V O L U ME 5 , ISS U E 6
JUN E 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
Lifting and Stretching Have Similar Effects on Range of
Motion
A recent meta-analysis found that stretching and resistance training are similarly
effective for increasing range of motion. Are these findings too good to be true?
24
BY MI CHAEL C. ZOUR DOS
Recovery Time Course Isn’t That Long, if You Train
Appropriately
Understanding recovery time course allows for appropriate planning of weekly training.
A new study found that lifters typically recover within 48 hours following eight sets
of pressing to failure. This article breaks down those findings and discusses how to
allocate weekly training volume appropriately.
37
BY ER I C HEL MS
Striving for Perfection Versus the Fear of Falling Short of It
Perfectionism is often viewed negatively in sports psychology, as striving for the
impossible goal of perfection sets you up to fail. But, not all forms of perfectionism
are the same. How perfectionism manifests itself in athletes can be the difference
between burnout and improved performance. However, there may be another way.
51
BY ER I C T R EXL ER
Revisiting the Effects of Vitamin D Supplementation on
Strength and Hypertrophy
In Volume 4 of MASS, our review of a meta-analysis indicated that vitamin D
supplementation enhanced strength gains, particularly in folks with low vitamin D
levels. However, a new study suggests otherwise. Read on for an update on the
potential value of vitamin D supplementation.
64
BY GR EG NUCKOL S
Has Science Finally Caught Up With the Training Methods
of Jacob Marley? The Effects of Chain-Based Training on
Strength Gains
Have you ever wondered, “How can I make my training 50% cooler and 200% noisier?”
If so, you’ll love this article examining the effects of chain-based resistance training.
79
BY MI CHAEL C. ZOUR DOS
When and How are Flexible Templates Actually Useful?
Flexible programming – choosing which training session you’ll do based on how you
feel that day – is a logical strategy. However, a new study adds to the surprisingly
null findings on the topic. This article discusses specific situations in which a flexible
template may have merit and how to implement flexibility.
96
BY ER I C T R EXL ER
Can Dietary Nitrate Enhance Strength and Hypertrophy
Adaptations Over Time?
In previous MASS issues, we’ve established that a single dose of dietary nitrate can
acutely enhance strength and power performance. Does that actually lead to better
gains over time? Read on to find out.
109
BY GR EG NUCKOL S
This is Theoretically an Article About the Effects of Cluster
Set Training for Powerlifters
An intrepid research reviewer was attempting to write an article about cluster sets,
when he stumbled across some improbable data patterns. You won’t believe what
happened next.
124
BY MI CHAEL C. ZOUR DOS
VIDEO: Novice Training Prescription Part II
In Volume 5 Issue 5, the video Novice Training Prescription Part 1 provided a
detailed example of how coaches can appropriately progress a novice lifter’s
training. This video picks up where that previous video left off and details how to
progress training following the initial novice phase.
126
BY ER I C HEL MS
VIDEO: Supplement Series Part II: Creatine & Caffeine
In the “Supplement Series” videos, Dr. Helms discusses effective, commonly
used supplements in strength and physique sport. Specifically covering what the
supplement is, how it works, data on its effectiveness, and considerations you
should be aware of. In the second installment, we cover creatine and caffeine.
Letter From the Reviewers
W
elcome to the June 2021 issue of MASS!
First, we are proud to say that with your help during the anniversary sale, we
donated just over $10,000 to the One Acre Fund during May. The One Acre
Fund supplies smallholder farmers in Africa with financing and materials to fight hunger and
poverty. Thank you for working with us to help this noble cause.
As for the June 2021 issue, Dr. Trexler spearheads the nutrition content this month with
reviews of new nitrate and vitamin D supplementation studies. Eric T. provides a nuanced
discussion of both supplements and explains why we shouldn’t give up on the possibility that
dietary nitrate supplementation can improve performance.
Dr. Helms rounds out the nutrition department content by continuing his video
supplement series, this time summarizing all you need to know about creatine and caffeine
supplementation. Further, Eric H. checks in with an excellent review on how mindsets known
as “perfectionist striving” and “perfectionist concerns” may impact performance. Ultimately,
Eric’s interpretation provides insight into how some might rethink their approach to goalsetting and attainment.
On the training side, Greg has fascinating reviews on two studies covering cluster sets and
accommodating resistance. For both of these reviews, Greg uses his uniquely trained eye
to point out some data inconsistencies, providing an in-depth look into the findings, and
modeling how to critically evaluate studies. Greg tackles a meta-analysis examining if
resistance training can improve flexibility to the same degree as stretching to round out his
content.
For the first time in four years, there is a new study on flexible training templates, which Mike
covers in-depth. Specifically, Mike discusses under what circumstances flexible templates
may be beneficial and how to implement them. Mike’s other written article examines the
time course of recovery from bench press performance. Finally, Mike’s video picks up right
where last month’s video left off and provides a sample program for the next phase of a novice
lifter’s training.
Every month we record audio roundtables on each written article, so be sure to check those
out for additional insight and discussion amongst multiple reviewers. Lastly, join us in the
Facebook group.
We hope you have a great month, and thank you for being a part of MASS.
Sincerely,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
5
Study Reviewed: Strength Training Versus Stretching for Improving Range of Motion: A
Systematic Review and Meta-Analysis. Afonso et al. (2021)
Lifting and Stretching Have Similar
Effects on Range of Motion
BY GREG NUCKOLS
A recent meta-analysis found that stretching and resistance training
are similarly effective for increasing range of motion. Are these
findings too good to be true?
6
KEY POINTS
1. Researchers systematically identified all of the randomized controlled trials that
assessed changes in range of motion and compared a supervised stretching
program to a supervised resistance training program.
2. It appears that resistance training and stretching are equally effective for increasing
range of motion.
3. We need to be careful about interpreting these results too broadly, because a
disproportionate amount of the studies assessed hamstrings flexibility. These
findings likely generalize to other muscle groups, but we need more research to
know for sure.
T
he impact of resistance training on
range of motion is a contentious
subject. Some people still cling to
the old notion that lifting weights will make
you stiff and “muscle bound,” while others
point to research showing that, in fact, lifting
weights can actually increase range of motion. However, when it comes to improving
range of motion, stretching is still most people’s go-to approach. But what if stretching
was unnecessary? What if resistance training was just as effective for increasing range
of motion as stretching?
A group of researchers recently dared to ask
such an audacious question, and actually put
in the legwork to answer it (1). They identified all of the randomized controlled trials
that assessed changes in range of motion and
compared a supervised stretching program
to a supervised resistance training program.
Eleven studies met the inclusion criteria, and
the researchers meta-analyzed the results to
summarize the relative effects of stretching
and resistance training on range of motion.
They found that stretching and resistance
training seem to be similarly effective for
improving range of motion. However, there
are some important caveats to keep in mind.
Read on to learn more.
Purpose and Hypotheses
Purpose
The purpose of this systematic review and
meta-analysis was to compare the effects of
resistance training and stretching for increasing range of motion.
Methods
The researchers searched six different databases in order to find all of the studies that
met the following criteria:
1. The study had to compare any form of supervised resistance training to any form of
supervised stretching in human subjects.
2. The study needed to assess changes in
joint range of motion, or changes in performance in a standardized flexibility test
(such as the sit-and-reach test).
7
3. The study needed to be a randomized controlled trial.
Furthermore, a study would be excluded if
either the resistance training or stretching
intervention was combined with some other
intervention (e.g. if a study compared stretching versus resistance training plus cardio, it
would be excluded due to the cardio component). Of note, the inclusion criteria were
quite broad. For example, the researchers defined resistance training broadly enough to
include plyometrics, and there were no population restrictions.
After identifying the studies that would be
included, the authors extracted the necessary
information from each study (demographic
information about the subjects, details about
the interventions, range of motion results,
etc.). From there, they completed a risk of
bias assessment, and then performed a series
of meta-analyses. The primary meta-analysis
looked at the pooled results of all of the studies comparing the effects of stretching versus
resistance training on range of motion. Secondary analyses compared the results of studies with high versus low risk of bias, studies that assessed active versus passive range
of motion, studies that specifically assessed
hip flexion range of motion, and studies that
specifically assessed knee extension range of
motion. Finally, they rated the overall certainty of evidence provided by this meta-analysis
using the GRADE criteria (13).
Findings
Eleven studies met the authors’ inclusion criteria (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12). Sample
sizes ranged from 27-124 subjects, for a total
of 452 subjects, and study durations spanned
from 5-16 weeks, with between 2-5 resistance
training or stretching sessions per week. Four
studies used trained subjects; three studies
used healthy, sedentary subjects; one study
used a mix of trained and healthy but sedentary subjects; one study used workers with
chronic neck pain; one study used subjects
with fibromyalgia; and one study used elderly participants who struggled with basic activities of daily living. Six studies investigated the effects of active static stretching, two
studies investigated the effects of dynamic
stretching, one study had both a static stretching and a dynamic stretching group, one study
investigated the effects of both static and dynamic stretching within the same group, and
one study investigated the effects of dynamic, static, and PNF stretching within the same
group. Seven studies used exclusively or predominantly female subjects, three studies used
exclusively or predominantly male subjects,
and one study used an equal number of male
and female subjects. Most of the studies assessed changes in hip range of motion (seven
studies; all assessed hip flexion range of motion), followed by knee range of motion (five
studies; four assessed knee extension range
of motion, while one assessed knee flexion
range of motion), shoulder range of motion
(four studies), elbow range of motion (two
studies), trunk range of motion (two studies),
cervical spine range of motion (one study),
and ankle range of motion (one study). The
studies were fairly split regarding whether
they measured active range of motion (eight
studies) or passive range of motion (seven
studies; four studies measured both, and sev-
8
en studies used exclusively active or passive
range of motion measures).
Of the 11 studies, 6 were rated as having high
risk of bias due to randomization procedures,
and 5 were rated as having high risk of bias
based on how outcomes were measured. In
practice, I’m not very concerned about the
studies deemed to have high risk of bias due
to randomization procedure. That just means
that the study didn’t adequately describe
how subjects were randomized into different groups. Studies are supposed to describe
randomization procedures in detail, but in
practice, most studies in our field simply
don’t. However, I trust that most competent
researchers know how to properly randomize subjects into different groups. High risk
of bias in outcome measures generally means
that the people taking the measurements
weren’t blinded to the subjects’ group allocations. In other words, the people measuring
changes in range of motion knew whether
subjects had been in the stretching group or
the resistance training group. That could potentially be a problem, if the people taking
the measurements had a strong bias in favor
of some particular outcome (even if the bias
was subconscious). However, as we’ll see in
the subanalyses, I don’t think non-blinded assessors had any major impact on the results.
In addition, Egger’s test for funnel plot asymmetry returned a non-significant result (p =
0.563), suggesting the risk of publication bias
within the full set of studies was low.
Within the main meta-analysis, stretching
and resistance training were similarly effective for increasing range of motion (ES = 0.22
in favor of stretching; p = 0.206). When you
9
examine the forest plot, you’ll see that there
appears to be one clear outlier, by Simão and
colleagues (11). I’ll address that study in the
Criticisms and Statistical Musings section.
None of the secondary analyses had particularly noteworthy results. Resistance training
and stretching led to improvements in range
of motion that did not significantly differ in
studies that had high versus low risk of bias
due to randomization procedures, studies
that had high versus low risk of bias due to
measurement procedures, measures of both
active and passive range of motion, studies
that specifically assessed hip flexion range of
motion, and studies that specifically assessed
knee extension range of motion.
The only secondary analysis worth zeroing in
on was the analysis of studies assessing knee
extension range of motion. The pooled effect
size was still small (ES = 0.245, in favor of
resistance training), but the difference was almost statistically significant (p = 0.066). You
could view that as weak evidence in favor
of resistance training promoting larger improvement in knee extension range of motion
than stretching, but I’d personally be skeptical of that interpretation. Ultimately, knee
extension range of motion and hip flexion
range of motion are both primarily assessing
hamstrings extensibility. The sub-analysis of
studies measuring hip flexion range of motion
included more studies (seven versus four)
and found an almost identical effect size in
the opposite direction (ES = 0.240 in favor of
stretching), with a p-value that wasn’t even
within spitting distance of statistical significance (p = 0.414). Thus, I suspect that if additional future studies compare the effects of
resistance training and stretching and knee
extension range of motion, the pooled effect
size would probably move back toward 0.
10
Using the GRADE scoring system, the authors
rated the confidence of the evidence provided
by this meta-analysis to be “moderate.”
Criticisms and Statistical
Musings
First off, I’d like to commend the authors of
the present meta-analysis (1). They pre-registered their protocol, provided their exact
search query for anyone who wanted to repeat their exact search (14), tested for publication bias, did a bunch of subgroup analyses
to make sure the observed (lack of) effect was
a robust finding, and summarized not just the
magnitude of the effect observed, but also
the strength of the evidence via the GRADE
rubric. In short, this was a very well-done
systematic review and meta-analysis. I only
have two complaints. The first is that I would
have liked to have seen a funnel plot, but in
light of all of the positive aspects of this meta-analysis, that’s a relatively small quibble.
The second is that I think the authors should
have done a leave-one-out sensitivity analysis to see if there were any individual studies
that had a large impact on the point estimate
or precision of the pooled effect size. Based
on visual inspection, the study by Simão and
colleagues appears to be a pretty clear outlier
11
(11) – it reported an effect size of 1.922 in
favor of stretching, while no other study reported an effect size larger than 0.616. That
large effect size didn’t wind up flipping any
of the pooled effect sizes from “non-significant” to “significant,” but I’m sure it had a
notable impact on the point estimate of all of
that analyses that included it, and I’m positive that it substantially widened the confidence interval of the pooled effect estimate,
negatively impacting the precision of the meta-analysis.
More to the point, when I see an outlier effect
estimate in a forest plot, I always examine
the corresponding study a bit more closely.
In this case, the results from the Simão study
raise some pretty major red flags.
It was a pretty straightforward study. 80 untrained female subjects were randomized into
four groups of 20 subjects – one group did
resistance training for 16 weeks, one group
stretched for 16 weeks, one group did both
resistance training and stretching for 16
weeks, and one group was a non-training
control group. Before and after the training
period, researchers assessed 10RM leg press
and machine bench press strength, along with
sit-and-reach test performance. You can see
the results in Tables 3 and 4.
Do you notice anything odd about those results?
First, even when accounting for the fact that
12
the subjects were untrained, the pre-training
strength numbers were oddly low. However,
it’s possible that the machines used for testing dramatically multiplied the actual force
applied per unit of weight loaded on the machine. More importantly, the standard deviations for all three measures display a few unusual patterns. For 10RM leg press strength,
the standard deviation is 10kg for all groups
at all time points. For bench press strength,
it’s either 5.0 or 2.5kg for all groups at all
time points (even if you’re loading weights in
2.5kg increments, there’s no reason to expect
standard deviations to only move in 2.5kg
increments). For sit-and-reach performance,
all of the standard deviations for all groups at
both time points ended in “.0cm”
My first thought was that the researchers may
have been using non-standard significant digit conventions. Technically, you’re not supposed to report data with more precision than
your measurements allow for. In other words,
if you’re reporting the group average of reps
performed in a set, assuming reps are binary
(you either make the rep or you miss the rep),
you should technically report the number of
reps completed as a whole number; for example, even if the group average is 7.2 reps, you’d
report it as 7 reps. Most people in exercise
science don’t follow this convention, but it’s
technically the proper thing to do. However, I
don’t think that’s what’s happening here. The
boxes you’d use for a sit-and-reach test generally have marks to measure performance to
the nearest millimeter, so you’d still expect to
see decimals other than “.0” for sit-and-reach
test performance (and if you weren’t measuring decimal values, why include decimals in
your standard deviations at all?). That would
also imply that the strength measures had absolutely horrendous precision. If strength was
assessed to the nearest 2.5kg for bench press
10RM, and the group means were just 5kg,
then a subject whose “true” 10RM was 4.5kg
would be able to complete 10 reps at 2.5kg
but not 5kg, leaving them with a pre-training
strength assessment that underrepresented
their true maximum strength by almost 45%.
That would be comparable to estimating your
1RM squat as 250lb when it was really 450lb.
However, if we assume the researchers
weren’t using non-standard significant digit
conventions, it’s incredibly unlikely we’d see
standard deviations following the patterns reported in the study. I ran a little simulation to
see how often we’d expect all of the 10RM
squat values to have standard deviations of
10kg, assuming standard deviations could
vary in increments of 1. I started by assuming that we were starting with a population of
80 lifters who squatted an average of 67.5kg,
with a standard deviation of 10kg. First, I
simulated 15,000 sets of 4 groups of 20 lifters
drawn from that population. Then, to mimic the strength gains observed in the present
study, I specified that the lifters in the first
group would improve their leg press 10RM
by 50 ± 10 kg, the lifters in the second group
would improve their leg press by 5 ± 2.5 kg,
the lifters in the third group would improve
their leg press by 40 ± 10 kg, and the lifters
in the fourth group would have effectively
no change in leg press strength – 0 ± 1 kg.
I’ll note, these assumptions are quite generous; changes in strength are generally way
more heterogeneous in the real world (and
13
in the literature), but assuming a high degree
of homogeneity is a more generous assumption, because it decreases the probability that
post-training standard deviations will change
substantially from pre-training. From there,
I had pre- and post-training data for 15,000
simulated groups of 20 subjects. I calculated
the standard deviation for each group at each
time point, and rounded them to the nearest
1kg. From there, I devised a distance metric:
a 1kg difference in standard deviation above
or below 10kg would represent one unit removed from all groups having a standard deviation of 10kg at both time points. So, for
example, if all standard deviations in each set
of 8 (four groups at two time points) was 10,
the “distance” would be 0. If they were 10,
10, 10, 10, 11, 8, 12, 15, the distance is 0 + 0
+ 0 + 0 + 1 + 2 + 2 + 5 = 7.
Figure 3 shows the distribution of these values. Out of 15,000 sets of four groups at two
time points, none had standard deviations of
10 across the board. The mean distance away
was 15.5kg, with a standard deviation of 4.3.
That means winding up with standard deviations of 10 across the board (a score of 0 in
this distance metric) would be about as likely as flipping a fair coin and having it land
“heads” 13 times in a row.
I repeated the same basic process with the sitand-reach scores. This time, I wanted to see
how often standard deviations would all be
whole numbers, assuming that all standard deviations are being rounded to the tenths digit.
The basic process was the same. I started by
assuming that we were drawing 4 samples of
20 subjects from a population with a mean sitand-reach score of 30.75cm, with a standard
14
deviation of 2cm. Then, each group improved
by 6 ± 2 cm, 9 ± 3 cm, 11 ± 3.67 cm, and 0 ± 1
cm (mirroring the mean values reported in the
paper, and assuming pretty low heterogeneity
to be generous). After simulating 8,000 sets
of 4 groups of 20 subjects at both time points,
I calculated the standard deviations, and then
calculated the distance between each standard
deviation and the nearest whole number (e.g.
1.8cm would be 0.2cm away from 2cm, 1.1cm
would be 0.1cm away from 1cm, etc.). Out of
8,000 sets of four groups at two time points,
none had all whole numbers for standard deviations (Figure 4). The mean distance away
(summed across each set of eight standard
deviations) from 0 was 1.88cm, with a standard deviation of 0.42cm. That means winding
up with whole numbers across the board for
standard deviations would be about as likely
as flipping a fair coin and having it land on
“heads” about 18 times in a row.
For what it’s worth, as additional evidence that
the researchers weren’t just being sticklers
about significant figures, the reported mean
ages were all whole numbers, but the reported standard deviations had values reported to
the tenths digits. Assuming the authors were
just being sticklers about the significant figures reported in standard deviations, including a tenths digit in the standard deviations
of ages implies that the researchers collected
subjects’ ages to at least one decimal point of
precision. With that in mind, I repeated a process similar to the one I just described for sitand-reach scores to find the probability of all
of the mean ages being whole numbers (Figure 5). The probability of all of the mean ages
rounding to a whole number was about the
same as having a fair coin land on “heads”
12 times in a row. That implies that, even if
means were reported using strict significant
figure conventions, the authors may not have
15
applied those same conventions to standard
deviations.
Also, for what it’s worth, the data reported
in the study doesn’t even match up between
tables. In Table 1 of the study, which reports
baseline characteristics, the reported bench
press 10RMs were (in order) 10 ± 5kg, 12.5
± 5kg, 12.5 ± 5kg, and 10 ± 5kg. In Table 3 of
the study, which reports changes in strength
over time, pre-training 10RM bench press
values are reported to be (in order) 5 ± 2.5kg,
5 ± 5kg, 5 ± 2.5kg, and 5 ± 2.5kg.
Basically, the most charitable reading of this
study (11) is that the measurement precision
was atrocious, the authors were sticklers
about significant figures (but only sometimes), and data reporting was pretty sloppy.
The less charitable reading is that data reporting was still sloppy, and the standard deviations reported for every primary outcome
variable are grossly improbable. Then you
also need to remember that this study reported an effect size for the difference between
strength training and stretching for range of
motion outcomes that was more than three
times larger than the next closest effect size
in the literature.
With all of that said, in my opinion, I think
we probably get a more accurate view of this
body of literature if we disregard the results
of the Simão study. So, what effect would its
exclusion have on the results?
Well, the pooled effect estimate would be
even closer to 0 – probably in the 0-0.10
range in favor of stretching, down from 0.22
– with a tighter confidence interval (e.g. more
precision). That may have been sufficient to
upgrade the GRADE assessment from moderate to high confidence, and, in my opinion,
it’s probably a more accurate representation
16
RESISTANCE TRAINING
CAN BE JUST AS EFFECTIVE
AS STRETCHING FOR
PROMOTING INCREASES
IN RANGE OF MOTION.
of the state of the literature. If you look at all
of the rest of the studies included in this meta-analysis, you see mostly trivial-to-small
effect sizes, pretty evenly distributed on both
sides of the “no difference” line.
Interpretation
The basic interpretation of this meta-analysis is very straightforward: resistance training can be just as effective as stretching for
promoting increases in range of motion.
However, there’s a pretty major caveat. A
disproportionate amount of the studies in the
present meta-analysis examined changes in
hip flexion or knee extension range of motion. In other words, we can be pretty confident that resistance training works just as
well as stretching for promoting hamstrings
flexibility, but it might be hasty to generalize
that finding to every joint and muscle of the
body.
With that being said, there’s a clear mechanistic rationale that explains how resistance
training improves joint range of motion.
There are two predominant ways to increase
range of motion: increase muscle fascicle
length, or increase stretch tolerance. We
know that resistance training can increase
muscle fascicle length, especially when it has
an eccentric component, and especially when
it’s performed through a long range of motion (15, 16, 17, 18). Thus, it’s not unreasonable to expect that resistance training could
promote improvements in range of motion at
most joints.
However, “resistance training can promote
improvements in range of motion at most
joints” and “resistance training does promote
improvements in range of motion at most
joints” aren’t identical statements. For example, a few years ago, Mike reviewed a study
showing that powerlifters have greater knee
extension range of motion than untrained
controls (supporting the idea that resistance
training can improve hamstrings flexibility),
but less shoulder extension, internal rotation,
RESISTANCE TRAINING
CAN INCREASE MUSCLE
FASCICLE LENGTH,
ESPECIALLY WHEN IT HAS AN
ECCENTRIC COMPONENT,
AND ESPECIALLY WHEN IT’S
PERFORMED THROUGH A
LONG RANGE OF MOTION.
17
and external rotation range of motion than
untrained controls (19). Why might that be?
cline of the bench as your shoulder extension range of motion improves.
While powerlifters clearly do a lot of resistance training, they (we) rarely perform exercises that train through a full shoulder extension, internal rotation, or external rotation
range of motion. As long as we have enough
internal rotation range of motion to bench
press and enough external rotation range of
motion to squat, we’re golden. The fact that
we perform exercises that train most muscles
of the shoulder girdle doesn’t automatically
improve our shoulder mobility in all directions. In much the same way, if your hamstrings-focused resistance training consisted
solely of lying hamstrings curls through just
the top half of the range of motion, I strongly
suspect that specific approach to resistance
training wouldn’t do much for hip flexion or
knee extension range of motion.
2. If you want to improve hip abduction
range of motion, wide-stance squats may
be a good option. Work on widening your
stance gradually over time, even if doing so temporarily comes at the expense
of weight on the bar. Alternatively, you
could do cossack squats. To make sure the
extended leg (the one whose groin is being stretched) stays under load, make sure
you’re driving the heel of your extended
leg into the ground throughout the movement, and especially as you initiate the
concentric phase of the lift.
In other words, if you want your resistance
training to improve range of motion for a particular joint action, make sure you’re choosing exercises that allow you to train near
end-ROM for that particular joint action, and
make sure you’re performing those exercises
through a full range of motion. Here are a few
examples:
1. If you want to improve shoulder extension
range of motion, you could perform incline
dumbbell curls (on an incline that allows
your elbows to hang behind your torso)
if tight biceps are limiting your shoulder
extension range of motion. If your front
delts are tight instead, you could perform
dumbbell front delt raises on an incline
bench instead, and aim to decrease the in-
3. If you want to improve hip extension
range of motion, one great option is single-leg leg raises from the end of a bench.
Position yourself so that about half of your
butt is hanging off the bench, plant your
opposite leg on the ground, and attempt
to tap the heel of your working leg to the
floor while maintaining neutral or posterior pelvic tilt. Reverse nordic curls are
also a great option if a tight rectus femoris is limiting your hip extension range of
motion. Again, focus on training through
the longest range of motion you can manage while maintaining neutral or posterior
pelvic tilt.
The point is, you probably can improve your
range of motion for most joint actions via resistance training, but the standard repertoire
of exercises may not cut it for all planes of
motion at every joint.
Another caveat worth mentioning is that resistance training has the potential to decrease
18
I SUSPECT THAT
STRETCHING IS
NECESSARY IF YOU
WANT TO ABSOLUTELY
MAXIMIZE YOUR RANGE
OF MOTION FOR CERTAIN
JOINT ACTIONS.
range of motion at some joints, simply due
to increases in muscularity. Probably the best
example of this phenomenon is how very
large calves and hamstrings can limit maximal knee flexion range of motion. Muscle
tissue is not infinitely compressible, so if
your calves are more like full-grown bulls
and your hamstrings are well-developed pork
cords, the sheer amount of flesh on the back
side of your legs can interfere with knee flexion range of motion. Now, to be clear, you
probably never need to worry about getting
so jacked that you’ll fall below the standard
threshold of “full” knee flexion range of motion (130-135 degrees), but very skinny people can have more than 150 degrees of knee
flexion range of motion, and if your calves
and hamstrings get big enough, that may not
be in the cards for you. Similarly, very large
lats could interfere with shoulder extension,
very large biceps and forearms could interfere with elbow flexion, and very large lower traps and rhomboids could interfere with
scapular retraction. For an example relevant
to powerlifters, thicker spinal erectors could
even interfere with your ability to achieve an
extreme bench press arch (e.g. maximize spinal extension range of motion). Again, to be
clear, you’d need to get enormously jacked
before losses in range of motion due to sheer
muscularity actually impede day-to-day function, but muscle mass does impose a mechanical constraint that could limit your ability to
absolutely maximize range of motion at some
joints.
Finally, I think it’s worth recognizing the
limited scope of the findings of the present
meta-analysis. The included studies lasted
16 weeks at most, and they weren’t investigating people who wanted to push the absolute boundaries of joint ranges of motion.
I suspect that stretching is necessary if you
want to absolutely maximize your range of
motion for certain joint actions. Athletes
whose sports require extreme ranges of motion (gymnasts, martial artists, dancers, etc.)
all do a ton of stretching in order to achieve
the extreme ranges of motion their sports demand. Even weightlifters commonly stretch
in order to develop the sort of mobility required to catch snatches and cleans in a very
deep squat position. I suppose it’s never been
proven that you can’t achieve extreme ranges
of motion purely via resistance training, but I
suspect that a hefty dose of stretching will be
required if you want to achieve otherworldly
flexibility and mobility.
