamstrings Growth?
We’ve previously discussed the acute effects of knee flexion-based versus hip
extension-based exercises on hamstrings activation, but are those proxy measures
actually predictive of longitudinal outcomes? A recent study on elite rugby players
provides some insight.
65
BY MI CHAEL C. ZOUR DOS
Origin and Modern-Day Implementation of Autoregulatory
Progressive Resistance Exercise
Autoregulatory Progressive Resistance Exercise, or APRE, had a resurgence about
a decade ago. A new study shows that using APRE as a load progression strategy
leads to greater strength gains than a fixed progression. This article discusses the
origin of APRE and provides a nuanced look at its practical implementation.
82
BY ER I C T R EXL ER
Does Hibiscus Tea Increase Satiety Or Energy Expenditure
(And Would It Actually Matter)?
Given the well-known challenges of fat loss, convenient and affordable interventions
that may reduce hunger and increase energy expenditure are easy to embrace. This
study sought to determine if hibiscus tea can meaningfully alter these outcomes.
98
BY GR EG NUCKOL S
Research Briefs
In the Research Briefs section, Greg Nuckols shares a few quick summaries of
recent studies. Briefs are short and sweet, skimmable, and focused on the need-toknow information from each study.
115
BY MI CHAEL C. ZOUR DOS
VIDEO: Foam Rolling Part 2
Similar to pre-training foam rolling, post-training foam rolling is widely used. But is
it effective? Part 2 of our series reviews the data on post-training foam rolling to
accelerate recovery of muscle soreness and strength performance.
117
BY ER I C HEL MS
VIDEO: Nutrition for Strength vs Physique Athletes Part 1
While there is a lot of overlap between the nutritional guidance for strength athletes
and physique athletes, there are also many nuanced differences. In this video series
we explore what those differences are and where the recommendations to optimize
strength and bodybuilding performance should differ. In part 1 we discuss broad
similarities, the source and magnitude of energetic differences, and phasic and
psychological differences related to nutrition.
Letter From the Reviewers
W
elcome to the October 2021 issue of MASS!
The video department once again delivers with some important content. Dr.
Helms kicks off a series discussing the similarities and differences in nutrition
recommendations for physique athletes versus powerlifters. Part 1 discusses the broad
similarities and differences in the nutrition needs of strength and physique athletes including
phasic differences, energetic differences, protein differences, and psychosocial factors . Mike
finishes up his two-part series on foam rolling, this time discussing the evidence for posttraining foam rolling to accelerate both strength and muscle soreness recovery.
The deservedly renowned training department checks in with another classic offering. Greg
reviews a cool longitudinal study comparing Nordic curls versus stiff-legged deadlifts for
hamstring architecture for one of his articles. In another article, Greg reviews a new study on
inter-set stretching and provides an overview of the totality of literature on the topic. Mike
checks in with an article on autoregulatory progressive resistance exercise and another one
on if blinding men and women to the load on the barbell affects maximal strength. Mike also
touches on the origins and history of autoregulatory progressive resistance exercise, and we
think you’ll be surprised about how the concept started.
The nutrition department is still around. First, Dr. Helms reviews an extensive secondary
data analysis of the Doubly-Labeled Water Database, which has crucial insights into the
relationship between energy expenditure and fat-free mass at different lifespan stages. Next,
Dr. Trexler reviews a study that examined if tracking dietary intake increased eating disorder
risk. Further, Dr. Trexler also provided an essential distinction between disordered eating and
eating disorders in his article. Finally, Eric T. rounds out his content by reviewing a study
investigating if hibiscus tea reduces hunger and increases energy expenditure.
Greg’s Research Briefs cover recovering from training as you age, whether people prefer
autoregulation or fixed training, the relationship between step-count and all-cause mortality,
and pec and triceps EMG at different loads during the bench press.
As always, be sure to check out the audio roundtables and join us in the Facebook group.
Lastly, the unheralded CEU department has continuing education for NSCA, ACSM, NASM,
and ACE.
We hope you have a great month and thank you for being a part of MASS.
Thanks,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
5
Study Reviewed: Loaded Inter-set Stretching for Muscular Adaptations in Trained Males:
Is the Hype Real? Wadhi et al. (2021)
Weighted Inter-Set Stretching:
Time To Temper the Hype?
BY GREG NUCKOLS
We previously reviewed a study suggesting that stretching
between sets may improve hypertrophy, but a recent study failed
to replicate those findings. So, where does that leave us?
6
KEY POINTS
1. Over eight weeks, two groups of trained males completed the same training
program, consisting of bench press and incline press. The only difference between
groups was that one group simply rested between sets, and one group stretched
their pecs for 30 seconds after each set using a cable machine.
2. Strength gains and pec hypertrophy were similar between groups. Neither group
increased strength endurance.
3. At this point, the jury is still out on inter-set stretching.
I
nter-set stretching (stretching between
sets) and loaded stretching are sometimes
promoted by bodybuilders and bodybuilding coaches to increase muscle growth. Perhaps most famously, Dante Trudel’s DC-style
training heavily featured loaded stretching.
However, the research examining the effects
of loaded stretching and inter-set stretching is
pretty sparse. For example, one study found
that loaded stretching of the calves (without
any resistance training) may cause some calf
growth in untrained individuals (2), one study
found that inter-set stretching might increase
muscle growth in untrained lifters (3), and one
conference abstract (that was never published
as a full paper) also suggested inter-set stretching could improve calf growth (4). However,
three studies doesn’t constitute a huge body of
literature. Furthermore, only two of the studies
are actually published in journals (2, 3), both
of the published studies use untrained subjects,
and only one of them included resistance training (3). So, overall, you could argue that some
degree of optimism about inter-set stretching
and weighted stretching is warranted, but it
would be hard to argue that inter-set stretching
and weighted stretching have a strong base of
evidentiary support.
The presently reviewed study by Wadhi and
colleagues (1) should serve to temper some of
that optimism. Two groups of trained lifters
completed an eight-week training program,
consisting of bench press and incline press,
performed twice per week. One group performed 30 seconds of weighted pec stretching between sets (using a cable machine), and
one group did not. Gains in pec thickness and
bench press 1RM did not significantly differ
between groups, and neither group experienced any meaningful change in bench press
strength endurance. Furthermore, measures
of perceived exertion and recovery were
similar between groups. Overall, this study
suggests that some of the optimism about
weighted stretching and inter-set stretching
may not be warranted.
Purpose and Hypotheses
Purpose
The primary purpose of the study was to
investigate the effects of loaded inter-set
stretching on hypertrophy, strength, and
strength endurance outcomes. The secondary purpose was to investigate the effects of
7
loaded inter-set stretching on perceived exertion and recovery.
Hypotheses
The researchers hypothesized that inter-set
stretching would increase muscle growth, but
also increase perceived exertion and decrease
perceived recovery. They also hypothesized
that loaded inter-set stretching would not affect strength or strength endurance outcomes.
Subjects and Methods
Subjects
36 subjects initially enrolled in the study,
and 26 subjects completed the study. Four
subjects per group withdrew due to personal reasons, or injuries unrelated to training.
Additionally, two subjects in the inter-set
stretching group withdrew due to pain or injuries related to the study.
To be included in the study, subjects needed
at least three years of bench press experience,
and they needed to have a 1RM bench press of
at least 120% of their body mass. More details
about the subjects can be seen in Table 1.
Experimental Design
Subjects were randomized into two groups,
counterbalanced based on baseline pec thickness. Both groups completed an eight-week
training program, consisting of bench press
and incline press. One group simply rested between sets, and one group performed
a 30-second pec stretch immediately after
completing each set, which was factored into
their total rest time of two minutes between
sets and three minutes between exercises.
To illustrate, the non-stretching group rested
the entire two second between sets, and three
minutes between exercises. The inter-set
stretching group stretched for 30 seconds and
then rested for 90 seconds (two minutes in
total) between sets, and stretched for 30 seconds and then rested for 150 seconds (three
minutes in total) between exercises.
The eight-week training intervention consisted of bench press followed by incline press,
performed twice per week. The number of sets
per exercise increased from 3 to 5 over the
course of the study, and each week of training
consisted of one heavier training day (sets of
4-6 reps) and one lighter training day (sets of
8
8-10 reps). The final set of incline press was
taken to failure in each session, while the rest
of the sets were terminated when subjects believed they had two reps in reserve. Weights
were adjusted within each training session
and between training sessions based on performance – if subjects were unable to complete the minimum number of reps for a given
set (4 reps on heavy days, and 8 reps on lighter days), loads would be decreased for subsequent sets; similarly, if subjects were able to
complete the maximum number of reps for a
given set (6 reps on heavy days, and 10 reps
on lighter days) “with ease,” loads were increased for subsequent sets. The loaded inter-set stretches were performed using a cable machine, and the load used was ~15% of
the subjects’ working weight, capped at 15kg
(Figure 1). Table 2 gives a quick overview of
the training protocol, and Figure 1 illustrates
the type of loaded stretch performed by the
inter-set stretching group
Figure 1
Demonstration of loaded stretch
9
Before the start of the training intervention,
and 48-72 hours after the final training session, the researchers assessed 1RM bench
press strength, bench press strength endurance, and pec muscle thickness for each
subject. Pec thickness was assessed via ultrasound at both a lateral site and the middle
of the muscle belly. Strength endurance was
assessed via a reps to failure test with 70% of
the subjects’ 1RM bench press at the time
of testing. In other words, if a subject got
stronger over the course of the study, they’d
perform their post-training reps to failure test
with a heavier load than their pre-training reps
to failure test. Furthermore, before each training session, perceived recovery was assessed
via the Perceived Recovery Scale (PRS; 5),
and perceived exertion was assessed immediately after each training session using the
effort-based CR-10 RPE scale (6).
Findings
Both groups got stronger, both groups experienced hypertrophy, neither group experienced
a significant change in bench press strength
endurance, and perceived recovery, perceived
exertion, and total volume load completed
were similar in both groups. In short, loaded
inter-set stretching didn’t significantly affect
any outcome (Figures 2 and 3).
Interpretation
The present study (1) serves as an interesting follow-up to a 2019 study by Evangelista
and colleagues (3). The design of the prior
study was very similar to that of the present
study. The three major differences with the
prior study were: 1) the subjects were untrained in the prior study, 2) more exercises were performed and more muscle groups
were assessed, and 3) the subjects only performed four sets for most muscle groups (7).
In the prior study, inter-set stretching resulted in significantly greater increases in vastus lateralis muscle thickness, but not biceps,
triceps, or rectus femoris thickness. However, non-significant differences also leaned
in favor of the group performing inter-set
stretching for biceps, triceps, and rectus femoris hypertrophy (all p < 0.2), and the sum of
10
So, let’s discuss potential reasons for the different results in these two studies.
to provide some degree of hypertrophic stimulus in untrained lifters, but that the amount
of tension generated by weighted stretching
simply isn’t sufficient to represent a hypertrophic stimulus for trained lifters.
Starting with training status, the subjects
were untrained in the prior study, and reasonably well-trained in the present study (1).
That may be significant, because just weighted stretches (without any resistance training),
if held for a long enough duration, may be
sufficient to cause hypertrophy in untrained
subjects (2). Stretching puts a muscle under
tension, albeit far less tension than a muscle
would experience when performing resistance training. It’s possible that the tension
generated by weighted stretching is sufficient
The muscle groups assessed may be relevant
as well. The prior study didn’t assess pec
growth, but it found that inter-set stretching
only resulted in significantly greater muscle
growth than lifting alone in one out of four
muscles assessed. It’s possible that some
muscles are more amenable to the effects of
inter-set stretching than others – maybe it increases vastus lateralis growth, but doesn’t
do much for the pecs. Personally, I think this
potential explanation is fairly tenuous. After
all, “statistical significance” is a somewhat
all muscle thicknesses increased to a significantly greater extent in the group performing
inter-set stretching (p<0.01).
11
arbitrary construct, and results for all four
muscle groups did lean in favor of the inter-set stretching group in the prior study (the
results observed in all four muscle groups
didn’t markedly differ). Furthermore, I can’t
think of a good a priori reason to assume that
stretching would present a greater stimulus to
the vastus lateralis than the pecs.
Finally, it’s possible that the results of these
two studies differed due to the per-muscle
training volumes employed in the two studies. In the prior study, subjects performed
four sets per muscle group, per training session; in the present study, subjects performed
6-10 sets per muscle group, per training session. It’s possible that the per-workout volume for the pecs in the present study was
high enough that adding a small additional
stimulus, in the form of inter-set stretching,
wasn’t enough to boost hypertrophy, whereas
the additional stimulus was able to improve
results when the resistance training stimulus
was smaller. Incidentally, the bodybuilding
tradition that places the greatest emphasis on
weighted stretching – DC-style training – is a
very low-volume training approach.
Of these three potential explanations, I think
the first and the third – training status and
overall training volume – are the most plausible. In fact, the perceived exertion and recovery results of the present study could bolster
either (or both) of these possibilities. Perhaps perceived effort and perceived recovery didn’t differ between groups because the
inter-set stretching represented such a small
additional stimulus to the trained lifters in the
present study, or perhaps perceived effort and
recovery didn’t differ between groups be-
cause the training protocol itself was a large
enough stimulus that the additional stimulus
provided by inter-set stretching simply didn’t
register.
However, there’s also a fourth option: random error resulting from sample selection or
group allocation. When we’re dealing with
a small body of literature (two studies on
the same topic is the smallest collection of
literature you could conceivably refer to as
a “body”), it’s entirely possible that studies
will have different results due to pure chance.
We can’t start estimating the “true” effect of
a particular intervention with a high degree
of precision until we have several studies to
draw upon (and potentially meta-analyze).
It’s entirely possible that inter-set stretching didn’t actually improve hypertrophy outcomes in the prior study; a large number of
hypertrophic high responders may have simply landed in the inter-set stretching group by
pure chance. It’s also possible that inter-set
stretching did improve results in the present
study (with the benefits masked by a larger
number of hypertrophic low responder landing in the inter-set stretching group). Of note,
I think the “random chance” explanation
seems more plausible for the prior study than
the present study, since the subjects in the
present study were counterbalanced based on
baseline pec thickness (which would suggest
that subjects in the two groups had, on average, previously responded to training similarly well), but we really couldn’t venture
a guess with much confidence until there’s
more literature on the topic. The main point is
that when you have a small body of literature
with small-sample studies and inconsistent
12
findings, you just need to wait until there’s
more research on the topic before reaching
strong conclusions.
Some readers may be alarmed by the fact that
there were two injuries related to the study
in the inter-set stretching group, versus zero
in the lifting-only group. Personally, I don’t
think alarm is warranted yet. Just as we need
to wait before drawing firm conclusions about
whether inter-set stretching improves hypertrophy outcomes, we also need to wait before
drawing firm conclusions about the injury
risk (or lack thereof) of inter-set stretching.
In a study we previously reviewed in MASS,
about 87% of powerlifters reported having
sustained an injury in the prior year (8), so
two lifting-related injuries out of a sample of
36 lifters isn’t anything extraordinary. Maybe
inter-set stretching is risky, or maybe two injuries just happened to occur by pure chance.
If the study was 10 times as large, and you saw
20 training-associated injuries in the inter-set
stretching group and still zero injuries in the
lifting-only group, that would be a cause for
concern, but I wouldn’t read much into a pair
of injuries in a relatively small study. They
could be a harbinger of ill, or they could be a
couple of completely random flukes – at this
point, there’s no way to know.
So for now, what can we do with this information? Well, if you favor liberal interpretations, you could potentially argue that the
balance of evidence still leans ever so slightly in favor of performing inter-set stretching
to improve hypertrophy outcomes. If you’re
a bit more conservative, you could argue
that the conflicting results of the two current
studies warrant a completely agnostic “wait
and see” approach. Either way, there’s no
evidence currently suggesting that inter-set
stretching reduces muscle growth. For whatever it’s worth, I personally tend to do a bit of
inter-set stretching for certain muscle groups,
just because it seems to improve my pumps,
and I enjoy getting big pumps solely for the
simple joy of getting big pumps. However,
before I’d recommend that you should do
inter-set stretching, I’d want to see a couple
more positive studies first.
Next Steps
There are a few directions I’d like to see future studies go. First, I’d love to see a study
similar to Evangelista’s (3), performed in
trained subjects: use a lower-volume training protocol, and simply assess more muscles in total. If such a study came back with
a slate of null findings, that would suggest
that training status really may be the determining factor – inter-set stretching improves
outcomes for untrained lifters, but perhaps
not trained lifters. If the study found that inter-set stretching improved some outcomes,
then follow-up research could investigate
whether the effects were dependent on training volume, or whether the effects were muscle group-specific. Alternately, I’d really like
to see research on DC-style “extreme stretching.” Instead of one group stretching for 30
seconds between sets with relatively little
resistance, the DC “extreme stretching” protocol involves heavier loads and longer-duration stretches, performed just once (after your
last set of a particular exercise). That’s the
weighted stretching protocol that some pro
bodybuilders swear by; since you’re ratch-
13
APPLICATION AND TAKEAWAYS
At this point, the weighted stretching and inter-set stretching literature contains
some optimistic results for untrained lifters, and now a single disappointing result
for trained lifters. Given the paucity of the literature, I’d still consider this to be a
wide open question, and I’m hesitant to make any firm recommendations. If you’re
plateaued (or just bored) and you want “one weird trick” to get the gains rolling again
(or just provide some novelty to your training), inter-set stretching and/or weighted
stretching probably won’t be the magic bullet you’re looking for, but I also wouldn’t
discourage anyone from experimenting with it.
eting up the stimulus (both the tension and
duration of the stretch), it may have effects
comparable to isometrics performed at long
muscle lengths, which seem to be pretty effective for promoting muscle growth (9).
14
References
1. Wadhi T, Barakat C, Evangelista AL, Pearson JR, Anand AS, Morrison TEA, O’Sullivan
J, Walters J, Souza EO. Loaded Inter-set Stretching for Muscular Adaptations in Trained
Males: Is the Hype Real? Int J Sports Med. 2021 Aug 10. doi: 10.1055/a-1529-6281.
Epub ahead of print. PMID: 34375990.
2. Simpson CL, Kim BDH, Bourcet MR, Jones GR, Jakobi JM. Stretch training induces
unequal adaptation in muscle fascicles and thickness in medial and lateral gastrocnemii.
Scand J Med Sci Sports. 2017 Dec;27(12):1597-1604. doi: 10.1111/sms.12822. Epub
2017 Jan 30. PMID: 28138986.
3. Evangelista AL, De Souza EO, Moreira DCB, Alonso AC, Teixeira CVS, Wadhi
T, Rauch J, Bocalini DS, Pereira PEA, Greve JMD. inter-set Stretching vs.
Traditional Strength Training: Effects on Muscle Strength and Size in Untrained
Individuals. J Strength Cond Res. 2019 Jul;33 Suppl 1:S159-S166. doi: 10.1519/
JSC.0000000000003036. PMID: 30688865.
4. Silva J, Lowery R, Antonio J et al. Weighted Post-Set Stretching Increases Skeletal
Muscle Hypertrophy. In: NSCA 2014 Annual Meeting. Abstract 22.
5. Laurent CM, Green JM, Bishop PA, Sjökvist J, Schumacker RE, Richardson MT,
Curtner-Smith M. A practical approach to monitoring recovery: development of a
perceived recovery status scale. J Strength Cond Res. 2011 Mar;25(3):620-8. doi:
10.1519/JSC.0b013e3181c69ec6. PMID: 20581704.
6. Shinya Yamauchi SM. Rating of Perceived Exertion for Quantification of the Intensity of
Resistance Exercise. Int J Phys Med Rehabil 2013; 1: 9. doi:10.4172/2329-9096.1000172
7. Since they performed both bench press and machine dips, and both rows and curls, you
could argue that they performed 8 sets of triceps and biceps exercises. I could potentially
buy that argument for the triceps, but not the biceps, since rows aren’t very effective for
biceps growth.
8. Strömbäck E, Aasa U, Gilenstam K, Berglund L. Prevalence and Consequences of
Injuries in Powerlifting: A Cross-sectional Study. Orthop J Sports Med. 2018 May
14;6(5):2325967118771016. doi: 10.1177/2325967118771016. PMID: 29785405;
PMCID: PMC5954586.
9. Oranchuk DJ, Storey AG, Nelson AR, Cronin JB. Isometric training and long-term
adaptations: Effects of muscle length, intensity, and intent: A systematic review. Scand
J Med Sci Sports. 2019 Apr;29(4):484-503. doi: 10.1111/sms.13375. Epub 2019 Jan 13.
PMID: 30580468.
█
15
Study Reviewed Effect of Bench Press Load Knowledge on One-Repetition Maximum
Strength. Snarr et al. (2021)
How Much Ya Bench? Honestly, I
Don’t Know.
BY MICHAEL C. ZOURDOS
If you had your training partners load the bar for you, you could
lift without knowing the load. Naturally, this practice would affect
your attentional focus. But, would it be positive or negative? A
new study finds out if the approach of load-blinding improves
bench press 1RM.
16
KEY POINTS
1. 10 women and 10 men performed a 1RM bench press under two conditions
in a randomized order. They knew the load on the barbell in one condition and
were blinded to the load in the other condition. The lifters also estimated their
1RM before each condition.
2. Researchers reported no statistically significant differences between conditions
for 1RM performance. However, the between-condition p-value for men was
close to significance (p = 0.07), as men lifted 2.3kg less in the load-blinded
condition. Women lifted about the same on each day. Lifters also tended to
underestimate their 1RM.
3. The presently reviewed study shows that load-blinding doesn’t have a huge
impact on 1RM strength, and any impact may affect men more than women.
The total body of literature doesn’t provide a clear reason to use load blinding;
however, this study is the first to examine load blinding and maximal strength
performance.
I
f someone misses a one-repetition maximum (1RM) attempt, we tend to think they
just aren’t strong enough for that weight
yet. That could be true, but various attentional
focus factors are affected by using either an
internal or external focus. For example, auditory and visual feedback can affect focus and
alter performance. Examples of such feedback
include verbal encouragement (2), velocity
feedback (3 – MASS Review , 4 – MASS Review, 5 – MASS Review), and cueing during a
biceps curl (6 – MASS Review). Further, Greg
covered visual stimuli affecting performance
when he reviewed a study that showed that the
visual presence of spotters improved bench
press reps to failure (7). The visual stimuli
can also be affected by blinding lifters to the
load. Data have shown that bench press reps
to failure at 70% of 1RM and perception of effort were unaffected by load-blinding (8), but
we don’t currently know how load-blinding
affects 1RM performance. The presently re-
viewed study from Snarr et al (1) was a crossover design in which 10 trained women and
10 trained men performed two bench press
1RMs, 48-72 hours apart. They knew the load
on the bar in one condition and were blinded to the load in the other condition. In both
conditions, subjects estimated 1RM before
the test. Also, in the load-blinded condition,
subjects estimated the load lifted after each
attempt. Researchers assessed the effort-based
session rating of perceived exertion (sRPE)
after all attempts in each condition. Findings
showed that load blinding did not statistically
affect 1RM performance or sRPE. However,
men did bench an average of 2.3kg less in the
load-blinded condition, and lifters tended to
underestimate their 1RM when predicting it
before the testing session. This study suggests
1) blinding lifters to their 1RM load did not
significantly affect weight lifted, and 2) lifters
tended to underestimate their 1RM. This article will:
17
1. Discuss why load-blinding may affect
performance.
of resistance training experience participated.
The available subject details are in Table 1.
2. Evaluate the potential use of load-blinding
under powerlifting meet or competitive
conditions.
Study Procedures
3. Consider why lifters may underestimate
their 1RM in a laboratory setting.
4. Discuss a path for future research on attentional focus to benefit lifting performance.
Purpose and Hypotheses
Purpose
The purpose of the reviewed study was to
examine if load-blinding affected 1RM performance and estimation capability in trained
men and women.
Hypotheses The researchers did not provide a hypothesis
and stated, “it is unclear whether differences
[between load knowledge and load-blinding]
would exist at a maximal load.”
Subjects and Methods
Subjects
10 women and 10 men with at least six months
The presently reviewed study was a crossover
design with two conditions (known-load and
load-blinded), which were completed in a randomized order and separated by 48-72 hours. In
both conditions, the men and women first estimated their 1RM for the day and then performed
a bench press 1RM. In the load-blinded conditions, cardboard was attached to the barbell to
cover the weights. Additionally, subjects in the
load-blinded condition estimated the weight
used for each 1RM attempt immediately after the
attempt. In both conditions, subjects rested for
2-4 minutes between 1RM attempts and gave an
sRPE value on the 1-10 effort-based Borg scale
after completing all attempts. Researchers compared the 1RM and sRPE between both conditions across the entire cohort and between sexes.
Further, researchers examined the accuracy of
the subjects’ 1RM estimations.
Findings
Before getting into specifics, the short version is that load-blinding did not significantly
affect 1RM or sRPE, and lifters tended to underestimate their 1RMs.
18
1RM Performance and sRPE
Actual 1RM performance did not significantly differ between conditions across the entire
subject cohort (p = 0.094), the women only (p
= 0.64), or the men only (p = 0.07). Notably,
the p-value of 0.07 for men was close to significance, and men recorded 1RMs that were
an average of 2.3kg lower in the load-blinded
condition. On the other hand, women lifted
only 0.4kg less in the load-blinded condition. For the entire cohort, the actual 1RMs
differed by 1.3kg between conditions (intraclass correlation coefficient = 0.99). Further,
there was no significant difference in sRPE
19
between conditions in women (p = 0.66) or
men (p = 0.39). Figure 2AB shows individual
subject 1RMs in each condition and Table 2
shows mean values.
