V O L U ME 3 , ISS U E 12
DEC EMBER 2 0 1 9
MASS
M ONTHLY A PPL ICATIO N S IN
STRE N G TH SPO R T
E R IC H E LMS | G R E G N UCK O LS | MIC HAEL ZO URDO S | ERIC T REXL E R
The Reviewers
Eric Helms
Eric Helms is a coach, athlete, author, and educator. He is a coach for drug-free strength and physique
competitors at all levels as a part of team 3D Muscle Journey. Eric regularly publishes peer-reviewed
articles in exercise science and nutrition journals on physique and strength sport, in addition to writing for
commercial fitness publications. He’s taught undergraduate- and 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 the
IPF at international-level events as an unequipped powerlifter.
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.
2
Letter from
the Reviewers
C
an you believe it’s the last issue of 2019? Neither can we, and we want to collectively take
a moment to say thank you for being a MASS subscriber. While our success in growing
the MASS subscriber base is something we each feel proud of, as it tells us we are providing value and utility, we are also incredibly encouraged that so many people value their continued
education. This bodes well for the fitness industry, and we want to thank you for making it a better
place.
With that said, this final issue of 2019 is a very interesting one! Guest reviewer Anne-Kathrin
Eiselt PhD, a neuroscientist, long-time trainer, and experienced CrossFit and powerlifting competitor, wrote an excellent review of how and why our brains make us hungry. Also, Greg discusses a seemingly science fiction-based intervention, as he reviews how lasers (AKA phototherapy)
may actually help you make gains. Additionally, Dr. Trexler reviews the evidence on betaine as a
body composition aid, and in his second article, the safety profile of sucralose.
On top of that, Dr. Zourdos reviews a study that highlights the practical limitations of low load,
high rep training, and also examines a study that compares various resistance training recovery-marker surrogates, including HRV. Greg also reviews a study on bench press ROM which
suggests the efficacy of partial range of motion training is movement specific, and examines an
analysis of the peak performance age for both powerlifters and weightlifters.
To round the issue out, we have video content from both Dr. Helms and Dr. Zourdos. Eric
covers how one of the possible regulatory systems which influences bodyweight could be total
energy expenditure, a novel perspective given most hypotheses involve body fatness and energy
intake. Mike, in his video, takes you through the competition-day elements of a powerlifting
meet, covering both practical considerations and also a logical, tactical approach to attempt selection to ensure you end your day with the largest total possible.
Once again, thank you. It’s been a pleasure and a privilege having your trust for the year, and
we look forward to putting out more reviews and content in 2020!
Sincerely,
The MASS Team
Eric Helms, Greg Nuckols, Mike Zourdos, and Eric Trexler
3
Table of Contents
6
BY G R EG NUCKOL S
Improving Strength Endurance with Frickin Lasers
It sounds like sci-fi, but using a low-power infrared or near-infrared laser on your muscles between sets
may improve strength and strength endurance.
17
BY M I CHAEL C. ZOUR DOS
Most People Find Low-Load Training to Failure Miserable
You may have heard that recent evidence suggested that low-load training produces similar
hypertrophy to more typical, moderate-load training. That may be true, but a new study shows that
people find low-load training to be pretty miserable in comparison to moderate-load training.
25
BY E R I C T R EXL ER
Betaine May Promote Body Composition Improvements When
Combined With Resistance Training
Betaine, which is found in beets, spinach, whole grains, and seafood, has been used to improve the
body composition of livestock. To see if these effects also apply to humans, a recent meta-analysis
sought to review the small body of literature evaluating betaine’s effects on fat loss in human subjects.
Read on to find out if betaine might help you achieve your physique-related goals.
37
BY G R EG NUCKOL S
Bench Press Range of Motion: An Exception to the Principle of
Specificity?
Much of what we know about range of motion specificity comes from single-joint studies and squat
studies. When we branch out to the bench press, things get more complicated.
49
BY M I CHAEL C. ZOUR DOS
The Usefulness of Heart Rate Variability in Resistance Training is Tenuous
The potential benefit of heart rate variability in resistance training is its ability to track recovery and be
used as a readiness indicator. However, does heart rate variability actually correlate with performance?
This article reviews a recent study and examines the totality of the literature to provide some answers.
4
58
72
BY A NN E-KAT HR I N EI SELT
How The Brain Controls Eating Behavior
Many people find it difficult to follow a calorie-restricted diet. Is your brain working against you?
This review of neuroscience research helps explain why dieting makes you feel lousy, why it is so
easy to overeat hyperpalatable food, and how you can use this knowledge to your advantage.
BY E R I C T R EXL ER
More Good News for Artificial Sweeteners: No Effect of Short-Term
Sucralose Ingestion on Glycemic Control or Gut Microbiome
Some view artificial sweetener consumption as a safe and healthy way to cut calories, while others
suspect that they’re too good to be true. A recent study found that one week of consuming highdose sucralose, also known as SPLENDA®, had no effect on glycemic control or the gut microbiome.
Read on to get the scoop on the potential health effects of artificial sweeteners.
86
98
100
BY G R EG NUCKOL S
What’s the Best Age to Dominate Strength Sports?
If you want to maximize your competitiveness in powerlifting or weightlifting, at what age should you
anticipate being at the peak of your prowess? It seems that weightlifting is a young person’s game,
but many powerlifters are still improving well into their 30s (or even 40s).
BY M I CHAEL C. ZOUR DOS
VIDEO: Powerlifting Game Day
MASS has many articles and videos on programming for powerlifting, but what can you expect at
the actual competition? This video breaks down everything you need to prepare, and what you need
to know about powerlifting game day as a coach and lifter.
BY E RI C HEL MS
VIDEO: New Perspectives on Activity and Bodyweight
We are still uncovering the mechanisms of how humans regulate body weight. Typically, this is viewed
from the perspective of our body attempting to maintain a certain level of adiposity or mass during an
energy deficit or surplus. However, there are likely regulatory mechanisms related to total daily energy
expenditure that influence body weight as well.
5
Study Reviewed: Muscular Preconditioning Using Phototherapy Improves the Physical
Work Capacity of the Quadriceps When Applied Between Repeated Bouts of Resistance
Exercise. Borsa et al. (2019)
Improving Strength Endurance
with Frickin Lasers
BY G RE G NUC KO LS
It sounds like sci-fi, but using a low-power infrared or nearinfrared laser on your muscles between sets may improve
strength and strength endurance.
6
KEY POINTS
1. Phototherapy between sets of isokinetic knee extensions improved torque,
power, and total work performed in a crossover study.
2. When digging into the literature, I was surprised to find that there’s actually a
considerable amount of evidence in favor of phototherapy; it seems to reliably
boost performance by improving recovery between sets, accelerating recovery
from training. It may even lead to larger strength gains and more muscle growth
over time.
3. Phototherapy works because infrared and near-infrared light interacts with protein
complexes involved in aerobic metabolism.
W
hen we think about the stimuli our muscles can respond
to, light probably isn’t the first
thing that comes to mind. After all, barring a catastrophic injury, your muscles
should reside safely under your skin,
mostly existing in darkness. When we
think about stimuli for our muscles, we
generally think of mechanical stimuli,
electrical stimuli, and chemical stimuli
first, for good reason. However, certain
protein complexes in your muscles can
respond to light through a process called
photobiomodulation.
This responsiveness to light, specifically infrared and near-infrared light, opens
up the possibility for lasers to impact
muscle function (referred to as phototherapy). That may sound like science fiction,
but actual science supports its efficacy. In
the present study (1), subjects completed the same fatigue protocol consisting
of four sets of isokinetic knee extensions
twice, in a crossover fashion. One time
subjects received phototherapy between
sets, and one time they received a sham
treatment. Peak torque, average torque,
average power, and total work performed
were greater in sets 2, 3, and 4 in the phototherapy condition. As it turns out, this
finding is right at home in the rest of the
literature on the topic.
Purpose and Hypotheses
Purpose
The purpose of this study was to examine the effects of phototherapy,
compared to a sham treatment, on neuromuscular performance of the quads
when applied between successive sets
of fatiguing training.
Hypotheses
The authors hypothesized that phototherapy would delay the onset and extent of fatigue compared to a sham treatment, allowing the subjects to maintain
higher torque and work output.
7
Figure 1
Timeline of study
Healthy volunteers
n=20
Volunteers excluded
n=1
Randomization for treatment order
n=19
Healthy volunteers
n=20
Familiarization training
Phase 1
Phase 2
72 hours recovery
5 minute warm-up
5 minute warm-up
Active phototherapy
n=9
Sham phototherapy
n=10
Fatigue protocol
Fatigue protocol
5 minute warm-up
5 minute warm-up
Active phototherapy
n=10
Sham phototherapy
n=10
Fatigue protocol
Fatigue protocol
72 hours recovery
Participants received a 72 hour recovery between familiarization training and start of phase 1, and between phase 1 and phase 2. All participants
completed both phases of the study
Subjects and Methods
Subjects
20 subjects volunteered for the study
(10 men and 10 women), with 19 successfully completing it. The only inclusion criteria were that the subjects needed to be injury-free and between the
ages of 18 and 30 years old. Thus, it’s
hard to know whether the subjects were
predominantly trained or untrained. In
the limitations section of the paper, the
authors note that future studies should
include highly competitive or elite athletes, so we can safely assume that such
labels wouldn’t apply to the subjects in
this study, but they don’t mention un-
8
Figure 2
Exercise protocol
Pre-exercise
Fatigue protocol: 4 bouts of 30 repetitions
4 minute
recovery
treatment
(active/sham)
Dose 1
5-minute warm-up
on cycle ergometer
Bout 1
4 minute
recovery
treatment
(active/sham)
Dose 2
Bout 2
4 minute
recovery
treatment
(active/sham)
Dose 3
Bout 3
Bout 4
Each subject completed a 5 minute warm-up on a cycle ergometer followed by the completion of four exercise bouts on an isokinetic dynamometer.