Next Steps
We need more studies comparing the effects
of stretching and resistance training on a wid-
19
APPLICATION AND TAKEAWAYS
Resistance training can be just as effective as stretching for improving joint ranges of
motion. However, to fully realize those benefits, you need to make sure your exercise
selection and execution are tailored toward increasing your range of motion for the
specific joint actions you’d like to improve.
er variety of joint actions. More longitudinal
studies that don’t primarily assess hip flexion
or knee extension range of motion would be
welcome.
20
References
1. Afonso J, Ramirez-Campillo R, Moscão J, Rocha T, Zacca R, Martins A, Milheiro AA,
Ferreira J, Sarmento H, Clemente FM. Strength Training versus Stretching for Improving
Range of Motion: A Systematic Review and Meta-Analysis. Healthcare (Basel).
2021 Apr 7;9(4):427. doi: 10.3390/healthcare9040427. PMID: 33917036; PMCID:
PMC8067745.
2. Alexander NB, Galecki AT, Grenier ML, Nyquist LV, Hofmeyer MR, Grunawalt JC,
Medell JL, Fry-Welch D. Task-specific resistance training to improve the ability of
activities of daily living-impaired older adults to rise from a bed and from a chair. J
Am Geriatr Soc. 2001 Nov;49(11):1418-27. doi: 10.1046/j.1532-5415.2001.4911232.x.
PMID: 11890578.
3. Aquino CF, Fonseca ST, Gonçalves GG, Silva PL, Ocarino JM, Mancini MC. Stretching
versus strength training in lengthened position in subjects with tight hamstring muscles:
a randomized controlled trial. Man Ther. 2010 Feb;15(1):26-31. doi: 10.1016/j.
math.2009.05.006. Epub 2009 Jul 25. PMID: 19632878.
4. Caputo GM, Di Bari M, Naranjo Orellana J. Group-based exercise at workplace: shortterm effects of neck and shoulder resistance training in video display unit workers
with work-related chronic neck pain-a pilot randomized trial. Clin Rheumatol. 2017
Oct;36(10):2325-2333. doi: 10.1007/s10067-017-3629-2. Epub 2017 May 2. PMID:
28466419.
5. Jones KD, Burckhardt CS, Clark SR, Bennett RM, Potempa KM. A randomized
controlled trial of muscle strengthening versus flexibility training in fibromyalgia. J
Rheumatol. 2002 May;29(5):1041-8. PMID: 12022321.
6. Leite T, de Souza Teixeira A, Saavedra F, Leite RD, Rhea MR, Simão R. Influence of
strength and flexibility training, combined or isolated, on strength and flexibility gains.
J Strength Cond Res. 2015 Apr;29(4):1083-8. doi: 10.1519/JSC.0000000000000719.
PMID: 25268286.
7. Li S, Garrett WE, Best TM, Li H, Wan X, Liu H, Yu B. Effects of flexibility and strength
interventions on optimal lengths of hamstring muscle-tendon units. J Sci Med Sport.
2020 Feb;23(2):200-205. doi: 10.1016/j.jsams.2019.09.017. Epub 2019 Oct 7. PMID:
31623958.
8. Morton SK, Whitehead JR, Brinkert RH, Caine DJ. Resistance training vs.
static stretching: effects on flexibility and strength. J Strength Cond Res. 2011
Dec;25(12):3391-8. doi: 10.1519/JSC.0b013e31821624aa. PMID: 21969080.
9. Nelson RT, Bandy WD. Eccentric Training and Static Stretching Improve Hamstring
21
Flexibility of High School Males. J Athl Train. 2004 Sep;39(3):254-258. PMID:
15496995; PMCID: PMC522148.
10. Racil G, Jlid MC, Bouzid MS, Sioud R, Khalifa R, Amri M, Gaied S, Coquart J. Effects
of flexibility combined with plyometric exercises vs isolated plyometric or flexibility
mode in adolescent male hurdlers. J Sports Med Phys Fitness. 2020 Jan;60(1):45-52. doi:
10.23736/S0022-4707.19.09906-7. Epub 2019 Oct 16. PMID: 31640314.
11. Simão R, Lemos A, Salles B, Leite T, Oliveira É, Rhea M, Reis VM. The influence of
strength, flexibility, and simultaneous training on flexibility and strength gains. J Strength
Cond Res. 2011 May;25(5):1333-8. doi: 10.1519/JSC.0b013e3181da85bf. PMID:
21386731.
12. Wyon MA, Smith A, Koutedakis Y. A comparison of strength and stretch interventions
on active and passive ranges of movement in dancers: a randomized controlled trial. J
Strength Cond Res. 2013 Nov;27(11):3053-9. doi: 10.1519/JSC.0b013e31828a4842.
PMID: 23439346.
13. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y,
Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ.
GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings
tables. J Clin Epidemiol. 2011 Apr;64(4):383-94. doi: 10.1016/j.jclinepi.2010.04.026.
Epub 2010 Dec 31. PMID: 21195583.
14. (((“strength training” [Title/Abstract] OR “resistance training” [Title/Abstract] OR
“weight training” [Title/Abstract] OR “plyometric*” [Title/Abstract] OR “calisthenics”
[Title/Abstract]) AND (“flexibility” [Title/Abstract] OR “stretching” [Title/Abstract]))
AND (“range of motion” [Title/Abstract])) AND (“random*” [Title/Abstract])
15. Valamatos MJ, Tavares F, Santos RM, Veloso AP, Mil-Homens P. Influence of full
range of motion vs. equalized partial range of motion training on muscle architecture and
mechanical properties. Eur J Appl Physiol. 2018 Sep;118(9):1969-1983. doi: 10.1007/
s00421-018-3932-x. Epub 2018 Jul 7. PMID: 29982844.
16. Marušič J, Vatovec R, Marković G, Šarabon N. Effects of eccentric training at longmuscle length on architectural and functional characteristics of the hamstrings. Scand J
Med Sci Sports. 2020 Nov;30(11):2130-2142. doi: 10.1111/sms.13770. Epub 2020 Jul
30. PMID: 32706442.
17. Bourne MN, Duhig SJ, Timmins RG, Williams MD, Opar DA, Al Najjar A, Kerr GK,
Shield AJ. Impact of the Nordic hamstring and hip extension exercises on hamstring
architecture and morphology: implications for injury prevention. Br J Sports Med. 2017
Mar;51(5):469-477. doi: 10.1136/bjsports-2016-096130. Epub 2016 Sep 22. Erratum in:
Br J Sports Med. 2019 Mar;53(6):e2. PMID: 27660368.
22
18. Gérard R, Gojon L, Decleve P, Van Cant J. The Effects of Eccentric Training on Biceps
Femoris Architecture and Strength: A Systematic Review With Meta-Analysis. J Athl
Train. 2020 May;55(5):501-514. doi: 10.4085/1062-6050-194-19. Epub 2020 Mar 27.
PMID: 32216654; PMCID: PMC7249279.
19. Gadomski SJ, Ratamess NA, Cutrufello PT. Range of Motion Adaptations in
Powerlifters. J Strength Cond Res. 2018 Nov;32(11):3020-3028. doi: 10.1519/
JSC.0000000000002824. PMID: 30204657.
█
23
Study Reviewed: Muscle Activation and Volume Load Performance of Paired Resistance
Training Bouts with Differing Inter-Session Recovery Periods. Paz et al. (2021)
Recovery Time Course Isn’t That Long,
If You Train Appropriately
BY MICHAEL C. ZOURDOS
Understanding recovery time course allows for appropriate
planning of weekly training. A new study found that lifters
typically recover within 48 hours following eight sets of pressing
to failure. This article breaks down those findings and discusses
how to allocate weekly training volume appropriately.
24
KEY POINTS
1. Researchers assessed the time course recovery from bench press training in 15
trained men. For three consecutive weeks, subjects performed an initial session
(Monday) on both the bench press and incline bench press consisting of 4 sets to
failure at an 8RM load. Then, each week subjects performed an identical session (a
recovery test session) at 24, 48, or 72 hours later, in a randomized order, to assess
recovery of volume performance.
2. Researchers found that volume performance was fully recovered, on the group
level, by 48 hours; however, volume performance was reduced by 18.8% when
subjects had only 24 hours of recovery time. Researchers also assessed EMG of
the pecs, delts, triceps, and biceps and found that biceps muscle activity tended
to increase at each recovery time point in the bench press, but all other muscle
groups showed similar activity as the initial session.
3. These findings suggest that, on average, lifters can effectively perform a volumebased bench press training session within 48 hours of a preceding training session.
However, these findings may be specific to the exercises tested and the magnitude
of training volume. Therefore, this article will discuss how recovery time courses
may vary when training variables are altered, and how lifters and coaches can
effectively allocate weekly training volume.
W
hen configuring weekly training,
it’s imperative to allocate training volume appropriately. Suppose you perform a session on Monday and
still cannot effectively train the same muscle
group again 48-72 hours later. In that case, fatigue may negatively impact your total weekly volume, so understanding recovery time
courses are paramount for planning weekly
training. Oftentimes, researchers investigate
the temporal muscle damage response to
training using indirect measures like creatine
kinase or muscle soreness, which is valuable, but elevated levels of creatine kinase
or soreness are not necessarily indicative
of one’s ability to train. Fortunately, the reviewed crossover design study from Paz et al
(1) quantified recovery by assessing changes
in actual training performance. Specifically,
15 trained men performed four sets to failure
at an eight-repetition maximum (RM) load
on both the barbell bench press and incline
bench press twice per week for three consecutive weeks. During each week, subjects
performed an initial session (Monday) and
then the second session (a recovery test session), 24, 48, or 72 hours later, in a randomized order, to assess recovery of volume performance. Electromyography (EMG) of the
pecs, delts, triceps, and biceps were compared
between the initial session and recovery test
session in each condition. Biceps EMG tended to increase within each condition during
the bench press at the recovery test sessions,
but otherwise, EMG was unchanged for the
other muscles tested. Total volume (bench
press + incline bench volume) did not recover after 24 hours of rest (-18.8%), but did re-
25
cover after 48 (-0.3%) and 72 (+2.5%) hours.
These findings suggest that volume performance is recovered within 48 hours following eight sets of pressing to failure in trained
lifters. While these findings provide a guide
for the time-course recovery, they are likely
exercise-, population-, volume-, and proximity to failure-specific. Therefore, this article
will aim to discuss:
3. Configuring training days to minimize recovery time course to allow for training a muscle
group on back-to-back days, if desired.
Purpose and Hypotheses
Purpose
1. How various training variables may impact recovery time course.
The purpose of this study was to compare the
recovery of volume performance and EMG
activity at 24, 48, and 72 hours following
eight sets of pressing to failure in trained men.
2. The importance of individual recovery
rates.
The researchers did not provide hypotheses.
Hypotheses 26
Subjects and Methods
Subjects
15 men with an average of almost seven years
of training experience completed the study.
Table 1 displays the available subject details.
Study Overview
Subjects completed this crossover study over
eight sessions. The first two sessions, separated by at least 48 hours, tested and retested the 8RM bench press and 30-degree incline bench press. A week after session two,
subjects began the first experimental condition, which consisted of four sets to failure
at their 8RM load on the bench press and
incline bench press with two minutes of interset rest. Lifters then performed the same
training session at either 24, 48, or 72 hours
later (a recovery test) to test recovery of volume performance. The following two weeks,
subjects completed the same protocol, except
they tested recovery of volume performance
at the time points not used in the first week.
Figure 1 shows the complete protocol.
Researchers also assessed the difference in
EMG activity from set one to set four in each
session. EMG was assessed on the pecs, delts,
triceps, and biceps in each condition. To allow
for comparisons, EMG data was normalized
(scaled) using the highest values obtained
in each session. Finally, the researchers calculated fatigue index (%) for each exercise
during every session: (fourth set reps / first
set reps) × 100. The paper actually notes that
the fatigue index is “third set / first set;” however, I assume the third set was a typo and
it’s actually the fourth (last) set. Further, the
paper the authors cited for the fatigue index
calculation uses the first and last sets.
Findings
EMG
Figure 2 shows that the only significant difference in EMG was for biceps EMG during
the 24- and 72-hour recovery tests. Specifically, activity tended to increase from set one
to set four during these recovery tests for the
biceps.
Total Volume and Fatigue Index
Subjects performed similar total volume at
the recovery test compared to the initial session in the 48- and 72-hour conditions. However, total volume decreased by 18.8% at the
recovery test in the 24-hour condition across
both exercises combined. Subjects also performed significantly lower volume for bench
press at the 24-hour recovery test than at the
recovery test in the other conditions. Table 2
shows the means and percentage changes in
volume performance. Fatigue index did not
differ between sessions or conditions.
Interpretation
A coach or lifter can write weekly training
with a frequency of three times per week
on an exercise or muscle group (Monday,
Wednesday, Friday); however, if the lifter is
too fatigued to complete the training, training
prescription should be altered. The presently
reviewed study (1) suggests that, on average,
volume performance in trained men fully recovers by 48 hours following eight sets of
pressing to failure. Taking these findings at
27
face value, one might say that challenging
training sessions could be performed three
times per week without issue. However, I’d
say not so fast, my friend, since recovery
time is highly individual and depends upon
various program design factors such as exercise selection, proximity to failure, and total
volume. This interpretation will:
1. Evaluate the program design variables affecting recovery time.
2. Discuss the individual rate of recovery.
3. Show a practical example of how to allocate training volume to manage recovery
time courses appropriately.
Overall Evaluation of Previous Research
There’s some recent research examining recovery time courses, most of which has been
reviewed in MASS articles or evaluated in
a recent applicable video. These include studies from Miranda et al (2 - MASS Review),
Ferreira et al (3 - MASS Review), Moran-Navarro et al (4 - MASS Review), Pareja-Blanco et al (5 - MASS Review), Belcher et al (6),
28
Camargo et al (7), and Soares et al (8). Evaluating these studies will help us determine a
more complete picture of performance recovery depending on the exercise (squat, bench
press, deadlift, or single-joint movements),
total volume, and proximity to failure. Table
3 summarizes the studies mentioned above.
Table 3 provides us with insight on recovery
time courses for the squat alone (6), bench
press alone (2, 3, 6), deadlift alone (6), squat
and bench press in the same session (4, 5),
single-joint exercises (7, 8), training to failure versus shy of failure (4, 5), and high (3)
and moderate volumes (6).
Volume and Recovery Time Course
The reviewed study from Paz et al (1), which
included eight sets of pressing (four flat bench
and four incline), is most similar to the studies
by Ferreira (3; 8 sets of pressing) and Miranda (2; 12 sets of pressing). Ferreira observed
the longest recovery time for volume performance, 96+ hours, of any study in Table 3.
However, Ferreira’s eight sets of bench press
between 50-70% of 1RM likely resulted in
the highest number of reps per set, potentially
exacerbating fatigue. Further, Ferreira’s volume performance test was on a dynamometer,
which is weaker evidence in comparison to the
studies by Paz and Miranda, which tested volume recovery on the same exercises used in
the initial session. On average, the Paz and Miranda studies both show complete bench press
performance recovery by 48 hours; however,
the Miranda study’s individual data reporting
shows that some lifters didn’t fully recover by
72 hours, and it’s possible some lifters were
still fatigued at 96 hours. Therefore, when performing bench press sets to failure, 48 hours
29
may be sufficient for recovery on average, but
it’s crucial that volume and recovery times are
individualized.
Interestingly, Belcher et al (6) showed recovery of some indirect muscle damage markers within 24-48 hours following four sets of
only bench press, squat, or deadlift training.
This faster recovery than Ferreira, Paz, and
Miranda could be due to lifters performing
only four sets; however, the recovery of biomarkers and joint range of motion does not
necessarily indicate volume performance recovery. Additionally, we should keep in mind
that the Belcher study used only one exercise
per session (Paz used two exercises and Miranda used three). Most likely, a lifter is performing three or more exercises per session,
which may cause more fatigue, especially if
those exercises require large ranges of motion, such as a dumbbell fly (more on specific
exercises later).
Before breaking down the rest of Table 3, let’s
pause for a moment and consider the cost of
inappropriate volume allocation. Suppose you
train in a way that elongates recovery time
beyond 48 hours. In that case, that may mean
30
lower weekly frequency and volume, which
could harm hypertrophy potential (lower volume) or strength potential (lower volume and
practice on major movements). Of course, a
lower frequency may be more appropriate
for some, but for those that train with higher frequencies (≥3 times per week per muscle
group), highly damaging training early in the
week isn’t ideal. However, other factors may
have an even greater impact on muscle damage and fatigue than high volume, which we’ll
evaluate in the following sections.
Failure and Recovery Time Course
Table 3 also reveals that training to failure, as
we’ve noted before, lengthens recovery time.
Specifically, the Moran-Navarro (4) and
Pareja-Blanco (5) studies show full recovery at 24 hours post-training with non-failure training on both the Smith machine squat
and bench press, compared to between 4896 hours with failure training. Additionally,
Moran-Navarro (4) not only shows faster recovery with non-failure training, but the rapid recovery occurred in a condition that was
volume equated to a failure training condition (3 × 10 at 10RM versus 6 × 5 at 10RM).
While higher volumes may elongate recovery time, failure training may have an even
greater impact. Notably, the non-failure conditions in those studies stayed pretty far from
failure, ~5 RPE with >8 reps and still only a
~7 or ~8 RPE when <6 reps were performed
in a set. It’s possible that training closer to
failure (7-9 RPE) with moderate reps or on a
free-weight exercise would have caused reductions in performance past 24 hours.
When mentioning “fatigue” with failure training, it’s always important to qualify the metric.
As previously noted, the currently reviewed
study (1) and Miranda et al (2) study measured
recovery with actual volume performance on
the same exercises used in the week’s initial
training session. The remainder of the studies
in Table 3 utilized indirect markers of muscle damage, assessed training volume via dynamometry, or measured velocity of a single
repetition at a moderate intensity (70-75% of
1RM). While we don’t directly compare volume performance with the other metrics from
these studies, we can’t be sure that high creatine kinase levels are predictive of volume
performance. Therefore, the Paz and Miranda
studies are likely the best current evidence we
have for the recovery of volume performance,
and we can only draw indirect conclusions
from the other studies.
Exercise-Specific Recovery Time Course
As I noted in a recent video, recovery time
course is exercise-specific. This concept is
somewhat supported in Table 3, even though
it doesn’t reveal much difference in the recovery rates of the squat, bench press, and
deadlift. In fact, Belcher et al (6) compared
the three, finding that the recovery time
course is similar between all three exercises. Pareja-Blanco (5) and Moran-Navarro
(4) also observed similar recovery rates between the squat and bench press following
both failure and non-failure training. However, that doesn’t mean some people won’t
have a harder time recovering from a squat
than a bench press if they have poor ankle
mobility and long femurs. The Camargo and
Soares studies, however, do suggest exercise-specific recovery time courses. Camargo
(7) shows, unsurprisingly, that it takes lon-
31
ger to recover from five sets of squats than
five sets of leg extensions. Soares (8) found
longer recovery time of the biceps following
preacher curls versus seated rows. Although
rows are a multi-joint movement and curls
are a single-joint movement, the Soares study
specifically tested fatigue (soreness and limb
swelling) directly of the biceps. Further, we
should consider that specific exercises that
are performed through large ranges of motion, such as Romanian deadlifts and dumbbell flyes, are significant drivers of localized
muscle damage and fatigue. Therefore, a
lifter should be cautious training particular-
ly damaging exercises to failure or with high
volume 48 hours before the next workout on
the same muscle group.
The Concept in Practice
The reviewed study, and various others, are
attempting to answer the question, “How long
does it take to recover from bench press training?” However, the more important question
is: “How can a lifter configure an appropriate
amount of volume while minimizing the recovery time course?” I don’t fault researchers
for investigating the former question in previous studies (I’ve been part of some of that
32
research too). Nonetheless, we have to keep
in mind that if a study wants to examine how
long it takes to recover, then the researchers
need to be sure their protocol induces a significant amount of fatigue and muscle damage. Similarly, if a concurrent training study
wants to see if the interference effect exists,
the researchers will not take it easy on the
participants. Therefore, all of the things that
I’d recommend keeping in check during the
early part of a training week (high-volume,
failure training, and damaging exercises) are
being specifically prescribed by researchers
to induce a damage response. So, just as we
shouldn’t use a study that has lifters run long
distances for four hours per week to conclude
that any endurance exercise will attenuate
strength and hypertrophy, we shouldn’t treat
the recovery time courses reported in the literature as if they tell the full story, because
some simple adjustments to training configuration can mitigate the fatigue response.
Therefore, Table 4 shows a conceptual example of allocating volume appropriately to
allow for greater weekly frequency and volume by avoiding failure training and damaging movements early in a week. The Monday, Wednesday, and Friday portion of this
table initially appeared in Volume 1 Issue 5,
and I’ve added Tuesday, Thursday, Saturday
for this article. As always, keep in mind the
table is just conceptual, and specific training
frequencies, volumes, and exercise selections
should be individualized.
Additional Thoughts
Here are some additional notes and opinions
now that we’ve reviewed the literature and
provided practical recommendations. There
was an 18.8% decrease in bench press volume
and volume for both exercises combined at
24 hours post-exercise in the reviewed study,
which isn’t that much. The average load used
in this study was 80.5kg. If a subject performed 4 (sets) × 8 (reps) × 80.5 (load), that
would be a volume of 2,576kg. Performing
18.8% less volume would mean the lifter
was averaging 6.5 reps per set. While I’ve
been clear that I wouldn’t recommend training within 24 hours after a damaging failure
workout, if you did, you could compensate
for any missed volume by reducing the number of reps, training shy of failure, and adding a set or two. Further, if you did want to
train with really high frequency (≥4 times per
week on a muscle group), that can be done;
it just needs to be structured appropriately. I
provided specific examples of how to accomplish those frequencies here and here.
While training to failure elongates the recovery time course, there are a few things
we don’t know. First, I wonder if training
to failure every week during an eight-week
program elicits the repeated bout effect to a
degree where recovery of volume capability fully occurs within 24-48 hours. Also, the
studies examining time courses of recovery
usually involve all sets being taken to failure,
or no sets being taken to failure. I’d be interested to see a temporal recovery comparison
between failure training, non-failure training,
and a hybrid group (i.e., one set to failure and
three sets not to failure).
I haven’t touched on the EMG findings yet
because they aren’t vital; they are mostly null
and the study reports actual performance data.
The tendency for biceps EMG to increase from
33
set one to set four during the recovery period
may signal that this specific muscle was not
fully recovered. Thus, it’s possible subjects
required enhanced motor unit recruitment of
the biceps to sustain the contraction during
the recovery tests. However, since the biceps
are not very active during a bench press, it’s
possible that the researchers just picked up
noise and the biceps were recovered.
Lastly, keep in mind that while the recovery
time course depends on all of the training
variables mentioned above, it is also highly
individual. Earlier I noted that biomechanical
factors could contribute to individual differences in recovery responses. Further, the Miranda study (2) reported individual recovery
of fatigue index. As Greg previously pointed out, some lifters were close to full recovery of volume performance after 24 hours of
pressing to failure, more had complete recovery at 48 hours, yet 9 out of 16 subjects only
lifted between 66-89% of their original volume at 72 hours following the initial session.
Therefore, when configuring weekly training, please be cognizant that your recovery
time course may vary from someone else’s,
and lifters and coaches should individualize
all training variables.
could train three times per week (Monday,
Wednesday, and Friday) for eight weeks,
with one group taking every set each day
to failure (would be miserable and perhaps
unfeasible on main movements) and another group training shy of failure. Before and
after training on Monday and before training
on Wednesday and Friday, indirect markers
of muscle damage could be assessed. During
the initial weeks, I’d expect that muscle
damage would be elevated to a greater extent before Wednesday and possibly Friday
training in the failure group. Still, the damage
response may be attenuated during the latter
weeks. For number two, researchers could
utilize a crossover design. Specifically, five
squat (or any exercise) sets to failure in one
condition, an equated volume squat training
session, not to failure in another condition,
and finally an equated volume condition with
only one set to failure with indirect measures
of muscle damage assessed at 24 hours and
volume performance at 48 hours could be the
study design.
Next Steps
To me, the most interesting underexplored
areas of research relating to the time course
of recovery are 1) evaluating the magnitude
of the repeated bout effect over time with
failure training, and 2) examining temporal
recovery between failure, non-failure, and
hybrid (both failure and non-failure) training
as noted above. For number one, two groups
34
APPLICATION AND TAKEAWAYS
1. The reviewed study found that the ability to perform volume on the bench press
and incline bench press was fully recovered on the group level by 48 hours posttraining, but not at 24 hours post-training, following eight sets of pressing (four
bench press and four incline bench) to failure.
2. An overarching look at the literature investigating the time course of recovery from
resistance training reveals that while high volume in a training session may elongate
the time course of recovery, training to failure and exercises that train your muscles
at very long muscle lengths have a particularly large impact.
3. Overall, when configuring a training week, a lifter should aim to allocate volume
appropriately. Specifically, if a lifter is looking to train with a frequency of two or
three times per week (or more) for a muscle group, they should avoid training to
failure on damaging exercises at the beginning of the week. Additionally, be advised
that recovery time courses are highly individual.
35
References
1. Paz GA, de Freitas Maia M, de Araújo Farias D, Miranda H, Willardson JM. Muscle
activation and volume load performance of paired resistance training bouts with differing
inter-session recovery periods. Science & Sports. 2021 Apr 1;36(2):152-9.
2. Miranda H, Maia MF, Paz GA, de Souza JA, Simão R, Farias DA, Willardson JM.
Repetition performance and blood lactate responses adopting different recovery periods
between training sessions in trained men. The Journal of Strength & Conditioning
Research. 2018 Dec 1;32(12):3340-7.
3. Ferreira DV, Gentil P, Ferreira-Junior JB, Soares SR, Brown LE, Bottaro M. Dissociated
time course between peak torque and total work recovery following bench press training
in resistance trained men. Physiology & behavior. 2017 Oct 1;179:143-7.
4. Morán-Navarro R, Pérez CE, Mora-Rodríguez R, de la Cruz-Sánchez E, GonzálezBadillo JJ, Sanchez-Medina L, Pallarés JG. Time course of recovery following resistance
training leading or not to failure. European journal of applied physiology. 2017
Dec;117(12):2387-99.
5. Pareja-Blanco F, Rodríguez-Rosell D, Aagaard P, Sánchez-Medina L, Ribas-Serna J,
Mora-Custodio R, Otero-Esquina C, Yáñez-García JM, González-Badillo JJ. Time course
of recovery from resistance exercise with different set configurations. The Journal of
Strength & Conditioning Research. 2020 Oct 1;34(10):2867-76.
6. Belcher DJ, Sousa CA, Carzoli JP, Johnson TK, Helms ER, Visavadiya NP, Zoeller RF,
Whitehurst M, Zourdos MC. Time course of recovery is similar for the back squat, bench
press, and deadlift in well-trained males. Applied Physiology, Nutrition, and Metabolism.
2019;44(10):1033-42.
7. de Camargo JB, Braz TV, Batista DR, Germano MD, Brigatto FA, Lopes CR.
Dissociated Time Course of Indirect Markers of Muscle Damage Recovery Between
Single-Joint and Multi-joint Exercises in Resistance-Trained Men. Journal of Strength
and Conditioning Research. 2020 Dec 24.
8. Soares S, Ferreira-Junior JB, Pereira MC, Cleto VA, Castanheira RP, Cadore EL, Brown
LE, Gentil P, Bemben MG, Bottaro M. Dissociated time course of muscle damage
recovery between single-and multi-joint exercises in highly resistance-trained men. The
Journal of Strength & Conditioning Research. 2015 Sep 1;29(9):2594-9.