Estimations
The only significant difference between
pre-training estimations and actual 1RM was
that the entire cohort significantly (p = 0.034)
underestimated their 1RM by an average of
5kg in the known-load condition. Of the 40
total estimations (20 in each condition), 21
were underestimations, 12 hit the nail on the
head, and 7 were overestimations. However,
post 1RM estimations in the blinded condition were within 0.3kg for women and 0.2kg
for men of the actual load lifted. Table 2
shows all actual 1RMs and estimates.
Interpretation
Lifters use strategies such as supplementation
and appropriate warmups to enhance acute
performance. If a lifter doesn’t perform as desired, the supplement stack or warmup strategy is often questioned. However, adjusting
your attentional focus from internal to external
or vice versa may sometimes be the culprit for
suboptimal performance. In lifting, an external
focus means that the lifter is focused on completing the movement, while an internal focus
is concentrating on the muscle contraction or
components of the movement technique. Further, external stimuli, both visual and auditory, can affect a lifter’s attentional focus. The
reviewed study from Snarr et al (1) aimed to
affect the lifter’s external focus by blinding
them to the load on the barbell. Overall, this
study found that maximal strength performance was not affected by load-blinding. Now
that some basic definitions are out of the way,
let’s analyze the body of literature in the area
of load-blinding, then discuss potential practical implications for load-blinding and shifting
attentional focus.
While the reviewed study is the first to examine load-blinding on maximal strength
performance, load-blinding has been investigated on bench press reps to failure (8) and
force production (9). Table 3 summarizes
those studies along with the presently reviewed study. One note about Table 3 is that
two studies from Halperin et al (10, 11) did
not tell subjects exactly how many reps they
were going to perform before a set (i.e., rep
blinding) instead of blinding them to the load.
20
Table 3 shows that the two studies that examined the effects of load blinding on performance found no significant effect on bench
press 1RM (1) or rep performance (8). The
two studies from Halperin took a different
approach and used “deception” to blind lifters
to the number of reps performed in a set. The
greater force output in both women (10) and
men (11) in the Halperin studies in the first
6 reps of a 12-rep set, despite the same load
in all conditions, suggests that subjects paced
themselves differently when they didn’t
know how many reps would be performed. In
other words, when subjects knew they were
performing 12 reps, they didn’t exert max-
imal intended effort on the early reps, even
though they were instructed to, as evidenced
by the lower force production compared to
the deception condition. The Hernandez-Davo et al study (9) used load blinding instead
of rep blinding but found similar results to
the Halperin research. Specifically, Hernandez-Davo observed greater force production
on the bench press throw when subjects were
blinded to the load at 50 and 70% of 1RM
during the first half of a 12-rep set. However,
there wasn’t a difference in force production
during reps 7-12, demonstrating that subjects
paced themselves when they had full knowledge of both the load and reps.
21
I think these findings from Halperin and Hernandez-Davo have a broader application in
research.
Although velocity declines during a set, it
doesn’t decline linearly throughout the entire set when moderate to high reps are performed. Hickmott (12) observed that the velocity decline was steeper at the end than at
the beginning of a 15-rep set. I do think velocity would decline linearly during a set to
failure if subjects truly used maximal intended velocity on each rep. However, this also
begs the question: Is it good for the lifter to
use maximal intended velocity on each rep?
The answer depends on the circumstance. If
performing a double at 90% of 1RM or something else heavy, then yes, maximal intent is
essential. However, if the goal is to complete
as many reps as possible on a set to failure at
a moderate intensity (i.e., 65-85% of 1RM), I
would advise “pacing” as Halperin described.
Typically, additional visual stimuli boost resistance training performance. We reviewed
a study previously that showed when lifters
could see spotters; they improved bench press
rep performance (13 - MASS Review). Also,
we covered a study that found when lifters
could view velocity, their average velocity
was faster than when not viewing velocity
during a non-failure set of squats (4 - MASS
Review). The present study’s authors framed
load-blinding as a visual stimulus, and it is,
but only in part. There’s also the psychological aspect of knowing or not knowing the
load. If you know the load you’ll have a preconceived notion about whether you can lift
it or how many reps you’ll be able to do, even
if cardboard is covering the plates. This psy-
chological aspect could be positive or negative, and possibly individually dependent. As
an anecdote, I was coaching the Florida State
Powerlifting team at the USAPL collegiate
state championships in 2011 and had a lifter
who was psyched out by the thought of 175kg
(385lbs). I knew it would be a tough squat, but
I thought he could do it. I went to the scorer’s
table and put in 175kg, but since he trained in
pound plates, I knew he wouldn’t know the
conversion or what the plates looked like on
the bar, so I told him it was 172.5kg (380lbs).
He smashed it. He may have also smashed it
if he knew the load, but he had missed 175kg
in training a few times. The point being,
load-blinding may have some applicability in
practice, but it has to be in the right circumstance, and probably suited to the individual.
Despite my use of deception with this athlete,
it’s important to note that deception should
be used responsibly and very rarely. In fact,
the instance above is the only time I’ve done
IF THE GOAL IS TO
COMPLETE AS MANY
REPS AS POSSIBLE
ON A SET TO FAILURE
AT A MODERATE
INTENSITY I WOULD
ADVISE “PACING”.
22
this. I felt comfortable in that specific circumstance because I had a very close relationship
with the individual and knew they would respond positively whether the lift was made
or missed. I would never take this approach
with someone I only coached online, someone with different personality traits, or just
in general, someone I didn’t know well. This
meet was also a local meet, and the lifter was
only looking for his own personal records. In
short, something like this should only be done
responsibly and in particular circumstances.
On a day-to-day basis, it’s tough to implement load-blinding, and there isn’t a great
justification to do so. If you’re training by
yourself, you can’t blind yourself to the load.
If you have training partners, then they can
load the bar for you and cover the plates with
trash bags or cardboard when you’re not
looking. Research doesn’t provide a clear
justification to do this. However, that doesn’t
mean it wouldn’t be fun to try on yourself.
If you have a high-volume day, you could
ask your training partners to blind you to the
load and see how your rep performance is. If
your rep performance is considerably higher or lower than usual, you might know if
load-blinding is for you. You could view using RIR stops (i.e., performing reps until you
hit a predetermined RIR) (14) as a method
of rep-blinding. However, since the goal is to
perform as many reps as possible, I think lifters would naturally pace themselves, similar
to the Halperin studies.
The appropriate attentional focus is dependent upon the goal of the training session or
even the goal of a specific exercise. If your
goal is to perform a 1RM or perform the most
THE APPROPRIATE
ATTENTIONAL FOCUS
IS DEPENDENT UPON
THE GOAL OF THE
TRAINING SESSION OR
EVEN THE GOAL OF A
SPECIFIC EXERCISE.
reps possible, you will most likely want an
external focus. An external focus in lifting involves focusing on completing the movement
itself. Further, factors such as preferred music
(15 - MASS Review), verbal encouragement
(2), verbal velocity feedback (16 - MASS Review), or visual velocity feedback (4 - MASS
Review) all promote an external focus. With
any of those external foci, I’d give the caveat that your preference should also factor in.
For example, even though music may help
on the group level, it may not be for you if
you’re not someone who gets overly excited
while training. On the other hand, if you’re
performing a biceps curl with the goal of
hypertrophy, then an internal focus may be
warranted. Greg previously reviewed a study
(6) in which untrained men performed biceps
curls and leg extensions three times per week
for eight weeks, and used either the internal
cue to “squeeze the muscle” or the external
cue to “get the weight up.” Subjects in the in-
23
APPLICATION AND TAKEAWAYS
1. Similar to previous research on bench press rep performance, Snarr et al (1) found
that load-blinding didn't significantly impact 1RM performance.
2. Load-blinding may alter a lifter's attentional focus. In general, the attentional
focus should be external when performing a 1RM or completing as many reps
as possible, and internal if targeting a specific muscle on an isolation exercise for
hypertrophy.
3. I don't see a huge application for load-blinding in practice, other than trying it
out for fun. There may be a time and place to incorporate load blinding into a
powerlifting meet, per the anecdote included in the interpretation, but we should
take that anecdote for what it is: an anecdote. Overall, if your attentional focus fits
your goal, then you're good to go.
ternal cueing group experienced significantly
greater biceps growth than the external cueing group, although internal cueing didn’t enhance hypertrophy of the quads. Nonetheless,
there is sufficient evidence to suggest that the
ideal type of attentional focus to employ is
goal-dependent.
To finish up, I think it’s important to point
out that although there was no statistically
significant difference between conditions,
men did lift 2.3kg less, on average, in the
load-blinded condition, which translated to
a p-value of 0.07. Women only lifted 0.4kg
less in the load-blinded condition with a between-condition p-value of 0.64; thus, if
load-blinding affected anybody, it was men.
Subjects also tended to underestimate their
1RM in the known-load condition. Previous
research (17) found that men and women
who were asked to estimate what they were
75% confident they could squat for a 1RM
underestimated the prediction by 3.5 ± 15.75
kg. While asking someone if they are 75%
sure they can squat is not the same as predict-
ing a 1RM, it’s somewhat similar and shows
a precedent that both men and women may
slightly underestimate their max when squatting in a lab.
Next Steps
I don’t see a ton of research coming down the
pipeline in this area. So, thinking outside the
box a little bit, it would be interesting to examine both 1RM and rep performance when
lifters are told the incorrect load on the bar.
Specifically, would rep performance at 70%
of 1RM improve if you told lifters it was actually 65% of 1RM? Likewise, would 1RM
improve if you loaded the bar to a 5kg personal record for the lifter but told the lifter it
was 5kg less? I’m not sure, but it would be
fun to find out.
24
References
1. Snarr RL, Adams K, Cook J. Effect of Bench Press Load Knowledge on One Repetition
Maximum Strength. The Journal of Strength & Conditioning Research. 2021 Aug
1;35(8):2121-6.
2. Weakley J, Wilson K, Till K, Banyard H, Dyson J, Phibbs P, Read D, Jones B. Show
me, tell me, encourage me: The effect of different forms of feedback on resistance
training performance. The Journal of Strength & Conditioning Research. 2020 Nov
1;34(11):3157-63.
3. Nagata A, Doma K, Yamashita D, Hasegawa H, Mori S. The effect of augmented
feedback type and frequency on velocity-based training-induced adaptation and retention.
The Journal of Strength & Conditioning Research. 2020 Nov 1;34(11):3110-7.
4. Weakley JJ, Wilson KM, Till K, Read DB, Darrall-Jones J, Roe GA, Phibbs PJ, Jones
B. Visual feedback attenuates mean concentric barbell velocity loss and improves
motivation, competitiveness, and perceived workload in male adolescent athletes. The
Journal of Strength & Conditioning Research. 2019 Sep 1;33(9):2420-5.
5. Jiménez-Alonso A, García-Ramos A, Cepero M, Miras-Moreno S, Rojas FJ, PérezCastilla A. Effect of Augmented Feedback on Velocity Performance During StrengthOriented and Power-Oriented Resistance Training Sessions. Journal of strength and
conditioning research. 2020 Jul 7.
6. Schoenfeld BJ, Vigotsky A, Contreras B, Golden S, Alto A, Larson R, Winkelman N,
Paoli A. Differential effects of attentional focus strategies during long-term resistance
training. European journal of sport science. 2018 May 28;18(5):705-12.
7. Sheridan A, Marchant DC, Williams EL, Jones HS, Hewitt PA, Sparks A. Presence of
spotters improves bench press performance: a deception Study. The Journal of Strength
& Conditioning Research. 2019 Jul 1;33(7):1755-61.
8. Beaudoin CM, Cox Z, Dundore T, Thomas T, Kim J, Pillivant D. Effect of Bench Press
Load Knowledge on Repetitions, Rating of Perceived Exertion, and Attentional Focus.
The Journal of Strength & Conditioning Research. 2018 Feb 1;32(2):514-9.
9. Hernández-Davó JL, Sabido R, Moya-Ramón M, Blazevich AJ. Load knowledge reduces
rapid force production and muscle activation during maximal-effort concentric lifts.
European journal of applied physiology. 2015 Dec;115(12):2571-81.
10. Halperin I, Aboodarda SJ, Basset FA, Behm DG. Knowledge of repetitions range
affects force production in trained females. Journal of sports science & medicine. 2014
Dec;13(4):736.
25
11. Halperin I, Aboodarda SJ, Basset FA, Byrne JM, Behm DG. Pacing strategies during
repeated maximal voluntary contractions. European journal of applied physiology. 2014
Jul;114(7):1413-20.
12. Hickmott LM. Relationship Between Velocity and Repetitions in Reserve in the Back
Squat, Bench Press, and Deadlift (Doctoral dissertation, Florida Atlantic University).
13. Sheridan A, Marchant DC, Williams EL, Jones HS, Hewitt PA, Sparks A. Presence of
spotters improves bench press performance: a deception Study. The Journal of Strength
& Conditioning Research. 2019 Jul 1;33(7):1755-61.
14. Helms ER, Cross MR, Brown SR, Storey A, Cronin J, Zourdos MC. Rating of perceived
exertion as a method of volume autoregulation within a periodized program. The Journal
of Strength & Conditioning Research. 2018 Jun 1;32(6):1627-36.
15. Ballmann CG, Favre ML, Phillips MT, Rogers RR, Pederson JA, Williams TD. Effect of
Pre-Exercise Music on Bench Press Power, Velocity, and Repetition Volume. Perceptual
and Motor Skills. 2021 Jun;128(3):1183-96.
16. Jiménez-Alonso A, García-Ramos A, Cepero M, Miras-Moreno S, Rojas FJ, PérezCastilla A. Effect of Augmented Feedback on Velocity Performance During StrengthOriented and Power-Oriented Resistance Training Sessions. Journal of strength and
conditioning research. 2020 Jul 7.
17. Haischer MH, Cooke DM, Carzoli JP, Johnson TK, Shipherd AM, Zoeller RF,
Whitehurst M, Zourdos MC. Impact of Cognitive Measures and Sleep on Acute Squat
Strength Performance and Perceptual Responses Among Well-Trained Men and Women.
The Journal of Strength & Conditioning Research. 2021 Feb 1;35:S16-22.
█
26
Study Reviewed: Daily Energy Expenditure Through the Human Life Course. Pontzer et al.
(2021)
The Energy Expenditure Exposé
BY ERIC HELMS
Discussions on age and sex as they relate to weight loss often
feature statements about “my metabolism.” But, what we think
we know about energy expenditure is not always grounded in
reality. In this article I review the latest and largest study to date
on human energy expenditure.
27
KEY POINTS
1. This study (1) is the largest (n = 6,421) human energy expenditure study to date,
reporting the relationships that age, sex, and fat-free mass share with free-living
total energy expenditure via doubly-labeled water, as well as basal metabolic
rate in 2008 of the subjects.
2. Energy expenditure is proportionally related to fat-free mass, but this
relationship is distinct at different life phases. Energy expenditure relative to
fat-free mass is highest in infants, then declines after the first birthday until ~20
years old, when it becomes stable until age 60. After 60, expenditure relative to
fat-free mass begins decreasing.
3. There aren’t energy expenditure sex differences per se; rather, any absolute
differences between sexes are due to differing levels of mass. Energy
expenditure relative to fat-free mass also does not decrease from ages 20-60;
rather, any absolute decreases are likely due to decreased physical activity and
subsequent drops in fat-free mass.
N
utrition and exercise science studies are fraught with low sample
sizes. However, when you step just
outside of this niche into clinical nutrition,
physiology, and public health research, you
start to find studies with hundreds, and sometimes even thousands, of participants. But
even then, large scale studies don’t necessarily have the most informative methods for
application. For example, data on the basal
metabolic rate of thousands of people exist
(2), but basal metabolic rate is just one component of total energy expenditure, which is
a more useful metric in the real world. Athletes, dieters, coaches, trainers, and dietitians
all want accurate approximations of energy
expenditure so they can estimate caloric intake for themselves, their clients, their athletes, or their patients. The present study (1)
bridges this gap because it is an analysis of
the free-living total daily energy expenditure
of a diverse group of 6,421 individuals from
29 different countries, 64% of whom are female, spanning almost the entire human lifespan from just over a week old to 95 years of
age. Using the open access Doubly-Labeled
Water Database, the authors had access to
total energy expenditure, fat-free mass, fat
mass, sex, and age data for their entire sample, and basal metabolic rate data for 2008 individuals within their sample. Analyzing the
relationships between these variables resulted in findings that, in some cases, contradicted common anecdotes about energy expenditure. For example, I can’t tell you how many
times older friends and family members have
told me “just wait until you’re <insert age 30,
40, or 50> – you won’t be able to maintain
that body eating as much as you do now.”
However, the authors found that energy expenditure relative to fat-free mass remains
constant from age 20 to 60, only decreasing
after this point. Indeed, fat-free mass has a
proportional relationship to energy expendi-
28
ture, meaning despite common belief, there
also aren’t sex differences in energy expenditure per se. Rather, any differences between
men and women are due to men typically having higher fat-free mass at the same weight.
In this review, I discuss other interesting details from this analysis, and how the authors
specifically came to their conclusions.
Purpose and Hypotheses
Purpose
The purpose of this analysis was to investigate the relative effects of physical activity
and age-related changes on total daily energy
expenditure across the lifespan. Additionally,
this study sought to determine if the known
declines in total daily energy expenditure associated with aging are due to concomitant
losses of fat-free mass and declines in physical activity, or if there is also an independent
reduction due to the aging process.
Hypotheses
This was a large-scale analysis of existing
data where the authors explored relationships
between body composition and sex with energy expenditure throughout the lifespan, and
thus, they had no hypotheses.
Subjects and Methods
Subjects
For those interested in the detailed methods
of this publication, they aren’t in the main paper. You’ll need to download the supplementary methods here, which are open access.
Using the Doubly-Labeled Water Database,
the authors analyzed total daily energy expenditure data from 6,421 individuals. This
database also provides basic demographic
characteristics, including age, sex, height,
and weight. The individuals in this analysis
were 64% female and from various geographical locations spread across 29 countries. The
age of the participants was also diverse, with
the youngest being 8 days old and the oldest
95 years old. This analysis also included data
on 136 infants, and 141 pregnant and postpartum women not from the database. Additionally, basal metabolic rate measured via
indirect calorimetry was available for 2,008
of the subjects.
Study Procedures
Briefly, doubly-labeled water is a validated
measure of free-living total daily energy expenditure (3). It requires a sample of participants’ body water (typically urine or saliva)
before they drink water that contains elevated
levels of the uncommon hydrogen and oxygen
isotopes deuterium (2H) and oxygen-18 (18O)
– hence “doubly-labeled” – and then another
body water sample at a time point days after
drinking the doubly-labeled water for comparison. Since oxygen primarily exits the body
through breathing and losses of body water,
while deuterium only exits the body through
losses of body water, the difference between
the two can be used to estimate carbon dioxide
(CO2) production and subsequently, energy expenditure. Metabolic chambers and metabolic
carts can also estimate energy expenditure using carbon dioxide production, but these labbased measures can’t be used in a free-living
environment like doubly-labeled water can,
which is what makes this method so useful.
29
Another interesting aspect of doubly-labeled
water measurements is that the process allows you to calculate total body water. Since
the hydration of fat-free mass is relatively
constant, if you know total body mass (which
the database provided), you can calculate a
two-compartment body composition (total
body mass - fat-free mass = fat mass) (4).
This is how the authors were able to analyze
the relationships between body composition
and energy expenditure.
For 2,008 of the individuals in the database,
basal metabolic rate data from indirect calorimetry was also available. This allowed the
authors to break down total energy expenditure into three components. Specifically, they
calculated physical activity energy expenditure in this sub-sample by subtracting basal
metabolic rate and 10% of total expenditure
(to represent the thermic effect of feeding)
from total energy expenditure. With these
data, the authors used general linear models
and regression analyses to determine the relationships between age, sex, fat-free mass, and
other factors with energy expenditure, the independent effects of each factor on energy
expenditure, and the degree to which different compartments of energy expenditure contribute to total energy expenditure at various
stages of life.
Findings
In Figure 1, you can see how body mass, fatfree mass, fat mass, and body-fat percentage
change over the lifespan in both men and
women. Note that body mass and fat mass
tend to peak in middle age, and then slowly
decline in old age as people generally become
less social and experience a number of physiological changes that collectively influence
30
them to eat less (decreased taste and smell
reducing hedonic food reward, increases in
some satiating hormones, potential gastrointestinal issues, and more [5]) and lose some
weight. Fat-free mass remains constant once
you hit adulthood, and then slowly declines
after middle age into old age. Finally, bodyfat percentage peaks in middle age (when fat
mass peaks), and then tends to remain constant (as lean mass and fat mass both decline
in old age, body-fat percentage stays about
the same). Finally, note that these patterns
are the same for men and women, except that
absolute body mass and fat-free mass are, on
average, higher at the same age in men, while
absolute fat mass and body-fat percentage are
higher in women.
In Figure 2, you can see how total energy expenditure is proportionally related to fat-free
mass, such that the higher your fat-free mass,
the more energy you expend. However, with
a heat map of data points by age, we can see
that juveniles under 20 years old have higher energy expenditures for the same amount
of fat-free mass compared to adults aged 20
to 60, while older adults over 60 have lower
energy expenditures for the same amount of
fat-free mass compared to adults aged 20 to
60. In panel B of Figure 2, we can see this
relationship results in a trend where energy
expenditure is mostly constant in adults until
about 60 years, after which it begins to decline. That pattern is the same in men and
women, just with higher and lower absolute
values on average, respectively (due to higher fat-free mass on average in men).
Figure 3 presents data from a handful of
large-scale accelerometer-derived moderate
and vigorous physical activity studies (6, 7,
8, 9) which show that physical activity steadily declines from middle age to old age. This
may partially explain the observed losses of
fat-free mass and increases in fat mass.
In Figure 4, we can see that both total and bas-
31
al energy expenditure values are nearly identical throughout the lifespan in men and women
when adjusting for fat-free mass and fat mass.
Further, we can see that both basal and total
energy expenditure when adjusting for fat-free
mass and fat mass remain constant in all stages
of pregnancy and in the postpartum period.
Finally, in Figure 5 we can see the authors’
attempts to model the observed relationship of
total, basal, and physical activity energy expenditure relative to fat-free mass throughout the
lifespan. Panel A shows what was observed.
Panel B displays a model that doesn’t match
the observed patterns of energy expenditure,
32
in which the authors modeled physical activity
and tissue metabolism (the energy expenditure
relative to organ tissue mass) to remain constant throughout the lifespan. Panel C displays
a model that does match the observed patterns
of energy expenditure, in which the authors
modeled physical activity to start declining in
middle age and relative tissue metabolism to
decline starting at 60. This indicates that observed reductions in energy expenditure due
to aging are likely due to a combination of
decreased activity, subsequent losses of fatfree mass, and also the independent effect of
decreased relative tissue metabolism (organs
being less metabolically active) with age.
Interpretation
The main findings of this analysis are straightforward, but despite being straightforward, a
number of the findings go against the grain
of common “metabolic wisdom.” Thus, I’ll
focus on these aspects of energy expenditure
in three different sections: the effect of sex
(including pregnancy), the effect of age, and
inter-individual differences.
33
The Effect of Biological Sex and Pregnancy
The first thing that might surprise readers is
that sex has no apparent independent effect
on energy expenditure per se. If you look
at Figure 2, Panel B, you can see that at the
same age, men on average have higher energy expenditures than women. However, if
you look at Figure 1 you can also see that
men have slightly higher total body mass,
slightly lower fat mass, and higher fat-free
mass at the same age than women. To bring
these figures together, if you look at Figure
4 you can see that when adjusting for fat and
fat-free mass, women and men follow nearly identical patterns of basal and total energy
expenditure throughout their lifespan.
This lack of a sex difference might surprise
some people who confuse differences in the
average mass and body composition between
men and women with true sex differences
in energy expenditure. Sure, you might burn
fewer calories than your brother who is about
WHEN ADJUSTING
FOR FAT AND FAT-FREE
MASS, WOMEN AND
MEN FOLLOW NEARLY
IDENTICAL PATTERNS OF
BASAL AND TOTAL ENERGY
EXPENDITURE THROUGHOUT
THEIR LIFESPAN.
the same age, but if he’s larger than you, or
even weighs the same as you but has a greater relative proportion of fat-free mass, that’s
just an indirect effect of sex rather than a true
sex difference. Does that matter in the real
world? Maybe.
In ideal circumstances, your energy intake is
coupled with your energy expenditure, and as
this analysis showed, energy expenditure is coupled to your fat-free mass. Thus, theoretically,
this should be a non-issue as people who burn
fewer calories would simply eat less. While this
is generally true on a population level, it can
be more complicated for individuals. As one
of our guest reviewers Dr. Anne-Katrin Eiselt
explained, eating behavior is complex. Take a
married couple for example, where one partner weighs more than the other and also has a
physically active job, while the lighter partner
has a desk job. They eat together, cook together, watch the same commercials about food,
go to restaurants together, and are generally
exposed to similar food-related sensory cues
and share a similar food environment. While
the heavier, more active partner should simply
eat more while the lighter, less active partner
should simply eat less, it might not work out
that way. As I’ve discussed previously, active
people generally have better regulated satiety
signals than sedentary individuals (10). Meaning, if you put two people in an “obesogenic”
high-calorie, highly palatable, food-cue-rich
environment, and one of them burns fewer calories and has poorer appetite regulation, that
person will probably gain disproportionately more weight as portion sizes at restaurants
aren’t scaled to body size, and a more active
person is more resistant to stimuli that induce
34
overeating. So to summarize, while there aren’t true sex differences in energy expenditure,
and rather just differences on average in body
composition and mass, these indirect effects
in some individual cases can appear like sex
differences in the real world. Fortunately, for
MASS readers, lifting weights puts on fat-free
mass and reduces your time spent being sedentary. So for the few, the proud, the jacked,
we have it a little better than the non-muscular
unwashed masses.