There was a standardized passive 4-minute recovery period between each exercise bout during which active or sham PBMT was administered to the
quadriceps muscle group.
trained subjects as a limitation. The authors also note that they instructed the
subjects to refrain from lifting weights
for at least 48 hours prior to testing. This
leads me to think that most (or at least
some) of the subjects likely had some
degree of prior training experience.
Experimental Design
The study used a standard crossover
design. After familiarization, subjects
were randomly assigned to complete a
fatigue protocol while receiving either
phototherapy between sets of knee extensions, or a sham treatment. After 72
hours of rest, the subjects completed the
same fatigue protocol, receiving whichever treatment they did not receive
during the first testing session.
To the researchers’ credit, the famil-
iarization was quite extensive. In each
familiarization session (separated by at
least 72 hours), the subjects completed
the exact same fatigue protocol that was
used during the experimental sessions.
They performed familiarization sessions
until their peak torque values differed by
less than 10% session to session. Thus,
all subjects had at least two familiarization sessions, with an average of 2.6.
The fatigue protocol consisted of 4 sets
of 30 maximal concentric isokinetic knee
extensions at an angular velocity of 75°
per second, with four minutes of rest between sets. Between sets, subjects either
received phototherapy on their quads using a laser emitting near-infrared light, or
a sham treatment using the same machine
emitting a different frequency of light at
a much lower power. The researchers
9
81
Normalized PT (Nm/BW)
Figure 5
Normalized peak torque for the four exercise bouts (marginal means)
78
Sham
phototherapy
2600
Active
phototherapy
2500
*
*
75
*
72
Total work (J)
Figure 3
*
2400
2300
*
2200
*
2100
69
2000
1
2
Bout
3
1
4
Participants produced higher peak torque (PT) during bouts 2, 3, and 4
after receiving active phototherapy compared to sham phototherapy.
* = statistically significant difference of p < 0.013
Figure 4
Average torque for the four exercise bouts (marginal means)
2
Bout
3
4
Participants produced more work during bouts 2, 3, and 4 after
receiving active phototherapy compared to sham phototherapy.
* = statistically significant difference of p < 0.013
Figure 6
Average power for the four exercise bouts (marginal means)
115
100
110
95
Average power (W)
Average torque (Nm)
Total work for the four exercise bouts (marginal means)
*
90
*
85
*
105
100
*
*
95
90
80
1
2
Bout
3
4
Participants produced more average torque during bouts 2, and 3
after receiving active phototherapy compared to sham phototherapy.
* = statistically significant difference of p < 0.013
took the important (though depressingly
uncommon) step of asking the subjects
in both experimental sessions whether
they thought they were getting the active treatment or the sham treatment;
the subjects couldn’t tell them apart (p =
0.63), indicating that the sham treatment
was applied successfully.
During the fatigue protocol, the re-
1
2
Bout
3
4
Participants produced more power during bouts 2, 3, and 4 after
receiving active phototherapy compared to sham phototherapy.
* = statistically significant difference of p < 0.013
searchers assessed peak torque (normalized to body mass), average torque, total
work, and average power.
Findings
During the first set of the fatigue protocol (before the first application of either treatment), normalized peak torque,
10
Figure 7
Comparison of phototherapy vs. placebo group
Study or subgroup
Mean difference
SE Weight
Mean difference
IV, Random, 95% CI
Mean difference
IV, Random, 95% CI
3.3.1 Immediately
dos Reis 2014
-8.9
4.41
7.7%
Higashi 2013
2.5
1.88
18.1%
2.50 [-1.18, 6.18]
10.16
2.64
14.0%
10.16 [4.99, 15.33]
Leal Junior 2008
Leal Junior 2009d
4.5
2
17.4%
4.50 [0.58, 8.42]
5
1.47
20.5%
5.00 [2.12, 7.88]
2.3
1.17
22.2%
2.30 [0.01, 4.59]
Leal Junior 2010
Toma 2013
-8.90 [-17.54, -0.26]
Total (95% CI)
100%
Heterogeneity: Tau = 8.23; Chi = 16.89, df = 5 (P = 0.005); I = 70%
2
2
2
Test for overall effect: Z = 2.41 (P = 0.02)
Test for subgroup differences: Not applicable
3.51 [0.65, 6.37]
-10
-5
Favors placebo
0
5
10
Favors phototheraphy
Outcome: exercise capacity (number of repetitions [count])
From Nampo et al. (2016)
average torque, total work, and average
power were similar between conditions,
as one would hope. For every subsequent
set, performance was maintained better
in the phototherapy condition than the
sham treatment condition. Specifically, in the phototherapy condition, there
was either an increase (normalized peak
torque and average torque) or a smaller
decrease (total work and average power)
in performance from set 1 to set 2 compared to the sham condition. After set
2, set-to-set decrements in performance
were similar in both conditions, though
absolute performance remained superior
in the phototherapy condition.
Interpretation
I found this study fascinating. I was
aware of the concept of phototherapy,
but I didn’t realize how far the research
had already progressed. As it turns out,
I was pretty far behind on the research.
Before I get ahead of myself, it’s worth
explaining how phototherapy works (2,
3). You have protein complexes within
your cells that react to light. That reactivity to light is known as photobiomodulation. Most importantly for exercise,
one of the cytochrome complexes in
your mitochondria, along with your hemoglobin (the molecule that carries oxygen in red blood cells) and myoglobin
(the molecule that stores oxygen in your
muscles) interact with red and infrared
light. In your mitochondria, red and infrared light cause inhibitory factors to
dissociate with cytochrome c oxidase.
When hemoglobin and myoglobin inter-
11
act with red or infrared light, they release
the oxygen they’re holding onto. As a
result, your mitochondria work more
efficiently, and you have more oxygen
freely available for oxidative metabolism. Essentially, red and infrared light
allow local aerobic energy production to
hum along a bit more efficiently. As a result, strength endurance improves a bit,
your muscle cells can move back toward
homeostasis a bit faster after a metabolic stressor, and you experience a bit less
total acidosis, leading to a bit less muscle damage. None of these things are
night-and-day differences, but they can
cumulatively make a noticeable impact.
As an added bonus for strength-focused
trainees, phototherapy also seems to increase intracellular calcium ion concentrations (2; though I’ll admit that I’m not
sure I totally understand the mechanism
by which this occurs).
As I alluded to, I was pretty far behind
on the research in this area. I assumed
that the present study was one of the first
few to examine the effects of phototherapy on exercise performance (1). As it
turns out, there were at least 16 extant
studies when a meta-analysis on the subject was published back in 2016 (4). The
headline findings were that phototherapy improves rep performance, improves
time to exhaustion, and attenuates blood
lactate levels. An earlier systematic review also suggested that phototherapy
attenuates muscle damage to some degree, leading to improved recovery after
THERE ARE AT LEAST FIVE
LONGITUDINAL STUDIES
INVESTIGATING THE
EFFECTS OF PHOTOTHERAPY
ON HYPERTROPHY OR
IMPROVEMENTS IN MUSCLE
PERFORMANCE, AND ALL FIVE
REPORTED POSITIVE EFFECTS
IN FAVOR OF TRAINING
WITH PHOTOTHERAPY.
training (5).
So, with all of that in mind, the results
of the present study (1) are right in line
with the rest of the literature on the subject. Phototherapy didn’t make a nightand-day difference in performance, but
it did make a consistent, measurable difference. For all four measures, subjects
performed ~7-10% better during sets
two, three, and four in the phototherapy
condition. Given its mechanism of action, it shouldn’t be too surprising that
phototherapy improved average torque,
total work, and average power. However, the striking thing to me is that it actually improved normalized peak torque
12
between sets 1 and 2, matching the 2016
meta-analysis which also found that
phototherapy improved peak torque (3).
This is likely due to phototherapy’s ability to increase intracellular calcium concentrations.
Just as one final note about this particular study: the results of phototherapy
are even more impressive, given that the
subjects were resting four minutes between sets. That should be plenty of time
to recover, especially from single-joint
exercise (although 30 maximal reps is
certainly challenging). If they only had
a minute to recover, it would be reasonable to wonder whether phototherapy
improved recovery, or if it just accelerated recovery. In other words, maybe one
minute of rest with phototherapy would
be equivalent to two minutes of rest
without phototherapy, but one could still
rest a bit longer and reach the same level
of recovery before the next set, which
would make one question the degree to
which phototherapy would actually be
useful and relevant in practical settings.
However, if it’s able to delay fatigue
when people are already resting four
minutes between sets, that strikes me as
an improvement that’s more meaningful
and relevant.
If phototherapy can improve strength
endurance and recovery from training,
and potentially even peak force, there
must be some downside, right? Since it
seems to attenuate muscle damage, my
first though was that it may work similar-
ly to NSAIDs or cold water immersion –
decreasing inflammation and hastening
recovery from training, while also attenuating longitudinal gains in strength and
muscle mass. However, if anything, the
opposite may be true. There are at least
five longitudinal studies investigating
the effects of phototherapy on hypertrophy or improvements in muscle performance, and all five reported positive
effects in favor of training with phototherapy (6, 7, 8, 9, 10).