█
36
Study Reviewed: The Role of Perfectionism in Predicting Athlete Burnout, Training Distress,
and Sports Performance: A Short-Term and Long-Term Longitudinal Perspective. Květon et
al. (2021)
Striving for Perfection Versus
the Fear of Falling Short of It
BY ERIC HELMS
Perfectionism is often viewed negatively in sports psychology,
as striving for the impossible goal of perfection sets you up
to fail. But, not all forms of perfectionism are the same. How
perfectionism manifests itself in athletes can be the difference
between burnout and improved performance. However, there
may be another way.
37
KEY POINTS
1. Research has identified two dimensions of perfectionism: perfectionist striving
is characterized by setting very high standards and striving for perfection, while
perfectionist concerns are characterized by the fear of mistakes, judgment from
others, and not reaching one’s expectations.
2. The present study evaluated relationships between both dimensions of
perfectionism in adolescent athletes at baseline, three months, and one
year, with psychometric measures of burnout and overtraining. Perfectionist
concerns were related to higher scores for burnout and training distress and
lower perceived performance, while perfectionist striving was related to higher
perceived performance and lower scores for burnout.
3. While this study suggests only perfectionist concerns are maladaptive, and
perfectionist striving may even aid athletes, other research has observed
shared variance between the two dimensions. Meaning, in the real world, it may
be difficult to foster a purely perfectionist striving mentality. However, recent
research indicates “striving for excellence” may be a mentality that keeps the
best aspects of perfectionist striving without its downsides.
D
uring my first competitive bodybuilding season, I remember telling my
wife, “I don’t know if I’d keep training if I didn’t think I could one day become
Mr. Natural Olympia.” As soon as the words
left my mouth, I knew I had a problem. Over
the years, to prevent myself from burning out,
I’ve had to address my perfectionist tendencies and adjust how I set standards for myself,
define success, and evaluate progress. The
present study (1) gets to the heart of this issue,
as it looks at the complex relationship between
perfectionism, athlete burnout, and overtraining. Importantly, while we often view perfectionism as a solitary construct, research into
perfectionism has identified that it consists of
two dimensions which don’t always overlap:
perfectionist striving (striving for perfection
and setting exceedingly high standards), and
perfectionist concerns (fear of mistakes, judg-
ment from others, and not reaching expectations). In the presently reviewed study, the researchers evaluated the associations between
both dimensions of perfectionism and psychometric scores for athlete burnout, perceived
performance, and training distress in adolescent athletes at baseline, after three months,
and after one year. At baseline, perfectionist
concerns were associated with higher scores
for athlete burnout and training distress, and
lower scores of perceived performance. On
the other hand, perfectionist striving was associated with lower scores for athlete burnout,
and higher scores for perceived performance.
These relationships were somewhat stable
over time as well. Perfectionist concerns were
associated with higher scores for athlete burnout at three months, and lower scores of perceived performance at both three months and
one year, while perfectionist striving was as-
38
sociated with lower scores for athlete burnout
and higher scores for perceived performance
at both three months and one year. In this article, I’ll discuss the nuances of these findings,
the two dimensions of perfectionism, and what
attitudes (if any) coaches should encourage.
Purpose and Hypotheses
Purpose
The purpose of this study was to test the two
dimensions of perfectionism to see if they
predicted higher scores for athlete burnout
and overtraining (training distress and lower
perceived performance) cross sectionally, after three months, and after one year.
Hypotheses The researchers hypothesized there would
be a cross-sectional relationship, such that
perfectionist concerns would predict higher
scores for athlete burnout and training distress, and lower scores for perceived performance, while perfectionist striving would
have the opposite relationship. For longitudinal relationships at three months and one
year, the authors had the same hypothesis for
training distress and perceived performance;
however, because of prior mixed evidence
on burnout, they added that this relationship
might follow the same trend, or might end up
nonsignificant.
Subjects and Methods
Subjects
A 50% male, 50% female sample of 228 adolescent athletes (aged 14 to 19) who attended
the same sports school in the Czech Republic
participated in the cross-sectional portion of
this study. The participants competed in a variety of individual and team sports, with 132
individual sport athletes in the initial sample.
As a whole, the most common sports were
athletics (track and field), swimming, tennis,
volleyball, basketball, and football (soccer).
The participants competed at the regional
(14.9%), national (50.9%) and international
(34.2%) level.
For the longitudinal portions of this study, the
authors intended to look specifically at the relationships among individual sport athletes,
so they invited only the 132 individual sport
athletes to participate in the three month and
one year follow ups. While 93 of these athletes
participated at the three-month mark (57.0%
female), only 56 participated at the one-year
mark. Thus, to bolster sample size, the authors
invited the team sport athletes to participate
at the one-year mark as well, resulting in a
sample size of 83 respondents at the one-year
follow up (56.6% female, 56 individual sport
athletes, 27 team sport athletes).
Study Overview
The students who participated completed a
pencil and paper questionnaire at school that
consisted of demographic questions, three
previously validated psychological scales,
and one question about their perceived performance at three time points: at baseline,
three months later, and then one year later.
The psychological scales at each testing occasion all showed high internal consistency –
how related all the questions are to the metric
they are supposed to assess – as Cronbach’s
Alpha scores were > 0.8 (a value for internal
consistency from 0-1, interpreted similarly to
39
an r-score). The specifics of each scale are
shown below in Table 1.
age, sex, and the baseline scores were calculated as well.
The single-item perceived performance score
was determined from a visual analogue scale
where the participants made a mark anywhere on the line from 0-100 (0 - “bad form”,
100 - “great form”). This question was adapted from a scale used to evaluate overtraining
syndrome in athletes across a range of sports.
Criticisms and Statistical
Musings
To examine the relationships between the
items on the questionnaire, the researchers
performed simple correlational analyses at
baseline between the various psychometric
scores as well as multiple regression analyses that considered the effects of age, sex,
perfectionist strivings, and perfectionist concerns on athlete burnout, training distress,
and perceived performance scores. At baseline the amount of variance explained by perfectionism (both strivings and concerns) was
calculated when accounting for age and sex,
and at three months and one year the amount
of variance explained when accounting for
I don’t have any major criticisms, but I
thought it might be helpful to discuss correlation and regression for those who want
to better understand the findings. Typically,
with normally distributed data, you calculate
linear correlations (also called associations
or bivariate correlations) as Pearson r values from -1 to 1, which you have probably
seen before. The closer the value is to -1 or
1, the stronger the relationship between two
variables. Two variables which have a perfect, positive (as one increases, so does the
other) correlation have a score of 1. When
plotted on a graph the lines connecting the
points would be straight and slope upward.
An example would be plotting the height of
a group of people in inches on the x axis, and
in centimeters on the y axis. Since there are
40
2.54 centimeters per inch, you would see 1
inch in height plotted for every 2.54 centimeters in height, for every person, producing a
straight, positively sloped line spanning the
entire range of the graph. If two variables had
a negative relationship (as one variable increases, the other decreases), the slope would
be the opposite. This is a great visual representation of various linear correlations.
Regression simply presents the mathematical
equation that expresses this relationship, such
that you could plug in the value of one variable, and it would produce the predicted value
of the second. So using the height example, if
you plugged 63 inches into a regression equation for this relationship, it would produce
160.02 centimeters. Remember, this prediction is only as accurate as the strength of the
relationship. While a regression equation to
predict height in centimeters from height in
inches would perfectly predict height (as it’s
the same variable, just measured by two different units), a regression equation for a correlation with a very low r score will produce
prediction values that contain a lot of error.
This error is due to other variables which influence the outcome variable or simple measurement error. To describe how much of the
variance in the outcome variable is related to
the predictor variable, you can square the r
value, to produce an r2 value or coefficient
of determination. The coefficient of determination can be interpreted as the percentage
of the variance in the outcome explained by
the predictor. So for example, a strong correlation with an r of 0.8 produces an r2 value
of .64, meaning changes in the predictor account for 64% of the variance in the outcome.
You can also calculate the relationship between multiple predictor variables with an
outcome variable. This is done with a multiple
linear regression, where you add multiple predictor variables to see the combined strength
of their relationship with an outcome. This
process produces an r2 value for the whole
model, which is interpreted the same way as
it would be for a bivariate correlation. Further,
you can include additional predictor variables
to control for their influence on an outcome.
In the present study, the authors used multiple regression models to quantify relationships
between baseline perfectionism dimensions
and burnout scores, training distress, and perceived performance, while controlling for the
effects of age and sex. They reported the combined predictive relationship of age, sex, perfectionist strivings, and perfectionist concerns
on athlete burnout scores, training distress, and
perceived performance, denoted with a combined r2 score for each outcome, and also reported the combined predictive relationship of
perfectionist strivings and concerns after controlling for the effect of age and sex, denoted
as Δr2. At the three-month and one-year mark,
in addition to sex and age, they controlled
for baseline scores of perfectionist strivings
and concerns, denoted as CVbaseline. This last
point is important, as you’ll notice that the
strengths of the Δr2 adjusted relationships at
three months and one year are weaker than
at baseline. However, you should expect that
to be the case since the authors controlled for
CVbaseline scores which, unsurprisingly, were
often moderately or strongly related to the
same scores at both follow ups (i.e., in almost
all cases, the strongest predictors of burnout,
training distress, and perceived performance
41
at three months and one year, were burnout,
training distress, and perceived performance
at baseline). In the interpretation, I’ll explain
why this is an important point.
Lastly, if you look at Tables 3-5 in the Findings, you’ll notice there are values for each of
the individual predictors within the multiple
regression models at baseline, three months,
and one year. These are beta coefficient values
(also called regression coefficients). Unstandardized beta coefficient values in a regression model represent the average change in the
outcome variable you’d expect to see for every one-unit increase in the predictor variable.
In this study, since the psychometric scales all
have scores operating on different scales, they
report standardized beta coefficient values.
Unlike unstandardized beta coefficients which
represent the average change in the outcome
variable for every one-unit increase in the predictor, standardized coefficients represent the
change in standard deviations in the outcome
variable for every one-standard deviation increase in the predictor.
As an example, cross reference the means
and standard deviations in Table 2 for perfectionist concerns and the total athlete burnout score, with the coefficients in Table 3. A
standardized beta coefficient of 0.33 between
perfectionist concerns and athlete burnout
means that if someone’s perfectionist concerns score increased by 0.48 (one standard
deviation from Table 2), you’d expect a one
third of a standard deviation increase in the
athlete burnout score (as the coefficient is
0.33 from Table 3), which would be a score
increase of 0.16 (one third of the athlete burnout standard deviation of 0.48).
Findings
Table 2 displays the correlation matrix between
all the measured variables, as well the means
and standard deviations of each variable’s
42
score along the bottom. Perfectionist striving
had significant, moderate, negative relationships (r = -0.35 to -0.43) with the total athlete
burnout score, the subscales for reduced sense
of accomplishment and sport devaluation (not
getting as much value and enjoyment from
sport) and had a significant, small, positive
relationship (r = 0.19) with perceived performance. In contrast, perfectionist concerns had
significant, small to moderate, positive relationships (r = 0.17 to 0.29) with all burnout
scores, a significant, moderate, positive relationship (r = 0.40) with training distress, and
a significant, small, negative relationship (r =
-0.20) with perceived performance.
As shown in Table 3, when evaluating these
relationships with multiple regression at
baseline, after controlling for sex and age,
the relationships which were significant were
almost identical, with the same directionality,
with standardized beta coefficient (ß) values
ranging from -0.22 to -0.46, and 0.21 to 0.38.
Further, you can see between 5-21% of the
variance in the athlete burnout subscales was
explained by the model (and 23% of the overall athlete burnout score, but this wasn’t quite
significant with a p-value of 0.08).
In Table 4, you can see that at the threemonth mark, after controlling for age, sex,
and baseline scores, perfectionist striving had
a significant negative relationship (ß = -0.20)
with the burnout subscale for exhaustion, and
a significant positive relationship (ß = 0.22)
with perceived performance. Perfectionist
concerns had significant positive relationships (ß = 0.15 to 0.17) with total burnout,
reduced sense of accomplishment, and devaluation, and a significant negative relationship
(ß = -0.24) with perceived performance.
Finally, as seen in Table 5, at the one-year
mark after controlling for age, sex, and baseline scores, perfectionist striving had a significant negative relationship (ß = -0.20)
with total burnout and reduced sense of accomplishment and had a significant positive
relationship (ß = 0.34) with perceived perfor-
43
mance. Perfectionist concerns only had a significant negative relationship (ß = -0.27) with
perceived performance.
Interpretation
The findings of this study are straightforward, but nuanced in their application. At
various time points, higher levels of perfectionist concerns predicted burnout and aspects of burnout, as well as higher training
distress and lower perceived performance,
both of which are markers for overtraining.
The most consistent relationship with perfectionist concerns, however, which was present
44
at all time points, was that it predicted lower
perceived performance. On the other hand,
perfectionist striving essentially had the opposite relationship, predicting lower levels of
burnout, lower aspects of burnout, and better perceived performance, but it lacked any
relationship with training distress. Similarly,
perfectionist striving consistently predicted
better perceived performance. The magnitudes of these relationships weren’t huge, indicating that the dimensions of perfectionism
aren’t the primary determinants of burnout or
overtraining, but they were noteworthy.
With that said, before you simply come away
with “perfectionist concerns bad, perfectionist striving good,” there are a few things to
consider. The most consistent relationship
was between perfectionist concerns and
worse perceived performance. Assuming
there is some relationship between perceived
performance and actual performance, it’s
very possible that reverse causality could be
muddying the waters here. In other words,
constant fear of falling short of perfection and
being judged by others might cause a poorer
evaluation of one’s performance, and maybe
even poorer actual performance as well due
to distraction. However, it’s also likely that
poorer performance might cause you to be
more concerned that you aren’t reaching expectations and standards, and that you could
be judged for it.
Another potential misinterpretation is that
the relationships between perfectionism and
burnout, distress, and perceived performance
weaken a large amount over time. If you look
at the strength of the significant correlations
at baseline, then at the adjusted Δr2 at both
BEFORE YOU SIMPLY COME
AWAY WITH “PERFECTIONIST
CONCERNS BAD,
PERFECTIONIST STRIVING
GOOD,” THERE ARE A FEW
THINGS TO CONSIDER.
three months and one year, you might conclude these relationships are far weaker at the
two timepoints after baseline. However, you
must remember that at the three-month and
the one-year marks, the multiple regression
models controlled for the strength of the relationships at baseline. Thus, the low Δr2 values for the three-month and one-year models
don’t necessarily indicate that these relationships weaken a ton over time, but moreso
that the relationships are mostly explained
by the scores at baseline. The relationships
might have weakened to some degree (notice
the ß coefficients for CVbaseline were 0.17-0.53
at one year, but were 0.32-0.78 after three
months), but certainly not as much as is indicated by just looking at the Δr2 scores at three
months and one year versus baseline. Thus,
while the assessed dimensions of perfectionism are related to burnout, distress, and perceived performance, they might decrease a
small amount over time, and when the scores
for perfectionism change, they don’t have a
perfectly proportionate effect on the change
in burnout, distress, and perceived perfor-
45
mance (to better understand how the multiple regression models were implemented and
how to interpret them, check out the Criticisms and Statistical Musings).
This article actually ties in nicely with some
of the other mindset and sports psychology reviews I’ve done in MASS. If you recall my review earlier this year “New Year, New You”,
I reviewed a large scale study that found approach-oriented (goals exemplified by moving toward an outcome or doing something)
New Year’s resolutions were more likely to
be successful than avoidance-oriented (exemplified by moving away from something
or stopping a behavior) resolutions (2). In a
similar fashion, you could view striving for
perfection as an approach-oriented attitude,
and perfectionist concerns as trying to avoid
being imperfect.
The present study also connects with my review on self compassion and fears of self compassion in athletes. In it, the authors hypothesized that competitive athletes would have
lower self compassion and higher fears of self
compassion as a way to motivate higher performance; however, they found no significant
differences in compassion or self compassion
at different athlete levels. In fact, higher fear
of self compassion predicted greater psychological distress (3). This outcome is quite similar to the present study’s finding that athletes
with higher perfectionist concerns had higher
scores for burnout and distress.
Finally, regarding the application of these
findings, at the beginning of this article I mentioned the applications were nuanced. Despite what’s been said thus far, you shouldn’t
YOU COULD VIEW
STRIVING FOR PERFECTION
AS AN APPROACHORIENTED ATTITUDE, AND
PERFECTIONIST CONCERNS
AS TRYING TO AVOID
BEING IMPERFECT.
view perfectionist striving as a purely positive trait. I hinted in the introduction that I
had some pretty lofty goals when I started
bodybuilding, and that those goals were a
double-edged sword. I was highly motivated
to be the best, but I remember not seeing the
point in lifting at all if I couldn’t become the
best. This scared me when I realized it, as prior to competing, I was in love with lifting for
its own sake. Importantly, I wasn’t motivated
by the fear of not being the best. I didn’t stress
about the possibility of losing, and while I
wanted the acknowledgement and praise for
being the best, I wasn’t too concerned about
how others might judge me if I fell short. If
I had to guess, I suspect I would have scored
high on perfectionist strivings and reasonably
low on perfectionist concerns. Certainly, my
attitude resulted in being very meticulous
and focused, but I didn’t like that the thing I
loved, lifting weights, was now at risk longterm because I was so focused on being the
best. Over time, I consciously shifted my fo-
46
cus to being motivated by self improvement
and a love for the process.
In support of my personal experience, prior
research indicates that perfectionist striving
is occasionally related to negative psychological outcomes and that there is also shared
variance between perfectionist striving and
perfectionist concerns, such that the positive
outcomes associated with striving are often
only apparent when controlling for concerns
(4). As I discussed in a previous MASS video,
this is similar to the shared variance between
flexible dietary restraint and rigid restraint
(5, 6). If you have to mathematically control
for shared variance to see positive relationships in a behavior or trait, it indicates that in
the real world they often overlap. Conceptually, while flexible dietary restraint is better
(having a non black or white view of foods
and dieting), it’s quite common for people to
slip into rigid behaviors while attempting to
be flexible (sure you can eat “whatever you
want” on your IIFYM diet, but you have a
panic attack if you don’t have your food scale
with you). To better explain, rigid control
and flexible control are separate dimensions
of dietary restraint (7), but that doesn’t mean
they are opposites or mutually exclusive. Perfectionist strivings and concerns have a similar relationship. You can score high on both,
low on both, or high on one and not the other (8). While it’s certainly possible to score
very high on perfectionist strivings and very
low on perfectionist concerns (I think I probably would have), it’s probably semi-rare. To
truly exemplify this archetype you’d have to
be someone who strives for flawlessness, is
highly organized, always plans ahead, and
sets incredibly high personal standards, while
simultaneously not being concerned about
making mistakes, not dwelling on them or
being self-critical when they occur, and not
being concerned about the approval of others
or sensitive to their criticism. I think I was
close to this, but certainly not that extreme.
Further, in my coaching experience, most of
my clients with perfectionist tendencies have
had traits that would classify as both perfectionist striving and concerns.
So if perfectionist striving isn’t necessarily
a good thing, then what should coaches encourage in athletes? Some researchers make
a specific distinction between perfectionist
striving and striving for excellence (9). Pursuing excellence puts the focus on excelling,
which implies reaching a high standard of
PURSUING EXCELLENCE
PUTS THE FOCUS ON
EXCELLING, WHICH
IMPLIES REACHING A
HIGH STANDARD OF
PERFORMANCE BUT NOT
NECESSARILY PERFECTION,
AND ITS CONNOTATION
IS ALSO RELATED TO THE
PROCESS OF IMPROVEMENT.
47
performance but not necessarily perfection,
and its connotation is also related to the process of improvement. However, the pursuit
of perfection goes far beyond just reaching
a high standard, focusing purely on the outcome of perfection. In support of the idea that
excellence is likely a better thing to encourage, a recent meta-analysis reported that personality traits associated with the pursuit of
excellence, but not perfection, were associated with more helpful, positive academic traits
among students (10).
To be fair, a limitation of the present study is
that the participants were adolescent athletes
in school. This wasn’t a group of elite or professional adult athletes. It’s possible that the
relationships observed here wouldn’t be the
same in such a group. Perfectionist concerns
might be a well-managed, natural part of being
a high level performer who is paid to compete,
has a lot of eyes watching them play regularly, and whose career and livelihood depend
on their performance. Perhaps they wouldn’t
experience the same negative effect? While
that’s possible, back in volume three I wrote
a review of a study in which the authors qualitatively interviewed 10 Olympic, Paralympic, and World Championship gold medalists
about what they believed contributed to their
competitive success (11). While this wasn’t
a random sample, and it didn’t provide quantitative evidence, the qualitative themes that
emerged didn’t have elements of perfectionist
concerns. They didn’t mention the pressure of
not letting coaches or their team down, or their
constant efforts to not make mistakes. Rather,
the themes “intrinsic motivation” emerged, as
well as “effective coping strategies and a pos-
itive mindset.” One athlete specifically stated
when asked about studying opponents, “I never, ever wasted time thinking about anybody
else, ever.” Another stated “I was never the
type of person that needed to be watched. I
didn’t need to have an audience. I just took off,
and it was like I left everyone and everything
behind me.” These quotes indicate the athletes
probably wouldn’t have scored high on perfectionist concerns. With that said, some of
the quotes and themes from these interviews
did line up with the more “positive“ elements
of perfectionist striving that are also featured
in striving for excellence. For example, the
theme of “self-confidence and dominance”
stemmed from quotes which had similarities
with high personal standards, and methodical
planning was mentioned as a coping strategy –
both features within perfectionist striving and
striving for excellence. Ultimately, we don’t
have enough information at this stage to know
how well the observed relationships with perfectionism in the present study would translate
to adult strength and physique athletes. But,
given the other lines of research I’ve mentioned, I suspect the relationships wouldn’t be
completely different. For that reason, it seems
like the lowest risk, highest potential for reward motivational strategy would be striving
for excellence, not perfection.
Next Steps
This study was a well done series of correlational analyses, which to some degree accounted for changes in the associated variables.
However, it wasn’t specifically an intervention that examined the effect of changing one
variable on the others over time. For ethical
48
APPLICATION AND TAKEAWAYS
Perfectionism has two dimensions: perfectionist striving and perfectionist concerns.
Perfectionist concerns are consistently related to maladaptive behaviors and
outcomes in many aspects of life (including sport), but perfectionist striving often
isn’t. While striving for perfection had neutral to beneficial relationships with burnout,
distress, and perceived performance in the present study, it often shares variance
with perfectionist concerns. Disentangling the dimensions of perfectionism in the
real world is challenging. So, a better motivational strategy with fewer potential
downsides is the pursuit of excellence, where one focuses on self improvement and
high personal standards.
reasons, perfectionist striving shouldn’t be
used as an intervention compared to striving
for excellence. However, I would like to see
a longitudinal study using similar measurements as the current study, in which a group
of adult strength and physique athletes who
scored high on perfectionist striving were divided into a control group and an intervention group. The intervention group could be
counseled by a sports psychologist to adopt
the pursuit of excellence as a mindset to see if
it resulted in lower scores for distress, burnout, and overtraining, without negatively impacting perceived performance, compared to
the controls.
49
References
1. Květon P, Jelínek M, Burešová I. The role of perfectionism in predicting athlete burnout,
training distress, and sports performance: A short-term and long-term longitudinal
perspective. J Sports Sci. 2021 Apr 4:1-11.
2. Oscarsson M, Carlbring P, Andersson G, Rozental A. A large-scale experiment on New
Year’s resolutions: Approach-oriented goals are more successful than avoidance-oriented
goals. PLoS One. 2020 Dec 9;15(12):e0234097.
3. Walton CC, Baranoff J, Gilbert P, Kirby J. Self-compassion, social rank, and
psychological distress in athletes of varying competitive levels. Psychology of Sport and
Exercise. 2020 Sep 1;50:101733.
4. Gäde JC, Schermelleh-Engel K, Klein AG. Disentangling the Common Variance
of Perfectionistic Strivings and Perfectionistic Concerns: A Bifactor Model of
Perfectionism. Front Psychol. 2017 Feb 13;8:160.
5. Tylka TL, Calogero RM, Daníelsdóttir S. Is intuitive eating the same as flexible dietary
control? Their links to each other and well-being could provide an answer. Appetite. 2015
Dec;95:166-75.
6. Linardon J, Mitchell S. Rigid dietary control, flexible dietary control, and intuitive eating:
Evidence for their differential relationship to disordered eating and body image concerns.
Eat Behav. 2017 Aug;26:16-22.
7. Westenhoefer J, Stunkard AJ, Pudel V. Validation of the flexible and rigid control
dimensions of dietary restraint. Int J Eat Disord. 1999 Jul;26(1):53-64.
8. Stoeber J, editor. The psychology of perfectionism: Theory, research, applications.
Routledge; 2017 Aug 22.
9. Gaudreau P. On the Distinction Between Personal Standards Perfectionism and
Excellencism: A Theory Elaboration and Research Agenda. Perspect Psychol Sci. 2019
Mar;14(2):197-215.
10. Osenk I, Williamson P, Wade TD. Does perfectionism or pursuit of excellence contribute to
successful learning? A meta-analytic review. Psychol Assess. 2020 Oct;32(10):972-983.
11. Burns L, Weissensteiner JR, Cohen M. Lifestyles and mindsets of Olympic, Paralympic
and world champions: is an integrated approach the key to elite performance? Br J Sports
Med. 2019 Jul;53(13):818-824.
█
50
Study Reviewed: Vitamin D Supplementation Does Not Enhance Resistance TrainingInduced Gains in Muscle Strength and Lean Body Mass in Vitamin D Deficient Young Men.
Savolainen et al. (2021)
Revisiting the Effects of Vitamin D
Supplementation on Strength
and Hypertrophy
BY ERIC TREXLER
In Volume 4 of MASS, our review of a meta-analysis indicated that
vitamin D supplementation enhanced strength gains, particularly
in folks with low vitamin D levels. However, a new study suggests
otherwise. Read on for an update on the potential value of
vitamin D supplementation.
51
KEY POINTS
1. The presently reviewed study (1) sought to determine if vitamin D supplementation
(8,000IU/day) would enhance strength or hypertrophy during a 12-week resistance
training program in young, vitamin D deficient males.
2. Vitamin D supplementation increased serum vitamin D levels substantially, but did
not significantly impact hypertrophy. As for strength outcomes, the placebo group
actually made significantly better gains than the vitamin D group for two out of seven
lifts tested.
3. It’s hard to argue that vitamin D supplementation will have a huge impact on strength
or hypertrophy, particularly for people with sufficiently high vitamin D levels. There are
clear downsides of excessive vitamin D levels, and clear downsides of insufficient
vitamin D levels. Targeted vitamin D supplementation can be a suitable strategy for
keeping blood vitamin D within an ideal range, which appears to be around 75-100
nmol/L, give or take.
W
hen we last discussed vitamin D
in MASS, it was in the form of
a meta-analysis. When you see
a meta-analysis, which happens to sit upon
the very top of the hierarchy of evidence, you
may be inclined to expect that it will provide
some pretty definitive answers to whatever
research question it’s addressing. However,
the previously reviewed meta-analysis looking at the effects of vitamin D on strength
in athletes contained very few studies, so
it should be viewed as more of a hypothesis-generating paper than a conclusion-generating paper. As such, we have to keep an
open mind and continue evaluating new vitamin D studies as they come out, and we have
to incorporate new findings into an updated
understanding of the relationships between
vitamin D, strength, and hypertrophy. The
presently reviewed study (1) assessed the effects of vitamin D supplementation (8,000IU/
day) on strength and hypertrophy during
a 12-week resistance training program in
young, vitamin D deficient males. Vitamin D
supplementation induced a large increase in
serum vitamin D levels, but did not favorably
impact strength or hypertrophy to a statistically significant degree. In fact, strength values were significantly different between the
vitamin D and placebo groups for two of the
seven lifts, with the placebo group making
better gains than the vitamin D group. As we
saw back in Volume 4, a thoughtful analysis
of vitamin D research demands some nuance
and attention to detail, so let’s dig in and see
if we can make sense of the inconsistent vitamin D literature.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was to determine if vitamin D supplementation would enhance hypertrophy and strength
adaptations during a 12-week resistance
52
training program in young, vitamin D deficient males.