As a last aside on this point, you might be
surprised by just how many perceived sex
differences dissapear or are greatly diminished when accounting for body composition. Take Table 1, which is from an analysis
comparing the performance of elite US male
and female weightlifters controlling for body
composition (11). As you can see, relative
to lean body mass, the females snatched and
clean and jerked 91% and 93% of what the
males could do, respectively. Further, these
sex differences in strength would likely de-
crease even more if scaled to skeletal muscle
mass, which makes up a smaller proportion
of total lean mass in females than males (12).
What I personally thought was the most surprising difference related to sex (and perhaps
the most surprising finding in the whole paper
for me) was that energy expenditure during
pregnancy and during the postpartum period
do not increase when you account for changes
in body mass and composition. You’d expect
with a rapidly growing fetus inside of you that
you’d burn more calories per unit of fat-free
mass, but apparently a fetus has a similar energy expenditure relative to fat-free mass as
an adult. The authors point out that energy expenditure relative to fat-free mass rapidly increases after birth until age 1, indicating this is
actually the most energy intensive maturation
period. Indeed, they cite data that this early
period of “metabolic acceleration” coincides
with the critical phase of early development
where maturation can be subsequently stunted
in malnourished children (13).
35
The Effect of Age
I mentioned above that the pregnancy-related findings were the most surprising to me
personally, but I recognize that the majority
of people (especially those in the non-lifting world) are probably most fixated on the
fact that energy expenditure relative to fatfree mass doesn’t decline until after age 60.
In most conversations I hear about aging, the
ages thrown around “where it all goes downhill” are typically 40 or 50, or sometimes
even 30; by age 60 it’s assumed that it’s been
downhill for at least a decade. However, if
you’re embedded in the natural lifting world
like me, it’s hard to see middle-aged folks at a
big disadvantage. I’ve seen Marshall Johnson
in his 50s win prestigious natural bodybuilding titles like the Pro USA in person (shown
here), beating my brother-from-another-mother Alberto Nuñez (an accomplished
pro, mind you) at the age of 31 back in 2014.
Or take another example, Dave Ricks (AKA
Superman), who just posted an elite 802.5kg
raw total at 93kg at the 2021 USAPL Raw
ENERGY EXPENDITURE
RELATIVE TO FATFREE MASS DOESN’T
DECLINE UNTIL
AFTER AGE 60
Nationals, placing 10th in the open division
at the ripe young age of 61. What’s crazy is
that his total isn’t far off his best-ever total
of 830kg, which he did at 57 years young.
Oh, and by the way, Dave wasn’t late to the
game – he did his first competition in 1981,
a couple years before I was born, when he
was only 21 years old (Openpowerlifting.org
profile here)!
Now, obviously I’m talking about elite outliers, and outcomes related to physique development and strength rather than energy
expenditure, but these aren’t completely disconnected concepts. As the data have shown,
fat-free mass is the primary factor that determines energy expenditure, and of course
fat-free mass is gained and maintained via
lifting weights. Again, the authors found
that energy expenditure relative to fat-free
mass doesn’t decrease until after age 60;
however, if you lose a substantial amount of
fat-free mass prior to age 60, your absolute
energy expenditure will go down, even if
your relative energy expenditure doesn’t. In
fact, the data collectively indicate that much
of what we attribute to negative effects of
middle age are actually indirect effects of
decreased physical activity and subsequent
fat-free mass losses. The authors did a good
job graphically representing (see Figure 3)
the data from studies which show that physical activity declines in middle age, and continues to decrease into old age (6). So, while
we all love lifting for the purposes of getting
bigger and stronger, it’s important to keep
lifting to maintain muscle mass as we age
in order to maintain our absolute energy expenditures as long as possible.
36
APPLICATION AND TAKEAWAYS
This was one of those “isn’t science neat?” studies that had the primary role of
helping us better understand the world around us, or in this case, better understand
ourselves. Thus, its primary application is being able to sound smarter than the
people around you when they say cliché stuff about age, sex, and metabolism.
However, beyond that (not that you need more), this can help you as a coach
or athlete to better understand how energy expenditure changes throughout
the lifespan, how important lifting is to maintain it, and just how variable energy
expenditure can be between individuals.
Individual Differences
The final aspect of this analysis I want to highlight is the variability in energy expenditure
between individuals. It won’t take much time
to discuss, but it’s important. Look back at Figure 2, Panel A, and look around the ~60kg fatfree mass point on the x-axis, which is a decent
approximation of the average lifter. Notice the
highest blue adult dot you can find on the graph
is just under 30 MJ/d or roughly ~7000kcal,
while the lowest blue adult dot is around 8
MJ/d or roughly ~1900kcal. Yes, that is nearly
a fourfold difference between two individuals
with the same amount of fat-free mass! Let that
sink in for a moment, and remember, individuals are not the mean. While many individuals
cluster around the mean, outliers are out there.
Importantly, outliers will seek coaching since
the calculators and textbooks tell them to eat far
more or less than they need.
Next Steps
WHILE MANY INDIVIDUALS
CLUSTER AROUND THE MEAN,
OUTLIERS ARE OUT THERE.
IMPORTANTLY, OUTLIERS
WILL SEEK COACHING
SINCE THE CALCULATORS
AND TEXTBOOKS TELL
THEM TO EAT FAR MORE OR
LESS THAN THEY NEED.
This was an eye-opening analysis that I really enjoyed reading. Given the nature of this
analysis of existing data, I don’t have a typical “what research to do next” tidbit to add
here. However, given how central a role fatfree mass and activity played in the analyses
and their interpretations, I think it would be
really cool to have a similar doubly-labeled
water database consisting of athletes to compare different sports people over a lifespan
to one another. It would be interesting to see
how a Junior weightlifter compares to a Masters weightlifter, to a Junior marathon runner
to a Masters marathon runner.
37
References
1. Pontzer H, Yamada Y, Sagayama H, Ainslie PN, Andersen LF, Anderson LJ, et al. Daily
energy expenditure through the human life course. Science. 2021 Aug 13;373(6556):808812.
2. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new
equations. Public Health Nutr. 2005 Oct;8(7A):1133-52.
3. Institute of Medicine (US) Committee on Military Nutrition Research; Carlson-Newberry
SJ, Costello RB. Emerging Technologies for Nutrition Research: Potential for Assessing
Military Performance Capability. Washington (DC): National Academies Press (US);
1997. Chapter 12, Doubly Labeled Water for Energy Expenditure.
4. Westerterp KR. Doubly labelled water assessment of energy expenditure: principle,
practice, and promise. Eur J Appl Physiol. 2017 Jul;117(7):1277-1285.
5. Morley JE. Decreased food intake with aging. J Gerontol A Biol Sci Med Sci. 2001
Oct;56 Spec No 2:81-8.
6. Wolff-Hughes DL, Bassett DR, Fitzhugh EC. Population-referenced percentiles for
waist-worn accelerometer-derived total activity counts in U.S. youth: 2003 - 2006
NHANES. PLoS One. 2014 Dec 22;9(12):e115915.
7. Wolff-Hughes DL, Fitzhugh EC, Bassett DR, Churilla JR. Waist-Worn Actigraphy:
Population-Referenced Percentiles for Total Activity Counts in U.S. Adults. J Phys Act
Health. 2015 Apr;12(4):447-53.
8. Hager ER, Gormley CE, Latta LW, Treuth MS, Caulfield LE, Black MM. Toddler
physical activity study: laboratory and community studies to evaluate accelerometer
validity and correlates. BMC Public Health. 2016 Sep 6;16(1):936.
9. Schmutz EA, Haile SR, Leeger-Aschmann CS, Kakebeeke TH, Zysset AE, MesserliBürgy N, et al. Physical activity and sedentary behavior in preschoolers: a longitudinal
assessment of trajectories and determinants. Int J Behav Nutr Phys Act. 2018 Apr
4;15(1):35.
10. Beaulieu K, Hopkins M, Blundell J, Finlayson G. Homeostatic and non-homeostatic
appetite control along the spectrum of physical activity levels: An updated perspective.
Physiol Behav. 2018 Aug 1;192:23-29.
11. Stone MH, Stone M, Sands WA. Page 211, Principles and practice of resistance training.
Human Kinetics; 2007.
12. Abe T, Kearns CF, Fukunaga T. Sex differences in whole body skeletal muscle mass
38
measured by magnetic resonance imaging and its distribution in young Japanese adults.
Br J Sports Med. 2003;37(5):436-40.
13. Alderman H, Headey D. The timing of growth faltering has important implications for
observational analyses of the underlying determinants of nutrition outcomes. PLoS One.
2018 Apr 25;13(4):e0195904.
█
39
Study Reviewed: Introducing Dietary Self-Monitoring to Undergraduate Women via a
Calorie Counting App Has No Effect on Mental Health or Health Behaviors: Results From a
Randomized Controlled Trial. Hahn et al. (2021)
Diet Tracking and Disordered
Eating: Which Comes First?
BY ERIC TREXLER
A common concern is that quantitatively tracking dietary
intake may give rise to disordered eating. A new randomized
controlled trial casts doubt on this idea, fueling optimism for
people who want to more actively manage their diet without
unintended consequences.
40
KEY POINTS
1. In the presently reviewed study (1), 200 female college students who did not closely
monitor their diet were randomly assigned to one month of diet tracking with
MyFitnessPal or no intervention (control).
2. The researchers did not observe significant negative effects on eating disorder risk,
anxiety, depressive symptoms, body satisfaction, quality of life, eating behaviors,
physical activity, screen time, or other forms of weight-related self-monitoring.
3. For individuals without a current or previous eating disorder diagnosis, tracking with
a diet app did not negatively impact psychological outcomes or increase eating
disorder risk. On the other hand, the mere act of tracking did not significantly improve
other health-related behaviors.
E
ating disorders are not to be trifled
with, as they can have extremely deleterious effects on physical health,
mental health, and quality of life. Unfortunately, eating disorder symptoms and other
subclinical indicators of disordered eating
can often manifest as actions and behaviors
that are common among many health and fitness enthusiasts, who may engage in these
actions and behaviors in the absence of psychological symptoms that are pathological in
nature. For example, I once distributed some
eating disorder questionnaires to a group of
physique athletes during contest preparation,
and some of the questions included:
“Have you been deliberately trying to limit
the amount of food you eat to influence your
shape or weight (whether or not you have
succeeded)?”
“Have you tried to follow definite rules regarding your eating (for example, a calorie limit) in order to influence your shape or
weight (whether or not you have succeeded)?”
“Have you had a strong desire to lose weight?”
Needless to say, if you ask a physique athlete
any of those questions during their contest
preparation, their only answer is a blank, confused stare. Questions related to these behaviors find their way onto eating disorder questionnaires, but the behaviors themselves are
not inherently deleterious when completed
in the absence of unfavorable psychological
symptoms. Along these lines, the definition
of “disordered eating” is a bit ambiguous, and
there doesn’t seem to be a unanimous consensus. Broad definitions make it seem like
just about any intentional dietary modification intended to influence body composition
could qualify as “disordered eating,” while
the more strict definitions can be difficult to
distinguish from clinical eating disorder diagnoses such as “other specified feeding or
eating disorders” and “unspecified feeding or
eating disorder.”
So, for the purposes of this article, I intend
to refer to “disordered eating habits” as po-
41
tentially pathological dietary attitudes and
behaviors that are accompanied or driven by
deleterious psychological symptoms related
to weight or body image. With this operational definition, an “increase in disordered
eating” among a group of individuals could
pertain to an increased prevalence of eating
disorder diagnoses, an increase in scores on
questionnaires designed to quantify the severity of eating disorder symptoms, or an
increase in the frequency or severity of potentially pathological dietary attitudes and
behaviors that are accompanied or driven by
deleterious psychological symptoms related to weight or body image. In this context,
someone with an eating disorder diagnosis
will display disordered eating habits, but a
subclinical increase in disordered eating habits does not necessarily warrant an eating
disorder diagnosis, and goal-oriented dietary
modifications that are implemented safely
and in the absence of deleterious psychological symptoms (such as a powerlifter modifying their diet to move up or down a weight
class for competitive purposes) would not
fit the description. I’m not necessarily suggesting that this is the one “true” definition
of disordered eating that should be adopted
broadly, but this is the most useful definition
for the purpose of this article.
It is often hard to draw the line between
healthy and unhealthy dietary manipulation,
so fitness enthusiasts and fitness professionals must be vigilant to avoid doing harm to
themselves or others. Whenever this discussion comes up in fitness circles, people often wonder if encouraging someone to track
their food intake, calories, or macros is a
risky directive that may cause eating disorders or subclinical (but still unfavorable)
disordered eating behaviors. This concern
is largely based on cross-sectional observations indicating that the use of diet and fitness
monitoring devices is correlated with eating
disorder symptomatology (2) and that people
with eating disorders track their dietary intake at a higher rate than people without eating disorders and tend to report the perception that their app usage contributes to their
eating disorder symptoms (3). However, with
these types of associations, it’s hard to say
whether diet tracking led to the development
of eating disorders, or whether people with
eating disorders were drawn to diet tracking.
We also can’t rule out the possibility that the
relationship between diet tracking and eating
disorder development or symptom severity is
moderated by the individual’s level of susceptibility to eating disorders, or the possibility that the relationship between diet tracking
and eating disorder development is substantially more complex than any of these proposed explanations.
The presently reviewed study (1) was a randomized controlled trial that sought to determine if one month of diet tracking with MyFitnessPal would significantly impact eating
disorder questionnaire scores, prevalence of
eating disorder behaviors, mental health, or
health behaviors. Results indicated that tracking with a diet app did not negatively impact
psychological outcomes or increase eating
disorder risk. However, tracking also failed
to significantly improve health behaviors related to physical activity and nutrition.
Before you read the rest of this article, I want
42
to disclose a clear conflict of interest: Greg and
I (and the rest of the team at Stronger By Science Technologies) released a diet app about
two weeks before this MASS article went live.
The reality is that it’s nearly impossible to
operate in the fitness space with an absolute
absence of conflicts, whether those conflicts
are directly or indirectly related to financial
incentives. Every fitness professional favors
particular approaches to eating or training
(hopefully based on an unbiased appraisal of
strong scientific evidence), and those preferences will be (and should be) reflected in that
professional’s content, partnerships, products, and services. In my opinion, the goal
shouldn’t be to get information from someone
with absolutely no biases or conflicts of interest (good luck with that). Rather, I try to get
my information from people who clearly and
transparently disclose their conflicts and make
an earnest effort to suspend their biases when
creating content. So, with that out of the way,
let’s dig into this study.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed
study was “to identify the effects of dietary
self-monitoring on eating disorder risk among
college women via a randomized controlled
trial.”
Hypotheses
The researchers hypothesized that “women
assigned to use an app for self-monitoring dietary intake would report an increase in eating disorder risk relative to women assigned
to the control condition.” They also hypoth-
esized that “dietary self-monitoring would
lead to poorer mental health outcomes given
the impacts of self-weighing on mental health
among this population.”
Subjects and Methods
Subjects
To recruit for this study, the researchers sent out
emails to 4,601 female undergraduate students,
indicating they were seeking participants for a
study evaluating the impact of smartphone apps
on the wellbeing of college students. The email
did not specifically mention anything about eating disorder risk as an outcome, in an effort to
avoid influencing study results. They specifically recruited female undergraduate college
students based on previous research indicating
that the prevalence of eating disorders and disordered eating behaviors are particularly high
within this population.
Participants were eligible to participate if
they were a female undergraduate student,
were fluent in English, had a smartphone,
and were at least 18 years old. Participants
were excluded if they reported a current or
previous eating disorder diagnosis, reported
a history of any medical condition that directly impacted the type or amount of food
they eat, or had tracked their food intake
within the past year. Participants were also
excluded if they had a score ≥2 on a preliminary questionnaire used to gauge eating
disorder symptoms and behaviors (EDE-QS).
The longer version of this questionnaire has
twice as many survey items, with scores ≥4
commonly classified as “within the clinical
range.” So, the researchers decided that a cut-
43
off of ≥2 would be analogous when using
the shortened version of the questionnaire. In
theory, this participant sampling procedure
and screening process should have allowed
the researchers to investigate the research
question within a population (female college
students) with a heightened propensity for
expressing disordered eating habits and eating disorder symptoms, while weeding out
participants who were already in the clinical
range for questionnaire scores related to eating disorder symptoms, which is an ethically
defensible approach to take.
Of the 4,601 students emailed, 808 completed
the screening survey, and 411 were deemed
eligible for participation. The first 201 eligible participants were invited to enroll in the
study. One participant was removed due to
a deviation from the study protocol, so 200
participants were randomly assigned to one
of two groups: the intervention group tracked
their diet for a month using the MyFitnessPal smartphone app, while the control group
maintained their typical habits and did not
monitor their diet. Eight participants from
the intervention group dropped out prior to
study completion, so the study yielded data
from 100 participants in the control group
and 92 participants in the intervention group.
The full sample had an average age of 20.2 ±
2.4 years, and an average BMI of 23.1 ± 4.8
kg/m2.
Methods
The methods for this study were very
straightforward. The study consisted of two
visits, separated by about a month. At the
pre-testing visit, participants had their height
and weight measured, and completed some
surveys related to eating disorder risk, anxiety, depressive symptoms, body satisfaction,
quality of life, eating behaviors, physical activity, screen time, and other health-related
outcomes and behaviors. After that, participants in the intervention group were given
instructions about how to track their food and
beverage intake using MyFitnessPal, and the
app was downloaded to their phones with energy requirements entered based on the Mifflin St. Jeor equation. They were instructed to
log everything they ate or drank immediately
after consumption for the following month,
whereas the control group made no modifications to their daily habits. After the month
was over, participants returned to the laboratory for post-testing, and the same procedures carried out in the pre-testing visit were
repeated. At the end of the post-testing visit,
participants were informed about the purpose
of the study, and were provided a list of locally available mental health resources.
Eating disorder risks and behaviors were
assessed using the “EDE-QS,” depressive
symptoms were assessed using the “Center
for Epidemiologic Studies Depression Scale
Revised,” state anxiety was assessed using
the state subscale of the “State-Trait Anxiety
Inventory,” body image was assessed using
the “Body Image States Scale,” overall quality of life was assessed using the “Brunnsviken Brief Quality of Life Scale,” nutrition and
physical activity behaviors were assessed using questions adapted from the “Youth Risk
Behavior Surveillance System Survey,” and
other miscellaneous sets of questions were
used to assess social media use, screen time,
self-weighing frequency, and physical activ-
44
ity self-monitoring. For dichotomous outcomes, statistical analyses sought to calculate
the odds of participants in the intervention
group experiencing the outcome in comparison to participants in the control group. For
continuous outcomes, statistical analyses
sought to numerically quantify the impact of
group membership (intervention or control)
on a given outcome.
Findings
Participants in the intervention group used
the diet app an average of 89.1% of the days
between pre-testing and post-testing (median
= 94.1% of days). For the total overall score
on the eating disorder questionnaire, there
was no significant difference between groups
(p = 0.17). Scores were actually a little lower
in the diet tracking group, but not to a degree
that would be considered practically or statistically significant. Furthermore, as shown in
Table 1, there were no significant differences
between groups for prevalence of any of the
individual eating disorder behaviors.
As shown in Table 2, there were no significant
differences between groups for state anxiety (p
= 0.48), depressive symptoms (p = 0.66), body
image (p = 0.81), or quality of life (p = 0.36).
45
In the original study, there was a huge table
presenting very detailed outcomes related
to eating behavior, dietary intake, physical
activity, social media use, and screen time.
However, these outcomes can be summarized
quite concisely, as no significant differences
were observed between the two groups (all p
> 0.05). The only significant between-group
difference in the study is presented in Table
3, which shows that self-weighing frequency
decreased from 0.66 to 0.33 times per week in
the tracking group, while self-weighing frequency increased from 0.44 to 0.60 times per
week in the control group. In the absence of
other changes related to eating disorder ques-
tionnaire scores, prevalence of eating disorder behaviors, self-monitoring habits, and
mental health outcomes, this isolated finding
doesn’t seem to be particularly impactful.
Interpretation
This is an important study, because the concerns giving rise to the research question are
plausible and have high potential for widespread impact. Observational evidence tells us
that diet and fitness tracking is correlated with
eating disorder symptomatology (2) and that
diet tracking is far more prevalent among people with eating disorders than the general population (3), so it’s natural to wonder if tracking
46
one’s diet might lead to a pathological degree
of focus and fixation on dietary intake, body
weight, body image, and so on. However, a
major shortcoming of observational research
reporting correlations is that we can’t make
confident inferences about causation. For
example, one might plausibly speculate that
higher rates of diet tracking among people with
eating disorders could suggest that diet tracking causes eating disorders. Conversely, in the
absence of additional evidence, one could suggest with a similar degree of plausibility that
people with eating disorders are simply more
likely to track their diet as a consequence, not
a cause, of their eating disorder. One could
also suggest that the relationship between diet
tracking and eating disorder development or
symptom severity is moderated by the individual’s level of susceptibility to eating disorders,
or that there is a far more complicated chain of
phenomena that indirectly link diet tracking to
eating disorders, without one directly causing
the other.
Fortunately, the presently reviewed study is
a randomized controlled trial, which circumvents this issue and gives us more stable footing for making claims about causation. This
study had a large sample of participants that
were drawn from the same population, then
randomly assigned to track their diet or maintain their normal habits. This means we can
have a reasonable degree of confidence that
both groups had generally similar characteristics, with the key difference between them
being the introduction of diet tracking. As a
result, we can observe the temporal impact
of changing one particular behavior, while
comparing these observations to a group of
very similar people who did not make that
change. The presently reviewed results indicate that the mere act of diet tracking did
not meaningfully impact BMI or a variety of
health-related behaviors, but it also didn’t do
any measurable harm with regards to mental
health or disordered eating.
47
THE MERE ACT OF DIET
TRACKING DID NOT
MEANINGFULLY IMPACT
BMI OR A VARIETY
OF HEALTH-RELATED
BEHAVIORS, BUT IT
ALSO DIDN’T DO ANY
MEASURABLE HARM.
Of course, we never want to place all of our
confidence in a single study. As reviewed by
Helms and colleagues (4), the evidence linking a variety of self-monitoring strategies to
eating disorder symptoms is a bit mixed, but
the presently reviewed study is not the first to
report fairly benign effects. In a study by Jospe
et al (5), 250 adults seeking treatment for overweight or obesity were randomly assigned to
one of five self-monitoring conditions: daily
self-weighing, diet tracking with MyFitnessPal, monthly consultations, self-monitoring
of hunger, or control (no monitoring). After
12 months of actively trying to lose weight,
the groups did not significantly differ in
terms of eating disorder questionnaire scores
or prevalence of binge eating, self-induced
vomiting, laxative misuse, or excessive exercise. While there haven’t been many randomized controlled trials assessing the impact of
dietary monitoring with smartphone apps,
some randomized controlled trials evaluating
other self-monitoring interventions have reported pretty negligible effects with regards
to outcomes related to eating disorders. For
example, Bailey and Waller reported that frequent body checking did not generally impact body dissatisfaction or disordered eating
attitudes to a significant degree (6). They did
observe a significant effect by which body
checking increased one specific survey item
(fear of uncontrollable weight gain after eating), but their analyses demonstrated that this
effect was specifically driven by unfavorable
responses in people with more pathological baseline eating attitudes. In other words,
body checking generally didn’t have a deleterious effect, but did negatively impact one
particular cognition related to eating pathology, specifically in predisposed individuals.
In addition, Steinberg et al reported that daily
self-weighing did not negatively affect mental health or outcomes related to disordered
eating (including depressive symptoms, anorectic cognitions, disinhibition, susceptibility
to hunger, and binge eating) to a significant
degree in overweight individuals undergoing
a weight loss intervention (7).
This is positive news for coaches who like to
use diet tracking as a tool for their clients, and
for individuals who are interested in tracking
(or already tracking) but are a bit nervous
about the correlation between diet tracking
and disordered eating. However, it’s important to acknowledge that there might be scenarios where tracking could be part of a plan
with potential to do harm. In the presently reviewed study, the researchers excluded par-
48
ticipants with baseline eating disorder questionnaire scores in the clinical range, which
means these results can’t be extrapolated to
people who have an active eating disorder
or elevated predisposition to eating disorder
development. So, despite the findings of the
presently reviewed study, it’s most likely a
bad idea to introduce diet tracking without
professional guidance if you have a history
of disordered eating or suspect that you’re at
an elevated risk for developing an eating disorder. As someone who manages a team of
fitness coaches, I have procedures in place to
ensure that all applicants who appear to have
an elevated eating disorder risk are directed toward a registered dietitian with clinical
training in the area of disordered eating. Unfortunately, you don’t have to look far to find
“horror stories” of people who’ve had bad
experiences with diet tracking, and I would
suspect that many of these unfavorable experiences involve a convergence of three factors: diet tracking, a predisposition to disordered eating, and an approach to dieting that
reinforces rigid restraint.
