MASS
ER I C H E L M S
G REG N U C K O L S
M IC HAEL Z O U R D O S
ERIC T R E X L E R
M O NTHLY A PP LICATIONS IN S TRENGTH S PO R T
Can You Stay Shredded?
Are there inevitable physiological consequences that make it difficult to stay really lean,
or are people just doing it wrong? p.7
VOLU ME 6 , I SSU E 7
J U LY 2022
The Reviewers
Eric Helms
Eric Helms is a coach, athlete, author, and educator. He is a coach for drug-free strength and physique
competitors at all levels as a part of team 3D Muscle Journey where he is also the Chief Science
Officer. Eric regularly publishes peer-reviewed articles in exercise science and nutrition journals on
physique and strength sport, in addition to contributing to the 3DMJ blog. He’s taught undergraduateand graduate-level nutrition and exercise science and speaks internationally at academic and
commercial conferences. He has a B.S. in fitness and wellness, an M.S. in exercise science, a second
Master’s in sports nutrition, a Ph.D. in strength and conditioning, and is a research fellow for the Sports
Performance Research Institute New Zealand at Auckland University of Technology. Eric earned pro
status as a natural bodybuilder with the PNBA in 2011 and competes in numerous strength sports.
Greg Nuckols
Greg Nuckols has over a decade of experience under the bar and a B.S. in exercise and sports
science. Greg earned his M.A. in exercise and sport science from the University of North Carolina
at Chapel Hill. He’s held three all-time world records in powerlifting in the 220lb and 242lb classes.
He’s trained hundreds of athletes and regular folks, both online and in-person. He’s written for many
of the major magazines and websites in the fitness industry, including Men’s Health, Men’s Fitness,
Muscle & Fitness, Bodybuilding.com, T-Nation, and Schwarzenegger.com. Furthermore, he’s had the
opportunity to work with and learn from numerous record holders, champion athletes, and collegiate
and professional strength and conditioning coaches through his previous job as Chief Content
Director for Juggernaut Training Systems and current full-time work on StrongerByScience.com.
Michael C. Zourdos
Michael (Mike) C. Zourdos, Ph.D., CSCS, has specializations in strength and conditioning and skeletal
muscle physiology. He earned his Ph.D. in exercise physiology from The Florida State University (FSU)
in 2012 under the guidance of Dr. Jeong-Su Kim. Prior to attending FSU, Mike received his B.S. in
exercise science from Marietta College and M.S. in applied health physiology from Salisbury University.
Mike served as the head powerlifting coach of FSU’s 2011 and 2012 state championship teams. He
also competes as a powerlifter in the USAPL, and among his best competition lifts is a 230kg (507lbs)
raw squat at a body weight of 76kg. Mike owns the company Training Revolution, LLC., where he has
coached more than 100 lifters, including a USAPL open division national champion.
Eric Trexler
Eric Trexler is a pro natural bodybuilder and a sports nutrition researcher. Eric has a PhD in Human
Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research
papers on various exercise and nutrition strategies for getting bigger, stronger, and leaner. In
addition, Eric has several years of University-level teaching experience, and has been involved in
coaching since 2009. Eric is the Director of Education at Stronger By Science.
Table of Contents
7
BY ER I C HEL MS
Can You Stay Shredded?
In the fitness industry many claim that with their system, supplement, or coaching,
you can have your lean, dream body 24/7. While some get really lean, few, including
bodybuilders, maintain a shredded physique year round. Why is this? Are there
inevitable physiological consequences that make it very difficult to stay really lean, or
are people just doing it wrong?
23
BY MI CHAEL C. ZOUR DOS
What's Worth Including in Your Warm-Up?
Our previous forays into foam rolling determined that it acutely increases range of
motion, but not performance. But does foam rolling enhance performance when
combined with dynamic stretching? This article breaks down the new findings.
36
BY ER I C T R EXL ER
Is Caffeine Tanking Your Testosterone?
Many of us grab a cup of coffee before we start our day, or ingest a caffeinated
supplement before a workout. A new observational study sought to investigate
whether a man’s caffeine habit might be driving his testosterone levels downward.
52
BY MI CHAEL C. ZOUR DOS
Accentuated Eccentrics are Overhyped
Since you are stronger on the eccentric phase than the concentric phase,
accentuated eccentric loading makes sense. However, the longitudinal data
supporting this practice for enhancing strength gains is underwhelming. Does a new
study turn the tides?
65
BY ER I C T R EXL ER
Rye Versus Wheat: Evidence-Based Sandwich Guidelines
A recent MASS article discussed the utility of fiber restriction for short-term weight
cuts, but also cautioned against long-term adherence to low-fiber diets. A new
study points to some potential mechanisms by which fiber might favorably impact
body composition and health.
81
BY GR EG NUCKOL S & ER IC TREX LER
Research Briefs
In the Research Briefs section, Greg Nuckols and Eric Trexler share quick
summaries of recent studies. Briefs are short and sweet, skimmable, and focused
on the need-to-know information from each study.
125
BY MI CHAEL C. ZOUR DOS
VIDEO: 1RM Prediction Part 2
Part 1 of this series suggested that reps performed equations have questionable
efficacy for predicting 1RM. This installment breaks down the existing literature on
submaximal velocity to predict 1RM. Does it fare better? Watch the video to find out.
127
BY ER I C HEL MS
VIDEO: Periodization for Hypertrophy Part 2
Back in Volume 1 Dr. Helms noted in his intro to periodization videos that
periodization for hypertrophy was a relatively unexplored topic. Five years later,
we now have a number of meta-analyses on this topic as well as a broader
understanding of how varying specific variables might impact hypertrophy. In part
2 of this video series, Dr. Helms covers the rationale specifically for periodizing
exercises for maximizing hypertrophy.
Letter From the Reviewers
V
olume 6, Issue 7 of MASS has arrived, and it’s one of our best yet. This month’s
issue features one concept review, 12 study reviews, and 2 practical video
lectures to help you take your training, nutrition, and coaching to the next level.
In this month’s cover story, Dr. Helms explores a frequently asked question: can you
stay shredded? Plenty of people aspire to get shredded at some point in their fitness
journey, and many can achieve it. However, very, very few can maintain a very lean
physique in perpetuity, or even for a period of several months. In this article, Dr.
Helms dives into the scientific literature to explain the challenges we face en route
to becoming shredded, the symptoms we might experience while attaining (and
maintaining) a shredded physique, the physiological factors that directly promote those
symptoms, the factors that make it easier or harder for certain individuals to stay lean,
and some practical tips for maximizing your ability to get (and stay) shredded.
On the training side, Dr. Zourdos has two fantastic articles this month. In the first,
he covers a new paper investigating whether performance is improved by warmup routines involving a combination of foam rolling and dynamic stretching. In the
second, he covers the popular concept of accentuated eccentrics. Some folks swear by
training strategies that involve increased emphasis on heavy, controlled eccentrics, but
this new study puts the idea to the test.
On the nutrition side, Dr. Trexler reviews a new observational study suggesting that
caffeine reduces testosterone levels. If that last sentence caused you to panic, take
a few deep breaths and check the article out before you throw away your stash of
coffee and pre-workout supplements. Dr. Trexler also covers an exploratory paper
investigating how switching from refined wheat products to high-fiber rye products
influences body composition, the gut microbiota, and the production of short-chain
fatty acids. If you’re unfamiliar with short-chain fatty acids, you might be surprised to
learn about their wide-ranging impacts throughout the body.
Greg and Dr. Trexler teamed up on this month’s Research Briefs, collectively bringing
you eight different study reviews on a diverse selection of training and nutrition topics.
This month’s briefs cover squatting with bands, stretch-induced muscle hypertrophy,
oral contraceptives, troubleshooting the weakest link of your split jerk, estimating the
energy cost of various exercise modalities and intensities, habituation to caffeine’s
ergogenic effects, krill oil’s effects on strength and hypertrophy, and how much
cortisol levels matter for your gains.
As for this month’s video content, Dr. Helms delivers part 2 of his video series on
periodizing training for hypertrophy, with his newest installation focusing on exercise
5
selection. Dr. Zourdos is also contributing part 2 of an ongoing video series. His
current series is all about practical strategies for estimating one-repetition maximum
strength, and part 2 addresses the use of submaximal velocities.
As always, be sure to check out the audio roundtables and join us in the Facebook
group. Lastly, if you need some CEUs to maintain your current certifications, be sure to
take advantage of our continuing education opportunities for NSCA, ACSM, NASM,
and ACE.
We hope you have a great month, and we thank you for being a part of MASS.
Sincerely,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
6
COVER STORY
Can You Stay
Shredded?
BY ERIC HELMS
In the fitness industry many claim that with their system,
supplement, or coaching, you can have your lean, dream body
24/7. While some get really lean, few, including bodybuilders,
maintain a shredded physique year round. Why is this? Are there
inevitable physiological consequences that make it very difficult
to stay really lean, or are people just doing it wrong?
7
COVER STORY
BA C KGRO UN D
Getting really lean is a common goal, which a
fair number of people regularly achieve, and
it’s easy to find trainers and books to help
you do so. However, getting lean and staying
lean is often viewed as a holy grail, at least
in bodybuilding-centric circles. People pursue this goal for many reasons, and it’s something many can relate to. As a bodybuilder
and fan of bodybuilding, I’m awestruck by
physiques lean enough to display all the anatomical muscular details of the human body.
Thus, when I go through the grueling process of contest prep to get shredded, there’s
always a part of me that wonders if maybe
I could stay shredded, or at least stay something close to shredded. If you look around
the fitness industry, and see what people buy,
click on, and try, it’s apparent I’m not alone.
The question is, why is it so hard for people
to stay shredded once they get there? Speaking generally, regardless of the end-point
body composition achieved, the difficulty of
maintaining clinically meaningful, long term
weight loss is well established (1). For those
interested in learning how difficult it is (and
why), I highly recommend reading Dr. Ben
House’s excellent, in-depth guest article on
this topic. However, while Dr. House’s review covers a related question, the present
article isn’t about how hard it is to maintain
weight loss. Rather, it specifically addresses
the question of whether it’s sustainable to
maintain a very low body fat.
To discuss sustainability, however, I must acknowledge the subjectivity of the word. Everyone can technically sustain an extremely
low level of body fat. Hypothetically, if you
were locked in a room and only fed sufficient
energy to lose weight until you got to essential levels of body fat, and then subsequently
only fed enough to maintain those levels of
body fat, you’d sustain a shredded physique.
Whether or not you’d have full physiological
functionality doing so, and whether you’d enjoy the experience enough for it to be worth it,
however, are the more relevant questions. Indeed, if you’ve ever spoken to bodybuilders,
they almost universally express sentiments of
how difficult it is to get shredded for competition. At 3DMJ we’ve collectively prepped
thousands of drug free physique competitors
in the last decade, and we’ve been intimately
involved in bodybuilding culture. When discussions of contest prep come up, we hear the
same anecdotal reports time and time again
of how it gets harder and harder as the weeks
pass. Physique athletes report getting hungrier, more food focused, lethargic, tired, and
irritable, and veterans notice they seem to get
ill and injured more frequently as they get
leaner. Indeed, many of these anecdotal experiences are mirrored in studies of physique
8
COVER STORY
competitors during contest preparation and
recovery. A collection of these findings are
shown in Table 1, adapted from a review I led
on the challenge of making physique sport a
sustainable practice (2). However, it’s difficult to parse out whether these experiences
and observations are caused by the state of
being really lean, the process of getting really
lean, or a combination of the two.
Energy availability and
RED-S
To better understand the causes of the negative
symptoms associated with getting really lean,
we must discuss “relative energy deficiency
in sport” (RED-S). RED-S describes the “impaired physiological functioning caused by
9
relative energy deficiency, and includes but
is not limited to impairments of metabolic
rate, menstrual function, bone health, immunity, protein synthesis, and cardiovascular
health” (3). Importantly, research directly
links RED-S to being in a chronic state of low
energy availability, defined as the amount of
calories consumed relative to lean body mass
(LBM) when taking exercise activity into account. Mathematically, this is expressed as:
(total energy intake - exercise expenditure) /
LBM. If this value gets too low, athletes experience increased prevalence of RED-S symptoms. As reviewed by Anne Loucks (4, 5), a
seminal researcher in this field, signs of metabolic and reproductive hormonal downregulation associated with RED-S are observed
in diverse populations from lean, male Army
Rangers during training, to exercising and
sedentary normal weight women, to women
with obesity undergoing rapid weight loss,
when energy availability falls below ~30kcal/
kg of LBM/day through any combination of
increased exercise energy expenditure and/or
decreased energy intake.
While 30kcal/kg/LBM/day is a decent rule
of thumb to keep in mind, it should not be
seen as a universal threshold that applies to
all (6). Furthermore, most physique athletes
in my experience simply won’t get into adequate contest shape without going lower than
30kcal/kg of LBM/day at a certain point,
and even if you can stay above it, there is
substantial individual variation as to when
symptoms of RED-S crop up (in many cases, the threshold among athletes is higher, in
the 30-45kcal/kg of LBM/day range). Differences in baseline non-exercise activity, one’s
composition of LBM, and other individual
physiological differences cause the appropriate energy availability for a given person to
10
vary (6). Regardless of where an individual’s
threshold for low energy availability lies, you
can view going below it as there not being
enough “left over” energy for physiological
function. When this continues chronically,
adaptive downregulation across various aspects of physiology occur, which can impact
performance and health (Figure 1).
RED-S is relatively common among athletes
with a high energy output, such as endurance
athletes, or among athletes who are likely to
restrict energy intake (3), such as physique
athletes, weight class athletes, or athletes
who benefit from a high power-to-weight ratio. When reflecting on the effects of RED-S
and how energy availability is calculated, you
might notice two things: 1) RED-S symptoms
line up with the experiences of bodybuilders
during contest prep, and 2) body fatness is
not part of the energy availability equation.
So, does this mean if a bodybuilder was to
diet down to stage condition, then simply increase their calories or decrease their training energy expenditure to get out of a deficit,
they’d be able to avoid all the RED-S symptoms and stay lean consequence free? Well,
despite the current understanding that the
singular cause of RED-S is low energy availability, independent of leanness, it is a little
more complicated than that.
Adaptive thermogenesis
MASS readers are likely more familiar with
the concept of metabolic adaptation, known
more commonly in the literature as “adaptive
thermogenesis,” than they are with RED-S
and energy availability. Briefly, adaptive
thermogenesis refers to a reduction in total
energy expenditure following weight loss
(or the increase following weight gain) beyond what would be predicted by changes
in body composition (7, 8). For a deep dive,
my colleague Dr. Trexler has a fantastic article that outlines its mechanisms and how
to address it while dieting, and during maintenance post-diet. While the study of adaptive thermogenesis is distinct from the study
of low energy availability, the two fields are
interrelated and describe the same phenomena from different perspectives. The fitness
industry focuses on adaptive thermogenesis
because this research has been around longer
and it attempts to understand how reductions
in energy expenditure manifest, and how
they impact efforts to lose weight and maintain weight loss. This lines up with the interests of the fitness industry, while low energy
availability research doesn’t line up quite as
well, as it addresses how to adequately fuel
athletes for health and performance.
Since adaptive thermogenesis is studied in
relation to weight loss, the focus is on energy
balance, rather than energy availability. People often have a difficult time conceptually
integrating the two concepts, especially if
they are new to the latter. The way to understand the link between the two is to consider
the effects of adaptive thermogenesis beyond
the simple quantitative reduction in energy
expenditure. The causes of reduced energy
expenditure are due to reduced sympathetic
and increased parasympathetic nervous system tone and downregulation of the hypothalamic pituitary-thyroid and -gonadal axes,
resulting in decreases in heart rate, thyroid
hormone production, increases in skeletal
11
muscle work efficiency at low intensities, decreases in non-exercise activity expenditure,
and reductions in sex hormone production
(8). But these physiological changes don’t
just reduce energy expenditure in a vacuum. Many of these changes also cause the
symptoms associated with RED-S. Adaptive
thermogenesis describes the degree to which
the downregulation of physiological systems
impacts energy expenditure, while RED-S
describes how the downregulation impacts
health and performance.
Importantly, you can be at energy balance
while being in a state of low energy availability and experiencing symptoms of RED-S.
Unfortunately, adaptive thermogenesis
doesn’t only occur during weight loss, but
can persist during weight maintenance. In a
classic study by Rosenbaum (7), seven trios
of weight and sex matched participants spanning a range of bodyweights were compared.
Each trio consisted of a participant who had
lost at least 10% of their bodyweight and was
maintaining that loss for 5-8 weeks, a participant who had lost at least 10% of their bodyweight and was maintaining it for at least a
year, and a participant at their usual weight.
Total energy expenditure was significantly
lower among the weight-reduced participants
compared to the participants at their usual
weight, regardless of whether the weight loss
had been maintained for 5-8 weeks, or a year
or longer. Further, the reductions in energy
expenditure were similar between the two
weight-reduced groups. This seems to be a
consistent trend when assessing the literature
broadly (8), as 10% weight-reduced study
participants display a ~15% lower total daily
ADAPTIVE THERMOGENESIS
DESCRIBES THE DEGREE TO
WHICH THE DOWNREGULATION
OF PHYSIOLOGICAL
SYSTEMS IMPACTS ENERGY
EXPENDITURE, WHILE
RED-S DESCRIBES HOW THE
DOWNREGULATION IMPACTS
HEALTH AND PERFORMANCE
energy expenditure on average compared to
their non-weight-reduced counterparts.
Considering the above, let’s do a little bit
of math. Using this calculator (9), a 170cm
(~5’6”), 70kg (~154lbs), 25 year old, very
lean woman at 12% body fat, who performs
moderate exercise 4-5 days per week has
an estimated daily energy expenditure of
2491kcals. If she was sedentary, she would
instead have an expenditure of 2041kcals; the
difference between these two values can be
used to represent her average exercise energy
expenditure of 450kcals per day. If this woman was previously 77kg, and had lost 10% of
her bodyweight to reach 70kg, we could reasonably expect a ~15% reduction in energy
expenditure based on the literature. Thus, her
daily energy expenditure of 2491kcals would
instead be ~2117kcals. At 70kg and 12%
body fat, she has 61.6kg of LBM. Thus, if she
was eating at maintenance following weight
loss, we could calculate her energy availabil-
12
ity using the previously mentioned equation
([total energy intake - exercise expenditure]
/ LBM) as follows: (2117kcals - 450kcals) /
61.6kg = 27.1kcal/kg of LBM/day. As you
can see, this intake, despite being her maintenance calories, is below the ~30kcal/kg of
LBM/day average threshold for low energy
availability where we’d anticipate symptoms
of RED-S would occur.
Certainly, not everyone experiences a 15% reduction in total energy expenditure after weight
loss; some experience less, some more. But, on
average, if we accept the current understanding that energy availability is the sole cause
of RED-S with no influence of body composition, it seems unlikely that the majority of
individuals would be able to maintain a very
low body fat after weight loss without experiencing some symptoms of RED-S. However,
this begs the question: if it just comes down to
energy availability, and body fat doesn’t enter
the equation, why does physiological function
remain downregulated in weight-reduced individuals in the first place?
Body fat “set points”
and leptin
To answer the question I just posed, I don’t
think it comes down to energy availability
exclusively. I think body fat plays a role,
and it’s hard to think otherwise when you
understand the physiology at play. If you’ve
observed discussions on dieting in the evidence-based fitness space, you might have
heard the concept of a “body fat set point”.
Generally, the idea is that people have a level of body fat that is “defended” (i.e., adap-
tive thermogenesis occurs) when fat loss
takes you below it, or when fat gain takes
you above it. This concept originated from
scientific research that’s been ongoing for
the better part of 70 years. Indeed, the set
point concept describes the original “lipostatic” model of body weight regulation proposed by Kennedy in 1953 (10). This model
states that, like a thermostat, adipose tissue
sends signals to the brain indicating whether body fat stores are below, at, or above a
person’s body fat set point. In response, the
brain sends signals to downregulate, maintain, or upregulate energy expenditure, and
increase, maintain, or decrease energy intake, respectively, to get back to the body fat
set point. This model was largely theoretical
until the discovery of leptin in the 1990’s
(11), a hormone that seemed to act as the
proposed signal from the lipostatic model.
Leptin is a hormone secreted by adipose tissue in proportion to the amount of adipose
tissue present (12), and, in initial animal
models, leptin would decrease with weight
loss, increase with weight gain, and returned
to baseline when animals compensatorily increased or decreased food intake following
these states to return to a seeming “set point”
(13). However, the pure lipostatic model has
a lot of problems, and is not the current model used to understand body weight regulation.
From an observational perspective, the lipostatic model fails to explain the obesity epidemic, and from a mechanistic perspective,
leptin doesn’t behave exactly like the lipostatic model’s signal is supposed to. Specifically, it seems leptin release from adipose tissue is impacted by metabolic hormones, such
13
as insulin and others, that respond acutely to
feeding and fasting (14). Leptin decreases
precipitously upon the initiation of fasting,
and this response precedes (and is disproportionate to) changes in body fat. Further,
as research on leptin continued, it was discovered that, while leptin is primarily produced by adipose tissue (15), it is produced
(and there are receptors for it) in other tissues as well, notably the stomach. Gastrically
produced leptin is thought to be a signaller
of short-term energy availability, while adipose tissue derived leptin may act as a long
term signal of energy availability (16). Indeed, changes in macronutrients and energy
intake can acutely change leptin (17). Also
out of step with the lipostatic model is that
leptin is much more effective at encouraging
weight gain when levels are low, as opposed
to encouraging weight loss when levels are
high. Indeed, circulating leptin is quite high
in those with common forms of obesity, but
does not suppress excess energy consumption enough to cause weight loss (18).
As reviewed by Speakman and colleagues
(13; notably this is open access and very informative), to account for these observations
and complexities, the “dual-intervention
model” of body weight regulation was eventually proposed, which arguably is the best
fit for the currently available data. It accounts
for environmental factors that can overcome
physiological set points, which lines up with
the obesity epidemic and the nuances of leptin
physiology. As shown in Figure 3, there are
upper and lower points where physiological
factors primarily influence energy intake and
expenditure, modifying adiposity. Between
these points, however, environmental factors
dominate. These upper and lower points are
thought to exist due to evolutionary predation
and famine selection pressures (i.e., being too
heavy and slow made you more likely to be
eaten, being too lean made you more vulnerable to famine), respectively (13). Arguably,
the latter was a greater threat to humans, resulting in a better defended lower intervention point, hence the struggles many have
with weight gain and regain.
This model provides hope for those interested
in maintaining a lower body fat. Based on the
model, if you can modify your environment
to do the opposite of what the modern, obesogenic environment has done to our collective waist lines, you should be able to hang
out closer to your lower, rather than your upper intervention point. In fact, by examining
people living in a non-modern environment,
we can see this is probably the case. One
14
such group, the Amish, live in traditionalist
communities that typically don’t adopt most
conveniences of modern technology. Bassett
and colleagues (19) examined the physical
activity and body composition of a sample of
98 Amish men and women from a community in Ontario that did not use electricity or
gas power, and of whom the majority of men
were farmers (78%) and the majority of women were homemakers (69%). The researchers
gave the Amish participants pedometers to
track their step count, and assessed their body
composition via bioelectrical impedance
measurements. In this agricultural community, they made their own food, and the requisite activity levels for day-to-day work were
very high compared to modern standards. The
men walked an average of 18,425 ± 4,685
steps per day, and the women an average of
14,196 ± 4,078. Interestingly, the men had an
average body fat percentage of 9.4 ± 4.3%,
and the women an average of 25.3 ± 6.7%.
Importantly, these are bioimpedance measurements, so they aren’t as accurate or reliable, even at the group level, as alternative
measurement options like DXA. However,
with a sample of nearly 100 individuals, they
are likely close to the true values. Notably,
the more active men were maintaining, on
average, a single digit body fat percentage.
The women weren’t as lean relatively, even
taking sex differences into account (the rough
female equivalent to a male at ~9-10% body
fat is ~17-18%), and also weren’t as active.
While it’s tempting to isolate this difference
in body fat percentage to the men being more
active, it’s not as though the women weren’t
reasonably active as well. Rather, other cultural or environmental aspects were likely at
play, which led to the women being relatively
higher in body fat (for example, the authors
noted Amish women have an average of
seven children, which can result in a higher
average body fat). So, if we assume the men
didn’t have RED-S - a reasonable assumption
as the community had an ample food supply
(earlier research on Amish men reports a daily energy intake of ~3600kcal/day [20]) and
they weren’t athletes trying to stay lean - this
suggests your environment plays a major role
in how lean you stay. In support of this contention, decreases in sedentary activity (21)
and ultra-processed food consumption (22)
can lead to maintaining lower body fat levels. However, it’s important to point out that
9.4 ± 4.3% body fat is not 5 ± 1% body fat.
These Amish dudes are lean, some more and
some less than others, but on average they aren’t ready to don posing trunks to show off
their striated glutes.
Putting it all together
If we put these models and observational data
together, we can construct a relatively clear,
albeit simplified (23) theoretical explanation
of what determines the level of leanness you
can sustainably maintain. Starting with the
dual intervention model as the backdrop, when
you are between your lower and upper intervention points of adiposity, you’ll likely feel
fine. However, bringing in the RED-S model,
this is only true until you reduce your energy
intake or increase your energy expenditure to
the point where you reach your threshold for
low energy availability. When this happens,
regardless of where your body fat level is between your intervention points, RED-S and
15
adaptive thermogenesis may occur. However,
if you can manipulate your body fat gradually, so that you don’t reduce energy intake to
the point where you reach a state of low energy
availability, you can mitigate adaptive thermogenesis and symptoms of RED-S. That is, until
you pass your lower intervention point, which
is where body fat comes into the picture.
As discussed, leptin transiently fluctuates
in response to meals and acute changes in
energy balance, and each time you eat you
can get a nice bump in leptin. However, the
largest contributor to your circulating leptin
levels is far and away fat mass. To put a specific number to it, Considine and colleagues
reported a strong correlation (r = 0.85, p <
0.001) between serum leptin and body fat
percentage across a combined sample of 136
normal-weight participants and 139 participants with obesity (12). Meaning, in this
large, diverse sample, body fat percentage
explained ~72% of the variance in leptin.
Thus, even if you’re eating at maintenance,
when you’re between meals (which is most
of the day), your leptin will fall to low levels
when below your lower intervention point.
As a consequence, total energy expenditure
will remain suppressed, keeping you in a
state of low energy availability, leading to
symptoms of RED-S. Indeed, we can’t discount the important effect of chronic leptin
levels; the only known intervention besides
regaining lost body fat that alleviates adaptive thermogenesis (and likely RED-S symptoms for some) in weight-reduced individuals
are multiple daily leptin injections (8).
I know what some of you are thinking: “but
Eric I know some people who walk around
THE LARGEST
CONTRIBUTOR TO
YOUR CIRCULATING
LEPTIN LEVELS IS FAR
AND AWAY FAT MASS
shredded who are just fine!” So do I, and this
still lines up with the theoretical understanding I’ve proposed. Importantly, there is a ton
of individual variation at play. Individual
variation exists in where one’s lower intervention point is (some people have a leaner
lower end point), the energy threshold for
when RED-S symptoms crop up (some people do okay at lower values), and whether
and how much a person experiences adaptive
thermogenesis during and after weight loss
(some people don’t experience much at all).
Thus, you’ll see people who maintain a variety of different body fat levels, despite living
in similar environments. For example, not
everyone in our modern obesogenic environment has obesity. Likewise, the Amish men
had a body fat standard deviation of 4.3%,
meaning (if we trust the bioelectrical impedance measurements) some were walking
around at 5% body fat, but just as many were
walking around at 14% (maybe; 24). Also
consider that when there are strong rewards
at play, people might be okay with living
with mild or even moderate RED-S symp-
16
toms. To harken back to a prior MASS article, I reviewed a paper on energy availability
in a group of elite female sprinters who were
maintaining reasonably lean (~20% body
fat) physiques (article; 25). Interestingly, the
sprinters with more indicators of low energy
availability had higher fat mass (13.0 ± 2.3kg
vs. 11.2 ± 1.6kg, p = 0.03) compared to the
leaner sprinters with fewer indicators. While
speculative, I guessed this was due to the
selection pressures of being an elite sprinter, where having less fat mass means you
can run faster. Thus, there were those with a
lower intervention point at a lower body fat
level who were able to stay leaner without
issue, while the rest who weren’t so lucky
had to stay in a perpetual weight-reduced,
low energy availability state to stay lean (but
not quite as lean). Simply put, athletes like
to win, and they are often okay with some
health and comfort trade-offs if being leaner
will improve their performance (I would also
note that influencers like your money and attention, and being leaner helps them get that
too). This is why athletes in sports where a
lower body fat improves performance tend to
be leaner (26), and, while many of these athletes have the genetics to be naturally lean,
not all of them do, which is why athletes in
these sports are also more likely to experience RED-S (2).
Testing the hypothesis
that body fat matters
We can assess the veracity of the theoretical
explanation I’ve presented that it’s not just
energy availability, but also your lower body
fat intervention point that dictates how lean
you can maintain. If body fat played no role,
and it just came down to energy availability, you’d expect dieting to impact people in
similar ways, regardless of their body fat level when starting the diet, but it doesn’t. For
example, authors of a recently published meta-analysis reported that caloric restriction
resulted in an increase in testosterone in the
majority of studies on men with overweight
or obesity, while the majority of studies on
normal weight men reported a decrease (27).
Likewise, muscle protein synthesis is blunted
during an energy deficit in overweight dieters
(28), but, in lean dieters, not only is protein
synthesis blunted, but protein breakdown increases as well (29). Furthermore, lean individuals utilize two to three fold more energy
from protein when fasting compared to individuals with obesity (30) and are more likely
to lose lean mass while dieting (31).
However, the most direct evidence we have to
test my hypothesis that body fat matters, are
observations of what happens when people
get very lean, and then try to stay very lean.
In a case series on physique athletes by Longstrom and colleagues, some of the competitors did just that, following conservative “reverse diets” to minimize fat gain post contest
by slowly increasing calories and decreasing
cardio (32). Longstrom measured body composition and metabolic hormones 1-2 weeks
prior to competition, as well as 8-10 weeks
post-contest once the competitors had carried
out their post competition strategies. Generally, Longstrom reported that those who
increased fat and body mass the most, experienced larger increases in leptin and resting
metabolic rate, while smaller increases or no
17
changes occurred in those who gained very
little fat and body mass.