Hypotheses
The researchers hypothesized that vitamin D
supplementation would enhance hypertrophy
and strength adaptations over the course of
the resistance training program.
Subjects and Methods
Subjects
The presently reviewed study was completed by young, healthy, untrained male participants. Potential participants were excluded
from the study if they had recently been consuming any dietary supplements containing
vitamin D, participated in any competitive
sport or structured resistance training within
the previous year, or had any chronic illnesses that could have influenced study outcomes.
60 subjects were interested in participating,
but the researchers excluded participants
with serum 25(OH)D levels (that is, blood
vitamin D levels) over 60nmol/L at the first
measurement (prior to the familiarization
phase), and also excluded any subjects whose
serum 25(OH)D levels were above 50nmol/L
one month later (when the main phase of the
study was about to begin). As such, all participants were below the threshold for vitamin D deficiency (50nmol/L) at the onset of
the main phase of the study. 11 subjects were
excluded due to high serum 25(OH)D levels,
and some other participants withdrew for a
variety of other reasons, so the researchers
ended up with 39 subjects for their analysis.
Baseline characteristics of those 39 subjects
are presented in Table 1.
Methods
This study utilized a double-blind design in
which participants supplemented with either
vitamin D (8000 IU) or a visually identical
but inert placebo daily for the duration of a
12-week resistance training program. Given
that the participants were untrained prior to
participation, the researchers implemented
a four-week preparatory period prior to the
onset of supplementation so the participants
could become familiarized with the training
program and acquire some of the early neural adaptations that are observed at the onset
of training. With a 4-week preparatory period followed by the 12-week intervention, the
study was 16 weeks in total, and visits were
completed in Estonia during the winter and
early spring (December to March), when vitamin D levels tend to be their lowest.
During the 12-week intervention period,
participants completed three supervised resistance training sessions per week. Each
training session consisted of seven exercises (chest press, leg press, lat pull-down, triceps push-down, seated row, knee extension,
53
and biceps curl). They weren’t completed in
a specific order, but at least one upper-body
exercise had to be completed between the leg
press and leg extension exercises. The concentric and eccentric phases were 1-2 seconds for each exercise, and participants rested 1-2 minutes between sets of an exercise
and 3 minutes as they transitioned from one
exercise to the next. The program essentially
had three, four-week blocks; in the first three
weeks subjects completed three sets of each
exercise, but in the fourth week they did only
one set per exercise and completed a 5RM
testing protocol to update their estimated
1RM for each lift. The first block of training
involved sets of 12-15 reps with 60-75% of
1RM, and intensity progressively increased
to sets of 8-12 with 75-85% of 1RM in the
third block. Subjects in both groups consumed a whey protein beverage after each
workout, which provided 20g of protein. Every four weeks throughout the intervention
period, participants were weighed, filled out
a four-day food diary, got their blood drawn,
and re-tested their 5RMs to update their 1RM
estimates (for programming purposes).
min D, and calcium intakes) from four-day
food diaries, several hormones (testosterone,
cortisol, growth hormone, IGF-1, and parathyroid hormone), and some other blood biomarkers (calcium, ionized calcium, urea, and
creatine kinase).
Findings
Diet and Blood Biomarkers
For both groups, calorie intake tended to be
around 2200-2400 kcal/day throughout the
intervention, with protein intake between
1.17-1.32 g/kg of body mass. Those protein
values do not include the post-workout protein beverage that was consumed on training
days only, which would bump the protein
numbers up to around 1.42-1.57 g/kg. There
were no significant differences between
groups, no significant changes over time,
and no significant group × time interactions
for any diet variables. As one would expect,
vitamin D supplementation induced a large
The primary outcomes of interest included serum 25(OH)D levels, estimated 1RM for all
seven training exercises (estimated based on
5RM testing), and changes in body composition (total body mass, total fat mass, bodyfat percentage, android fat mass, android fat
percentage, total lean mass, trunk lean mass,
arms lean mass, and legs lean mass), measured via dual-energy x-ray absorptiometry
(DXA). The researchers also measured and
analyzed a bunch of other stuff, including dietary variables (energy, macronutrient, vita-
54
increase in serum 25(OH)D levels, whereas
values trended slightly downward in the placebo group (Figure 1).
With regards to blood metabolites, a significant main effect of time was observed for
ionized calcium (p < 0.0001), with values decreasing from week 0 to week 8 but rebounding back near baseline at week 12. Similarly,
a significant main effect of time was observed
for creatine kinase (p < 0.0001), with values
decreasing from week 0 to week 8. They increased a bit from week 8 to week 12, but
remained below week 0 values. No significant time effects were observed for calcium
or urea levels, and there were no significant
group effects or group × time interactions for
any of these blood metabolites.
As for hormones, a significant main effect of
group was observed for cortisol, with values
generally being higher in the placebo group
(460.4 ± 94.0 nmol/L) compared to the control group (414.1 ± 82.4 nmol/L). In contrast,
there was no significant main effect of time
or group × time interaction effect for cortisol
values. A significant main effect of time was
observed for IGF-1, with values generally
being higher at week 12 (217.6 ± 44.0 μg/L)
compared to week 0 (206.4 ± 38.2 μg/L), but
there was no significant main effect of group
or group × time interaction effect for IGF-1
values. No significant main effects or interactions were observed for testosterone, growth
hormone, or parathyroid hormone levels (all
p > 0.05), which were all within normal ranges. Previous research has closely evaluated
links between vitamin D and testosterone
(2), so it’s worth noting that effects on testosterone levels were negligible, with values
falling from 23.8 ± 7.9 nmol/L to 22.3 ± 5.5
nmol/L in the placebo group and from 22.6 ±
6.2 nmol/L to 21.7 ± 7.5nmol/L in the vitamin D group.
Strength
For all seven exercises, a significant main
effect of time was observed, with values
increasing from week 0 to week 12 (all p
55
< 0.0001). No significant main effects of
group were observed for any of the lifts.
There were two lifts for which significant
group time × interactions were observed; in
both cases (chest press and seated row), the
placebo group had larger strength increases
from week 0 to week 12 than the vitamin D
group (both p < 0.05). All strength values
are presented in Figure 2.
Body Composition
Main effects of time were observed for total
body mass, fat mass, body-fat percentage, android fat mass, android fat percentage, total
lean mass, trunk lean mass, arm lean mass, and
leg lean mass (all p < 0.001). Increases were
observed for total body mass and all indices of
lean mass, whereas decreases were observed
for all fat mass outcomes. Group-specific
changes for the most notable body composition variables are presented in Table 2.
The researchers also assessed some correlations at week 0 and found that serum 25(OH)
D levels were positively correlated with total
lean mass (r = 0.432, p = 0.006), trunk lean
mass (r = 0.427, p = 0.007), arm lean mass
(r = 0.378, p = 0.018), and leg lean mass (r =
0.390, p = 0.014), but were not significantly
correlated with BMI, total fat mass, or android fat mass (all p > 0.05).
Interpretation
I think the best way to interpret these results is
to begin by recapping three recent meta-analyses on the effects of vitamin D supplementation on strength. First, there was a 2015
meta-analysis by Tomlinson et al (3). Their
56
analysis included seven studies and concluded that vitamin D supplementation increased
upper-body and lower-body strength in
healthy adults. A second meta-analysis was
published by Zhang et al in 2019 (4). This
analysis included eight studies and concluded that vitamin D supplementation increased
lower-body strength, but not upper-body
strength, in athletes. They also found that
vitamin D supplementation appeared to be
significantly more effective in athletes who
trained indoors, indicating that even the small
amount of sun exposure received during outdoor training might be enough to influence
the results of a vitamin D trial. Finally, another meta-analysis from 2019 was reviewed
in Volume 4 of MASS. In this analysis (5),
Han et al included five studies featuring six
independent samples of participants. The researchers concluded that vitamin D failed to
significantly increase bench press strength,
leg extension strength, and overall strength
(bench press and leg extension combined),
but I identified some issues during the review
that required recalculation. After recalculating the meta-analysis, the updated results indicated that vitamin D supplementation significantly increased leg extension strength,
but did not significantly increase bench press
strength. In addition, larger effects tended to
be observed in the studies in which circulating vitamin D levels were lower at baseline.
This quick assessment of the most recent vitamin D meta-analyses equips us with a little checklist of important factors to consider
when interpreting a vitamin D trial. We need
to carefully consider the exact strength test
being evaluated, participants’ baseline vitamin D levels, and (by extension) the degree
to which vitamin D levels were changed by
supplementation.
In light of prior research in this area, we
shouldn’t be terribly surprised that the presently reviewed study found no significant effect of vitamin D on the strength tests evaluated. As a reminder, strength tests included
5RMs for gym machines (leg press, knee
extension, chest press, triceps push-down,
biceps curl, lat pull-down, and seated row),
with 5RMs being used to estimate 1RM values. I love the meta-analysis conducted by
Tominson et al (3) because it very clearly
presents strength results for both upper-body
and lower-body outcomes, and clearly identifies which outcomes were tested via free
weights, gym machines, or dynamometers.
If you look at the upper-body results from
Tomlinson et al, you’ll see that all five of the
WE SHOULDN’T BE
TERRIBLY SURPRISED
THAT THE PRESENTLY
REVIEWED STUDY
FOUND NO SIGNIFICANT
EFFECT OF VITAMIN
D ON THE STRENGTH
TESTS EVALUATED.
57
largest effect sizes came from dynamometer
tests. Two effect sizes came from free weight
or gym machine strength tests; one reported
a pretty sizable negative effect size (ES =
-0.55), while the other reported a trivial effect size (ES = 0.15; effect sizes below 0.2
are often categorized as being trivial). If you
look at the lower-body tests, you’ll see that
all five of the highest effect sizes were from
studies using dynamometer tests, and none of
the free weight or gym machine strength assessments yielded non-trivial effect sizes.
While some analyses suggest that vitamin D
may differentially affect upper-body versus
lower-body strength outcomes, the more consistent pattern seems to be that dynamometer-based tests reflect improvements far more
consistently than tests involving free weights
or gym machines, and it just so happens that
dynamometers are more commonly used to
assess lower-body strength rather than upper-body strength in applied supplementation trials assessing large muscle groups. As
I discussed back in Volume 4, dynamometers
allow for more precise and repeatable assessments of muscle strength. They allow participants to give a maximal effort in simple muscle actions that are relatively independent of
skillful coordination. For example, consider
1RM testing of the squat. We have to make
an educated guess about your 1RM load,
then test it and see how right (or wrong) we
were. If we’re repeatedly wrong and testing
requires numerous attempts, you may fatigue
before we get an accurate number. If we don’t
have fractional plates, our precision of measurement is limited by the smallest plates we
have access to. If you aren’t a highly skilled
squatter, your 1RM strength will be hard to
predict and variable from day to day.
In contrast, we can put you on a dynamometer, adjust every single setting of the chair
to perfectly fit your body, and just tell you
to push as hard as you can. We don’t have to
predict your strength, and the precision of our
measurement is not limited by the constraints
of plate denominations. When you return for
post-testing, the chair is set up the exact same
way, and all you have to do is push again.
While there is meta-analytic evidence to suggest that vitamin D enhances lower-body
strength more than upper-body strength (4),
these findings are challenged by direct experimental evidence. Grimaldi et al (6) tested both upper-body and lower-body strength
using dynamometry in a large sample of 419
healthy men and women. Blood vitamin D
levels were significantly associated with both
upper-body and lower-body strength outcomes, and if anything, the results were more
robust for upper-body outcomes than lower-body outcomes. While I suspect that the
discrepancy between dynamometer measurements and other strength measurements is
mostly related to precision and reliability, we
can’t totally discount the fact that dynamometers are often used to assess muscle actions
that are technically different from the typical
isotonic free weight exercise. For example,
dynamometers are often used to measure isometric muscle actions, and dynamic muscle
actions are usually measured isokinetically
(contraction velocity is constrained). So, it’s
possible that vitamin D specifically impacts
isometric and isokinetic muscle actions to a
greater degree than dynamic muscle actions
58
with unconstrained velocities, but we’ll need
more research to sort out the details.
In the presently reviewed study, the likelihood
of obtaining precise and reliable strength values was not only threatened by the use of
gym machines rather than dynamometry, but
also by the decision to predict 1RM strength
based on 5RM testing. Plus, while we’re on
the topic of obtaining precise and reliable
measurements, I think it’s also valuable to
consider training status. It’s true that meta-analyses have reported favorable strength
effects of vitamin D for both athletes (4) and
non-athletes (3), but it can be hard to parse
very small, supplement-induced differences
when assessing strength changes using free
weights or gym machines in untrained subjects. In the presently reviewed study, none of
the participants had been resistance training
in the previous year leading up to enrollment.
While the authors made a great decision to
implement a four-week familiarization phase
to induce some of the early-stage neural adaptations to training, some very substantial
beginner gains still occurred throughout the
main phase of the study. For example, the
placebo group added over 100kg to their leg
press and nearly doubled their leg extension
strength. The effect of just about any supplement, regardless of efficacy level, is going to
seem like fairly meaningless noise within the
context of such robust and substantial gains,
and may be hard to reliably detect.
In reality, we’d have to assume that any effect
of vitamin D supplementation on strength or
hypertrophy is going to be modest in magnitude. We’re talking about the type of intervention that aims to optimize gains, not to
revolutionize gains. The mere fact that over
half of the athletic population tends to have
inadequate blood vitamin D levels (7) should
reinforce the reality that performance doesn’t
absolutely deteriorate with vitamin D insufficiency; I struggle to believe that over half of
athletes in today’s ultra-competitive athletic
climate are in a perpetual state of dramatically impaired performance that’s only a few
cheap capsules away from rectification. So,
overall, this study had a relatively low likelihood of detecting significant effects from
vitamin D supplementation. On the strength
side, the likelihood of detection was threatened by using gym machines for strength testing, using 5RMs to predict 1RM values, and
the possibility of letting the small supplement
effects get totally overshadowed by huge beginner gains. Similarly, on the hypertrophy
side, the likelihood of detection was threatened by using DXA, which has a limited ability to detect small differences in hypertrophy
over relatively short time scales (12 weeks in
this case), in addition to lack of control over
dietary factors influencing the magnitude of
hypertrophy, while sizable beginner gains
in lean mass also have the potential to overshadow the small anticipated effect of vitamin D supplementation. The observed correlations between fat-free mass and vitamin
D levels at baseline also lend some support
to the idea that insufficient vitamin D levels
could threaten lean mass retention or accretion. However, muscle mass has the potential to store vitamin D (8), which limits our
ability to make strong conclusions about the
direction of this relationship. In other words,
greater blood vitamin D levels could facilitate maintenance or accretion of lean mass,
59
but greater muscle mass could also lead to
better maintenance of blood vitamin D levels.
One additional consideration put forth by the
researchers was the possibility that vitamin
D levels may have gotten a bit too high. As
they explained, very high doses of vitamin
D can lead to a significantly lower ratio of
1,25[OH]2D to 24,25[OH]2D, which can reduce the activity of the vitamin D receptor
(9). We have a tendency to assume that more
hormone in circulation means more hormone
action, but that’s not necessarily the case; at
the end of the day, the actual activity depends
upon the hormone binding to its receptor
and initiating a response. In the presently reviewed study, average blood 25(OH)D levels
reached a peak level of 142.4 ± 21.9 nmol/L
in the vitamin D group, which is substantially
higher than the typical values in the studies
included in previous meta-analyses reporting
ergogenic effects (3–5). From a health perspective, we’ve long understood that there is
an optimal range, with values above or below
the range being suboptimal. Insufficient vitamin D levels have been linked to impaired
strength, but this idea of an upper limit to the
optimal vitamin D range for strength performance is pretty new. Given the lack of available data, the idea is a bit speculative at this
point, but it’s an intriguing possibility that
warrants further investigation.
As discussed in a previous MASS article, vitamin D has multifactorial roles in the body.
Insufficient vitamin D levels have been associated with depression, cognitive decline,
poor bone health, and decreased neuromuscular function, and previous studies have
linked vitamin D levels to higher aerobic fit-
ness levels, greater muscle force production,
higher testosterone levels, better immune
function, reduced post-exercise inflammation, and more rapid recovery from intense
exercise. People often argue about what constitutes “sufficient” blood levels of 25(OH)
D; some suggest that values should be above
20 ng/mL (50 nmol/L), while others suggest
it should be above 30 ng/mL (75 nmol/L). As
more evidence becomes available, it seems
advisable to keep blood 25(OH)D levels below 140nmol/L; beyond this level, the likelihood of hypercalcemia increases (10), and
levels that high also seem to be linked to increased 24,25[OH]2D levels (9), which could
threaten the efficacy of vitamin D supplementation by reducing the activity of the vitamin
D receptor. So, while the presently reviewed
study failed to identify significant benefits
of vitamin D supplementation for strength or
hypertrophy, and the vitamin D literature is
generally a bit inconsistent overall, maintaining vitamin D levels within an optimal range
(let’s say between 75-100 nmol/L, give or
take) seems advisable.
As recommended previously, you probably
don’t want blood vitamin D levels to be too
low or too high, for reasons related to both
performance and general health. While highdose supplementation in the absence of blood
testing can be counterproductive if blood
25(OH)D levels get too high, targeted supplementation with repeated blood testing can
be a good strategy for maintaining blood vitamin D levels within the optimal range. Ideally, this process would be supervised by a
qualified healthcare professional. Finally, as
a reminder, blood levels of 25(OH)D can be a
60
YOU PROBABLY DON’T
WANT BLOOD VITAMIN
D LEVELS TO BE TOO
LOW OR TOO HIGH, FOR
REASONS RELATED TO
BOTH PERFORMANCE
AND GENERAL HEALTH.
bit misleading for individuals with a relatively high degree of skin pigmentation, so more
nuanced testing might be required to guide a
targeted supplementation strategy.
Next Steps
It’d be great if future studies could get to the
bottom of the inconsistent effects within the
vitamin D literature. More specifically, I’m
interested in the fact that dynamometer-based
tests seem to show strength benefits more
frequently than isotonic tests that utilize free
weights or weight machines, and that some
studies have reported more notable effects
on lower-body strength than upper-body
strength. Studies typically use either free
weight tests or dynamometry tests rather than
using both for a given muscle action, so it’s
hard to know if the difference is truly related
to the type of strength test being used, or is
actually attributable to some other character-
istic of the studies (such as geographical region, training status of participants, baseline
vitamin D levels, vitamin D dose, post-test
vitamin D levels, or any number of other factors that can vary from study to study). So, I’d
like to see follow-up studies that investigate
vitamin D supplementation in a big sample of
people with insufficient vitamin D levels at
baseline while using a large battery of tests.
Such tests would assess isometric strength
via dynamometry, isokinetic strength via dynamometry, and isotonic strength via a few
different free weight exercises, while testing
a combination of upper-body and lower-body
muscle actions for each mode of testing. Studies like this would help us fill in some gaps
and resolve some of the uncertainties in the
vitamin D literature. Hopefully, plenty of additional studies like this will be completed so
we can gather even more information about
the ideal blood vitamin D level for optimizing strength, which will most likely end up
being a range including both a lower boundary and an upper boundary.
61
APPLICATION AND TAKEAWAYS
The vitamin D literature is pretty inconsistent. When comparing two studies, you
might have participants living at different latitudes, participating at different times
of the year, starting with different baseline vitamin D levels, consuming different
vitamin D doses, and completing different exercise tests. As such, it’s no wonder
that comparing results from study to study can be challenging. Overall, it seems that
vitamin D may have modest but positive effects on strength, particularly in studies
that use precise and reliable strength assessments. While direct evidence from
large randomized controlled trials is lacking, there are also multiple lines of evidence
pointing toward positive effects of sufficient vitamin D levels on the maintenance
and accretion of muscle mass, in addition to positive effects on overall health. As
such, it seems advisable to maintain blood vitamin D levels within an ideal range that
probably spans from around 75-100 nmol/L, give or take. More vitamin D isn’t always
better, but targeted supplementation strategies guided by repeated blood testing
can facilitate the maintenance of optimal blood vitamin D levels, preferably under the
supervision of a qualified healthcare professional.
62
References
1. Savolainen L, Timpmann S, Mooses M, Mäestu E, Medijainen L, Tõnutare L, et al.
Vitamin D supplementation does not enhance resistance training-induced gains in muscle
strength and lean body mass in vitamin D deficient young men. Eur J Appl Physiol. 2021
Apr 5; ePub ahead of print.
2. Santos HO, Howell S, Nichols K, Teixeira FJ. Reviewing the Evidence on Vitamin D
Supplementation in the Management of Testosterone Status and Its Effects on Male
Reproductive System (Testis and Prostate): Mechanistically Dazzling but Clinically
Disappointing. Clin Ther. 2020 Jun;42(6):e101–14.
3. Tomlinson PB, Joseph C, Angioi M. Effects of vitamin D supplementation on upper and
lower body muscle strength levels in healthy individuals. A systematic review with metaanalysis. J Sci Med Sport. 2015 Sep;18(5):575–80.
4. Zhang L, Quan M, Cao Z-B. Effect of vitamin D supplementation on upper and
lower limb muscle strength and muscle power in athletes: A meta-analysis. PloS One.
2019;14(4):e0215826.
5. Han Q, Li X, Tan Q, Shao J, Yi M. Effects of vitamin D3 supplementation on serum
25(OH)D concentration and strength in athletes: a systematic review and meta-analysis of
randomized controlled trials. J Int Soc Sports Nutr. 2019 Nov 26;16(1):55.
6. Grimaldi AS, Parker BA, Capizzi JA, Clarkson PM, Pescatello LS, White MC, et al.
25(OH) vitamin D is associated with greater muscle strength in healthy men and women.
Med Sci Sports Exerc. 2013 Jan;45(1):157–62.
7. Farrokhyar F, Tabasinejad R, Dao D, Peterson D, Ayeni OR, Hadioonzadeh R, et al.
Prevalence of vitamin D inadequacy in athletes: a systematic-review and meta-analysis.
Sports Med. 2015 Mar;45(3):365–78.
8. Abboud M, Puglisi DA, Davies BN, Rybchyn M, Whitehead NP, Brock KE, et al.
Evidence for a specific uptake and retention mechanism for 25-hydroxyvitamin D
(25OHD) in skeletal muscle cells. Endocrinology. 2013 Sep;154(9):3022–30.
9. Owens DJ, Tang JCY, Bradley WJ, Sparks AS, Fraser WD, Morton JP, et al. Efficacy
of High-Dose Vitamin D Supplements for Elite Athletes. Med Sci Sports Exerc. 2017
Feb;49(2):349–56.
10. Dahlquist DT, Dieter BP, Koehle MS. Plausible ergogenic effects of vitamin D on
athletic performance and recovery. J Int Soc Sports Nutr. 2015;12:33.
█
63
Study Reviewed: Comparison of Traditional and Accommodating Resistance Training with
Chains on Muscular Adaptations in Young Men. Arazi et al. (2021)
Has Science Finally Caught Up With the
Training Methods of Jacob Marley? The Effects
of Chain-Based Training on Strength Gains
BY GREG NUCKOLS
Have you ever wondered, “How can I make my training 50% cooler
and 200% noisier?” If so, you’ll love this article examining the
effects of chain-based resistance training.
64
KEY POINTS
1. Twenty trained males completed eight weeks of training, with half using constant
resistance (i.e. just barbells and plates), and half using chains that replaced 10-15%
of the load on the bar. Ten additional subjects served as non-training controls.
2. Gains in squat 1RM strength, medicine ball throw distance, and countermovement
jump power were similar between training groups, but the group training with
chains experienced a larger increase in bench press 1RM, and squat and bench
press strength endurance (reps to failure at 60% of 1RM).
3. I think the results of this study are inaccurate, for reasons explained in my cluster
set study this month. However, the interpretation section includes a nice little review
of the literature examining chain-based resistance training.
T
he first time I saw someone squatting
with chains on the bar, I knew I’d
need to experiment with training with
chains. They were loud, they looked really
cool, and they gave every guy within earshot
license to talk about how they had a buddy
who lifted with chains and benched 500 in
high school – all good things in my book.
The idea behind chains is simple. As you
lower the bar during the eccentric phase of a
lift, more and more of the chains pile up on
the ground, thus deloading the bar. As you
lift the bar during the concentric phase, more
and more of the chains come off the ground,
providing increasing resistance as the rep
progresses. Since you’re typically weaker at
the bottom of a lift and stronger at the top of
a lift, training with chains should allow you
to better match the resistance curve of an exercise with your natural strength curve. Since
training with chains accommodates your natural strength curve, training with chains (or
elastic bands, which function on similar principles) is often referred to as training with
accommodating resistance. In theory, by
matching the resistance curve of the exercise
with your natural strength curve, you have to
exert more effort through a larger proportion
of the lift, which is purported to result in larger strength gains. But does this theory hold
up in practice?
In the present study (1), 30 trained young men
were randomized into three different groups.
One group trained with constant resistance
(just a barbell and weight plates), one group
trained with chain resistance (10-15% of 1RM
came in the form of chain resistance; the rest
of the resistance was supplied by the bar and
plates), and one group was a non-training control group. The training intervention lasted for
eight weeks. Training with chains led to larger
improvements in bench press 1RM, and squat
and bench press strength endurance (reps
to failure at 60% of 1RM). The chain group
and the constant resistance group experienced
similar increases in squat 1RM, medicine ball
throw distance, and peak power output during
countermovement jumps. However, there are
reasons to be skeptical about the results of the
presently reviewed study.
65
Purpose and Hypotheses
Purpose
The purpose of this study was to compare the
effects of traditional resistance training versus resistance training with chains on maximal squat and bench press strength, strength
endurance, body composition, limb circumferences, and countermovement jump performance.
Hypotheses
The researchers hypothesized that “[resistance training with chains] would induce
greater improvements compared to [traditional resistance training].”
Subjects and Methods
Subjects
30 young, male subjects completed this study.
They were required to have at least one year
of training experience, and no musculoskele-
tal issues that would interfere with their ability to train.
Experimental Design
Subjects were randomized into three groups:
a traditional training group, a chain group,
and a control group. The control group did
not train throughout the duration of the study.
The traditional training group and chain group
both trained three times per week for eight
weeks, following the training protocol seen
in Table 1. Briefly, in each session, subjects
performed back squats, lying leg curls, bench
press, and pull-downs. Training intensity linearly increased from 65% to 80% of 1RM
throughout the study, and the number of sets
performed gradually increased from three
sets to five sets per exercise. Subjects rested
for 2-3 minutes between sets. If a subject was
unable to complete the assigned number of
reps for a particular set with a given load, the
load was reduced by 2.5% for the next set.
The only difference between programs was
that a portion of the load was replaced with
66
chains in the chain group when performing
back squats and bench press. For example,
in week 1, the traditional training group performed 3 sets of 10 reps with 65% of 1RM,
while the chain group performed 3 sets of 10
reps with 55% of 1RM in plate-loaded resistance, with an additional 10% of 1RM added
to the bar in the form of chains. Presumably,
the chains were adjusted so that all or most
of the links of the chains were off the ground
at the top of each rep, and all or most of the
links were on the ground at the bottom of
each rep (though that isn’t spelled out in the
manuscript).