However, there is a catch: I’m a little
skeptical about whether consumer-grade
phototherapy devices are effective. A
cursory search revealed that research- or
medical-grade phototherapy units cost
between $10,000-$40,000 USD. However, there are consumer products retailing on Amazon for $100-$150. Immediately, I noticed that the device used in
the present study (1) was considerably
more powerful than the consumer products on Amazon (10W vs. 1-3W). However, other studies have used less powerful devices, which have also seemed to
be effective. I know that research equipment is often massively overpriced (it’s
generally purchased with grant money,
and most organizations awarding grants
have DEEP pockets), but a 100- to 400fold markup seems a little insane, assuming the consumer products are also
effective. Do with that what you will.
I could just be overly skeptical due to
obscene price gouging, and the consumer products may be awesome. Or,
13
APPLICATION AND TAKEAWAYS
Phototherapy seems to reliably improve acute training performance and accelerate
recovery from training, and it may even lead to larger strength gains and more muscle
growth over time. However, due to the massive cost disparity between research-grade
and consumer-grade devices, I’m still a bit skeptical of the consumer products on the
market. With that being said, a phototherapy device may be worth the splurge if you’re
looking for an edge.
the consumer products may be a sham
that would be hard to detect (since phototherapy doesn’t cause any noticeable
sensation).
Seeing as consumer phototherapy devices are reasonably affordable, and
since the research on phototherapy is so
promising, investing in a phototherapy
device may not be a bad investment if
you’re looking for something to give
you an edge. On one hand, it’s a fairly
sizeable one-time purchase, but on the
other hand, there are people who spend
more money than that per month on
supplements that are probably less effective. I don’t think a dose-response relationship for phototherapy is known, so
your best bet would probably be to just
use it on your prime movers between
each set of your workout. A lot of the
studies mention holding the device over
each point to which it’s applied for 10+
seconds, so that would be my recommendation as well (since we know the
experimental protocols are effective);
if you were using it on your biceps, for
example, instead of continually moving
it over your biceps, hold it in place for
10-30 seconds over 3-4 spots along each
of your biceps.
Overall, phototherapy is an exciting
technology with much more support
than I realized. I’m still skeptical about
the consumer products, but it’s an area
of research you should keep your eye
on. It likely won’t make a night-and-day
difference, but for a one-time purchase,
it seems to be more effective than most
supplements, which would make a reliable phototherapy device a pretty good
investment in the long-run for people
who are willing to do a little extra in order to maximize their performance.
Next Steps
At this point, I’m pretty sold on phototherapy’s ability to improve acute
performance and recovery, but I’d like
to see more longitudinal research on
whether it improves strength gains and
hypertrophy over time. While there’s
some research on that subject already,
I’d just like to see more before I’d feel
14
comfortable recommending phototherapy with the same confidence I’d recommend something like creatine. I’d also
like to see validation studies on some of
the affordable consumer-grade products
on the market.
15
References
1. Borsa PA, Dale RB, Levine D, Crow JA. Muscular Preconditioning Using Phototherapy
Improved the Physical Work Capacity of the Quadriceps when Applied between Repeated
Bouts of Resistance Exercise. J Athl Enhanc 2019, 8:1.
2. de Freitas LF, Hamblin MR. Proposed Mechanisms of Photobiomodulation or Low-Level
Light Therapy. IEEE J Sel Top Quantum Electron. 2016 May-Jun;22(3).
3. Ferraresi C, Huang YY, Hamblin MR. Photobiomodulation in human muscle tissue: an advantage in sports performance? J Biophotonics. 2016 Dec;9(11-12):1273-1299.
4. Nampo FK, Cavalheri V, Dos Santos Soares F, de Paula Ramos S, Camargo EA. Low-level
phototherapy to improve exercise capacity and muscle performance: a systematic review
and meta-analysis. Lasers Med Sci. 2016 Dec;31(9):1957-1970.
5. Borsa PA, Larkin KA, True JM. Does phototherapy enhance skeletal muscle contractile function and postexercise recovery? A systematic review. J Athl Train. 2013 Jan-Feb;48(1):57-67.
6. Ferraresi C, de Brito Oliveira T, de Oliveira Zafalon L, de Menezes Reiff RB, Baldissera V,
de Andrade Perez SE, Matheucci Júnior E, Parizotto NA. Effects of low level laser therapy
(808 nm) on physical strength training in humans. Lasers Med Sci. 2011 May;26(3):349-58.
7. Vieira WH, Ferraresi C, Perez SE, Baldissera V, Parizotto NA. Effects of low-level laser therapy (808 nm) on isokinetic muscle performance of young women submitted to endurance
training: a randomized controlled clinical trial. Lasers Med Sci. 2012 Mar;27(2):497-504.
8. Ferraresi C, Bertucci D, Schiavinato J, Reiff R, Araújo A, Panepucci R, Matheucci E Jr,
Cunha AF, Arakelian VM, Hamblin MR, Parizotto N, Bagnato V. Effects of Light-Emitting
Diode Therapy on Muscle Hypertrophy, Gene Expression, Performance, Damage, and Delayed-Onset Muscle Soreness: Case-control Study with a Pair of Identical Twins. Am J Phys
Med Rehabil. 2016 Oct;95(10):746-57.
9. Baroni BM, Rodrigues R, Freire BB, Franke Rde A, Geremia JM, Vaz MA. Effect of low-level laser therapy on muscle adaptation to knee extensor eccentric training. Eur J Appl Physiol.
2015 Mar;115(3):639-47.
10. Toma RL, Vassão PG, Assis L, Antunes HK, Renno AC. Low level laser therapy associated
with a strength training program on muscle performance in elderly women: a randomized
double blind control study. Lasers Med Sci. 2016 Aug;31(6):1219-29.
█
16
Study Reviewed: Acute Effects of Different Training Loads on Affective
Responses in Resistance-Trained Men. Ribeiro et al. (2019)
Most People Find Low-Load
Training to Failure Miserable
BY MIC HAE L C . ZO URD O S
You may have heard that recent evidence suggested that low-load training produces
similar hypertrophy to more typical, moderate-load training. That may be true,
but a new study shows that people find low-load training to be pretty miserable in
comparison to moderate-load training.
17
KEY POINTS
1. This crossover design study compared subjects’ perceived exertion and discomfort
during low-load training (25-30RM) and moderate-load training (8-12RM) on the
bench press, hack squat, and lat pulldown.
2. Perceived exertion and perceived discomfort were both rated significantly higher
following a training session with low loads versus high loads. Further, subjects
noted more “displeasure” with the low-load training session versus the moderateload session.
3. Although low-load and moderate-load training can produce similar hypertrophy,
serious practical limitations exist with using solely low-load training. This study
points out that, on average, people simply perceive low-load training to failure to
be more fatiguing and less enjoyable than more typical moderate-load training.
W
hen research reveals similar
outcomes between training protocols, I often think, “cool, do
what you do want.” Low-load training
(25-30RM, or ~30-40% of one-repetition
maximum [1RM]) versus moderate-load
training (8-15RM, or ~60-80%) is one
of those do-what-you-want scenarios, as
a previous meta-analysis (reviewed by
Greg) showed no difference in hypertrophy between low- and moderate-load
training (2). However, there are practical limitations associated with low-load
training, including a limited ability to
progressively increase load over time and
potentially greater acute fatigue and discomfort than moderate-load training. If
training is more difficult, less enjoyable,
and only produces similar outcomes to
easier training, then why choose the more
difficult option? This crossover design
study (1) had 12 trained men perform the
bench press, hack squat, and lat pulldown
for 3 sets of 8-12RM in one session and
3 sets of 25-30RM in another session.
Following both training sessions, researchers asked the participants to rate
their perceived exertion, discomfort, and
pleasure/displeasure on 10-point Likert
scales. The low-load condition led to significantly greater exertion, discomfort,
and displeasure than the moderate load
condition. These results suggest that although two different protocols may produce similar hypertrophy, we should look
deeper into what could affect long-term
progress. This article will discuss the
practical limitations of low-load training
as a standalone strategy, and when lowload training may be useful.
Purpose and Research
Questions
Purpose
The purpose of this study was to com-
18
Table 1
Subject characteristics
Subjects
Age (years)
Height (cm)
Body mass (kg)
Training experience (years)
12 men
26.7 ± 3.5
174.9 ± 9.9
85.1 ± 17.5
2.3 ± 0.9
Data are mean ± SD
Subject characteristics from Ribeiro et al. 2019 (1)
pare a lifter’s perception of exertion, discomfort, and pleasure/displeasure following 3 sets of 3 exercises using low
loads (25-30RM) versus moderate loads
(8-12RM).
and 4, subjects performed 3 sets at the
8-12RM load on the bench press, hack
squat, and lat pulldown on one day and
3 sets of the 25-30RM load on the other
day. There were 120-second rest intervals
between sets.
The authors hypothesized that low-load
training would lead to greater ratings of
exertion, discomfort, and displeasure
compared to moderate-load training.
Perceptual Scales
Hypotheses
Subjects and Methods
Subjects
12 men with 1-4 years of training experience participated. The available descriptive details of the subjects are in Table 1.
Study Protocol
All subjects completed this crossover
design study over four lab visits separated by 48-72 hours. The first visit was
for anthropometric testing (i.e. height,
weight, body composition) and the second visit was to determine the 8-12RM
and 25-30RM loads that would be used
for each subject during the experimental visits (visit 3 and 4). During visits 3
The outcome measures in this study
were three perceptual scales which were
completed 15 minutes after training on
visits 3 and 4. The scales rated perceived
exertion, perceived discomfort, and perceived pleasure/displeasure for the entire training session. More details of the
scales are in Table 2.