If you examine Figures 3 and 4 from this
study, you can see that two of the male competitors (M1 and M2) only increased their
body fat by ~2%, staying below 10% body
fat even 8-10 weeks post competition. Likewise, one female competitor (F4) increased
her body fat by just 2.7%, only getting up to
~15% body fat 8-10 weeks post show, which
was the body fat that the other three females
achieved at the end of their diets. Notably,
these two male competitors experienced no
appreciable change in leptin, and F2 had the
lowest leptin value of the female competitors.
Likewise, resting metabolic rate only slightly increased (M3), stayed the same (M2), or
slightly decreased (F2) among these competitors. Finally, at the group level, the observations were also consistent with the hypothesis
that fat mass does indeed play a role in hormonal and metabolic recovery. The change
in fat mass was strongly associated (33) with
the change in resting metabolic rate (τ = 0.90;
p = 0.001) and the change in body fat percentage was strongly associated with changes
in leptin (τ = 0.88; p = 0.003).
Takeaways
It’s difficult to piece together complex, distinct lines of research on how humans adapt
to changes in short and long term energy
availability. Different models tell a piece of
the story, but not all of it. A pure focus on
low energy availability can lead one to think
that body fat plays no role in the symptoms
we associate with RED-S, but effectively ignores ~70 years of research on body composition regulation. Similarly, a pure focus on
adaptive thermogenesis can neglect the effect of these adaptations on health and performance, focusing only on how it changes
energy expenditure. In totality, it’s likely
that energy availability is the dominant variable impacting your physiology when you’re
between your upper and lower body fat intervention points. However, when you go
below your lower intervention point, you’ll
18
be persistently fought by your body and you
probably won’t be able to get your calories
high enough (without fat gain) to alleviate
the negative effects you experience. With
that said, some people can stay really lean,
as they happen to have a leaner lower intervention point. For those of us that are not
so lucky, that doesn’t mean all hope is lost.
Rather, it just means that we have to respect
wherever our lower intervention points might
be. Further, you can do all the things we’ve
talked about in MASS time and time again
(like eating sufficient protein and lots of low
energy density, high fiber fruits and vegetables, increasing activity and reducing sedentary time, reducing ultra-processed and highly palatable food intake, and of course, lifting
lots of weights) to modify your environment
so you can stay close to it.
19
References
1. Hall, K. D., & Kahan, S. (2018). Maintenance of Lost Weight and Long-Term
Management of Obesity. The Medical clinics of North America, 102(1), 183–197.
2. Helms, E. R., Prnjak, K., & Linardon, J. (2019). Towards a Sustainable Nutrition
Paradigm in Physique Sport: A Narrative Review. Sports (Basel, Switzerland), 7(7), 172.
3. Mountjoy, M., Sundgot-Borgen, J., Burke, L., Ackerman, K. E., Blauwet, C., Constantini,
et al. (2018). International Olympic Committee (IOC) Consensus Statement on Relative
Energy Deficiency in Sport (RED-S): 2018 Update. International Journal of Sport
Nutrition and Exercise Metabolism, 28(4), 316–331.
4. Loucks A. B. (2004). Energy balance and body composition in sports and exercise.
Journal of Sports Sciences, 22(1), 1–14.
5. Loucks A. B. (2003). Energy availability, not body fatness, regulates reproductive
function in women. Exercise and sport sciences reviews, 31(3), 144–148.
6. Burke, L. M., Lundy, B., Fahrenholtz, I. L., & Melin, A. K. (2018). Pitfalls of
Conducting and Interpreting Estimates of Energy Availability in Free-Living Athletes.
International Journal of Sport Nutrition and Exercise Metabolism, 28(4), 350–363.
7. Rosenbaum, M., Hirsch, J., Gallagher, D. A., & Leibel, R. L. (2008). Long-term
persistence of adaptive thermogenesis in subjects who have maintained a reduced body
weight. The American Journal of Clinical Nutrition, 88(4), 906–912.
8. Rosenbaum, M., & Leibel, R. L. (2010). Adaptive thermogenesis in humans.
International Journal of Obesity (2005), 34 Suppl 1(0 1), S47–S55.
9. Click the settings icon, then use the Katch-McArdle equation which takes body fat
percentage into account to replicate.
10. Kennedy G. C. (1953). The role of depot fat in the hypothalamic control of food intake
in the rat. Proceedings of the Royal Society of London. Series B, Biological Sciences,
140(901), 578–596.
11. Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L., & Friedman, J. M.
(1994). Positional cloning of the mouse obese gene and its human homologue. Nature,
372(6505), 425–432.
12. Considine, R. V., Sinha, M. K., Heiman, M. L., Kriauciunas, A., Stephens, T. W., Nyce,
et al. (1996). Serum immunoreactive-leptin concentrations in normal-weight and obese
humans. The New England Journal of Medicine, 334(5), 292–295.
20
13. Speakman, J. R., Levitsky, D. A., Allison, D. B., Bray, M. S., de Castro, J. M., Clegg,
D. J., et al. (2011). Set points, settling points and some alternative models: theoretical
options to understand how genes and environments combine to regulate body adiposity.
Disease Models & Mechanisms, 4(6), 733–745.
14. Ahima, R. S., & Flier, J. S. (2000). Leptin. Annual Review of Physiology, 62, 413–437.
15. Kasacka, I., Piotrowska, Ż., Niezgoda, M., & Łebkowski, W. (2019). Differences in
leptin biosynthesis in the stomach and in serum leptin level between men and women.
Journal of Gastroenterology and Hepatology, 34(11), 1922–1928.
16. Picó, C., Oliver, P., Sánchez, J., & Palou, A. (2003). Gastric leptin: a putative role in the
short-term regulation of food intake. The British Journal of Nutrition, 90(4), 735–741.
17. Izadi, V., Saraf-Bank, S., & Azadbakht, L. (2014). Dietary intakes and leptin
concentrations. ARYA Atherosclerosis, 10(5), 266–272.
18. Myers, M. G., Cowley, M. A., & Münzberg, H. (2008). Mechanisms of leptin action and
leptin resistance. Annual Review of Physiology, 70, 537–556.
19. Bassett, D. R., Schneider, P. L., & Huntington, G. E. (2004). Physical activity in an Old
Order Amish community. Medicine and Science in Sports and Exercise, 36(1), 79–85.
20. Weale, V.W., (1980). Eating patterns and food energy and nutrient intake of old order
amish in Holmes county, Ohio (Doctoral dissertation, The Ohio State University).
21. Júdice, P. B., Hetherington-Rauth, M., Magalhães, J. P., Correia, I. R., & Sardinha, L. B.
(2022). Sedentary behaviours and their relationship with body composition of athletes.
European Journal of Sport Science, 22(3), 474–480.
22. Hall, K. D., Ayuketah, A., Brychta, R., Cai, H., Cassimatis, T., Chen, K. Y., et al. (2019).
Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient
Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metabolism, 30(1), 67–77.
e3.
23. I call this “simplified” because it holds up when conceptualizing what happens with
normal weight individuals attempting to get lean and stay lean; however, it does not for
individuals with obesity and/or metabolic disease. Large amounts of fat gain can change
one’s intervention points, and leptin resistance, which is common in those with obesity,
can impair the physiological responses which attempt to prevent further weight gain.
24. Standard deviations only accurately represent normally distributed data (i.e., shaped like
a bell curve). It’s quite possible, given how the dual intervention model works, that body
fat wasn’t normally distributed. There may have been just a few outlier men who were
close to 5%, and then a lot clustering around 9-12% to produce the mean.
25. Sygo, J., Coates, A. M., Sesbreno, E., Mountjoy, M. L., & Burr, J. F. (2018). Prevalence
21
of Indicators of Low Energy Availability in Elite Female Sprinters. International Journal
of Sport Nutrition and Exercise Metabolism, 28(5), 490–496.
26. Jeukendrup, A. and Gleeson, M., (2018). Sport Nutrition. Human Kinetics.
27. Smith, S. J., Teo, S., Lopresti, A. L., Heritage, B., & Fairchild, T. J. (2022). Examining
the effects of calorie restriction on testosterone concentrations in men: a systematic
review and meta-analysis. Nutrition Reviews, 80(5), 1222–1236.
28. Hector, A. J., McGlory, C., Damas, F., Mazara, N., Baker, S. K., & Phillips, S. M.
(2018). Pronounced energy restriction with elevated protein intake results in no change
in proteolysis and reductions in skeletal muscle protein synthesis that are mitigated by
resistance exercise. FASEB Journal: Official Publication of the Federation of American
Societies for Experimental Biology, 32(1), 265–275.
29. Carbone, J. W., Pasiakos, S. M., Vislocky, L. M., Anderson, J. M., & Rodriguez,
N. R. (2014). Effects of short-term energy deficit on muscle protein breakdown and
intramuscular proteolysis in normal-weight young adults. Applied Physiology, Nutrition,
and Metabolism, 39(8), 960–968.
30. Elia, M., Stubbs, R. J., & Henry, C. J. (1999). Differences in fat, carbohydrate, and
protein metabolism between lean and obese subjects undergoing total starvation. Obesity
Research, 7(6), 597–604.
31. Helms, E. R., Zinn, C., Rowlands, D. S., & Brown, S. R. (2014). A systematic review
of dietary protein during caloric restriction in resistance trained lean athletes: a case for
higher intakes. International Journal of Sport Nutrition and Exercise Metabolism, 24(2),
127–138.
32. Longstrom, J. M., Colenso-Semple, L. M., Waddell, B. J., Mastrofini, G., Trexler, E.
T., & Campbell, B. I. (2020). Physiological, Psychological and Performance-Related
Changes Following Physique Competition: A Case-Series. Journal of Functional
Morphology and Kinesiology, 5(2), 27.
33. For those unfamiliar with the “τ” symbol, it represents Kendall’s tau, which is a
nonparametric correlation coefficient, interpreted similarly to Pearson’s r. A value of zero
reflects no correlation, and values closer to 1 or -1 represent stronger correlations, with
the sign of the tau value (positive or negative) reflecting the direction of the association.
█
22
Study Reviewed: An Intense Warm-Up Does Not Potentiate Performance Before or After a
Single Bout of Foam Rolling. Konrad et al. (2022)
What’s Worth Including in
Your Warm-Up?
BY MICHAEL C. ZOURDOS
Our previous forays into foam rolling determined that it acutely
increases range of motion, but not performance. But does foam
rolling enhance performance when combined with dynamic
stretching? This article breaks down the new findings.
23
KEY POINTS
1. Researchers compared the acute effects of three warm-up conditions: 1)
foam rolling only, 2) dynamic stretching followed by foam rolling, and 3) foam
rolling followed by dynamic stretching. Outcomes assessed were sit-andreach test performance (hamstring range of motion), hamstring strength, and
countermovement jump performance.
2. Findings showed that sit-and-reach performance improved from pre- to postwarm-up in all conditions, but with no difference between conditions. Strength
and jump performance did not significantly change in any condition. Lastly, men
increased their hamstring range of motion 7% more than women in the foam
rolling first condition.
3. This study shows that combining dynamic stretching with foam rolling fails to
acutely improve performance. However, previous literature indicates that dynamic
stretching alone may improve performance. Therefore, it remains good practice to
include dynamic stretching in a warm-up. Foam rolling can be included for range
of motion based on personal preference and individual needs.
T
here’s no doubt that foam rolling
acutely increases range of motion.
However, its benefits on post-exercise recovery are small (2). Further, three
different systematic reviews and meta-analyses (2, 3, 4) have concluded that pre-training
foam rolling does not significantly improve
acute strength performance. To be fair, foam
rolling doesn’t seem to harm performance
when completed as part of the warm-up (2, 3,
4, 5), and it comes with little downside. Research has also suggested that static stretching in conjunction with foam rolling does not
enhance acute performance (6); however,
research is mixed on the combination of dynamic stretching and foam rolling (7) to improve acute strength performance.
The reviewed crossover design study from
Konrad et al (1) assessed the sit-and-reach
test (hamstring range of motion), dynamic
and isometric hamstring strength, and coun-
termovement jump height in men and women
before and after three different warm-up protocols. In one condition, subjects performed
dynamic stretching followed by foam rolling
(foam rolling second). In another condition,
the subjects foam rolled first (foam rolling
first), then completed the dynamic stretching.
Finally, in a third condition, subjects only foam
rolled (i.e., no dynamic stretching). Findings
showed that all conditions increased range of
motion pre- to post-warm-up, with no significant differences between conditions. Dynamic strength, isometric strength, and countermovement jump height failed to significantly
improve from pre- to post-warm-up. When
comparing the sexes, researchers found that
range of motion increased significantly more
in men than in women (p < 0.001) in the foam
rolling first condition. These findings suggest
that pairing dynamic stretching and foam
rolling in a warm-up does not improve ham-
24
string strength performance or range of motion more than foam rolling alone. Therefore,
we can confidently state that foam rolling, on
average, is unlikely to produce a meaningful
benefit for acute strength performance. This
article will aim to:
in men and women. Further, the researchers
compared the magnitude of change for each
outcome measure between the sexes.
1. Review the present findings and discuss
what we can and can’t infer from the study
design.
Subjects and Methods
2. Evaluate the research combining foam rolling and dynamic stretching in warm-ups.
27 “recreational to well-trained” soccer players (13 women and 14 men) completed the
study. While the average age of the subjects
was over 18 years old, some participants were
under 18, as the authors stated, “Participants
or (if under 18) their legal representatives,
signed a written informed consent form.”
Additional subject details are in Table 1.
3. Examine if foam rolling in conjunction
with other warm-up strategies (i.e., static
stretching) can benefit acute strength performance.
4. Provide recommendations for a well-structured lifting warm-up.
Purpose and Hypotheses
Purpose
The presently reviewed study compared
warm-ups consisting of foam rolling only,
foam rolling + dynamic stretching (foam
rolling first), and dynamic stretching + foam
rolling (foam rolling second) for acute changes in hamstring range of motion, hamstring
strength, and countermovement jump height
Hypotheses The researchers did not provide hypotheses.
Subjects
Study Protocol
The reviewed study was a counterbalanced
crossover design with three conditions separated by at least 48 hours. In all three conditions, subjects completed low-intensity
cycling for five minutes and then performed
sit-and-reach (hamstring range of motion),
dynamic and isometric torque, and countermovement jump height tests. Next, subjects
underwent a condition-specific warm-up protocol and repeated the tests. The three condition-specific warm-up protocols were:
25
1. Foam rolling only.
2. Foam rolling followed by dynamic stretching (foam rolling first).
3. Dynamic stretching followed by foam
rolling (foam rolling second).
Foam Rolling Specifics
Foam rollers consisted only of foam and not
hard plastic. Each subject rolled the posterior thigh on each leg for two total minutes.
Rolling was performed at a cadence of 2 seconds from distal to proximal and 2 seconds
from proximal to distal (i.e., 2 seconds up the
thigh and 2 seconds down). Each individual
rolled with their body weight, and subjects
were instructed to put pressure on the roller
to the point of discomfort. The “point of discomfort” was intended as a 7 out of 10 on a
10-point visual analog scale.
Dynamic Stretching Specifics
The dynamic stretching protocol included
three sets of 30 reps of butt kicks on each
leg with 15 seconds between sets. Next, subjects laid face down while researchers placed
a swiss ball on their backs, and then kicked
the ball with their heel (leg curl motion), alternating each foot; however, the researchers
did not specify the sets and reps for this exercise. An illustration of the swiss ball exercise
can be seen in Figure 2.
Findings
The findings were simple. Range of motion
significantly increased from pre- to postwarm-up in all conditions, but with no significant difference between conditions. Dynamic
26
and isometric torque and countermovement
jump did not statistically change in any condition. Between-condition comparisons for
all outcome measures are in Table 2.
Sex-based comparisons revealed that women
had a significantly greater range of motion at
baseline than men in all conditions. Range of
motion increased significantly more in men
(+9.34%) than in women (+2.30%) in the
foam rolling first condition. Men also experienced greater percentage increases in range
of motion in the foam rolling second condition (men: +7.63%; women: +3.96%) and the
foam rolling only condition (men: +6.34%;
women: +3.97%); however, these differences were not statistically significant. As a
disclaimer, I estimated all percentage changes from WebPlotDigitizer; thus, slight discrepancies may exist from the actual values.
Changes in range of motion for both sexes
can be seen in Figure 3.
Interpretation
The presently reviewed study from Konrad
et al (1) showed that a warm-up consisting
of foam rolling alone or foam rolling com-
27
bined with dynamic stretching increased
acute range of motion, but not strength or
jump performance. I’m not surprised that the
foam rolling only condition failed to acutely improve performance. While a handful of
studies show that foam rolling enhances acute
strength performance (8, 9), two meta-analyses (2, 4) and a systematic review (3) have
found that foam rolling does not significantly
improve acute strength. Further, those same
meta-analyses and systematic reviews show
that foam rolling enhances acute range of
motion; thus, the presently reviewed study’s
findings agree with the majority of the literature examining a foam rolling only warm-up.
The findings for the two dynamic stretching
conditions are interesting for two reasons.
First, foam rolling aside, dynamic stretching may improve acute strength performance. Mechanistically, dynamic stretching increases acute muscle blood flow
and muscle fiber conduction velocity (7,
10). However, I say “may improve” because, although various reviews have determined a positive effect of dynamic stretching on strength (10, 11) some individual
studies have shown no performance benefit
with dynamic stretching (12, 13). Dynamic stretching may not improve performance
if the protocol is too demanding such that
it fatigues the athlete. However, dynamic
stretching didn’t seem to be too fatiguing
in this study since performance did not decline from pre- to post-warm-up in either
dynamic stretching condition. It’s also possible that the foam rolling protocol was too
fatiguing. The foam rolling protocol was a
7/10 discomfort, which is on the upper end
of our recommendations (slide 12 here), so
it wasn’t anything crazy.
The second reason that the findings of the
dynamic stretching condition are interesting
is that a review (7) and a handful of recent
studies (14, 15, 16, 17, 18, 19, 20, 21) have
addressed the concept of warming up with
foam rolling in conjunction with dynamic
stretching and observed mixed results. Those
studies are summarized in Table 3.
When reviewing the eight studies in Table
3, there seems to be some efficacy for the
combination of dynamic stretching and foam
rolling. Seven of the eight studies that used
a pre- to post-warm-up crossover design
found that dynamic stretching + foam rolling improved some metrics (range of motion
or performance) from pre- to post-warm-up.
A warm-up consisting of dynamic stretching + foam rolling also improved jump performance more than walking (14) and improved jump performance and bench press
strength more than jogging (15). Five studies (16, 17, 18, 19, 20) compared dynamic
stretching + foam rolling to dynamic stretching alone; however, among the 12 comparisons in Table 3, dynamic stretching + foam
rolling was more beneficial for only one
performance outcome – hamstring muscle
endurance (17). This finding was observed
by Chen et al., and the study protocol utilized vibration foam rolling, which also has
a mixed record on improving acute strength
performance (22). Overall, the results on the
efficacy of dynamic stretching + foam rolling, coupled with the fact that foam rolling
alone doesn’t typically improve strength
performance (2, 3, 4), seem to confirm that
28
a combination of both warm-up modalities
doesn’t boost acute performance above dynamic stretching alone. However, the presently reviewed study by Konrad et al. (1) did
not directly compare dynamic stretching only
to dynamic stretching + foam rolling.
The fact that foam rolling only and dynamic
stretching + foam rolling result in similar increases in acute range of motion is unsurpris-
ing, but useful for the lifter. If a lifter performs
dynamic stretching, but skips foam rolling,
they are unlikely to miss out on the range of
motion benefits. Of course, we can only directly apply the findings from the presently reviewed study to the sit-and-reach test. Further,
foam rolling provides similar acute range of
motion benefits to static strength (5, 6), and
dynamic stretching usually provides similar
29
range of motion benefits to static stretching
(11). Therefore, when considering all of the
range of motion literature, I suspect that dynamic stretching provides similar acute range
of motion benefits to foam rolling. Still, if you
want to incorporate foam rolling to increase
range of motion, I don’t see the downside other than the extra time required.
The last finding to discuss from the presently reviewed study is the greater increase
in acute hamstring range of motion in men
versus women in the foam rolling first condition. This difference could be attributed
to the 5.27 cm shorter sit-and-reach at baseline in men (31.46 ± 5.15 cm) compared to
women (36.73 ± 3.33 cm) in the foam rolling
first condition (it was similarly different in
all conditions). Therefore, as the researchers
suggested, men seemingly had more potential to increase their range of motion. Notably, even with a 7.04% greater increase in
the sit-and-reach test, men still had a shorter
range of motion on the sit-and-reach test than
women post-warm-up (men: 34.4 cm; women: 37.57 cm). Previous data also found that
women have a greater range of motion than
men in various joints (23, 24). Given the sexbased difference in baseline levels, men may
require a greater absolute increase in acute
range of motion pre-training to maximize
performance; however, we currently lack the
requisite data to substantiate this speculation.
When considering how to structure a warmup, lifters usually consider four options before moving to an empty barbell and light
weights. Those options are: 1) low-intensity
walking/cycling, 2) static stretching, 3) foam
rolling, and 4) dynamic stretching. Some may
consider other warm-up techniques, such as
massage, partner-assisted stretching, or even
cooling or heating therapies; however, the
aforementioned four are the most common
and relevant to this article. If creating a hierarchy (dare I say a pyramid?) of these options, dynamic stretching would rank first for
inclusion in a warm-up, as it’s the only one to
at least somewhat consistently improve performance. Further, dynamic stretching probably results in similar increases in range of
motion as both foam rolling and static stretching. Static stretching would rank last on the
list unless a physical therapist suggests its inclusion for some reason for a specific individual. You may be gearing up to remind me that
if static stretching is kept short and not too in-
30
tense, performance is unlikely to be harmed.
I agree, and I’ve articulated that position
in MASS on various occasions (one, two).
However, static stretching also doesn’t offer
any unique benefits and it is more likely to
decrease acute performance compared to other warm-ups options. I’d rank foam rolling
and low-intensity walking/cycling as two and
three, respectively, without a strong opinion
on the order. It’s really personal preference
if someone wants to include foam rolling or
low-intensity walking/cycling. I don’t see either option having a positive or negative effect on performance if performed appropriately, so if someone feels good doing them,
then go for it, as long as time permits. Lastly,
an important rule of warming up is to ensure
that the warm-up doesn’t harm performance.
So, even when considering dynamic stretching, I would keep the practice to about 5 minutes to avoid fatigue. Similarly, I would keep
the duration and intensity of walking/cycling
and foam rolling in check. Figure 4 represents
a basic flow chart of warm-up prescriptions
with optional items denoted with asterisks.
shoulder joint) and lower body ranges of motion. Then, squat and bench press 1RM could
be assessed before and after the warm-up period. It would also be nice to assess the combination of dynamic stretching and foam rolling on muscle endurance (reps performed). In
that case, reps performed could be evaluated
instead of 1RM or after 1RM. However, if
researchers wanted to do both and avoid the
potential fatigue of performing reps to failure
after a 1RM test, they could use a within-subject design with a unilateral exercise such as
the leg extension, and test 1RM on one leg
and reps performed at a moderate load (i.e.,
70% of 1RM) on the other leg.
Next Steps
While I found this study interesting, it would
have been more valuable for MASS readers
if it included range of motion in other joints
and examined performance with free-weight
exercises (e.g., squat, bench press, and deadlift). Therefore, I’d like to see this study replicated with a few changes. First, I would
add a fourth condition of dynamic stretching
only. Second, I would expand the foam rolling and dynamic stretching routines to target
the upper body and then test upper body (e.g.,
31
APPLICATION AND TAKEAWAYS
1. Konrad (1) found that combining dynamic stretching and foam rolling did not
increase acute performance, nor did it improve acute hamstring range of motion
more than foam rolling alone.
2. The warm-up literature as a whole suggests that dynamic stretching may improve
performance, foam rolling is unlikely to increase performance, and there is
probably a similar acute range of motion benefit between the two strategies.
3. It is good practice to include dynamic stretching as a part of your warm-up,
but foam rolling may not provide additional benefits to dynamic stretching.
Nonetheless, lifters should feel free to include it based upon personal preference
or a physical therapist’s recommendation.
4. Most importantly, the first rule of a warm-up is to ensure that it’s not too fatiguing
and does not harm performance. Therefore, a dynamic stretching routine should
remain relatively short (~5 minutes) and should be adjusted by the individual lifter
to best fit their needs.
32
References
1. Konrad A, Bernsteiner D, Reiner MM, Nakamura M, Tilp M. An Intense Warm-Up Does
Not Potentiate Performance Before or After a Single Bout of Foam Rolling. Journal of
Sports Science and Medicine. 2022 Mar 4;21(2):145-52.
2. Wiewelhove T, Döweling A, Schneider C, Hottenrott L, Meyer T, Kellmann M, Pfeiffer
M, Ferrauti A. A meta-analysis of the effects of foam rolling on performance and
recovery. Frontiers in physiology. 2019:376.
3. Cheatham SW, Kolber MJ, Cain M, Lee M. The effects of self‐myofascial release
using a foam roll or roller massager on joint range of motion, muscle recovery, and
performance: a systematic review. International journal of sports physical therapy. 2015
Nov;10(6):827.
4. Skinner B, Moss R, Hammond L. A systematic review and meta-analysis of the effects of
foam rolling on range of motion, recovery and markers of athletic performance. Journal
of Bodywork and Movement Therapies. 2020 Jul 1;24(3):105-22.
5. Konrad A, Nakamura M, Paternoster FK, Tilp M, Behm DG. A comparison of a single
bout of stretching or foam rolling on range of motion in healthy adults. European Journal
of Applied Physiology. 2022 Mar 17:1-3.
6. Konrad A, Nakamura M, Bernsteiner D, Tilp M. The accumulated effects of foam rolling
combined with stretching on range of motion and physical performance: a systematic
review and meta-analysis. Journal of Sports Science & Medicine. 2021 Sep;20(3):535.
7. Anderson BL, Harter RA, Farnsworth JL. The acute effects of foam rolling and
dynamic stretching on athletic performance: a critically appraised topic. Journal of sport
rehabilitation. 2020 Aug 13;30(3):501-6.
8. Su H, Chang NJ, Wu WL, Guo LY, Chu IH. Acute effects of foam rolling, static
stretching, and dynamic stretching during warm-ups on muscular flexibility and strength
in young adults. Journal of sport rehabilitation. 2017 Nov 1;26(6):469-77.
9. Morton RW, Oikawa S, Phillips SM, Devries MC, Mitchell CJ. Self-Myofascial Release:
No Improvement of Functional Outcomes in” Tight” Hamstrings. International Journal of
Sports Physiology & Performance. 2016 Jul 1;11(5).
10. Behm DG, Chaouachi A. A review of the acute effects of static and dynamic stretching
on performance. European journal of applied physiology. 2011 Nov;111(11):2633-51.
11. Behm DG, Blazevich AJ, Kay AD, McHugh M. Acute effects of muscle stretching on
physical performance, range of motion, and injury incidence in healthy active individuals:
33
a systematic review. Applied physiology, nutrition, and metabolism. 2016;41(1):1-1.
12. Curry B.S., Chengkalath D., Crouch G.J., Romance M., and Manns P.J. 2009. Acute
effects of dynamic stretching, static stretching, and light aerobic activity on muscular
performance in women. J. Strength Cond. Res. 23: 1811–1819.
13. Franco B.L., Signorelli G.R., Trajano G.S., Costa P.B., and de Oliveira C.G. 2012. Acute
effects of three different stretching protocols on the Wingate test performance. J. Sports
Sci. Med. 11: 1–7.
14. Richman ED, Tyo BM, Nicks CR. Combined effects of self-myofascial release and
dynamic stretching on range of motion, jump, sprint, and agility performance. The
Journal of Strength & Conditioning Research. 2019 Jul 1;33(7):1795-803.
15. Peacock CA, Krein DD, Silver TA, Sanders GJ, Von Carlowitz KP. An acute bout
of self-myofascial release in the form of foam rolling improves performance testing.
International journal of exercise science. 2014;7(3):202.
16. Smith JC, Pridgeon B, Hall MC. Acute effect of foam rolling and dynamic stretching on
flexibility and jump height. The Journal of Strength & Conditioning Research. 2018 Aug
1;32(8):2209-15.
17. Chen CH, Chiu CH, Tseng WC, Wu CY, Su HH, Chang CK, Ye X. Acute effects of
combining dynamic stretching and vibration foam rolling warm-up on lower-limb muscle
performance and functions in female handball players. J. Strength Cond. Res. 2021 Mar
2.
18. Seçer E, Kaya DÖ. Comparison of Immediate Effects of Foam Rolling and Dynamic
Stretching to Only Dynamic Stretching on Flexibility, Balance, and Agility in Male
Soccer Players. Journal of Sport Rehabilitation. 2021 Sep 20;31(1):10-6.
19. de Cunha JC, Monteiro ER, Fiuza A, Neto VG, Araujo GS, Telles LG, de Meirelles
AG, Serra R, Vianna JM, Novaes JS. Acute Effect of Foam Rolling Before Dynamic
Stretching on the Active Hip Flexion Range-of-Motion in Healthy Subjects. Journal of
Exercise Physiology Online. 2021 Apr 1;24(2):81-90.
20. Lin WC, Lee CL, Chang NJ. Acute effects of dynamic stretching followed by vibration
foam rolling on sports performance of badminton athletes. Journal of sports science &
medicine. 2020 Jun;19(2):420.
21. Hsu FY, Tsai KL, Lee CL, Chang WD, Chang NJ. Effects of dynamic stretching
combined with static stretching, foam rolling, or vibration rolling as a warm-up exercise
on athletic performance in elite table tennis players. Journal of Sport Rehabilitation. 2020
Apr 28;30(2):198-205.
22. Alonso-Calvete A, Lorenzo-Martínez M, Padrón-Cabo A, Pérez-Ferreirós A, Kalén
34
A, Abelairas-Gómez C, Rey E. Does Vibration Foam Roller Influence Performance
and Recovery? A Systematic Review and Meta-analysis. Sports Medicine-Open. 2022
Dec;8(1):1-0.
23. Miyamoto N, Hirata K, Miyamoto-Mikami E, Yasuda O, Kanehisa H. Associations of
passive muscle stiffness, muscle stretch tolerance, and muscle slack angle with range of
motion: individual and sex differences. Scientific reports. 2018 May 29;8(1):1-0.