Before and after the training intervention, the
researchers assessed body composition (via
calipers), squat and bench press 1RMs, squat
and bench press strength endurance (reps to
failure with 60% of time-specific 1RMs), upper arm and thigh circumferences, and countermovement jump height and peak power.
Testing was spread across two testing sessions, with circumferences and 1RM strength
67
assessed during the first testing session, and
strength endurance and countermovement
jump performance assessed during the second
testing session, 72 hours later. I think countermovement jump assessments occurred after strength endurance testing, so I’d interpret
the countermovement jump results with a big
grain of salt (I can’t imagine people perform
their best possible countermovement jumps
after performing a set of 15 squats to failure).
Findings
Body composition and limb circumferences
changes were similar in both training groups.
Improvements in squat 1RM strength were
similar in both training groups. Improvements in bench press 1RM were significantly
(p = 0.045) greater in the chain group than
the traditional training group. Back squat
and bench press strength endurance also
increased to a greater degree (p < 0.05) in
the chain group than the traditional training
group. Countermovement jump displacement
and peak power increased to a similar degree
in both training groups.
Across the board, the control group failed to
improve in any metric, and tended to regress a
bit (which is what you’d expect from trained
lifters who didn’t train for eight weeks).
Criticisms and Statistical
Musings
I have a few major criticisms of the present
study (1), but they’re criticisms that are shared
between the present study and the cluster set I
68
reviewed this month (which is from the same
lab; 2). In the interest of not just typing the
same things twice in one issue, I’ll expand
on all of the issues in the other article. But
briefly, there are numerical similarities in
the body fat percentages in these two studies
that would be unlikely to occur by chance,
and there are reported standard deviations for
strength-to-body mass ratios in both of these
studies that are mathematically impossible.
Interpretation
The idea behind accommodating resistance –
using bands or chains in addition to barbell
weight – is simple. As a band stretches or as
more chain links come off the ground, resistance increases as you proceed through the
concentric phase of a lift. Since most exercises have an ascending strength curve – you’re
stronger at the top of a lift than the bottom of
a lift – accommodating resistance allows you
to better match your natural strength curve
with the resistance curve of an exercise. For
example, in the bench press, your 1RM may
be 200lb (because 200lb is all you can move
through the bottom of the lift), but you may
be able to do a half-rep with 250lb. Thus,
when you bench 200lb, the first half of the
rep is really hard, but the second half is fairly
easy. However, if you instead pressed 175lb
with 50lb of chains attached to the bar, the
resistance curve of the exercise would better
match your strength curve – the force exerted
by the bar would be about 25lb below your
maximal force output at the bottom when all
of the chains are on the floor (175lb), and still
about 25lb below your maximal force output
near the top of the lift once the chains are off
the floor (225lb).
People theorize that matching strength curves
and resistance curves should increase strength
gains and improve hypertrophy outcomes.
After all, if you can challenge your muscles
through the entire range of motion, instead
of just challenging them through the hardest portion of the range of motion (near the
PEOPLE THEORIZE THAT
MATCHING STRENGTH CURVES
AND RESISTANCE CURVES
SHOULD INCREASE STRENGTH
GAINS AND IMPROVE
HYPERTROPHY OUTCOMES.
69
start of the concentric phase for most lifts),
shouldn’t that lead to more muscle growth
and larger strength gains?
As appealing as that theory sounds, we
don’t have much evidence for it. There’s
not much research investigating whether
matching strength and resistance curves improves hypertrophy outcomes, but what we
do have isn’t particularly promising (3). Although limb circumferences certainly aren’t a
gold-standard hypertrophy measure, the presently reviewed study provides further weak
evidence suggesting that matching strength
and resistance curves don’t improve hypertrophy outcomes. For strength outcomes, a
recent meta-analysis found that lifting plain
old barbell weight was just as effective for
strength development as using accommodat-
ing resistance (4), and results of the present
study don’t shift the balance of evidence that
much. Squat strength gains were similar in
both groups, and there wasn’t a night-and-day
difference in bench press strength gains. Both
groups experienced robust improvements in
1RM bench press strength, and if you added
the results of the present study to the prior
meta-analysis, they certainly wouldn’t flip
the results from non-significant to significant.
So, does that mean accommodating resistance serves no purpose?
Not necessarily.
First, I’m not yet going to rule out the possibility that matching strength and resistance
curves could improve hypertrophy outcomes.
As we’ve discussed before, muscular tension
70
at long muscle lengths seems to be more important for hypertrophy than tension at short
muscle lengths (5; and accommodating resistance generally increases tension at short
muscle lengths by increasing resistance as
the concentric phase progresses), but there’s
just not much research directly investigating
the impact of accommodating resistance on
hypertrophy. However, since it’s possible
that constant tension training improves hypertrophy outcomes (6), it’s also possible that
accommodating resistance could increase the
range of motion through which one can maintain a high degree of active tension on the
muscles they’re training, which may lead to
more growth. Overall, though, we just need
more research on the topic.
Second, it’s possible that accommodating
resistance could improve power- and velocity-based outcomes (like jumping, throwing,
or sprinting). The present study observed
similar increases in jump performance in
both groups, but as I mentioned in the methods section, I think we may need to take those
findings with a pretty big grain of salt, since
it seems that jump performance was assessed
after strength endurance. With power-based
performance, your goal is to exert as much
force as you can, as fast as you can, all the
way until you lose contact with whatever
you were applying force against (the ground
or ball you’re throwing). As such, the ability to exert force near the top of the concentric phase is just as important as the ability
to exert force near the bottom. Traditional
resistance training exercises primarily increase your ability to exert force near the bottom of the concentric phase when performed
IT’S POSSIBLE THAT
ACCOMMODATING RESISTANCE
COULD IMPROVE POWERAND VELOCITY-BASED
OUTCOMES (LIKE JUMPING,
THROWING, OR SPRINTING).
through a full range of motion with just a barbell. Manipulating range of motion (e.g. also
doing half-reps or quarter-reps) can improve
force output through the top of the concentric phase to plug the gap, but accommodating resistance could theoretically be used to
improve strength through the entire range
of motion more efficiently. In other words,
squatting with chains through a full ROM
may not increase full-ROM squat 1RM to a
greater degree than squatting through a full
ROM with just barbell weight, but squatting
with chains through a full ROM may increase
half-squat and quarter-squat 1RM to a greater degree than squatting through a full ROM
with just barbell weight. Those strength increases through the top part of the concentric
phase wouldn’t provide much of a benefit for
powerlifters, but they may be very helpful
for athletes competing in sports with more
jumping, running, or throwing. Furthermore,
training with constant resistance forces you
to purposefully decrease force output through
the top part of the concentric phase in order
to decelerate the bar prior to lockout; with
accommodating resistance, you don’t have to
71
“let off the gas” to nearly the same extent,
which does a better job of mimicking the sort
of effort you’d put into a max height jump.
Third, it is actually possible that accommodating resistance can actually lead to larger
strength gains, but the typical study design
masks its benefits. First, many of the studies on accommodating resistance take the
approach of the present study: simply replacing some percentage of the weight on the
bar with resistance from bands or chains. In
other words, if a traditional training group is
training with 75% of 1RM, an accommodating resistance group may train with 65% of
1RM in barbell weight, and 10% of 1RM in
force from chains or bands. This means that
the traditional training group is actually training at a higher intensity through the bottom
end of the ROM (which is typically the most
important part of the range of motion). If instead, the accommodating resistance group
trained with 75% of 1RM in barbell weight
plus an additional 10% of 1RM in force from
bands or chains (based on the recognition
that accommodating resistance allows you to
handle more resistance through the top part
of the ROM, where you’re stronger), it’s possible that you’d see larger strength gains with
accommodating resistance (7). Second, and
more importantly (in my opinion), studies
compare the effects of training with barbell
weight against training exclusively with the
use of accommodating resistance. Before and
after training, strength is assessed with barbell weight. In other words, the traditional
training groups are training in a manner that’s
more similar to the test. With that in mind,
you could view it as somewhat impressive
that accommodating resistance still increases
1RM strength with barbell weight to the same
extent as just training with barbell weight
does, in spite of the training stimulus being
less specific to the test. It’s possible that either
a) a block of traditional training following a
block of training with accommodating resistance or b) a mix of both traditional training
and training with accommodating resistance
would produce larger strength gains than exclusively training with barbell weight. To be
clear, I’m not suggesting that training with
accommodating resistance does actually lead
to larger strength gains that always training
with barbell resistance; I’m merely suggesting that, in spite of a meta-analysis suggesting that accommodating resistance doesn’t
increase strength gains (4), we shouldn’t necessarily assume that the door is closed on accommodating resistance. Some methodological tweaks in future research might reveal
that training with accommodating resistance
does increase strength gains. We’ll just need
more research to see.
While “accommodating resistance” is often
used as a single, overarching term for training with bands and chains, I think it’s worth
noting that bands and chains don’t behave
the same way in training contexts. In a static
sense, they’re comparable: as a band stretches, the amount of force it exerts increases linearly, and as more chain links come off the
ground, the amount of force exerted by the
chain increases linearly. However, they produce very different feelings when used during
dynamic movements. I think that stems from
the fact that a bar loaded with chains tries to
accelerate downward at the same rate a bar
72
loaded with iron plates would: at the rate of
acceleration due to gravity. Bands, on the
other hand, exert force by attempting to pull
the bar toward the floor at a rate exceeding
that of gravity. In other words, if you deadlifted a bar that had either 200lb of chains attached or 200lb of band tension in play, and
you dropped the bar after completing the rep,
the band-loaded bar would hit the platform
before the plate-loaded bar. As a result, training with chains still subjectively feels pretty similar to training with straight weight,
whereas training with bands can take a bit of
adjustment. If you’re not careful, bands will
try to dramatically speed up your eccentrics,
and it can be a bit easier to lose control of the
bar. For my money, I prefer the overall feel of
chains, but your preferences may vary.
Keeping in mind that bands and chains aren’t
identical, rather than rely solely on the meta-analysis on the effects of accommodating
resistance for strength gains (which includes
studies on both bands and chains), I think it’s
worth briefly reviewing the results of the studies on chains specifically. I believe the pres-
TRAINING WITH CHAINS STILL
SUBJECTIVELY FEELS PRETTY
SIMILAR TO TRAINING WITH
STRAIGHT WEIGHT, WHEREAS
TRAINING WITH BANDS CAN
TAKE A BIT OF ADJUSTMENT.
ently reviewed study (1) was the fourth study
comparing resistance training with constant
resistance to resistance training with chains.
Again, it found that training with chains produced larger bench press strength gains than
training with constant resistance (21.7 vs.
14.2 kg), but gains in squat strength were similar in both groups (16.2 vs. 15 kg). A study
by Ataee and colleagues compared the effects
of training with chains versus constant resistance in wrestling and Kung Fu athletes (8).
It found that training with chains led to larger
increases in squat strength than training with
constant resistance (45.5 vs. 25.5 kg), but that
gains in bench press strength were similar in
both groups (12.5kg vs. 11.4 kg). Of note, the
Ataee study actually equated barbell intensity between groups (both groups trained with
85% of their max loaded on the barbell), and
the chain group added an additional 20% of
their 1RM in chain resistance, allowing them
to handle heavier loads near the top of the
lift where they were stronger. However, I’d
take those results with a grain of salt, since
the study only lasted four weeks (and, if I’m
being honest, a 45.5kg increase in 1RM squat
strength in four weeks makes me raise my
eyebrows a bit). A study by McCurdy and
colleagues tested the effects of benching with
constant resistance and chain resistance over
nine weeks (9). In this study, the chain group
used exclusively chain resistance (no plates
were put on the barbell. Only chains). The
chains were seven feet long, and were hung so
that a lot of the chain was still off the ground
at the bottom of each rep. Furthermore, in this
study, bench press 1RMs were assessed preand post-training with both constant resistance (e.g. plates loaded on the bar) and with
73
exclusively chain resistance. The chain group
gained about as much strength in the constant
resistance bench press as the constant resistance group (5.9 vs. 6.4 kg), but way more
strength in the chain bench press (22.4 vs.
11.4 kg). Finally, Ghigiarelli and colleagues
studied the effects of seven weeks of constant
load and chain-loaded bench press training in
college football players (10). The chain load
on every set for every subject in the chain
group was 85-90lb. It’s not clear what sorts
of plate loads were used in either group. They
did 5-6 sets of 4-6 “heavy” reps one day, and
6 sets of 3 “speed reps” on another day. Their
main bench press work was followed by a
lot of accessory lifts (floor press, DB bench
press, and triceps extensions one day, and DB
incline press, drop push-ups, dumbbell floor
press, and triceps pushdowns on the other
day). Bench press strength gains were similar
in both groups (9.1kg in the chain group versus 7.7kg in the constant resistance group).
However, it seems that the subjects’ bench
press training was a pretty small minority of
their overall pressing training, so it’s hard to
know how much of an independent effect the
chains had.
Overall, in these four studies, you could definitely weave a tale where chains look pretty
good. The chain group gained more bench
press strength in the present study, more squat
strength in the Ataee study, and more strength
in the chain bench press in the McCurdy study.
Furthermore, three of the four non-significant
differences in these four studies still leaned in
favor of training with chains. Conversely, you
could take a critical view of these studies. I lack
confidence in the results of the present study
(read the “Criticisms and Statistical Musings”
section of the cluster set article), the results of
the Ataee study could be explained by the fact
that the chain group actually added load over
time while the constant resistance group didn’t
(and the training intervention only lasted four
weeks), the McCurdy study lacks some degree
of ecological validity because the chain group
loaded the bar with only chains (no plates),
and the Ghigiarelli study lacks some degree
of internal validity because chain versus constant-load bench was such a small minority of
74
the subjects’ overall bench press training, and
we don’t even know what sorts of intensities
that subjects were training with. You’d be justified to say that the current batch of studies
paints a somewhat rosy view of training with
chains if you take the results at face value, but
that we shouldn’t take the results at face value.
As much as it pains me to say, I think the second interpretation is the more justifiable one.
I really like training with chains. Subjectively, I just think they feel really good, they allow me to feel heavier weights on my back or
in my hands (which is fun for me), and they
make a lot of noise (also a definite plus in
my book). However, the evidence in favor of
training with chains leading to larger strength
gains is just very weak. To be clear, I don’t
think the evidence leans against training with
THE CURRENT BATCH
OF STUDIES PAINTS A
SOMEWHAT ROSY VIEW
OF TRAINING WITH
CHAINS IF YOU TAKE
THE RESULTS AT FACE
VALUE, BUT THAT WE
SHOULDN’T TAKE THE
RESULTS AT FACE VALUE.
chains producing larger strength gains; I just
don’t think we can take much of anything
away from these studies, period.
For now, I’d primarily recommend training
with chains in three circumstances (for people interested in maximizing strength gains):
1. If you get scared when handling heavy
loads. With chains, you can feel heavier
weights at the top of a lift to desensitize
you to the feeling of near-max (or even
super-maximal) loads, while still being
able to train through a full range of motion.
2. If your joints are happier when you remove some of the load from the bottom of
a lift. For example, I know a fair number
of older lifters whose shoulders or hips aren’t happy with them when they bench or
squat heavy through a full range of motion. By replacing some plate weight with
chain weight, they can still train through a
full range of motion while decreasing the
load at the bottom of the lift, which keeps
their joints happier.
3. If you simply want to work some variety
into your training. Every indication suggests that training with accommodating
resistance isn’t less effective than training with constant loads, so if you’ve been
looking for a new lift variation to try, and
chains look like they’d be fun, you aren’t
going to lose out on anything by taking
them for a whirl. If you compete raw, I
generally wouldn’t recommend replacing
more than 20% of the load on the bar with
chain weight.
75
APPLICATION AND TAKEAWAYS
Training with chains can help you acclimate to the feeling of heavy loads on your back
or in your hands, and simply allow for a bit more variety in your training, but the present
evidence suggests that training with constant resistance and chain resistance are
similarly effective for strength development.
Next Steps
I’d love to see a longitudinal study where one
group trains without chains, and one group
performs half of their training with chains
and the other half without chains. If chains
do actually promote larger strength gains that
are merely obscured by the fact that chainless
1RM testing favors subjects who train without chains, such a study would give chains
the opportunity to shine.
76
References
1. Arazi H, Mohammadi M, Asadi A, Nunes JP, Haff GG. Comparison of traditional and
accommodating resistance training with chains on muscular adaptations in young men.
J Sports Med Phys Fitness. 2021 Apr 19. doi: 10.23736/S0022-4707.21.12049-3. Epub
ahead of print. PMID: 33871234.
2. Arazi H, Khoshnoud A, Asadi A, Tufano JJ. The effect of resistance training set
configuration on strength and muscular performance adaptations in male powerlifters.
Sci Rep. 2021 Apr 12;11(1):7844. doi: 10.1038/s41598-021-87372-y. PMID: 33846516;
PMCID: PMC8041766.
3. Staniszewski M, Mastalerz A, Urbanik C. Effect of a strength or hypertrophy training
protocol, each performed using two different modes of resistance, on biomechanical,
biochemical and anthropometric parameters. Biol Sport. 2020 Mar;37(1):85-91.
doi: 10.5114/biolsport.2020.92517. Epub 2020 Feb 6. PMID: 32205914; PMCID:
PMC7075227.
4. dos Santos N, Divino W, Gentil P, Ribeiro A, Vieira C, Martins W. Manuscript
clarification: Effects of Variable Resistance Training on Maximal Strength: A Metaanalysis. J Strength Cond Res. 2018 Nov;32(11):e52-e55
5. Schoenfeld BJ, Grgic J. Effects of range of motion on muscle development during
resistance training interventions: A systematic review. SAGE Open Med. 2020 Jan
21;8:2050312120901559. doi: 10.1177/2050312120901559. PMID: 32030125; PMCID:
PMC6977096.
6. Goto M, Maeda C, Hirayama T, Terada S, Nirengi S, Kurosawa Y, Nagano A, Hamaoka
T. Partial Range of Motion Exercise Is Effective for Facilitating Muscle Hypertrophy and
Function Through Sustained Intramuscular Hypoxia in Young Trained Men. J Strength
Cond Res. 2019 May;33(5):1286-1294. doi: 10.1519/JSC.0000000000002051. PMID:
31034463.
7. Some studies basically split the difference. Instead of matching intensity at the bottom
of the ROM or matching intensity at the top of the range of motion, they basically
match the average intensity throughout the range of motion. For example, 75% of 1RM
in barbell weight, versus 70% of 1RM in barbell weight plus 10% of 1RM in force
from chains or bands. In that scenario, the accommodating resistance group would be
overcoming a force equal to 70% of 1RM at the bottom of the lift, and 80% of 1RM at
the top, with an average of 75% of 1RM. I do think that’s a good way to test the effects
of accommodating resistance, and it’s worth noting that those studies (one example here)
don’t tend to show that accommodating resistance leads to larger strength gains
77
8. Ataee J, Koozehchian MS, Kreider RB, Zuo L. Effectiveness of accommodation and
constant resistance training on maximal strength and power in trained athletes. PeerJ.
2014 Jun 17;2:e441. doi: 10.7717/peerj.441. PMID: 25024910; PMCID: PMC4081144.
9. McCurdy K, Langford G, Ernest J, Jenkerson D, Doscher M. Comparison of chainand plate-loaded bench press training on strength, joint pain, and muscle soreness in
Division II baseball players. J Strength Cond Res. 2009 Jan;23(1):187-95. doi: 10.1519/
JSC.0b013e31818892b5. PMID: 19050650.
10. Ghigiarelli JJ, Nagle EF, Gross FL, Robertson RJ, Irrgang JJ, Myslinski T. The effects
of a 7-week heavy elastic band and weight chain program on upper-body strength and
upper-body power in a sample of division 1-AA football players. J Strength Cond Res.
2009 May;23(3):756-64. doi: 10.1519/JSC.0b013e3181a2b8a2. PMID: 19387404.
█
78
Study Reviewed: Effects of a Flexible Workout system on Performance gains in Collegiate
Athletes. Walts et al. (2021)
When and How are Flexible
Templates Actually Useful?
BY MICHAEL C. ZOURDOS
Flexible programming – choosing which training session you’ll do
based on how you feel that day – is a logical strategy. However, a
new study adds to the surprisingly null findings on the topic. This
article discusses specific situations in which a flexible template
may have merit and how to implement flexibility.
79
KEY POINTS
1. Collegiate lacrosse players performed eight weeks of either flexible training or fixed
order training. Athletes tested hex bar deadlift and bench press strength before and
after training, along with vertical jump and agility performance.
2. The flexible training group could choose between four options for their daily
workout, while the fixed order group had a predetermined weekly training order.
Each group trained three times per week.
3. The findings showed very similar rates of progress in all outcome measures
between groups. This article discusses the circumstances in which flexible
templates may enhance strength performance, and whether or not the autonomy
that flexible templates offer is a positive for lifters.
A
utoregulating training session load
and volume helps match the daily training stimulus to your daily
readiness. However, quantitative assessments of daily readiness can sometimes be
hard to implement. Not everyone has access
to a device they can use to assess bar velocity, and not everyone can accurately assess
repetitions in reserve (RIR)-based rating
of perceived exertion (RPE) values. Therefore, it may be worth having a mechanism to
choose the overall workout structure based
on your subjective readiness when you enter
the gym instead of having a specific day of
the week pre-planned. Enter flexible training
templates, which I’ve covered before (one,
two). A basic example of a flexible template
is having six heavy, six moderate, and six
light training sessions within a month. In this
case, you could perform each training session
whenever you feel ready for it (i.e., if poor
sleep, choose a light session) rather than in
a set order (i.e., moderate, light, and heavy
on M, W, F). One previous study has shown
flexible training to enhance strength (2),
while another showed a slight improvement
in training adherence (3 – MASS Review).
However, there isn’t much more data on the
topic. The reviewed study from Walts et al (1)
had collegiate athletes perform either flexible
non-linear periodization (flexible group), or
fixed-order non-linear periodization (fixed
group) for two four-week training blocks
(eight weeks total). Before each session, the
flexible group would indicate their state of
readiness (green, yellow, or red) and then
choose a corresponding workout. A green
response meant the subject could choose either a high-volume or high-intensity session,
while researchers matched a yellow response
with a low volume or low-intensity session.
A readiness response of red meant the athlete skipped that day’s training session. The
fixed order group performed training sessions in a predetermined order but could still
select red and skip a session. Researchers
equated the specific number of each training
session (green or yellow) between groups.
80
Findings showed that all outcome measures
(bench press and hex bar deadlift 1RM, vertical jump, sprinting speed, and agility) improved, but without significant differences
between groups (p > 0.05). Despite the lack
of significant between-group differences, I
don’t believe we should discard flexible templates entirely. A positive spin is that a lifter
may receive the same training benefit while
avoiding the typical rigidity of a fixed order
program. However, I think the more salient
argument is that the current study didn’t provide a framework for the flexible template to
work. In other words, a flexible template is
probably most beneficial when readiness to
train is often low due to either consistently
fatiguing training or extenuating life circumstances. Therefore, this article will aim to deliver the following information:
1. Break down the existing data on flexible
training templates.
2. Discuss in what situations flexible training may be most useful.
3. Discuss different levels of flexibility (i.e.,
weekly, monthly, or all-time flexibility).
4. Examine the efficacy of various metrics to
assess daily readiness.
5. Provide practical examples of how to implement this concept.
Purpose and Hypotheses
Purpose
The purpose of this study was to compare the
effects of flexible and fixed-order training
templates equated for session type on gains in
strength, power, and agility over eight weeks
in both men and women.
Hypotheses The researchers hypothesized that flexible
training would enhance performance in all
outcome measures.
Subjects and Methods
Subjects
32 Division III collegiate lacrosse players (15
men and 17 women) completed the study.
The researchers provided no information regarding previous resistance training experience, but all athletes completed a six-week
familiarization phase before the study (more
below). Further, the full manuscript indicated
that some athletes were freshmen (which can
also be inferred by the average age in Table
81
1). Overall, I suspect that subjects had some
structured resistance training experience, but
this experience varied between individuals,
which is not uncommon for young athletes
at the Division III level. Table 1 provides the
available details of the subjects.
Study Overview
The athletes were split into two training
groups for eight weeks. A flexible group performed a daily training session that matched
their readiness. In contrast, the non-flexible
(fixed order) group performed training sessions in a predetermined order (details in the
next section). Athletes trained three times
per week for eight weeks on non-consecutive days, but the first session of week one
and last session of week eight served as preand post-training testing sessions; thus, each
group had 22 training sessions. Both groups
performed non-linear (or undulating, if you
prefer) periodization. Pre- and post-testing
measures were one-repetition maximum
(1RM) bench press and hex bar deadlift, vertical jump height, sprint time (28.7m), and an
agility test consisting of a 6.1m fly-in sprint
followed by a turn and 6.1m sprint back.
Lastly, all subjects completed a six-week familiarization program before the eight-week
intervention. However, researchers did not
provide any further details regarding the familiarization.
Training Protocol
The original paper doesn’t provide many details of the training program. However, the
senior author (Dr. Kenneth Clark) put me in
touch with the first author (Cory Walts), who
graciously suffered through a phone call and
various email exchanges with me to provide
details. Huge thanks to these gentlemen for
their assistance.
Researchers split the eight-week program
into two four-week training blocks. There
were 11 total training sessions in each fourweek block (remember pre- and post-testing
bookended weeks one and eight, respectively). There were two strength-focused sessions
(i.e., traditional strength exercises) each week
and one power-focused session (i.e., Olympic lift variations), except in weeks one and
eight, when there were only two strength-focused sessions (plus a testing day). Each week
also fluctuated volume and intensity; thus,
the programming was non-linear both within and between weeks. There were two main
workout categories, “Green” and “Yellow,”
and each category had two subtypes (green:
high volume or high intensity; yellow: low
volume or low intensity) to make four different session-type possibilities. Further, the
workouts were rated on a 1-4 scale (arbitrary
units) for both volume and intensity. In other
words, the workout with the highest volume
was rated a 4, and the lowest volume workout was rated a 1. Table 2 displays all session
types and their volume/intensity ratings.
In both groups, athletes answered the question
(using the TeamBuildr phone app) “based on
how your body feels and your current mindset, how ready are you for today’s training?”
Subjects had the option of answering “green
(good feel and mindset),” “yellow (fair feel
or mindset),” or “red (poor feel or mindset).”
In the fixed group, subjects performed training weeks in the order of yellow (low intensity), green (high volume), yellow (low vol-
82
ume), and green (high intensity) regardless of
their response, as seen in Figure 1. The only
caveat is that if an athlete in the fixed group
answered “red,” then they skipped the training session and performed one less training
session for that block.
Similar to the fixed group, a flexible athlete
also skipped a workout if they answered “red”
to the readiness question. However, on each
training day, flexible group athletes had four
workouts to choose from (the two greens and
the two yellows), but no session type could be
performed more than once on a specific day
of the week during each block. For example,
there were four Wednesdays in each training
block, and if an athlete answered green on
Wednesday of week one, then they chose a
high volume or high intensity workout. If they
chose high volume, then that workout could
not be completed again on a future Wednesday during the first training block. Athletes
followed the same procedures for each individual day during the training blocks. One of
the four workouts was not chosen on a Monday
during block one since there were only three
Monday training days (i.e., the first Monday
was pre-testing), and the same for Friday in
block two since that’s when post-testing was
conducted. Table 3 displays a possible example of the protocol in the flexible group for a
four-week training block.
Additional Notes
It’s also worth noting that researchers did not
supervise training in this study. Athletes an-
83
swered the daily question on the TeamBuildr
app, which then provided the workout based
on their choice. While this is a limitation, it’s
inherently not the researchers’ fault. Since
the study used Division III NCAA collegiate
athletes, NCAA rules had to be followed,
which meant that training workouts had to
be self-selected. The researchers couldn’t officially report training adherence or volume
because that would amount to “tracking” an
intercollegiate athlete’s off-season training.