Findings
Very simply, the subjects rated exertion, discomfort, and displeasure all significantly higher in the low-load condition versus the moderate-load condition.
In other words, subjects perceived the
low-load, high-rep training to be more
fatiguing, and they felt worse afterward.
The mean values for each scale and effect
sizes between conditions are in Table 3.
The individual subject ratings for each
scale are in Figure 1.
19
Table 2
Description of perceptual scales
Session rating of perceived
exertion scale
Session rating of perceived
discomfort scale
Feelings of pleasure
and displeasure
An 11-point scale ranging from -5 to +5
The OMNI 0-10 scale was used
0 = resting
0 = neutral
A 0-10 scale
0 = no discomfort at all
10 = maximum discomfort
+1 to +5 = positive
(pleasure ratings)
-1 to -5 = negative
(displeasure ratings)
Protocol from Ribeiro et al. 2019 (1)
Interpretation
We’ve known for quite some time that
when sets are equated between lowload (~30% of 1RM) and moderate-load
(~60-80% of 1RM, called high-load by
some) training, hypertrophy outcomes
are similar in studies lasting at least six
weeks, while strength unsurprisingly favors the moderate-load training (2). Oftentimes, some will use these previous
results to state that you should feel free
to use whichever strategy (low- or moderate-load) you wish. While on the surface that seems true, I have never been
a believer of using solely low loads in
the long term, and – in my opinion – the
study reviewed here adds to that skepticism.
Specifically, I believe low-load training has some important practical limitations over the long run. First, it seems as
though progressive overload would be
difficult over the long-term with a lowload strategy. While I cannot say that for
certain, it logically seems more difficult
to continually achieve progressive overload with low loads versus moderate
loads over the very long term. Therefore, while low loads might produce
similar hypertrophy to moderate loads in
the short-term (i.e. 6-12 weeks), I would
be surprised if continually using lower
loads produced similar hypertrophy over
the longer term (6 months to 1 year) for
the limitations mentioned above. Another practical consideration with low loads
is that, to date, all of the low-load versus
moderate-load training studies have the
low-load group training to failure, so we
don’t yet know if training shy of failure
with low loads can maximize hypertrophy. In contrast, with moderate loads,
there is much more evidence to suggest
that training shy of failure with moderate loads can maximize hypertrophy.
Further, we know that training to failure
elongates fatigue and the time course
of recovery compared to submaximal
training (3). Thus, if solely low loads are
20
Table 3
Results for each perceptual scale
Scale
Low-load
Moderate-load
sRPE
6.4 ± 0.7*
5.5 ± 1.0
1.06
Discomfort
8.7 ± 1.0*
6.7 ± 1.7
1.48
Pleasure/Displeasure
-2.3 ± 1.9*
1.2 ± 1.3
2.19
*Significantly greater than the moderate-load condition; RPE = Session Rating of Perceived Exertion
All effect sizes are large and suggest greater ratings in the low-load condition.
used, training to failure may be necessary, which could in turn lead to a lower
frequency and volume due to elongated
recovery. However, the recovery piece
aside, I think the current study reveals
the biggest practical limitation to lowload failure training, which is that lowload training is pretty miserable for most
lifters. The results presented in this article clearly show greater acute fatigue
and discomfort in the low-load condition
versus the high-load condition (Table 3).
Further, Figure 1 shows that the greater
exertion, discomfort, and displeasure
following training occurred in almost
every subject, and the difference between conditions was pretty drastic for
some individuals. I’m not sure if the difference in the outcome measures would
be this drastic if the low-load condition
trained shy of failure; however, if you
train shy of failure with low loads, then
hypertrophy may be suboptimal. From
a practical perspective, can you imagine completing such a miserable training session 2-3 times per week over the
long-term? Probably not. Therefore, I
would suspect that adherence could become an issue by solely using low-load
training, even if you could overcome the
progressive overload limitation. Table 4
provides a quick list of the key practical
limitations of using low-load training
exclusively.
Despite the previous paragraph’s justified negativity toward low-load training, there may be an appropriate time
and place to utilize low-load training.
As with many training topics, people
tend to look at low-load or moderate/
high-load training as a binary decision,
when of course it is not. Perhaps you enjoy loading a bit lighter, but just not all
the time. Perhaps you have trouble add-
Table 4
Practical limitations of using solely
low-load training for hypertrophy
High levels of discomfort, displeasure, and fatigue
Requires close proximity to failure
21
A
Individual subject ratings in each condition
B
10
8
Discomfort scale
8
6
sRPE
C
10
Pleasure / displeasure scale
Figure 1
4
2
6
4
2
0
0
Low-Load
Moderate-Load
5
3
1
-1
-3
-5
Low-Load
Moderate-Load
Low-Load
Moderate-Load
From Ribeiro et al. (1)
Data are ratings for each individual subject in each condition
sRPE = Session RPE
ing weight to the bar for a 30RM over
the long term, but you still want to use
low loads at times. In these cases, you
could easily train a muscle group twice
per week, once with low loads and once
with moderate loads a la daily undulating programming. Also, you could
alternate low-load training blocks and
moderate-load training blocks. These
basic strategies would mitigate some
of the limitations but still allow you to
perform low-load training if you wish.
Of course, low-load training could still
be used on assistance movements more
often than on the main lifts. Doing sets
of 25-30 on curls, triceps extensions,
and rows is more feasible and enjoyable
to do consistently than on the squat or
deadlift. So, you can certainly train with
more typical loads and rep ranges on the
main lifts and lower loads with high reps
on assistance movements. I bet a lot of
people do that anyway. To that point, this
study took perceptual measures of the
entire session, which included two main
movements (bench press and hack squat)
and one true assistance movement (lat
pulldown). Therefore, it is entirely possible that the displeasure from the session
solely stemmed from the main movements, specifically the hack squat. We
cannot know if results would have been
the same if this study only used assistance
movements. Besides, if it is necessary
to take low loads to failure to maximize
muscle growth, then it is more feasible to
train to failure consistently on assistance
movements than on the main lifts.
22
APPLICATION AND TAKEAWAYS
1. Training to failure at low loads leads to greater perceived exertion, discomfort, and
displeasure, on average, compared to moderate-load training.
2. Although data do exist showing that hypertrophy is similar when low-load training
is compared to moderate-load training over an eight-week study, coaches and
lifters should be mindful of the practical limitations of low-load training.
3. The limitations of low-load training include difficulty implementing progressive
overload in the long-term, along with the low enjoyment factor reported in the
present study. Therefore, when programming for yourself or others, consider what
will lead to greater long-term adherence, and use low-load training appropriately
rather than as a full-time standalone training strategy.
Lastly, low-load training may be easier on the joints for some, or may be useful when recovering from an injury. I’m
sure some who are returning from an injury can load a barbell to a certain point
without aggravating the issue, but as the
weight goes up, the injury becomes a bit
bothersome. In this case, a block of lowload training may be beneficial in the
same way blood-flow restriction training could be beneficial to avoid heavy
loading. These aren’t necessarily standalone training strategies; however, they
have their place when the time is right. I
also don’t think that you would have to
take the low loads to failure when coming back from an injury. First, staying
shy of failure would help to avoid aggravating the injury, and second, if you
weren’t doing the movement at all and
now you are doing it, you should still
see some progress upon the initial return
to training.
Next Steps
As with anything, a very long-term
(i.e. 6 months to a year) low-load versus moderate-load study would be great.
Also, in a long-term study, it would be
necessary to gather the perceptual responses that were examined in the presently reviewed study over time. Just
because low-load training is more miserable acutely doesn’t mean that the enjoyment can’t increase over time. To be
clear, I don’t think the enjoyment would
increase, but if enjoyment did improve,
that would enhance the case to utilize
low-load training. For a more direct follow-up to the specific data discussed
in this article, I’d like to see the acute
exertion, discomfort, and displeasure/
pleasure responses for single-joint assistance movements in response to lowand moderate-load training.
23
References
1. Ribeiro AS, dos Santos ED, Nunes JP, Schoenfeld BJ. Acute Effects of Different Training
Loads on Affective Responses in Resistance-trained Men. International journal of sports
medicine. 2019 Sep 9..
2. Schoenfeld BJ, Grgic J, Ogborn D, Krieger JW. Strength and hypertrophy adaptations between low-vs. high-load resistance training: a systematic review and meta-analysis. The
Journal of Strength & Conditioning Research. 2017 Dec 1;31(12):3508-23.
3. Pareja-Blanco F, Rodríguez-Rosell D, Aagaard P, Sánchez-Medina L, Ribas-Serna J, Mora-Custodio R, Otero-Esquina C, Yáñez-García JM, González-Badillo JJ. Time Course of
Recovery From Resistance Exercise With Different Set Configurations. Journal of strength
and conditioning research. 2018 Jul.
█
24
Study Reviewed: Effect of Betaine on Reducing Body Fat—A Systematic Review and
Meta-Analysis of Randomized Controlled Trials. Gao et al. (2019)
Betaine May Promote Body
Composition Improvements When
Combined With Resistance Training
BY E RI C T RE X LE R
Betaine, which is found in beets, spinach, whole grains, and seafood, has
been used to improve the body composition of livestock. To see if these
effects also apply to humans, a recent meta-analysis sought to review the
small body of literature evaluating betaine’s effects on fat loss in human
subjects. Read on to find out if betaine might help you achieve your
physique-related goals.
25
KEY POINTS
1. The current meta-analysis (1) summarized the literature evaluating the effects of
betaine (trimethylglycine) supplementation on fat loss.