In the context of dieting, rigid restraint describes an approach that sets a lot of inflexible and dichotomous boundaries, with clear
delineations between acceptable and unacceptable intakes. For example, someone dieting with rigid restraint would only eat a
small list of “diet foods,” insist upon hitting
macronutrient or calorie targets with exceptional precision, and maintain a regimented
and hyper-specific meal schedule. With this
approach, perfection is the goal, and there
is little room for flexibility, adaptability, or
approximation. There are also very few gray
areas, so behaviors can be quite easily categorized as unequivocal successes or failures.
You could argue that rigid restraint reinforces some “perfectionist concerns” that were
covered in a previous MASS article by Dr.
Helms. While that article focused on training
and performance, there are some pretty clear
parallels to nutrition, and perfectionist concerns were a recipe for burnout and distress.
In contrast, someone dieting with flexible restraint would allow for a wide variety of food
sources, accept a goal-appropriate margin of
error with regards to daily macronutrient or
calorie targets, and shift meal composition
and timing when necessary.
Broadly speaking, rigid restraint creates a dieting environment that emphasizes precision,
perfection, and a stark delineation between
success and failure, whereas flexible restraint
creates a dieting environment that is adaptable, malleable, and accommodating. In more
practical terms, a person with rigid restraint
might “miss a meal” or be “off their diet,”
whereas a person with flexible restraint might
shift calories from lunch to dinner, or notice
that they’re over their carbohydrate target and
lower their fat intake a little bit to account
for it. When a person with rigid restraint deviates from their strict plan, it’s categorized
and internalized as a failure that gets paired
with a negative emotion, whereas someone
with flexible restraint might simply shift their
focus to a pragmatic adjustment that can be
made to accommodate the small deviation
within their flexible plan. Unsurprisingly, as
reviewed by our very own Dr. Helms (and
colleagues), rigid dietary restraint is associated with a wide range of negative outcomes,
49
including disordered eating behaviors and attitudes, body image concerns, psychological
distress, and poorer well-being (4).
Diet tracking and other forms of self-monitoring can be helpful tools. When a new dieter learns the skill of tracking, it can reinforce
the flexible nature of constructing a diet, the
importance of portion sizes, the misguidedness of fad diets and weight loss “tricks,”
and the arbitrary nature of rigid lists outlining which foods are acceptable or off limits.
Aside from this utility during active dieting
phases, tracking can also support weight
maintenance after a given body composition
goal is achieved. The National Weight Control Registry was developed to study and
understand characteristics of individuals
who are able to successfully lose substantial amounts of weight and keep it off. More
than 10,000 people have joined this registry,
and research on registry members indicates
that decreased frequency of self-weighing is
associated with weight regain (8). Self-monitoring also appears to have a high level of
feasibility; in the presently reviewed study,
participants used the diet app on an average
of 89.1% of days (median = 94.1% of days),
and daily food tracking in MyFitnessPal can
be a bit cumbersome, particularly for individuals with no prior tracking experience.
In addition to the benefits of diet tracking
with a flexible approach that have already
been described, Dr. Helms has previously
covered studies documenting slightly better
body composition outcomes and micronutrient intakes when using flexible diets with
macro tracking compared to more rigid,
rule-based diets.
However, it’s important to note that – just like
any other tool – the effects of diet tracking
depend on how it is used. As the presently reviewed study indicates, merely tracking alone
does not automatically impart a favorable impact on other health-related behaviors. A diet
app can support self-regulation, but if your
aim is to make some major changes related to
your health, fitness, or physique, you’ll want
to pair it with other components of successful
behavior change interventions. For example,
diet tracking could be used in conjunction
with intentional modifications to your diet or
physical activity habits, in addition to other
intervention components that aim to increase
nutrition-related knowledge, bolster self-efficacy, and provide social support. You’ll also
want to avoid a plan of action that involves
excessively rigid restraint, as the “horror stories” of diet tracking seem to have a lot more
to do with rigid restraint, perfectionist concerns, excessively restrictive guidelines, and
internalization of perceived failures than diet
IT’S IMPORTANT TO
NOTE THAT – JUST LIKE
ANY OTHER TOOL –
THE EFFECTS OF DIET
TRACKING DEPEND
ON HOW IT IS USED.
50
tracking per se. It’s also important to recognize that tracking is not for everyone, all the
time. As stated previously, anyone with a history of disordered eating or significantly elevated eating disorder risk probably shouldn’t
venture into the world of diet tracking or diet
manipulation without guidance from a qualified professional. I don’t have any clinical
training or experience in the realm of disordered eating, so that’s not a professional
opinion, but a better-safe-than-sorry opinion
that errs on the side of doing no unintentional
harm. For all others seeking a practical breakdown of circumstances in which diet tracking
makes sense, and how to go about learning
the process, Dr. Helms has a great three-part
video series covering the topic in the MASS
archive (one, two, three).
evaluated in people with no prior tracking
history, with half of the participants receiving instructions that reinforce rigid restraint
and the other half receiving instructions that
reinforce flexible restraint. I would expect
the results to indicate that dietary tracking
is still benign (in terms of mental health and
eating disorder symptoms) for the majority of
individuals within the context of flexible restraint, but more likely to induce unfavorable
effects when rigid restraint is applied, specifically in individuals who are particularly predisposed to eating disorders.
Next Steps
There are a couple ways I’d like to see this
work built upon in the future, with varying
degrees of ethical acceptability. I’d be interested to see a study very similar to this one,
but with one small change: Rather than simply giving participants (with no history of diet
tracking) access to the app and passively putting in their estimated energy needs, participants would self-select a weight-related goal
(gain, lose, or maintain) and receive a specific set of macro targets to aim for each day.
This would crank up the intensity, and shift
the intervention from a more passive state of
observation to a more active state of manipulation. On the slightly-less-ethical (but still
probably ethical-enough-to-justify) side, I’d
also be interested to see a study in which dietary monitoring on smartphone apps was
51
APPLICATION AND TAKEAWAYS
Quantitative diet tracking is a tool; no more, no less. Tracking dietary intake on a
smartphone app did not lead to deleterious effects related to mental health, eating
disorder questionnaire scores, or prevalence of eating disorder behaviors. On the
other hand, the mere act of tracking nutrition alone did not lead to the improvement or
adoption of other health-related behaviors. While a lot of people with eating disorders
track their diet, diet tracking did not appear to increase the frequency or severity of
eating disorder symptoms in this sample of participants with baseline eating disorder
questionnaire scores below the clinical range. As a result, diet tracking within the
context of dietary guidelines that encourage flexible restraint can be generally viewed
as an effective method of modifying dietary intake without inducing disordered
eating symptoms or other negative effects on mental health. The huge caveat is that
some individuals are particularly predisposed to developing eating disorders, and
these individuals should not undergo any intervention involving weight monitoring,
diet monitoring, or dietary manipulation without guidance from a qualified medical
professional with ample training and experience in the area of disordered eating.
52
References
1. Hahn SL, Kaciroti N, Eisenberg D, Weeks HM, Bauer KW, Sonneville KR. Introducing
Dietary Self-Monitoring to Undergraduate Women via a Calorie Counting App Has No
Effect on Mental Health or Health Behaviors: Results From a Randomized Controlled
Trial. J Acad Nutr Diet. 2021 Aug 19;S2212-2672(21)00734-6.
2. Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: Associations
with eating disorder symptomatology. Eat Behav. 2017 Aug;26:89–92.
3. Levinson CA, Fewell L, Brosof LC. My Fitness Pal calorie tracker usage in the eating
disorders. Eat Behav. 2017 Dec;27:14–6.
4. Helms ER, Prnjak K, Linardon J. Towards a Sustainable Nutrition Paradigm in Physique
Sport: A Narrative Review. Sports. 2019 Jul 16;7(7):172.
5. Jospe MR, Brown RC, Williams SM, Roy M, Meredith-Jones KA, Taylor RW. Selfmonitoring has no adverse effect on disordered eating in adults seeking treatment for
obesity. Obes Sci Pract. 2018 Jun;4(3):283–8.
6. Bailey N, Waller G. Body checking in non-clinical women: Experimental evidence
of a specific impact on fear of uncontrollable weight gain. Int J Eat Disord. 2017
Jun;50(6):693–7.
7. Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. Daily selfweighing and adverse psychological outcomes: a randomized controlled trial. Am J Prev
Med. 2014 Jan;46(1):24–9.
8. 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.
█
53
Study Reviewed: Muscular Adaptations to Training Programs using the Nordic Hamstring
Exercise or the Stiff‑Leg Deadlift in Rugby Players. Marchiori et al. (2021)
Are Knee Flexion or Hip Extension
Exercises Better for Hamstrings Growth?
BY GREG NUCKOLS
We’ve previously discussed the acute effects of knee flexionbased versus hip extension-based exercises on hamstrings
activation, but are those proxy measures actually predictive of
longitudinal outcomes? A recent study on elite rugby players
provides some insight.
54
KEY POINTS
1. A team of Brazilian premier league rugby players was split into two groups. For
five weeks, one group did Nordic curls (eccentric, bodyweight hamstrings curls)
as their only hamstrings training, and one group did stiff-legged deadlifts as
their only hamstrings training.
2. There were no significant differences between groups for any outcome
assessed: changes in eccentric and concentric knee flexion torque, concentric
knee extension torque, countermovement jump height, and biceps femoris
thickness, pennation angle, and fascicle length.
3. While this study is certainly a good first step, the difference in training intensity
between groups makes the results of the present study difficult to generalize to
other knee flexion-based and hip extension-based hamstrings exercises.
H
amstrings exercises generally fall
into two categories: knee flexion-based exercises and hip extension-based exercises. We’ve previously
discussed studies (one, two) looking at the
acute effects of knee flexion-based and hip
extension-based hamstrings exercises on
measures of muscle activation and usage (2,
3), but as we know, proxy measures aren’t
always indicative of longitudinal outcomes.
So, what are the effects of different types
of hamstrings exercises on hypertrophy and
hamstrings strength?
significantly differed between groups, but
hypertrophy and fascicle length outcomes
leaned in favor of the Nordic curl group,
and gains in countermovement jump height
leaned in favor of the stiff-legged deadlifts
group. However, as we’ll discuss in the interpretation section of this article, the training intervention used in this study makes it
difficult to know how well these results will
generalize to other hip extension-based and
knee flexion-based exercises.
In the present study (1), an elite rugby team
was split into two groups – one group did
Nordic curls for the entirety of their hamstrings training, and one group did stifflegged deadlifts for the entirety of their
hamstrings training for five weeks. The researchers assessed changes in biceps femoris architecture (muscle thickness, fascicle
length, and pennation angle), various isokinetic strength outcomes, and countermovement jump height. None of the outcomes
Purpose
Purpose and Hypotheses
The purpose of this study was to investigate
the effect of Nordic curls and stiff-legged
deadlifts on hamstrings strength, muscle architecture (thickness, pennation angle, and
fascicle length), and jump height.
Hypotheses
No hypotheses were directly stated.
55
Subjects and Methods
Subjects
23 subjects completed this study; all were
rugby players on a Brazilian premier league
team (including five players who’d played for
the national squad). To be included, subjects
needed at least “2 years of experience playing competitive rugby and … regularly participating in the team’s training routine.” So,
resistance training experience wasn’t specifically mentioned, but I assume national- and
international-level rugby teams generally include resistance training as part of their training routine, so I think it’s safe to assume that
all subjects had at least two years of resistance training experience. The subjects had
been playing rugby for an average of about
9-10 years.
Experimental Design
Subjects were randomized into two groups.
One group only performed Nordic curls
(bodyweight eccentric hamstrings curls) for
their hamstrings training, while the other
group only performed stiff-legged deadlifts.
Both groups also performed a variety of other exercises for other muscle groups, but the
training programs for those exercises didn’t
differ between groups.
fall through the last several degrees of knee
extension, catching yourself with your arms).
Stiff-legged deadlifts were initially performed with 75% of 1RM, and loads could
be increased if the subjects felt like they had
more than one rep in reserve at the end of
a training session. The range of motion for
stiff-legged deadlifts wasn’t strictly regulated, but subjects kept a slight bend in their
knees, and lowered the bar on each rep until
attaining approximately 100° of hip flexion; I
assume that’s more-or-less where their hamstrings stopped them (they didn’t touch the
bar to the floor on each rep). Over the course
of the five-week intervention, training volume progressed from 2 sets of 8 reps to 4 sets
of 12 reps for both groups.
One week before and one week after the
training intervention, researchers assessed
the muscle architecture of the subjects’ biceps femoris via ultrasound (muscle thickness, pennation angle, and fascicle length,
assessed at the midpoint of the femur). Quadriceps and hamstrings concentric peak torque,
and hamstrings eccentric peak torque, were
assessed via isokinetic dynamometry at 60°
per second. Conventional hamstrings:quadri-
Hamstrings training was conducted twice
per week for five weeks. Nordic curls were
performed with just body weight as resistance, at a cadence that allowed subjects to
control each rep for three seconds before
reaching the “break point” near the bottom
of each rep (unless your hamstrings are very
strong, you’ll generally just lose control and
56
ceps ratio (concentric peak hamstrings torque
divided by concentric peak quad torque) and
functional hamstrings:quadriceps ratio (eccentric peak hamstrings torque divided by
concentric peak quadriceps torque) were
calculated for each subject. Finally, countermovement jump height was assessed both
pre- and post-training. Of note, all isokinetic
testing was performed unilaterally with both
legs, but there were no significant differences between the left and right legs, so the researchers averaged the left and right leg values within each subject for analysis.
Findings
There were no significant differences between groups for any outcome. If you inter-
pret the results liberally, you might contend
that Nordic curls may have led to slightly
larger increases in biceps femoris thickness
and fascicle length (7.7% and 13.7% increases, versus 3.7% and 5.8% increases in the
stiff-legged deadlift group), and that stifflegged deadlifts led to larger improvements
in countermovement jump height (4.7% versus 1.1%), but the absolute differences were
all small enough that I don’t think they’re
worth getting worked up about. For example,
the “difference” in hypertrophy was <1mm,
and the “difference” in countermovement
jump height improvements was ~1.5cm.
Interpretation
Just as a quick note before we get rolling on the
57
interpretation section: a lot of this interpretation section may sound more like “Criticisms
and Statistical Musings,” but I’m combining
the two sections because the “Criticisms and
Statistical Musings” section didn’t revolve
around statistical complaints or minutia (we
primarily started including that stuff in a separate section so that folks who don’t care as
much about stats would have an easier time
skipping over statistical rambling), and the
interpretation section would have been pretty
sparse otherwise.
When a study has null findings across the
board, it’s easy to fall into the trap of criticizing the study for not recruiting more subjects
or not using a longer training intervention.
However, it’s worth acknowledging the constraints the researchers are dealing with – in
this case, the constraints relate to the fact that
the researchers were studying elite-level athletes (1). The sample size was determined by
the size of the team being studied, and I’m
sure the duration of the intervention was limited by the training requirements of the team.
The coach of a professional sports team was
willing to grant the researchers five weeks to
study the players, which is already amazing.
However, I doubt the coach would be very receptive if a researcher asked to constrain the
athletes’ hamstrings training to just one exercise for several months. In general, we all
value longer studies, with larger sample sizes,
great control of variables, and high-level athletes as subjects. However, it’s almost impossible to tick all four of those boxes in a single
study. If you want a long-duration study in
high level athletes, you’ll probably need to
sacrifice control (or just perform retroactive
analyses), and your sample will probably be
limited. If you want a well-controlled, long
duration study, you probably can’t be quite
as selective about your subject pool. All of
this is to say, I suspect some readers may balk
at the idea of a five-week training study, but
given the circumstances, that’s probably the
longest duration that was feasible, and I applaud the researchers for pulling it off.
However, I do have one substantive criticism
of the present study: the researchers should
have taken more total measurements of the
hamstrings.
To start with, knee flexion-based and hip
extension-based hamstrings exercises result in different regional activation patterns
of the hamstrings (4), so it’s not unreasonable to suspect they may result in different
regional changes in hamstrings architecture
(thickness, fascicle length, and pennation angle). Unfortunately, the researchers only took
measurements at the midpoint of the femur,
instead of also taking measurements at proximal and distal sites. In fact, this limitation
specifically weakens the findings in a way
that affects the authors’ intended application
of the present study’s results. While I care
most about the hypertrophy outcomes, the researchers were more interested in assessing
predictors of injury risk. Specifically, they
wanted to see whether stiff-legged deadlifts
had the potential to decrease biceps femoris
strains to the same extent as Nordic curls.
Most severe biceps femoris strains occur near
the distal tendon in rugby athletes (5), so it
would make sense to also measure muscle
thickness, fascicle length, and pennation angles in the distal region of the biceps femoris.
58
For the sake of thoroughness (because proximal biceps femoris strains do occur as well),
a proximal measurement also wouldn’t hurt.
Measurements of the semitendinosus and
semimembranosus wouldn’t be as relevant for
the authors’ intended application (reducing the
risk of biceps femoris strains), but they could
have expanded the scope of the paper (after
all, strains of the medial heads of the hamstrings aren’t particularly uncommon) without
requiring too much additional effort. As I see
it, if you’re able to run a training study on elite
athletes, you should collect as much data as is
feasible. For a bit of additional effort at each
data collection session, this study could have
investigated the effects of Nordic curls and
stiff-legged deadlifts on muscle architecture
of the hamstrings globally; instead, it just tells
us about what happens in the middle of the biceps femoris. To be clear, this was still a very
interesting and well-done study, but it could
have been quite a bit better without too much
additional effort.
tent with torque requirements in excess of
one’s concentric 1RM or very close to one’s
concentric 1RM. Conversely, the subjects in
the stiff-legged deadlift group were training
at loads around ~70-75% of 1RM. This isn’t
a criticism per se; rather, it illustrates how
achieving high ecological validity (the Nordic curl group used a training prescription
that’s within the realm of “normal” for Nordic curls, and the stiff-legged deadlift group
used a training prescription that’s within the
realm of “normal” for stiff-legged deadlifts)
sometimes comes at the expense of equating
all training variables. In this case, assisted
Nordic curls could have been used so that intensity was equated between groups. However, since Nordic curls are typically used for
the purpose of eccentrically overloading the
hamstrings, equating all training variables
(reducing the intensity of the Nordic curls,
such that the eccentric component of each rep
was no longer particularly difficult) would
have resulted in a loss of ecological validity.
I also wanted to call attention to a key component of the training prescription that’s
worthy of remark: the Nordic curl group was
functionally training at a much higher intensity than the stiff-legged deadlift group. Each
rep in the Nordic curl group was essentially performed at a supramaximal intensity,
at least in the way we generally understand
training intensity as “percentage of concentric 1RM.” Since the subjects were elite-level rugby players, it’s possible that some of
them would have been capable of performing
a couple strict, concentric Nordic curls, but
for most people (even people who are quite
strong), bodyweight Nordic curls are consis-
That’s important, because it means the results of the present study are specifically relevant to “normal” training prescriptions for
the stiff-legged deadlift and Nordic curls. If
all training variables were actually equated between conditions, we might be able to
make more generalizable statements about
knee flexion-based and hip extension-based
exercises. For example, hip extension-based
exercises seem to specifically activate the biceps femoris and semimembranosus over the
semitendinosus (6), and exercises (like stifflegged deadlifts) that involve deep hip flexion
with minimal knee flexion allow you to train
the hamstrings at longer muscle lengths than
59
you can with Nordic curls. With that background, you’d potentially expect more biceps
femoris hypertrophy with stiff-legged deadlifts than Nordic curls, and you’d strongly expect greater increases in fascicle length with
stiff-legged deadlifts. That’s not what we see
in the present study, though. The changes in
biceps femoris thickness and fascicle length
didn’t significantly differ between groups,
but the non-significant differences leaned in
favor of the subjects performing Nordic curls.
However, if intensity was equated (if subjects
did assisted Nordic curls, or supramaximal
eccentric-only stiff-legged deadlifts), results
may have tilted in favor of the stiff-legged
deadlift group. In other words, the present
study tells us that Nordic curls result in biceps femoris architectural changes that are at
least as large as those observed after training
stiff-legged deadlifts when both exercises
are performed in their customary fashion.
However, it doesn’t tell us much about the
effects of knee flexion-based versus hip extension-based hamstrings exercises generally. The results of the present study may have
been driven by the different types of exercises performed, but they also may have been
driven by the different training intensities
used. In other words, I’d love to see a similar
study where both groups train with submaximal loads, or where both groups train with
supramaximal loads; that would give us more
insight into the general effects of knee flexion-based versus hip extension-based hamstrings training.
Though MASS isn’t a research review focused primarily on injury prevention or rehab
(for that content, I’ve heard good things about
Physio Network), I figure it’s worth taking a
few steps back to explain the line of thinking
underpinning this study (I personally care the
most about the hypertrophy and concentric
strength outcomes, but there’s more to life
that being jacked and strong). Nordic curls
are used by athletes in many sports because
they’re stunningly effective at reducing the
risk of hamstrings strains – they decrease the
risk of hamstrings injuries by approximately 50% (7). Hamstrings strains are one of the
primary non-contact injuries experienced by
athletes in many sports, so any exercise that
can reduce your risk by approximately half
is very valuable. Nordic curls are thought
to reduce your risk of hamstrings strains in
two major ways. First, heavy eccentric stress
can increase muscle fascicle length (8), and
athletes with longer hamstrings fascicles are
less likely to experience hamstrings strains
(9). Second, Nordic curls increase eccentric
hamstrings strength, and muscles with greater eccentric strength are less likely to experience a strain injury (9). Stiff-legged deadlifts
(generally) don’t involve the same degree of
eccentric overload as Nordic curls, but they
have two key advantages over Nordic curls.
First, hip extension-based hamstrings exercises seem to preferentially target the biceps
femoris (6), and the biceps femoris is the
hamstring muscle most likely to experience
a strain injury. Second, stiff-legged deadlifts
allow you to train your hamstrings at longer
muscle lengths than Nordic curls allow. Deep
hip flexion with very slight knee flexion (the
bottom position of a stiff-legged deadlift)
results in longer hamstrings muscle lengths
than full hip extension and varying degrees
of of knee flexion (as you experience in a
60
Nordic curl), and training a muscle through
long muscle lengths can increase fascicle
length. With that in mind, it’s logical to test
whether stiff-legged deadlifts are as effective
as Nordic curls for improving correlates of
hamstrings strain risk. In the present study,
there weren’t significant differences between
the two exercises for any of the measured outcomes, but non-significant differences in outcomes related to hamstrings strain risks tended to lean in favor of Nordic curls. However,
stiff-legged deadlifts did still result in significant improvements in some key outcomes related to hamstrings strain risk (including fascicle length, and hamstrings eccentric peak
torque). Furthermore, as previously alluded
to, it’s still possible that stiff-legged deadlifts
could lead to even larger improvements in
eccentric strength and even larger increases
in biceps femoris fascicle length if they were
performed at a higher intensity. Finally, it’s
worth noting that this study was simply looking at outcome variables associated with the
risk of hamstrings strains – the study wasn’t
actually assessing rates of hamstrings injuries
following Nordic curl and stiff-legged deadlift training, so these results should be interpreted very tentatively.
Before closing out, I just want to share one fun
fact that the Discussion section of the present
study mentioned in passing. For whatever
reason, I believed that sarcomeres (the basic functional units of skeletal muscles) had
a fixed length, such that increases in muscle
fascicle length could only be accomplished
by adding more sarcomeres in series. However, individual sarcomeres can apparently
increase in length, so increases in sarcomere
length may partially underpin the increases
in fascicle length observed after Nordic curl
training (10). I’m not sure that this little tidbit
of information is actually useful, but I always
enjoy learning new fun facts about muscle
physiology.
So, in closing, I don’t think the study really changes our general recommendations
for hamstrings training. Your best bet is to
use both knee flexion-based and hip extension-based exercises, and to choose exercises
that allow you to train through long muscle
lengths (for example, seated hamstrings curls
may be a bit better than lying hamstrings
curls). If you’re choosing hamstrings exercises for injury risk reduction, stiff-legged
deadlifts might be a good option, but Nordic
curls should still be the primary tool in your
toolbox.
Next Steps
I’d love to see a study with three groups: one
group only trains the stiff-legged deadlift, one
group only trains seated leg curls, and one
group performs both stiff-legged deadlifts
and seated leg curls. Ideally, the study would
assess hypertrophy at proximal, middle, and
distal sites of all three biarticular heads of
the hamstrings (along with middle and distal
sites for the short head of the biceps femoris). I’d predict that stiff-legged deadlifts and
seated leg curls would cause comparable hypertrophy overall, with regional differences
between exercises. I also think the group performing both stiff-legged deadlifts and seated
leg curls would experience the most growth
overall.
61
APPLICATION AND TAKEAWAYS
For well-rounded hamstrings development, it’s a good idea to perform both knee
flexion-based exercises and hip extension-based exercises. Whether one of those
categories of exercises produces better results generally is still an open question, but I
can’t see a good reason to not include exercises from both categories in your training.
62
References
1. Marchiori CL, Medeiros DM, Severo-Silveira L, dos Santos Oliveira G, Medeiros TM, de
Araujo Ribeiro-Alvares JB, Baroni BM. Muscular adaptations to training programs using
the Nordic hamstring exercise or the stiff-leg deadlift in rugby players. Sport Sci Health
(2021). https://doi.org/10.1007/s11332-021-00820-0
2. Yanagisawa O, Fukutani A. Muscle Recruitment Pattern of the Hamstring Muscles in
Hip Extension and Knee Flexion Exercises. J Hum Kinet. 2020 Mar 31;72:51-59. doi:
10.2478/hukin-2019-0124. PMID: 32269647; PMCID: PMC7126262.