Specific interset rest intervals were not listed;
however, all workouts lasted approximately
60 minutes.
Outside of lifting, the athletes also participated in speed and agility sessions twice per
week for 60 minutes each time, and one “conditioning” session, which was not otherwise
described.
Findings
The findings were simple. All outcome measures tended to increase in both groups; however, there were no group differences. Vertical jump increased by 3.9% and 6.4% in the
flexible and fixed groups, respectively. Agility performance improved by 0.8% and 1.6%
in the flexible and fixed groups, respectively.
Tables 4 and 5 show the findings for bench
press and hex bar deadlift 1RM strength
along with percentage changes.
Interpretation
The reviewed study from Walts et al (1) didn’t
show flexible training to augment strength
gains, but that doesn’t mean we should write
off the concept. One could argue that similar
strength gains but more autonomy over training decisions is a win for flexible templates, for
84
starters. However, I think there’s much more
to discuss. We should also consider the readiness indicator used, how fatiguing the program is, and the degree of flexibility allowed
(i.e., weekly, monthly, or all-time). Therefore,
this interpretation will provide a nuanced discussion of the above considerations.
Current and Previous Research
Although the concept of flexible templates is
well-known, there are only three resistance
training studies directly tackling the idea.
Two of those studies – the currently reviewed
study from Walts (1) and a study from Colquhoun et al (3 - MASS Review) – failed to
show a benefit for flexible training versus
fixed order training. The Colquhoun study
compared a group of trained lifters using a
fixed weekly order of hypertrophy-focused
(Monday), power-focused (Wednesday),
and strength-focused (Friday) sessions for
nine weeks to a flexible group. The flexible
group performed hypertrophy, power, and
strength sessions within the same week, but
lifters could choose the order. Lifters used a
five-point Likert scale before each session to
assess motivation and readiness to train. Further, subjects performed the last set to failure
on both the hypertrophy and strength sessions
each week in Colquhoun’s study. Weekly
load changes were based on repetition performance, which allowed for between-group
volume and intensity calculations. Colquhoun
reported similar squat, bench press, and deadlift increases between groups, and no group
differences for volume or percentage of 1RM
used. Colquhoun did show a lower dropout
rate in the flexible group (12.5%) versus the
fixed group (31%), and fewer total missed
sessions in the flexible (four) versus the fixed
group (eight).
Walts’ argument regarding autonomy seems
to have some value, based on Colquhoun’s
adherence reporting. I’ll return to autonomy
in a bit, but in the short term, I don’t think
Colquhoun’s flexible group experienced enhanced performance because the fixed group
was already set up well. Specifically, the fixed
order of hypertrophy, power, and strength allocates weekly volume appropriately. Higher
volume training sessions (i.e., traditionally
hypertrophy-focused) tend to result in the
most muscle damage. Thus, inserting a lighter (i.e., power) session in the middle of the
week considers that a lifter may be fatigued
48 hours after hypertrophy-type training. The
lifter is then recovered for Friday’s strength
session and may even get a 48-hour priming
effect from the power session to enhance Friday’s strength performance (4 – MASS Review). Indeed, previous data have shown this
hypertrophy, power, strength set-up to result
in greater volume on the strength day than a
hypertrophy, strength, power configuration
(5). Other factors such as sleep, travel, stress,
and early morning training can affect readiness and warrant a flexible template, which
may have accounted for the greater adherence in Colquhoun’s flexible group. The other study to directly address this concept is from McNamara and Stearne (2), and
was published over a decade ago. These researchers split 16 subjects (both men and
women) with a little over one year of training experience into a fixed order and flexible groups and measured leg press and chest
press strength before and after 12 weeks of
85
training. The subjects trained only twice
per week using one set of various exercises,
but every set was to failure. The fixed order
group rotated 20, 15, and 10RM sessions in
that order, while the flexible group completed a 0-10 Likert scale to assess energy levels and then chose which session they wanted that day. The flexible group in this study
had more autonomy than the flexible groups
in the Walts or Colquhoun studies, in that
flexibility was not restricted to within-week.
Instead, lifters had to perform each session
type eight times, but could do so in whatever order they chose. McNamara and Stearne
reported no group differences for chest press
increase, but leg press improvement in the
flexible group roughly tripled (Figure 1 here)
the fixed group’s progress. Table 6 provides
a summary of both the Colquhoun and McNamara studies.
Flexible templates are generally viewed positively. I mostly share that view; however,
it’s worth noting that only one strength measure (McNamara and Stearne leg press) out
of seven strength tests from three studies improved more with a flexible versus a fixed
training order. However, the McNamara and
Stearne study may have been better designed
to see group differences than the other two
studies. Specifically, as noted above, both
the Colquhoun and Walts studies had power-focused training sessions in the middle of
the training week, which may have helped
mitigate fatigue. Further, while I’m not entirely sure about proximity to failure in the
Walts study, subjects in Colquhoun’s study
only went to failure on the last set of the
hypertrophy and strength day (two sets per
week for squats, two for bench, and one for
deadlift). On the other hand, McNamara and
Stearne’s subjects did at least 14 sets to failure each week on various exercises and only
had a little over a year’s training experience,
while Colquhoun’s lifters had been training
86
for three years on average. Therefore, fatigue
may have been greater in the McNamara and
Stearne study, which provides a stronger justification for a flexible program. McNamara
and Stearne prescribed the same volume in
each group, but subjects in the flexible group
may have been able to progress loads more
frequently than subjects in the fixed group,
which would explain the flexible group’s enhanced rate of strength gain. Although, even
if volume or intensity was greater in the flexible group, I’m not entirely sure how to account for the roughly three times greater leg
press strength increases in favor of the flexible training group in the McNamara and Stearne study. One possible explanation is that
subjects in the flexible group chose mostly
lighter sessions (20RM and 15RM) early in
the program and thus, performed more of the
heavier (10RM) sessions closer to post-testing than the fixed order group. However, that
is purely speculative.
In general, the cornerstone of flexible templates is that lifters can match the day’s session to their readiness. This flexibility is beneficial when training or life circumstances (or
both) are really demanding. In other words, if
a training program isn’t that demanding (i.e.,
not high-volume or a lot of failure training)
and life circumstances aren’t extraordinary, is
a flexible template vital to optimize progress?
Even though a flexible template may not always shine without demanding life circumstances, that doesn’t mean flexible templates
still aren’t a good idea for some individuals.
The discussed studies, including the currently reviewed one, show similar performance
changes between flexible and fixed pro-
grams; thus, individuals should choose whatever they prefer. Having autonomy in a program is indeed a good thing, but we should
also be cautious of too much autonomy. The
specific population may also matter in terms
of autonomy. For example, the reviewed
study (1) used Division III collegiate athletes
during voluntary off-season workouts, which
I’m intimately familiar with as both a former
Division III NCAA athlete (i.e., average athletic adult human) and former Division III
strength coach. In this specific case, autonomy is probably positive. First and foremost,
these athletes must voluntarily choose to do
the workouts, and a coach wants the athletes
to buy into the program since NCAA rules
do not allow coaches to monitor athletes in
the off-season. Some athletes will train and
do exactly as instructed; however, others will
take a bit more convincing, so on the whole,
flexibility is a positive for the Walts study
population. In the context of team sport athletes, flexible training may also be helpful
in-season, as some athletes play more minutes in a game or match than others. For instance, in NCAA soccer, there are typically
two games per week. If an athlete is playing
90 minutes and another is barely touching
the field, these athletes should have different workloads (both on the field and in the
weight room) during the week, and a flexible template is a vehicle to get them there.
On the other hand, some autonomy is a positive for the individual strength athlete, but
others hire a coach because they want exact
programming. Therefore, some lifters come
to a coach precisely to avoid having to make
training decisions. In other words, I don’t
think autonomy is the most salient defense of
87
flexible templates for the strength athlete.
Flexibility Situations and Degrees of Flexibility
As previously stated, specific circumstances
should be present to warrant use of a flexible
template:
1. Extremely demanding training.
2. Currently exhaustive life (work/school/
family) schedule.
3. Consistent travel with inconsistent gym
access.
Although, I’d argue that extremely demanding
training isn’t necessarily a reason to utilize a
fully flexible training program. If performing
an overreaching block or sustained high volume hypertrophy-type training, coaches and
lifters should organize programming within a
week to account for training fatigue. For example, if you are training a muscle group three
times per week (i.e., M, W, F) and there are
high RPE, moderate RPE, and low RPE days,
then the default program structure should be
to do moderate RPE training on Monday, low
RPE on Wednesday, and high RPE on Friday.
Similarly, if thinking in terms of high-, low, and moderate-volume days or hypertrophy-,
power-, and strength-type training sessions,
the weekly order should be: high-volume/
hypertrophy (Monday), low-volume/power
(Wednesday), and moderate-volume/strength
(Friday). The point being, no matter what type
of programming you prefer, each week should
be programmed to allocate training volume
appropriately. If you already do this, such as
Colquhoun’s fixed order group, the need for a
flexible template is minimized. If you are con-
stantly fatigued going into your next session,
then a flexible template is not the solution. Instead, I’d recommend rearranging your training, or consider lowering your training volume
or proximity to failure (i.e., training variables
which elongate recovery).
Flexible programming may shine when life
gets busy, such as a month-long work project, studying for that elusive D+, preparing
for family holidays, or extended travel – in
other words, situations in which you have
consistently lower sleep and higher stress,
which can impact performance along with inconsistent gym access. The next step is not
just implementing a flexible model, but also
considering the degree of necessary flexibility. Suppose all of the above situations last
approximately one month, and your fatigue
levels and gym access are entirely unknown.
In that case, you might use within-month
or within-block flexibility such as the Mc-
IF YOU ARE CONSTANTLY
FATIGUED GOING INTO
YOUR NEXT SESSION,
THEN A FLEXIBLE TEMPLATE
IS NOT THE SOLUTION
CONSIDER LOWERING
YOUR TRAINING VOLUME
OR PROXIMITY TO FAILURE.
88
Namara and Stearne study. For example, if
you have six low RPE days, six high RPE
days, and six moderate RPE days, then you
would perform six of each session type anytime in the month in whatever order you
choose. It may also be wise to scale back to
four of each session type to ensure feasibility during stressful times. If someone doesn’t
want to scale back, they could still aim for 24
total sessions, but use a breakdown with more
“easier” days such as 4 high RPE, 8 moderate
RPE, and 12 low RPE sessions.
Another option is within-week flexibility,
such as in the Walts (1) and Colquhoun (3)
studies. For within-week flexibility, there are
usually two or three different types of training sessions during the week (whatever is
best for the specific individual), and the lifter
completes each session type during the week,
but in whatever order they choose. However,
I’m not sure that within-week flexibility offers much benefit if the week’s volume is already appropriately allocated so that fatigue
from one session doesn’t bleed into another.
If you’re already appropriately allocating
volume within the week, within-week flexibility doesn’t provide much benefit. While
daily stress could still warrant flexing in easier sessions, this could present problems with
a within-week model. For example, if stress
levels are high on Monday in a Monday (hypertrophy), Wednesday (power), and Friday
(strength) setup, then you may choose to perform the power session. Then, on Wednesday,
you complete one of the two more fatiguing
sessions (hypertrophy or strength). Still, now
fatigue from Wednesday may bleed into the
hypertrophy or strength session (whichever
is left) on Friday. Ironically, an actual within-week model of flexibility could exacerbate
the same issue it’s trying to mitigate. Instead of a true “within-week only” flexible
model, I’d prefer a fixed order, but with an
“all-time flexible option.” For example purposes, let’s assume we’re training a muscle
group three times per week. I’d set up training so that you are already allocating volume
to the best of your ability. Table 7 shows a
conceptual example of this setup where the
heaviest training day (Friday) is positioned
89
the farthest from the high-volume day. Before looking at Table 7, just know that there
are many other ways to configure training
and many different exercises to include; this
is solely intended for conceptual purposes.
Although the program in Table 7 isn’t easy
training, the predetermined order is sound, so
training fatigue alone probably won’t be an
issue, assuming this is the appropriate magnitude of volume for a specific person. However, since life situations still pop up, you could
have a few “easy days” in your back pocket to allow for all-time flexibility as needed.
These easy days could be a low volume power session or a solely assistance work-focused
session at a low RPE, or another option you
prefer. In this design, you would perform the
predetermined order, plug in one of the easy
options when needed, and then continue with
the next pre-planned day. Table 8 presents
this option.
In Table 8, you can see a power day on the
main lifts or a session focused on assistance
work was flexed in when needed. Then, the
lifter carried on with their next scheduled
session-type after the flex day. This type of
90
I WOULD ALMOST
ALWAYS HAVE A
FLEX OPTION IN A
TRAINING PROGRAM.
model may not be best when traveling and
having limited gym access; however, I think
it works well when you are not planning for
disruptions. In other words, I would almost
always have a flex option in a training program to account for unforeseen circumstances. In addition to just inadequate sleep or elevated acute stress, a lifter may have to train
early in the morning unexpectedly or may
suddenly be short on time. In both of these
situations, having flex options works well. If
we understand this concept conceptually, we
can create a whole host of flex options that
serve a specific purpose. For example, this
video provides flexible examples for an afternoon lifter who has to train in the early morning. Of course, even in a flexible template,
there should also be the option not to train
if fatigue and motivation are just too low; in
that case, I’d just push everything back one
day. If you have to miss two days, then I’d
probably repeat the training week. Our video on program troubleshooting provides additional flexible options when traveling or
completely missing training.
Determining Readiness
I’ve previously covered readiness indicators
in great detail (one, two), so I’ll just provide
some brief considerations here. The three
flexible studies (McNamara and Stearne,
Colquhoun, and Walts) vary in their methods
of determining pre-training readiness. McNamara and Stearne (2) used a 0-10 Likert
scale, Colquhoun (3) used a 0-5 scale assessing readiness and motivation, and Walts (1)
used a specific question (quoted earlier) asking about mindset and readiness. Importantly, if using a readiness indicator to influence
training choice, that indicator should have
some capacity to predict performance. Yet,
many common readiness indicators lack empirical support to predict lifting performance.
The perceived recovery status scale (0-10
Likert scale) has a strong inverse correlation
with muscle damage following very damaging sprinting (6). A general Likert scale might
pick up large magnitudes of fatigue; however, if you have extreme soreness when you go
into the gym, then you should consider allocating your volume differently, as discussed
earlier. Further, a scale such as the perceived
recovery status scale doesn’t assess well-being (anxiety and mood state), which may affect performance. While well-being scales
may have merit in team sports (7), their ability to predict acute strength performance has
not fleshed out (8). Even technological tools
such as heart rate variability have also failed
to show promise to relate to recovery of resistance training performance (9 - MASS Review) or enhance strength gains when used
to guide flexible programming (10 - MASS
Review). Perhaps the readiness indicator
with the most empirical support to predict
lower body lifting performance is vertical
91
jump height. Watkins et al (11) assessed lifters’ vertical jump height and performed four
squat sets to failure at 80% of 1RM. 48 hours
later, Watkins retested both measures and
observed that decreased vertical jump height
was correlated with reduced squat reps (r =
0.65). A lifter could perform a quick vertical jump before each training session and set
a cutoff (e.g., 1.5 cm); if their vertical jump
drops below that target, they choose an easier training session. Of course, vertical jump
height wouldn’t apply to upper body performance, but conceptually the Watkins study
design is how researchers can determine if
recovery of a particular metric is indicative
of performance.
Since becoming interested in this topic about
12 years ago and writing this piece for Stronger by Science a few years back, I’ve started to wonder how much readiness indicators
matter. In other words, how fatigued do you
need to be to change training? Suppose you
are training with a well-designed setup where
volume is allocated appropriately, and you’re
probably not ever too fatigued going into the
next session. In that case, you probably don’t
need to be 100% recovered to train effectively. Additionally, intra-session load can
always be adjusted (up or down) to match
performance using RPE or velocity, which
mitigates the need to completely change the
day’s session if you’re feeling just a touch
fatigued. Let’s use the perceived recovery
status scale as a simple example. If a lifter
plans a heavy session when their perceived
recovery is between 8-10 on a 10-point scale,
does that mean performance will be worse
if they do a heavy session on a day where
they’d rate their perceived recovery status
a 7? Probably not. I think a lifter generally
knows if he or she feels completely trashed
or good enough to perform. If feeling good
enough to perform, tools such as RPE and velocity are there for intra-session adjustments.
If the lifter is feeling trashed, then perhaps a
day off is warranted, or the athlete can insert
a light/power day. If a specific circumstance
(i.e., morning training or travel) arises, using
one of the specific flexibility options noted
above is a good idea.
Conclusions and Thoughts
Overall, there’s merit in the idea of being able
to flex in a different type of training session
than was initially planned. Still, unless major
circumstances are present (high fatigue, inadequate sleep, travel, etc.), I wouldn’t expect
a huge benefit from flexible templates. The
presently reviewed study does have a high
ecological validity for team sport athletes
training in the offseason. It’s often difficult to
get those athletes to adhere to an off-season
lifting program; given that a flexible approach
did not hinder progress in this study, flexibility could be viewed as a potential approach
to enhance adherence without sacrificing efficacy. However, for strength sports athletes,
I’d be more likely to implement flexibility
in specific circumstances, or always having
a power/light day on hand in case it’s needed. Lastly, training flexibility isn’t limited to
just session-type. A study (12) previously reviewed by Dr. Helms showed that allowing a
lifter to choose from a pool of exercises each
day may enhance strength. While a powerlifter needs to squat, bench press, and deadlift, a coach could give the athlete a choice
92
APPLICATION AND TAKEAWAYS
1. The reviewed study found that choosing each weekly session’s volume or
intensity did not enhance strength performance compared to a fixed weekly
training schedule.
2. The concept of flexible training has been around for a while and has merit;
however, it’s probably most beneficial when training readiness is low due to
extenuating life circumstances.
3. Ultimately, if life circumstances aren’t extenuating, then I’d prefer a fixed order
weekly configuration. However, I’d always keep a light or power training session
on hand to flex in just in case readiness is low due to unexpected poor sleep,
early morning training, or time restraints.
of what assistance exercises to perform. For
instance, I often program “back assistance
(your choice)” to provide athletes with autonomy. Of course, not everyone may want
that autonomy, so this is not a blanket statement to always offer this choice; instead, this
is just to say that there are other ways (more
than listed here) to implement flexibility into
your training program.
Next Steps
versus fixed order training when training
or life circumstances are really demanding,
with my preference being the latter. Potentially, college students who report typically
being stressed and sleeping less during the
last month of a semester would be good candidates for this study. In that case, we’d see
if flexible training could enhance volume and
intensity when lifters chose the hard days in
the flexible group and if that led to improved
outcomes over a month.
Although flexible templates are logical and
should work, I still feel this area needs proof
of concept for resistance training. When early studies are conducted, the intervention is
often overly demanding to examine if the
idea is worth continuing. For example, an
early static stretching study from Fowles et
al (13) had subjects hold stretches for >100
seconds and found decreased acute muscle
stiffness and force production. We now know
that if your stretches are pretty short (i.e.,
<10 seconds), the risk of a strength decrease
is negligible. The point being, I’d like to see
a longitudinal study that compares flexible
93
References
1. Walts CT, Murphy SM, Stearne DJ, Rieger RH, Clark KP. Effects of a Flexible Workout
System on Performance Gains in Collegiate Athletes. The Journal of Strength &
Conditioning Research. 2021 Mar 25.
2. McNamara JM, Stearne DJ. Flexible nonlinear periodization in a beginner college weight
training class. The Journal of strength & conditioning research. 2010 Jan 1;24(1):17-22.
3. Colquhoun RJ, Gai CM, Walters J, Brannon AR, Kilpatrick MW, D’Agostino DP,
Campbell WI. Comparison of powerlifting performance in trained men using traditional
and flexible daily undulating periodization. The Journal of Strength & Conditioning
Research. 2017 Feb 1;31(2):283-91.
4. Tsoukos A, Veligekas P, Brown LE, Terzis G, Bogdanis GC. Delayed effects of a lowvolume, power-type resistance exercise session on explosive performance. The Journal of
Strength & Conditioning Research. 2018 Mar 1;32(3):643-50.
5. Zourdos MC, Jo E, Khamoui AV, Lee SR, Park BS, Ormsbee MJ, Panton LB, Contreras
RJ, Kim JS. Modified daily undulating periodization model produces greater performance
than a traditional configuration in powerlifters. The Journal of Strength & Conditioning
Research. 2016 Mar 1;30(3):784-91.
6. 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. The Journal of Strength & Conditioning Research. 2011
Mar 1;25(3):620-8.
7. Mielgo-Ayuso J, Zourdos MC, Clemente-Suárez VJ, Calleja-González J, Shipherd
AM. Can psychological well-being scales and hormone levels be used to predict acute
performance of anaerobic training tasks in elite female volleyball players?. Physiology &
behavior. 2017 Oct 15;180:31-8.
8. 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.
9. Flatt AA, Globensky L, Bass E, Sapp BL, Riemann BL. Heart Rate Variability,
Neuromuscular and Perceptual Recovery Following Resistance Training. Sports. 2019
Oct;7(10):225.
10. de Oliveira RM, Ugrinowitsch C, Kingsley JD, da Silva DG, Bittencourt D, Caruso FR,
Borghi-Silva A, Libardi CA. Effect of individualized resistance training prescription with
94
heart rate variability on individual muscle hypertrophy and strength responses. European
journal of sport science. 2019 Jan 30:1-9.
11. Watkins CM, Barillas SR, Wong MA, Archer DC, Dobbs IJ, Lockie RG, Coburn JW,
Tran TT, Brown LE. Determination of vertical jump as a measure of neuromuscular
readiness and fatigue. The Journal of Strength & Conditioning Research. 2017 Dec
1;31(12):3305-10.
12. Rauch JT, Ugrinowitsch C, Barakat CI, Alvarez MR, Brummert DL, Aube DW, Barsuhn
AS, Hayes D, Tricoli V, De Souza EO. Auto-regulated exercise selection training
regimen produces small increases in lean body mass and maximal strength adaptations in
strength-trained individuals. The Journal of Strength & Conditioning Research. 2020 Apr
1;34(4):1133-40.
13. Fowles JR, Sale DG, MacDougall JD. Reduced strength after passive stretch of the
human plantarflexors. Journal of applied physiology. 2000 Sep 1;89(3):1179-88.
█
95
Study Reviewed: Influence of Dietary Nitrate Supplementation on Physical Performance
and Body Composition Following Offseason Training in Division I Athletes. Townsend et al.
(2021)
Can Dietary Nitrate Enhance Strength and
Hypertrophy Adaptations Over Time?
BY ERIC TREXLER
In previous MASS issues, we’ve established that a single dose
of dietary nitrate can acutely enhance strength and power
performance. Does that actually lead to better gains over time?
Read on to find out.
96
KEY POINTS
1. The presently reviewed study (1) sought to determine if 180mg/day of dietary
nitrate from red spinach extract would improve maximal strength, sprint
performance, and body composition over the course of an 11-week resistance
training program in male collegiate baseball players.
2. Nitrate supplementation did not significantly impact strength, sprint, or body
composition outcomes, but the study had several limitations including a small
sample size, a small nitrate dose, and a truncated period of time between
supplementation and the onset of exercise.
3. Given the data showing acute benefits and the mechanistic justification
supporting chronic benefits, I still think a cost-effective strategy with plenty of
upsides and minimal downsides would be to get 400-800 mg/day of nitrate from
vegetables while supplementing with 4-6g of citrulline (6-9g of citrulline malate)
about an hour before training sessions.
I
n previous MASS issues, we’ve looked
at dietary nitrate supplementation from
a few different angles. The first nitrate
article provided a thorough overview of what
nitrate is, what it does, and how it (favorably)
impacts acute exercise performance, while
also exploring potential sex-based differences in responses to nitrate. In more recent
articles, we’ve looked at how acute dietary
nitrate supplementation (in the form of beetroot juice) specifically enhanced bench press
power and strength endurance, and how a
combination of dietary nitrate and citrulline
supplementation enhanced strength adaptations over the course of an eight-week training program. Along the way, I’ve openly
speculated that dietary nitrate supplementation may have the potential to enhance hypertrophy responses to resistance training, given
that there are multiple plausible mechanisms
that could drive such an effect. The presently
reviewed study (1) put this speculative idea
to the test by evaluating the effects of red
spinach extract, providing 180mg of dietary
nitrate, on strength, sprint, and hypertrophy
outcomes over 11 weeks of training in male
collegiate baseball players. The statistical
analysis revealed no significant advantage of
the red spinach extract in comparison to a placebo, but based on some key methodological
considerations, I think it may be premature to
give up on dietary nitrate as a supplement for
facilitating gains in strength, power, and hypertrophy. Read on to find out why this study
shouldn’t necessarily crush our optimism
about nitrate supplements.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was “to investigate the potential of daily dietary nitrate supplementation, in the form of
red spinach extract, on body composition and
97
physical performance following 11 weeks of
offseason training in Division I male baseball
athletes.”
Hypotheses
The researchers hypothesized that daily supplementation with red spinach extract would
enhance maximal strength, sprint performance, and body composition over the course
of the 11-week resistance training program.
Subjects and Methods
Subjects
This study was completed by 16 Division I
male baseball players. In order to participate,
members of this baseball team were required
to be free from any medical, muscular, or metabolic contraindications, and participants were
excluded if they reported the use of ergogenic
aids. This study didn’t provide a ton of information regarding specific inclusion and exclusion criteria, which makes sense given the
context. I assume they partnered up with the
baseball team, and basically allowed all members of the baseball team to participate, as long
as they didn’t violate any very basic exclusion
criteria. Baseline characteristics of the study
participants are presented in Table 1.
Methods
The presently reviewed study had a very
straightforward design. The sample was randomly divided into two groups; one group
received the red spinach extract supplement,
and the other received a placebo treatment.
Throughout the study, both groups participated in a structured, supervised, 11-week offseason training program. The program was
described as a triphasic undulating periodized
program; the athletes completed 2-3 workouts per week, the set and rep schemes varied within each microcycle, and each phase
focused on a different type of muscle action
(or “phase of movement”). Weeks 1-4 had an
eccentric focus, weeks 5-8 had an isometric
focus, and weeks 9-12 had a concentric focus. Overall, the program was well suited to
promote gains in strength and hypertrophy.
Supplementation continued in a double-blind
manner for 12 straight weeks (11 training
weeks, and 1 week of post-testing). The red
spinach extract supplement was a 2g dose,
which provided 180mg of dietary nitrate,
while the placebo capsules contained maltodextrin. On training days, supplements were
ingested 15 minutes prior to the onset of training, which ended up being about 30 minutes
prior to their first working set of the training
98
session. On non-training days, participants
were instructed to consume their supplement
with a meal. All participants were instructed
to refrain from using antibacterial mouthwash
for the duration of the study, as antibacterial
mouthwash can dramatically blunt the necessary conversion of nitrate to nitrite (if this is
blunted, nitrate supplements will have no effect). After every workout, both groups were
provided with a recovery drink containing 20g
of protein, 46g of carbohydrate, and 4.5g of
fat. In addition, participants completed threeday food logs during weeks 1, 9, and 12 of the
study so the researchers could assess any potential impacts related to dietary changes.
Outcomes of interest included changes in
body composition, strength, sprint performance, and cardiovascular measures (heart
rate, systolic blood pressure, and diastolic
blood pressure). Body composition was estimated using a four-compartment model that
utilizes DXA in conjunction with body water
estimates derived from bioelectrical imped-
ance analysis. Thicknesses of the rectus femoris and vastus lateralis muscles were measured via ultrasound to directly assess muscle
hypertrophy. Bench press 1RM was the lone
strength assessment, and anaerobic power
was assessed via Wingate testing. Briefly,
participants completed a maximal intensity
30-second sprint against a pretty heavy resistance (7.5% of body mass), and the researchers assessed absolute and relative peak power, average power output, minimum power,
and fatigue index (the drop in power output
from the beginning to the end of the test).