2. Betaine did not significantly alter body weight, body mass index, or waist
circumference, but significantly reduced fat mass (−2.25kg) and body-fat
percentage (−2.44%) in comparison to placebo.
3. Betaine (2.5g/day for ≥ 6 weeks) may modestly facilitate fat loss and lean mass
accretion when combined with resistance training, and effects may be larger in
males than females. However, few studies are available, so definitive conclusions
cannot be drawn yet.
B
etaine (trimethylglycine) finds
its way into pre-workout supplements from time to time, but it isn’t
a particularly well-known supplement.
Betaine can be found in many foods,
such as beets, spinach, whole grains, and
seafood, and average daily betaine intake is around 100-400mg in adults (1).
Betaine has been used as an additive in
the feed of livestock (2), with evidence
suggesting that it reduces fat storage
and increases meat yield (3), but there is
relatively minimal research evaluating
its effects on human body composition.
The current meta-analysis (1) sought to
summarize the currently available literature investigating the effects of betaine
supplementation on several fat-related parameters in humans. The search
revealed only six qualifying studies,
which were all published between 2002
and 2018. According to the analysis,
betaine supplementation (2.0-9.9g/day,
over a span of 10 days to 24 weeks) did
not significantly alter body weight, body
mass index (BMI), or waist circumference. In contrast, betaine supplementation significantly reduced both fat mass
(−2.25kg; 95% CI: −3.96, −0.54kg) and
body-fat percentage (−2.44%; 95% CI:
−4.20, −0.68%). In addition, the largest
effects were observed in the two studies that implemented resistance training
programs in conjunction with supplementation. This article discusses what
we currently know about betaine supplementation and its likelihood of being
a worthwhile supplement for lifters.
Purpose and Hypotheses
Purpose
The purpose of the current study was
to “provide an up-to-date evaluation of
the roles of betaine in obesity.” To that
end, the authors performed a meta-analysis on the randomized controlled trials
evaluating the effects of betaine supplementation on various measures of adi-
26
posity, such as body weight, BMI, waist
circumference, fat mass, and body-fat
percentage.
Hypotheses
The authors hypothesized that “betaine reduces body fat in humans.” This
hypothesis is most directly related to
the studies that reported fat mass as an
outcome, but obviously closely relates
to the other outcomes analyzed (body
weight, BMI, waist circumference, and
body-fat percentage).
Subjects and Methods
The authors systematically reviewed
the betaine literature, and only included studies that met the following criteria
(1):
1. The study used adult subjects.
2. The study was a randomized controlled trial.
3. Betaine was the only intervention
that differed between groups in the
study.
4. The study provided enough information to extract the values of interest.
The authors ended up with six qualifying studies, with 195 total subjects.
Study durations ranged from 10 days to
24 weeks, and betaine doses ranged from
2.0g/day to 9.9g/day. Notably, only two
studies involved a training component,
and both utilized well-designed resis-
tance training protocols (4, 5).
For the analysis, the authors compared
changes observed in the betaine group
to changes observed in the placebo
group for each study. Notably, some key
details appear to be omitted from the
methods. With this type of meta-analysis, the authors typically need to calculate a change score standard deviation.
This almost always requires the authors
to make an educated guess about the
correlation between pre-test and posttest values; as I noted in a recent MASS
article, this estimate can have a meaningful impact on the results of the analysis. Theoretically, I could do a bunch of
reverse-engineering to figure it out, but
for reasons I’ll discuss later, that would
provide us with some information that
isn’t particularly valuable.
Findings
The analysis for body weight revealed
no statistically significant effect from
betaine supplementation (−0.29kg; 95%
CI: −1.48, 0.89kg). Similarly, the effect
on BMI was not statistically significant
(−0.10kg/m2; 95% CI: −5.1, 0.31kg/m2),
nor was the effect on waist circumference (0.68cm; 95% CI: −1.72, 3.09cm).
Notably, these outcomes are all fairly indirect measures of adiposity, and there
were only three studies for the BMI
analysis and two studies for the waist
circumference analysis.
For the more direct measures of adi-
27
Figure 1
Effects of betaine supplementation on fat mass
Total body fat mass
Study ID
WMD (95% CI)
% Weight
Cholewa, 2018
-1.30 (-8.94, 6.34)
4.91
Cholewa, 2013
-3.20 (-4.96, -1.44)
68.83
Favero, 2011
-0.60 (-5.19, 3.99)
13.13
Schwab, 2002
0.70 (-3.90, 5.30)
13.12
Overall (I-squared = 6.6%, p = 0.360)
-2.25 (-3.96, -0.54)
100.00
-8.94
0
8.94
The weighted mean difference (WMD) for fat mass was a reduction of 2.25kg, which was statistically significant
posity, the results were a bit more promising. Betaine supplementation led to
significant reductions for both fat mass
(−2.25kg; 95% CI: −3.96, −0.54kg; Figure 1) and body-fat percentage (−2.44%;
95% CI: −4.20, −0.68%; Figure 2). Still,
it’s important to note that there were
only four studies available for each of
these analyses. In addition, it looks like
the fat mass results that are reported in
the abstract and the text of the results do
not match the results reported in the figure. I am going to assume that the values in the figure are the accurate values,
because the figure was probably auto-
matically generated by their software
(and therefore less prone to typos), and
the reported values in the text yield an
asymmetrical confidence interval.
Interpretation
There are a couple of different ways
to view a meta-analysis. When a body
of literature is large and well-developed,
you can use a meta-analysis to form
something of a working “conclusion”
about whether or not a treatment works,
and to identify some critical criteria that
increase or decrease its efficacy. How-
28
Figure 2
Effects of betaine supplementation on body-fat percentage
Body fat percentage
Study ID
WMD (95% CI)
% Weight
Schwab, 2002
0.30 (-4.18, 4.78)
15.42
Favero, 2011
-0.40 (-6.31, 5.51)
8.86
Cholewa, 2018
-1.70 (-8.21, 4.81)
7.29
Cholewa, 2013
-3.40 (-5.53, -1.27)
68.44
Overall (I-squared = 0.0%, p = 0.435)
-2.44 (-4.20, -0.68)
100.00
-8.21
0
8.21
The weighted mean difference (WMD) for body-fat percentage was a reduction of 2.44 percentage points,
which was statistically significant.
ever, when a body of literature is small,
it’s basically an update about the current state of the literature. For the current meta-analysis, the small number of
studies would preclude us from making
anything resembling a firm conclusion.
Six total studies made the cut, with
anywhere from 2-5 studies used in each
individual analysis. When we look at the
most direct body composition outcomes
a lifting or athletic population would
be interested in (fat mass and body-fat
percentage), a couple of studies are particularly notable. For both outcomes,
the studies by Cholewa in 2013 (4) and
2018 (5) show the largest effects, and
these also happen to be the only studies done in young, healthy subjects
performing resistance training while
supplementing. More importantly, the
meta-analysis is weighted, meaning that
the studies do not all contribute equally to the overall analysis. For both fat
mass and body-fat percentage, the studies by Cholewa collectively account for
70-75% of the weight of the analysis.
So, from a theoretical perspective, we’d
be most interested in the results of the
Cholewa studies, as the sampled popu-
29
Figure 3
Effects of betaine on arm cross-sectional area (4)
*
60
Arm CSA (m2)
50
40
Pre-treatment
30
Post-treatment
20
10
0
Placebo
Betaine
Values estimated via girth and skinfold measurements, in resistance-trained men
lation and intervention are most relevant
to the MASS readership. From a mathematical perspective, we’re still most
interested in the results of the Cholewa
studies, as they are largely driving the
meta-analysis results for fat mass and
body-fat percentage.
I previously mentioned that I didn’t
have a keen interest in reverse-engineering the meta-analysis statistics; this is
because we’ve got more to gain from just
directly looking at the Cholewa studies
that are most relevant to us and that are
pulling the weight of the analysis anyway. In the spirit of full disclosure, I’ve
collaborated with Dr. Cholewa (but not
on any of the studies in this article), and
I consider him a friend. Nonetheless, I
think I can give an unbiased summary
of his studies, and my choice to focus on
them is purely based on the study characteristics and mathematical considerations described above. So, let’s take a
look at them.
Key studies
The first of the two Cholewa studies
was published in 2013 (4), and the subjects were 23 resistance trained men between the ages of 18 and 35 who were
able to bench press at least 100% of
body weight and squat at least 125% of
30
Figure 4
Effects of betaine on lean body mass (4)
80
*
75
70
LBM (kg)
65
60
55
Pre-treatment
50
Post-treatment
45
40
35
30
Placebo
Betaine
Values estimated via skinfold measurements, in resistance-trained men
body weight. Subjects were randomly
assigned to consume betaine (2.5g/day)
or placebo for six weeks, with groups
matched based on training experience
and body-fat percentage. Throughout
the study, participants completed a periodized training program consisting of
three, two-week microcycles that involved two upper-body and two lower-body sessions per week. The most
notable outcome variables of interest
included training volume, lean mass,
fat mass, body-fat percentage, vertical
jump, bench press one-rep max (1RM),
squat 1RM, and cross-sectional area of
the arm and thigh musculature. Body
composition was estimated based on
skinfold measurements, and cross-sectional areas were estimated using a
method that combines skinfold measurements with limb circumferences.
In terms of training volume, betaine
did not consistently outperform placebo
for bench press or squat. Betaine did not
significantly alter thigh cross-sectional
area, but arm cross-sectional area was
significantly increased in the betaine
group (Figure 3), as was lean body mass
(Figure 4).