24. Hwang J, Jung MC. Age and sex differences in ranges of motion and motion patterns.
International Journal of Occupational Safety and Ergonomics. 2015 Apr 3;21(2):173-86.
█
35
Study Reviewed: The Association Between Caffeine Intake And Testosterone: NHANES
2013-2014. Glover et al. (2022)
Is Caffeine Tanking Your
Testosterone?
BY ERIC TREXLER
Many of us grab a cup of coffee before we start our day, or ingest
a caffeinated supplement before a workout. A new observational
study sought to investigate whether a man’s caffeine habit might
be driving his testosterone levels downward.
36
KEY POINTS
1. The presently reviewed study sought to determine whether urinary levels of
caffeine (and 14 of its metabolites) were associated with blood testosterone
levels among 372 men in the 2013-2014 NHANES cohort. As such, it was an
observational, retrospective analysis of previously collected data.
2. Blood testosterone levels were negatively associated with urinary caffeine,
along with 10 of its metabolites. However, 3 metabolites were positively
associated with testosterone levels, and analyses looking at various quartiles of
caffeine and two of its key metabolites (theobromine and theophylline) yielded
very inconsistent results.
3. Due to some methodological shortcomings and inconsistent results within this
study, along with a lack of compatibility with other studies on this topic, there is
currently insufficient evidence to suggest that high caffeine intake will negatively
impact testosterone levels in men.
A
lot of men are interested in optimizing their testosterone levels, and it’s
not hard to figure out why. Testosterone levels can impact body composition,
vitality, and libido, and also happen to decline
as men age. Naturally, a lot of men are interested in the possibility of supporting optimal
testosterone levels throughout all stages of
adulthood, and keeping age-related testosterone reductions at bay (to the extent that such
a feat is possible).The presently reviewed
study (1) proposes a nightmarish suggestion:
lay off the caffeine.
To address this question, the researchers
leaned on data from the National Health and
Nutrition Examination Survey (NHANES).
NHANES is a huge research program that’s
been going on since the 1960s, and involves
data collection in a variety of different forms,
ranging from questionnaires to physiological
measurements and blood tests. The NHANES
researchers and staff currently aim to collect
data from a representative sample of about
5,000 Americans per year, and epidemiologists publish findings from the NHANES
data set extremely frequently. In the present
study, the researchers gathered and retrospectively analyzed complete data, including demographic characteristics, blood testosterone
levels, and urinary concentrations of caffeine
and 14 of its metabolites, from 372 men that
participated in the 2013-2014 NHANES data
collection cycle.
In their primary regression analysis, the researchers found that urinary caffeine levels
(and the levels of 10 other caffeine metabolites) were negatively associated with blood
testosterone levels. However, they also found
that 3 caffeine metabolites were positively
associated with testosterone levels. The researchers also used regression to compare
quartiles stratified by urinary concentrations
of caffeine and a couple of its key metabolites (theobromine and theophylline). These
37
models yielded very inconsistent results,
and testosterone levels did not consistently
drop when progressing from lower quartiles
to higher quartiles of urinary caffeine or its
primary metabolites. Finally, the researchers constructed a bunch of logistic regression
models, which collectively found that urinary
caffeine and its metabolites were not significantly predictive of an individual’s risk for
having clinically low testosterone (<300 ng/
dL). The researchers concluded that caffeine
was inversely associated with testosterone,
and that “these effects of caffeine may serve
as important risk factors in the etiology of
low testosterone and reproductive dysfunction.” Read on to find out why I beg to differ.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was “to quantify the strength and direction of
the association between caffeine and testosterone.”
Hypotheses
The researchers hypothesized “that caffeine
consumption is significantly associated with
testosterone in men.” However, they did not
commit to a particular direction or pattern by
which varying levels of caffeine consumption would correlate with testosterone levels.
38
Subjects and Methods
Subjects
These researchers retrospectively analyzed
data from the 2013-2014 NHANES cohort,
which consisted of 10,175 participants. For
this analysis, the researchers excluded all female participants and individuals under the age
of 18, which left 2,958 adult males remaining.
From this group of 2,958, only participants
with complete data for the outcomes and covariates of interest (serum testosterone and sex
hormone binding globulin, urinary creatinine
and caffeine metabolites, demographic information, anthropometric data, and information
about alcohol use, diabetes status, ethnicity,
and smoking status) could be included. This
resulted in a final sample of 372 men, whose
data (stratified by quartiles of urinary caffeine
levels) are presented in Table 1.
Methods
For the original data collection, participants
reported to a lab after an overnight fast to complete all assessments. Participants provided
a urine sample for determination of caffeine
metabolites, a blood sample for determination
of serum testosterone levels, went through a
quick testing battery to collect anthropometric measurements, and provided all necessary
information pertaining to their demographic
characteristics and health-related information.
As for statistical analysis, the researchers took
a few different approaches. First, the sample
was divided into four quartiles (stratified by
urinary caffeine levels), which were compared
to one another using ANOVAs (for continuous variables) and Chi-squared tests (for categorical variables). After that, the researchers
constructed regression models to explore relationships between various caffeine metabolites and serum testosterone levels. Based on
the methods, it sounds like these models were
constructed one at a time (one for each caffeine metabolite), with each model adjusted
for covariates including age, BMI, smoking,
drinking, and urinary creatinine levels.
The researchers also used regression to compare quartiles stratified by urinary concentrations of caffeine and a couple of its key metabolites (theobromine and theophylline). For
each of the three metabolites, these models
directly compared the mean predicted change
in testosterone level when comparing the 2nd,
3rd, and 4th quartiles to the 1st quartile. The
researchers also used logistic regression to determine if caffeine, theobromine, or theophylline were predictive of the likelihood of having clinically low testosterone levels, defined
as serum levels <300 ng/dL. For these models,
each quartile was compared to the first quartile
for comparison purposes. For example, they
constructed logistic regression models that answered the following questions:
• Compared to individuals in the 1st quartile
of urinary caffeine concentrations, were individuals in the 2nd quartile more likely to
have low testosterone levels (<300 ng/dL)?
• Compared to individuals in the 1st quartile of urinary caffeine concentrations,
were individuals in the 3rd quartile more
likely to have low testosterone levels?
• Compared to individuals in the 1st quartile of urinary caffeine concentrations,
were individuals in the 4th quartile more
likely to have low testosterone levels?
39
This same sequence of questions was answered by building similar models to compare among the quartiles of urinary theobromine concentrations and theophylline
concentrations.
Findings
Testosterone levels, stratified by urinary
caffeine quartiles, are presented in Table 2.
When I first saw these data, I noticed that the
quartiles had pretty similar testosterone levels. For example, quartiles 1 through 3 were
all in the range of 430-440 ng/dL; physiologically speaking, the difference between 430
and 440 is inconsequential. You could argue
that the drop-off from quartile 3 (430 ng/
dL) to quartile 4 (398 ng/dL) is potentially
approaching a physiologically relevant magnitude (not for hypertrophy or body composition, but perhaps for small impacts on things
like perceived energy level or libido), but,
generally speaking, raw testosterone values
didn’t differ much across the four quartiles.
I noticed that the reported p-value for the
ANOVA comparing the four quartiles was p
= 0.02, which is considered statistically significant. However, when I tried to replicate
this ANOVA based on the reported means
and standard errors, my results weren’t even
close to being statistically significant. I think
it’s possible that my inability to replicate the
finding relates to something called “weighting.” The NHANES data are weighted, which
involves using mathematical adjustments to
make the sample data more representative of
the population it was drawn from. You can
see the impact of this weighting in Table 2;
the four different quartiles have very different
sample sizes, which is never the case for unweighted data. So, the raw data aren’t particularly interesting with regards to testosterone
levels across quartiles, but the weighted analysis appears to tell a different story. I don’t
personally have experience with weighted
analyses of this nature, so I’m left to assume
40
consistency. For caffeine, the predicted mean
testosterone values for each quartile were,
from 1st to 4th: 427, 506, 405, and 400 ng/
dL. The 2nd caffeine quartile had significantly higher testosterone levels than the 1st
quartile, but the 4th quartile had significantly
lower testosterone levels than the 1st quartile. For theobromine, the predicted mean
testosterone levels by quartile were 473, 398,
396, and 473 ng/dL. In other words, having
moderate urinary theobromine levels, but not
high urinary theobromine levels, was associated with statistically significant reductions
in testosterone. For theophylline, the predicted mean testosterone levels by quartile were
463, 464, 410, and 414. The only statistically
significant comparison was between the 1st
and 4th quartile.
that weighting is the cause for my inability to
replicate the calculation.
Next, the researchers constructed a series of
regression models, adjusted for a bunch of
covariates, to examine the relationships between individual caffeine metabolites and
blood testosterone levels. The results are
presented in Table 3. In short, caffeine itself
was negatively correlated with testosterone
levels, and the same was true for 10 other
caffeine metabolites. However, 3 metabolites
were positively correlated with testosterone
levels. There was only one remaining metabolite that was not significantly correlated
with testosterone levels in either direction.
Looking at the regression models comparing
quartiles grouped by caffeine, theobromine,
and theophylline levels, there was a lot of in-
As for the logistic regression models, none
of them were statistically significant. In other
words, quartiles grouped by urinary levels of
caffeine, theobromine, and theophylline were
not significantly predictive of an individual’s
risk for having clinically low testosterone
(<300 ng/dL). Nonetheless, the researchers
concluded that they had “observed an inverse
association between caffeine and serum testosterone,” and that “these effects of caffeine
may serve as important risk factors in the etiology of low testosterone and reproductive
dysfunction.”
Criticisms and Statistical
Musings
When I read through this study the first time,
I was surprised by a lack of clarity in the
methods and results, and a lack of nuance and
41
specificity in the discussion section. Sometimes, if I am surprised enough by an apparent lack of clarity, I dig a little deeper to figure out why that might be the case. I looked
into the other research from this lead author,
and it ended up being a fruitful exercise.
With a little bit of digging, I found out that
this lead author has published only one other paper as the primary author, so I checked
out the other one. Upon reading it, I noticed
that the research team had submitted a paper
about associations between 2,4-dichlorophenoxyacetic acid (2,4-D) and testosterone levels in July of 2021 (for context, the presently reviewed study was submitted about four
months later, in November of 2021). Four
months prior to submitting the presently reviewed paper on caffeine, they submitted a
totally different paper that utilized data from
the same exact NHANES cohort from 20132014, and looked at the same exact outcome
variable. In many spots, the present caffeine
paper seemed like a paraphrased version of
the 2,4-D paper that happened to use a different predictor in the statistical models (urinary
caffeine instead of 2,4-D). In some sections,
the methods were hardly even paraphrased,
with a few instances of entirely duplicated
text. On the one hand, this duplicated text
isn’t a huge deal; I don’t personally care if
you find a different way to tell me how you
did the same thing in your methods section.
However, a comparison of these two papers
provided a couple of important insights. First,
this paper’s methods probably lacked important caffeine-specific considerations because
the methods appear to be adapted from another project that had nothing to do with caf-
feine. Second, this is the lead researcher’s
first venture into caffeine research (according to PubMed, at least). These observations
probably tell us a lot about why the methods
seemed so non-specific to the research question about caffeine, and why the discussion
left so much to the imagination. Now, let’s
discuss some specific quibbles I had, in no
particular order.
First, the methods state that blood draws occurred in a fasted state, but do not elaborate
on the specific fasting instructions. On the
surface level, one might assume the most
simplistic definition of fasted: no foods or
beverages other than plain water. If adopting
this assumption, then any caffeine identified
in blood samples was from the day before,
and higher urinary values could potentially reflect high intake from the day before
(which probably correlates, to some extent,
with high habitual intake), or could possibly
reflect a general preference for evening or
nighttime caffeine ingestion.
To get to the bottom of this, I had to dig up
the old NHANES handbooks and guidelines
to figure out the standard procedure for fasted
examinations. When NHANES study participants arrive, the research staff asks them some
questions. The first question is: “When was
the last time you ate or drank anything other
than plain water? Do not include diet soda or
black coffee or tea with artificial sweeteners
like Sweet’N Low, NutraSweet, Equal, or
Splenda.” Follow-up questions ask for details
about the timing of various food, beverage,
supplement, and medication intakes, but there
is no mechanism to determine if a non-caloric,
caffeinated beverage was recently consumed,
42
and the fasting instructions do not restrict the
intake of such beverages. Black coffee, plain
tea, diet soda, and even some diet energy
drinks would all be on the table.
That’s kind of a big deal. In this context, urinary caffeine biomarkers don’t specifically reflect how much caffeine a participant typically
consumes; rather, they reflect when a participant happened to consume caffeine, and the
dose of this particular caffeinated product, in
temporal proximity to a one-time measurement. This also means that participants who
happen to like their coffee black might have
enjoyed some while traveling to the lab that
morning, whereas those who prefer cream or
sugar would have been more likely to abstain
or delay consumption that morning. While
some might interpret the results of this study
to make inferences about “heavy” or “light”
caffeine consumers, that’s not really what is
being measured here. Urinary caffeine levels
in this study aren’t directly indicative of how
much or how frequently a participant consumes caffeine, as these urinary metabolites
may be influenced by other caffeine-related
preferences or tendencies, such as a participant’s preferred caffeine source, the way they
flavor their coffee in the morning, or what
beverage happened to pair well with their dinner the night before testing.
Moving past the unknown timing of caffeine
intake prior to the testing visit, there was also
a lack of detail regarding typical sleep habits,
sleep quality or quantity in the days leading
up to testing, and habitual caffeine intake. In
2013-2014, the NHANES researchers definitely collected data pertaining to sleep and
dietary intakes (including caffeine), and in-
corporating this information would have
been very valuable for the present study.
Many nutritional epidemiologists are fond of
using biomarkers for nutrient intakes rather
than self-reported intake data, whenever it’s
possible and feasible to do so. For several
dietary outcomes, this makes a lot of sense.
For example, very few people can give you
a really good estimate of their folate intake,
or even of their typical food choices and portion sizes from which an estimate of folate
intake could be derived. If you can measure
something in the blood or urine that accurately and objectively reflects a person’s habitual
folate intake without relying on their memory or attention to detail, that’s a really nice
way to quantify folate intake and observe its
relationship to a variety of health-related outcomes. In contrast, it’s not particularly difficult to get a reasonably accurate estimate of
a person’s approximate caffeine intake from
a quick survey or interview. In addition, research suggests that concentrations of caffeine and its metabolites derived from “spot
urine samples” (samples collected at a single
time point, like those in the present study)
only have weak to moderate correlations with
caffeine intake (2). In summary, I’m fairly
certain that pertinent details related to sleep
and self-reported dietary habits are available
within the NHANES data set, and this information would’ve been very informative.
It’s possible that adding these variables and
requiring “complete” data for all subjects
would have further reduced their sample size
to an untenable degree, but they also could’ve
rectified that issue by expanding their sample
to include more NHANES cohorts from different data collection cycles.
43
My final quibble is that, frankly, I’m not seeing a clear connection between the reported
findings and the researchers’ conclusions.
Some metabolites were positively correlated with testosterone levels, some were negatively correlated with testosterone levels,
and there was no consistent pattern by which
testosterone levels changed as you jump from
one quartile to the next. Further, the discussion section focused largely on rodent data
and prenatal exposure to caffeine, while failing to acknowledge a great deal of evidence
that is more directly relevant to the research
question at hand. I was also surprised to see
the conclusion that “these effects of caffeine
may serve as important risk factors in the etiology of low testosterone and reproductive
dysfunction,” given that they used logistic
regression to directly test whether urinary
caffeine, theobromine, or theophylline concentrations were predictive of clinically low
testosterone levels, and none of them returned
a statistically significant result. If there’s a
clear and internally consistent link between
the reported findings and the stated conclusions, I’m unable to find it.
Interpretation
If you typically skip the “Criticisms and Statistical Musings” section, I encourage you
to read it this time around – it’s not overly technical, but it provides a detailed justification for my skepticism of the present
study’s findings. The short version is that
the reported analysis isn’t very compelling,
even when interpreted at the surface level.
However, when you dive deeper, there’s
even more reason for skepticism.
For starters, there’s insufficient evidence to
confidently identify a plausible mechanism
by which commonly observed levels of caffeine intake would directly cause testosterone reductions in male adults. The most
plausible mechanism, in my opinion, is a
somewhat indirect relationship mediated by
sleep disruption. Certain patterns of caffeine
intake (but not all patterns of caffeine intake) could impair sleep, and impaired sleep
can lead to reduced testosterone. However,
as noted in the “Criticisms and Statistical
Musings” section, we don’t know enough
about these individuals’ caffeine habits or
sleep habits to draw informed conclusions
about this possibility. If the higher caffeine
levels in the present study were mostly from
people who ingested large doses of caffeine
right before bed on the night prior to testing, and this is representative of their habitual caffeine intake and timing, then it’s
very possible that caffeine is hindering their
testosterone levels through chronic sleep
impairment. However, that’s also an easy
fix – restricting caffeine to the morning or
early afternoon would probably rectify that
situation. Similarly, it’s possible that some
people ingest such large doses of caffeine in
the morning and afternoon that it has yet to
clear their system by bed time; once again,
this could be rectified by simply lowering
habitual caffeine intake to a more suitable
dose. Unfortunately, spot assessments of
urinary caffeine metabolites don’t give us
much information about a person’s habitual caffeine intake, nor do they allow us to
make inferences about the specific timing of
caffeine intake. In other words, the present
study simply doesn’t provide information
44
that would allow us to draw strong conclusions about the relationship between caffeine and testosterone.
Nonetheless, if one wishes to link caffeine intake to low testosterone, they cannot rule out
the possibility that high caffeine intake is more
of an effect than a cause. For example, people
with sleep issues might be consuming more
caffeine because of their sleep impairment,
rather than directly causing the sleep impairment by consuming large caffeine doses at inadvisable times of day. In this situation, poor
sleep would be directly contributing to lower
testosterone levels, and the caffeine could
merely be a response to the sleep issue that
has no direct impact on testosterone. For another example, it’s important to recognize that
low levels of perceived energy, vitality, and
vigor are among the most common symptoms
of low testosterone in men. It’s very plausible to suggest that men experiencing some of
these symptoms from low testosterone might
be more inclined to reach for extra caffeine
for an energy boost; in such a scenario, testosterone reductions lead to high caffeine intake rather than high caffeine intake leading
to testosterone reductions. Finally, stress is a
relevant confounding factor to consider. I personally tend to consume more caffeine when
I’m quite busy, and these busy stretches of
time are when I tend to experience heightened
levels of chronic stress. Acute stress responses
can be a bit variable, but chronic stress is associated with testosterone reductions in men (3),
so it’s possible that stress could be an underlying factor that is simultaneously driving caffeine intake upward while driving testosterone
levels downward.
THE PRESENT STUDY
SIMPLY DOESN’T
PROVIDE INFORMATION
THAT WOULD ALLOW
US TO DRAW STRONG
CONCLUSIONS ABOUT
THE RELATIONSHIP
BETWEEN CAFFEINE
AND TESTOSTERONE
While we’re on the topic of causation, it’s
important to contextualize the present study
within the broader research linking caffeine
intake and testosterone levels. As I mentioned previously, the discussion section of
the present study allocates a great deal of focus toward mechanistic studies in non-human
research models, with some findings indicating that very high caffeine intake can lead to
lower testosterone levels in rodents. However, we should be very wary of generalizing rodent testosterone responses to human testosterone responses. For example, consider the
melatonin literature. I’ve heard very well-respected fitness influencers caution against
melatonin supplementation purely because it
has been shown to reduce testosterone levels
in rodents. However, there is a robust body
of literature showing that this effect is highly
dependent on contextual factors of melatonin
45
administration and the specific species being
studied (4), and there’s plenty of evidence
showing that exogenous melatonin supplementation has inconsequential effects on human testosterone levels (5). Shifting focus
away from rodent research, these researchers
also acknowledged some observational studies indicating that prenatal caffeine exposure
is associated with decreased testosterone levels later in life. I’m not an expert on fetal development, so I’ll sit that debate out for now,
but it’s important to highlight that prenatal
caffeine exposure has virtually nothing to do
with the present study’s findings, which spe-
cifically investigate the impact of recent caffeine intake in adult males.
I found it odd that the researchers directed
such minimal focus toward other observational studies that aimed to investigate the
very same relationship between caffeine intake and testosterone among adult men. They
did acknowledge that a study by Lopez and
colleagues (6), which also used data from the
NHANES study, found non-linear associations between caffeine intake and testosterone levels. However, they didn’t elaborate on
the actual pattern of this association, probably because it looked like this:
46
As you can see in Figure 1, Lopez and colleagues didn’t find a single category of
caffeine intake that was associated with
meaningfully lower testosterone levels than
consuming no caffeine at all. Of course, there
are other observational studies exploring the
relationship between the intake of caffeinated beverages and testosterone levels among
adult males. For example, Svartberg and colleagues looked at the relationship between
a variety of lifestyle factors and testosterone levels in 1,563 men (7). Results actually
found that higher habitual coffee consumption (>4 cups/day) was associated with significantly higher levels of total testosterone
and free testosterone when compared to lower consumption (1-4 cups per day).
There are even some human trials that give
us hints about how acute and chronic caffeine
consumption might impact testosterone levels in men. Acutely, Beaven and colleagues
(8) reported a dose-response relationship
by which caffeine increased testosterone
responses to exercise. While the results reported by Beaven et al might be related to
achievement of a greater workload during the
exercise bout, Wu also reported that caffeine
increases the acute testosterone response to
resistance exercise with a fixed workload
(9). Ormsbee et al (10) studied six weeks
of supplementation with multi-ingredient
pre-workout supplementation (whey protein,
casein protein, branched-chain amino acids,
creatine, beta alanine, and caffeine) in resistance-trained men. While the study was confounded by a long list of ingredients, both the
placebo group and supplement group experienced similar testosterone increases over
time. On a related note, a study by MacKenzie and colleagues (11) investigated the
effect of daily caffeine intake (200mg, twice
per day for seven days) on two androgens
that are related to testosterone (dehydroepiandrosterone [DHEA] and androstenedione).
Neither androgen was significantly impacted
by daily caffeine supplementation.
A well-constructed epidemiological paper
will take steps to summarize the existing
knowledge related to the research topic, establish the biological plausibility of the research question, formulate a set of methods
to directly address that question, describe
those methods with a high level of clarity, report the observed results in conjunction with
a nuanced discussion about any apparent inconsistencies or contradictions, and lean on a
thorough understanding of the research topic
to contextualize the newly reported findings
within the broader literature. The presently reviewed study falls a bit short in each of these
areas, and therefore fails to provide convincing support for the idea that men interested
in optimizing their testosterone levels should
necessarily restrict caffeine intake. As I noted
in a recent Research Brief, men interested in
supporting optimal testosterone levels should
aim to be lean enough (but not too lean), and
to achieve adequate total energy intake with
a fairly moderate macronutrient distribution.
Beyond that, a great article by the team at examine.com covers the rest of the current best
practices for optimizing testosterone levels:
adequate sleep, regular physical activity, and
sufficient micronutrient status (with a specific focus on vitamin D, zinc, and magnesium)
should have you covered. As long as your
47
caffeine intake isn’t messing up your sleep,
I’m simply not seeing convincing evidence
that negative effects on testosterone levels
are likely.
In many cases, I write MASS articles to inform readers about helpful, practical, evidence-based strategies that can improve their
approach to training or nutrition. However,
I have to play defense sometimes, which involves writing articles that proactively “get
out ahead” of unreliable or poorly supported recommendations that readers might encounter. Given that the presently reviewed
paper combines two hot topics with immense
public interest (caffeine and testosterone), I
wouldn’t be surprised if a number of influencers in the realm of health, fitness, or biohacking take the surface-level interpretation
and run with it, leading to recommendations
to limit caffeine intake in order to optimize
testosterone levels. However, as a MASS
reader, you’ve got the inside scoop, and you
know that this study simply doesn’t provide
strong evidence to support those recommendations.
sponse relationship between caffeine intake
and testosterone levels should become apparent. Personally, I am very skeptical that such
a relationship exists, but it shouldn’t be hard
to identify with a very simple and straightforward randomized controlled trial.
AS LONG AS YOUR
CAFFEINE INTAKE ISN’T
MESSING UP YOUR SLEEP,
I’M SIMPLY NOT SEEING
CONVINCING EVIDENCE
THAT NEGATIVE EFFECTS
ON TESTOSTERONE
LEVELS ARE LIKELY
Next Steps
It would be very easy to address this research
question head-on, and would probably make
for a nice master’s thesis project. All you’d
need to do is recruit a group of men and randomly allocate them to some treatment arms.
Personally, I’d opt for 4 different conditions,
if possible: 0mg caffeine (placebo), 200mg
caffeine, 400mg caffeine, and 600mg caffeine. After having the participants ingest
their allocated supplement (or placebo) every
morning for 2-4 weeks, any potential dose-re-
48
APPLICATION AND TAKEAWAYS
If you want to support optimal testosterone levels, you’ll want to get to a bodyfat level that is compatible with testosterone optimization; obesity can reduce
testosterone levels, but testosterone levels also tend to drop as we go from lean to
shredded. In addition, you’ll want to make sure you’re eating enough total calories
(that is, avoiding low energy availability) within a fairly balanced macronutrient
distribution. Beyond that, try to get adequate sleep, regular physical activity, and
sufficient micronutrient intake (especially vitamin D, zinc, and magnesium), and try
to minimize chronic stress. If you heard about this paper and were worried about
choosing between your caffeine and your testosterone levels, don’t sweat it – there
is currently insufficient evidence to suggest that high caffeine intake will negatively
impact testosterone levels in men.
49
References
1. Glover FE, Caudle WM, Del Giudice F, Belladelli F, Mulloy E, Lawal E, et al. The
Association Between Caffeine Intake And Testosterone: Nhanes 2013-2014. Nutr J. 2022
May 17;21(1):33.
2. Rybak ME, Sternberg MR, Pao CI, Ahluwalia N, Pfeiffer CM. Urine Excretion Of
Caffeine And Select Caffeine Metabolites Is Common In The Us Population And
Associated With Caffeine Intake. J Nutr. 2015 Apr;145(4):766–74.
3. Smith GD, Ben-Shlomo Y, Beswick A, Yarnell J, Lightman S, Elwood P. Cortisol,
Testosterone, And Coronary Heart Disease: Prospective Evidence From The Caerphilly
Study. Circulation. 2005 Jul 19;112(3):332–40.
4. Yu K, Deng SL, Sun TC, Li YY, Liu YX. Melatonin Regulates the Synthesis
of Steroid Hormones on Male Reproduction: A Review. Molecules. 2018 Feb
17;23(2):447.
5. Luboshitzky R, Levi M, Shen-Orr Z, Blumenfeld Z, Herer P, Lavie P. Long-Term
Melatonin Administration Does Not Alter Pituitary-Gonadal Hormone Secretion In
Normal Men. Hum Reprod. 2000 Jan;15(1):60–5.
6. Lopez DS, Advani S, Qiu X, Tsilidis KK, Khera M, Kim J, et al. Caffeine Intake
Is Not Associated With Serum Testosterone Levels In Adult Men: Cross-Sectional
Findings From The Nhanes 1999-2004 And 2011-2012. Aging Male. 2019
Mar;22(1):45–54.
7. Svartberg J, Midtby M, Bønaa KH, Sundsfjord J, Joakimsen RM, Jorde R. The
Associations Of Age, Lifestyle Factors And Chronic Disease With Testosterone In Men:
The Tromsø Study. Eur J Endocrinol. 2003 Aug;149(2):145–52.
8. Beaven CM, Hopkins WG, Hansen KT, Wood MR, Cronin JB, Lowe TE. Dose Effect
Of Caffeine On Testosterone And Cortisol Responses To Resistance Exercise. Int J Sport
Nutr Exerc Metab. 2008 Apr;18(2):131–41.
9. Wu BH. Dose Effects Of Caffeine Ingestion On Acute Hormonal Responses To
Resistance Exercise. J Sports Med Phys Fitness. 2015 Oct;55(10):1242–51.
10. Ormsbee MJ, Mandler WK, Thomas DD, Ward EG, Kinsey AW, Simonavice E, et al.
The Effects Of Six Weeks Of Supplementation With Multi-Ingredient Performance
Supplements And Resistance Training On Anabolic Hormones, Body Composition,
Strength, And Power In Resistance-Trained Men. J Int Soc Sports Nutr. 2012 Nov
15;9(1):49.
50
11. MacKenzie T, Comi R, Sluss P, Keisari R, Manwar S, Kim J, et al. Metabolic And
Hormonal Effects Of Caffeine: Randomized, Double-Blind, Placebo-Controlled
Crossover Trial. Metabolism. 2007 Dec;56(12):1694–8.
█
51
Study Reviewed: Short-Term Effects of Eccentric Overload Versus Traditional Back Squat
Training on Strength and Power. Munger et al. (2022)
Accentuated Eccentrics are
Overhyped
BY MICHAEL C. ZOURDOS
Since you are stronger on the eccentric phase than the concentric
phase, accentuated eccentric loading makes sense. However, the
longitudinal data supporting this practice for enhancing strength
gains is underwhelming. Does a new study turn the tides?
52
KEY POINTS
1. The presently reviewed study was a parallel-groups design that split 33 trained
men into three groups for five weeks. The men performed: 1) squats with
added load on the eccentric (accentuated eccentric group), 2) traditional squats
(traditional group), or 3) their regular training (unsupervised group).
2. Squat 1RM and 20m sprint performance improved from pre- to post-study with
no group differences. Eccentric squat 1RM and countermovement jump height
increased significantly more in the accentuated eccentric and traditional groups
than in the unsupervised group.
3. This study shows that accentuated eccentric loading does not enhance maximal
squat strength more than regular squatting. Overall, the support in the literature
for accentuated eccentric loading to enhance long-term strength is thin, and the
practice is currently too cumbersome to recommend.