These outcomes were measured before and
after the 11-week intervention, with participants instructed to abstain from alcohol and
vigorous exercise for at least 48 hours prior
to each laboratory testing visit.
Findings
Supplement compliance was not significantly different between groups (94.1% in the red
99
spinach group, 95.7% in the placebo group).
There were no between-group differences for
average daily intakes of total calories, carbohydrate, fat, or protein (all p > 0.05), and both
groups ate plenty of protein (around 176g/
day in the red spinach group [~1.96g/kg] and
around 141g/day [~1.55g/kg] in the placebo
group; I wouldn’t get too worked up over
a differences of this magnitude when using
self-reported diet log data, and it’s unclear if
these values include the post-workout recovery drink that was provided). There were no
significant group × time interaction effects
for heart rate (p = 0.374), systolic blood pressure (p = 0.563), or diastolic blood pressure
(p = 0.490), and no significant differences
between groups. Heart rate did not change
significantly over time, but a main effect of
time indicated that systolic blood pressure
values were generally higher at post-testing
than pre-testing (+7.7mmHg; p = 0.044), and
diastolic blood pressure values increased to a
degree that was near the threshold for statistical significance (p = 0.086).
There were no significant group × time interaction effects for any of the strength or
sprint outcomes (all p > 0.05). A main effect
of time was observed for bench press 1RM (p
< 0.001), with values generally increasing by
about 18.7kg from pre-testing to post-testing,
but no significant main effects were observed
for any of the sprint outcomes. Full performance data are presented in Table 2.
There were no significant group × time interaction effects for any of the body composition
or hypertrophy outcomes (all p > 0.05), and
no significant differences between groups.
Total body mass did not significantly change
over time, but main effects of time indicated
that fat-free mass and muscle thicknesses increased significantly, whereas fat mass and
body-fat percentage decreased significantly, irrespective of group membership (all p
100
< 0.05). Full body composition and muscle
thickness data are provided in Table 3.
Criticisms and Statistical
Musings
This isn’t a major criticism, but it is a small
point of clarification and a recommendation
for researchers conducting similar experiments in the future. This is a randomized
study using a two group, pre-post design.
This means there are two different groups
of people that were randomly assigned to
receive a particular treatment (in this case,
supplement or placebo), and both groups are
measured twice (once before the intervention, and once after). You’ll sometimes see
researchers use a two-way analysis of variance (ANOVA) with repeated measures over
time, as the researchers did in the presently
reviewed study. With this type of test, you
assess the main effect of group, the main effect of time, and the group × time interaction,
which allows you to determine if values generally differed between groups, generally different between time points, and if the change
over time for one group was meaningfully
different than the change over time for the
other group. Based on the research question
for this study, we’re most interested (by far)
in the group × time interaction effect, which
helps us determine if changes from pre-testing to post-testing were larger for the supplement group than they were for the placebo
group. You might also see researchers who
simply calculate the change score for each
group (quantifying the change from pre-testing to post-testing within each group), then
do an independent samples t-test to compare
the two groups’ change scores to one another.
For our purposes, these two approaches are
mathematically equivalent.
However, you’ll also see situations in which
researchers will use an analysis of covariance
for this type of study design. They will directly compare the two groups’ change scores to
one another, while controlling for the baseline (pre-test) value. Alternatively, they may
compare the two groups’ post-test scores to
one another, while once again controlling
for the baseline (pre-test) value. Whether
you compare the change scores or post-test
scores, the two approaches are mathematically equivalent when you control for the
pre-test value. The major advantage of this
approach is that it tends to have more statistical power, and increases the likelihood that
you’ll find a statistically significant effect
of supplementation if one truly exists. For a
small study (such as the presently reviewed
study), this preservation of statistical power
would be pretty favorable.
You’ll also see situations in which researchers use the ANOVA-ish approach of testing
for the main effect of group, the main effect
of time, and the group × time interaction, but
they instead indicate that they used a linear
mixed model with random intercepts for the
analysis. Instead of identifying “time” as a
repeated measures effect, this strategy allows
each study participant to have their own intercept. If there are no missing data, this approach is equivalent to the standard ANOVA
with repeated measures over time. However,
the linear mixed model approach is advantageous when you have missing data, because
it can incorporate missing data into the model
101
far more effectively than the other approaches.
As discussed by O’Connell et al (2), these
approaches are all considered appropriate,
but they have distinct characteristics. In my
opinion, the best choice can vary based on
the circumstances of the study (for example,
based on whether or not your groups were
randomly or non-randomly assigned, whether or not the main effects of time or group
are relevant to your research question, how
large or small your sample is, and whether or
not you have missing data). In the presently reviewed study, the researchers used the
two-way ANOVA with repeated measures
approach, which is by far the most common
and absolutely appropriate. However, based
on the focused research question, the small
sample size, and the lack of missing data, I
personally would’ve preferred to see the ANCOVA approach used instead. I don’t think it
would have made a large impact on the findings of this particular study, but in the future
I think it’d be great to see the field gravitate
toward using the ANCOVA approach more
often when the researchers have fairly complete data, and the linear mixed model approach when they have a substantial amount
of missing data.
Interpretation
Before we explore reasons why dietary nitrate failed to promote significantly greater
training adaptations in the presently reviewed
study, we should briefly address reasons why
one might expect favorable effects in the first
place. First and foremost, there is evidence to
suggest that nitrate supplementation can acutely enhance strength and power outcomes. As
reviewed in a previous MASS article, nitrate
supplementation can increase nitric oxide production, and nitric oxide can enhance the contractile function of muscle by increasing calcium release from the sarcoplasmic reticulum
and increasing myofibrillar sensitivity to the
calcium that’s released. The review went on
to summarize the more “mechanistic” studies
that used highly technical, lab-based strength
and power metrics to assess the effectiveness
of nitrate supplementation. These studies generally indicated that acute nitrate supplementation favorably impacts strength and power,
particularly during explosive, high-velocity
muscle actions in a fatigued state. While these
lab-based strength and power assessments are
well-controlled and informative, they lack
ecological validity; for example, we can learn
a lot from high-velocity concentric leg extensions on a dynamometer or electrically stimulated muscle contractions, they don’t mimic
the everyday training of the typical athlete or
lifting enthusiast.
Fortunately, more relevant studies do exist.
For example, Mosher et al (3) found that six
days of beetroot juice supplementation (providing 400mg nitrate) allowed participants to
complete significantly more bench press repetitions in a series of three sets to failure with
60% of 1RM. As we covered in a fairly recent
MASS review, Williams et al (4) reported
that a single dose of beetroot juice (providing
400mg nitrate) significantly enhanced bench
press power, bar velocity, and reps to failure
(with 70% of 1RM) in comparison to a placebo. Even more recently, Ranchal-Sanchez
et al (5) assessed the effects of beetroot juice
supplementation (providing 400mg nitrate)
102
on back squat and bench press performance
across three sets to failure using 60%, 70%,
and 80% of 1RM. Supplementation did not
significantly impact bench press repetitions
to fatigue or metrics related to bar velocity
and power. However, beetroot juice significantly increased squat reps to fatigue when
using 60% of 1RM and 70% of 1RM. Given
the combination of mechanistic and applied
evidence, it seems plausible to speculate that
regular nitrate supplementation could facilitate greater training volume per session,
which could theoretically promote better
training adaptations over time.
Aside from a potential increase in training
volume, there are other plausible mechanisms by which nitrate supplementation
could increase hypertrophy. As I explained
in an old article, nitrate supplements enhance blood flow during exercise by increasing vasodilation via nitric oxide production.
If more blood flow is directed to the active
muscle during resistance training, this could
potentially augment acute swelling of the
muscle cell, which is believed to play a role
in promoting growth of the muscle fiber (6).
This mechanism is admittedly speculative,
but plausible nonetheless. Another potential mechanism pertains to satellite cells, as
there is some evidence to suggest that nitric
oxide mediates the process of activating satellite cells (7), which can then fuse with myofibers to promote muscle growth. There are
also some fascinating rodent studies that actually assess longitudinal hypertrophy, with
results suggesting that overload-induced hypertrophy can be increased by administration
of pharmacological nitric oxide boosters (8),
and can be blunted by administration of pharmacological nitric oxide blockers (9). So, if
nitrate enhances acute strength performance
and has mechanistic links to hypertrophy promotion, why didn’t it induce any statistically
significant effects in the presently reviewed
study?
The potential explanations are multifactorial. As I mentioned in the statistical musings
section, a study this small isn’t likely to have
adequate statistical power to pick up on small
differences in training adaptations, particularly when using analysis of variance as the
statistical approach. Other research has also
suggested (in a speculative manner) that there
may be nonresponders to dietary nitrate supplementation (10), which could be particularly impactful in smaller studies if it turns out to
be true. In the presently reviewed study, the
researchers tested a red spinach extract product yielding 180mg of nitrate; the majority of
studies reporting performance benefits from
dietary nitrate supplementation tend to provide doses of around 400-800mg, and tend to
use other nitrate sources like beetroot juice
or sodium nitrate. The dosing issue is noteworthy and self-explanatory, but the nitrate
source could also be a big deal. For example,
it’s possible that other ingredients in beetroot
juice may act synergistically with high-dose
nitrate, so a lower nitrate dose coming from
red spinach extract may not be sufficient to
get the job done. In addition, the actual nitrate content of beetroot supplements (11)
and fresh produce (12) is notoriously inconsistent, and it doesn’t appear that an independent analysis of the nitrate content of the red
spinach extract was conducted.
103
Finally, supplements were provided 15 minutes prior to training sessions, and 60 minutes
prior to post-testing. While the researchers provided justification for this short time frame, the
vast majority of studies provide nitrate supplements 2-3 hours before exercise to allow plenty of time for a robust increase in plasma nitrite concentrations. I’m speculating here, but
I wonder if the decision to sample competitive
athletes factored into this truncated wait time.
In the few opportunities I’ve had to conduct
research with a full team of collegiate athletes,
scheduling has always been a big hurdle, as
these teams typically have a schedule packed
full of practices, games, workouts, travel, and
team meetings. Whether or not busy schedules
were a contributing factor, the presently reviewed study did not measure or report blood
nitrate or nitrite levels after supplementation,
so it’s hard to say whether or not blood nitrate
and nitrite levels were increased substantially
enough to have any chance of impacting per-
IT’S HARD TO VIEW THE
NULL FINDINGS FROM THE
PRESENTLY REVIEWED STUDY
AS A CRUSHING BLOW TO THE
IDEA OF SUPPLEMENTING
WITH DIETARY NITRATE TO
ENHANCE PERFORMANCE
OR TRAINING ADAPTATIONS.
formance or hypertrophy. When you consider
the small sample size, underpowered statistical analysis, low and unverified nitrate dose,
nonstandard nitrate source, and short waiting
period between supplementation and the onset of exercise, it’s hard to view the null findings from the presently reviewed study as a
crushing blow to the idea of supplementing
with dietary nitrate to enhance performance or
training adaptations. It’d be great if we could
lean on the other applied studies evaluating
hypertrophy in response to nitric oxide precursor supplements, but the few studies that exist
have their own share of notable limitations.
For example, we can’t really make strong inferences about nitric oxide precursor supplements and training adaptations from studies
involving multi-ingredient pre-workout supplements, as they are confounded by the presence of creatine, caffeine, and other ergogenic ingredients. Campbell et al found arginine
alpha-ketoglutarate to significantly increase
gains in bench press 1RM and peak cycling
power over eight weeks of resistance training
(14), but lean body mass was not significantly impacted. Having said that, it’s important
to recognize that arginine supplements have
largely fallen out of favor in the supplement
world, as arginine has poor bioavailability in
comparison to other nitric oxide precursors like
citrulline and nitrate. More recently, Hwang et
al (13) studied the impact of 2g citrulline malate, 2g L-citrulline with 200mg glutathione,
or a placebo on strength and hypertrophy over
eight weeks of resistance training. Supplementation did not significantly impact strength
gains. You might be able to argue that the citrulline + glutathione condition led to slightly
104
more favorable changes in fat-free mass, but
the between-group difference was only significant at week four mid-point testing (the difference was no longer significant at post-testing
in week eight). The training program generally failed to induce hypertrophy or increase
bench press strength, and the citrulline doses
were quite low in comparison to the majority of studies reporting ergogenic effects from
citrulline-based supplements, so definitive
conclusions are hard to derive. Finally, as you
may recall from Volume 4 Issue 12, researchers recently studied the combined effects of
citrulline (6g) and dietary nitrate (520mg) on
adaptations to an eight-week training program
(14). The supplement group had significantly
larger improvements in maximal isometric leg
extension force, and non-significantly larger improvements in leg extension muscular
endurance and maximal aerobic endurance
performance, but supplementation did not significantly influence changes in fat-free mass.
However, it’s important to note that the training program was not hypertrophy-focused; it
included a combination of resistance training,
interval training, and aerobic training, and neither of the groups gained more than 0.7kg of
fat-free mass.
In summary, we find ourselves in a tricky spot
with this particular area of research. We have
growing evidence that nitric oxide precursors
(specifically citrulline and dietary nitrate)
can acutely enhance strength and power performance when dosed and timed appropriately. We have plausible mechanisms by which
such supplements could theoretically enhance strength and hypertrophy adaptations
over time in response to resistance training.
However, there is scant longitudinal research
evaluating training adaptations in response to
nitric oxide precursor supplementation, and
the few existing studies tend to report null
findings but have major limitations. The findings of the presently reviewed study, in light
of the limitations related to dosage, timing,
and sample size, don’t provide strong enough
evidence to materially change my recommendations related to nitric oxide precursor
supplementation. As such, I still think the
best bet is to try to get a daily ergogenic dose
(at least 400-800mg) of nitrate from vegetables, while supplementing with 4-6g of citrulline about 60 minutes before workouts (if
consuming citrulline malate with a 2:1 citrulline-to-malate ratio, this would translate to a
dose of 6-9g). Nitrate-rich vegetables (such
as spinach, beets, celery, arugula/rocket, and
other leafy greens) typically have high avail-
I STILL THINK THE BEST BET
IS TO TRY TO GET A DAILY
ERGOGENIC DOSE (AT
LEAST 400-800MG) OF
NITRATE FROM VEGETABLES,
WHILE SUPPLEMENTING
WITH 4-6G OF CITRULLINE
ABOUT 60 MINUTES
BEFORE WORKOUTS.
105
APPLICATION AND TAKEAWAYS
So far, the results from longitudinal studies assessing chronic supplementation with
nitric oxide precursors look pretty underwhelming, but there are major limitations
that hinder our ability to draw strong conclusions from them. Given that we have
pretty good evidence of acute benefits and pretty strong mechanistic justifications
for the expectation of chronic benefits, it seems hard to view nitric oxide precursor
supplementation with a terribly pessimistic outlook. On the other hand, it would be a
shame to sink a ton of money into a supplementation strategy with uncertain efficacy.
So, my recommended approach remains the same: getting plenty (400-800mg/day)
of nitrate from vegetables, while supplementing with 4-6g of citrulline (or 6-9g of
citrulline malate) about an hour before workouts. On the high-nitrate vegetable side
of the strategy, it’s all upsides: there are plenty of accessible nitrate-rich vegetables,
and by seeking them out you’ll be fortifying your diet with plenty of fiber, vitamins, and
phytonutrients that will improve the overall quality of your diet. On the citrulline side of
the strategy, the acute benefits are fairly well-documented, and citrulline is a relatively
cost-effective supplement with an excellent safety profile.
ability in most food environments, and citrulline malate has pretty solid data supporting acute performance benefits while tasting
great and falling pretty low on the cost-perdose spectrum when compared to most other
ergogenic supplements. We don’t have rock
solid data indicating that this strategy will
enhance training adaptations over time, but
it’s a cost-effective strategy with several potential benefits, minimal downsides, and a
strong mechanistic justification.
Next Steps
forward replication of this study, but with a
larger sample of participants, an ergogenic
amount of nitrate (400-800mg) coming from
a third party tested beetroot juice supplement,
and a waiting period of 2-3 hours between
supplement ingestion and exercise. While the
decision to sample high-level athletes was a
strength of the presently reviewed study, I
would gladly sacrifice that aspect for a design that allows for more total participants
and greater schedule flexibility to allow for
a longer wait time between supplementation
and the onset of exercise.
I don’t want to sound too critical of the presently reviewed study, because it has several
strengths and I appreciate the researchers for
devoting their time and effort toward making
it happen. Nonetheless, the limitations related to sample size, dosage, and supplement
timing could be rectified in future studies.
Ultimately, I’d like to see a pretty straight-
106
References
1. Townsend JR, Hart TL, Haynes JT, Woods CA, Toy AM, Pihera BC, et al. Influence
of Dietary Nitrate Supplementation on Physical Performance and Body Composition
Following Offseason Training in Division I Athletes. J Diet Suppl. 2021 Mar 23;1–16.
2. O’Connell NS, Dai L, Jiang Y, Speiser JL, Ward R, Wei W, et al. Methods for Analysis
of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods. J Biom
Biostat. 2017 Feb 24;8(1):1–8.
3. Mosher SL, Sparks SA, Williams EL, Bentley DJ, Mc Naughton LR. Ingestion of a Nitric
Oxide Enhancing Supplement Improves Resistance Exercise Performance. J Strength
Cond Res. 2016 Dec;30(12):3520–4.
4. Williams TD, Martin MP, Mintz JA, Rogers RR, Ballmann CG. Effect of Acute Beetroot
Juice Supplementation on Bench Press Power, Velocity, and Repetition Volume. J
Strength Cond Res. 2020 Apr;34(4):924-928.
5. Ranchal-Sanchez A, Diaz-Bernier VM, De La Florida-Villagran CA, Llorente-Cantarero
FJ, Campos-Perez J, Jurado-Castro JM. Acute Effects of Beetroot Juice Supplements
on Resistance Training: A Randomized Double-Blind Crossover. Nutrients. 2020 Jun
28;12(7):1912.
6. Cholewa J, Trexler E, Lima-Soares F, de Araújo Pessôa K, Sousa-Silva R, Santos AM,
et al. Effects of dietary sports supplements on metabolite accumulation, vasodilation and
cellular swelling in relation to muscle hypertrophy: A focus on “secondary” physiological
determinants. Nutr. 2019;60:241–51.
7. Anderson JE. A role for nitric oxide in muscle repair: nitric oxide-mediated activation of
muscle satellite cells. Mol Biol Cell. 2000 May;11(5):1859–74.
8. Leiter JRS, Upadhaya R, Anderson JE. Nitric oxide and voluntary exercise together
promote quadriceps hypertrophy and increase vascular density in female 18-mo-old mice.
Am J Physiol Cell Physiol. 2012 May 1;302(9):C1306-1315.
9. Smith LW, Smith JD, Criswell DS. Involvement of nitric oxide synthase in skeletal
muscle adaptation to chronic overload. J Appl Physiol. 2002 May;92(5):2005–11.
10. James PE, Willis GR, Allen JD, Winyard PG, Jones AM. Nitrate pharmacokinetics:
Taking note of the difference. Nitric Oxide. 2015 Aug 1;48:44–50.
11. Gallardo EJ, Coggan AR. What’s in Your Beet Juice? Nitrate and Nitrite Content of Beet
Juice Products Marketed to Athletes. Int J Sport Nutr Exerc Metab. 2019 01;29(4):345–9.
12. Nuñez de González MT, Osburn WN, Hardin MD, Longnecker M, Garg HK, Bryan NS,
107
et al. A survey of nitrate and nitrite concentrations in conventional and organic-labeled
raw vegetables at retail. J Food Sci. 2015 May;80(5):C942-949.
13. Hwang P, Morales Marroquín FE, Gann J, Andre T, McKinley-Barnard S, Kim C, et
al. Eight weeks of resistance training in conjunction with glutathione and L-Citrulline
supplementation increases lean mass and has no adverse effects on blood clinical safety
markers in resistance-trained males. J Int Soc Sports Nutr. 2018 Jun 27;15(1):30.
14. LE Roux-Mallouf T, Vallejo A, Pelen F, Halimaoui I, Doutreleau S, Verges S. Synergetic
Effect of NO Precursor Supplementation and Exercise Training. Med Sci Sports Exerc.
2020 Nov;52(11):2437–47.
█
108
Study Reviewed: The Effect of Resistance Training Set Configuration on Strength and Muscular
Performance Adaptations in Male Powerlifters. Arazi et al. (2021)
This is Theoretically an Article About
the Effects of Cluster Set Training for
Powerlifters
BY GREG NUCKOLS
An intrepid research reviewer was attempting to write an article
about cluster sets, when he stumbled across some improbable
data patterns. You won’t believe what happened next.
109
KEY POINTS
1. 24 male powerlifters were randomized into three groups: a cluster set group, a
traditional set group, and a control group. All three groups completed two training
sessions per week involving traditional sets, while the cluster set group and the
traditional set group performed an additional three training sessions per week,
during which the traditional set group performed traditional sets, and the cluster set
group performed … you guessed it, cluster sets that were matched for volume and
intensity with the traditional set group.
2. Cluster sets promoted larger gains in bench press 1RM, medicine ball throw
distance, and peak power output during countermovement jumps, and also
tended to promote larger gains in squat and deadlift 1RM strength. The cluster
set group and traditional training group experienced similar improvements in body
composition.
3. I don’t think the results of the present study are accurate, for reasons explained in
the “Criticisms and Statistical Musings” section. Read on to learn a little bit about
cluster sets, and a lot about critically reading research.
C
luster sets involve splitting sets of an
exercise up into smaller mini-sets,
with short rest intervals in between
each mini-set. For example, instead of squatting 80% of 1RM for 3 sets of 6 reps with 2
minutes of rest between sets, you could perform each set of 6 reps as 2 mini-sets of 3 reps,
with 20 seconds between mini-sets, and 2 minutes of rest between each cluster of mini-sets.
Cluster sets have grown in popularity in recent
years, and we’ve discussed them several times
in MASS (one, two, three, four).
A recent study compared the effects of cluster sets and traditional sets in powerlifters (1).
Male powerlifters with at least three years of
powerlifting experience were randomized into
three groups: a cluster set group, a traditional
set group, and a control group. All three groups
completed two training sessions per week involving traditional sets, while the cluster set
group and the traditional set group performed
an additional three training sessions per week.
During these three additional sessions, the traditional set group performed traditional sets,
and the cluster set group performed cluster
sets that were matched for volume and intensity with the traditional set group. The cluster set
and traditional set groups experienced similar
increases in squat and deadlift strength, while
the cluster set group experienced larger increases in bench press strength, medicine ball
throw distance, and peak power output during
countermovement jumps. However, some improbable data patterns threaten our ability to
have confidence in the results of the presently
reviewed study.
Purpose and Hypotheses
Purpose
The purpose of this study was to compare
the effect of training with traditional set con-
110
figurations versus cluster sets on maximal
strength, power-based performance, and limb
circumferences in powerlifters.
Hypotheses
The researchers hypothesized that traditional
sets would lead to larger strength gains, but
that cluster sets would improve power-based
performance to a greater degree.
Subjects and Methods
Subjects
24 college-aged male powerlifters participated in this study. They all had at least three
years of powerlifting experience.
Experimental Design
Subjects were randomized into three groups:
a traditional set group, a cluster set group, and
a control group. All three groups trained twice
per week using the same “off-season pow-
erlifting program” (so the control group was
still performing at least a little bit of training)
which employed traditional sets. Few details
are provided about this portion of the training, except that all sessions included 4-5 sets
of bench press, back squat, and deadlift with
loads ranging from 60-95% of 1RM. The traditional set group and cluster set group also
performed an additional three training sessions per week (thus, they trained five times
per week in total). In these training sessions,
they performed bench press, military press,
curls, triceps extensions, back squats, leg
press, knee extensions, and deadlifts. The traditional set group performed four sets per exercise, while the cluster set group performed
four sets of two mini-sets per exercise, with
20 seconds between mini-sets. For example,
in the first workout, people in the traditional
set group performed one set of 10 reps at 70%
of 1RM, 1 set of 10 reps at 75% of 1RM, 1 set
111
of 8 reps at 80% of 1RM, and 1 set of 6 reps
at 85% of 1RM, while the cluster set group
performed 2 mini-sets of 5 reps (with 20 seconds between mini-sets) at 70%, 2 mini-sets
of 5 reps at 75%, 2 mini-sets of 4 reps at 80%,
and 2 mini-sets of 3 reps at 85%. Both groups
rested for two minutes between sets and three
minutes between exercises. The researchers
reassessed the subjects’ strength every two
weeks to adjust training loads. It’s unclear
what sort of adjustments were made (if any) if
a subject failed to complete all of the assigned
reps in a particular set. You can see the full
training program (for the three training days
that differed between groups) in Table 2.
Before and after the training period, researchers
assessed the subjects’ body composition (via
calipers), arm and thigh circumferences, 1RM
squat and bench press strength, and countermovement jump and medicine ball throw performance. Testing was spread across two sessions. Anthropometric measurements, squat
1RM, and bench press 1RM were assessed
during the first testing session, and medicine
ball throw distance, countermovement jump
peak power, and deadlift 1RM were assessed
during the second testing session.
Findings
Changes in body fat percentage, upper arm
circumference, and thigh circumference
were similar in all three groups. Medicine
ball throw and countermovement jump performance increased significantly more in
the cluster set group than the traditional set
group. Squat, bench press, and deadlift 1RM
strength tended to increase to a greater degree in the traditional set group than the cluster set group (Table 4, Figure 1). I think the
difference was statistically significant for
the bench press, but not the squat or deadlift
(more on that in the “Criticisms and Statistical Musings” section).
Criticisms and Statistical
Musings
The present study (1) and the study investigating the effects of chains that I also reviewed
112
113
this month (2) came from the same lab, and I
have some pretty serious concerns about both
of them.
My first concern stems from the shocking similarities between the reported body fat percentages in both studies. In the chains study, for all
groups at all time points, the reported means
and standard deviations for body fat percentage are exactly 1% higher than the corresponding figures in the present study. For example,
for the first group listed in the table (the chains
group in the chains study and the cluster group
in the present study), pre-training body fat percentage is 18.9 ± 4.1% in the chains study, and
17.9 ± 3.1% in the present study. For the last
group listed in the table (the control groups in
both studies), post-training body fat percentage is 18.2 ± 3.6% in the chains study, and 17.2
± 2.6% in the present study. That same pattern
holds true for all pairs of body fat percentage
means and standard deviations between pairs
of groups at both time points. Furthermore,
the relationship between groups is identical
in both studies between all pairs of groups
at all time points. For example, in the chains
study, the first group had 1.2% more body
fat than the third group pre-training (18.9%
vs. 17.7%), and the standard deviation in the
first group is 0.8% higher than the third group
(4.1% vs. 3.3%). In the present study, the first
group had 1.2% more body fat than the third
group pre-training (17.9% vs. 16.7%), and the
standard deviation in the first group is 0.8%
higher than the third group (3.1% vs. 2.3%).
So, that clearly seems very improbable. But
just how improbable is it? Thankfully, I can
simulate a dataset to find out. Since group
allocation was randomized, for both stud-
114
ies, we start by sampling from a population
of people that have fat percentages with a
distribution similar to the subjects in both
studies (18.6 ± 3.8% body fat for the chains
study, and 17.6 ± 2.8% for the present study).