The betaine group also had favorable
(and statistically significant) fat loss results; the betaine group lost 2.9kg of fat
31
mass and 3.2 body-fat percentage points,
compared to a gain of 0.3kg and 0.2 percentage points in the placebo group. Results were not significantly different for
vertical jump, bench press 1RM, or squat
1RM. In summary, the study showed favorable effects of betaine on both lean
mass and fat mass, but with pretty negligible effects on performance.
The second of the Cholewa studies
was published in 2018 (5). This study
featured a sample of 36 untrained, college-aged female participants, with 23
of them completing the entire study.
Subjects were randomly assigned to
consume betaine (2.5g/day) or placebo
for nine weeks. Throughout the study,
subjects completed three days per week
of progressive resistance exercise,
with two lower-body days and one upper-body day per week. The program
was split into two mesocycles, which
were separated by a week of active
rest during week five. Body composition was measured via air displacement
plethysmography (BodPod), rectus femoris thickness was measured via ultrasound, and performance outcomes included vertical jump, bench press 1RM,
and squat 1RM.
For performance outcomes, results of
the study indicated that all performance
measurements improved over time, with
no difference between betaine and placebo groups. In contrast, betaine significantly altered some of the body composition outcomes. The betaine group had
IT WOULD APPEAR THAT
BETAINE INFLUENCES BODY
COMPOSITION MORE SO THAN
PERFORMANCE, AND ITS
EFFECTS ARE MORE LIKELY
TO BE OBSERVED WHEN
DOSED AT 2.5G/DAY OR MORE
FOR AT LEAST SIX WEEKS.
a larger reduction in fat mass (-2.0kg)
and body-fat percentage (-3.3%) than
the placebo group (-0.8kg and -1.7%),
but betaine did not significantly alter
changes in lean mass or rectus femoris
muscle thickness. So, while betaine had
favorable effects in this study (5), the
effects were smaller in magnitude (in
comparison to the placebo group) than
the effects observed in the 2013 study
with male subjects (4).
To be clear, both of Cholewa’s studies
utilized durations of six weeks or longer.
Creatine can induce some pretty rapid
effects with a short-term loading phase,
but that’s not the case for betaine. Del
Favero et al (6) investigated the effects
of short-term (10-day) supplementation
with betaine or creatine in untrained
32
subjects. Unsurprisingly, 20g/day of creatine increased muscle phosphocreatine
content, along with squat and bench
press strength and power. In contrast,
2g/day of betaine did not increase muscle phosphocreatine content, nor did it
improve squat or bench press outcomes.
It would appear that betaine influences
body composition more so than performance, and its effects are more likely to
be observed when dosed at 2.5g/day or
more for at least six weeks, in conjunction with a well-structured resistance
training program.
Mechanisms
While there is obviously value in observing outcomes in response to betaine
supplementation, it’s important to identify the mechanisms that may underlie
or explain the effect we’re observing.
This is especially important when very
few studies are available; if we observe
something a couple of times, but can’t
fathom a reason why, we’re more likely to have doubts that we’re observing a
“real” effect.
Authors of the current meta-analysis
did a fantastic job laying out the potential mechanisms by which betaine could
potentially reduce body-fat percentage
(1). The proposed mechanisms include:
1. Betaine may increase lipolysis and
reduce lipogenesis by influencing a
ton of pathways associated with fat
metabolism by influencing PPAR-α,
SREBP-1c, acetyl-CoA carboxylase,
carnitine accretion, fatty acid synthase, and more
2. Betaine may decrease the amount
of triglycerides that fat cells take up
from circulating lipoproteins by reducing the expression of lipoprotein
lipase.
3. Betaine may increase mitochondrial content of liver cells and fat cells,
and make white fat cells more like
brown fat cells, which are more thermogenic.
4. Betaine may decrease levels of homocysteine via transmethylation to
methionine; there is evidence to suggest that this may promote lipolysis
and, possibly, the hypertrophic effects of betaine (5).
5. Betaine may promote protein synthesis (and, by extension, muscle
hypertrophy) by increasing growth
hormone secretion, increasing insulin-like growth factor 1 (IGF-1)
secretion, stimulating the mTOR
pathway, or reducing homocysteine
thiolactone.
At this point in time, we’ve got a couple of pretty relevant studies demonstrating beneficial effects of betaine on body
composition, and a handful of pretty
plausible mechanisms by which betaine
might be favorably influencing fat loss
and lean mass accretion. We’re far from
having a conclusive body of literature,
but for lifters with physique-oriented
goals who like to experiment fairly lib-
33
erally with dietary supplements, there’s
probably enough preliminary evidence
to pique their interest.
sibility of a sex-based difference cannot
be automatically discounted. In fact, as
explained by Cholewa et al (5), there are
some plausible sex-linked mechanisms
worth looking into. Activity of the enzyme that breaks down betaine to assist
in the transmethylation of homocysteine
to methionine is increased in the presence of estrogen (7). When the activity of
this enzyme increases, it’s possible that
less betaine will be available for muscle
uptake (5). Compared to males, females
appear to have lower plasma levels of betaine (8), so differential responses to betaine supplementation would not be implausible. Nonetheless, we shouldn’t get
carried away; the effects in Cholewa’s
male study were a bit larger than Cholewa’s female study, but betaine still had a
beneficial effect in the female study, and
there were several notable methodological differences between the studies.
Potential sex differences
Conclusions
THE RESULTS OF THIS
META-ANALYSIS, AND MORE
SPECIFICALLY THE RESULTS
OF THE STUDIES BY CHOLEWA
ET AL SUGGEST THAT
BETAINE MAY HAVE MODEST
BUT FAVORABLE EFFECTS
ON BODY COMPOSITION.
It is noteworthy that the results from
Cholewa’s 2013 study (4), in terms of
fat loss and hypertrophy, were more favorable than Cholewa’s 2018 study (5).
There are several methodological considerations that might explain this difference, including differences in the training
programs, methods of body composition
measurement, and the training status of
participants, among others. However,
the 2013 study was conducted with male
subjects and the 2018 study was conducted with female subjects, so the pos-
On the topic of dietary supplements, a
prolific sports nutrition researcher named
Dr. Ron Maughan has said, “If it works,
it’s probably banned; if it’s not banned, it
probably doesn’t work. But there may be
a few exceptions.” My outlook on supplements isn’t quite as skeptical, particularly when it comes to performance, but I
bring up this quote for a very specific application: If you’re looking for a dietary
supplement that’s going to have massive
effects on fat loss, and isn’t going to be
unsafe and swiftly banned, you might
34
APPLICATION AND TAKEAWAYS
Based on the small number of studies available, it’s hard to draw firm conclusions.
Nonetheless, the limited evidence available suggests that 2.5g/day of betaine for ≥
6 weeks may facilitate fat loss and lean mass gains when combined with resistance
training. It is possible that the effects may be larger in males than females, but more
research is needed to confirm this potential sex-based difference.
want to temper your optimism. However, the results of this meta-analysis, and
more specifically the results of the studies by Cholewa et al (4, 5), suggest that
betaine may have modest but favorable
effects on body composition. Results indicate that 2.5g/day for at least six weeks,
in conjunction with a well-structured resistance training program, may facilitate
increases in lean mass and reductions in
fat mass. Effects on performance don’t
seem to be as notable, and effects could
potentially be smaller in females than
males. However, given the very small
number of studies available, these conclusions should all be viewed as very,
very tentative. Hopefully we’ll be seeing
more studies on betaine in the future, so
we can either confirm or revise our current understanding.
Finally, it’s prudent to note that you
could feasibly increase your dietary betaine intake by 2.5g/day without using
supplements. The average human dietary
betaine intake is roughly 100-400mg/day
(9), but a number of foods provide pretty
substantial betaine doses per 100g serving (wheat bran = 1339mg, wheat germ =
1241mg, spinach = 600-645mg, beets =
114-297mg, pretzels = 237mg, shrimp =
219mg, and wheat bread = 201mg) (10).
So, if you’re curious about exploring the
potential benefits of betaine, but not curious enough to pay for a supplement,
you could always try working some extra spinach and wheat germ into your diet
(conveniently, both are pretty easy and
painless additions to protein shakes).
Next Steps
At this point, we basically just need more
data in general. We’ve still got plenty of
work to do before we can conclusively
say that betaine supplementation reliably
enhances fat loss or muscle hypertrophy,
let alone give confident recommendations for optimal dosing strategies. Ideally, more randomized controlled trials will
be conducted in conjunction with resistance training programs, and it’d be great
if they took a closer look at the possibility of more favorable responses in males
than females.
35
References
1. Gao X, Zhang H, Guo X-F, Li K, Li S, Li D. Effect of Betaine on Reducing Body Fat-A
Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients. 2019
Oct 16;11(10).
2. Eklund M, Bauer E, Wamatu J, Mosenthin R. Potential nutritional and physiological functions of betaine in livestock. Nutr Res Rev. 2005 Jun;18(1):31–48.
3. Matthews JO, Southern LL, Higbie AD, Persica MA, Bidner TD. Effects of betaine on
growth, carcass characteristics, pork quality, and plasma metabolites of finishing pigs. J
Anim Sci. 2001 Mar;79(3):722–8.
4. Cholewa JM, Wyszczelska-Rokiel M, Glowacki R, Jakubowski H, Matthews T, Wood R, et
al. Effects of betaine on body composition, performance, and homocysteine thiolactone. J Int
Soc Sports Nutr. 2013 Aug 22;10:39.