3. Boyer A, Hug F, Avrillon S, Lacourpaille L. Individual differences in the distribution
of activation among the hamstring muscle heads during stiff-leg Deadlift and
Nordic hamstring exercises. J Sports Sci. 2021 Aug;39(16):1830-1837. doi:
10.1080/02640414.2021.1899405. Epub 2021 Mar 7. PMID: 33678131.
4. Schoenfeld BJ, Contreras B, Tiryaki-Sonmez G, Wilson JM, Kolber MJ, Peterson MD.
Regional differences in muscle activation during hamstrings exercise. J Strength Cond
Res. 2015 Jan;29(1):159-64. doi: 10.1519/JSC.0000000000000598. PMID: 24978835.
5. Kenneally-Dabrowski C, Serpell BG, Spratford W, Lai AKM, Field B, Brown NAT,
Thomson M, Perriman D. A retrospective analysis of hamstring injuries in elite rugby
athletes: More severe injuries are likely to occur at the distal myofascial junction. Phys
Ther Sport. 2019 Jul;38:192-198. doi: 10.1016/j.ptsp.2019.05.009. Epub 2019 May 29.
PMID: 31176259.
6. Bourne MN, Timmins RG, Opar DA, Pizzari T, Ruddy JD, Sims C, Williams MD, Shield
AJ. An Evidence-Based Framework for Strengthening Exercises to Prevent Hamstring
Injury. Sports Med. 2018 Feb;48(2):251-267. doi: 10.1007/s40279-017-0796-x. PMID:
29116573.
7. van Dyk N, Behan FP, Whiteley R. Including the Nordic hamstring exercise in injury
prevention programmes halves the rate of hamstring injuries: a systematic review and
meta-analysis of 8459 athletes. Br J Sports Med. 2019 Nov;53(21):1362-1370. doi:
10.1136/bjsports-2018-100045. Epub 2019 Feb 26. PMID: 30808663.
8. Hody S, Croisier JL, Bury T, Rogister B, Leprince P. Eccentric Muscle Contractions:
Risks and Benefits. Front Physiol. 2019 May 3;10:536. doi: 10.3389/fphys.2019.00536.
PMID: 31130877; PMCID: PMC6510035.
9. Timmins RG, Bourne MN, Shield AJ, Williams MD, Lorenzen C, Opar DA. Short biceps
femoris fascicles and eccentric knee flexor weakness increase the risk of hamstring
injury in elite football (soccer): a prospective cohort study. Br J Sports Med. 2016
63
Dec;50(24):1524-1535. doi: 10.1136/bjsports-2015-095362. Epub 2015 Dec 16. PMID:
26675089.
10. Pincheira PA, Boswell MA, Franchi MV, Delp SL, Lichtwark GA. Biceps femoris long
head sarcomere and fascicle length adaptations after three weeks of eccentric exercise
training. bioRxiv 2021.01.18.427202; doi: https://doi.org/10.1101/2021.01.18.427202
█
64
Study Reviewed: Auto-regulatory Progressive Training Compared to Linear Programming
on Muscular Strength, Endurance, and Body Composition in Recreationally Active Males.
Ghobadi et al. (2021)
Origin and Modern-Day
Implementation of Autoregulatory
Progressive Resistance Exercise
BY MICHAEL C. ZOURDOS
Autoregulatory Progressive Resistance Exercise, or APRE, had a
resurgence about a decade ago. A new study shows that using
APRE as a load progression strategy leads to greater strength
gains than a fixed progression. This article discusses the origin of
APRE and provides a nuanced look at its practical implementation.
65
KEY POINTS
1. Researchers compared changes in maximal strength and reps to failure at 75% of
1RM on the leg press and chest press in men following eight weeks of training with
autoregulated versus fixed linear progression. Anaerobic power was also measured.
2. The autoregulated group used autoregulatory progressive resistance exercise
(APRE). APRE progresses load based upon reps performed in the previous session.
3. Most outcomes leaned in favor of the APRE group. Most importantly, the APRE
group increased leg press and chest press strength 7.0% and 9.8% more than the
fixed linear progression group. Overall, these data show that individualizing training
progression can lead to greater strength improvements on the group level. This
article points out that APRE has positives and negatives, and is just one of many
ways to individualize load progression.
I
n research, some ideas gain traction; however, some fail to gain traction, even if the
initial study produces novel findings. For
example, as Dr. Trexler recently mused, a paper (2) from 25 years ago suggested caffeine
attenuates the benefits of creatine when ingested together. You’d think this idea would
gain traction since it’s showing that the effectiveness of the world’s second best supplement is blunted by the world’s number one
ranked supplement. Similarly, since Mann
et al (3) found that a load progression strategy called autoregulatory progressive resistance exercise (APRE) led to greater strength
gains than a typical linear load progression;
the only scientific literature on the topic has
been a rehabilitation case report (4) and multiple reviews discussing the concept (5, 6).
Fortunately, the presently reviewed study
from Ghobadi et al (1) returns to the APRE
arena. This study had two groups (APRE
and fixed linear progression) of trained men
lift three days per week for eight weeks. At
pre-, mid-, and post-study both groups test-
ed one-repetition maximum (1RM), reps to
failure at 75% of 1RM on the leg press and
chest press, Wingate performance (cycling
anaerobic power), and body composition.
The APRE group performed four sets in each
session and progressed load for the next session based upon fourth set performance. The
fixed linear progression group increased load
by 5% each week with a concomitant decrease in reps. Findings showed significantly greater increases in leg press (+5.7%) and
chest press (+5.4%) strength in the APRE
group versus the linear progression group.
The APRE group also performed more reps
to failure at 75% of 1RM at post-study. There
were no significant group differences for
changes in body composition or anaerobic
power. These findings suggest that individualizing load progression with APRE leads to
greater strength than a fixed linear progression. However, these results don’t necessarily show the magical power of APRE; rather,
they illustrate the importance of individualization. Therefore, this article will aim to:
66
1. Examine the origins and history of APRE.
2. Discuss the differences in definitions between training theory and periodization,
programming, and progression schemes.
3. Discuss both the positives and negatives
of APRE.
4. Provide practical strategies to implement
APRE progression into training.
Purpose and Hypotheses
Purpose
The purpose of the reviewed study was to
compare changes in strength, reps to failure,
body composition, and anaerobic power between men training with an APRE progression scheme versus a fixed linear progression
scheme over eight weeks.
Hypotheses The researchers hypothesized that all outcome measures would improve to a greater
extent with APRE progression versus the
fixed linear progression.
Subjects and Methods
Subjects
24 men with at least 6 months of resistance
training experience completed the study. The
available subject details are in Table 1.
Study Overview
The presently reviewed study was a parallel group design with two groups 1) APRE
(n=12) and 2) fixed linear progression (n=12).
Both groups trained three times per week for
eight weeks with outcome measures assessed
at pre-, mid-, and post-study. Subjects trained
the back squat, chest press, lat-pulldown,
barbell curl, leg curl, and triceps pushdown
in each session. Table 2 lists all outcome
measures. As a quick note, the researchers
assessed strength on the leg press although it
was not trained during the study.
APRE Training
For day 1 of week 1, the researchers estimated a 6RM load by simply calculating 85% of
the pre-study 1RM for each individual. Then,
in each training session, subjects performed
four sets as follows:
• Set 1: 10 reps with 50% of the 6RM load.
• Set 2: 6 reps with 75% of the 6RM load.
67
• Set 3: 6RM load performed to failure.
• Set 4: Subjects performed set four to failure, and researchers adjusted the load used
for set four based upon performance in set
three. Further, performance on set four
was used to adjust load for the following
session. The authors cited the all-knowing
Supertraining textbook (7) for these setto-set and session-to-session load adjustments. Keeping in mind the target range
of six repetitions, the specifics of these
load adjustments are in Table 3.
Following the mid-study testing at the end of
week four, subjects used a new 6RM (85%
of 1RM) based upon the mid-study testing to
start training on day 1 of week 5.
Fixed Linear Progression Training
Subjects in the fixed linear progression group
performed all three sessions in a week at the
same percentage of 1RM, but the load pro-
68
gressed by 5% each week. Load started at 70%
of 1RM in week one, then increased to 75, 80,
and 85% over the next three weeks. Following the mid-study testing in week four, 70%
of the new 1RM was used for week five training, then 75, 80, and 85% of the new 1RM
were used for loading in weeks 6, 7, and 8.
The number of sets undulated between three
and four each week, and repetitions decreased
within each four-week training block. Lastly,
subjects rested only 60 seconds between sets.
Table 4 shows the specific program each week
in the fixed linear progression group.
Findings
Summary
Before getting into all measures, here’s a simple
summary of the findings. Both groups tended
to improve outcome measures, and the APRE
group tended to improve more than the fixed
linear group. Most importantly, subjects in
the APRE group gained 7.0% and 9.8% more
strength than the fixed linear progression group
on the leg press and chest press, respectively.
Total Volume and sRPE
I included these in Table 2 as outcome measures because researchers tracked them;
however, the authors strangely did not perform statistical analyses on these outcomes.
Figure 1AB plots total volume in each week
and sRPE following every session. Just viewing the graphs, the values for sRPE are pretty
close and maybe just a bit higher in the APRE
group. However, from extracting the means
with webplotdigitizer (so possibly not exact),
69
the total volume was 22.67% higher in the
APRE group than the fixed linear group.
Anaerobic Power
There is no table or figure of anaerobic power
as these findings are less important for MASS
readers. However, both groups increased anaerobic power from pre- to post-study with
significantly greater increases in favor of the
APRE group for lower (p=0.014) and upper
(p=0.028) body power.
Body Composition
Lifters significantly increased body mass and
skeletal muscle mass. In addition, body mass
in the APRE group significantly increased
(p=0.05; +0.9kg) more than in the fixed linear group. However, there were no significant
group differences for skeletal muscle mass or
body fat percentage. Table 5 shows the changes in body mass and skeletal muscle mass.
Strength and Rep Performance
Both groups improved strength, with the
APRE group improving both leg press and
chest press 1RM significantly more (p<0.05)
from pre- to post-study than the fixed linear
group. In addition, the number of reps to fail-
70
ure at 75% of 1RM significantly decreased
over time in both groups on both exercises,
and subjects in the APRE group tended to perform more reps at post-study than the fixed
linear group. However, there was no group
× time interaction. Table 6 shows the means
and standard deviations for each performance
variable, while Figure 1 displays the pre- to
post-study relative strength findings.
Statistical Criticisms and Musings
While there are other ways to analyze this data,
the 2 (group) × 3 (time) repeated measures
ANOVA was perfectly acceptable. However,
it’s worth mentioning that data were almost
certainly reported incorrectly in a few spots.
First, the authors reported that chest press
reps to failure were significantly different (p =
0.001; effect size = 1.01) at post- versus midstudy in the APRE group, yet the mean (11.8
reps) was the exact same at each time point.
By definition, the effect size is 0 if the means
are the same; thus, either the p-value and effect size are wrong, or the presented values
are incorrect. Second, the authors also noted a
significant change from mid- to post-study (p
= 0.015; effect size = 0.64) in the fixed linear
group for chest press reps to failure, yet reps
only decreased by 0.2. In both of the cases,
it seems the researchers reported something
(mean or p-value/effect size) incorrectly.
The authors also reported relative strength
(1RM/body mass). From pre- to post-study
relative leg press 1RM improved from 2.3 ±
0.3 to 2.8 ± 0.4 (+21.7%) and 2.2 ± 0.4 to
2.5 ± 0.4 (+13.6%) in the APRE and fixed
linear groups, respectively. The authors reported a “significant interaction” (p = 0.034)
for this comparison, which should indicate
that relative leg press strength increased significantly more from pre- to post-study in the
APRE group versus the fixed linear group.
Those values are not too far apart, but that
reporting seems fine. However, the authors
then went on to say, “there was (were) no
significant differences between the APRE
and LPRE [fixed linear group] groups in leg
press strength (p = 0.001).” This last quote is
confusing on two accounts. First, the authors
have noted no significance but have provided
a significant p-value of 0.001, which makes
it unclear what they are reporting in this case.
I’m not sure what “group differences” are
referring to here. A group × time interaction
compares the rate of change between groups,
but a group difference just compares the raw
values between groups. However, a group
difference is not meaningful in and of itself.
What’s meaningful is the rate of change over
time. When you look at the average of the
raw values (average of pre- and post-study
in each group) for relative leg press strength,
they are 2.55 (APRE) versus 2.35 (fixed linear), which are pretty close. Therefore, if this
is referring to the difference in raw values
between the two groups, I bet the p-value
is incorrect, as the reported p-value is really low for absolute values that are so close.
But, what’s important here is that the authors
stated no significant difference but reported
a p-value of 0.001, and there are multiple instances of unclear data reporting.
The good news is that the most evident findings are for strength, and 1RM strength will
be the interpretation’s focus. Whatever the
case with the aforementioned reporting issues,
it’s clear that 1RM strength increased in both
71
groups and tended to favor APRE for both leg
press (+7.0% greater) and chest press (+9.8%
greater) compared to the fixed linear group.
Interpretation
The presently reviewed study from Ghobadi
et al (1) showed 7.0% (APRE: +22.6% versus fixed linear: +15.6%) and 9.8% (APRE:
+29.9% versus fixed linear: +20.1%) greater strength gains in the leg press and chest
press, respectively, with APRE progression
than with a fixed linear progression. The most
surface-level conclusion is that APRE is a
magical program which we should all adopt.
While APRE isn’t a bad idea, there’s nothing magical about the present results. Instead,
APRE essentially amounts to individualized
progression, versus the fixed progression
used in the other group. When concepts like
APRE are presented, they are often discussed
as overarching training theories or even
forms of periodization, of which modern-day
APRE is neither. Of course, there are many
other ways to progress load, and all methods
of progression have some positives and some
negatives. Therefore, this interpretation will:
1. Discuss the history of APRE.
2. Explain the differences between training
theory and periodization and progression
methods.
3. Interpret the present results along with a
previous APRE study from Mann et al (3).
4. Discuss the positives and negatives of
various progression methods.
5. Demonstrate practical examples of load
progression.
THERE ARE MANY OTHER
WAYS TO PROGRESS
LOAD, AND ALL METHODS
OF PROGRESSION HAVE
SOME POSITIVES AND
SOME NEGATIVES.
Brief History of APRE
The first mention of APRE is often attributed
to Veroshansky and Siff’s textbook “Supertraining” (7). However, Knight (8) first examined APRE, which he termed DAPRE (daily
adjustable progressive resistive exercise), in
1979. Since 1979, the concept of APRE (or
DAPRE) has been implemented precisely as
Knight outlined. Knight’s explanation for the
training prescription was the same as outlined
above in the “APRE Training” subsection,
and Knight’s original progression scheme was
the same as illustrated in Table 3. Ignoring
Knight’s paper as the origin of APRE is unfortunate because the original paper explains why
he developed APRE. In Dr. Helms’ birth year
of 1945, Delorme (9) developed “progressive
resistive exercise’’ or PRE (really just progressive overload) for postsurgical patients. The
idea was simply to provide progressive overload during rehabilitation. Then, in 1975, Dena
Gardner wrote a book titled “The Principles
of Exercise Therapy” (10). She discussed the
principles of progressive overload in rehabilitation, but specifically noted that insufficient
72
loading wouldn’t allow for maximal progress.
Based on Gardner’s writing, Knight noted that
the progressive resistive exercise proposed by
Delorme was inadequate because it 1) did not
necessarily ensure a patient was working at
their maximal level and 2) did not account for
individual differences in strength. Due to these
two factors, Knight developed APRE and actually said, “We have solved this problem [limitations of Delorme] with a program that we
call DAPRE.” Importantly, what we now call
APRE was explicitly developed for resistance
training in a rehabilitation setting and not for
well-trained athletes. Just because Knight designed APRE for rehabilitation doesn’t mean
we can’t apply it to athletes and lifters (we can
and should, which is what Siff and Verkoshansky described). However, Knight’s original paper explains why it wasn’t developed as just a
progression method and a specific fixed program (see “APRE Training”).
In the modern-day, APRE can be adapted to be
more of a progression concept, discussed later.
Following Knight’s paper, the Supertraining
textbook discussed DAPRE. Then in 2010,
Mann et al (3) published a widely cited study
in football players comparing APRE progression to linear progression, which truly brought
the term back to the forefront. The Mann paper came out right when I was designing my
dissertation study, and was a huge influence
on me at the time. However, since then, APRE
or DAPRE has only been used in a case report
(4) or discussed in review papers (5, 6).
Evaluating the Present Data
I won’t spend much time on the non-strength
findings, so let’s get those out of the way.
The men in this study were not very well-
trained. Therefore, it’s not surprising that
all measures improved to a large degree. Although the APRE group gained significantly
more body mass than the fixed linear group,
I wouldn’t read too much into it, as the difference was only 1.2%. Further, the change
in muscle mass was similar between groups.
While the researchers did not compare total volume between groups statistically, the
APRE group performed ~23% more volume.
Volume is, of course, related to greater increases in muscle size (11), but novice trainees only need so much volume to grow. In
the present study, I think both groups performed more than sufficient training volume
for muscle growth purposes. The added volume in the APRE group was probably past
the point of diminishing returns for these
lifters. Reps performed to failure at 75% significantly decreased on both exercises and
in both groups from pre- to post-study. The
APRE group tended to perform more reps
at post-study than the fixed linear group, albeit non-significantly. Not only did APRE
perform more total volume, but they almost
certainly performed more total reps since the
fixed linear group only performed more than
six reps during weeks one and five when they
performed 3 × 8 at 70% of 1RM. Thus, the
APRE group had a chance to perform more
than six reps in every session, which might
explain why they experienced slightly smaller decreases in strength endurance. Now,
onto the strength findings.
First, Mann et al (3) used the same exact
protocol as the presently reviewed study
to compare the rate of change in squat and
bench press 1RM and bench press reps to
73
failure with ~102.5kg (225lb) in American
collegiate football players over six weeks.
That study also found that the APRE group
increased squat and bench press 1RM more
than the linear progression. Further, Mann
reported that the football players improved
their 102.5kg to failure bench press by ~3
reps, while the fixed linear progression group
experienced no change in bench press reps to
failure. Since the Mann study used the same
protocol as the present study, the explanations below apply to both studies.
The greater strength increase in the APRE
group in the present study can be explained,
in my opinion, by three factors 1) greater load
(percentage of 1RM), 2) individualization and
3) more volume (maybe). I say “maybe” for
more volume, because that is probably the least
likely contributor in this case. Volume is related to strength (12 - MASS Review); however, that relationship isn’t as strong as the relationship between volume and muscle growth.
Above, I noted the relatively novice subjects
in the presently reviewed study likely reached
the point where additional volume would result in diminishing returns for muscle growth.
Thus, I’m not confident the added volume in
the APRE group contributed to the additional
strength gains, but it isn’t impossible.
The APRE group undoubtedly trained at a
greater average percentage of 1RM and probably a greater peak percentage of 1RM than
the fixed linear group. The fixed linear group
topped out at 85%, which they only used in
weeks 4 and 8, whereas the APRE group was
training ~85% of 1RM in week one and could
increase the load. We know that the fixed linear group trained at an average of 77.5% of
1RM over the entire study. From the total volume data in Figure 1, it seems there were fluctuations in the load used on sets 3 and 4 in the
APRE group. For example, for set 4 on days
1 and 2, the load, taking the average of 85%
on both the leg press and chest press, would
have been 109.57kg. The volume on both days
one and two on set four was 562.33kg (estimated from data extraction), meaning lifters
performed an average of 5.13 reps per set. On
average, based on Table 3, lifters would have
then kept the same load for the following week
session (session three), but volume dipped to
400.90kg, which translates to an average of
3.66 reps per set, which would have stipulated
a load decrease for the next session. That load
decrease would have been 2.5kg taking the
subjects to an average of 83.1% of 1RM. The
volume then remained pretty stable until day
three of week three, increasing to 605.38kg.
Following the mid-testing 1RM, volume was
a bit higher due to greater absolute load, and
there were visually more peaks in the second
half of the training program. Of course, more
volume peaks could be due to more reps rather
than more load; however, when enough reps
are performed, a load increase is made for the
next session, which occurred at some points.
Therefore, even with a possible load reduction
on the group level, subjects probably trained
with a load between 80-90% for most of the
study in the APRE group, which was a higher
average and peak than the fixed linear group.
The other positive component of APRE is
individualization. The original purpose of
autoregulation and Knight’s DAPRE was to
develop a system in which individuals could
provide the appropriate amount of stress for
74
their capabilities on a given day. While other
autoregulation methods allow for more individualization than APRE, APRE did provide
more individualization than the fixed linear
group. Often, we frame individualization as
the ability to decrease training load when
we’re not feeling good. However, in the present study, I doubt the percentage-based group
would have had much trouble completing
their training, as their training protocol was
pretty feasible. That’s not to say there was
never a time where someone failed a rep; that
probably happened, but overall the protocol
was reasonable. I think the individualization
benefit in this study was that it allowed the
APRE group to increase load from set 3 to 4
in each session, thus allowing the subjects to
use a higher percentage of 1RM when needed. Of course, the session-to-session individualization is beneficial, but subjects in this
group had a chance to feel good on any given
day. Individualization aside, it’s also possible
that a higher level of individualization wasn’t
a significant factor. As discussed in the previous paragraph, the greater starting intensity may be solely responsible for the greater
strength gains observed in the APRE group.
Therefore, other than the higher percentage
of 1RM used, it’s difficult to make definitive statements about why the APRE group
gained more strength than the fixed linear
group; however, the three possible factors
given (percentage of 1RM, individualization,
and volume) are the likely contributors.
APRE Within the Context of Progression
After delving into the history, we understand
that Knight originally developed APRE as a
specific training program aimed at post-sur-
gical patients. More recently, APRE has been
referred to as a form of periodization (3). However, as we’ve discussed before, periodization
refers to long-term changes or trends in training variables such as volume, intensity, and
frequency. The concept of APRE doesn’t fit
definitions of periodization or programming
since it involves a fixed set and rep scheme.
Programming refers to short-term fluctuations
in variables or certain methods (i.e., cluster,
rest-pause, and supersets) used to produce the
desired outcome on a specific day. Specifically, if volume and intensity increase and decrease gradually over six months, that is periodization, but if these variables also fluctuate
a little bit during an individual week, then that
is programming. This distinction can be seen
when considering a daily undulating program
within a traditional periodization scheme. For
example, if you train the squat three days per
week on Monday, Wednesday, and Friday and
perform 4 × 10, 4 × 8, and 5 × 6 for four weeks,
followed by 3 × 8, 3 × 5, and 4 × 3 on the respective days over the next four weeks, that’s
undulating programming within a week, fit
within an overall traditional periodized model.
Table 7, initially printed in Volume 3 Issue 8,
demonstrates undulating programming within
an overall traditional (linear) periodized program. For more information on programming
and periodization, please refer to that article
from Volume 3, as the interpretation is essentially a de facto concept review on the topic.
Although understanding APRE’s history
is helpful, most people think of it as a load
progression strategy, and that’s a good thing.
There are many other positive and negative
considerations with APRE, which we’ll get
75
to in a moment. However, I want to note that
there are many different strategies to progress load. We could discuss each strategy in
an individual article, but that would be a bit
excessive for the purposes of this interpretation section; thus, I’ll keep this article focused
solely on APRE. The good news is we have
articles and videos on those strategies. Here
are links on how to apply load and set progression with RIR-based RPE, session RPE
(one, two), arbitrary values (one, two, three),
and exact load prescription. Further, here’s an
older article I wrote for Stronger By Science
on load progression as a whole. Let’s just
think of APRE as a load progression strategy
referring to increasing load based upon the
number of reps performed. Again, there are
numerous positives and negatives to discuss,
along with various nuanced iterations.
APRE Positives and Negatives
The positive aspect of APRE is the individualized progression. Across a group, the individual rates of adaptation can be widely different (13, 14); thus, individualized progression
is recommended. However, if performing a
plus set (or AMRAP) at the end of a training
session (e.g., 4 × 3+), a negative is that performance on that one set is responsible for
load progression for the entirety of the following session or week. Relying on the plus
set for progression can be an issue for two
reasons. First, APRE still doesn’t take into
account daily fluctuations in performance;
76
thus, if you feel terrible, warming up using
the APRE progression from the previous session or week may not be appropriate. Second,
with this strategy, only one set is driving the
entire progression. For example, if performing 4 × 3 with a plus set on the last set, the
first three sets might be at 2 RIR (predicted 5
total reps), but the lifter may have more excitability on set four because they know it’s a
plus set and perform seven reps. Seven reps
might constitute a greater load increase than
five reps, and this larger load increase may be
inappropriate. It’s not possible or desirable to
keep the same level of arousal that you conjure up on a plus set for all sets throughout a
week; thus, this strategy could lead to a load
progression strategy that is too aggressive.