W
e’ve covered accentuated eccentrics on a few occasions
(one, two, three, four), and the
idea behind the practice is logical. People can
handle more weight on the eccentric phase of
a lift than on the concentric portion. Therefore, accentuated eccentrics typically employ weight releasers, which add load to the
barbell on the eccentric then detach from the
barbell at the bottom of the movement to lessen the load for the concentric phase. Some
data suggest that accentuated eccentrics can
increase acute one-repetition maximum
(1RM) via an immediate potentiation effect
(2). However, the current body of research
on the efficacy of accentuated eccentrics for
long-term strength is lukewarm, at best. Although a study from Douglas et al (3 - MASS
Review) showed that accentuated eccentrics
led to greater increases in squat strength than
traditional training, the accentuated eccentric
group trained at a much higher percentage of
1RM, which clouds the findings. Further, a
meta-analysis from Buskard et al (4) found
that accentuated eccentrics did not enhance
1RM strength more than traditional loading.
So, does a new study suggest a more positive
outlook for accentuated eccentric loading?
The reviewed study from Munger et al (1)
tested squat 1RM, eccentric squat 1RM, sprint
performance, and countermovement jump
height before and after five weeks of training
in three groups of trained men. Two groups
maintained their normal training, but replaced
two lower body sessions per week with squat
training under the supervision of the researchers. One of these groups squatted with accentuated eccentrics (accentuated eccentric group),
while the other group performed traditional
squats (traditional group). The third group
(unsupervised group) simply maintained their
normal training. Squat 1RM increased and
sprint time decreased in all groups. Changes
in eccentric 1RM and jump height were significantly greater in the accentuated eccentric
53
and traditional training groups than in the unsupervised group. These findings suggest that
accentuated eccentrics do not further enhance
squat 1RM when relative load (percentage of
1RM) is equated. At this point, I cannot confidently recommend accentuated eccentrics as
a surefire strategy to boost strength more than
traditional training. However, accentuated eccentrics are also unlikely to be harmful; thus,
the strategy still warrants consideration. This
article will aim to:
1. Review the present findings and evaluate
the study design.
2. Evaluate the state of the literature on accentuated eccentrics for both acute and
long-term strength.
3. Review the difference between accentuated
eccentrics and eccentric overload training.
4. Discuss the implementation and practicality of accentuated eccentrics.
Purpose and Hypotheses
Purpose
The purpose of the presently reviewed study
was to compare changes in squat 1RM, eccentric squat 1RM, sprint performance, and jump
performance in trained men after performing
five weeks of supervised accentuated eccentric training, supervised traditional training, or
their regular training on their own.
Hypotheses The researchers hypothesized that subjects in
the accentuated eccentric training group would
improve performance in all outcome measures
to a greater extent than the other two groups.
Subjects and Methods
Subjects
33 men who had at least one year of training
experience and could squat at least their body
weight participated in the study. Additional
subject details are in Table 1.
Study Overview
This study was a parallel-groups design. Subjects were counterbalanced by 1RM squat
strength into three groups: 1) accentuated
eccentric loading group, 2) traditional group,
and 3) unsupervised group. The accentuated
eccentric and traditional groups maintained
their normal training, but replaced two lower
body sessions per week with either accentuated eccentric squat or traditional squat training under the supervision of the researchers.
Each squat training session lasted ~30 minutes. The unsupervised group continued their
normal lower body training on their own.
They also performed 30 minutes of self-selected upper body training twice per week
with the researchers. Squat 1RM, eccentric
squat 1RM, countermovement jump height,
and 20m sprint assessed before and after the
five-week training program. The researchers analyzed the change in 20m sprint time
during four distance windows (0-5 m, 5-10
m, 10-20 m, and 0-20 m).
It’s worth briefly describing the eccentric
testing protocol, since this is not a method we
see often. For the test, the power rack’s safety bars were set at a height where the thigh
was parallel to the ground. To successfully
complete an eccentric squat 1RM attempt,
the lifters had to lower the barbell to the safe-
54
ty bars over the course of at least three seconds. In other words, if the subjects descended too fast (i.e., <3 seconds), the attempt was
deemed unsuccessful.
Accentuated Eccentric and Traditional
Groups
The researchers split training into three blocks
(weeks 1-2, weeks 3-4, and week 5) in the
accentuated eccentric and traditional groups.
During each block, the average percentage
of 1RM (i.e., an average of concentric and
eccentric phases) used in the accentuated eccentric group equaled the percentage of 1RM
used in the traditional group. For example, in
weeks 2-3, subjects in the traditional group
used 80% of 1RM, while subjects in the accentuated eccentric group squatted 105% of
1RM on the eccentric and 55% of 1RM on the
concentric [(105% + 55%) ÷ 2 = 80%]. The
eccentric overload group performed all squats
with a 3-second eccentric phase and maximal
intended velocity during the concentric phase.
The researchers did not provide details of the
eccentric and concentric cadence for the traditional group. However, I suspect the traditional
group trained with a self-selected eccentric and
maximal intent on the concentric. The specific
sets, reps, and relative loads in the accentuated
eccentric and traditional groups are in Table 2.
Findings
Changes in 1RM squat, 1RM eccentric squat,
and countermovement jump height was analyzed with a 3 (group) × 2 (time) repeated
55
measures analysis of variance (ANOVA).
A “main time effect” indicates a significant
change in the outcome measure for the full
sample, ignoring the impact of group assignment. A “group × time interaction” indicates
differences in the magnitude of change from
pre- to post-study between groups. In the
event of a group × time interaction, I’ll identify the group(s) in which the outcome measure changed significantly more than the other group(s). Changes in sprint performance
were analyzed with a 3 (group) × 2 (time) × 4
(distance window) ANOVA; thus, I’ll report
if there was a three-way interaction, and if
there was a significant change in sprint time
during the specific distance windows.
Squat 1RM and Eccentric Squat 1RM
There was a main time effect for squat 1RM
(p < 0.001), indicating an increase in squat
strength; however, there was not a significant
group × time interaction (p = 0.093). There
was a significant group × time interaction
for eccentric squat 1RM (p = 0.001), which
was driven by significantly greater increases in the accentuated eccentric (+16.9kg) and
traditional groups (+12.7kg) than the unsupervised group (+2.0kg). Although the accentuated eccentric group gained 4.2kg more
on their eccentric 1RM than the traditional group, this difference was only a 3.22%
greater percentage increase, and a trivial between-group effect size (g = 0.10). The eccentric squat findings can be seen in Figure 1.
Countermovement Jump Height and Sprint
Performance
There was a significant group × time interaction (p = 0.026) for countermovement
jump height. This interaction was driven by
significantly greater increases in the accentuated eccentric (+3.8cm) and traditional
groups (+2.9cm) compared to the unsupervised group (+0.0cm) (Figure 2). The 0.9 cm
56
greater change in jump height in the accentuated eccentric than in the traditional group
only equated to a 1.25% greater percentage
increase, and a trivial between-group effect
size (g = 0.10).
There was no significant three-way interaction
(p = 0.318) for sprint performance, indicating
that sprint times did not change significantly
more in one group than another. However, a
significant time × distance window effect (p =
0.027) indicated a decrease in sprint time from
pre- to post-study in all groups combined. The
changes were -0.022 seconds (0-20 m), -0.018
seconds (5-10 m), +0.031 (10-20 m), and
-0.035 seconds (0-5 m).
Criticisms and Statistical
Musings
Squat 1RM increased from 120.8 ± 31.4kg to
129.3 ± 31.3kg in all groups combined; how-
ever, the researchers did not report the changes in squat 1RM for each group. Even though
there was no significant group × time interaction, knowing the changes in each group
would have allowed me to calculate percentage changes and between-group effect sizes
to see if findings leaned in favor of one training program or another. If the p-value for
the group × time interaction was high, this
wouldn’t matter, but in this case the p-value
was 0.093 (i.e., close to 0.05). Based on the
eccentric 1RM findings, I would imagine any
meaningful differences in squat 1RM would
be due to greater increases in the traditional and eccentric loading groups compared to
the unsupervised group, but obviously I don’t
know that for sure.
Since subjects in the accentuated eccentric
and traditional groups were “instructed to
maintain habitual lower body training frequency, but to replace two of their leg train-
57
ing days with exercise prescribed during the
study,” it’s likely that lifters trained with different lower body frequencies. Furthermore,
the researchers did not state if they instructed the subjects to exclude squats from lower
body sessions performed outside of the lab. I
suspect most didn’t squat, because two days
of high load squatting was probably enough,
but I don’t know which lower body exercises the lifters performed in those sessions.
Similarly, since subjects in the unsupervised
group were “encouraged to maintain their
lower body training regimen to prevent lower
body strength loss,” we don’t know the overall lower body training frequency or specific
squat training frequency of the subjects in the
unsupervised group.
Interpretation
Accentuated eccentric loading can be supramaximal or submaximal. Supramaximal
accentuated eccentrics use an eccentric load
greater than the lifter’s concentric 1RM,
while submaximal eccentrics use a load less
than concentric 1RM. The reviewed study
from Munger et al (1) used supramaximal
eccentric loading. So, when interpreting the
presently reviewed study, we’ll do so only in
the context of other supramaximal eccentric
studies.
Before reviewing this study from Munger et
al (1), I was already of the opinion that accentuated eccentrics didn’t boost long-term
strength. Specifically, a meta-analysis from
Buskard et al (4) found that accentuated eccentric loading did not significantly (p = 0.20)
improve 1RM lower body strength compared
to traditional training. Buskard did report an
effect size of 0.33 in favor of accentuated eccentrics versus traditional loading, but this
effect was driven by a single study by Cook
et al (5). Further, Buskard’s meta-analysis
only included four studies and seven total effects for maximal strength, and two of these
studies did not actually compare accentuated eccentric loading to traditional training.
One compared two eccentric only loading
paradigms (6), and the other compared eccentric only versus concentric only training
(7). Further, the Buskard meta did not analyze four studies (3 - MASS Review, 8, 9,
10) that compared accentuated eccentrics to
traditional loading. Altogether, seven longitudinal studies have examined the effects of
accentuated eccentrics on long-term strength
gains. Those seven studies (1, 3, 5, 8, 9, 10,
11) are summarized in Table 3.
After reviewing Table 3, you can probably
see why I don’t hold accentuated eccentrics in
high regard as a strategy to enhance strength.
Three of the previous studies show no clear
benefit of accentuated eccentrics; with the
addition of the presently reviewed study,
that total is now four of seven studies showing no benefit of accentuated eccentrics for
long-term strength. Further, the three studies
(1, 3, 5) that suggest a possible benefit come
with significant caveats. First, Douglas et al
(3), which Greg reviewed, did not equate for
load between the traditional and accentuated
eccentric groups. Therefore, the ~5% higher
load in the accentuated eccentric group could
account for their greater increase in squat
strength. Second, the findings from Cook et
al (5) were pretty remarkable. Cook found
that rugby players who performed eccentric
58
only (i.e., not followed by a concentric) increased regular squat 1RM more than traditional training. Third, it’s not possible to fully
interpret the results of English et al (11). English examined changes in leg press and calf
raises strength in five groups, all of which performed the same concentric load on each rep
(between 55-96% of 1RM). The five different
groups lifted either 120%, 100%, 66%, 33%,
or 0% of 1RM on the eccentric phase. The
120% group gained more leg press strength
than the 0, 33, and 66% groups, but not more
than the 100% group. Therefore, since most
concentric loading was greater than 66% of
1RM, we can conclude the accentuated eccentric led to greater leg press 1RM gains
59
than submaximal eccentrics. Still, we cannot
infer if accentuated eccentrics would have
outperformed traditional training. Further,
English also reported no difference between
accentuated and submaximal eccentrics for
calf strength gains. Therefore, on balance,
four longitudinal studies show no benefit for
accentuated eccentrics to enhance strength
over traditional training, and the three other
studies all come with significant caveats.
I think the presently reviewed study from
Munger et al (1) is one of the better designed
longitudinal accentuated eccentric studies.
First, this study equated load between the
accentuated eccentric and traditional groups,
and is one of only four in Table 3 to employ
trained individuals. Further, Munger also tested eccentric 1RM, which is unique from other
studies. Perhaps most importantly, Munger’s
study is the only longitudinal study to date
to train the free-weight squat with weight releasers. I’d wager that weight releasers are
perhaps the most common method for lifters
interested in max strength to apply an accentuated eccentric. So, in my book, the lack of
benefit for accentuated eccentrics for strength
in the presently reviewed study weighs pretty
high compared to the other studies. Of course,
this study is not without its faults, some of
which were already mentioned in the “Criticisms and Statistical Musings” section, and
others I’ll discuss in the “Next Steps” section. I do think it’s debatable if equating load
between groups by decreasing the concentric
load on the accentuated eccentric group is the
best strategy. On one hand, if someone is doing repeated eccentrics, their concentric force
production may be impaired, so concentric
load may have to be decreased. On the other hand, one could argue that the accentuated
eccentric group in this study was training in
a mode less specific to a 1RM, as their concentric load never reached more than 65% of
1RM.
The previous paragraph said, “On the one
hand, if someone is doing repeated eccentrics, their concentric force production may
be impaired, so concentric load may have to
be decreased.” That statement is true, but, if
someone only does one supramaximal eccentric, that may actually increase immediate
concentric strength. In this way, lifters can
also use accentuated eccentrics to potentiate
acute performance. I’ll keep this discussion
of accentuated eccentrics and acute strength
brief since we’ve discussed it twice before
(one, two). In short, similar to the body of literature on longitudinal studies, the data from
studies examining a supramaximal eccentric
to potentiate 1RM performance immediately
are underwhelming. In general, these acute
studies test if supramaximal eccentrics potentiate performance by loading a barbell to
a lifter’s 1RM attempt. These studies typically add weight releasers to the barbell for the
eccentric phase and see if this increases concentric 1RM or concentric velocity. For example, Doan et al. (2) found that an eccentric
squat with a load of 105% of 1RM increased
actual 1RM between 5-15kg for all subjects
compared to a squat 1RM test without a supramaximal eccentric. Ojasto and Hakkinen
(12) observed that eccentrics of 105, 110, and
120% of 1RM did not enhance actual 1RM
performance; however, subjects did all of
these conditions in the same session; thus,
60
it’s likely fatigue prevented any potential for
increased performance. Lates et al (13) found
that using 105% on the eccentric did not enhance concentric velocity at 80% of 1RM in
the bench press. Similarly, Wagle et al (14)
reported that accentuated eccentrics did not
improve acute squat concentric kinematics at
80% of 1RM. Another study from Merrigan
et al (15) found that a 120% of 1RM squat
eccentric improved squat velocity at 65% of
1RM on the concentric, but not at 80% of
1RM. Thus, the Merrigan findings suggest
that the benefits of accentuated eccentrics on
acute squat performance are load-dependent.
Additional Thoughts
Some additional thoughts and bits of information as we finish up. First, although not the
focus of this article, there are data on accentuated eccentric loading and hypertrophy. Of
the longitudinal studies (9, 16, 17) comparing
accentuated eccentrics to traditional training
for hypertrophy, none have shown accentuated eccentrics to boost muscle growth further.
Let’s briefly clarify some terminology. I have
used the term accentuated eccentric loading
throughout this article. Sometimes, you will
see the term eccentric overload used. While
both are technically correct, eccentric overload is often used when referring to flywheel
training. Flywheel training is technically an
accentuated eccentric. However, it would
be submaximal and not supramaximal eccentric loading, so I didn’t discuss it in this article.
However, we have a previous article evaluating the efficacy of eccentric overload with a
flywheel to potentiate concentric performance.
Lastly, perhaps the biggest hurdle to regularly implementing accentuated eccentrics
is practicality. Many studies (see Table 3)
implemented supramaximal eccentrics with
a dynamometer or specific machines, which
are absent in most gyms. The currently reviewed study and the aforementioned acute
studies used weight releasers, which may be
more well-known to MASS readers. However, weight releasers aren’t readily accessible
or practical to use. You could buy weight
releasers for about ~$100-200 USD (which
isn’t cheap but isn’t an atmospheric price) or
build some for about $50. It would be annoying to lug these to the gym, so you’d hope to
convince the gym staff to let you leave them
there. But the biggest hurdle is that, if you’re
going to use them to apply a supramaximal
eccentric load on every rep (such as in the
longitudinal studies), you would need two
training partners to attach the weight releasers
to the barbell after each rep. Obviously, this
is very cumbersome, and not practical if you
train alone. Some gyms that primarily serve
the strength athlete may have a community of
people willing to help with weight releasers,
since everybody tends to help each other out
in those environments. However, if you train
at a typical commercial gym, applying accentuated eccentrics all the time is probably not
feasible. Aside from the practical limitations,
the data supporting accentuated eccentrics is
underwhelming; thus, I wouldn’t go to the
trouble to implement them.
Next Steps
Despite covering seven different longitudinal
accentuated eccentric loading studies, we are
61
APPLICATION AND TAKEAWAYS
1. The reviewed study from Munger et al (1) found that, when percentage of 1RM was
equated between accentuated eccentric loading and traditional squatting, there
was no difference in strength gains after five weeks of training.
2. Accentuated eccentric training makes sense on the surface. Since lifters can
handle more weight on the eccentric, using a heavier load on that phase of a lift is
logical. However, the longitudinal data do not consistently suggest that accentuated
eccentric training enhances strength compared to traditional training.
3. Overall, I wouldn’t expect a huge boost in long-term strength with accentuated
eccentrics. I also wouldn’t recommend somebody rush out and implement the
practice due to cost and logistical concerns. However, there also doesn’t seem
to be a downside to accentuated eccentrics. So, if you are at a gym with weight
releasers and some friends (or even just acquaintances) are willing to help, then
give it a shot.
still in desperate need of more, since only one
of them used weight releasers on a free-weight
barbell exercise. I’d like to see this current
study replicated, but with two changes. First, I
would make the study at least eight weeks long
to provide more time for any between-group
differences to flesh out. I’d also replace the
unsupervised group with a group performing
accentuated eccentrics with a concentric load
equated for velocity loss and/or repetitions in
reserve to the traditional loading group. Equating concentric load for velocity loss or reps in
reserve would take some pilot testing. For example, let’s say the lifters could perform an
average of six reps to a 2RIR at 80% of 1RM
on the squat. Then, if the loading on the supramaximal eccentric was 105% of 1RM, the
researchers should determine the concentric
load resulting in six reps to a 2RIR following
the supramaximal eccentric. This would allow
the study design to equate the level of effort (at
least in terms of velocity loss and repetitions
in reserve) on the set.
62
References
1. Munger CN, Jones BC, Halloran IJ, Eggleston GG, Post PG, Brown LE, Berning JM.
Short-Term Effects of Eccentric Overload Versus Traditional Back Squat Training on
Strength and Power. International Journal of Kinesiology and Sports Science. 2022 Jan
30;10(1):1-8.
2. Doan BK, Newton RU, Marist JL, Triplett-McBride NT, Koziris LP, Fry AC, Kraemer
WJ. Effects of increased eccentric loading on bench press 1RM. The Journal of Strength
& Conditioning Research. 2002 Feb 1;16(1):9-13.
3. Douglas J, Pearson S, Ross A, McGuigan M. Effects of accentuated eccentric loading on
muscle properties, strength, power, and speed in resistance-trained rugby players. The
Journal of Strength & Conditioning Research. 2018 Oct 1;32(10):2750-61.
4. Buskard AN, Gregg HR, Ahn S. Supramaximal eccentrics versus traditional loading
in improving lower-body 1RM: A meta-analysis. Research Quarterly for Exercise and
Sport. 2018 Jul 3;89(3):340-6.
5. Cook CJ, Beaven CM, Kilduff LP. Three weeks of eccentric training combined with
overspeed exercises enhances power and running speed performance gains in trained
athletes. The Journal of Strength & Conditioning Research. 2013 May 1;27(5):1280-6.
6. Schroeder ET, Hawkins SA, Jaque SV. Musculoskeletal adaptations to 16 weeks of
eccentric progressive resistance training in young women. The Journal of Strength &
Conditioning Research. 2004 May 1;18(2):227-35.
7. Johnson BL, Adamczyk JW, Tennoe KO, Stromme SB. A comparison of concentric and
eccentric muscle training. Medicine and science in sports. 1976 Jan 1;8(1):35-8.
8. Yarrow JF, Borsa PA, Borst SE, Sitren HS, Stevens BR, White LJ. Early-phase
neuroendocrine responses and strength adaptations following eccentric-enhanced
resistance training. The Journal of Strength & Conditioning Research. 2008 Jul
1;22(4):1205-14.
9. Walker S, Häkkinen K, Haff GG, Blazevich AJ, Newton RU. Acute elevations in
serum hormones are attenuated after chronic training with traditional isoinertial but
not accentuated eccentric loads in strength‐trained men. Physiological Reports. 2017
Apr;5(7):e13241.
10. Godard MP, Wygand JW, Carpinelli RN, Catalano S, Otto RM. Effects of accentuated
eccentric resistance training on concentric knee extensor strength. The Journal of
Strength & Conditioning Research. 1998 Feb 1;12(1):26-9.
11. English KL, Loehr JA, Lee S, Smith SM. Early-phase musculoskeletal adaptations to
63
different levels of eccentric resistance after 8 weeks of lower body training. European
journal of applied physiology. 2014 Nov;114(11):2263-80.
12. Ojasto T, Häkkinen K. Effects of different accentuated eccentric load levels in eccentricconcentric actions on acute neuromuscular, maximal force, and power responses. The
Journal of Strength & Conditioning Research. 2009 May 1;23(3):996-1004.
13. Lates AD, Greer BK, Wagle JP, Taber CB. Accentuated eccentric loading and cluster
set configurations in the bench press. The Journal of Strength & Conditioning Research.
2022 Jun 1;36(6):1485-9.
14. Wagle JP, Cunanan AJ, Carroll KM, Sams ML, Wetmore A, Bingham GE, Taber CB,
DeWeese BH, Sato K, Stuart CA, Stone MH. Accentuated eccentric loading and cluster
set configurations in the back squat: A kinetic and kinematic analysis. The Journal of
Strength & Conditioning Research. 2021 Feb 1;35(2):420-7.
15. Merrigan JJ, Tufano JJ, Falzone M, Jones MT. Effectiveness of Accentuated Eccentric
Loading: Contingent on Concentric Load. International Journal of Sports Physiology and
Performance. 2020 Nov 12;1(aop):1-7.
16. Brandenburg, J.P.; Docherty, D. The effects of accentuated eccentric loading on strength,
muscle hypertrophy, and neural adaptations in trained individuals. J Strength Cond Res
2002, 16, 25-32.
17. Friedmann-Bette, B.; Bauer, T.; Kinscherf, R.; Vorwald, S.; Klute, K.; Bischoff, D.;
Muller, H.; Weber, M.A.; Metz, J.; Kauczor, H.U., et al. Effects of strength training with
eccentric overload on muscle adaptation in male athletes. Eur J Appl Physiol 2010, 108,
821-836.
█
64
Study Reviewed: The Effects of High Fiber Rye, Compared to Refined Wheat, on Gut
Microbiota Composition, Plasma Short Chain Fatty Acids, and Implications for Weight Loss
and Metabolic Risk Factors (the RyeWeight Study). Iversen et al. (2022)
Rye Versus Wheat: Evidence-Based
Sandwich Guidelines
BY ERIC TREXLER
A recent MASS article discussed the utility of fiber restriction
for short-term weight cuts, but also cautioned against longterm adherence to low-fiber diets. A new study points to some
potential mechanisms by which fiber might favorably impact body
composition and health.
65
KEY POINTS
1. A 2021 study indicated that a hypocaloric diet with heavy intake of rye products
led to greater weight loss and reductions in C-reactive protein (an inflammation
biomarker) than the same intervention with refined wheat products. The present
study sought to determine if these effects were related to changes in the gut
microbiota or circulating short-chain fatty acid levels.
2. Gut microbiota data are always a bit messy, but the results generally hint at the
idea that rye and wheat led to divergent changes in the gut microbiota, which
led to divergent changes in plasma short-chain fatty acid levels, which may
have played a small role in the rye group’s more favorable body composition
and C-reactive protein changes.
3. Preliminary studies suggest that short-chain fatty acids are associated
with a wide range of positive physiological effects, and might be partially
responsible for some of the health benefits linked to high fiber intake. Adequate
consumption of fiber and other non-digestible carbohydrates is currently the
best way to promote the production of short-chain fatty acids.
B
ack in Issue 5 of Volume 6, I wrote
an in-depth article about short-term
fiber restriction. The results of that
study indicated that acute fiber restriction
can facilitate short-term weight cuts lasting
less than a week (2), but I also cautioned that
fiber restriction is not an advisable long-term
strategy for healthy lifters with no clinically
relevant gastrointestinal symptoms or conditions. The reasoning for this recommendation is very straightforward: fiber does a ton
of good stuff, through a number of potential
mechanisms. In that article, I included a brief
acknowledgement of one particularly fascinating mechanism by stating, “It’s possible
that fiber is impacting these outcomes by
promoting more favorable diversity of the gut
microbiome and increasing the production of
short-chain fatty acids and other metabolites
with wide-ranging physiological effects.”
That’s precisely what the presently reviewed
study (1) investigated. This study was a
secondary analysis from a 12-week weight
loss study called the RyeWeight Study (3),
in which 242 males and females with overweight or obesity were randomly assigned to
two different intervention groups. One group
was instructed to eat high-fiber rye products,
while the other was instructed to eat refined
wheat products, all within the context of a
12-week hypocaloric diet. 207 participants
completed the full dietary intervention, with
results indicating that rye consumption led
to significantly larger reductions in weight,
body-fat percentage, and C-reactive protein
levels (a biomarker of inflammation) than refined wheat consumption. The present analysis included data from all 207 study completers to determine if the positive effects
of the rye intervention might be related to
66
changes in gut microbiota composition (measured from fecal samples) or short-chain fatty
acid levels (measured from plasma samples).
In short, the results suggested that the rye intervention led to some small changes in gut
microbiota composition and plasma shortchain fatty acid levels, which appeared to be
related to changes in body composition and
metabolic risk factors. Of course, the devil is
in the details with any research involving the
gut microbiota, so let’s dive in and see what
we can learn from this study.
Purpose and Hypotheses
Purpose
This was an exploratory analysis of a previously conducted weight loss trial. The primary purpose was to investigate “the effects of
a dietary intervention on gut microbiota and
plasma short-chain fatty acids and their potential roles as mediators of weight-loss induced by a hypocaloric diet rich in high fiber
rye foods [versus] refined wheat in a 12-week
weight-loss trial.” The secondary purpose
was to investigate “if improvements in clinical risk markers caused by the intervention
could be related to changes in gut microbiota
and [short-chain fatty acids] in plasma and if
baseline microbiota was associated with the
response to the intervention.”
Hypotheses
The authors did not explicitly state hypotheses, which likely reflects the exploratory
nature of the analysis (in other words, they
were seeking to observe and learn rather than
confirm or deny). Having said that, the introduction section of the paper clearly suggests
that these researchers were at least interested
in the possibility that high-fiber rye products
might favorably impact body composition
and metabolic risk factors by influencing gut
microbiota composition and short-chain fatty
acid production.
Subjects and Methods
Subjects
The present study recruited males and females between the ages of 30-70 years old to
participate. Study participants were required
to have a BMI of 27-35 kg/m2 at the time of
enrollment, along with sufficiently low values
for serum thyroid stimulating hormone levels
(≤4.00 mIU/L), plasma low-density lipoprotein (LDL) cholesterol levels (<5.3 mmol/L)
levels, plasma triglyceride levels (≤1.8
mmol/L), and blood pressure (<160/105
mmHg), in addition to sufficiently high hemoglobin levels (≥120 g/L). Subjects were
excluded from participation if they used nicotine products, did more than ten weekly hours
of strenuous physical activity, implemented a
weight loss program within the previous six
months, used any weight loss medications or
supplements within the previous six months,
had any relevant cardiometabolic or gastrointestinal health issues, or were unable to
consume any of the food products provided
by the researchers. After screening 590 interested participants, 242 participants eventually began the dietary intervention; each diet
group had 121 total participants, with roughly 61% of them being female. Both groups
were around 56-57 years old, weighed 8889 kg, and had 39-41% body-fat and a BMI
of about 30, with no significant differences
67
between groups. Of the 242 individuals who
began the dietary intervention, 207 of them
completed it (108 in the rye group, and 99 in
the wheat group). The baseline characteristics for the 207 study completers were quite
similar to the full sample of 242 study starters, and were very similar when comparing
across groups.
Methods
After enrollment, 317 participants were provided with dietary guidance and wheat-based
food products in order to complete a 2-week
introductory period before the actual dietary
intervention. This introductory period allowed for some degree of baseline standardization, and also allowed the researchers to
exclude any participants who had insufficient
adherence to instructions or were particularly
resistant to weight loss in the context of this
specific diet intervention. Participants were
excluded from participation if they failed to
meet a particular weight target during the
2-week introductory period (0.5kg of weight
loss for non-menstruating participants, and
weight stability for menstruating participants), but they were not made aware of this
requirement ahead of time (which could have
modified their behavior). Of the 317 participants who began the introductory period, 242
moved forward to the next step, which was
to be randomly assigned to a group (wheat or
rye) for the 12-week dietary intervention.
During the intervention, participants were
given dietary guidance that was intended
to promote a daily energy deficit of around
500kcal, with a macronutrient breakdown of
45-60% carbohydrate, 10-20% protein, and
25-40% fat. In conjunction with this general
dietary guidance, participants were provided an assortment of wheat or rye products
(depending on their group assignment), including a variety of breakfast cereals, crip
breads, and soft breads. They were instructed to consume enough of these products to
reach approximately 650kcal/day, which was
about 30-50% of their total energy intake for
the day. The wheat and rye products were
individually packaged in neutral and unlabeled containers, but the study wasn’t strictly
“blinded” due to inherent differences in appearance and taste when comparing wheat
products to rye products. The rye products
provided around 30g/day of fiber, whereas the refined wheat products only provided
around 8g/day of fiber.
In the original paper from this study (3), the
researchers primarily focused on outcomes
related to body composition, appetite, and
measures related to cardiometabolic risk factors (such as blood pressure, blood lipids,
and biomarkers related to inflammation and
insulin sensitivity). The presently reviewed
study (1) expands upon the primary findings
by measuring changes in the gut microbiota (via fecal samples) and changes in plasma
levels of short-chain fatty acids. With these
outcome measures, the researchers were specifically interested in observing how the two
dietary interventions impacted the gut microbiota and circulating short-chain fatty acid
levels, and exploring how any such changes
might be related to the previously published
changes in body composition and cardiometabolic risk factors. Outcomes were measured
at weeks 0, 6, and 12 of the intervention.