5. Cholewa JM, Hudson A, Cicholski T, Cervenka A, Barreno K, Broom K, et al. The effects
of chronic betaine supplementation on body composition and performance in collegiate females: a double-blind, randomized, placebo controlled trial. J Int Soc Sports Nutr. 2018 Jul
31;15:37.
6. del Favero S, Roschel H, Artioli G, Ugrinowitsch C, Tricoli V, Costa A, et al. Creatine but
not betaine supplementation increases muscle phosphorylcreatine content and strength performance. Amino Acids. 2012 Jun;42(6):2299–305.
7. Finkelstein JD, Kyle W, Harris BJ. Methionine metabolism in mammals. Regulation of homocysteine methyltransferases in rat tissue. Arch Biochem Biophys. 1971 Sep;146(1):84–92.
8. Lever M, Atkinson W, George PM, Chambers ST. Sex differences in the control of plasma
concentrations and urinary excretion of glycine betaine in patients attending a lipid disorders
clinic. Clin Biochem. 2007 Nov;40(16–17):1225–31.
9. Obeid R. The Metabolic Burden of Methyl Donor Deficiency with Focus on the Betaine Homocysteine Methyltransferase Pathway. Nutrients. 2013 Sep 9;5(9):3481–95.
10. Craig SA. Betaine in human nutrition. Am J Clin Nutr. 2004 Sep 1;80(3):539–49.
█
36
Study Reviewed: Bench Press at Full Range of Motion Produces Greater
Neuromuscular Adaptations than Partial Executions After Prolonged
Resistance Training. Martínez-Cava et al. (2019)
Bench Press Range of Motion:
An Exception to the Principle of
Specificity?
BY G RE G NUC KO LS
Much of what we know about range of motion specificity comes from
single-joint studies and squat studies. When we branch out to the bench
press, things get more complicated.
37
KEY POINTS
1. Subjects trained for 10 weeks, doing either full bench press reps or one of two
partial ranges of motion (⅓ reps or ⅔ reps). They tested strength and velocity at
all three ranges of motion pre- and post-training.
2. Unexpectedly, the full range of motion group tended to improve the most in all
measures at all ranges of motion, not just the full range of motion measures. The
⅓ range of motion group tended to improve the least in all measures, even for the
⅓ range of motion tests.
3. While the principle of specificity has a tremendous amount of support, we need
to remember that it’s a principle, not an iron-clad law of the universe. In the
interpretation section, I’ll discuss when it may or may not apply.
O
ne of the first things you learn
about when you start consuming
strength training content is the
principle of specificity. The principle of
specificity has wide-reaching implications, but one of the well-known applications is range of motion specificity:
you gain the most strength in the range
of motion you train for. In other words,
if I want to improve my deep squat, I’d
want to do deep squats, but if I want to
improve my half squat, I’d be better off
doing half squats.
However, we need to keep in mind
that the principle of specificity is more
of a strong heuristic rather than an ironclad law of the universe. Sometimes, it
doesn’t apply. And when it doesn’t, we
can learn something by thinking through
the factors that may be able to “override”
such an important principle.
In the present study (1), three groups of
subjects trained the bench press through
either a full range of motion, a 2/3 range
of motion, or a 1/3 range of motion, with
strength and veloity testing for all three
ranges of motion pre- and post-training.
The full range of motion group improved
the most for tests through all ranges of
motion, while the 1/3 range of motion
group got the worst results, including on
the tests in the range of motion they were
actually training. The interpretation section will dig into factors that may explain
why the results of this study run counter
to what we’d expect, given the principle
of specificity.
Purpose and Hypotheses
Purpose
The purpose of the study was to investigate the effects of bench press range of
motion on strength and velocity adaptations.
38
Table 1
Subject characteristics
Number
Age (years)
Height (cm)
Weight (kg)
Body fat (%)
1RM smith machine
bench press (kg)
Bench press relative to
body mass
49 males
24.0 ± 4.7
176.2 ± 8.4
73.4 ± 9.9
10.6 ± 4.3%
71.8 ± 14.2
0.98 ± 0.18
Hypotheses
No hypotheses were given.
Subjects and Methods
Subjects
49 young men who had been benching
2-4 times per week for at least 6 months
completed the study. More details about
the subjects can be seen in Table 1.
Experimental Design
Before any performance testing, the
subjects underwent nine familiarization
sessions: three sessions of 1/3 bench
reps, three sessions of 2/3 bench press,
and three sessions of full bench reps.
After familiarization was completed,
the subjects underwent three testing sessions. In each session, they completed
a load-velocity profile and a 1RM test
with one bench press range of motion
(i.e. 1/3 reps, 2/3 reps, or full reps). The
order of the testing sessions was randomized for each subject, and the same
order was repeated for post-testing. The
load-velocity testing started at 20kg,
with loads increased by 10kg per set until mean propulsive velocity (2) fell below 0.5 m/s, after which time loads increased by 2.5-5 kg per set until a 1RM
was reached. Subjects performed 3 reps
per set with light loads (<50% 1RM),
2 reps per set with moderate loads (5080% 1RM), and 1 rep per set with heavy
loads (>80% 1RM).
After the initial testing sessions were
completed, subjects were assigned to
one of four groups in a counterbalanced
fashion based on pre-training bench
press strength. One group trained doing
1/3 reps, one group trained doing 2/3
reps, one group trained doing full reps,
and a control group didn’t train at all.
The three experimental groups trained
twice per week for 10 weeks, using a
linearly periodized training program.
Loads were selected and adjusted using
velocity targets that were intended to
correspond with the target intensity for
the day. Details of the training program
can be seen in Table 2. After 10 weeks
of training, the performance tests were
repeated.
The researchers did a good job of
standardizing as many aspects of the
study as possible. The subjects trained
using a Smith machine (this probably
wasn’t necessary, but it does make velocity data a little more accurate since
all of the movement is completely vertical, though this accuracy comes at the
cost of a bit of ecological validity), grip
39
Table 2
Descriptive characteristics of the resistance training programs
Scheduled
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
%1RM
~60
~60
~65
~65
~70
~70
~75
~75
~80
~80
4x8
5x8
4x8
5x8
4x6
5x6
4x5
5x5
4x4
5x4
Group BPFull
0.76
0.76
0.68
0.68
0.61
0.61
0.54
0.54
0.47
0.47
Group BP2/3
0.64
0.64
0.57
0.57
0.51
0.51
0.45
0.45
0.40
0.40
Group BP1/3
0.48
0.48
0.43
0.43
0.38
0.38
0.34
0.34
0.30
0.30
Sets x reps
Target MPV (m•s )
-1
BPFull = full bench press; BP2/3 = two-thirds bench press; BP1/3= one-third bench press
Target MPV = maximal intended velocity repetition performed at the end of each session’s warm-up to ensure that the load (kg) to be used matched the velocity associated with the intended % 1RM
width was standardized (5-7 cm outside
of shoulder width), and safety bars were
used to ensure that range of motion was
appropriate and consistent for each rep.
The subjects lowered the bar to safety
pins and paused for two seconds before
pressing each rep, making the demarcation between the eccentric and concentric crystal clear, thus theoretically
improving velocity measurements and
ensuring a consistent range of motion.
Furthermore, subjects were instructed to
maintain a velocity of 0.45-0.65m/s for
their eccentrics (with the aid of visual
and audio feedback from the velocity
device used in the study), and to press
each concentric as explosively as possible.
Findings
Surprisingly, the group doing full reps
tended to improve the most in all measures, and the 1/3 reps group tended
to improve the least. According to the
principle of specificity, one would have
expected that the full reps group would
improve the most at full reps, the 2/3
reps group would improve the most at
2/3 reps, and the 1/3 reps group would
improve the most at 1/3 reps. However, that was not the case. Figure 1 tells
the story. Note that not all differences
between groups were statistically significant (and there are 72 potential pairwise comparisons; it’s not worth going
through all of them one by one), but the
overall pattern is crystal clear.
Interpretation
Upon reading the title of this article,
I thought it was going to be a straightforward article that I could review in
my sleep. “There’s this thing called the
‘principle of specificity,’ here are all of
the studies backing it, and the present
study adds one more to the pile.” In and
out, easy peasy.
However, this study does not fit that
narrative. Training through a full range
of motion was best for improving performance through a full range of motion, but training through a partial range
of motion wasn’t best for improving
40
Figure 1
Changes in relative strength ratio (A) and velocity developed against all (B) low (C) and high (D) loads
common to pre-test and post-test
1RM/BM
MPVALL
Training group assigned
BPFULL
ES = 0.8 - 1.2
BP2/3
ES = 0.30 - 0.6
BP1/3
CON
ES = 0.1 - 0.4
BPFULL
ES = -0.3 - -0.5
0.20
25
20
*#†
#†
*#†
15
#†
#†
10
#†
5
†
†
†
0
-5
MPV Change (m·s-1)
1RM/BM Change (%)
30
Training group assigned
0.15
BP2/3
ES = 0.7 - 2.0
BPFULL
A
#†
#†
0.10
#†
†
BPFULL
BP2/3
†
†
0.00
BP2/3
BP1/3
Exercise Tested
MPV>50% 1RM
BP2/3
Training group assigned
BP1/3
ES = 0.3 - 0.8
CON
ES = 0.3 - 0.6
*#†
#†
*#†
#†
BPFULL
ES = -0.1 - -0.2
*#†
0.10
†
0.05
0.00
-0.05
0.20
MPV Change (m·s-1)
BPFULL
ES = 1.5 - 0.7
BP1/3
Exercise Tested
B
MPV<50% 1RM
MPV Change (m·s-1)
#†
†
0.05
Training group assigned
-0.10
C
ES = 0.0 - -0.4
*#†
-15
0.15
CON
ES = 0.4 - 0.7
-0.05
-10
0.20
BP1/3
ES = 0.4 - 0.7
0.15
0.10
0.05
BP2/3
ES = 1.0 - 2.4
BP1/3
ES = 0.9 - 1.7
CON
ES = 0.3 - 0.0
ES = -0.9 - -0.6
*#†
#†
#†
#†
†
†
#† #†
†
0.00
-0.05
-0.10
BPFULL
BP2/3
BP1/3
Exercise Tested
D
BPFULL
BP2/3
BP1/3
Exercise Tested
BPFULL = full bench press; BP2/3 = two-thirds bench press; BP1/3 = one-third bench press; CON = control; * = significant differences when compared with the BP2/3 group;
# = significant differences when compared with the BP1/3 group; † = signficiant differences when compared with the CON group; ES = range of effect size for each resistance
training group
Data are presented as mean ± SEM
performance through a partial range of
motion. In fact, the 1/3 range of motion
group had the worst gains though the
1/3 range of motion in three of the four
measures. So, I can’t just type out my
stock “principle of specificity” spiel and
call it a day. However, while that makes
this article more difficult to interpret, it
also makes it much more fun.