Absolute Progression Versus Percentages
If using APRE to progress load for a group,
coaches and athletes should use percentage
progressions instead of absolute values. If
lifters with 100kg and 200kg squat 1RMs, respectively, both have 4 × 4+ at 85% of 1RM
and perform seven reps on the plus set (4
IF USING APRE TO PROGRESS
LOAD FOR A GROUP,
COACHES AND ATHLETES
SHOULD USE PERCENTAGE
PROGRESSIONS INSTEAD
OF ABSOLUTE VALUES.
more than prescribed), that would stipulate a
load progression of 5-7.5kg. If 7.5kg is used,
that would increase the 100kg lifter’s training load from 85kg to 92.5kg (+8.1% of load
used) and the 200kg lifter’s load from 170kg
to 177.5kg (+4.4% of load used). Using absolute values is going to cause the lifter with the
lower 1RM to stall more quickly. Therefore,
progressing load based on a percentage value (percentage of load used) creates a more
level playing field. Table 8 shows an example of applying percentage-based progression
to APRE. Also, instead of using the number
of reps performed in Table 8, I’ve used the
number of reps above or below a target. This
way, we can apply the table to any target rep
prescription.
A few other notes regarding Table 8: If you
notice, I did not use the same progression
77
standards as the originally APRE table. Instead, the load decreases more than it increases. So, 1-2 reps under the rep target would
decrease your working weights by 3%, but
1-2 reps above the rep target would increase
your working weights by only 1-2%. Nothing
in Table 8 is unassailable. In fact, I would encourage you to change it to whatever fits your
needs or your lifter’s needs if you’re a coach.
The point is that concepts can and should be
individualized.
Weekly or Session Load Progression
Although the original APRE (or DAPRE) iteration is for rep performance in one training
session to progress load for the next training
session, I’d progress it weekly. The original
iteration progresses for the next session because each session is the same, but if you’re
using a daily undulating programming strategy as outlined above and have a 10-rep day,
7-rep day, and 4-rep day within a week, then
each day can apply to the corresponding day
for the following week. Table 9 presents an
example of this strategy.
An individual may simply be better at performing lower versus higher reps or vice versa; thus, it makes sense to progress a day in
each week based on your performance in the
corresponding training in the prior week if you
use a seven-day training split. One limitation
of APRE, even in this model, is that at least
one set needs to be to failure. I wouldn’t recommend failure training on the main lifts every session (although one set might be okay).
So, in the example above, you could perform
the last set to 1 RIR on days 1 and 2 instead of
going all the way to failure, thus avoiding failure most of the week. Of course, if you are not
training to failure, you could just progress load
based upon RIR (i.e., more RIR = greater load
IT MAKES SENSE TO
PROGRESS A DAY IN
EACH WEEK BASED ON
YOUR PERFORMANCE IN
THE CORRESPONDING
TRAINING IN THE
PRIOR WEEK.
78
APPLICATION AND TAKEAWAYS
1. Ghobadi et al (1) found that APRE training resulted in greater strength
improvement than a fixed load progression on the group level.
2. Importantly, this study only compared APRE to a fixed progression and not to
other forms of autoregulated load progression.
3. In reality, the current form of APRE can be improved upon (see Tables 8 and
9). Ultimately, your load progression strategy should be encompassed within a
training program that has a solid foundation and is individualized to your goals.
increase; fewer RIR = smaller load increase),
and the articles and videos linked above walk
you through that strategy.
exercises. The APRE group could progress
load using the percentage progression method (Tables 8 and 9).
Next Steps
There are a lot of directions to go here since
only one form of APRE has been used in
the research. The next step could compare
APRE’s current form versus a periodized
program that uses RIR or velocity to autoregulate session-to-session load. In that design, I think the RIR or velocity group would
have superior strength gains for two reasons.
First, both RIR and velocity do a better job
than APRE of considering low readiness on
a given day. Second, the RIR or velocity autoregulation approaches could be used with a
periodized protocol, whereas APRE involves
training with the same sets and reps each day.
Additionally, I’d like to see a new iteration of
APRE using some of the strategies I laid out
in Tables 8 and 9. Researchers could design a
periodized program in this design, which was
used in both an APRE group and a fixed progression group. The groups could train three
days per week and perform one set to failure each day on the training program’s main
79
References
1. Ghobadi H, Attarzadeh Hosseini SR, Rashidlamir A, Forbes SC. Auto-regulatory
progressive training compared to linear programming on muscular strength, endurance,
and body composition in recreationally active males: Resistance training programming.
European Journal of Sport Science. 2021 Aug 3(just-accepted):1-9.
2. Pakulak A, Candow DG, Totosy de Zepetnek J, Forbes SC, Basta D. Effects of Creatine
and Caffeine Supplementation During Resistance Training on Body Composition,
Strength, Endurance, Rating of Perceived Exertion and Fatigue in Trained Young Adults.
Journal of Dietary Supplements. 2021 Mar 16:1-6.
3. Mann JB, Thyfault JP, Ivey PA, Sayers SP. The effect of autoregulatory progressive
resistance exercise vs. linear periodization on strength improvement in college athletes.
The Journal of strength & conditioning research. 2010 Jul 1;24(7):1718-23.
4. Horschig AD, Neff TE, Serrano AJ. Utilization of autoregulatory progressive resistance
exercise in transitional rehabilitation periodization of a high school football‐player
following anterior cruciate ligament reconstruction: A case report. International journal
of sports physical therapy. 2014 Oct;9(5):691.
5. Suchomel TJ, Nimphius S, Bellon CR, Hornsby WG, Stone MH. Training for Muscular
Strength: Methods for Monitoring and Adjusting Training Intensity. Sports Medicine.
2021 Jun 8:1-6.
6. Zhang X, Li H, Bi S, Cao Y, Zhang G. Auto-regulation method vs. fixed-loading method
in maximum strength training for athletes: a systematic review and meta-analysis.
Frontiers in Physiology. 2021;12:244.
7. Verhoshansky Y, Siff M. Supertraining sixth edition-Expanded version. 2009.
8. Knight KL. Knee rehabilitation by the daily adjustable progressive resistive exercise
technique. The American journal of sports medicine. 1979 Nov;7(6):336-7.
9. DeLorme TL. Restoration of muscle power by heavy-resistance exercises. JBJS. 1945
Oct 1;27(4):645-67.
10. Gardner MD The Principles of Exercise Therapy London, G Bell and Sons, Ltd, 1975,
11. Schoenfeld BJ, Ogborn D, Krieger JW. Dose-response relationship between weekly
resistance training volume and increases in muscle mass: A systematic review and metaanalysis. Journal of sports sciences. 2017 Jun 3;35(11):1073-82.
12. Ralston GW, Kilgore L, Wyatt FB, Baker JS. The effect of weekly set volume on strength
gain: a meta-analysis. Sports Medicine. 2017 Dec;47(12):2585-601.
80
13. Hubal MJ, Gordish-Dressman HE, Thompson PD, Price TB, Hoffman EP, Angelopoulos
TJ, Gordon PM, Moyna NM, Pescatello LS, Visich PS, Zoeller RF. Variability in muscle
size and strength gain after unilateral resistance training. Medicine & science in sports &
exercise. 2005 Jun 1;37(6):964-72.
14. Erskine RM, Jones DA, Williams AG, Stewart CE, Degens H. Inter-individual variability
in the adaptation of human muscle specific tension to progressive resistance training.
European journal of applied physiology. 2010 Dec;110(6):1117-25.
█
81
Study Reviewed: Hibiscus Sabdariffa Tea Affects Diet-Induced Thermogenesis and Subjective
Satiety Responses in Healthy Men, But Not in Women: A Randomized Crossover Trial. de Faria
et al. (2021)
Does Hibiscus Tea Increase Satiety Or
Energy Expenditure (And Would It
Actually Matter)?
BY ERIC TREXLER
Given the well-known challenges of fat loss, convenient and
affordable interventions that may reduce hunger and increase
energy expenditure are easy to embrace. This study sought to
determine if hibiscus tea can meaningfully alter these outcomes.
82
KEY POINTS
1. The presently reviewed study (1) evaluated the effects of hibiscus tea on energy
expenditure and satiety up to four hours after ingestion, in addition to selfreported food intake the rest of the day.
2. The researchers reported that male participants experienced lower hunger and
desire to eat, along with higher satiety, fullness, and energy expenditure in the
hibiscus condition, whereas effects were not significant for female participants.
3. With an unblinded design, insufficient statistical support, no clear mechanism
for sex-based differences, and no impact on subsequent food intake, it’s hard
to have confidence that hibiscus tea will meaningfully impact fat loss, even for
males.
F
at loss is simple, but it’s far from
easy. The plan of attack is pretty
straightforward: we need to establish
a caloric deficit in conjunction with training
habits, macronutrient intakes, and micronutrient intakes that are compatible with good
health and lean mass retention (or accretion,
when possible). The actual implementation
can be substantially more challenging. As we
restrict calories, we may experience a level of
hunger that is unpleasant enough to threaten
our dietary adherence. As we do more physical activity to burn more calories, we may
find that the process consumes a lot of time,
effort, and energy. As a result, any easy, safe,
convenient, and affordable intervention that
may reduce hunger and increase energy expenditure is sure to be embraced by dieters
aiming to lose fat.
There is some very preliminary evidence that
components of hibiscus tea may reduce hunger and increase energy expenditure, so the
presently reviewed study (1) sought to determine if this preliminary evidence would
pan out in a more applied setting. 21 participants (10 males and 11 females) reported to
the laboratory on two separate occasions to
consume a standardized breakfast meal with
either hibiscus tea or water. The researchers evaluated energy expenditure and satiety
before and up to four hours after ingestion,
in addition to self-reported food intake the
rest of the day. The researchers reported that
male participants experienced lower hunger
and desire to eat, along with higher satiety,
fullness, and energy expenditure in the hibiscus condition, whereas effects were not significant for female participants. That sounds
pretty promising for about half of our MASS
readers, but I’m not totally convinced that
hibiscus tea is the next big thing for dieters.
Read on to find out why.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was “to investigate the effect of Hibiscus
sabdariffa tea on energy expenditure, as well
83
as its effect on satiety response and energy
intake for both sexes and between women
and men.”
Hypotheses
The researchers did not directly state a hypothesis.
Subjects and Methods
Subjects
The presently reviewed study recruited a
mixed sample of males and females aged 18
to 40 years old, with BMIs between 18.5 and
26 kg/m2. 28 participants volunteered for the
study; three were removed due to protocol violations and four voluntarily withdrew from
the study, so data from 21 participants were
available for statistical analysis. The average
age of these participants was 27.6 ± 6.2 years
old, and the sample was 52.4% female (11 females, 10 males). Participants were not using
any medications or dietary supplements (other than oral contraceptives), were non-smokers, were weight-stable, and “made rare use
of peppers and caffeine,” although “rare use”
was not specifically defined in the paper. Data
collection did not take place during menstruation for female participants. Throughout the
trial, participants were instructed to maintain
their normal diet, avoid intense physical activity, abstain from alcohol the day prior to
testing, and abstain from physical exercise on
the day of laboratory visits. Participant characteristics are presented in Table 1.
Methods
Participants completed two separate testing
visits for this study, separated by at least seven days. For each visit, they arrived at the
laboratory in a fasted state (12 hours) and
completed baseline assessments including
height, weight, waist circumference, hip circumference, body composition (via bioelec-
84
trical impedance), resting energy expenditure
and substrate oxidation (via indirect calorimetry), and subjective assessments of hunger,
satiety, fullness, and desire to eat (via visual
analog scales). In addition to the more common non-protein estimate of resting energy
expenditure (non-nitrogen energy expenditure), the researchers also collected urine to
assess nitrogen excretion, which also allowed
the researchers to calculate an outcome they
called “nitrogen energy expenditure.” This
is not the amount of energy obtained from
metabolizing protein alone, but rather an energy expenditure estimate that accounts for
the small amount of protein metabolism contributing to energy expenditure. Researchers
usually just report non-nitrogen expenditure,
because the inclusion of nitrogen takes an
extra step, protein should only account for
about 4% of energy expenditure at rest, and
the exclusion of protein only introduces an
estimation error of about 1-2% (2). Nonetheless, these researchers reported both versions
of the energy expenditure estimates. They
also quantified diet-induced thermogenesis,
or the energy expenditure increase attributable to energy intake, by subtracting the fasted energy expenditure value from the energy
expenditure values measured after the standardized meal was consumed.
After baseline measurements, participants
were given 15 minutes to consume a standardized meal of approximately 500kcals
(60% carbohydrate, 15% protein, and 25%
fat). The breakfast included 200mL of orange
juice, but participants were also given the experimental beverage (hibiscus tea) or control
beverage (water) to accompany the meal. As
for brewing procedures, 5g of the hibiscus
tea was added to boiling water, which was
strained after five minutes of steeping. Resting energy expenditure and substrate oxidation were re-tested 40, 120, and 240 minutes
after ingestion of the standardized breakfast.
Outcomes assessed via visual analog scales
(hunger, satiety, fullness, and desire to eat)
were re-tested about 15, 60, 120, and 180
minutes after breakfast consumption. The researchers also assessed energy intake at the
next meal after breakfast, and energy intake
throughout the entire day of each testing visit. In order to facilitate these assessments,
participants were asked to record all of their
food and beverage intake after leaving the
testing center on the day of each laboratory
visit. Data were analyzed using pretty standard analyses including analyses of variance,
t-tests, and the nonparametric equivalent of
t-tests (as needed).
Findings
Participants did not have significantly different body composition values when arriving
for the two different testing visits (p > 0.05),
which is a good thing. For the more common
non-nitrogen estimate of energy expenditure,
there was a significant main effect of time,
indicating that energy expenditure increased
after meal consumption and then dropped
back toward baseline values, but no significant differences between conditions (hibiscus tea versus water control) were observed.
The researchers decided to split the sample
by sex, and analyze male and female data
separately. In doing so, they found no significant condition effect or time × condition
85
interaction effect for females, but reported
what they called a “tendency to significance”
for the time × condition interaction effect in
males. In theory, this interaction would suggest that males had similar resting energy expenditure before meal ingestion in both study
conditions, but the hibiscus tea led to elevated
energy expenditure levels around 240 minutes after the standardized meal. Non-nitrogen energy expenditure results are presented
in Figure 1.
The researchers also analyzed nitrogen energy expenditure data (that is, energy expenditure values that incorporate estimated
protein utilization into the estimate). For the
full sample, there were no significant effects
of time, condition, or the interaction between
them. The same was true for the female-only
data. For the male-only data, there was a significant time × condition interaction effect (p
= 0.045). When looking at the pre-breakfast
and 240-minute time points, it appears that
nitrogen energy expenditure dropped from
1613 ± 258.9 to 1501 ± 290.7 kcal/day in the
water condition, but rose from 1599 ± 223.4
to 1619 ± 288.9 kcal/day in the hibiscus tea
condition. In other words, nitrogen energy
expenditure dropped by 112 ± 118.5 kcals
from baseline to 240 minutes in the water
condition, but increased by 20 ± 190 kcals in
the hibiscus tea condition.
In terms of substrate utilization, no significant effects were found for the full sample
or for males only. A significant main effect
of condition was observed for fat oxidation
in females only, indicating that greater fat
oxidation occurred during the hibiscus tea
condition (p = 0.034). However, the utility
of this finding is questionable due to the lack
of a significant time × condition interaction
effect, the absence of an increase in energy
expenditure, and curiously low baseline fat
oxidation rates for females in the water condition (1.2g/hour, with a fasting non-nitrogen
respiratory quotient of 0.91 units at rest).
Results for subjective ratings of hunger, sa-
86
tiety, and fullness all followed the same general pattern. In the full sample, there was a
main effect of condition (p < 0.05), but no
time × condition interaction effect. When
splitting the sample by sex, a significant main
effect of condition was observed in males (p
< 0.05), but not in females. These effects reflected lower hunger along with higher satiety and fullness within the hibiscus condition.
The hunger results are presented in Figure 2.
The results for desire to eat were a little different, but not by much. In the full sample,
neither the main effect of condition nor the
time × condition interaction effect were statistically significant. However, once again, a
significant main effect of condition was observed in males, but not in females. This main
effect reflected generally lower desire to eat
values in the hibiscus condition for male participants. Despite higher subjective ratings for
satiety-related outcomes, this did not translate
to lower energy intake in the next meal after
testing, or lower total energy intake on the
day of testing. None of the outcomes related
to energy intake were statistically significant,
but the mean values for the male subsample
seemed to reflect higher intakes within the
hibiscus tea condition. In the full sample, energy intakes were 690 ± 97.6 kcals in the first
meal and 1726 ± 167.6 for the full day within
the hibiscus condition, compared to only 609
± 53.7 kcals and 1588 ± 103.7 kcals within
the water condition. For males only, intakes
were 858 ± 165.0 kcals in the first meal and
2098 ± 232.6 kcals for the full day within the
hibiscus condition, compared to only 607 ±
67.5 kcals and 1553 ± 117.3 kcals within the
water condition.
Criticisms and Statistical
Musings
I have a number of considerations to highlight in this section, so I’m just going to list
them as concisely as possible to prevent this
section from taking up too much real estate
87
in this article. One issue is that the study was
an open-label, unblinded design with no placebo. Sometimes it’s unethical to conceal
treatments or difficult to find a suitable placebo, but it seems both ethical and feasible
to run this study back as a double-blinded
trial with a placebo condition (some kind of
physiologically inert beverage with color or
flavor) instead of a control condition (water).
For a study that leans heavily on subjective
responses (that is, all of the hunger and satiety outcomes), failing to include a placebo is
a major weakness.
Shifting from study design to statistical analysis, two major issues jumped out to me.
First, the researchers analyzed the data as a
full sample, then split the sample into males
only and females only. In order to justify doing this, you’d want to first test a three-way
analysis of variance, with time, condition,
and sex as predictors. If you find that sex is
interacting with other factors, then you have
statistical justification to split the sample by
sex for further analysis. If not, you don’t. By
failing to take this step in the analysis, the
researchers inflated the type 2 error rate (that
is, the risk of false positives) due to an increased number of statistical tests. This is
compounded by the fact that each subsample (males only and females only) was very
small (10-11 participants), which leaves the
door wide open for false positives related to
sampling error.
Once you start splitting groups by sex without
statistical justification, you also tend to run
into erroneous inferences about “sex differences.” As we’ve discussed before, you have
to be very careful when testing stuff within
two separate groups and making inferences
about differences between the groups. If one
group increases their bench press significantly (p = 0.04) and one group increases their
bench press to a non-significant degree (p =
0.06), those groups had very similar responses to the intervention, and there’s no way
that a direct comparison would reveal differing results between those two groups. So, if
we see a significant effect in the males-only
subsample but no significant effect in the females-only subsample, we can’t start making
inferences about differences between sexes
based on that alone.
As we move on, we come across a statistical concern that actually found its way into
one of my articles last month as well. As I
explained in detail last month, we have to
carefully distinguish between the main effect
of condition and the time × condition interaction effect when baseline measurements
are involved. I would direct readers to last
month’s article for a more verbose explanation, but the short version is as follows: the
main effect of condition is determined based
on measurement values within each condition
averaged across all measured time points,
including the baseline measurement. In this
study, baseline measurements preceded treatment ingestion (hibiscus tea or water), which
means that values that cannot be impacted
by the treatment are being lumped into the
main effect of condition. In an ideal scenario, baseline values would be very similar for
both conditions; if the treatment “worked” or
did something interesting, then values would
start to diverge in measurements taken after
the treatment was actually ingested, and this
88
would be reflected as a significant time ×
condition interaction effect. If the treatment
didn’t do anything, then values would remain
similar, and no time × condition interaction
effect or main effect of condition would be
found. When you’ve got a main effect of
condition in this design, that can mean that
people just had higher or lower values during
a particular visit, whether those values were
measured before or after the treatment was
actually ingested. For example, look back at
Figure 2 in this article. Hibiscus tea didn’t reduce hunger in males; those guys just showed
up hungry for their control (water) visit. They
were hungrier at baseline, and at every time
point thereafter, to a similar degree. In the
presently reviewed study, there were several
occasions in which main effects were interpreted in a way that seemed to overstate the
effects of hibiscus ingestion.
Finally, a word on the energy expenditure and
substrate oxidation values. When looking at
non-nitrogen energy expenditure, there are
no statistically significant effects, even when
splitting the sample by sex. The authors note
a “trend” for the interaction effect in males,
but an examination of Figure 1 shows that
energy expenditure values were pretty much
identical at three of the four time points, and
it’s hard to imagine that the effect becomes
practically meaningful out of nowhere at 240
minutes (that is, I can’t think of a mechanistic
explanation for a delayed effect on this time
scale). For nitrogen energy expenditure, there
is no statistical justification for splitting the
sample, so an analysis done “by the book”
would reveal no significant effect. However,
the analysis was stratified by sex, and the in-
teraction effect was significant in males only.
It’s important to note this effect was driven
by a decrease in the water condition more
so than an increase in the hibiscus tea condition. In the water condition, males had lower
energy expenditure at 240 minutes (by an average of 112kcals) than they did in the fasted
state (baseline), which is not what we would
expect. Energy expenditure may or may not
drop all the way back down to baseline by 240
minutes after a meal, but we wouldn’t expect
it to drop substantially below a value that
was measured in the rested state after a 12hour fast. The unfortunate reality of resting
energy expenditure is that it’s an inherently
fickle measurement. Participants are required
to rest during the measurement, but if they
doze off, fidget a lot, or get a little nervous
or excited about something, it can impact the
value. Similarly, if a participant is a little late
for their visit and has to briskly walk through
the parking lot and building to find the laboratory, their baseline measurement might
end up being too high. Or, there could be air
escaping the hood, mask, or mouthpiece being used for data collection, or the equipment
could be poorly calibrated, or the pump (if
you’re using one) could be set to an incorrect
flow rate. All of that is to say, we have to
carefully scrutinize resting energy expenditure values measured via indirect calorimetry, and I don’t view the values presented as
strong evidence favoring hibiscus tea.
The researchers also reported, based on indirect calorimetry, that fat oxidation was increased by hibiscus tea in females only. Once
again, there was insufficient statistical justification to split the sample by sex, and this was
89
another instance where the observed effect
was a main effect rather than an interaction.
It’s quite clear that this main effect was driven by the fact that females had unusually high
respiratory quotient values (0.91 units) when
they showed up for their water visit. I would
expect this to be down around 0.84-0.85, give
or take (3), and a value up above 0.90 represents a high proportion of carbohydrate utilization and a low proportion of fat utilization
at rest, which was not replicated when these
same participants reported for their hibiscus
tea visit. These values could have been elevated due to poorer adherence to pre-visit fasting
and exercise instructions, a more brisk walk
on the way into the lab, differences in psychological state or breathing rate during the test,
lack of control for menstrual cycle phase, or
insufficient resting procedures before the onset of measurement, among other potential
explanations. In any case, the reported effect
pertaining to elevated fat oxidation in females
is more accurately described as unusually low
fat oxidation when they showed up for the water visit, before any study treatments were actually ingested.
Interpretation
There’s no point in burying the lede – I don’t
see compelling evidence to suggest that hibiscus tea is your “one weird trick” to accelerate fat loss and curb hunger. To avoid
being repetitious, I’ll begin by acknowledging that, based on the factors outlined in the
“Criticisms and Statistical Musings” section,
I don’t believe the new data generated within this study are indicative of a practically
meaningful impact of hibiscus tea on energy
I DON’T SEE COMPELLING
EVIDENCE TO SUGGEST
THAT HIBISCUS TEA IS
YOUR “ONE WEIRD TRICK”
TO ACCELERATE FAT LOSS
AND CURB HUNGER.
expenditure, satiety, or prospective weight
loss success. Aside from the considerations
that have already been highlighted, it’s also
important to emphasize that the purported effect on satiety was not sufficient to actually
reduce energy intake in the subsequent meal
or throughout the day of testing. As noted
in the results section, the findings suggested
that the satiety-inducing effects of hibiscus
tea were specifically observed in male participants, but male participants consumed
about 250kcals more at the next meal, and
over 500kcals more over the course of the
testing day when they received hibiscus tea
instead of water. So, within the study itself,
it’s challenging to suggest that hibiscus tea
had effects that promoted more negative energy balance to support weight loss.
Even if we adopt an excessively charitable
view of the data within this specific study,
there are still some roadblocks when it comes
to tying them into the broader literature. For
example, the presently reviewed study reported significant satiety effects in males only,
90
but the strongest human data tying hibiscus
tea to satiety effects was from a study by
Boix-Castejón et al (4), and that study only
included female participants. In addition, the
presently reviewed paper indicated that the
main bioactive ingredients in hibiscus tea are
suspected to be lutein, chlorogenic acids, and
a type of polyphenol known as anthocyanin,
with the majority of studies attributing most
actions of hibiscus tea to their polyphenol
content. In the presently reviewed paper, the
researchers suggest that hibiscus tea may be
able to increase energy expenditure by activating AMP-activated protein kinase, and
may be able to increase satiety by modulating hormones related to hunger and appetite.
However, the researchers do not present a
mechanistic explanation for their interpretation that hibiscus differentially impacted
males and females. They did acknowledge
that failing to account for menstrual cycle
phase might have been a confounding factor, but in the previously mentioned study
by Boix-Castejón and colleagues that linked
hibiscus tea to appetite regulation (4), the researchers sampled females between the ages
of 30-75 (and presumably had plenty of eumenorrheic participants), took appetite-related measurements every 15 days (thereby
ensuring a lack of menstrual phase standardization), and obtained data that appear to be
incompatible with the idea that satiety-related effects were masked by menstrual phase
within the presently reviewed study (1).