68
Findings
To fully describe the results of this study with
a high level of detail would be an inefficient
exercise for the purposes of MASS readers. To
illustrate my point, the results section of this
study spans from page 5 to page 15 in the published PDF version, which is about five times
longer than the discussion section. In addition,
that’s just referring to the results of the newest
paper from this study; I also need to recap some
results from the first paper in order to properly
contextualize the findings from the new paper.
As a result, my goal is to concisely highlight
the most pertinent findings from this study.
Both subjective and objective measures of
compliance suggested that both groups ad-
hered to their instructions to consume the
assigned wheat or rye products. Furthermore, weighed food logs suggested that both
groups reduced their calorie intake by around
100-200 kcal/day; not exactly the intended
target of 500 kcal/day, but enough to promote
weight loss, and a similar calorie reduction
among both groups. Due to differences in
the prescribed food products, the rye group
increased daily fiber intake from 22g/day to
around 37g/day, while the wheat group maintained a steady fiber intake of around 19-21g/
day. The rye intervention led to significantly
greater weight loss than the wheat condition;
at week 12, there was a 1.08kg difference between groups, after adjusting for baseline differences. As shown in Table 1, the rye group
had significantly lower values for BMI, waist
69
circumference, hip circumference, fat mass,
and android fat (after adjusting for baseline
values) when compared to the wheat group.
The rye intervention also led to statistically
significant differences in C-reactive protein
levels at week 6 and week 12; a drop was observed in the rye group, whereas values remained pretty stable in the wheat group. Even
after removing some influential outliers, the
rye group had CRP values that were 21% and
28% lower than the wheat group at weeks 6
and 12, and differences at both time points
remained significant whether outliers were
retained or removed from the analysis. There
was a tendency to observe lower LDL levels
in the rye group as well; the between-group
difference was statistically significant at week
6 (difference = 0.14 mmol/L; p = 0.013), but
not at week 12 (difference = 0.10 mmol/L; p
= 0.095). One might assume that these differ-
ences in C-reactive protein and LDL might
relate to weight loss, but these analyses were
adjusted for the observed change in body
weight, and correlations between weight
change and changes in C-reactive protein
and LDL were not statistically significant.
However, there were no other consistent and
statistically significant between-group differences for the other cardiometabolic outcomes
assessed or for subjective appetite outcomes.
As for the gut microbiota results, the researchers analyzed changes in 110 different bacteria
(grouped and compared at the genus level).
With this many different statistical tests and
comparisons occurring simultaneously, it’s
critically important to adjust the analysis to
minimize the likelihood of false positives. Of
the 110 bacterial genera (the plural term for
“genus”) tested, 45 differed significantly between the two groups. However, after adjust-
70
ing for multiple comparisons, between-group
comparisons remained statistically significant for only 8 of these 45 genera at either the
6-week or 12-week mark. The relative abundance values of these 8 bacteria at week 0, 6,
and 12 are presented in Table 2. Relative to the
wheat group, the rye group experienced larger reductions in abundance of (Ruminococcus) torques group, (Eubacterium) ventriosum
group, Anaerofilum, and Holdemania, and
larger increases in Agathobacter, UCG-003,
and Haemophilus. Both groups experienced
decreases in Anaerotruncus (with a larger decrease observed in the rye group), and both experienced increases in Bifidobacterium.
The researchers measured plasma levels of
nine different short-chain fatty acids, which
are presented in Table 3. Generally speaking,
between-group comparisons were not statistically significant. Acetic acid levels were
significantly higher in the rye group than the
wheat group at week 6, but this was no longer the case at week 12. However, butyric
acid levels increased within the rye group,
and were significantly higher than the wheat
group at weeks 6 and 12.
Finally, the researchers tested a bunch of correlations to explore relationships among variables of interest (such as gut bacteria abundance, short-chain fatty acid levels, body
composition, and cardiometabolic risk factors). It wouldn’t be productive to include results for all of the individual correlation tests,
as there were dozens and dozens presented in
71
the paper, and many of them were quite weak
and, frankly, likely to be spurious. However,
the general results of the numerous correlation tests can be summarized as follows:
• Baseline abundance of certain bacteria
were correlated with changes in body
composition, C-reactive protein, and other cardiometabolic risk factors, but not in
a consistent manner.
• Changes in the abundance of certain bacteria were correlated with changes in body
composition, C-reactive protein, and other cardiometabolic risk factors, but not in
a consistent manner.
• In the rye group, changes in butyric acid
and propionic acid were inversely correlated with body composition changes,
and changes in succinic acid were inversely correlated with changes in C-reactive protein. In the wheat group, changes
in succinic acid were inversely correlated
with body composition changes.
The researchers used nonparametric Spearman correlation tests (rather than parametric
Pearson correlation tests), so the correlation
coefficients are presented as Spearman’s rho
values rather than Pearson’s r values. Nonetheless, both types of correlation coefficients
refer to the relative strength of the correlation between two variables and range from
-1 to 1, so they’re interpreted in a similar
manner. In the vast majority of cases in the
presently reviewed study, correlations were
fairly weak (ranging from -0.25 to +0.25),
and could possibly be confounded by changes in body weight over the course of the 12week weight loss intervention. Many of the
observed correlations were also quite inconsistent, despite meeting the threshold for statistical significance (for example, abundance
of was Barnesiella was positively correlated
with C-reactive protein changes in the rye
group, but negatively correlated with C-reactive protein changes in the wheat group).
This inconsistency was particularly noteworthy for correlations involving bacterial abundance values.
Criticisms and Statistical
Musings
If you look closely at Table 2, some of the
p-values might catch you by surprise. For
example, check out the p-values for Agathobacter – the corrected p-value comparing the
groups was p = 0.01 at week 6 (well below
the significance threshold), but p = 0.985 at
week 12 (nearly as high and non-significant
as a p-value can get). What gives?
There are two important considerations to
keep in mind when assessing these microbiota comparisons. First, it’s important to recognize that the gut microbiota changes a ton,
even in the absence of any particular intervention. For example, a recent study found
that for 78% of the microbial genera they
tested, day-to-day variation (within the same
individual) for absolute abundance was way
larger than the variation between individuals, and up to 100-fold changes in abundance
were observed during the study period (4).
The presently reviewed study looked at relative abundance, which is a little more stable than measures of absolute abundance, but
the point still holds: the gut microbiota is a
72
rapidly shifting landscape with considerable
day-to-day variation, and any study using
stool samples from an isolated point in time
is looking at a single snapshot of a constantly
moving target.
The second consideration is statistical in
nature. Due to the huge number of bacterial
genera tested, the researchers needed to adjust their analysis to limit the risk of false
positives. To accomplish this, they used the
“false discovery rate” method, which is one
of my favorites. This is a sequential adjustment method, and that the first step of the
procedure is to complete the huge list of
comparisons (in this case, comparing rye
versus wheat values for the relative abundance of a long list of bacterial genera), then
to rank-order their raw (unadjusted) p-values from smallest to largest. The next step
is to apply a correction to each p-value, but
the actual magnitude of adjustment varies as
you progress from higher-ranked p-values
to lower-ranked p-values. As a result, the
degree to which a particular p-value is adjusted can change based on where it lands
in this rank-ordered list of p-values. This
means the adjusted p-value for a particular
bacterial genus can change from week 6 to
week 12, even if the unadjusted p-value was
exactly the same; if it moved up or down the
ranking list (due to changes in the p-values
corresponding to other bacterial genera), the
magnitude of adjustment would change. We
can think of this like a bench press competition; you could bench 150kg and win your
competition in June, but you could bench
the exact same weight at another competition a few months later and come in 9th
place, solely because the competitors at the
second meet put up better numbers.
To extend that metaphor, these adjusted
p-values for bacterial abundance outcomes
are like looking at the competition placings
of a powerlifter whose strength levels vary
wildly from day-to-day, and who happens to
be competing against a huge field of lifters
whose strength levels also vary wildly from
day-to-day. As such, the variability observed
in Table 2’s adjusted p-values is to be expected, and we should always interpret gut microbiota data with day-to-day and person-to-person variability at top of mind.
Interpretation
The results leave us with a lot to chew on,
so a quick summary is warranted. When randomly assigned to weight loss interventions
involving a bunch of high-fiber rye consumption or a bunch of low-fiber refined wheat
consumption, the rye group lost more weight
and had larger decreases in C-reactive protein, which is a biomarker for inflammation.
The two different dietary interventions led to
subtle, but in some cases statistically significant, divergences related to the gut microbiota. Bacteria in the gut feed on fiber and other
digestion-resistant carbohydrates to create
short-chain fatty acids, and the rye and wheat
interventions also led to subtle divergences
related to plasma short-chain fatty acid concentrations. A huge collection of fairly weak
and inconsistent correlations were presented, which is to be expected for correlations
involving bacteria abundance from single
fecal samples. However, the results collectively hint at the idea that rye and wheat led
73
to divergent changes in the gut microbiota,
which led to divergent changes in plasma
short-chain fatty acid levels, which may have
played a small role in the rye group’s more
favorable body composition and C-reactive
protein changes.
It’s important to acknowledge that these data
don’t offer rock-solid evidence for a causative chain of events by which carb source
selection leads to gut bacteria changes, gut
bacteria changes lead to circulating shortchain fatty acid changes, and short-chain
fatty acids cause clinically relevant changes
in body composition, inflammation, or cardiometabolic risk factors. These findings are
generally, loosely compatible with such a hypothesis, but are far from offering convincing
proof. Having said that, if you’re the type of
person who requires rock-solid evidence that
is intuitive and consistent, you might want
to stay on the sidelines of the gut microbiota
research for the next couple of decades; it’s
very interesting, but it’s very messy.
It’s very possible that all of this talk about
short-chain fatty acids has taken you by surprise. In the fitness world, you hear all about
plenty of other acids – essential amino acids,
branched-chain amino acids, and even essential fatty acids make their way into all sorts
of evidence-based content. You rarely hear
about short-chain fatty acids, but I believe
they deserve more attention than they get.
Over the last few decades, there has been a
considerable amount of research effort dedicated to understanding how short-chain fatty
acids are formed, and what they actually do
in the body.
For starters, the short-chain fatty acids circulating in our blood are primarily created
by the bacterial fermentation of non-digestible carbohydrates in the large intestine. As
a result, production of short-chain fatty acids
depends on having the right bacteria present
in the colon, and feeding them the substrates
they like. These substrates include nonstarch
polysaccharides, resistant starch, oligosaccharides, disaccharides, and certain sugar alcohols (5). It might sound like meal planning
has just become a complex puzzle to solve,
but the reality is much simpler; you’re very
likely to provide a large and diverse supply
of these substrates if you’re consuming a
well-rounded diet with plenty of fruits, vegetables, whole-grain products, or legumes
(5). The presently reviewed study measured
nine different short-chain fatty acids, but the
main ones are acetate (acetic acid), propionate (propionic acid), and butyrate (butyric
acid). These three short-chain fatty acids represent up to 90-95% of the short-chain fatty
acid content in the colon (6), which are typically produced in a ratio of approximately
3:1:1 (7).
Our understanding of the physiological activities of short-chain fatty acids is preliminary,
and largely fueled by mechanistic studies in
non-human research models, or observational studies identifying correlations between
certain outcomes and short-chain fatty acid
concentrations in plasma or in stool samples.
However, the preliminary evidence seems
to suggest that short-chain fatty acids have
wide-ranging positive impacts throughout the
body. Given that short-chain fatty acids are
produced in the gastrointestinal tract, their
74
localized effects are a straightforward place
to start. Butyrate is a key energy source for
cells of the gastrointestinal tract, and shortchain fatty acids (and butyrate in particular)
have been associated with beneficial effects
on constipation (5), irritable bowel syndrome
(8), colon cancer (9), and other medical conditions of the GI tract. The short-chain fatty
acids that are not locally metabolized within
the GI tract are then transported to the liver via the portal vein. Research suggests that
short-chain fatty acids can reduce hepatic
inflammation, hepatic cholesterol synthesis,
and liver fat deposition, which has spurred
interest in investigating the clinical potential
of short-chain fatty acids in the context of
nonalcoholic fatty liver disease (10). Moving
on to more directly MASS-relevant topics, a
recent review paper by Byrne and colleagues
(7) detailed the apparent positive (but modest) effects of short-chain fatty acids on appetite regulation (e.g., increased satiety),
energy homeostasis (e.g., increased energy
expenditure), and blood lipids. In addition,
a separate review by Canfora and colleagues
explored the apparent positive (but modest)
effects of short-chain fatty acids on glycemic
control and insulin sensitivity (11). Shortchain fatty acids have even been linked to the
nervous system, with recent reviews by Silva
et al (12) and Mirzaei et al (13) discussing
preliminary findings related to anxiety, depression, cognition, and a number of clinical
conditions impacting the brain and other tissues of the nervous system.
When first hearing about the potential beneficial effects of short-chain fatty acids, one might
wonder why they haven’t seen a lot of research
I WOULDN’T BE SURPRISED
IF SHORT-CHAIN FATTY ACIDS
PLAY AN IMPORTANT ROLE
IN MEDIATING SOME OF THE
POSITIVE EFFECTS ASSOCIATED
WITH THE INTAKE OF VARIOUS
HIGH-FIBER FOODS OR
CERTAIN PATTERNS OF GUT
BACTERIAL ABUNDANCE
on “real-world” outcomes stemming from this
long list of proposed mechanisms. I don’t want
to oversell these preliminary observations or
overstate their importance or generalizability,
but there is one striking observation that leads
me to view these findings with more optimism
than your typical mechanistic or observational
findings: in many cases, the preliminary research on short-chain fatty acids points toward
mechanisms that seem to be very compatible
with the less-understood benefits of dietary
fiber. For example, research has suggested
(14) that high-fiber diets might favorably impact inflammation, depressive symptoms, and
the immune system, but the mechanistic links
are very unclear. I am speculating here, but I
wouldn’t be surprised if short-chain fatty acids
play an important role in mediating some of
the positive effects associated with the intake
of various high-fiber foods or certain patterns
of gut bacterial abundance. In other words, the
75
real-world evidence related to short-chain fatty acids could be right under our noses, buried
within interventions that focus on dietary fiber
intake or the gut microbiota.
You might be wondering why we’re several
pages into an article that seeks to explain a
1kg difference in weight loss from switching
from rye bread to wheat bread. As I see it,
many consumers of evidence-based fitness
content have collectively gathered at two opposite ends of a spectrum. On one end, you’ll
find the simplifiers. They might argue that
you should eat the right number of calories,
consume plenty of protein, and lift a few
times per week, with any attention devoted to
other details representing the futile exercise
of “majoring in the minors” (I’m being a bit
hyperbolic, but you get the idea). On the other
end of the spectrum, you’ll find the optimizers; they’re eager to act upon even the most
speculative and physiologically inconsequential ideas, as long as there’s a semi-plausible mechanism by which the idea may lead
to some type of miniscule improvement that
represents a tiny step toward optimization.
When it comes to carbohydrate sources, dietary fiber, and downstream effects on the
gut microbiota and short-chain fatty acid production, I encourage both the simplifiers and
the optimizers to move toward the middle of
the spectrum.
For the simplifiers, it’s important to recognize that simplifying is good, but oversimplifying can be counterproductive. Different
foods and beverages provide unique combinations of nutrients within unique food matrices, and the physiological effects of a particular food or beverage are more nuanced
than their calorie or macronutrient content.
While the “major” factors like calorie and
protein intake are certainly where most of
our focus should be placed, we can’t be
overly reductive in the way we view foods
and beverages. The presently reviewed RyeWeight Study doesn’t suggest that we need
to lose sleep over every little detail of our
diet, but it does provide an example of how a
seemingly minor food swap can have a measurable impact on tangible physiological
outcomes. When combined with the growing body of evidence pertaining to shortchain fatty acids, these findings suggest that
we can use food source selection as a tool to
optimally support our health and wellness.
If your sole focus is on weight change and
body composition, there’s no question that
calorie intake, protein intake, and resistance
training are your biggest priorities. However, if you’re also interested in feeling your
best throughout the process, and potentially
making small swaps and substitutions that
could impact hunger, satiety, gastrointestinal
comfort, and cardiometabolic risk factors,
it would be advisable to seek out a diverse
selection of food sources that provide some
non-digestible carbohydrate, and to aim for
a minimum daily fiber target. As mentioned
in a previous MASS article, a good starting
point for daily fiber intake is about 14g of fiber per 1,000kcals in the diet, but dieters can
individualize from there based on gastrointestinal comfort, satiety, stool frequency and
consistency, and personal preference.
For the optimizers, it’s important to understand that we are currently working with a
fairly incomplete understanding of the gut
76
microbiota and short-chain fatty acids. As a
result, we shouldn’t overestimate the degree
to which we can intentionally induce very
specific alterations in gut bacterial abundance or short-chain fatty acid production.
For many optimizers, the knee-jerk response
to hearing about promising short-chain fatty acid findings might be to order a targeted
probiotic supplement, or even to supplement
with a short-chain fatty acid like butyrate directly. However, that’s probably not the best
route to take at this point in time. As noted in
a previous MASS article, the probiotic supplementation research lacks clarity and consistency, and there are very few guarantees
when it comes to targeted, standalone probiotic supplementation. There are also logistical
hurdles related to the ingestion of short-chain
fatty acids. If we use butyrate as an example,
we see that amounts consumed from conventional food products are relatively low (5), and
supplement formulation is made challenging
by butyrate’s poor oral bioavailability (15)
and its pungent smell and taste (5). For now,
the optimizers should resist their biohacking
tendencies; rather than opting to directly supplement with short-chain fatty acids or very
specific probiotic bacterial strains, a more advisable (and very boring) strategy is to simply
consume a well-balanced diet with plenty of
fruits, vegetables, whole-grain products, or
legumes. Some researchers have suggested
that incorporating some fermented foods or
beverages into one’s diet might also be helpful for increasing short-chain fatty acid levels
(16); it certainly wouldn’t hurt, but I’m personally a bit skeptical that this would move
the needle to a physiologically meaningful degree. It’s also possible that there may be some
beneficial impact of a combined supplement
with prebiotics plus a thoughtfully constructed blend of probiotic bacterial strains, but
when it comes to promoting short-chain fatty
acid production, a food-focused approach is
certainly the simplest (and potentially most
effective) option. In summary, the dietary
recommendations to support short-chain fatty acid production are virtually identical to
best-practice recommendations for supporting “gut health” and microbial diversity, and
that’s not coincidental.
The results from the first paper from the RyeWeight Study (3) suggest that we can leverage fiber intake and food selection as a tool to
modestly (but favorably) impact changes in
weight and body composition. The presently
reviewed follow-up paper (1) suggests that
the beneficial effects induced by opting for
rye products rather than refined wheat products might have been mediated, at least in part,
by differential effects on the gut microbiota
and subsequent production of short-chain fatty acids. This is compatible with preliminary
mechanistic and observational evidence indicating that short-chain fatty acids can have
small but physiologically relevant impacts on
a variety of different tissues, organ systems,
and physiological processes. For now, there
doesn’t seem to be a particularly specific way
to “biohack” your way to these positive outcomes, but the classic advice to consume a
well-rounded diet with plenty of fruits, vegetables, whole-grain products, or legumes
should go a long way. There is no question
that calorie intake, protein intake, and resistance training are the most important and
impactful factors driving changes in body
77
APPLICATION AND TAKEAWAYS
When we make food choices, we aren’t just feeding ourselves – we’re feeding our
gut bacteria as well. By consuming more nonstarch polysaccharides, resistant
starch, oligosaccharides, disaccharides, and certain sugar alcohols, it’s very possible
that we can influence the production of short-chain fatty acids by our gut microbiota,
which appear to have wide-ranging effects across several different tissues, organ
systems, and physiological processes. A modest shift in short-chain fatty acid
production probably won’t be a huge “game changer” for any singular outcome, but
might confer small advantages for a broad selection of outcomes, including body
composition and appetite regulation. We still have a lot to learn about short-chain
fatty acids, but for now it seems advisable to aim for adequate daily fiber intake
(starting with a target of 14g of fiber per 1,000kcals in the diet and individualizing
from there), and to eat a well-balanced diet with plenty of fruits, vegetables, wholegrain products, or legumes.
composition. However, adequate total fiber
intake and consumption of a diverse selection
of non-digestible carbohydrate sources is associated with numerous benefits. There’s no
need to sweat over every little food choice,
but strategic food source selection offers
an opportunity to modestly improve a wide
range of diet-related outcomes within the
framework of flexible dieting.
ty acid production, and studies clearly documenting the physiological consequences of
successfully altering short-chain fatty acid
levels. When those types of studies become
available, we’ll have a much more thorough
understanding of the true potential of shortchain fatty acids, and the feasibility of actually acting upon that information with practical
dietary strategies.
Next Steps
The research related to short-chain fatty acids
is very promising, but very preliminary. At
this point, we have applied studies exploring
the positive effects of fiber intake, mechanistic and observational studies indicating
what short-chain fatty acids appear to do in
the body, and observational studies hinting
at a link between the two areas of research.
Moving forward, we’ll need some longitudinal human trials in which targeted dietary
interventions are implemented with the specific intention of influencing short-chain fat-
STRATEGIC FOOD SOURCE
SELECTION OFFERS AN
OPPORTUNITY TO MODESTLY
IMPROVE A WIDE RANGE OF
DIET-RELATED OUTCOMES
WITHIN THE FRAMEWORK
OF FLEXIBLE DIETING.
78
References
1. Iversen KN, Dicksved J, Zoki C, Fristedt R, Pelve EA, Langton M, et al. The Effects of
High Fiber Rye, Compared to Refined Wheat, on Gut Microbiota Composition, Plasma
Short Chain Fatty Acids, and Implications for Weight Loss and Metabolic Risk Factors
(the RyeWeight Study). Nutrients. 2022 Apr 17;14(8):1669.
2. Foo WL, Harrison JD, Mhizha FT, Langan-Evans C, Morton JP, Pugh JN, et al. A ShortTerm Low-Fiber Diet Reduces Body Mass in Healthy Young Men: Implications for
Weight-Sensitive Sports. Int J Sport Nutr Exerc Metab. 2022 Mar 21;1–9; ePub ahead of
print.
3. Iversen KN, Carlsson F, Andersson A, Michaëlsson K, Langton M, Risérus U, et al.
A Hypocaloric Diet Rich In High Fiber Rye Foods Causes Greater Reduction In Body
Weight And Body Fat Than A Diet Rich In Refined Wheat: A Parallel Randomized
Controlled Trial In Adults With Overweight And Obesity (The Ryeweight Study). Clin
Nutr ESPEN. 2021 Oct;45:155–69.
4. Vandeputte D, De Commer L, Tito RY, Kathagen G, Sabino J, Vermeire S, et al.
Temporal Variability In Quantitative Human Gut Microbiome Profiles And Implications
For Clinical Research. Nat Commun. 2021 Nov 18;12(1):6740.
5. Pituch A, Walkowiak J, Banaszkiewicz A. Butyric Acid In Functional Constipation. Prz
Gastroenterol. 2013;8(5):295–8.
6. Ríos-Covián D, Ruas-Madiedo P, Margolles A, Gueimonde M, de Los Reyes-Gavilán
CG, Salazar N. Intestinal Short Chain Fatty Acids and their Link with Diet and Human
Health. Front Microbiol. 2016;7:185.
7. Byrne CS, Chambers ES, Morrison DJ, Frost G. The Role Of Short Chain Fatty Acids In
Appetite Regulation And Energy Homeostasis. Int J Obes. 2015 Sep;39(9):1331–8.
8. Załęski A, Banaszkiewicz A, Walkowiak J. Butyric Acid In Irritable Bowel Syndrome.
Prz Gastroenterol. 2013;8(6):350–3.
9. Hamer HM, Jonkers D, Venema K, Vanhoutvin S, Troost FJ, Brummer RJ. Review
Article: The Role Of Butyrate On Colonic Function. Aliment Pharmacol Ther. 2008 Jan
15;27(2):104–19.
10. Juárez-Hernández E, Chávez-Tapia NC, Uribe M, Barbero-Becerra VJ. Role Of Bioactive
Fatty Acids In Nonalcoholic Fatty Liver Disease. Nutr J. 2016 Aug 2;15(1):72.
11. Canfora EE, Jocken JW, Blaak EE. Short-Chain Fatty Acids In Control Of Body Weight
And Insulin Sensitivity. Nat Rev Endocrinol. 2015 Oct;11(10):577–91.
79
12. Silva YP, Bernardi A, Frozza RL. The Role of Short-Chain Fatty Acids From Gut
Microbiota in Gut-Brain Communication. Front Endocrinol. 2020 Jan 31;11:25.
13. Mirzaei R, Bouzari B, Hosseini-Fard SR, Mazaheri M, Ahmadyousefi Y, Abdi M, et
al. Role Of Microbiota-Derived Short-Chain Fatty Acids In Nervous System Disorders.
Biomed Pharmacother. 2021 Jul;139:111661.
14. Barber TM, Kabisch S, Pfeiffer AFH, Weickert MO. The Health Benefits of Dietary
Fibre. Nutrients. 2020 Oct 21;12(10):E3209.
15. Steliou K, Boosalis MS, Perrine SP, Sangerman J, Faller DV. Butyrate Histone
Deacetylase Inhibitors. BioResearch Open Access. 2012 Aug;1(4):192–8.
16. Annunziata G, Arnone A, Ciampaglia R, Tenore GC, Novellino E. Fermentation of Foods
and Beverages as a Tool for Increasing Availability of Bioactive Compounds. Focus on
Short-Chain Fatty Acids. Foods. 2020 Jul 25;9(8):999.
█
80
Research Briefs
BY GREG NUCKOLS & ERIC TREXLER
In the Research Briefs section, Greg Nuckols and Eric
Trexler shares quick summaries of recent studies. Briefs
are short and sweet, skimmable, and focused on the needto-know information from each study.
82
88
94
102
107
110
116
119
Squatting with Bands May be Ideal for
Improving Jump Performance
Estimating the Energy Cost of Exercise to
Inform Dietary Adjustments
Can Stretching Directly Cause Muscle
Growth?
An Update on Caffeine Habituation and
Sensitivity to Ergogenic Effects
Oral Contraceptives Still Don’t Impact Muscle
Growth and Strength Gains
How Much Do Cortisol Levels Matter For
Training Adaptations?
Is Your Split Jerk Limited by Upper Body or
Lower Body Strength?
Should You Take a Krill Pill To Enhance
Strength Or Hypertrophy?
81
Study Reviewed: Effects of Variable Resistance Training Within Complex Training on
Neuromuscular Adaptations in Collegiate Basketball Players. Shi et al. (2022)
Squatting with Bands May be Ideal for Improving
Jumping Performance
BY GREG NUCKOLS
Looking through the MASS archives, I was
surprised to see that we haven’t directly reviewed a study investigating the impact of
barbell training with bands since Volume 1
(2). In fact, the one article we wrote about
training with bands can’t even be found in the
archives. It was part of a mini bonus issue we
released to incentivize sign-ups during the
pre-sale period of our launch sale.
However, there’s a reason why we haven’t
written much about training with bands, in
spite of the fact that training with bands is
still somewhat popular: there just hasn’t been
much longitudinal research on the topic lately. There have been a few studies looking at
acute effects (things like bar velocity, power
output, post-activation potentiation, and electromyographic measures within a single training session), but I think a 2018 meta-analysis by dos Santos and colleagues really put
the brakes on longitudinal studies (3). That
meta-analysis found that variable resistance
training (training with bands or chains) didn’t
lead to larger strength gains than training
with plain old metal plates, and I suspect that
many sports scientists didn’t want to invest a
ton of time into studying an intervention that
was likely to end with null results.
With that said, there’s a major application of
training with bands that’s (weirdly) under-researched: training for power output and explosiveness. Training with bands or chains
seems like it would be ideal for improving
jumping performance. When you’re training
for maximal strength, you’re ultimately limited by your ability to produce force through
a specific (rather small) “sticking region”
of a particular lift. Both bands and straight
weight can present a near-maximal challenge
to the lifter through the sticking region, so it
makes sense that they’re similarly effective
for promoting maximal strength (assessed
via 1RMs of barbell exercises). However,
jump performance is dictated by your ability to produce force rapidly through a much
longer range of motion: you start accelerating your body at the start of the concentric
phase of a jump, and you need to rapidly apply force until the moment your feet leave the
ground. With traditional barbell exercises,
the top part of the range of motion is typically pretty easy – you can ease up after you
82
make it through the sticking region, and still
complete the lift. With bands, on the other
hand, the effective load on the bar increases
as you progress through the concentric phase
of the squat, requiring more effort through a
larger portion of the concentric phase than
you’d experience with straight weight. Thus,
squatting with bands seems like it should be
a more specific stimulus than squatting with
straight weight for the purpose of improving
jump performance.
With that in mind, the present study by Shi and
colleagues aimed to compare the effects of
squatting with straight weight versus squatting
with bands for improving maximal strength
and explosive performance (1). 21 collegiate
basketball players completed this study. Lack
of resistance training experience wasn’t an exclusion criterion, but most collegiate basketball players have spent at least a bit of time
in the weight room, and the subjects squatted
approximately 125kg (275lbs) pre-training, so
I suspect that all of the subjects had some prior
resistance training experience.
Subjects were randomized into two groups
that each completed an eight-week training
program, with two training sessions per week.
Pre- and post-training, researchers assessed
subjects’ maximal squat strength (1RM),
countermovement jump height, squat jump
height, standing broad jump distance, and
10-20m sprint times. The training sessions
focused exclusively on lower body strength
and power development, and consisted of
complex training (one set of a strength exercise, followed by one set of a plyometric
or high-velocity exercise). Subjects rested 3
minutes after each set of squats, and 4 minutes after each set of jumps. Details of the
training program can be seen in Table 1.
One group of subjects completed all of their
squats with straight weight: just a barbell and
metal plates. The other group of subjects replaced a portion of the load with resistance
from elastic bands that were anchored to the
floor and looped over the barbell. The resistance provided by the bands was approximately 35% of 1RM at the top of the lift, and
83
approximately nil at the bottom of the lift.
Since the resistance provided by the bands
(averaged over the entire range of motion)
was about 17% of 1RM, the load provided by
metal plates was reduced by approximately
17% of 1RM to equalize the total loading between the groups.