“Subjects” from both of these populations
are randomized into three separate groups
of 10 subjects apiece. From there, subjects
in each group experience changes in body
fat percentage that mirror those of the two
studies (-1.0% for the first group, -0.9% for
the second group, and +0.5% for the third
group). We don’t know the standard deviations for changes in body fat percentages in
the chain study, so I just assumed there was
very low variability in both studies (±0.1%
for all three groups), which is a very conservative and charitable estimation (in the present study, those standard deviations were
±0.3-0.5%). Once all of that was specified,
I simulated 5,000 sets of groups. After the
groups were simulated, there were 12 variables of interest: Are all means and standard
deviations in corresponding groups at corresponding time points one unit higher in the
chain study than the present study?
Once I’d simulated my dataset and defined
my variables, the rest was pretty straightforward. For each mean and standard deviation
in the simulated “chains study samples,” I
subtracted the corresponding mean and standard deviation from the simulated “cluster
set study samples.” I counted up the number
of times the result was exactly 1, and then
divided by 5000 for each of the 12 pairs of
variables (because there were 5,000 simulated sets of groups). That gave me the independent probabilities for each set of variables to
differ by exactly one unit. For each one, the
probability was between 1% and 3%. From
there, since each of those 12 comparisons are
independent, it was just a matter of multiplying all 12 probabilities together.
The resulting probability: 2.75 × 10-20. In other words, a 1 in 275,000,000,000,000,000,000
chance of the observed relationships between
body fat percentage means and standard deviations in these two studies occuring by
chance. That’s about the same probability
as flipping a fair coin and having it land on
“heads” 65 times in a row.
115
Now, to be clear, that’s not an exact figure.
It’s at least partially based on assumptions
about the variability of changes in body fat
percentage within each group, after all; if we
assumed there was less variability, the probability would be higher, and if we assumed
there was more variability, the probability
would be lower. However, even if I’m off by
a factor of a trillion, we’d still be looking at
a probability equivalent to having a coin land
on “heads” 28 times in a row. Basically, this
is very improbable.
Now, if I was a reasonable person, I’d probably
pack it in, having made my point about why I’m
fairly skeptical of the data in these two studies.
But no, I’m not done. What if I told you there’s
data in these studies with something worse than
one in 275 million trillion odds? Dear reader,
there’s data reported in these studies that is
mathematically impossible.
Look back at Table 4. Pay close attention to
the relative strength data (squat 1RM per kilogram of body mass, for example). Notice
anything suspicious?
If you said, “Now that you mention it, those
are some implausibly large standard deviations,” you’re correct. In fact, they’re impossibly large standard deviations.
How can we know that? With a bit of math,
of course. You see, since we know the mean
and standard deviation of both body mass and
squat 1RM strength, we can approximate the
maximum possible standard deviation for the
ratio between squat 1RM strength and body
mass within this sample. We just need to use
this handy little equation:
Let’s use pre-training relative squat strength
in the cluster set group for this example.
A and B are your two group means (126.5 for
squat and 78.6 for body mass), and |ƒ| is the
absolute value of the ratio between the two
variables you’re interested in (in this case, it’s
reported the average relative squat was 1.56
times body mass). The little bars mean you
need to use the absolute value of f (f can’t be
negative). Finally, σA and σB are the standard
deviations of A and B (9.8 for squat, and 4.5
for body mass), and σAB is the covariance between the two variables. Covariance is just
the product of the standard deviations of the
two variables, multiplied by the correlation
coefficient for the relationship between the
two variables (squat 1RM and body mass).
Since we’re dividing two variables, the lowest possible covariance (the largest possible
negative covariance) will give us the highest possible standard deviation, so we want
to use -1 as our correlation coefficient. That
means our assumed covariance is 9.8 × 4.5 ×
-1 = -44.1. From there you just need to plug
your numbers into the equation. When you
do that, you get an approximate maximum
possible standard deviation for relative squat
strength of 0.21kg/kg.
Now, in practice, the maximum possible
value may be a shade higher, due to rounding issues (the standard deviation for body
mass may be 9.84kg, which rounds down to
9.8kg) or a skewed or kurtotic distribution,
but if the formula spits out 0.21kg/kg as a
maximum value, anything above ~0.3kg/kg
116
is probably off the table. That’s not even in
the same ballpark as the 0.87kg/kg reported
in Table 4 as the standard deviation of relative squat strength in the cluster set group.
Also, keep in mind, a standard deviation of
0.21kg/kg implies a perfect negative correlation between body mass and squat strength
– in other words, lighter people squat more,
and heavier people squat less. If we assume
even a modest positive correlation between
body mass and squat strength (r = 0.5, for
example, which would give us a covariance
of 9.8 × 4.5 × 0.5 = 22.05 to plug into the
formula), the standard deviation for relative
squat strength would be just 0.11kg/kg.
Across these two studies – the present study
(1) and the chains study (2) – every single
relative strength standard deviation runs into
this problem. There’s only one within spitting distance of being possible. In the chains
study, the post-training bench press relative
strength standard deviation in the chains
group was reported as 0.4kg/kg. When we run
the numbers through the formula that tells us
the maximum possible standard deviation, we
get a maximum standard deviation of 0.30kg/
kg. However, that’s still not that close. We’d
need to increase the standard deviations for
both body mass and bench press strength by
about 30% (from 4.9 to 6.4kg, and from 14.4
to 18.7kg) before a relative strength standard
deviation of 0.4 was possible, and that’s still
assuming a perfect negative correlation between body mass and bench press strength.
With a moderate positive correlation between
body mass and bench press strength (r = 0.5),
the body mass and bench press 1RM standard
deviations would need to be 125% larger than
the figures reported in the study. Basically, not
only are none of the relative strength standard
deviations reported in either of these studies
possible – none of them are particularly close
to being possible, much less plausible.
For reference, Table 6 shows the pre-training
relative strength standard deviations reported
in the present study, compared to the maximum possible standard deviations. I also
made a little calculator you can use to find
117
the approximate maximum and minimum
possible standard deviations in a scenario
like this (any time a third variable is created
by dividing two other variables with known
means and standard deviations).
Is it possible that these standard deviations
are just typos? Yes, actually. So far, I’ve focused on how you can figure out the highest
possible standard deviation, but you can also
figure out the lowest possible standard deviation when dividing two distributions with
known means and standard deviations. Instead of plugging in the lowest possible correlation coefficient to calculate your assumed
covariance, you can plug in the highest possible correlation coefficient (r = 1). Going back
to the relative squat strength in the cluster
set group, if we plug in 1 for the correlation
coefficient instead of -1 (meaning our covariance is 44.1 instead of -44.1), we can see
that the lowest possible standard deviation is
0.032kg. Therefore, it’s possible that all of
the relative strength standard deviations were
just mistyped to be an order of magnitude
too large. Instead of a standard deviation of
0.87kg, the relative squat strength standard
deviation for the cluster set group might be
0.087kg. That would imply a positive correlation between body mass and 1RM squat
strength of approximately r = 0.7. That’s an
imminently plausible correlation. So, in a
way, even though the numbers reported in the
text for relative strength standard deviations
are mathematically impossible, there may
be a simple explanation for them, whereas I
honestly can’t conceive of a simple explanation for the body fat percentages.
To further support the idea that the relative
strength standard deviations are just typos,
off by an order of magnitude, look back at
the standard deviations for height in the present study (Table 1). Subjects in the cluster
set group are reported to stand 1.75 ± 0.45 m
tall. In other words, within one standard deviation, they’d have subjects who were 1.3m
tall (4’3”) and subjects who were 2.2m tall
(7’3”). That’s almost certainly supposed to
be 1.75 ± 0.045m tall.
Are we done with data issues now?
Nope!
As I alluded to in the “Findings” section, I
think the researchers messed their statistical
analysis up somehow. They reported no significant group x time interactions for gains in
squat, bench press, or deadlift strength, and
no significant differences in strength gains
between groups. I’m not sure that’s correct
for squat or deadlift strength (3), but I’m almost positive that’s incorrect for bench press
strength. I extracted the bench press strength
data from the bar graphs in Figure 1 using
WebPlotDigitizer; it appears that bench press
strength improved by 15.7 ± 3.1 kg in the
cluster set group, 27 ± 5.8 kg in the traditional set group, and 2.4 ± 4.1 kg in the control
group. I can’t recreate the authors’ two-way
ANOVA, but I can run a one-way ANOVA
on those change scores, which should give
the same statistical result as looking for the
interaction effect in a two-way ANOVA. So,
what sort of result does that give us?
The between-groups p-value in a one-way
ANOVA is p < 0.00001. The authors’ reported p-value for their group x time interaction
was p = 0.74. I’m honestly baffled. I don’t
118
know how that could possibly be correct.
Using t-tests as rough post-hoc tests, for the
comparison between the cluster set group
and the traditional set group, the p-value is
0.0003. For the comparison between the cluster set group and the control group, the p-value is 0.000004. Finally, for the comparison
between the traditional set group and the control group, the p-value is 0.0000001. I cannot
conceive of how the researchers’ ANOVA
failed to detect a significant group x time interaction.
That’s gotta be all, right?
In terms of data issues, yes. However, I’d
like to register a bit of incredulity about the
training program employed in this study. If
I understand it correctly, the subjects in the
cluster set and traditional set groups were
performing squats and deadlifts fairly heavy,
for 4-5 sets per session, 5 days per week. We
don’t know what the two training days they
shared with the control group looked like, but
the three days of training per week associated with the intervention would have had the
subjects training to failure or pretty close to
failure on most sets. For example, in the traditional set group, one of your workouts in
week 7 would involve 8 reps at 85% of 1RM
, 6 reps at 85% of 1RM, 4 reps at 90% of
1RM, and 4 reps at 95% of 1RM. You’d repeat that workout two more times throughout
the week, and also perform 2 more workouts
of “4-5 sets … with 60 to 95% of 1RM.” Also
keep in mind, that week 7 workout would be
performed with training maxes that had recently been updated after retesting 1RMs following week 6. Call me crazy, but as a lifter
and coach, I doubt most people would even
complete that week of training (at least not
without missing a lot of reps), and I’m a bit
surprised that there were no injuries or dropouts over the course of the study, given the
training protocol.
While I’m just caterwauling, I also find the
lifters’ 1RMs somewhat suspicious (Table
1). I have a hard time believing that young
male powerlifters with at least three years of
powerlifting experience (not just general resistance training experience) had 1RM deadlifts that averaged less than 300lb/140kg.
I’m also really struggling with the fact that
the subjects squatted slightly more than they
deadlifted, and benched almost as much as
they deadlifted. I’ve sunk enough time into
simulating and quantifying probabilities for
this article already, so I really don’t want to
download the OpenPowerlifting dataset to see
how improbable these subjects’ squat:bench,
squat:deadlift and bench:deadlift ratios are,
but I’ll give you my completely non-quantitative assessment: they’re … highly atypical.
They’d be weird if the subjects were just general strength trainees, but they’re absolutely
bizarre for people who allegedly have three
years of powerlifting experience.
Just to wrap this section up, the last thing that
prevents me from reconciling these issues is
that the lead author has not yet returned an
email asking if we could see his data. The
journal the present study was published in
(Scientific Reports) has a data sharing policy that stipulates authors must share their
data under most circumstances (“A condition
of publication in a Nature Portfolio journal
is that authors are required to make unique
materials promptly available to others with-
119
out undue qualifications”). I’d really like to
see the data, for reasons that should now be
obvious, but the corresponding author hasn’t
yet responded to emails regarding data sharing. Until the corresponding author responds
and shares the data so that I can determine the
cause of these issues, I can’t take these results
at face value. However, if there’s an explanation for these data patterns I haven’t thought
of, or if I’ve made any errors in my analysis
that the raw data would lay bare (which is entirely possible), I’d love to know so that I can
re-interpret these results in a more charitable
and favorable light.
Interpretation
If we take the results of this study at face value, they largely confirm the recent cluster set
meta-analyses I reviewed in a previous issue
(4, 5). Those meta-analyses found that cluster
sets and traditional sets were similarly effective at promoting strength gains and muscle
growth, but that cluster sets may be slightly better for improving velocity and power
output. In the present study, strength gains in
120
two of the three exercises were pretty similar
between traditional and cluster sets, hypertrophy was similar between traditional and cluster sets (though the method used for assessing hypertrophy – limb circumferences – is
far from the gold standard), and power-based
performance (medicine ball throw and peak
power during jumping) improved to a greater
extent following cluster set training.
However, if you read the “Criticisms and Statistical Musings” section, you’ll know that I don’t
feel confident taking the results at face value.
This isn’t my favorite type of article to write
– I prefer to be able to focus on practical application rather than challenging the results
presented in a study – but I do think there’s a
lot of value in it. Before you can think about
how to apply research findings, you first need
to establish a few things about the research
itself: Was the study adequately designed to
answer the question it was asking? Were the
measurements valid and reliable? Does the
study design and population allow us to generalize the findings? And finally, do the data and
results themselves seem plausible (and if not,
are there obvious reasons why the results seem
implausible)? Far too often, people blow by
these fundamental questions when interpreting
research, which can lead to erroneous interpretations, or excessive emphasis on findings
that may be a bit shaky. The present study is
well-designed, used a mix of good (1RM tests
and the two tests of power) and not-so-good
(limb circumferences) measurements, and was
conducted in a great population for anyone
who wants to generalize the results to other
lifters. However, the present study didn’t clear
the last hurdle; some of the reported results are
highly implausible. In this case, I figured a lot
of people would see “cluster sets” (which are
currently a fairly hot topic) and “powerlifters”
(people tend to get excited about studies that
use powerlifters as subjects) in the title, share
the study all over social media, and pay more
attention to the general findings than the actual data. In my opinion, that would be a pretty
grave mistake, and I wanted to circumvent it
for MASS readers.
In fact, reviewing this study turned out to be a
blessing in disguise, because the issues relating to the similarities in body fat percentages
between the pair of Arazi studies I reviewed
this month is a really big problem, in my opinion. Everything else discussed in the “Criti-
IF YOU HAVE STRENGTH
OR HYPERTROPHY GOALS,
AND YOU JUST LIKE DOING
CLUSTER SETS, FEEL FREE
TO USE THEM. THEY
DON’T SEEM TO BE ANY
BETTER OR WORSE THAN
TRADITIONAL SETS, ON
AVERAGE, SO PERSONAL
PREFERENCE CAN GUIDE
YOUR DECISION MAKING.
121
APPLICATION AND TAKEAWAYS
Cluster sets probably won’t improve (or hinder) strength or hypertrophy outcomes,
but they may have a small positive effect on improvements in velocity and power
output with submaximal loads. They’re a great option for many types of athletes,
especially if your primary aim is improving power-based outcomes.
cisms and Statistics Musings” section could
be written off as mistakes or statistical errors
(which we’re very used to dealing with), but
the coupling of body fat data between these
two studies (1, 2) is extremely improbable
and inexplicable. If I wouldn’t have reviewed
both the chains study and the present study, I
never would have spotted it, and if I wouldn’t
have spotted that issue, I may have wound up
writing a far less cautious review.
Finally, let this serve as your periodic reminder to read studies closely. When you’re
reading research, you can’t always take the
reported results at face value. If you see numbers in a study that don’t make sense to you,
try to work out exactly what seems off about
them, and if you’re stumped, feel free to ask
about it in the MASS group.
Since I don’t think we should take much
away from the present study, my discussion
of cluster sets as a general concept would
perfectly mirror the interpretation section of
my article about the recent pair of cluster set
meta-analyses. If cluster sets interest you and
you want to get into the weeds a bit, I’d recommend checking out that article. The basics
are simple, though: if you have strength or
hypertrophy goals, and you just like doing
cluster sets, feel free to use them. They don’t
seem to be any better or worse than traditional sets, on average, so personal preference
can guide your decision making. However, if
you have velocity or power-based goals (improving your sprint speed, improving your
jumping ability, etc.), cluster sets are a great
tool to have in your toolbelt. They’re a bit
less time-efficient than traditional sets, but
they may promote larger increases in velocity
and power per unit of training volume.
Myo-reps are a popular approach to training that is similar to cluster sets, but with a
focus on promoting hypertrophy. However,
they’ve never been rigorously investigated.
I’d love to see a longitudinal training study
comparing the effects of traditional sets and
myo-reps on hypertrophy outcomes.
Next Steps
122
References
1. Arazi H, Khoshnoud A, Asadi A, Tufano JJ. The effect of resistance training set
configuration on strength and muscular performance adaptations in male powerlifters.
Sci Rep. 2021 Apr 12;11(1):7844. doi: 10.1038/s41598-021-87372-y. PMID: 33846516;
PMCID: PMC8041766.
2. Arazi H, Mohammadi M, Asadi A, Nunes JP, Haff GG. Comparison of traditional and
accommodating resistance training with chains on muscular adaptations in young men.
J Sports Med Phys Fitness. 2021 Apr 19. doi: 10.23736/S0022-4707.21.12049-3. Epub
ahead of print. PMID: 33871234.
3. Due to the inclusion of a control group that barely gained any strength, I suspect there
would have been a group x time interaction, and post-hoc testing would reveal that the
cluster set and traditional set groups both gained significantly more squat and deadlift
strength than the control group.
4. Davies TB, Tran DL, Hogan CM, Haff GG, Latella C. Chronic Effects of Altering
Resistance Training Set Configurations Using Cluster Sets: A Systematic Review and
Meta-Analysis. Sports Med. 2021 Apr;51(4):707-736. doi: 10.1007/s40279-020-01408-3.
Epub 2021 Jan 21. PMID: 33475986.
5. Jukic I, Van Hooren B, Ramos AG, Helms ER, McGuigan MR, Tufano JJ. The Effects of
Set Structure Manipulation on Chronic Adaptations to Resistance Training: A Systematic
Review and Meta-Analysis. Sports Med. 2021 May;51(5):1061-1086. doi: 10.1007/
s40279-020-01423-4. Epub 2021 Jan 8. PMID: 33417154.
█
123
VIDEO: Novice Training
Prescription Part II
BY MICHAEL C. ZOURDOS
In Volume 5 Issue 5, the video Novice Training Prescription Part 1 provided a
detailed example of how coaches can appropriately progress a novice lifter’s
training. This video picks up where that previous video left off and details how
to progress training following the initial novice phase.
Click to watch Michael's presentation.
124
Relevant MASS Videos and Articles
1. How Should Load be Prescribed for Novice Lifters. Volume 4 Issue 11.
2. Novice Training Prescription Part 1. Volume 5 Issue 6.
█
125
VIDEO: Supplement Series
Part II: Creatine & Caffeine
BY ERIC HELMS
In the “Supplement Series” videos, Dr. Helms discusses effective, commonly
used supplements in strength and physique sport. Specifically covering what the
supplement is, how it works, data on its effectiveness, and considerations you
should be aware of. In the second installment, we cover creatine and caffeine.
Click to watch Eric's presentation.
126
Relevant MASS Videos and Articles
1. How Much Does Caffeine Boost Your Strength?. Volume 1 Issue 3.
2. Should I Stop Drinking Caffeinated Drinks So My Pre-Workout Works Better?. Volume 1 Issue
6.
3. Your Caffeine Stash is Empty. Is it All Over for the Day?. Volume 1 Issue 9.
4. Is Caffeine an Effective Appetite Suppressant?. Volume 2 Issue 10.
5. What’s an Appropriate Daily Dose of Creatine?. Volume 2 Issue 12.
6. Is Caffeine Still Ergogenic After Repeated Use?. Volume 3 Issue 4.
7. Caffeine Supplementation Has Similar Effects in Both Men and Women. Volume 3 Issue 7.
8. Does Caffeine Reduce Soreness in Males More than Females?. Volume 3 Issue 8.
9. Creatine is Great, But it’s Not Making You Lean. Volume 3 Issue 11.
10. Is Theacrine the New Caffeine?. Volume 4 Issue 1.
11. How Low Can You Go With Your Caffeine Dose?. Volume 4 Issue 7.
12. Variability in Caffeine Responses: Is It All in Your Genes?. Volume 5 Issue 2.
13. Do Short-Term Caffeine Benefits Translate to Better Training Adaptations Over Time?. Volume
5 Issue 3.
14. Creatine Monohydrate, Tried and True. Volume 5 Issue 4.
15. Creatine and Caffeine: Do You Have to Pick One?. Volume 5 Issue 5.
References
16. Candow DG, Forbes SC, Chilibeck PD, Cornish SM, Antonio J, Kreider RB. Variables
Influencing the Effectiveness of Creatine Supplementation as a Therapeutic Intervention for
Sarcopenia. Front Nutr. 2019 Aug 9;6:124.
17. Hultman E, Söderlund K, Timmons JA, Cederblad G, Greenhaff PL. Muscle creatine loading in
men. J Appl Physiol (1985). 1996 Jul;81(1):232-7.
18. Purchas RW, Rutherfurd SM, Pearce PD, Vather R, Wilkinson BH. Concentrations in beef and
lamb of taurine, carnosine, coenzyme Q(10), and creatine. Meat Sci. 2004 Mar;66(3):629-37.
19. Green AL, Hultman E, Macdonald IA, Sewell DA, Greenhaff PL. Carbohydrate ingestion
augments skeletal muscle creatine accumulation during creatine supplementation in humans.
Am J Physiol. 1996 Nov;271(5 Pt 1):E821-6.
20. Kreider RB, Kalman DS, Antonio J, Ziegenfuss TN, Wildman R, Collins R, et al. International
127
Society of Sports Nutrition position stand: safety and efficacy of creatine supplementation in
exercise, sport, and medicine. J Int Soc Sports Nutr. 2017 Jun 13;14:18.
21. Lanhers C, Pereira B, Naughton G, Trousselard M, Lesage FX, Dutheil F. Creatine
Supplementation and Upper Limb Strength Performance: A Systematic Review and MetaAnalysis. Sports Med. 2017 Jan;47(1):163-173.
22. Lanhers C, Pereira B, Naughton G, Trousselard M, Lesage FX, Dutheil F. Creatine
Supplementation and Lower Limb Strength Performance: A Systematic Review and MetaAnalyses. Sports Med. 2015 Sep;45(9):1285-1294.
23. Branch JD. Effect of creatine supplementation on body composition and performance: a metaanalysis. Int J Sport Nutr Exerc Metab. 2003 Jun;13(2):198-226.
24. Guest NS, VanDusseldorp TA, Nelson MT, Grgic J, Schoenfeld BJ, Jenkins NDM, et al.
International society of sports nutrition position stand: caffeine and exercise performance. J Int
Soc Sports Nutr. 2021 Jan 2;18(1):1.
25. Porkka-Heiskanen T, Kalinchuk AV. Adenosine, energy metabolism and sleep homeostasis.
Sleep Med Rev. 2011 Apr;15(2):123-35.
26. Domaszewski P, Pakosz P, Konieczny M, Bączkowicz D, Sadowska-Krępa E. Caffeine-Induced
Effects on Human Skeletal Muscle Contraction Time and Maximal Displacement Measured by
Tensiomyography. Nutrients. 2021 Mar 2;13(3):815.
27. Polito MD, Souza DB, Casonatto J, Farinatti P. Acute effect of caffeine consumption on isotonic
muscular strength and endurance: a systematic review and meta-analysis. Science & Sports.
2016 Jun 1;31(3):119-28.
28. Grgic J, Trexler ET, Lazinica B, Pedisic Z. Effects of caffeine intake on muscle strength and
power: a systematic review and meta-analysis. J Int Soc Sports Nutr. 2018 Mar 5;15:11.
29. Grgic J, Pickering C. The effects of caffeine ingestion on isokinetic muscular strength: A metaanalysis. J Sci Med Sport. 2019 Mar;22(3):353-360.
█
128
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. Brazil et al. A comprehensive biomechanical analysis of the barbell hip thrust
2. Kuikman et al. A Review of Nonpharmacological Strategies in the Treatment of Relative
Energy Deficiency in Sport
3. Patus. Accentuated Eccentric Loading is Superior to Traditional Loading for Improving
Acute Countermovement Jump Performance in Adult, Resistance-Trained Males
4. Fabresse et al. Analysis of pharmaceutical products and dietary supplements seized from
the black market among bodybuilders
5. Vilton et al. Capsaicinoid and Capsinoids as an Ergogenic Aid: A Systematic Review and
the Potential Mechanisms Involved
6. Sim et al. Dietary Nitrate Intake Is Positively Associated with Muscle Function in Men
and Women Independent of Physical Activity Levels
7. Kim and Yoon. Does Obesity Affect the Severity of Exercise-Induced Muscle Injury?
8. Medeiros et al. Effect of Nordic Hamstring Exercise Training on Knee Flexors Eccentric
Strength and Fascicle Length: A Systematic Review and Meta-Analysis
9. Vidić et al. Effects of calorie restricted low carbohydrate high fat ketogenic vs. nonketogenic diet on strength, body-composition, hormonal and lipid profile in trained
middle-aged men
10. Ramos-Campo et al. Effects of resistance training intensity on sleep quality and strength
recovery in trained men: a randomized cross-over study
11. Yanase et al. Epimuscular myofascial force transmission from biarticular rectus femoris
elongation increases shear modulus of monoarticular quadriceps muscles
12. Nunes et al. Equating Resistance-Training Volume Between Programs Focused on
Muscle Hypertrophy
13. Paquin et al. Exercising for Insulin Sensitivity – Is There a Mechanistic Relationship
With Quantitative Changes in Skeletal Muscle Mass?
14. Flockhart et al. Excessive exercise training causes mitochondrial functional impairment
129
and decreases glucose tolerance in healthy volunteers
15. Watanabe et al. Fish Protein Ingestion Induces Neural, but Not Muscular Adaptations,
Following Resistance Training in Young Adults
16. Rudkowska. Genomics and Personalized Nutrition
17. Santos et al. Intermuscular Coordination in the Power Clean Exercise: Comparison
between Olympic Weightlifters and Untrained Individuals-A Preliminary Study
18. Yamaguchi et al. Kinetics of Muscle Carnosine Decay after β-Alanine Supplementation: A
16-wk Washout Study
19. Rodrigo et al. Lower limb muscle and joint forces during front and back squats performed
on a Smith machine
20. Sánchez-Moreno et al. Monitoring Training Volume Through Maximal Number of
Repetitions or Velocity-Based Approach
21. Sung et al. Muscle activities of lower extremity and erector spinae muscles according to
ankle joint position during squat exercise
22. Lavin et al. Muscle transcriptional networks linked to resistance exercise training
hypertrophic response heterogeneity
23. Snijders et al. Myonuclear content and domain size in small versus larger muscle fibres in
response to 12 weeks of resistance exercise training in older adults
24. Behm et al. Non-local Muscle Fatigue Effects on Muscle Strength, Power, and Endurance
in Healthy Individuals: A Systematic Review with Meta-analysis
25. Sandau et al. Predictive Validity of the Snatch Pull Force-Velocity Profile to Determine the
Snatch One Repetition-Maximum in Male and Female Elite Weightlifters
26. Murugappan et al. Rapid Weight Gain Following Weight Cutting in Male and Female
Professional Mixed Martial Artists
27. Wohlgemuth et al. Sex differences and considerations for female specific nutritional
strategies: a narrative review
28. Callahan et al. Skeletal Muscle Adaptive Responses to Different Types of Short-Term
Exercise Training and Detraining in Middle-Age Men
29. Martins-Costa et al. The effect of different resistance training protocols equalized by time
under tension on the force-position relationship after 10 weeks of training period
30. Thomas et al. The effect of resistance training programs on lean body mass in
postmenopausal and elderly women: a meta-analysis of observational studies
31. Filip-Stachnik et al. The effects of different doses of caffeine on maximal strength and
130
strength-endurance in women habituated to caffeine
32. Grider et al. The Influence of Mindful Eating and/or Intuitive Eating Approaches on
Dietary Intake: A Systematic Review
33. Guedes et al. ß-hydroxy-ß-methylbutyrate supplementation benefits the effects of
resistance training on body fat reduction via increased irisin expression in white adipose
tissue
131
Thanks for
reading MASS.
The next issue will be released to
subscribers on July 1, 2021.
Graphics and layout by Kat Whitfield
132