Since I’ve brought up the study by Boix-Castejón and colleagues twice now, there are a couple of important things to keep in mind about
it. That lab group has published at least a few
papers reporting increases in outcomes related to satiety, and modest reductions in body
weight, following longitudinal supplementation with a product that includes hibiscus tea
extract (4, 5, 6). However, this group’s papers involve a mixture of hibiscus tea extract
(35%) and Lippia citriodora extract (65%)
rather than brewed hibiscus tea alone, and it
looks like the company that owns the patent
for this combination was involved enough to
have an employee on the author line in these
studies. That doesn’t mean we should discard
the findings entirely, but we certainly want to
cross reference them with findings from other research groups using hibiscus alone.
When we do that, the findings aren’t quite as
promising. For example, Chang et al did not
find a significant difference in weight loss
over 12 weeks when taking a hibiscus extract
versus a placebo treatment (7). They did report a significant interaction effect for bodyfat percentage, but the body-fat reduction
in the hibiscus group was very small (less
than one percentage point). In addition, body
composition was measured using bioimpedance analysis, and hibiscus is thought to have
a slight diuretic effect (8), so I wouldn’t put
too much stock in that. Kuriyan et al assessed
changes in body weight over 90 days of supplementation with hibiscus extract, and found
no significant difference when compared to a
placebo (9). Similarly, Mozaffari-Khosravi
and colleagues published studies in 2009 (10)
and 2013 (11) in which hibiscus tea failed to
significantly decrease body weight in patients
with type 2 diabetes. A recent meta-analysis
pooled data from five studies evaluating the
effects of hibiscus tea on body weight (134
91
total participants), and six studies evaluating
the effects of hibiscus tea on BMI (152 total
participants). Results did not indicate that hibiscus had a significant effect on either outcome, with a weighted mean difference of
-0.1 units for BMI (p = 0.77) and -0.3kg for
body weight (p = 0.82).
So far, the data don’t suggest that hibiscus
tea is our shortcut to weight loss, but there
are some simple things we can do to facilitate
higher satiety and lower desire to eat, as outlined back in Volume 4 of MASS. If you’re
trying to organize an energy restricted diet
to support higher satiety, you might consider
avoiding hyperpalatable meals, and structuring your meals with high protein, fiber, and
water content, low energy density, and plenty of unprocessed or minimally processed
foods. There’s also some evidence to suggest
that eating more slowly can facilitate higher satiety levels (12), although whether or
THE DATA DON’T SUGGEST
THAT HIBISCUS TEA IS OUR
SHORTCUT TO WEIGHT
LOSS, BUT THERE ARE
SOME SIMPLE THINGS
WE CAN DO TO FACILITATE
HIGHER SATIETY AND
LOWER DESIRE TO EAT.
not this contributes to lower ad libitum food
intake is debatable (13), and eating slowly
might be less effective for people with high
dietary restraint (14). If you insist on seeking
out something to add into your diet as a “bonus,” there is some evidence that consuming
meals with green tea, hot pepper (capsaicin),
and even non-pungent capsaicinoids can lead
to suppressed hunger and increased satiety
(15), in addition to (very modest) short-term
thermogenic effects that may elevate resting
metabolic rate when high enough doses are
ingested. Caffeine could be contributing to
the effects observed for green tea, but whether or not caffeine alone can influence hunger or energy expenditure enough to make a
noteworthy difference for body composition
management is debatable. Many supplements
that claim to increase metabolic rate have a
small and transient effect on energy expenditure, which could easily be counteracted
by a small compensatory reduction in resting
energy expenditure or non-exercise activity
later in the day, or by a small compensatory
increase in energy intake. I wouldn’t expect
huge effects from these interventions, and the
data supporting them are far from conclusive
or unanimous, but they have more support
than hibiscus for these specific outcomes.
I’m going to go on a little detour here, but
another strategy I’ve found incredibly useful
is to mentally reframe hunger and palatability during energy restriction. To be totally
candid, I originally wrote this section based
largely on anecdotal evidence from my experiences as a coach and physique athlete, then
the good Dr. Helms informed me that there
was actually some pretty relevant empirical
92
support for it (16). When real-world experiences and scientific evidence combine, that’s
a pretty nice thing. For ambitious weight loss
goals, low satiety and low overall diet satisfaction are likely to become unavoidable at
some point in the process. Many people try
to get around this by pursuing strategies that
emphasize change or control. For example,
some focus on making their low-calorie meals
as palatable (or hyperpalatable) as possible,
with elaborate combinations of low-calorie
diet foods meant to replicate a decadent dessert. Others sacrifice palatability to focus on
food volume, hoping that eating a large bucket of broccoli and chicken breast will keep
hunger at bay. There’s nothing wrong with
trying to fit your diet to your preferences, but
when you start fixating on hunger or palatability, you’ve given your diet way too much
control over your mental and emotional state,
and you’ve set yourself up to fail. In contrast,
you could opt for a strategy that leans on acceptance more so than change or control.
This could involve adopting a more mindful
approach to eating by really focusing your attention on the meal as you’re eating it. Even
if the meal has fairly modest palatability, you
can make an effort to appreciate the food
without comparing it to some hypothetical
meal that would’ve been more palatable, or
comparing it to some hypothetical meal that
would’ve had larger serving sizes. Before
and after the meal, you can take a moment
to think about your goals, contextualize the
meal within those goals, and appreciate the
nutritional quality of the meal and the role it
plays in supporting your goals.
Similarly, as hunger arises throughout the
day, you can mindfully acknowledge it, contextualize it, and accept it for what it is. For
people who have sufficient food security and
are voluntarily dieting on a safe, healthy, and
well-constructed diet, hunger is essentially a
“false alarm” alerting us to a perceived shortfall in nutritional resources. Hunger is an unpleasant sensation, so we naturally have a tendency to attach negative mental and emotional
states to it. Nonetheless, we know why hunger
is present, we know the unpleasant sensation
is transient in nature, and we know that hunger itself will not harm or derail us in any way
(again, we’re assuming that you’re on a safe,
healthy, and well-constructed diet). When we
contextualize hunger this way, it increases the
likelihood that we can successfully accept and
coexist with it instead of fixating on the goal
of changing or controlling it.
Being mindful of hunger and satiety cues is
very useful, but it’s important to recognize
that the goal is not complete avoidance of
hunger; what we’re trying to do is objectively
observe hunger and satiety cues, contextualize them within our goals, and then respond
accordingly. Everyone is different when it
comes to their baseline levels of hunger and
appetite, and observed levels of hunger and
satiety transiently ebb and flow above and
below baseline throughout any given day. In
addition, the baseline levels themselves will
fluctuate as body composition goals change.
We might feel like we’re at our “standard”
baseline hunger level when we’re aiming to
maintain weight or induce very conservative
weight gain or weight loss, whereas aggressive weight gain can totally blunt an individual’s baseline appetite level, and weight loss
93
goals that involve rapid weight loss, large
amounts of weight loss, or acquisition of a
very low body-fat level can raise our baseline hunger level substantially. So, if you’re
trying to achieve a fairly ambitious weight
loss goal, some degree of hunger is to be expected. When you experience hunger, you
can aim to objectively examine it within the
context of your current diet and goals, and
then respond accordingly. If your approach
to dieting involves aiming for a specific set
of macro targets each day, then “responding
accordingly” may involve eating a meal a
bit earlier, shifting some calories around to
allow for a snack, or simply acknowledging
and accepting the hunger for what it is.
These mental strategies for reframing diet
satisfaction and hunger might outwardly
seem as simple as just eating plain food and
ignoring hunger, but there’s a lot more to it.
They require some mental effort and discipline, and these types of strategies can be
quite challenging to implement and sustain.
Nonetheless, they facilitate a totally different perspective relative to diet satisfaction
and hunger. Instead of desperately trying to
salvage super high palatability on a diet with
ever-decreasing calories, we’re embracing
the palatability of simple foods and releasing
ourselves from the need for overly palatable
meals. Instead of fighting against hunger or
desperately trying to avoid it, we’re acknowledging it, contextualizing it, and (when we’ve
got a fairly ambitious weight loss goal) coexisting with it.
In conclusion, my hopes are not high for beverages or supplements that aim to increase
energy expenditure for fat loss purposes; their
effects are generally too small and short lived
to really move the needle for total daily energy balance, and effects of this magnitude can
be easily counteracted by compensatory adjustments to energy intake or expenditure. As
for hunger, I personally view all of the teas,
pills, powders, and potions as “Band-Aids,”
whereas substantive changes in food selection and macronutrient intakes have a larger
impact. I think spending some time to mentally reframe hunger and diet satisfaction is
even more impactful when implemented successfully, but the good news is that these categories are not mutually exclusive. If you’re
struggling with hunger, you could implement
these reframing strategies while also avoiding hyperpalatable meals and structuring
your meals with high protein, fiber, and water content, low energy density, and plenty of
unprocessed or minimally processed foods.
If that’s not getting the job done, you could
stack one more intervention on top by adding
some type of food, beverage, or supplement
that is purported to increase energy expenditure or reduce hunger. However, based on the
evidence available, a hibiscus product would
not be my first choice; I’d be more inclined
to drink some green tea with a meal or work
more capsaicinoids into the meal itself.
Next Steps
As an empiricist, I’m not a big fan of using my
imagination. I don’t want to perform speculative cost/benefit analyses based on extrapolated expectations from transient changes in
resting energy expenditure or self-reported
satiety. Rather, I want to actually see the outcome of interest. So, I’d like to see a dou-
94
APPLICATION AND TAKEAWAYS
At this point in time, there is not convincing evidence that hibiscus tea has consistent
and meaningful effects on energy expenditure, satiety, or fat loss. Some practical
strategies that may support higher satiety levels during energy restriction involve
eating more slowly, avoiding hyperpalatable meals, and structuring your meals with
high protein, fiber, and water content, low energy density, and plenty of unprocessed
or minimally processed foods. If you want to add something to your diet to boost
energy expenditure, increase satiety, or reduce desire to eat, foods and supplements
containing capsaicinoids, green tea, or caffeine have more supporting evidence
than hibiscus tea. However, before waging war on our personal hunger, we should
carefully consider whether or not it’s a battle worth fighting. Mentally fixating on
changing or controlling hunger can potentiate its negative impact on our subjective
experience while dieting. A mental approach to dieting that acknowledges hunger as
part of the energy restriction process can be difficult to implement effectively, but can
be quite empowering when utilized successfully.
ble-blinded, placebo-controlled, randomized
trial that specifically assesses changes in
energy intake and body composition over a
period of 8-12 weeks, with one group consuming hibiscus tea with their breakfast, and
another consuming a suitable placebo. I totally understand the desire to conduct shortterm experiments with proxy measures like
the presently reviewed study, as they can
often help us understand if a larger or more
resource-intensive study is even warranted.
Researchers might be reluctant to run a longer study assessing body composition directly, and might justify their hesitation based on
the fact that longer studies are more expensive to run, or that it’s hard to capture small
changes in total body composition over the
span of only a couple months. However, the
counterarguments are obvious: do we really
want to drum up interest in an intervention
that is too expensive to implement consistently, or yields effects that are too small to
confidently discern over a timespan of mul-
tiple months? Before I get excited about a
tea that is purported to increase energy expenditure or suppress appetite, I need to see
that it actually yields tangible effects that are
practically relevant and meaningful. Once we
get a solid quantification of the effect size we
can expect, the cost/benefit analysis becomes
pretty simple.
95
References
1. Faria NC de, Soares AP da C, Graciano GF, Correia MITD, Pires MC, Valenzuela
VDCT, et al. Hibiscus sabdariffa tea affects diet-induced thermogenesis and subjective
satiety responses in healthy men, but not in women: a randomized crossover trial. Appl
Physiol Nutr Metab. 2021 Aug 9; ePub ahead of print.
2. Gupta RD, Ramachandran R, Venkatesan P, Anoop S, Joseph M, Thomas N. Indirect
Calorimetry: From Bench to Bedside. Indian J Endocrinol Metab. 2017;21(4):594–9.
3. Wingfield HL, Smith-Ryan AE, Melvin MN, Roelofs EJ, Trexler ET, Hackney AC, et al.
The acute effect of exercise modality and nutrition manipulations on post-exercise resting
energy expenditure and respiratory exchange ratio in women: a randomized trial. Sports
Med - Open. 2015 Jun 5;1(1):11.
4. Boix-Castejón M, Herranz-López M, Pérez Gago A, Olivares-Vicente M, Caturla N,
Roche E, et al. Hibiscus and lemon verbena polyphenols modulate appetite-related
biomarkers in overweight subjects: a randomized controlled trial. Food Funct. 2018 Jun
20;9(6):3173–84.
5. Boix-Castejón M, Herranz-López M, Olivares-Vicente M, Campoy P, Caturla N, Jones
J, et al. Effect of metabolaid® on pre- and stage 1 hypertensive patients: A randomized
controlled trial. J Funct Foods. 2021 Sep 1;84:104583.
6. Herranz-López M, Olivares-Vicente M, Boix-Castejón M, Caturla N, Roche E, Micol
V. Differential effects of a combination of Hibiscus sabdariffa and Lippia citriodora
polyphenols in overweight/obese subjects: A randomized controlled trial. Sci Rep. 2019
Feb 28;9(1):2999.
7. Chang H-C, Peng C-H, Yeh D-M, Kao E-S, Wang C-J. Hibiscus sabdariffa extract
inhibits obesity and fat accumulation, and improves liver steatosis in humans. Food
Funct. 2014 Apr;5(4):734–9.
8. Herrera-Arellano A, Miranda-Sánchez J, Avila-Castro P, Herrera-Alvarez S, JiménezFerrer JE, Zamilpa A, et al. Clinical effects produced by a standardized herbal medicinal
product of Hibiscus sabdariffa on patients with hypertension. A randomized, doubleblind, lisinopril-controlled clinical trial. Planta Med. 2007 Jan;73(1):6–12.
9. Kuriyan R, Kumar DR, R R, Kurpad AV. An evaluation of the hypolipidemic effect of an
extract of Hibiscus Sabdariffa leaves in hyperlipidemic Indians: a double blind, placebo
controlled trial. BMC Complement Altern Med. 2010 Jun 17;10:27.
10. Mozaffari-Khosravi H, Jalali-Khanabadi B-A, Afkhami-Ardekani M, Fatehi F, NooriShadkam M. The effects of sour tea (Hibiscus sabdariffa) on hypertension in patients
96
with type II diabetes. J Hum Hypertens. 2009 Jan;23(1):48–54.
11. Mozaffari-Khosravi H, Ahadi Z, Barzegar K. The effect of green tea and sour tea on
blood pressure of patients with type 2 diabetes: a randomized clinical trial. J Diet Suppl.
2013 Jun;10(2):105–15.
12. Hawton K, Ferriday D, Rogers P, Toner P, Brooks J, Holly J, et al. Slow Down:
Behavioural and Physiological Effects of Reducing Eating Rate. Nutrients. 2018 Dec
27;11(1):50.
13. Ferriday D, Bosworth ML, Lai S, Godinot N, Martin N, Martin AA, et al. Effects of
eating rate on satiety: A role for episodic memory? Physiol Behav. 2015 Dec 1;152(Pt
B):389–96.
14. Privitera GJ, Cooper KC, Cosco AR. The influence of eating rate on satiety and
intake among participants exhibiting high dietary restraint. Food Nutr Res. 2012 Jan
5;56:10.3402/fnr.v56i0.10202.
15. Reinbach HC, Smeets A, Martinussen T, Møller P, Westerterp-Plantenga MS. Effects of
capsaicin, green tea and CH-19 sweet pepper on appetite and energy intake in humans in
negative and positive energy balance. Clin Nutr. 2009 Jun;28(3):260–5.
16. Lillis J, Kendra KE. Acceptance and Commitment Therapy for weight control: Model,
evidence, and future directions. J Context Behav Sci. 2014 Jan;3(1):1–7.
█
97
Research Briefs
BY GREG NUCKOLS
In the Research Briefs section, Greg Nuckols shares a
few quick summaries of recent studies. Briefs are short
and sweet, skimmable, and focused on the need-to-know
information from each study.
99
If You Keep Lifting, You’ll Retain Your Capacity
To Recover From Training As You Age
103
Do People Prefer Being Told What To Do In
The Gym?
106
Walking Away From An Early Grave
109
No, Your Triceps Aren’t Extra-Important For
Bench Press 1RM Attempts
98
Recovery from Eccentric Squat Exercise in Resistance-Trained Young and Master Athletes
with Similar Maximum Strength: Combining Cold Water Immersion and Compression.
Schmidt et al. (2021)
If You Keep Lifting, You’ll Retain Your Capacity To
Recover From Training As You Age
We’ve previously discussed a pair of studies
investigating recovery from training in young
versus middle-aged lifters (2, 3), but those
studies had some drawbacks. The first, which
was covered way back in Volume 1, used untrained subjects (2). The second, which was
just covered two months ago, employed a
great study design, but utilized a statistical
approach that made it difficult to interpret the
results of the study with much granularity (3).
However, the present study by Schmidt and
colleagues isn’t plagued by either of those
drawbacks (1).
The subjects competed in a variety of sports
at the regional or national level, and all had
at least one year of resistance training experience. One group of subjects was young (n
= 8; 22.1 ± 2.1 years old) and one group was
middle-aged (n = 8; 52.4 ± 3.5 years old).
You can see more information about the subjects in Table 1.
Each subject completed a fatigue protocol in
two separate testing sessions, with the two sessions separated by two weeks. In each testing
session, subjects completed nine sets of eight
half squats at a controlled cadence (4-second
eccentrics and 2-second concentrics), followed by a final set performed to concentric
failure. After one session, subjects underwent 15 minutes of cold water immersion
(at 12° C), and then wore lower body compression garments for 48 hours post-training.
The other session served as a control condition – no specific recovery intervention was
provided. Maximum voluntary isometric leg
press and half squat strength, resting muscle
twitch force, countermovement jump height,
creatine kinase levels, subjective levels of
muscle soreness, and perceived physical performance capability (4) were assessed before
each squat session, immediately after each
session, and 24, 48, and 72 hours following
each session.
Once again, age didn’t seem to have much of
an effect on recovery. The middle-aged subjects were a bit weaker and had lower countermovement jump heights than the young
subjects, but the overall recovery trajectories for all measures were similar between
age groups. The only significant differences
occurred immediately post-training, when
99
young subjects actually reported more soreness and lower perceived physical performance capability than the middle-aged subjects. The recovery intervention (cold water
immersion and compression garment usage)
improved recovery of perceived physical performance capability and mitigated post-training increases in muscle soreness a bit, but
didn’t affect any of the objective measures of
performance recovery.
One study could just be a fluke, but at this
point, we’ve seen three consecutive studies
suggesting that active, healthy middle-aged
folks recover from resistance training about
as well as younger folks do (1, 2, 3). I think
the “active” and “healthy” modifiers are important, though. All three of these studies
have included subjects who are generally active and in good health (and two of the three
specifically used resistance-trained subjects).
There are plenty of anecdotes of folks who
were sedentary for a couple of decades, and
really struggle to recover from training when
they get back into the gym. However, if you
stay active and keep lifting, it seems that your
ability to recover from training in your 50s is
pretty comparable to your ability to recover
from training in your 20s.
Regarding the effects of the recovery intervention, I think it’s noteworthy that the subjective, perceptual measures were improved
by the introduction of cold water immersion
and compression garments, but the objective
measures of performance weren’t. That suggests to me that the placebo effect may have
been the driving force behind the significant
differences that were observed. In general,
the placebo effect is more potent for subjective measures (for example, pain, nausea,
mood, etc.) than objective measures (jump
100
101
height, range of motion, 1RM bench press,
etc.). However, we shouldn’t write cold water immersion and compression garments off
entirely – a previous meta-analysis (reviewed
in MASS) found that they were both effective recovery interventions (5). They just
didn’t seem to do much in the present study.
Though, even if the recovery intervention
did prove effective in the present study, I
probably still wouldn’t recommend cold water immersion as a go-to recovery aid, since
chronic usage of cold water immersion has
been shown to mitigate muscle growth and
strength gains (6).
102
Exploring the Acute Affective Responses to Resistance Training: A Comparison of the
Predetermined and the Estimated Repetitions to Failure Approaches. Schwartz et al. (2021)
Do People Prefer Being Told What To Do In The Gym?
Here at MASS, we’re pretty big fans of autoregulation generally, and autoregulation
employing reps in reserve (RIR) in particular. However, RIR-based autoregulation approaches may not be appropriate for everyone. For example, if someone simply isn’t
good at estimating how many reps they have
in the tank near the end of a set, they probably won’t benefit from RIR-based autoregulation. Furthermore, it’s important to take
preferences into account when designing a
training program, which begs the question:
do people actually enjoy RIR-based autoregulation?
the participants rated their affective valence
using the Feeling Scale (8), where 5 denotes
feeling “very good,” 0 denotes feeling “neutral,” and -5 denotes feeling “very bad.”
In a recent study (7), 20 women with “extensive Pilates experience but without [resistance training] experience” completed a
standardized workout under two conditions.
In one condition, subjects completed a predetermined training prescription (3 sets of 10
reps with 70% of 1RM) for each exercise; in
the other condition, subjects terminated each
set when they felt they were two reps away
from concentric failure (3 sets at 70% with
2 RIR). The exercises performed were leg
press, knee extensions, pull-downs, and machine chest press. Before and after each set,
The researchers also recorded the number of
reps completed per set in the RIR-based condition. On average, subjects wound up performing 8-9 reps per set of knee extensions,
chest press, and pull-downs in the RIR-based
workout, and about 17 reps per set of leg press.
There was considerable individual variability
about those averages, as you can see in Figure 2. Finally, the subjects provided subjective descriptions of why they preferred either
the predetermined training prescription or the
RIR-based training prescription; you can see
some examples of that feedback in Table 1.
On average, participants enjoyed the predetermined training prescription slightly more
than the RIR-based approach (p = 0.006).
The Feeling Scale score was 3.29 ± 0.89 in
the predetermined condition, and 3.01 ± 0.95
in the RIR-based condition (Figure 1). Overall, 12 subjects preferred the predetermined
training prescription, and eight preferred the
RIR-based training prescription.
103
Training prescription involves a balancing
act between designing the sort of program
you (or your clients) will enjoy, and designing the sort of program that will provide the
training stimulus you (or your clients) need.
This study suggests that people generally enjoy resistance training (which confirms my
biases, so it must be true), as evidenced by
nearly unanimous positive Feeling Scale
ratings with both styles of training prescription. However, some individuals had clear
preferences for either predetermined training
prescriptions or RIR-based training prescriptions. For example, one individual had a leg
press Feeling Scale rating of 4.5 with a predetermined training prescription, and a Feeling Scale rating of just 1 with an RIR-based
prescription. Conversely, one individual had
a knee extension Feeling Scale rating of 0
with a predetermined training prescription,
and a Feeling Scale rating of 2.5 with an RIRbased prescription. For most individuals, the
difference between conditions was less than
1 point on the Feeling Scale, meaning that
both methods of training prescription were
similarly enjoyable.
We’ve discussed the benefits of RIR-based
autoregulation pretty frequently in MASS,
and the data from this study strongly illustrates one of those benefits: when you’re
104
assigning training loads using percentages
of 1RM, one-size-fits-all rep targets may be
unrealistic, because strength endurance can
vary so widely between individuals. A rep
target that’s appropriate for one individual
may leave another individual 10 reps from
failure. With RIR-based autoregulation, on
the other hand, most folks will wind up at an
appropriate proximity from failure. However, if someone isn’t good at assessing their
reps in reserve (which does improve with experience), or if they simply prefer a predetermined training prescription, you can certainly
make a predetermined training prescription
work by either personalizing training intensities (if you want people to train in a particular rep range, you’d have people with better
strength endurance train at a slightly higher
intensity than people with worse strength endurance) or personalizing rep targets (if you
want people to train at a particular intensity,
you’d assign higher rep ranges to people with
better strength endurance).
105
Daily Step Count and All-Cause Mortality: A Dose-Response Meta-Analysis of Prospective
Cohort Studies. Jayedi et al. (2021)
Walking Away From An Early Grave
One of the reasons I started the “Research
Briefs” was to give myself a bit more leeway to discuss important studies that aren’t
squarely within the typical scope of MASS
(research that can help strength and physique
athletes and coaches), but that are still related
to exercise, physical activity, or nutrition. A recent meta-analysis examining the relationship
between daily step counts and all-cause mortality (9) fits the bill perfectly – walking a bit
more probably isn’t going to make you huge,
shredded, or freakishly strong, but it may have
a fairly massive impact on longevity.
The present meta-analysis sought to determine the relationship between daily step
counts and all-cause mortality. The researchers started by scouring several databases
to find all of the prospective cohort studies
that quantified the relationship between step
counts and all-cause mortality rate. From
there, they extracted all of the relevant data,
performed a pretty standard random-effects
meta-analysis, tested for moderating variables, and assessed the certainty of their conclusions using the GRADE criteria (10).
Seven studies were included in the meta-anal-
ysis, accounting for 28,141 total participants,
175,370 person-years, and 2,310 deaths. The
researchers found that rates of all-cause mortality were about 12% lower per 1,000 steps
per day (hazard ratio = 0.88; 95% CI = 0.830.93). The potential moderators examined
(studies with longer versus shorter observation periods, studies from Europe versus the
US versus Asia, studies with older versus
younger participants, etc.) didn’t impact the
findings to any meaningful degree – the hazard ratio fell within the range of 0.81-0.93 for
all subgroups of studies tested. According to
the GRADE criteria, we can have a high degree of certainty in the relationship between
step counts and all-cause mortality. Comparing the lowest step counts to the highest
step counts reported in the studies included in
this meta-analysis, walking 16,000 steps per
day was associated with a 66% reduction in
all-cause mortality compared to walking just
2,700 steps per day. Stated conversely, walking 2,700 steps per day was associated with
a three-fold greater risk of all-cause mortality
than walking 16,000 steps per day.