For example, if a subject had a 200kg squat
1RM, and they were training with 80% of
1RM, they’d just use 160kg if they were in
the straight weight group. If they were in
the bands group, the bands on the bar would
provide approximately 70kg of resistance at
the top of the lift (200 × 35%), and virtually
no resistance at the bottom of the lift. The
average resistance provided would be 35kg,
so the load on the bar would be reduced by
35kg. So, they’d complete their set of squats
with 125kg of straight weight (from the barbell and metal plates), plus the bands. The
total resistance would be approximately
195kg at the top of the lift, and 125kg at the
bottom of the lift, for an average resistance
of 160kg.
The results of the study were pretty straightforward: squatting with bands improved vertical jumping performance more than squatting with straight weight. The difference was
statistically significant for squat jump improvements (+21.4% vs. +12.9%; p = 0.008),
but not quite statistically significant for countermovement jump improvements (+12.9 %
vs. +5.6%; p = 0.056). Changes in all other
outcomes were similar between groups: large
increases in squat 1RM, small increases in
broad jump distance, and minimal changes in
10m and 20m sprint times. You can see these
results in Figure 1 and Table 2.
The results of this study comport well with
studies by Katushabe and Kramer (4) and by
Joy and colleagues (5). Katushabe and Kramer studied male soccer players over 6 weeks,
and found that squatting with bands tended to
improve squat jump height to a greater extent
than squatting with straight weight (+2.67cm
vs. +1.38cm; p = 0.055). Like the present
study, other measures of explosive performance didn’t differ between groups – chang-
84
es in 40m sprint times and performance in
an agility drill were similar between groups.
Joy and colleagues studied collegiate basketball players with at least one year of resistance training experience over 5 weeks. They
also found that training with bands tended
to result in larger increases in vertical jump
height, maximal power (assessed during
vertical jumping), and rate of power development than squatting with straight weight.
Furthermore, improvements in 40-yard dash
times didn’t differ between groups.
However, a study by Andersen and colleagues attained different results that were
nonetheless illuminating (6). Trained women
completed 10 weeks of training, and completed countermovement jump tests at three dif-
ferent depths: descending until they achieved
approximately 60°, 90°, and 120° of knee
flexion. Improvements in countermovement
jump height didn’t significantly differ between groups, but the nominal increase was
larger in the band group at 60° of knee flexion
(+3cm vs. +2.6cm), and larger in the straight
weight group at 90° and 120° of knee flexion
(+1.9cm vs. +2.9cm, and +1.5 vs. 2.4cm).
I suspect that the study by Andersen and colleagues obtained different results, primarily
due to differences in the band resistance used
in these four studies. In the present study
by Shi and colleagues (1), band tension was
35% of 1RM at the top of the lift, and ~0%
of 1RM at the bottom. In the study by Joy
and colleagues (5), band tension was 30% of
85
1RM at the top of the lift, and ~0% of 1RM
at the bottom. In the study by Katushabe and
Kraemer (4), they report there was “20% of
load coming from the power bands, and the
difference coming from the weight plates”
(which is somewhat vague, but appears to be
within the same general ballpark as the Shi
and Joy studies). However, band resistance
accounted for more than 40% of the total resistance in the Andersen study (6): 58% of
the resistance at the top of the lift and 44% of
the resistance at the bottom of the lift at the
start of the study, and 38% of the resistance at
the top of the lift and 27% of the resistance at
the bottom of the lift by the end of the study.
When you squat with a lot of band tension,
you can completely transform the typical
resistance curve of a squat. When band tension is dialed in just right, you wind up with
a smooth resistance curve that matches your
natural strength curve pretty well, resulting in
a consistent, high level of effort throughout
the lift. With excessive band tension, on the
other hand, the feel of the lift is completely
inverted: instead of being hard at the bottom
and easy at the top (as it would be with straight
weight), the lift becomes easy at the bottom
and very hard at the top. With heavy enough
band tension, you may hit your sticking point
when you’re in a quarter squat position.
Through that lens, the results of the Andersen
study make a lot of sense – they comport well
with the principle of specificity, and don’t actually conflict with the results of Shi, Joy, and
Katushabe. The subjects in the band group in
the Andersen study experienced pretty large
increases in countermovement jump performance at 60° of knee flexion, because that
matches the range of motion where they were
actually being challenged by their squat training. However, they experienced smaller improvements at 90° and 120° of knee flexion
because their squat training wasn’t providing
quite as much of a challenge at deeper knee
flexion angles. In the other three studies, band
tension was high enough to still provide more
of a challenge than straight weight through
the top portion of the concentric phase, but
not so high that the bottom portion of the
concentric became meaningfully easier.
Overall, we can take three things away from
the research on squatting with bands:
1. Squatting with bands doesn’t increase
your 1RM squat to a greater extent than
squatting with straight weight, on average.
2. Squatting with bands probably does increase
your jumping ability to a greater extent than
squatting with straight weight. However,
using bands probably doesn’t increase your
performance in less specific tests of power
output and explosive performance (sprinting, broad jumps, or change of direction) to
a greater extent than squatting with straight
weight. Both of these findings comport well
with the principle of specificity.
3. Excessive band tension can be counterproductive. Using band resistance equal
to ~30-40% of 1RM at the top of the lift
(and virtually no resistance at the bottom
of the lift) to replace an amount of plates
equal to 15-20% of 1RM seems to get the
job done. More band tension beyond that
point is unlikely to further improve results, and may actually result in smaller
improvements in jump performance.
86
To be clear, these takeaways are somewhat
tentative. Four studies isn’t a huge body of
literature, and while the outcomes related to
jump performance certainly lean in favor of
squatting with bands, only a handful of the
differences between groups achieved statistical significance, and we’re also not dealing
with enormous effect sizes. However, I’m a
bit more willing to interpret these results liberally because they fit well within the firm
conceptual framework of the principle of
specificity. With that said, a more conservative wait-and-see approach to interpreting
these results is certainly still very justifiable.
References
1. Shi L, Lyons M, Duncan M, Chen S, Chen
Z-X, Guo W, Han D. (2022). Effects of
variable resistance training within complex
training on neuromuscular adaptations in
collegiate basketball players. Journal of
Human Kinetics. 2022
Training on Sprint Speed, Agility, Vertical
Jump Height, and Strength in Collegiate
Soccer Players. Int J Exerc Sci. 2020
Aug 1;13(4):950-963. PMID: 32922637;
PMCID: PMC7449328.
5. Joy JM, Lowery RP, Oliveira de Souza E,
Wilson JM. Elastic Bands as a Component
of Periodized Resistance Training. J
Strength Cond Res. 2016 Aug;30(8):21006. doi: 10.1519/JSC.0b013e3182986bef.
PMID: 23669815.
6. Andersen V, Fimland MS, Kolnes
MK, Saeterbakken AH. Elastic Bands
in Combination With Free Weights
in Strength Training: Neuromuscular
Effects. J Strength Cond Res. 2015
Oct;29(10):2932-40. doi: 10.1519/
JSC.0000000000000950. PMID:
25807031.
2. Rivière M, Louit L, Strokosch A, Seitz LB.
Variable Resistance Training Promotes
Greater Strength and Power Adaptations
Than Traditional Resistance Training
in Elite Youth Rugby League Players. J
Strength Cond Res. 2017 Apr;31(4):947955. doi: 10.1519/JSC.0000000000001574.
PMID: 27465633.
3. Nilo Dos Santos WD, Gentil P, Lima de
Araújo Ribeiro A, Vieira CA, Martins
WR. Effects of Variable Resistance
Training on Maximal Strength: A Metaanalysis. J Strength Cond Res. 2018
Nov;32(11):e52-e55. doi: 10.1519/
JSC.0000000000002836. PMID:
30540285.
4. Katushabe ET, Kramer M. Effects of
Combined Power Band Resistance
87
Study Reviewed: Estimating Energy Cost of Body Weight Resistance Exercise Using a
Multistage Exercise Test. Nakagata et al. (2022)
Estimating the Energy Cost of Exercise to Inform
Dietary Adjustments
BY ERIC TREXLER
This is going to be a rather atypical Research
Brief, because I plan to discuss the reviewed
study extremely briefly. The justification for
this decision is two-fold: the findings largely reinforce the conclusions of a previous
MASS article (while adding a tiny bit of nuance), and I want to provide some very practical recommendations that are described with
a high degree of detail and clarity. As such,
the vast majority of this particular Research
Brief will focus on how to practically apply
the information.
You might recall a recent MASS article
about a study investigating the energy cost
of resistance exercise. The researchers found
that average (group-level) energy expenditure during low-, moderate-, and high-load
exercise was approximately 6kcal/min, with
individual values consistently falling within
the 4-8 kcal/min range (2). Greg even created
a very helpful calculator you can use to determine your added energy cost from resistance
exercise (that is, the additional energy expenditure induced from resistance exercise,
above and beyond your typical resting level
of energy expenditure).
The presently reviewed study (1) also investigated the energy cost of resistance-type
exercise, using a very different approach.
Their study investigated bodyweight exercise, which can be operationally viewed as
a unique type of low-load resistance training, and their study design lacked ecological
validity (that is, it’s not particularly representative of a typical training protocol), but
allows for some helpful observations. Participants (15 men) completed three different
bodyweight exercises (calf raise, squat, and
push-up) with very slow repetition cadences. Each repetition lasted six seconds (with
a 3-second concentric phase and a 3-second
eccentric phase), and repetitions were performed with a variety of different frequencies
(1, 2, 3, 4, 5, or 6 reps per minute, equally
spaced throughout the minute). For example,
participants completed a repetition every 30
seconds in the 2 reps per minute condition,
and completed a rep every 10 seconds in the
6 reps per minute condition. This means that
the work:rest ratio ranged from 6:54 (in the 1
rep/min condition) to 36:24 (in the 6 rep/min
condition).
88
The energy expenditure for each condition is
plotted in Figure 1. As we can see, energy expenditure varies from exercise to exercise, and
is particularly low when dealing with small
muscle groups (in this case, the calves). Expenditure is also influenced by relative intensity; bodyweight squats activate a large amount
of muscle mass, but are also considerably easier, rep-for-rep, when compared to push-ups.
Easier exercises (calf raise and squat) stayed
well below the 6kcal/min heuristic value associated with traditional resistance training, but
as push-ups started to get more challenging,
it looks like they were starting to level off as
they approached ~6kcal/min (although more
data would be needed at higher rep frequencies
to determine if this was a legitimate trend or a
couple of noisy data points). Generally speaking, these findings seem to be fairly compatible with the general heuristic that challenging
resistance-type exercise is likely to fall within
the range of 4-8kcal/min, and that you’re more
likely to end up near the higher end of that
range if you’re activating large muscle groups,
working at a high relative intensity, or training
with a relatively high work:rest ratio.
exercise energy expenditure, which has multiple practical use cases. For example, you
might want a good estimate of exercise energy expenditure because you’re adding some
extra exercise to your weekly routine during
a cut, and you’d like to know approximately
how large of an energy deficit you’re creating.
Conversely, you might be adding some extra
exercise for health purposes during a bulk,
and you’d like to make sure you aren’t unintentionally shrinking your energy surplus.
Alternatively, you might be doing a particular bout of exercise that is a departure from
your normal routine, and you’d like to make
sure you’re fueling your body adequately for
the energy demands of the activity.
If you’re primarily interested in maintaining
a particular level of energy balance (whether it’s a deficit, surplus, or maintenance) and
you’re tracking your body weight and calorie
intake regularly, one option is to skip the estimation process altogether and simply adjust
I’m well aware that this study falls short of
delivering any earth-shattering revelations
that fundamentally change the way we view
resistance exercise. However, I thought it
was valuable to cover this study for two reasons. First, it lends some additional support to
Greg’s proposed heuristic for resistance-type
exercise from Issue 3 of Volume 6, while
adding a little bit of nuance and expanding the scope of the conversation to include
bodyweight exercises. Second, it gives us an
opportunity to revisit the topic of estimating
89
your calorie intake as you go. Your weight
changes observed after implementing the new
exercise activity will tell you whether your
energy balance is more positive or more negative than you intended. For example, if you
were losing weight at your intended rate, but
started losing weight faster than intended after adding some exercise to your weekly routine, then an increase in calorie intake would
be warranted. In this scenario, you’d increase
calorie intake incrementally until you were
back on track with your intended rate of
weight loss. This strategy (explained in more
detail here) is a fantastic option that relies on
the fewest assumptions possible, but it’s hard
to implement without the help of good software that smoothes out some of the “noise”
in your weight data and provides quantitative
analytics to guide changes in calorie intake. It
also doesn’t address the specific use case of
prospectively planning a fueling strategy for
an arduous exercise bout that isn’t typically
part of your habitual exercise routine.
Another option is to use simple heuristics
rather than customized or individualized estimation methods. If you’re doing any kind of
ambulatory exercise like walking, jogging, or
running, then a decent heuristic is to assume
that you’ll burn 100kcal per mile (3). For
most forms of resistance exercise, it’s safe
to assume that you’re burning around 6kcal/
min, although it could range from roughly
4-8 kcal/min depending on your body mass
and the characteristics of the exercise bout
(2). Heuristics are great when you’re looking for a simple strategy (which is made even
simpler by Greg’s calculator), but they are a
bit oversimplified and lack flexibility for in-
dividualized estimates that can be adjusted to
accommodate a wide range of exercise modalities and intensities.
In this research brief, I’d like to propose a third
option, which is individualized MET-based
calculations. A MET, or metabolic equivalent,
is a standardized unit of measurement that
quantifies the rate of oxygen consumption observed in humans at rest. Well, kind of – the
original MET was calculated by measuring the
resting oxygen consumption of one 40-yearold guy (4), so I guess it actually quantifies the
resting oxygen consumption of one human.
Nonetheless, one MET equals 3.5 milliliters
of oxygen consumption per kilogram of body
mass per minute (3.5mLO2/kg/min). This metric is very useful for two reasons. First, it gives
us a standardized unit for quantifying the metabolic demand of exercise, scaled to a resting
level. If a particular exercise increases oxygen
consumption to 7mLO2/kg/min, this can also
be quantified as 2 METs, which indicates that
oxygen consumption is 2x the typical resting
level. Likewise, if a particular exercise increases oxygen consumption to 14mLO2/kg/min,
this can also be quantified as 4 METs, which
indicates that oxygen consumption is 4x the
typical resting level. That might not seem like
a huge deal, but it has allowed for researchers to compile enormous lists assigning MET
values to a wide range of exercise modalities
and intensities. For example, the 2011 Compendium of Physical Activities compiled by
Ainsworth and colleagues (5) provides MET
values for over 820 distinct forms of exercise
and non-exercise physical activity.
Second, and most importantly, oxygen consumption (and by extension, METs) can be
90
directly converted to an estimated energy
cost, in kilocalories. A detailed justification
of this conversion process is beyond the scope
of a research brief, but the simplified version
is that oxygen is consumed (and carbon dioxide is produced) when we metabolize energy
substrates to create ATP. This relationship
directly links energy oxygen consumption
to energy expenditure, and allows us to estimate the energy cost of various activities
based on the amount of oxygen consumed.
In fact, just about every energy expenditure
value we’ve ever mentioned in MASS that
was obtained from a lab-based measurement,
whether at rest or during exercise, is based
on this relationship. These measures are often
reported as kilocalories (a unit of energy), but
the measurement technique most commonly
used is called indirect calorimetry, which actually functions by measuring the oxygen and
carbon dioxide concentrations of inhaled and
exhaled breath rather than directly measuring
energy production (hence the name, indirect
calorimetry).
It’s often said that 1 MET is equivalent to
3.5mLO2/kg/min, which converts to an energy expenditure rate of 1 kcal/kg/hour. This is
actually an oversimplification; if you crunch
the numbers, you’ll find that 3.5mLO2/kg/
min actually converts to an energy expenditure rate of 1.05 kcal/kg/hour. Surprisingly,
the minimal amount of information we’ve
covered gives us everything we need to calculate a decent energy cost estimate for a huge
range of exercise modalities and intensities.
First, you need to estimate the MET value of
your exercise bout; the 2011 Compendium of
Physical Activities is the list I prefer to use,
but you can certainly find accurate MET values elsewhere. Second, you need to calculate
your estimated resting energy expenditure
(REE). You may be stunned to hear this, but
the resting metabolic characteristics of that
one 40-year-old guy aren’t perfectly representative of the entire human population.
As a result, researchers have demonstrated
that you can enhance the accuracy of exercise energy cost calculations by adjusting for
your own resting energy expenditure (4). I’d
personally recommend using the Cunningham equation (6) for this: REE = 500 + fatfree mass (in kg) × 22. This gives you REE
in kcal/day, which you can divide by your
weight (in kg) and 24 (hours per day) to convert your REE to kcal/kg/hour (REE [kcal/
kg/hour] = REE [kcal/day] ÷ weight (kg) ÷
24). To estimate the energy cost of your exercise bout, enter this information (along with
the duration of the bout, in minutes) into the
following equation:
However, we aren’t quite done yet. As Greg
pointed out in his recent research brief, we’re
actually interested in the added energy expenditure (beyond the resting level), not total energy expenditure. So, we should convert our REE (kcal/day) to kcals per minute
(REE [kcal/min] = REE [kcal/day] ÷ 1440),
then multiply that by the duration of our exercise bout to figure out how much energy
we would have burned if we rested instead
of exercising. We’ll subtract that value from
the energy cost of exercise, but there’s one
final step: we need to deal with energy com-
91
pensation, which has been covered in previous MASS articles (one, two, three). In short,
when we increase our exercise energy expenditure, we often experience compensatory reductions in other areas of energy expenditure.
Our current “best estimate” (7) is to anticipate
an average of 30% compensation (e.g., adding 100 extra kilocalories of exercise would
only increase total daily energy expenditure
by 70 kilocalories because you compensated
for 30 of them), but a typical range probably
spans from ~10-50%. If you’re pretty shredded, in an aggressive calorie deficit, and very
active, you’re likely to end up closer to 50%;
if you’re not shredded, in neutral-to-positive
energy balance, and have a low level of daily
energy expenditure from exercise and other
physical activity, you’re probably closer to
10%. In order to calculate a “compensation
adjustment,” you’ll need to make an educated guess about your most likely magnitude
of energy compensation, then calculate the
correction factor as follows: Compensation
Adjustment (CA) = (100 - % compensation)
÷ 100. So, if you anticipate a 30% magnitude
of energy compensation, your compensation
adjustment would be 0.7, and if you anticipate 40% compensation, the adjustment value would be 0.6.
The final step is to subtract resting energy expenditure from the energy cost of exercise,
then to multiply the result by the compensation adjustment (CA):
I know, that’s a lot of steps. However, this
approach to energy cost estimation is valu-
able because it can be individualized based
on your body size and estimated energy expenditure, and can be applied to any exercise
modality and intensity for which you can
find (or estimate) a reasonably accurate MET
value. For example, the 2011 Compendium
of Physical Activities provides estimates for
light calisthenics, vigorous calisthenics, circuit training, various intensities of resistance
training, and over 800 other types of exercise.
In addition, I made this spreadsheet to take
care of the actual calculations for you. So,
between the 2011 Compendium of Physical
Activities and the online calculator, you can
estimate the added energy cost of just about
any exercise or physical activity you complete. The estimate won’t be perfect, as it can
be impacted by extraneous factors like genetics, cardiorespiratory fitness level, mechanical efficiency, and environmental conditions,
but it’s probably our best equation-based option for prospective energy cost predictions.
References
1. Nakagata T, Yamada Y, Naito H. Estimating Energy Cost of Body Weight Resistance Exercise Using a Multistage Exercise Test. J Strength Cond Res. 2022 May
1;36(5):1290–6.
2. João GA, Almeida GPL, Tavares LD,
Kalva-Filho CA, Carvas Junior N, Pontes FL, et al. Acute Behavior of Oxygen
Consumption, Lactate Concentrations,
and Energy Expenditure During Resistance Training: Comparisons Among
Three Intensities. Front Sports Act Living. 2021;3:797604.
92
3. Loftin M, Waddell DE, Robinson JH,
Owens SG. Comparison Of Energy Expenditure To Walk Or Run A Mile In
Adult Normal Weight And Overweight
Men And Women. J Strength Cond Res.
2010 Oct;24(10):2794–8.
4. Byrne NM, Hills AP, Hunter GR, Weinsier RL, Schutz Y. Metabolic Equivalent:
One Size Does Not Fit All. J Appl Physiol. 2005 Sep;99(3):1112–9.
5. Ainsworth BE, Haskell WL, Herrmann
SD, Meckes N, Bassett DR, Tudor-Locke
C, et al. 2011 Compendium Of Physical
Activities: A Second Update Of Codes
And Met Values. Med Sci Sports Exerc.
2011 Aug;43(8):1575–81.
6. Cunningham JJ. A Reanalysis Of The
Factors Influencing Basal Metabolic Rate
In Normal Adults. Am J Clin Nutr. 1980
Nov;33(11):2372–4.
7. Careau V, Halsey LG, Pontzer H, Ainslie PN, Andersen LF, Anderson LJ, et
al. Energy Compensation And Adiposity In Humans. Curr Biol. 2021 Oct
25;31(20):4659-4666.e2.
93
Study Reviewed: Influence of Long-Lasting Static Stretching on Maximal Strength, Muscle
Thickness and Flexibility. Warneke et al. (2022)
Can Stretching Directly Cause Muscle Growth?
BY GREG NUCKOLS
Stretching is one of a handful of topics where
I consistently find myself at odds with other “evidence-based” fitness folks. The popular narrative is that stretching is useless at
best (“It doesn’t actually increase range of
motion long-term!” and “It doesn’t reduce
injury risk!”) and counterproductive at worst
(“It hinders performance!” and “It reduces
muscle growth!”). However, when you actually dig into the research on stretching, an
interesting, nuanced picture emerges. For example, intense stretching immediately before
exercise might reduce muscle growth (2), but
light stretching between sets may actually increase muscle growth (3). Similarly, intense,
long-duration stretching right before an exercise test may reduce force and power output
(4), but longitudinal stretching interventions
may actually increase strength over time (5).
In short, stretching isn’t all good or all bad
– whether it helps or hinders you largely depends on the timing, intensity, and duration
of your stretching sessions.
Lately, there’s been more interest in
stretch-mediated hypertrophy. We pretty
consistently observe that training through a
full range of motion results in more muscle
growth than training through the top part of a
range of motion (i.e., deep squats cause more
quad growth than half squats; 6). There are
two key differences between training through
a full range of motion and training through
the top half of a range of motion: 1) the total
range of motion is different, and 2) training
through the top part of a range of motion generally involves not training your prime movers at long muscle lengths. So, which of these
differences explains why training through a
full range of motion results in more muscle
growth?
Recent research suggests that the second factor – training at long muscle lengths – is far
more important than the total range of motion
you train through. If that weren’t the case,
only training the top half of a lift would result in just as much muscle growth as only
training the bottom half of a lift, and both
would result in less muscle growth than training through a full range of motion. However, that’s now what the research shows. Partial range of motion training at long muscle
lengths (for example, just doing the bottom
94
half of a squat) causes at least as much muscle
growth as training through a full range of motion, and considerably more muscle growth
than partial range of motion training at short
muscle lengths (7, 8).
This research suggests that there’s something special about training at long muscle
lengths. At the moment, the leading hypothesis to explain these findings is the existence
of “stretch-mediated hypertrophy.” In other
words, there’s something about tension on a
muscle in a stretched position that more effectively promotes hypertrophy than tension
on a muscle in a shortened position. And,
while I personally think that the existence
of stretch-mediated hypertrophy provides us
with a plausible, elegant idea that ties this
entire line of research together, there’s one
problem with it: there’s not a ton of evidence
that stretching can directly cause hypertrophy. We do know that stretching can put a
lot of tension on a muscle – sufficiently intense stretching can lead to muscle damage and DOMS, much like resistance training (14) – so sufficiently intense stretching
should directly result in muscle hypertrophy
if the notion of stretch-mediated hypertrophy
is correct. If it doesn’t, then we need to find
some other explanation for why training at
long muscle lengths results in more muscle
growth than training at short muscle lengths.
en’t complete slam dunks. For example, Panidi and colleagues found that a stretching intervention increased gains in gastrocnemius
cross-sectional area in adolescent volleyball
players (9), but a skeptic might note that
while gains in cross-sectional area differed
between conditions, increases in muscle
thickness didn’t differ between the stretching and non-stretching conditions. Furthermore, Simpson and colleagues found that a
six-week stretching intervention increased
gastrocnemius thickness in a sample of 11
males (10), but this finding also has a slight
asterisk: when comparing stretched versus
nonstretched legs, the increase in muscle
thickness was slightly greater in the stretched
legs (p = 0.04 for the time-by-condition interaction effect), but you can see the results
for yourself in Figure 1. It’s certainly not a
night-and-day difference.
So, the stretch-mediated hypertrophy hypothesis finds itself in a weird spot. It would
At this point, there have been dozens of studies on stretching, but hypertrophy following
stretching interventions has only been observed a handful of times, so a skeptic could
easily argue that these findings were false
positives, swimming in a sea of “true” null
results. Furthermore, the positive findings ar-
95
explain the results of studies examining the
effect of range of motion on hypertrophy. It
would explain why isometrics at long muscle lengths may result in more muscle growth
than isometrics at short muscle lengths (11).
It also has a lot of support from animal studies (on birds, rodents, and cats), finding that
intense stretching interventions result in a
ton of muscle growth (both hypertrophy and
fiber hyperplasia; 12). However, there’s not
much human evidence supporting the idea
that a stretch stimulus effectively and independently promotes muscle growth.
In situations like this, it’s nice to have a
proof-of-concept study to fall back on. In
proof-of-concept studies, you stack the deck
in favor of the effect you’d like to observe.
We’ve discussed this concept previously in
the context of concurrent training. The first
concurrent training study by Hickson was a
great proof-of-concept study (13). It found
that when you put subjects on a really intense training program and a really intense
endurance training program, subjects gain
less strength than they would when following a program without any endurance training. After Hickson established the existence
of this “interference effect,” subsequent research was able to flesh out the details: how
much endurance training is required to result
in significant interference? What populations
are most likely to experience the interference
effect? How does the timing of endurance
and resistance training affect the interference
effect?
Until recently, however, there wasn’t a great
proof-of-concept study investigating the direct impact of stretching on muscle growth.
The ideal proof-of-concept study would use
an intervention that would likely exceed anything that would ever be used in the “real
world,” to simply establish that stretching
can independently cause hypertrophy. If such
a study failed to find that stretching directly causes hypertrophy, that would put the
concept of stretch-mediated hypertrophy on
shakier footing. However, if such a study did
find that stretching can cause hypertrophy in
humans, it would put the idea of stretch-mediated hypertrophy on firmer evidentiary
grounds, and open the door for subsequent
studies to flesh out the details.
As you might suspect, the study I’m reviewing in this research brief is the exact sort of
proof-of-concept study I’ve been waiting on
(1).
52 subjects were randomized into two groups:
a stretching group and a non-stretching control group. Furthermore, the legs of the subjects in the stretching groups were randomly
divided within-subject: one leg underwent
the stretching intervention, and the other leg
served as a non-stretching control leg. All
subjects were “athletically active,” having
“performed two or more training sessions per
week in a gym or a team sport continuously
for the previous six months.”
The stretching intervention was quite intense. Each subject used an orthotic device
that locked the foot in place while pulling
the ankle into dorsiflexion. The orthotic is illustrated in Figure 2. The amount of stretch
provided by this device could be manually adjusted, and subjects were instructed to
cinch the stretching mechanism to the point
96
that the stretch resulted in pretty significant
discomfort (an 8 on a subjective 1-10 pain
scale). From there, they sat upright in a chair,
propped their leg up on another chair of the
same height, and stretched their calf for a
full hour. This setup can be seen in Figure
2. The stretching intervention lasted for six
weeks, and subjects stretched their calf for a
full hour every day. As their range of motion
improved, they were instructed to pull their
ankle into more and more dorsiflexion using the orthotic device, to maintain the same
discomfort rating throughout the intervention. Subjects were also instructed to keep a
stretching diary, noting their daily stretching
duration and intensity (the dorsiflexion angle
of the orthotic device).
For our purposes, the most important outcome was the change in gastrocnemius
thickness. However, changes in dorsiflexion
range of motion were also assessed, as were
changes in dynamic and isometric plantarflexion strength. Hypertrophy was assessed
via ultrasound. Flexibility was assessed via
the knee-to-wall test, and by measuring the
maximal dorsiflexion angle that could be
achieved on the orthotic device used in the
stretching intervention. Strength was assessed unilaterally on a leg press (subjects
performed maximal isometric contractions
and calf raise 1RM tests).
Isometric strength increased significantly
more in the stretching leg of the stretching
group (+16.8%) than in the non-stretching
leg of the strength group (+1.4%), whereas
the control group experienced small reductions in strength (reductions of 1.4-1.6%).
Dynamic strength followed a similar pattern,
97
though the non-stretching legs in the stretching group also experienced a notable increase
in strength, suggesting that some amount of
cross-education occurred: calf raise 1RMs increased by 25.1% in the stretching leg of the
stretching group and 11.4% in the non-stretching leg of the stretching group, whereas the
control group experienced small reductions
in strength (reductions of 1.2-3.6%). These
results can be seen in Table 1.
Changes in flexibility followed a similar pattern. Knee-to-wall test performance increased
substantially in the stretching leg of the stretching group (+13.2%), while all other groups and
conditions experienced small reductions in
performance (reductions of 0.8-2.4%). Maximum dorsiflexion angle on the orthotic device
increased by 27.3% in the stretching leg of the
stretching group and 7.5% in the non-stretching leg of the stretching group (suggesting that
98
some cross-education occurred), whereas the
control group experienced minimal changes
(increases of 0-0.7%). These results can be
seen in Table 2.
Finally, and most importantly, gastrocnemius thickness increased substantially in
the stretching legs of the stretching group
(+15.3%), while the non-stretching legs experienced a much smaller increase (+2.1%).
This was a large (ŋ2 = 0.406; an eta squared
of 0.406 is comparable to a Cohen’s d effect
size of about 1.65), statistically significant (p
= 0.015) difference. These results can be seen
in Table 3.
This study demonstrates that static stretching
with sufficient intensity and volume can directly cause hypertrophy in humans. While
this isn’t a completely novel finding (Simpson and Panidi previously observed similar
effects; 9, 10), the results of this study are
stronger and more conclusive than those observed in prior research. This is a pretty important finding, because it places the idea
of stretch-mediated hypertrophy on firmer evidentiary grounds. Furthermore, this
study confirms that longitudinal stretching
interventions can directly increase dynamic
strength and isometric force output (5).
At first, I was tempted to write that, like most
proof-of-concept studies, the results of the
present study likely can’t be directly translated into “real world” practice. However, upon
further reflection, I actually think that someone could directly apply the intervention
used in the present study. If you don’t mind
shelling out some money for an ankle stretching orthosis, and you spend at least an hour
per day sitting around (watching TV, playing video games, working on your computer,
etc.), you could conceivably try this intervention out for yourself, with minimal disruption
to your day-to-day life. It’s just a question of
how much discomfort you’re willing to endure to grow your calves.
Realistically, though, this study is just a first
step. Future research should examine other
muscles and manipulate stretching duration
and intensity to see just how much stretching
is required to provide an adequate stimulus
for muscle growth. Finally, we’re still a long
way from fully understanding stretch-mediated hypertrophy. We can observe its effects,
and we’ve now established its underlying
assumption (stretch per se can independent-
99
ly contribute to hypertrophy in humans), but
there’s a lot of work left to do before we understand the mechanistic underpinnings of
this phenomenon.
References
1. Warneke K, Brinkmann A, Hillebrecht
M and Schiemann S. Influence of LongLasting Static Stretching on Maximal
Strength, Muscle Thickness and Flexibility.
Front. Physiol. 2022, 13:878955. doi:
10.3389/fphys.2022.878955
2. Evangelista AL, De Souza EO, Moreira
DCB, Alonso AC, Teixeira CVS, Wadhi
T, Rauch J, Bocalini DS, Pereira PEA,
Greve JMD. Interset Stretching vs.
Traditional Strength Training: Effects on
Muscle Strength and Size in Untrained
Individuals. J Strength Cond Res. 2019
Jul;33 Suppl 1:S159-S166. doi: 10.1519/
JSC.0000000000003036. PMID:
30688865.
3. Junior RM, Berton R, de Souza TM,
Chacon-Mikahil MP, Cavaglieri CR.
Effect of the flexibility training performed
immediately before resistance training on
muscle hypertrophy, maximum strength
and flexibility. Eur J Appl Physiol. 2017
Apr;117(4):767-774. doi: 10.1007/s00421016-3527-3. Epub 2017 Mar 1. PMID:
28251401.
4. Chaabene H, Behm DG, Negra Y,
Granacher U. Acute Effects of Static
Stretching on Muscle Strength and Power:
An Attempt to Clarify Previous Caveats.
Front Physiol. 2019 Nov 29;10:1468.
doi: 10.3389/fphys.2019.01468. PMID:
31849713; PMCID: PMC6895680.
5. Medeiros DM, Lima CS. Influence of
chronic stretching on muscle performance:
Systematic review. Hum Mov Sci.
2017 Aug;54:220-229. doi: 10.1016/j.
humov.2017.05.006. Epub 2017 May 18.
PMID: 28527424.
6. 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.
7. Pedrosa GF, Lima FV, Schoenfeld BJ,
Lacerda LT, Simões MG, Pereira MR,
Diniz RCR, Chagas MH. Partial range
of motion training elicits favorable
improvements in muscular adaptations
when carried out at long muscle lengths.
Eur J Sport Sci. 2021 May 23:1-11. doi:
10.1080/17461391.2021.1927199. Epub
ahead of print. PMID: 33977835.
8. Sato S, Yoshida R, Kiyono R, Yahata
K, Yasaka K, Nunes JP, Nosaka K,
Nakamura M. Elbow Joint Angles in
Elbow Flexor Unilateral Resistance
Exercise Training Determine Its Effects on
Muscle Strength and Thickness of Trained
and Non-trained Arms. Front Physiol.
2021 Sep 16;12:734509. doi: 10.3389/
fphys.2021.734509. PMID: 34616309;
PMCID: PMC8489980.
9. Panidi I, Bogdanis GC, Terzis G, Donti
A, Konrad A, Gaspari V, Donti O. Muscle
Architectural and Functional Adaptations
Following 12-Weeks of Stretching in
Adolescent Female Athletes. Front Physiol.
2021 Jul 16;12:701338. doi: 10.3389/
fphys.2021.701338. PMID: 34335307;
PMCID: PMC8322691.
10. Simpson CL, Kim BDH, Bourcet MR,
Jones GR, Jakobi JM. Stretch training
induces unequal adaptation in muscle
fascicles and thickness in medial and lateral
gastrocnemii. Scand J Med Sci Sports.
100
2017 Dec;27(12):1597-1604. doi: 10.1111/
sms.12822. Epub 2017 Jan 30. PMID:
28138986.
11. Oranchuk DJ, Storey AG, Nelson AR,
Cronin JB. Isometric training and longterm adaptations: Effects of muscle
length, intensity, and intent: A systematic
review. Scand J Med Sci Sports. 2019
Apr;29(4):484-503. doi: 10.1111/
sms.13375. Epub 2019 Jan 13. PMID:
30580468.
12. Antonio J, Gonyea WJ. Skeletal muscle
fiber hyperplasia. Med Sci Sports Exerc.
1993 Dec;25(12):1333-45. PMID:
8107539.
13. Hickson RC. Interference of strength
development by simultaneously training for
strength and endurance. Eur J Appl Physiol
Occup Physiol. 1980;45(2-3):255-63. doi:
10.1007/BF00421333. PMID: 7193134.
14. Apostolopoulos N, Metsios GS, Flouris
AD, Koutedakis Y, Wyon MA. The
relevance of stretch intensity and position-a
systematic review. Front Psychol.
2015 Aug 18;6:1128. doi: 10.3389/
fpsyg.2015.01128. PMID: 26347668;
PMCID: PMC4540085.
101
Study Reviewed: Can I Have My Coffee and Drink It? A Systematic Review and Meta-analysis
to Determine Whether Habitual Caffeine Consumption Affects the Ergogenic Effect of
Caffeine. Carvalho et al. (2022)
An Update on Caffeine Habituation and Sensitivity
to Ergogenic Effects
BY ERIC TREXLER
Caffeine is a very popular ergogenic aid. It’s
been a staple in multi-ingredient pre-workout formulas since they hit the market, and
it’s been shown to improve many different
types of exercise performance, including endurance, strength, and power (2). However,
caffeine is also very popular for non-exercise
applications, with some research suggesting
that up to 85% of the US population consumes at least one caffeinated beverage per
day (3). Any regular caffeine user can tell
you that some of caffeine’s effects diminish
over time as we become habituated to regular consumption, which leads to important
questions: do performance improvements
fade away over time? Do we need to cycle
on and off caffeine in order to resensitize our
receptors and restore caffeine’s ergogenic
effects? The answers to these questions are
surprisingly unclear, despite the large body
of performance-related caffeine research, and
they are questions we’ve repeatedly revisited
in MASS (one, two, three, four).
The presently reviewed meta-analysis (1)
sought to determine if habitual caffeine consumption influences the acute ergogenic ef-
fect of caffeine. The researchers systematically searched the literature for studies that
met the following criteria:
• Included healthy males or females, with
no restrictions on age or training status,
within a randomized single-blind or double-blind study design.
• Quantified habitual caffeine consumption
of participants in mg/kg/day, or provided sufficient data for the meta-analysts to
calculate habitual intakes in these units.
• Assessed the acute ergogenic effect of
caffeine (at any dose and in any form)
consumed before an exercise task, with a
direct comparison to a placebo group or
placebo condition.
Once the literature search was complete and
the researchers excluded all of the studies
that failed to meet their criteria, they were
left with 60 studies. Collectively, these studies included 1,137 total participants; 958
were males and 179 were females; 718 were
“trained,” 400 were “untrained,” and 19 were
“elite.” At that point, they went through to
extract a ton of data for comparison purposes.
102
For example, they went through each study
and noted the age, weight, sex, training status, and habitual caffeine consumption of
participants, the characteristics of the exercise task, the supplementation protocol, and
the primary exercise outcome tested.
The researchers used meta-regression to determine if habitual caffeine intake was predictive of caffeine’s effect size for performance
outcomes. The primary model indicated that
caffeine had a statistically significant ergogenic effect (effect size [ES], expressed as
Hedges’ g, = 0.25)
Figure 1 shows the results of their comparisons. In short, the results indicated that caffeine
had a small but positive effect on endurance,
power, and strength outcomes. However, habitual caffeine intake (in mg/kg/day) did not
significantly influence the observed effect size
(p = 0.59). The researchers dug deeper to explore a number of other comparisons, which
are presented in Figure 1. In short, caffeine
was ergogenic across different exercise types
(endurance, strength, and power), training statuses (trained or untrained), and sexes (male or
female), with fairly similar effect sizes across
these categories, and no significant influence
of habitual caffeine intake within these comparisons. Acute caffeine was effective whether the experimental dose was higher or lower
than the habitual intake of study participants,
and the ergogenic effect was not meaningfully influenced by the duration of the caffeine
withdrawal periods prior to the caffeine intervention. The only thing that seemed to threaten
the ergogenic effect of caffeine was the dose
ingested; doses below 3mg/kg and between
3-6 mg/kg were similarly effective, but doses
above 6mg/kg led to a smaller pooled effect size
(which was not statistically significant) and a
wider confidence interval. This lines up pretty
well with experimental evidence showing that
higher caffeine doses (>6mg/kg) are more likely to induce uncomfortable side effects which
may impair average performance at the group
level and increase the inter-individual variability of performance responses (4).
Since we’ve visited this topic several times
in previous MASS issues (one, two, three,
four), I’ll focus on the highlights and conclusions from this literature instead of retreading
the study-by-study trajectory of this research
topic. I think this is a really useful meta-analysis, because it confirms something that’s
been observed many times in isolated studies:
when you evaluate the acute ergogenic effect
of caffeine, it tends to work for self-reported habitual caffeine consumers and non-consumers alike, and the effect sizes tend to be
pretty similar when compared directly. This
meta-analysis also provides several additional comparisons that have a ton of practical
utility – it’s very helpful to see head-to-head
comparisons of effect sizes for different sexes, exercise types, training statuses, dosing
ranges, and so on. At this point, I think we
can make a few conclusions with a reasonable
degree of confidence: caffeine still works for
habitual caffeine users, caffeine works across
a broad range of exercises, caffeine works
across different sexes and training statuses,
and your “best bet” for dosing is probably in
the range of 3-6mg/kg.
Having said that, I do want to highlight a few
considerations to prevent overconfidence in
some of these conclusions. A meta-analysis
103
can only enable inferences based on the experimental data available, and there are some
shortcomings in the caffeine literature. First,
the literature skews heavily toward male subjects (in this meta-analysis, 84% of the data
came from male participants), which isn’t
rare for supplement research. All signs point
to similar effects between males and females,
but we should remember that we’re working
with a limited set of female data. Speaking
of limited data, the presently reviewed meta-analysis noted that only 24% of the studies in their search reported the mean habitual
caffeine consumption of their participants. I
still feel pretty confident that 60 studies give
us plenty of data to draw from, but we should
acknowledge that these findings come from
less than a quarter of the relevant caffeine
research to date, and may not be perfectly
representative of the literature as a whole. Finally, and most importantly (by far): the randomized controlled trial is a very powerful
tool, and we should use it whenever possible.
Most of our knowledge about this caffeine
habituation question comes from research
that is observational in nature. Sure, they are
employing randomized, placebo-controlled
methods when determining the acute effect of
caffeine intake, but the determination of habitual caffeine intake is based on self-reported
habits rather than experimental manipulation
of caffeine habituation status. The presently
104
reviewed meta-analysis provides our current
“best guess” about the impact of habituation
on the ergogenic effect of caffeine, but there
are two major limitations of the underlying
literature. First, we’re looking at habitual
caffeine intake from each study at the group
level, not the individual level. This meta-analysis is not strictly comparing the effects of
caffeine among individuals who habitually
consume large amounts versus small amounts
of caffeine; rather, it’s comparing average,
group-level effects of caffeine among groups
of people with high versus low average daily intakes of caffeine. This approach should
generally point us in the right direction, but
there’s absolutely no question that there are individuals who consume very minimal caffeine
who have been grouped into samples that are
categorized, on average, as high caffeine consumers. Second, the time course of caffeine
habituation (and re-sensitization) isn’t fully
understood with a tremendous amount of detail, but appears to occur over periods of days
rather than months. Let’s say you’re participating in a caffeine study and the research
team asks how much caffeine you typically
consume. You typically consume about 5 cups
of coffee per day, but you haven’t been consuming much over the last 10-14 days due to
changes in your schedule (because of work,
social events, family obligations, and so on).
So, how does a researcher classify your habitual intake? You’re generally consuming a
pretty high level of daily caffeine intake, but
you might not be particularly habituated at the
time of testing. Individual studies don’t typically go into a lot of detail about this consideration, but it’s a pretty big deal when it comes
to nuanced interpretation.
The reason I focus so much on the observational nature of categorizing habitual caffeine
consumption is because Greg reviewed one
of the only studies (to my knowledge) that
actually addressed the question of caffeine
habituation and exercise performance from a
truly experimental perspective. In the study
(5), Lara and colleagues studied the ergogenic effect of pre-exercise caffeine supplementation (3mg/kg) over a 20-day period. The results generally suggested that the magnitude
of caffeine’s ergogenic effect decreased with
repeated use. However, the relative degree of
effect size reduction varied among the different performance outcomes measured, and an
effect size getting smaller is not the same as
an effect size disappearing entirely. It’s also
unclear if the effect sizes were on a trajectory involving continuous decreases over time,
or if the effect size reductions had effectively
plateaued at a lower (but still non-zero) magnitude of performance enhancement. Looking at the broader literature, I have no hesitation when stating that habitual caffeine users
can still enjoy an ergogenic effect from acute
caffeine use. It also seems that effect sizes
are pretty similar when comparing self-reported heavy caffeine users to self-reported
light caffeine users. However, the limited experimental evidence available causes me to
hesitate a little bit when suggesting that there
is absolutely no attenuation of caffeine’s ergogenic effects when it is consumed habitually. I wish there were more randomized
controlled trials to sort out the discrepancy,
but I find it difficult to unequivocally accept
the conclusions from observational findings
while entirely ignoring the small amount of
experimental data available. We never want
105
to place a disproportionate amount of confidence in a single study, but we also have
to reconcile experimental findings with observational findings when both are available,
and experimental data generally tend to warrant a heightened level of consideration when
compared to observational data.
In summary, I’m quite confident that habitual
caffeine consumers are still able to attain ergogenic benefits from acute caffeine consumption. However, in order to develop a more
nuanced understanding of the time course by
which caffeine habituation develops and reverses, and how performance varies across
these processes, we probably need to lean on the
good old-fashioned experiment. I’d love to see
more randomized controlled trials that explore
this question directly, and they are feasible
studies to complete. Caffeine has an excellent
safety profile within ergogenic dosing ranges
and is an extremely affordable study ingredient. The biggest logistical challenge would be
finding participants who are willing to come
into the lab for very frequent performance testing, and selecting a performance outcome that
is simultaneously sensitive enough to capture
small changes in neuromuscular performance,
easy enough to avoid soreness or performance
decrements when completed frequently, but
familiarized well enough to avoid time-related
performance improvements over the course of
the study. I think there’s a very intuitive series
of studies on caffeine tolerance, habituation,
and withdrawal time courses that would make
for a great PhD dissertation or thesis project,
and if I were entering a PhD program today
(a daunting prospect that I’d prefer not to entertain, even hypothetically), it’s definitely the
route I would pursue. Until more randomized
controlled trials with experimental manipulation of caffeine habituation status become
available, the current evidence suggests that
it probably isn’t a huge deal for performance
outcomes, and that caffeine is still ergogenic
(to a fairly similar degree) when comparing
heavy caffeine users to people who consume
minimal caffeine.
References
1. Carvalho A, Marticorena FM, Grecco BH,
Barreto G, Saunders B. Can I Have My
Coffee and Drink It? A Systematic Review
and Meta-analysis to Determine Whether
Habitual Caffeine Consumption Affects the
Ergogenic Effect of Caffeine. Sports Med.
2022 May 10; ePub ahead of print.
2. Grgic J, Grgic I, Pickering C,
Schoenfeld BJ, Bishop DJ, Pedisic
Z. Wake Up And Smell The Coffee:
Caffeine Supplementation And Exercise
Performance-An Umbrella Review Of 21
Published Meta-Analyses. Br J Sports Med.
2020 Jun;54(11):681-688.
3. Mitchell DC, Knight CA, Hockenberry
J, Teplansky R, Hartman TJ. Beverage
Caffeine Intakes In The U.S. Food Chem
Toxicol. 2014 Jan;63:136–42.
4. 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.
5. Lara B, Ruiz-Moreno C, Salinero JJ, Del
Coso J. Time Course Of Tolerance To The
Performance Benefits Of Caffeine. PloS
One. 2019;14(1):e0210275.
106
Study Reviewed: Effects of Oral Contraceptive Use on Muscle Strength, Muscle Thickness,
and Fiber Size and Composition in Young Women Undergoing 12 Weeks of Strength
Training: A Cohort Study. Sung et al. (2022)
Oral Contraceptives Still Don’t Impact Muscle
Growth and Strength Gains
BY GREG NUCKOLS
Near the end of Volume 4, I reviewed a study
investigating the impact of oral contraceptives on strength and hypertrophy outcomes
(2). In the interpretation section, I provided
a thorough review of the state of the literature on the topic. If you’re interested in doing a deep dive, you should check out that
article. But, to briefly summarize the state of
the literature at the end of 2020: second- and
third-generation combined oral contraceptives don’t seem to meaningfully impact either strength gains or hypertrophy outcomes.
Since then, I’m only aware of two new papers on the topic. The first was a paper by
Reichman and Lee (3), which actually wasn’t
entirely new. In my prior review of the literature, I included a conference abstract by
Lee et al from 2009, which hadn’t been formally published in a peer-reviewed journal
(4). The recent Reichman paper is the formal, published document based on the same
data Lee and colleagues presented in their
conference abstract; as such, the findings in
the Reichman paper findings are the same
as those reported in the Lee abstract. I’m
glad the study was finally published, but
it doesn’t provide net new information for
MASS readers.
The second new study on the topic is the subject of this research brief (1). Fair warning:
this will be a research brief. It just adds a few
more data points to the conversation, but it
doesn’t change the overall weight of the evidence on the topic. If anything, it just further
solidifies the takeaways of my prior article
on oral contraceptives.
74 subjects completed this study by Sung and
colleagues, including 40 subjects who didn’t
use oral contraceptives (and who hadn’t used
oral contraceptives within the past year), and
34 who were presently using second-generation oral contraceptives (and who had been
using oral contraceptives for at least one
year). Subjects were not resistance-trained,
but were generally active and healthy.
Subjects in both groups completed 12 weeks
of resistance training. The subjects performed three leg press sessions per week,
following a simple linear progression. Each
session consisted of three sets of leg press
to failure, with two minutes of rest between
107
sets. When a subject completed more than
12 reps in a set, their training loads increased
by 10%. Subjects also completed one recovery session of bodyweight squats per week,
consisting of three sets of 15-20 reps, with
3-5 minutes of rest between sets.
Before and after the 12-week training pro-
gram, researchers assessed the subjects’
strength (via an isometric leg press test at
90° of knee flexion) and quadriceps size (via
ultrasound measurements of rectus femoris, vastus intermedius, and vastus lateralis
cross-sectional area). They also took vastus
lateralis biopsies to assess changes in muscle
fiber cross-sectional and myonuclear density.
108
No outcomes significantly differed between
groups. The oral contraceptive group tended to gain a bit more strength (+28.02kg vs.
+23.30kg), but the difference wasn’t statistically significant (p = 0.073). No other outcome was even within spitting distance of
statistical significance (all p > 0.25). You can
see the results in Tables 1 and 2.
Overall, this study reinforces the overall
theme of my previous article on the topic:
oral contraceptives don’t seem to meaningfully affect strength or hypertrophy outcomes. However, this study is actually a pretty important addition to the literature. It’s the
biggest study on the topic (in terms of sample
size), and the researchers did a really good
job of controlling for menstrual cycle phase,
just to ensure that performance fluctuations
throughout the menstrual cycle wouldn’t add
noise to their results. The researchers monitored the subjects for two full cycles before
the start of the training intervention to ensure
that all subjects had a consistent cycle, and to
ensure that strength testing took place at the
same point in the menstrual cycle for all subjects. New studies confirming prior findings
are always nice to see, but large, methodologically rigorous studies confirming prior
findings are even nicer to see.
PMC9092708.
2. Oxfeldt M, Dalgaard LB, Jørgensen EB,
Johansen FT, Dalgaard EB, Ørtenblad N,
Hansen M. Molecular markers of skeletal
muscle hypertrophy following 10 wk of
resistance training in oral contraceptive
users and nonusers. J Appl Physiol (1985).
2020 Dec 1;129(6):1355-1364. doi:
10.1152/japplphysiol.00562.2020. Epub
2020 Oct 15. PMID: 33054662.
3. Riechman SE, Lee CW. Oral Contraceptive
Use Impairs Muscle Gains in Young
Women. J Strength Cond Res. 2021 May
14. doi: 10.1519/JSC.0000000000004059.
Epub ahead of print. PMID: 33993156.
4. Lee CW, Newman MA, Riechman SE.
Oral Contraceptive Use Impairs Muscle
Gains in Young Women. FASEB. 2009
Apr;23:51. doi: 10.1096/fasebj.23.1_
supplement.955.25.
References
1. Sung ES, Han A, Hinrichs T, Vorgerd M,
Platen P. Effects of oral contraceptive use
on muscle strength, muscle thickness, and
fiber size and composition in young women
undergoing 12 weeks of strength training: a
cohort study. BMC Womens Health. 2022
May 10;22(1):150. doi: 10.1186/s12905022-01740-y. PMID: 35538569; PMCID:
109
Study Reviewed: Influence of Training-induced Testosterone and Cortisol Changes on
Skeletal Muscle and Performance in Elite Junior Athletes. Bailey et al. (2022)
How Much Do Cortisol Levels Matter For Training
Adaptations?
BY ERIC TREXLER
There is absolutely no question that high levels of cortisol, a glucocorticoid hormone, can
impact body composition when they get into
high enough ranges. When people experience clinically high glucocorticoid levels for
a prolonged period of time, due to medical
scenarios involving Cushing’s syndrome or
pharmacological treatment with exogenous
glucocorticoids, they commonly gain weight
and develop abdominal obesity and metabolic
syndrome (2). In addition, muscle loss can be
experimentally induced by administering exogenous glucocorticoids at high enough doses
(3). While it might seem tempting (if not intuitive) to demonize cortisol, our relationship
with cortisol is a bit too complicated for that.
As reviewed by Hackney and Walz (4), cortisol is necessary and helps the human body
liberate energy substrates during exercise, prioritize and orchestrate substrate utilization,
and remodel proteins. In summary, as they put
it: “cortisol and the other glucocorticoids are
not the ‘bad guys’ of exercise endocrinology
as some have made them out to be.”
Nonetheless, the fitness industry has generally labeled glucocorticoids as “bad guys” that
threaten recovery while simultaneously promoting fat gain and impairing strength and
muscularity. Despite the overwhelmingly bad
reputation of cortisol among lifting enthusiasts, it’s interesting to note that most of the
fitness industry’s collective opinion about the
relationship between cortisol and long-term
changes in strength and body composition
is informed by research with minimal applicability. Some of this evidence comes from
unique clinical scenarios, such as studies on
Cushing’s syndrome or exogenous glucocorticoid administration, which involve substantially higher cortisol levels than a typical lifter
would experience. Some comes from studies
assessing overtraining syndrome (often characterized by a low testosterone-to-cortisol
ratio), which is rarely experienced by lifters,
and often a secondary effect of low energy
availability. Some of it comes from research
exploring acute cortisol responses to certain stressors rather than chronic elevations,
and some of that research even implies that
larger cortisol responses to training might be
predictive of better gains (5). Some comes
from observation research linking cortisol to
weight gain (6), but we know that there’s a
110
lot more to the psychological determinants of
eating behavior and physical activity than the
cortisol response alone. A much better and
more applicable type of evidence would assess resting cortisol levels at different time
points throughout a training program, while
aiming to observe concurrent fluctuations in
muscle thickness and strength.
As you probably inferred, that’s exactly what
the presently reviewed study did (1). Before
we get into the details, I want to acknowledge a major caveat, because I’m well aware
that this study fails to perfectly replicate the
circumstances and training habits of the typical MASS reader. This study was conducted on elite junior sprinters, with a mean age
of around 15-16 years. That’s certainly a big
limitation, but we can still draw some useful
inferences from it.
Now, on to the details. The researchers enrolled 28 sprinters, in addition to 13 “non-athletic” controls, with an approximately even
split of males and females within each group.
They measured salivary hormone levels (testosterone and cortisol), muscle thickness,
and muscle strength at several different time
points throughout the training year. Saliva
samples were taken between 2:30-3:00pm
(after school, but before training) in order
to control for diurnal variations throughout
the day. To quantify muscle thickness, the
researchers used A-mode ultrasound to take
panoramic scans of the knee extensors and
flexors, and used cumulative muscle thickness
values from both scans. To quantify muscle
strength, they measured maximal isometric
knee flexion and extension using a handheld
dynamometer, and once again used a cumu-
lative value (incorporating both flexion and
extension) for statistical analysis. Measurements were taken at four different time points
across a 7-month training schedule. The first
(T1) was during the general preparation period (at baseline), the second (T2) was during
the specific preparation period (around month
3-4), the third (T3) was during the pre-competition period (around month 5-6), and the
fourth (T4) was taken during the competition
period (month 7). The researchers referred
to T1-T2 as the “preparation phase” and T3T4 as the “competition phase.” Training involved a combination of resistance training
and running, and generally transitioned from
high-volume/low-intensity training in the
preparation phase to low-volume/high-intensity training in the competition phase.
As this is a Research Brief, we’ll focus on the
outcomes most relevant to MASS readers:
longitudinal changes in hormones, muscle
thickness, and strength, and the correlations
between them. The researchers also incorporated sprint performance and some more nuanced hierarchical regression modeling, but I
think that would add a ton of complexity (and
minimal value) to this brief. Starting with the
basic information (longitudinal changes within each group), Table 1 shows values for testosterone, cortisol, the testosterone-to-cortisol
ratio, muscle thickness, and muscle strength
for each group at each time point.
Now, moving on to the relationships between
variables. Table 2A reports the correlations
between changes in each variable during the
preparatory phase (that is, from T1 to T2).
Table 2B reports the correlations between
changes in each variable during the competi-
111
112
tion phase (that is, from T3 to T4). Both portions of Table 2 are presented as correlation
matrices in which each variable is numbered
(one through five). If, for example, you wanted to see the correlation coefficient (r value)
for the correlation between cortisol (numbered as variable #2) and muscle thickness
(numbered as variable #4), you’d find the
intersection between the 2nd column (since
cortisol is #2) and the fourth row (since
muscle thickness is #4). In other words, you
identify each individual correlation coefficient by finding the “intersection” of the two
variables you’re interested in, which makes
the correlation matrix a very efficient way to
communicate information about numerous
correlations among several different pairings
of variables.
The researchers concluded that “this study
demonstrated that [cortisol] levels increased in
response to sprint training, to levels where it
induced negative implications on both performance and skeletal muscle adaptation during
the competition season. Such a catabolic physiological status resulted in significant losses
in muscular force and [muscle thickness].”
When I first read this paper, I came across this
set of conclusions and immediately grew concerned that people would point to this paper to
sell a bunch of unnecessary “cortisol blocker”
supplements, to fuel even more catastrophizing about cortisol, or to promote an unwarranted narrative discouraging effortful training.
Looking at the tables presented within this Research Brief, I really don’t see justification for
this line of thinking.
The quoted conclusion made by the authors
isn’t strictly false or baseless, but it doesn’t
really add up from my perspective. First and
foremost, the cortisol increase from training
simply wasn’t that big. I could get into all
sorts of convoluted explanations about population-specific reference ranges and the trajectory of cortisol peaks and valleys throughout the day, but this one’s pretty simple: at
no point in the 7-month training period did
the athletes have higher cortisol levels than
the healthy, non-athletic controls. The idea
that training put these athletes into a catabolic state by driving cortisol levels through the
roof appears, from my perspective, to lack
face validity.
There was only one time point where training
did appear to drive athletes’ cortisol levels
near, but still below, the levels of non-athletic controls. Whatever the athletes were doing
from T1 to T2 (presumably pretty high-volume training) certainly seemed to move the
needle, with cortisol increasing from 1.97
to 4.15 nmol/L (Table 1). During this time
period, cortisol levels changed substantially,
and there was also a generally higher level of
variance in cortisol levels (the standard deviation at T2 was, by far, the largest of any
time point for the athletes). If meaningful
correlations between cortisol levels and muscle-related outcomes were to be observed,
the preparatory phase (T1 to T2) would be
the time for them to stand out – there were
clearly some folks in the sample experiencing large increases in cortisol, and others experiencing much smaller increases, so any
causative effect by which cortisol impaired
gains would have been most pronounced in
this time window. However, as we can see in
Table 2A, these cortisol changes were weak-
113
ly (but positively) correlated with changes in
muscle thickness and strength, with r values
of r = 0.37 and r = 0.31. It was only during the
competition period that cortisol levels were
negatively correlated with muscle thickness
(r = -0.30), and totally unrelated to muscle
strength (r = 0.04). I suspect that the inverse
correlation with muscle thickness was probably spurious, mostly because there wasn’t a
ton of meaningful cortisol variance to model
in the first place. Cortisol levels from T3 to T4
were virtually unchanged (2.89 to 2.84 nmol/L), and the standard deviations were pretty
low (1.62 to 1.47). If cortisol levels were really driving muscle adaptations at the individual level, it should be most pronounced in
the time period where cortisol levels actually
increased meaningfully and had a substantial amount of variance (T1 to T2), and this
time window provided virtually no evidence
pointing toward a negative impact from cortisol. In fact, the correlation coefficients, while
non-significant, point the opposite direction.
I don’t want to oversimplify my stance and
sound like a cortisol denier, because I’m not.