Before interpreting these results, I want to
make one thing crystal clear: I’m not falling
106
into the trap of assuming that correlation implies causation. It’s entirely possible that people who are healthier simply tend to walk more
than people who are less healthy, and daily
step counts are therefore merely a proxy for
general health, and don’t have an inverse causal relationship with all-cause mortality. How-
ever, I don’t think that’s the case – at least not
entirely. For example, a 2015 meta-analysis
(11) found that group-based walking interventions, all lasting one year or less, led to significant decreases in systolic blood pressure, diastolic blood pressure, resting heart rate, body
fat percentage, body mass index, total cholesterol, and depression scores, while increasing
VO2max, 6-minute walk distance, and score
on the SF-36 physical functioning inventory.
107
Most walking intervention studies don’t use
particularly strenuous walking programs either – generally 20-30 minutes of walking per
day, which works out to ~2,400-3,600 steps for
most people. So, if a bit of walking can beneficially modify ten different risk factors for allcause mortality in less than a year, I think we
can make a pretty strong case that the inverse
relationship between step counts and all-cause
mortality is more than mere association.
I think it’s worth contextualizing how striking these findings are. I’m sure most MASS
readers would agree that cigarette smoking
isn’t great for longevity. However, smoking
seems to be associated with ~70-80% higher
rates of all-cause mortality (12, 13). Relative
to people who walk 16,000 steps per day,
walking just 2,700 steps per day is associated
with ~200% higher rates of all-cause mortality. It’s also not uncommon for people in the
fitness industry to discuss the risks associated with obesity, and for good reason. Higher
BMIs are associated with greater all-cause
mortality risk (14). However, a BMI of 30 is
associated with a ~4% greater all-cause mortality risk than a BMI of 23, and a BMI of 40
is associated with a ~74% greater all-cause
mortality risk (Table D, “All participants, all
studies”). Thus, you could argue that being
very sedentary (relative to being very active,
as the standard of comparison) is a larger independent risk factor for all-cause mortality
than smoking status or obesity.
outside of the gym. Dedicated training is great,
and building and maintaining muscle mass
and strength will probably help you live longer (and maintain your ability to comfortably
perform activities of daily living further into
your twilight years), but there’s no substitute
for simply moving more. Research suggests
that adults in the US average ~5,100-6,500
steps per day (15, 16). The present meta-analysis (9) suggests that getting just 6,000 steps
per day is associated with an all-cause mortality risk ~126% higher than the all-cause
mortality risk associated with taking 16,000
steps per day. I’m sure that 6,000 steps plus
dedicated resistance training is better than
6,000 steps with no resistance training, but
it’s difficult to overstate the importance of
simply being on your feet and moving a lot.
Before I wrap up, I want to make it clear that
I’m not arguing that we should start sedentary-shaming people. If you have an office
job, you don’t live in a walkable city, and you
have a lot of obligations outside of work, it
may be hard to carve out the time to get a lot
of steps in. If you live in an unsafe neighborhood, it may be harder to get a lot of steps
in. There are plenty of diseases that make it
harder (or impossible) to get a lot of steps
in. I just want you, as an individual, to be informed – if you want to live a long time, it
never hurts to go for a walk.
I think it’s easy for lifters to fall into the trap
of assuming that being in the gym for a few
hours per week and maintaining a healthy
body composition are sufficient to maximize
longevity, despite being relatively sedentary
108
Change in EMG and Movement Velocity During a Set to Failure Against Different Loads in
the Bench Press Exercise. Tsoukos et al. (2021)
No, Your Triceps Aren’t Extra-Important For Bench
Press 1RM Attempts
This is more of a brief note than a research
brief. Back in Volume 1, I reviewed a study
investigating how electromyography (EMG)
responses of the prime movers in the bench
press changed with increasing loads (18). In
short, triceps EMG increased way more between 70% and 100% 1RM loads than pec or
front deltoid EMG amplitudes. Many people
have interpreted this study to mean that the
triceps are disproportionately important for
benching heavy loads, relative to the other
prime movers. I posited, however, that this
finding was merely due to the head of the
triceps the researchers measured. They measured EMG responses in the long head of the
triceps, and the long head of the triceps is
a biarticular muscle (it crosses both the elbow and the shoulder). In general, biarticular
muscles seem to contribute less to compound
exercises than monoarticular muscles (muscles that just cross one joint), especially when
one of the joint actions of a biarticular muscle
would oppose one of the joint actions being
accomplished in a particular compound exercise. For example, the rectus femoris is both
a knee extensor and a hip flexor. In the squat,
a forceful rectus femoris contraction would
aid one of the joint actions you’re trying to
accomplish (knee extension), but oppose one
of the other joint actions you’re trying to accomplish (hip extension). Since it opposes
one of the important joint actions in the lift,
it’s not nearly as active as the other heads of
the quads (all of which are monoarticular;
19).
I posited that the same logic may apply to
the long head of the triceps bench press. The
long head of the triceps is a shoulder extensor, in addition to being an elbow extensor;
since you’re trying to accomplish shoulder
flexion in the bench press, it makes sense that
the long head of the triceps may not be particularly active when benching lighter loads.
However, as you approach 1RM loads, biarticular muscles seem to contribute more and
more (20), essentially serving as a strength
reserve you can tap into when your nervous
system’s preferred activation pattern (relying mostly on monoarticular muscles) is no
longer sufficient. That would explain why
the long head of the triceps would show a
large increase in EMG at 1RM loads, but it
109
wouldn’t suggest that the triceps were necessarily more important than the other prime
movers for benching heavy loads. Specifically, the lateral and middle heads of the triceps (the two monoarticular heads, which are
more important for bench press performance)
might experience an increase in EMG with
increasing loads that more closely mirrors the
other prime movers.
With that lengthy intro out of the way, my
discussion of the present study will be brief
(17). In short, it confirms my suspicions.
Fourteen young men with at least three years
of “strength and power training” experience
participated in the present study. Subjects
completed three testing visits, separated by
5-7 days. In each visit, they completed a
single set of Smith machine bench press to
failure, pressing each rep as explosively as
possible, with either 40%, 60%, or 80% of
1RM; the subjects completed testing sessions
with these three loads in a randomized order.
Pec EMG and EMG of the lateral head of
the triceps were assessed on each rep. The
researchers normalized the EMG values obtained during the testing session against EMG
values obtained during maximum voluntary
isometric contractions, and reported normalized EMG values at the start, in the middle,
and at the end of each set to failure.
In short, the relative impact of both load and
intra-set fatigue on EMG was virtually identical for the pecs and the lateral head of the
triceps. EMG amplitudes were greater at 60%
and 80% of 1RM than 40% of 1RM, with no
major differences between 60% and 80%
of 1RM. Furthermore, EMG amplitudes increased substantially from the start of each
set to the middle of each set, but didn’t increase much from from the middle of the set
to the end of the set. In short, it seems like the
monoarticular triceps respond to increased
loading and increased fatigue in basically
the same way the pecs do, suggesting that
110
the findings in the previously reviewed study
were driven by the choice to assess EMG in
the long head of the triceps, rather than one
of the (relatively more important) monoarticular heads.
While this research brief may seem like a bit
of a vanity project (just bragging that I was
right about an idea I posited several years ago),
my primary reason for discussing the present
study is that I frequently see folks draw (what
I believe to be) erroneous conclusions from
the study I reviewed back in Volume 1 (18).
If you wouldn’t argue that improving rectus
femoris strength is the real key to improving
your max squat, you shouldn’t interpret the
previously reviewed study as strong evidence
that the triceps are any more or less important
than the pecs or front delts for maximizing
bench press strength.
111
References
1. Schmidt J, Ferrauti A, Kellmann M, Beaudouin F, Pfeiffer M, Volk NR, Wambach
JM, Bruder O, Wiewelhove T. Recovery From Eccentric Squat Exercise in ResistanceTrained Young and Master Athletes With Similar Maximum Strength: Combining Cold
Water Immersion and Compression. Front. Physiol. 2021. 12:665204. doi: 10.3389/
fphys.2021.665204
2. Gordon JA 3rd, Hoffman JR, Arroyo E, Varanoske AN, Coker NA, Gepner Y, Wells AJ,
Stout JR, Fukuda DH. Comparisons in the Recovery Response From Resistance Exercise
Between Young and Middle-Aged Men. J Strength Cond Res. 2017 Dec;31(12):34543462. doi: 10.1519/JSC.0000000000002219. PMID: 28859014.
3. Romero-Parra N, Maestre-Cascales C, Marín-Jiménez N, Rael B, Alfaro-Magallanes
VM, Cupeiro R, Peinado AB. Exercise-Induced Muscle Damage in Postmenopausal
Well-Trained Women. Sports Health. 2021 May 27:19417381211014134. doi:
10.1177/19417381211014134. Epub ahead of print. PMID: 34039086.
4. Kellmann, M., and Kölling, S. (2019). Recovery and stress in sport: a manual for testing
and assessment. London, UK: Routledge.
5. Dupuy O, Douzi W, Theurot D, Bosquet L, Dugué B. An Evidence-Based Approach for
Choosing Post-exercise Recovery Techniques to Reduce Markers of Muscle Damage,
Soreness, Fatigue, and Inflammation: A Systematic Review With Meta-Analysis. Front
Physiol. 2018 Apr 26;9:403. doi: 10.3389/fphys.2018.00403. PMID: 29755363; PMCID:
PMC5932411.
6. Fyfe JJ, Broatch JR, Trewin AJ, Hanson ED, Argus CK, Garnham AP, Halson SL,
Polman RC, Bishop DJ, Petersen AC. Cold water immersion attenuates anabolic
signaling and skeletal muscle fiber hypertrophy, but not strength gain, following wholebody resistance training. J Appl Physiol (1985). 2019 Nov 1;127(5):1403-1418. doi:
10.1152/japplphysiol.00127.2019. Epub 2019 Sep 12. PMID: 31513450.
7. Schwartz H, Emanuel A, Rozen Samukas II, Halperin I. Exploring the acute affective
responses to resistance training: A comparison of the predetermined and the estimated
repetitions to failure approaches. PLoS One. 2021 Aug 18;16(8):e0256231. doi: 10.1371/
journal.pone.0256231. PMID: 34407124; PMCID: PMC8372906.
8. Hardy CJ, Rejeski WJ. Not what, but how one feels: The measurement of affect during
exercise. J Sport Exercise Psy. 1989; 11: 304–317. https://doi.org/10.1123/jsep.11.3.304
9. Jayedi A, Gohari A, Shab-Bidar S. Daily Step Count and All-Cause Mortality: A DoseResponse Meta-analysis of Prospective Cohort Studies. Sports Med. 2021 Aug 21. doi:
10.1007/s40279-021-01536-4. Epub ahead of print. PMID: 34417979.
112
10. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann
HJ; GRADE Working Group. GRADE: an emerging consensus on rating quality of
evidence and strength of recommendations. BMJ. 2008 Apr 26;336(7650):924-6. doi:
10.1136/bmj.39489.470347.AD. PMID: 18436948; PMCID: PMC2335261.
11. Hanson S, Jones A. Is there evidence that walking groups have health benefits?
A systematic review and meta-analysis. Br J Sports Med. 2015 Jun;49(11):710-5.
doi: 10.1136/bjsports-2014-094157. Epub 2015 Jan 19. PMID: 25601182; PMCID:
PMC4453623.
12. Yang JJ, Yu D, Wen W, Shu XO, Saito E, Rahman S, Gupta PC, He J, Tsugane S, Xiang
YB, Gao YT, Koh WP, Tamakoshi A, Irie F, Sadakane A, Tsuji I, Kanemura S, Matsuo
K, Nagata C, Chen CJ, Yuan JM, Shin MH, Park SK, Pan WH, Qiao YL, Pednekar
MS, Gu D, Sawada N, Li HL, Gao J, Cai H, Grant E, Tomata Y, Sugawara Y, Ito H,
Wada K, Shen CY, Wang R, Ahn YO, You SL, Yoo KY, Ashan H, Chia KS, Boffetta
P, Inoue M, Kang D, Potter JD, Zheng W. Tobacco Smoking and Mortality in Asia:
A Pooled Meta-analysis. JAMA Netw Open. 2019 Mar 1;2(3):e191474. doi: 10.1001/
jamanetworkopen.2019.1474. PMID: 30924901; PMCID: PMC6450311.
13. Gellert C, Schöttker B, Brenner H. Smoking and all-cause mortality in older people:
systematic review and meta-analysis. Arch Intern Med. 2012 Jun 11;172(11):837-44. doi:
10.1001/archinternmed.2012.1397. PMID: 22688992.
14. Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, Romundstad P, Vatten LJ. BMI
and all cause mortality: systematic review and non-linear dose-response meta-analysis of
230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ. 2016
May 4;353:i2156. doi: 10.1136/bmj.i2156. PMID: 27146380; PMCID: PMC4856854.
15. Tudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, Hatano
Y, Inoue S, Matsudo SM, Mutrie N, Oppert JM, Rowe DA, Schmidt MD, Schofield
GM, Spence JC, Teixeira PJ, Tully MA, Blair SN. How many steps/day are enough?
For adults. Int J Behav Nutr Phys Act. 2011 Jul 28;8:79. doi: 10.1186/1479-5868-8-79.
PMID: 21798015; PMCID: PMC3197470.
16. Belgian, Swiss, Japanese, and Australian adults all walk more than US adults; I assume
adults in most other countries average more steps than adults in the US
17. Tsoukos A, Brown LE, Terzis G, Wilk M, Zajac A, Bogdanis GC. Changes in EMG
and movement velocity during a set to failure against different loads in the bench press
exercise. Scand J Med Sci Sports. 2021 Jul 30. doi: 10.1111/sms.14027. Epub ahead of
print. PMID: 34329514.
18. Król H, Gołaś A. Effect of Barbell Weight on the Structure of the Flat Bench Press. J
Strength Cond Res. 2017 May;31(5):1321-1337. doi: 10.1519/JSC.0000000000001816.
PMID: 28415066; PMCID: PMC5400411.
113
19. Escamilla RF, Fleisig GS, Zheng N, Lander JE, Barrentine SW, Andrews JR, Bergemann
BW, Moorman CT 3rd. Effects of technique variations on knee biomechanics
during the squat and leg press. Med Sci Sports Exerc. 2001 Sep;33(9):1552-66. doi:
10.1097/00005768-200109000-00020. PMID: 11528346.
20. Bryanton MA, Carey JP, Kennedy MD, Chiu LZ. Quadriceps effort during squat exercise
depends on hip extensor muscle strategy. Sports Biomech. 2015 Mar;14(1):122-38. doi:
10.1080/14763141.2015.1024716. Epub 2015 Apr 21. PMID: 25895990.
█
114
VIDEO: Foam Rolling Part 2
BY MICHAEL C. ZOURDOS
Similar to pre-training foam rolling, post-training foam rolling is widely used.
But is it effective? Part 2 of our series reviews the data on post-training foam
rolling to accelerate recovery of muscle soreness and strength performance.
Click to watch Michael's presentation.
115
Relevant MASS Videos and Articles
1. Foam Rolling May Enhance Recovery, but is it a Standalone Modality? Volume 1 Issue 8.
2. What’s the Best Way to Recovery from Training. Volume 2 Issue 6.
References
1. Cheatham SW, Kolber MJ, Cain M, Lee M. The effects of self‐myofascial release using a foam
roll or roller massager on joint range of motion, muscle recovery, and performance: a systematic
review. International journal of sports physical therapy. 2015 Nov;10(6):827.
2. Wiewelhove T, Döweling A, Schneider C, Hottenrott L, Meyer T, Kellmann M, Pfeiffer
M, Ferrauti A. A meta-analysis of the effects of foam rolling on performance and recovery.
Frontiers in physiology. 2019 Apr 9;10:376.
3. Skinner B, Moss R, Hammond L. A systematic review and meta-analysis of the effects of foam
rolling on range of motion, recovery and markers of athletic performance. Journal of Bodywork
and Movement Therapies. 2020 Jul 1;24(3):105-22.
4. Kerautret Y, Di Rienzo F, Eyssautier C, Guillot A. Selective effects of manual massage and
foam rolling on perceived recovery and performance: current knowledge and future directions
toward robotic massages. Frontiers in physiology. 2020 Dec 21;11:1567.
5. Fleckenstein J, Wilke J, Vogt L, Banzer W. Preventive and regenerative foam rolling are equally
effective in reducing fatigue-related impairments of muscle function following exercise. Journal
of sports science & medicine. 2017 Dec;16(4):474.
6. Behm DG, Alizadeh S, Anvar SH, Mahmoud MM, Ramsay E, Hanlon C, Cheatham S. Foam
rolling prescription: A clinical commentary. The Journal of Strength & Conditioning Research.
2020 Nov 1;34(11):3301-8.
7. Zorko N, Škarabot J, Garcia-Ramos A, Štirn I. The acute effect of self-massage on the shortterm recovery of muscle contractile function. Kinesiologia Slovenica. 2016 Sep 1;22(3):31.
8. D’Amico AP, Gillis J. Influence of foam rolling on recovery from exercise-induced muscle
damage. The Journal of Strength & Conditioning Research. 2019 Sep 1;33(9):2443-52.
9. Macdonald GZ, Button DC, Drinkwater EJ, Behm DG. Foam rolling as a recovery tool after an
intense bout of physical activity. Med Sci Sports Exerc. 2014 Jan;46(1):131-42.
10. Konrad A, Nakamura M, Bernsteiner D, Tilp M. The Accumulated Effects of Foam Rolling
Combined with Stretching on Range of Motion and Physical Performance: A Systematic Review
and Meta-Analysis. Journal of Sports Science & Medicine. 2021 Sep;20(3):535.
█
116
VIDEO: Nutrition for Strength vs.
Physique Athletes Part 1
BY ERIC HELMS
While there is a lot of overlap between the nutritional guidance for strength athletes
and physique athletes, there are also many nuanced differences. In this video series
we explore what those differences are and where the recommendations to optimize
strength and bodybuilding performance should differ. In part 1 we discuss broad
similarities, the source and magnitude of energetic differences, and phasic and
psychological differences related to nutrition.
Click to watch Eric's presentation.
117
Relevant MASS Videos and Articles
1. VIDEO: Nutritional Peaking for Strength and Physique Athletes, Part 1. Volume 1, Issue 6.
2. VIDEO: Nutritional Peaking for Strength and Physique Athletes, Part 2. Volume 1, Issue 7.
3. How Much Does Training Volume Affect the Rate of Strength Gains? Volume 1, Issue 6.
4. How Many Calories Do You Burn Lifting Weights? Volume 3, Issue 4.
References
1. Slater G, Phillips SM. Nutrition guidelines for strength sports: sprinting, weightlifting, throwing
events, and bodybuilding. J Sports Sci. 2011;29 Suppl 1:S67-77.
2. Helms ER, Aragon AA, Fitschen PJ. Evidence-based recommendations for natural bodybuilding
contest preparation: nutrition and supplementation. J Int Soc Sports Nutr. 2014 May 12;11:20.
3. Iraki J, Fitschen P, Espinar S, Helms E. Nutrition Recommendations for Bodybuilders in the
Off-Season: A Narrative Review. Sports (Basel). 2019 Jun 26;7(7):154.
4. Morton RW, Murphy KT, McKellar SR, Schoenfeld BJ, Henselmans M, Helms E, et al. A
systematic review, meta-analysis and meta-regression of the effect of protein supplementation
on resistance training-induced gains in muscle mass and strength in healthy adults. Br J Sports
Med. 2018 Mar;52(6):376-384.
5. Roberts BM, Helms ER, Trexler ET, Fitschen PJ. Nutritional Recommendations for Physique
Athletes. J Hum Kinet. 2020 Jan 31;71:79-108.
6. Lytle JR, Kravits DM, Martin SE, Green JS, Crouse SF, Lambert BS. Predicting Energy
Expenditure of an Acute Resistance Exercise Bout in Men and Women. Med Sci Sports Exerc.
2019 Jul;51(7):1532-1537.
7. Ralston GW, Kilgore L, Wyatt FB, Baker JS. The Effect of Weekly Set Volume on Strength
Gain: A Meta-Analysis. Sports Med. 2017 Dec;47(12):2585-2601.
8. Schoenfeld BJ, Ogborn D, Krieger JW. Dose-response relationship between weekly resistance
training volume and increases in muscle mass: A systematic review and meta-analysis. J Sports
Sci. 2017 Jun;35(11):1073-1082.
9. Nolan D, Lynch AE, Egan B. Self-Reported Prevalence, Magnitude, and Methods of Rapid
Weight Loss in Male and Female Competitive Powerlifters. J Strength Cond Res. 2020 Jan 3.
10. Slater GJ, Dieter BP, Marsh DJ, Helms ER, Shaw G, Iraki J. Is an Energy Surplus Required to
Maximize Skeletal Muscle Hypertrophy Associated With Resistance Training. Front Nutr. 2019
Aug 20;6:131.
█
118
Just Missed the Cut
Every month, we consider hundreds of new papers, and they can’t all be included in MASS.
Therefore, we’re happy to share a few pieces of research that just missed the cut. It’s our
hope that with the knowledge gained from reading MASS, along with our interpreting research
guide, you’ll be able to tackle these on your own. If you want to peruse our full journal sweep,
you can find it here, and you can find our historical archive here.
1. Gough et al. A critical review of citrulline malate supplementation and exercise performance
2. Grgic et al. Acute effects of caffeine supplementation on resistance exercise, jumping,
and Wingate performance: no influence of habitual caffeine intake
3. Martins et al. Association between ketosis and metabolic adaptation at the level of resting
metabolic rate
4. Burton et al. Background Inactivity Blunts Metabolic Adaptations to Intense Short-Term
Training
5. Musolino et al. Bigger isn’t always better: an exploration of social perception bias against
high levels of muscularity in women
6. Murton et al. Comparison of flywheel versus traditional resistance training in elite academy
male Rugby union players
7. Beethe et al. Differences in compound muscle activation patterns explain upper extremity
bilateral deficits
8. Salvador et al. Early resistance training-mediated stimulation of daily muscle protein
synthetic responses to higher habitual protein intake in middle-aged adults
9. Badenhorst et al. Effect of the Growth Spurt on Training of Strength and Power During
Mid-Adolescence in Boys
10. Appel et al. Effects of Genetic Variation on Endurance Performance, Muscle Strength, and
Injury Susceptibility in Sports: A Systematic Review
11. Larsen et al. Effects of Stance Width and Barbell Placement on Kinematics, Kinetics, and
Myoelectric Activity in Back Squats
12. Hendrickse et al. Endurance training-induced increase in muscle oxidative capacity
without loss of muscle mass in younger and older resistance-trained men
13. Marshall et al. Fatigue, pain, and the recovery of neuromuscular function after consecutive
days of full-body resistance exercise in trained men
14. van Doorslaer de Ten Ryen et al. Higher strength gain after hypoxic vs normoxic resistance
training despite no changes in muscle thickness and fractional protein synthetic rate
15. Robinson et al. Interoception, eating behaviour and body weight
16. Wolf et al. Is Physical Activity Associated with Less Depression and Anxiety During the
COVID-19 Pandemic? A Rapid Systematic Review
119
17. Iglesias-Soler et al. Load-velocity Profiles Change after Training Programs with Different
Set Configurations
18. Vann et al. Molecular Differences in Skeletal Muscle After 1 Week of Active vs. Passive
Recovery From High-Volume Resistance Training
19. Happ and Behringer. Neuromuscular Electrical Stimulation Training vs. Conventional Strength
Training: A Systematic Review and Meta-Analysis of the Effect on Strength Development
20. Behm et al. Non-local Muscle Fatigue Effects on Muscle Strength, Power, and Endurance
in Healthy Individuals: A Systematic Review with Meta-analysis
21. Spence et al. Range of Motion Is Not Reduced in National-Level New Zealand Female
Powerlifters
22. Bailey et al. Relative variability in muscle activation amplitude, muscle oxygenation, and
muscle thickness: Changes with dynamic low-load elbow flexion fatigue and relationships
in young and older females
23. Hogan et al. Scapular Dyskinesis Is Not an Isolated Risk Factor for Shoulder Injury in
Athletes: A Systematic Review and Meta-analysis
24. Naimo et al. Skeletal Muscle Quality: A Biomarker for Assessing Physical Performance
Capabilities in Young Populations
25. Mesquita et al. Skeletal Muscle Ribosome and Mitochondrial Biogenesis in Response to
Different Exercise Training Modalities
26. Zuraikat et al. Sleep and Diet: Mounting Evidence of a Cyclical Relationship
27. Shi et al. The Association Between Food Insecurity and Dietary Outcomes in University
Students: A Systematic Review
28. Nederveen et al. The Importance of Muscle Capillarization for Optimizing Satellite Cell
Plasticity
29. Leuchtmann et al. The Role of the Skeletal Muscle Secretome in Mediating Endurance and
Resistance Training Adaptations
30. Gurney et al. Twenty-one days of spirulina supplementation lowers heart rate during
submaximal cycling and augments power output during repeated sprints in trained cyclists
31. McCarthy and Berg. Weight Loss Strategies and the Risk of Skeletal Muscle Mass Loss
120
Thanks for
reading MASS.
The next issue will be released to
subscribers on November 1, 2021.
Graphics and layout by Kat Whitfield
121