It’s very clear that supraphysiological cortisol levels have unfavorable effects on body
composition, in terms of both abdominal obesity and muscle atrophy. There’s also some
experimental evidence suggesting that exogenous cortisol, within physiological ranges,
may induce muscle breakdown (7). However, I suspect that regular resistance training
might attenuate this effect, as research has
suggested that inactivity markedly exacerbates the catabolic effects of exogenous glucocorticoids (3). This observation, combined
with the fact that it’s exceedingly difficult to
intentionally induce overtraining syndrome
by resistance training, lead me to believe that
the typical MASS reader shouldn’t spend a
single moment worrying about training-induced increases in cortisol. Even in the present study, which included a combination of
resistance training and sprint training, resting
cortisol levels of athletes never even exceeded those of non-athlete controls. We’ll certainly experience some acute cortisol increases from hard training, but they’ll virtually
never approach the cortisol levels observed
with Cushing’s syndrome or pharmacological glucocorticoid interventions, and are likely offset by nocturnal suppression of cortisol
levels (4).
So, you probably aren’t training yourself into
a state of chronically high, catabolic levels
of cortisol. However, you might have high
resting cortisol levels for different reasons altogether, such as a medical condition, insufficient sleep, or chronic psychogenic stress.
While I’ve downplayed the importance of
training-induced changes in cortisol, you’ll
definitely want to address high cortisol levels
if they’re resulting from any of these other
causative factors. Certainly any symptomatic medical condition warrants a visit to the
doctor, who can give you information about
various treatment options. When it comes to
sleep, there are a million reasons to correct
sleep insufficiencies; it will most likely improve your gains, but will also dramatically
enhance quality of life. Similarly, it’s quite
possible that making an intentional effort to
alleviate chronic psychogenic stress could
enhance your gym-related progress, but it
would most likely have a substantial posi-
114
tive impact on quality of life. In other words,
there are cortisol-elevating things that are
worth addressing (and might even impair
your gains), but cortisol is merely an indicator of the underlying problem, not the root of
the problem itself. If your cortisol is high because you aren’t sleeping enough, you have
a sleep problem; if your cortisol is high because you’re chronically stressed, you have
a stress problem. Your best bet is to address
the sleep and the stress, not the cortisol itself.
In summary, I wouldn’t worry about training-related cortisol fluctuations, and I certainly wouldn’t hold back on training effort
to avoid cortisol elevations. If I had chronically elevated cortisol levels for other reasons, I’d make an effort to sort that out – not
by purchasing a questionably efficacious
“cortisol-blocker” supplement, but by getting
to the root cause, and consulting with a qualified healthcare professional if necessary.
4. Hackney AC, Walz EA. Hormonal
Adaptation And The Stress Of Exercise
Training: The Role Of Glucocorticoids.
Trends Sport Sci. 2013;20(4):165–71.
5. West DWD, Phillips SM. Associations Of
Exercise-Induced Hormone Profiles And
Gains In Strength And Hypertrophy In A
Large Cohort After Weight Training. Eur J
Appl Physiol. 2012 Jul;112(7):2693–702.
6. Chao AM, Jastreboff AM, White MA,
Grilo CM, Sinha R. Stress, Cortisol,
And Other Appetite-Related Hormones:
Prospective Prediction Of 6-Month
Changes In Food Cravings And Weight.
Obes. 2017 Apr;25(4):713–20.
7. Simmons PS, Miles JM, Gerich JE,
Haymond MW. Increased Proteolysis. An
Effect Of Increases In Plasma Cortisol
Within The Physiologic Range. J Clin
Invest. 1984 Feb;73(2):412–20.
References
1. Bailey J, Irving R, Dawson P, Brown DR,
Campbell E. Influence of Training-induced
Testosterone and Cortisol Changes on
Skeletal Muscle and Performance in Elite
Junior Athletes. Am J Sports Sci Med.
2021 Dec 16;9(1):13–23.
2. van der Valk ES, Savas M, van Rossum
EFC. Stress and Obesity: Are There More
Susceptible Individuals? Curr Obes Rep.
2018;7(2):193–203.
3. Ferrando AA, Stuart CA, Sheffield-Moore
M, Wolfe RR. Inactivity Amplifies the
Catabolic Response of Skeletal Muscle to
Cortisol. J Clin Endocrinol Metab. 1999
Oct 1;84(10):3515–21.
115
Study Reviewed: How Does Lower-Body and Upper-Body Strength Relate to Maximum Split
Jerk Performance? Soriano et al. (2022)
Is Your Split Jerk Limited by Upper Body or Lower
Body Strength?
BY GREG NUCKOLS
MASS may stand for “Monthly Applications in Strength Sport,” but we certainly
don’t write about all strength sports with
similar frequency. While I love all strength
sports equally, there’s just a lot of juicy research that’s directly related to powerlifting
performance, quite a bit less research directly related to weightlifting performance, and
even less research directly related to strongman performance (and virtually no research
related to more niche strength sports, like
the Highland Games or Basque stone lifting). In addition, the research that does exist
for weightlifting is valuable, but probably
wouldn’t make for a great MASS article.
Most of the research related to weightlifting focuses on applications of weightlifting training for improving the performance
of team sport athletes – very little research
focuses on improving weightlifting performance for its own sake.
However, we certainly haven’t forgotten
about the long-suffering weightlifters who
read MASS, and this research brief may be
pretty valuable if you struggle with split jerk
performance. A recent study allowed me to
develop a little tool that may help you diagnose the weak link in your split jerk (1).
In a study by Soriano and colleagues, researchers assessed 1RM split jerk, strict
overhead press, and back squat strength in
33 competitive weightlifters (20 males and
13 females). Back squats were performed
weightlifting-style: with a high bar position
and ass-to-grass depth.
From there, the researchers assessed the independent relationships between overhead
press strength and split jerk strength, and between squat strength and split jerk strength
via linear regression. Furthermore, they assessed the relationship between split jerk
strength and a combination of both squat and
overhead press strength via multiple linear
regression.
Unsurprisingly, they found that overhead
press strength, squat strength, and a combination of squat and overhead press strength
were all strongly predictive of split jerk performance (r > 0.9). You can see these associations in Figures 1-3. The researchers also
provided regression equations for all of these
116
Then, fill in your 1RM strict overhead press,
squat, and split jerk numbers in cells B1-3.
From there, the spreadsheet will take care of
all of the necessary calculations. In cells B57, it will calculate a) your predicted split jerk
1RM based solely on overhead press performance, b) your predicted split jerk 1RM based
solely on squat performance, and c) your predicted split jerk 1RM based on a combination
of squat and overhead press performance.
linear relationships, which allowed me to develop a little tool to help you assess the weak
link in your split jerk performance.
First, pull up this spreadsheet. Make a copy
or download it. Don’t request editing access.
Based on this data, cell B9 will tell you your
likeliest weak link. If your actual split jerk
1RM is more than 15% lower than would be
predicted based on your squat and overhead
press strength, then the spreadsheet will identify some combination of speed, skill, and
technique as the most likely factor limiting
your split jerk performance. If your actual
split jerk 1RM is within 15% of your predicted 1RM, the sheet will check whether lower body strength (squat 1RM) or upper body
strength (overhead press 1RM) is most likely
to be your limiting factor. If your predicted
117
split jerk 1RM based on overhead press performance is higher than your predicted split
jerk 1RM based on squat performance, the
spreadsheet will identify squat strength as
your most likely limiting factor. If your predicted split jerk 1RM based on overhead press
performance is lower than your predicted
split jerk 1RM based on squat performance,
the spreadsheet will identify overhead press
strength as your most likely limiting factor.
Furthermore, the spreadsheet will assign
a confidence rating to its predictions. For
example, if you “should” split jerk 120kg
(based on your squat and overhead press
strength), but your 1RM split jerk is only
90kg, the spreadsheet will identify speed/
skill/technique as your current limiting factor, and it will rate that as a high-confidence
prediction, because there’s a huge gap between your predicted performance and actual performance. However, if your 1RM
split jerk is 105 kg, the spreadsheet will still
identify speed/skill/technique as your most
likely limiter, but it will be a lower-confidence prediction, since the gap between
your actual performance and predicted performance is considerably smaller. Similarly, if your predicted split jerk 1RM based
on squat strength is 40kg higher than your
predicted split jerk 1RM based on overhead
press strength, the spreadsheet would identify overhead press strength as your most
likely limiter, and it would have high confidence in that prediction. However, if the gap
between those two predicted 1RMs was only
5kg, the spreadsheet would be less confident
in predicting that overhead press strength is
limiting your split jerk performance.
To be clear, I don’t think this is a foolproof,
100% perfect tool for diagnosing weaknesses in the split jerk. However, if you’re struggling with your split jerk, this little tool may
just help point you in the right direction.
References
1. Soriano M, Jiménez-Ormeño E, Amaro-Gahete FJ, Haff GG, Comfort P. How
Does Lower-Body and Upper-Body
Strength Relate to Maximum Split Jerk
Performance? Journal of Strength and
Conditioning Research: June 1, 2022. doi:
10.1519/JSC.0000000000004289
118
Study Reviewed: The Effect Of Krill Oil Supplementation On Skeletal Muscle Function And
Size In Older Adults: A Randomised Controlled Trial. Alkhedhairi et al. (2022)
Should You Take a Krill Pill To Enhance Strength Or
Hypertrophy?
BY ERIC TREXLER
Fish oil is a very popular dietary supplement
among lifters and non-lifters alike, and we’ve
covered it in MASS a few times now (one, two,
three, four). As described in a previous article,
eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are the primary omega-3
fatty acids in fish oil, and they are important
components of cell membranes and found in
a variety of cell types, including blood cells,
immune cells, cardiac tissue, skeletal muscle,
eye tissue, brain cells, and other tissues of the
nervous system. The general premise of fish
oil supplementation is that chronically high
intakes of EPA and DHA will increase their
abundance in multiple body tissues, ultimately influencing a variety of different outcomes
related to health and body function. Based on
the specific tissues in which EPA and DHA
are thought to be particularly impactful, researchers have studied the effects of fish oil
supplementation on cognition, mental health,
inflammation, immunity, muscle protein balance, neuromuscular function, and more. In
the general population, fish oil is typically
taken in hopes of reducing inflammation and
oxidative stress while favorably impacting
brain, eye, immune, or cardiovascular health.
However, a lot of lifters have their eye on
some promising (but very preliminary and
context-dependent) findings linking fish oil
to recovery, muscle protein synthesis, and
body composition.
The presently reviewed study (1) investigated
the effects of krill oil supplementation on outcomes related to strength, hypertrophy, and
physical function. Krill are tiny crustaceans
who happen to have high content of omega-3
fatty acids, including EPA and DHA. After
fish oil supplementation started getting popular, krill oil started to get marketed pretty
heavily in the supplement world. While most
fish oil supplements provide omega-3 fatty
acids in triacylglycerol or ethyl ester form, a
substantial portion of the omega-3 fatty acids
in krill oil are in phospholipid form. Some
studies have suggested that this phospholipid form might have enhanced bioavailability,
and the authors of the present study point to
other short-term trials suggesting that krill oil
can increase plasma EPA+DHA levels and
omega-3 index values more efficiently than
a typical fish oil supplement (that is, they can
achieve larger increases at an equated dose,
119
or similar increases at a smaller dose when
compared to regular fish oil). Krill oil also
contains a decent amount of choline and astaxanthin, which may have independent or
synergistic effects of their own.
The present study was a six-month trial, which
began with a large, mixed-sex sample of 102
participants. Subjects were required to be at
least 65 years old, have a BMI under 35, and
complete less than an hour of weekly structured exercise. Participants were ineligible
if they had any relevant medical conditions,
used any medications or dietary supplements
that might interfere with study outcomes, were
allergic to seafood, or regularly consumed
more than two servings of oily fish per week.
After enrollment, participants were random-
ly allocated to the krill oil group or placebo
group for six months of supplementation in
a double-blinded fashion. The krill oil group
consumed 4g/day of krill oil, with 2g taken
with lunch and 2g taken with dinner. This
4g daily dose provided a total of 1288mg of
omega 3 fatty acids, 772mg of EPA, 384mg of
DHA, 1156mg of combined EPA+DHA, and
316mg of choline. Throughout the 6-month
supplementation period, participants were
encouraged to maintain their normal diet and
exercise habits, so there was no training or exercise program completed in conjunction with
supplementation. While the study began with
102 participants, there were some individuals
who were unable to finish the study, resulting
in 49 study completers (26 female, 23 male)
in the krill oil group and 45 study completers
120
(27 female, 18 male) in the placebo group. The
researchers measured a number of outcomes
related to strength, hypertrophy, and physical
function, which were measured at baseline and
re-measured after six weeks and six months of
supplementation.
After six months, krill oil effectively increased the EPA and DHA levels within red
blood cells. The researchers observed statistically significant interaction effects, indicating that the krill oil group experienced more
favorable changes over time, for maximal
isometric knee extensor torque, grip strength,
and muscle thickness of the vastus lateralis
(Figure 1). However, between-group comparisons related to a physical performance
test (designed to reflect activities of daily living), neuromuscular function, body composition (body-fat percentage and muscle mass),
blood biomarkers (glucose, insulin, C-reactive protein, and blood lipids), and quality of
life (measured via validated questionnaire)
were all non-significant, with the exception
of one neuromuscular function outcome
(M-wave amplitude) with limited utility to
MASS readers.
If you follow the supplement literature related to strength and hypertrophy, you’re used to
seeing null (non-significant) findings. Sure,
there are always some “flash-in-the-pan”
findings that spark some excitement here and
there, but the list of dietary supplements that
have stood the test of time and are believed
to reliably enhance strength or hypertrophy
outcomes are few and far between. As a result, when you open up a paper with some
low p-values related to direct measures of
muscle strength and muscle thickness, a nat-
ural knee-jerk reaction might be to embrace
the positive finding and expand your supplement stack without much additional thought
or consideration. However, it’s important to
resist the temptation to be swayed solely by
statistical significance or a particularly low
p-value. When critically appraising the potential utility of a supplement, you should
consider a few important questions:
• Which specific population(s) stand to
benefit from this supplement?
• What magnitude of improvement can realistically be anticipated?
• When combined with resistance training,
are these anticipated effects additive, synergistic, or redundant?
As I work through these three bullet points, I
will refer to fish oil and krill oil synonymously. From my read of the literature, there’s
currently insufficient evidence to suggest that
krill oil’s effects are truly distinct from those
of fish oil. Rather, krill oil can be operationally viewed as a subtype of fish oil, which
might have slightly better bioavailability that
could allow for similar supplementation effects at lower relative doses.
As for the first bullet point, there is good reason to believe that older adults may experience improvements in muscle mass or function from fish oil or krill oil supplements. As
reviewed by Bird and colleagues (2), inflammation plays an important role in sarcopenia,
or the progressive, age-related loss of skeletal
muscle mass and function. As a result, risk
factors for sarcopenia include age, physical
inactivity, obesity, and certain chronic dis-
121
eases, which are all linked to increased levels of chronic inflammation and oxidative
stress. While fish oil appears to have positive
effects (2) related to muscle mass and function in people at high risk for sarcopenia (i.e.,
people with fairly elevated levels of chronic
inflammation and oxidative stress), effects in
young, healthy individuals are far less promising. The literature generally indicates that
young people fail to experience the same
degree of benefit related to strength or hypertrophy, outside of extreme physiological
scenarios, such as muscular inactivity due to
immobilization (3). These observations have
a high degree of biological plausibility, as
they’re quite consistent with the literature on
antioxidant supplementation. As reviewed by
Ismaeel and colleagues, certain antioxidant
supplements have neutral to slightly negative
effects on resistance training adaptations in
young, healthy subjects, but neutral to slightly positive effects on older subjects with
higher baseline levels of chronic inflammation and oxidative stress (4). For a much more
detailed look at antioxidant supplementation
and training adaptations, be sure to check out
this Stronger By Science article.
If you’re an avid exerciser in your 50s, 60s,
70s, or beyond, you might be reading the previous paragraph and thinking that fish oil is
your key to success as you begin planning
ahead for the potential impacts of sarcopenia. However, a recent review by Murphy
and McGlory (5) casts a little bit of doubt on
this idea. In the review, they note that the potential muscle-specific benefits of fish oil are
quite inconsistent in the literature, with some
of the more promising findings coming from
studies with low quality or a high risk of bias.
Ultimately, they conclude that “the available
evidence does not indicate that ingestion of
[long-chain omega-3 fatty acids] above current population recommendations (250–500
mg/day; 2 portions of oily fish per week)
enhances exercise performance or recovery
from exercise training in master athletes.”
When discussing this particular topic, it’s
also important to reinforce an important point
from a recent MASS article by Dr. Helms:
the sarcopenia-driving inflammation that we
associate with age is heavily influenced by
age-related drops in physical activity level.
As he puts it, “if you’re a lifter aged 35-60,
you’re more similar to a younger version of
yourself than you are to a version of yourself
at the same age who doesn’t lift.” As such,
it’s very possible that avid exercisers and
masters athletes experience training-related
reductions in chronic inflammation that effectively nullify the muscle-specific impacts
of fish oil, but more research is needed in this
specific area.
As for the second bullet point (regarding
the magnitude of effects), check out Figure
1. We could approach this from a painfully
detailed quantitative perspective, or we can
let our eyeballs do the work. Look at, for
example, the muscle thickness data, we can
see that fish oil is no game changer when it
comes to muscularity or body composition.
The researchers reported a 3.5% increase in
muscle thickness for the krill oil group, with
a baseline value that appears to be around
33mm, give or take. In other words, we’re
talking about an advantage that amounts to
roughly one millimeter, with a measurement
122
tool that makes it exceedingly challenging to
reliably identify a real change of 1mm. Furthermore, the present study found no significant effects in terms of whole-body indices
of body composition. As seen in Figure 1, the
observed effects for strength and hypertrophy
are small, but you might still be interested in
a tiny effect, as long as it’s additive (that is,
extra gains that occur above and beyond the
effects of training alone). But is it actually
additive?
That brings us to the third bullet point. A systematic review published by López-Seoane
and colleagues in 2022 found that fish oil supplementation positively impacted outcomes
related to muscle hypertrophy in the absence
of exercise (6). That’s pretty cool, but here’s
the bad news: the exact same researchers published a separate systematic review the year
prior, and found that fish oil supplementation
did not significantly impact outcomes related
to muscle hypertrophy, strength, or muscle
biomarkers of inflammation when combined
with exercise (7). So, when we combine our
observations related to these three bullet
points, we’re likely to conclude that fish oil
has the highest potential to improve muscle
strength or hypertrophy outcomes in older,
sedentary individuals who are not engaging
in structured exercise. When an effective
hypertrophy-promoting resistance training
program is thrown into the mix, the muscular benefits may very well become redundant
rather than additive. Furthermore, when you
look at populations with a tendency to have
lower baseline levels of chronic inflammation and oxidative stress (such as younger individuals, or older, healthy individuals who
regularly exercise), the likelihood of strength
or hypertrophy benefits appears to diminish.
So, I don’t think it’s necessarily inadvisable
to recommend that your sedentary family
members take some fish oil, but I am skeptical that the typical MASS reader stands to
meaningfully influence their strength or hypertrophy outcomes through fish oil or krill
oil supplementation.
Just to clarify, I’m certainly not suggesting
that fish oil is entirely useless. In fact, I feel
quite the opposite; EPA and DHA do important things in a wide range of tissues, and I believe it’s very important to seek out adequate
intakes of essential fatty acids. As I’ve noted
previously, getting at least 0.3-0.5g/day of
combined EPA + DHA appears to be a positive thing for many different outcomes related
to health and wellness, whether you’re achieving that intake from a few weekly servings of
oily fish or a dietary supplement. Personally, I
aim for around 0.5-1.0g/day of combined EPA
+ DHA, and I use an algae oil supplement (a
plant-based fish oil alternative that’s rich in
EPA and DHA) to facilitate that goal. There
is also some evidence suggesting that fish oil
can facilitate recovery from particularly arduous training sessions, even in healthy young
people, but there’s also some directly contradictory evidence, so that particular question remains unsettled. In conclusion, fish oil
might yield some small but detectable benefits
for strength and hypertrophy in older and relatively sedentary adults, or in other individuals
with elevated levels of chronic inflammation
or oxidative stress. As a young-ish, healthyish, relatively active person, I’m not holding
my breath when it comes to obtaining strength
123
or hypertrophy benefits from my algae oil
supplementation. However, there are plenty of
good reasons to intentionally seek out dietary
sources of EPA and DHA, and to incorporate
adequate amounts of them into your diet on a
regular basis.
References
1. Alkhedhairi SA, Aba Alkhayl FF,
Ismail AD, Rozendaal A, German M,
MacLean B, et al. The Effect Of Krill
Oil Supplementation On Skeletal Muscle
Function And Size In Older Adults: A
Randomised Controlled Trial. Clin Nutr.
2022 Jun;41(6):1228–35.
Induced By N-3 PUFA Supplementation
In Absence Of Exercise: A Systematic
Review Of Randomized Controlled Trials.
Crit Rev Food Sci Nutr. 2022 Feb 3;1–11;
ePub ahead of print.
7. López-Seoane J, Martinez-Ferran M,
Romero-Morales C, Pareja-Galeano H.
N-3 PUFA As An Ergogenic Supplement
Modulating Muscle Hypertrophy And
Strength: A Systematic Review. Crit Rev
Food Sci Nutr. 2021 Jun 15;1–21; ePub
ahead of print.
2. Bird JK, Troesch B, Warnke I, Calder
PC. The Effect Of Long Chain Omega-3
Polyunsaturated Fatty Acids On Muscle
Mass And Function In Sarcopenia: A
Scoping Systematic Review And MetaAnalysis. Clin Nutr ESPEN. 2021 Dec
1;46:73–86.
3. Heileson JL, Funderburk LK. The Effect
Of Fish Oil Supplementation On The
Promotion And Preservation Of Lean
Body Mass, Strength, And Recovery From
Physiological Stress In Young, Healthy
Adults: A Systematic Review. Nutr Rev.
2020 Dec 1;78(12):1001–14.
4. Ismaeel A, Holmes M, Papoutsi E, Panton
L, Koutakis P. Resistance Training,
Antioxidant Status, and Antioxidant
Supplementation. Int J Sport Nutr Exerc
Metab. 2019 Sep 1;29(5):539–47.
5. Murphy CH, McGlory C. Fish Oil for
Healthy Aging: Potential Application
to Master Athletes. Sports Med. 2021
Sep;51(Suppl 1):31–41.
6. López-Seoane J, Jiménez SL, Del Coso J,
Pareja-Galeano H. Muscle Hypertrophy
124
VIDEO: 1RM Prediction Part 2
BY MICHAEL C. ZOURDOS
Part 1 of this series suggested that reps performed equations have questionable
efficacy for predicting 1RM. This installment breaks down the existing literature
on submaximal velocity to predict 1RM. Does it fare better? Watch the video to
find out.
Click to watch Michael's presentation.
125
Relevant MASS Videos and Articles
1. Practical and Effective Ways to Predict Your 1RM. Volume 3 Issue 4.
References
1. Banyard HG, Nosaka K, Haff GG. Reliability and validity of the load–velocity relationship
to predict the 1RM back squat. The Journal of Strength & Conditioning Research. 2017 Jul
1;31(7):1897-904.
2. Pestaña-Melero FL, Haff GG, Rojas FJ, Pérez-Castilla A, García-Ramos A. Reliability of the
load–velocity relationship obtained through linear and polynomial regression models to predict
the 1-repetition maximum load. Journal of Applied Biomechanics. 2018 Jun 1;34(3):184-90.
3. García-Ramos A, Haff GG, Pestaña-Melero FL, Pérez-Castilla A, Rojas FJ, BalsalobreFernández C, Jaric S. Feasibility of the 2-point method for determining the 1-repetition
maximum in the bench press exercise. International Journal of Sports Physiology and
Performance. 2018 Apr 1;13(4):474-81.
4. Pérez-Castilla A, Suzovic D, Domanovic A, Fernandes JF, García-Ramos A. Validity of different
velocity-based methods and repetitions-to-failure equations for predicting the 1 repetition
maximum during 2 upper-body pulling exercises. The Journal of Strength & Conditioning
Research. 2021 Jul 1;35(7):1800-8.
5. Pérez-Castilla A, Fernandes JF, Garcia-Ramos A. Validity of the bench press one-repetition
maximum test predicted through individualized load-velocity relationship using different
repetition criteria and minimal velocity thresholds. Isokinetics and Exercise Science. 2021 Jan
1;29(4):369-77.
6. Thompson SW, Rogerson D, Ruddock A, Greig L, Dorrell HF, Barnes A. A novel approach
to 1RM prediction using the load-velocity profile: a comparison of models. Sports. 2021 Jun
22;9(7):88.
7. Jiménez-Alonso A, García-Ramos A, Cepero M, Miras-Moreno S, Rojas FJ, Pérez-Castilla
A. Velocity performance feedback during the free-weight bench press testing procedure: an
effective strategy to increase the reliability and one repetition maximum accuracy prediction.
Journal of Strength and Conditioning Research. 2022 Apr 8;36(4):1077-83.
8. Macarilla CT, Sautter NM, Robinson ZP, Juber MC, Hickmott LM, Cerminaro RM, Benitez
B, Carzoli JP, Bazyler CD, Zoeller RF, Whitehurst M. Accuracy of Predicting One-Repetition
Maximum from Submaximal Velocity in the Barbell Back Squat and Bench Press. Journal of
Human Kinetics. 2022 Apr 15;82(1):201-12.
█
126
VIDEO: Periodization for
Hypertrophy Part 2
BY ERIC HELMS
Back in Volume 1 Dr. Helms noted in his intro to periodization videos that
periodization for hypertrophy was a relatively unexplored topic. Five years
later, we now have a number of meta-analyses on this topic as well as a
broader understanding of how varying specific variables might impact
hypertrophy. In part 2 of this video series, Dr. Helms covers the rationale
specifically for periodizing exercises for maximizing hypertrophy.
Click to watch Eric's presentation.
127
Relevant MASS Videos and Articles
1. When it Comes to Hypertrophy, Not All Multi-joint Exercises are Created Equal. Volume 3,
Issue 9.
2. Variety is the Spice of Life: If You Want Well-Rounded Triceps Growth, You Need Both
Compound and Single-Joint Exercises. Volume 4, Issue 5.
3. Guiding Shoulder Hypertrophy Training with EMG. Volume 4, Issue 10.
4. Do You Need to Incline Press to Build your Upper Chest?. Volume 4, Issue 11.
5. Squats are Great, but Bodybuilders Need More. Volume 5, Issue 9.
References
1. Physiopedia.com
2. Gentil, P., Soares, S., & Bottaro, M. (2015). Single vs. Multi-Joint Resistance Exercises: Effects
on Muscle Strength and Hypertrophy. Asian Journal of Sports Medicine, 6(2), e24057.
3. Kubo, K., Ikebukuro, T., & Yata, H. (2019). Effects of squat training with different depths on
lower limb muscle volumes. European Journal of Applied Physiology, 119(9), 1933–1942.
█
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. Gibbs et al. Does A Powerlifting Inspired Exercise Programme Better Compliment Pain
Education Compared To Bodyweight Exercise For People With Chronic Low Back Pain?
A Multicentre, Single-Blind, Randomised Controlled Trial
2. Proost et al. How to Tackle Mental Fatigue: A Systematic Review of Potential
Countermeasures and Their Underlying Mechanisms
3. García et al. Movement Velocity As A Determinant Of Actual Intensity In Resistance
Exercise
4. Boxman-Zeevi et al. Prescribing Intensity in Resistance Training Using Rating of Perceived
Effort: A Randomized Controlled Trial
5. Wojdala et al. A Comparison Of Electromyographic Inter-Limb Asymmetry During A
Standard Versus A Sling Shot Assisted Bench Press Exercise
6. Nicklas et al. A Meta-Analysis On Immediate Effects Of Attentional Focus On Motor Tasks
Performance
7. Saeterbakken et al. Acute Effects of Barbell Bouncing and External Cueing on Power
Output in Bench Press Throw in Resistance-Trained Men
8. Gantois et al. Analysis Of Velocity- And Power-Load Relationships Of The Free-Weight
Back-Squat And Hexagonal Bar Deadlift Exercises
9. Fyksen et al. Cardiovascular Phenotype Of Long-Term Anabolic-Androgenic Steroid
Abusers Compared With Strength-Trained Athletes
10. Lum et al. Comparing the Effects of Long-Term vs. Periodic Inclusion of Isometric Strength
Training on Strength and Dynamic Performances
11. Liu et al. Effects of Exercise Training Intensity and Duration on Skeletal Muscle Capillarization
in Healthy Subjects: A Meta-analysis
12. Vieira et al. Effects of Resistance Training to Muscle Failure on Acute Fatigue: A Systematic
Review and Meta-Analysis
13. Bell et al. “I Want to Create So Much Stimulus That Adaptation Goes Through the Roof”:
High-Performance Strength Coaches’ Perceptions of Planned Overreaching
14. Latella et al. Long-Term Adaptations in the Squat, Bench Press, and Deadlift: Assessing
Strength Gain in Powerlifting Athletes
15. Steele et al. Long-Term Time-Course of Strength Adaptation to Minimal Dose Resistance
129
Training Through Retrospective Longitudinal Growth Modeling
16. Langer et al. Myofibrillar Protein Synthesis Rates Are Increased In Chronically Exercised
Skeletal Muscle Despite Decreased Anabolic Signaling
17. Xie. Prevention Methods of Fitness and Bodybuilding Exercise Injury Based on Data Mining
18. van den Tillaar et al. The Acute Effects of Attaching Chains to the Barbell on Kinematics and
Muscle Activation in Bench Press in Resistance-Trained Men
19. Wender et al. The Effect of Chronic Exercise on Energy and Fatigue States: A Systematic
Review and Meta-Analysis of Randomized Trials
20. Čretnik et al. The Effect of Eccentric vs. Traditional Resistance Exercise on Muscle Strength,
Body Composition, and Functional Performance in Older Adults: A Systematic Review With
Meta-Analysis
21. Mansingh et al. Time to Train: The Involvement of the Molecular Clock in Exercise Adaptation
of Skeletal Muscle
130
Thanks for
reading MASS.
The next issue will be released to
subscribers on August 1, 2022.
Copy editing by Lauren Colenso-Semple
Graphics and layout by Kat Whitfield
131