Before I get ahead of myself, let’s just
recap the results: a longer range of motion seems to be the way to go for …
everything, at least in this study. When
I thought about it for a moment, I realized that the key studies people generally cite to support the concept of range
41
TRAINING THROUGH A
FULL RANGE OF MOTION
WAS BEST FOR IMPROVING
PERFORMANCE THROUGH
A FULL RANGE OF MOTION,
BUT TRAINING THROUGH A
PARTIAL RANGE OF MOTION
WASN’T BEST FOR IMPROVING
PERFORMANCE THROUGH A
PARTIAL RANGE OF MOTION.
of motion specificity use the squat (3) or
single joint exercises (4), rather than the
bench press.
What about the bench press specifically? There are three other relevant
studies, but only one of them tested
strength through multiple ranges of motion. Clark et al compared the effects of
benching through a full ROM against
benching through a variable ROM (a
combination of full reps, 1/4 reps, 1/2
reps, and 3/4 reps; 5). Neither group experienced significant increases in force
output through a full range of motion
(which calls into question the usefulness of the training program used in the
study), but the variable ROM group had
a larger increase in force output through
a 1/2 rep ROM. Two studies by Massey
et al were nearly identical, except that
one of the studies used male participants
(6) while the other used female participants (7). Both studies had three groups:
one trained through a full range of motion, one group trained through a partial range of motion (not lowering the
bar below the sticking point), and one
group did half of their sets through a full
ROM and half though a partial ROM.
In both studies, full-ROM bench press
strength was the only outcome measure.
In the study on males, the full and partial ROM groups had similar increases
in strength, while the group doing both
full and partial reps tended to gain a bit
less strength. In the study on females, on
the other hand, the full ROM group had
the largest increase in 1RM strength,
while the partial ROM and mixed ROM
groups had slightly smaller increases. Putting all four of these studies together, it becomes clear that doing full
ROM training is important for building
strength through a full ROM on bench
press (benching through a full ROM
built strength through a full ROM or
through the bottom of the ROM as well
as or better than all other conditions in
all studies). However, ROM specificity
isn’t as clearly supported. In the present
study (1), benching through a full ROM
built more strength through a partial
ROM than benching through a partial
ROM did. In the Clark study, while par-
42
Figure 2
Theoretical differences in squat and bench press strength curves
Force output relative to the weakest point in the lift
Bench strength
Squat strength
200%
175%
150%
125%
100%
0%
20%
40%
60%
80%
Percentage of concentric completed
tials built more strength through the top
part of the ROM, benching through a
full ROM failed to increase strength off
the chest to a greater degree than doing
partials. In the Massey study on males,
benching through a full ROM and doing partials proved equally effective for
building strength through a full ROM.
And finally, in the Massey study on females, the group using mixed ROMs (and
thus still doing some training through a
full ROM) failed to increase full ROM
strength more than the group only doing
partials. All in all, it’s a murky picture.
So, what might explain these results? If the principle of specificity is so
well-supported, why does ROM speci-
ficity in the bench press look so iffy?
My first thought is that the magnitude
of strength fluctuations throughout a full
range of motion probably has an impact.
In other words, are you 20% stronger at
the top of a rep than at the bottom of a
rep, or are you 100% stronger? In the
case of squats, it’s not terribly uncommon to be able to lift WAY more for partials than for full reps. Speaking from
experience, I’ve done quarter squats
with 1000+lb when my 1RM through a
full ROM was closer to ~650, and I’m
sure that disparity would be even larger
if I trained quarter squats with as much
focus as I put into full ROM squats.
With bench, on the other hand, there’s
43
maybe a 15% difference between my
full-ROM bench press and the heaviest
weight I could use for high pin presses
or board presses. The exact ratios may
differ for you, but I’d be surprised if that
same principle didn’t apply to almost
everyone reading this: there’s a bigger gap between partial-ROM strength
and full-ROM strength in the squat
than in the bench press. By extension,
one would then assume that full-ROM
bench press would have a larger effect
on partial-ROM bench press strength
than full-ROM squatting would have
on partial-ROM squat strength. If an
appropriate load for full-ROM bench
press is 200lb and an appropriate weight
for partials is 230, doing reps at 200 is
still probably heavy enough to do something for partial-ROM strength. However if an appropriate load for full-ROM
squats is 400lb and an appropriate load
for half squats is 650, doing full-ROM
reps with 400 probably isn’t doing much
to improve partial-ROM strength directly (beyond simply building more muscle
mass). The opposite principle may also
be true – you may get better carryover
from partial-ROM training to full-ROM
strength when the strength curve of a
movement is flatter. Half reps on bench
press feel more similar to full-ROM
bench press than half reps on squat feel
when compared to full-ROM squats. If
you’ve ever converted an athlete from
partial-ROM training to full-ROM training, you’ve probably seen this firsthand
– their first session benching through a
full ROM is a little humbling because
they need to take some weight off the
bar, but they still perform reasonably
well, and the weights they can handle
are at least somewhat comparable to the
loads they were using before for partials,
unless they were previously using a very
partial ROM. With squats, on the other
hand, shifting from half squats to full
squats often requires a complete rebuild
of their squatting mechanics, and necessitates slashing their training weights at
least in half.
In the present study (1), another factor in play is how the subjects actually
performed their reps. To keep ranges of
motion consistent, the subjects benched
to pins, allowing the bar to briefly rest
on the pins between sets. This is a great
way to ensure that the range of motion
was consistent, but it possibly decreases ecological validity a bit. When you
switch between the eccentric and concentric portions of a lift, if there’s not a
physical impediment to help you decelerate the bar (i.e. the floor when deadlifting), you get a rather large spike in
force output when transitioning between
lowering the bar and explosively lifting
the bar. Lowering the bar to pins negates
that spike in force output. That does mirror the way that many people would do
partial bench press reps (i.e. pin press or
board press), but it may not capture all
of the ways that someone could apply
partial range of motion training when
bench pressing (namely, simply revers-
44
ing each rep yourself without touching
your chest). It’s plausible that partial
ROM exercises that require the athlete
to actively decelerate and reverse the
load build more partial ROM strength
than exercises, like pin presses, that allow another physical object to decelerate the load.
Finally, it’s important to think about
these results conceptually, rather than
simply accepting one study as the final word on the topic. We have good
research indicating that longer ranges
of motion tend to lead to more muscle
growth (3, 8). Since the subjects weren’t
very well-trained, they likely still had
plenty of room to grow more muscle.
Hypertrophy wasn’t assessed in this
study, but I don’t think we’d be unjustified to assume that that full range of motion group likely experienced the most
muscle growth. That could be enough to
explain the superior gains in performance,
even through partial ranges of motion.
Reasoning by analogy, two studies on
squatting and jump performance immediately come to mind. In one study on untrained lifters (3), deep squats led to greater improvements in jump height than half
squats (the positioning of half squats more
closely mirrors jumping mechanics than
the positioning of full squats). In another
study on high level athletes (9), half squats
led to larger improvements in jump height
than full squats. In my opinion, the most
likely explanation is that for untrained or
semi-trained athletes, more hypertrophy
IS IT POSSIBLE THAT
OPTIMIZING TECHNIQUE FOR
SHORT-TERM PERFORMANCE
COULD ACTUALLY LIMIT
LONG-TERM DEVELOPMENT,
ASSUMING YOU DO MOST OF
YOUR BENCH PRESS TRAINING
WITH A COMPETITIONSTYLE SET-UP? I CERTAINLY
THINK IT’S POSSIBLE.
can occur, and that muscular development
can lead to robust performance improvements. For highly trained lifters, on the
other hand, much less hypertrophy can occur, so optimizing for movement specificity rather than hypertrophy leads to larger performance improvements. Thus, if
this study was repeated on trained lifters,
I would expect that specificity would apply to a greater degree, with the full ROM
group having the largest strength gains
through a full ROM, the 2/3 ROM group
having the largest strength gains through
a 2/3 ROM, and the 1/3 ROM group having the largest strength gains through a
1/3 ROM.
45
APPLICATION AND TAKEAWAYS
While the principle of specificity is a cornerstone of training theory, it’s important
to remember that it’s a principle, not an iron-clad law. Specifically, range of motion
specificity may not hold up quite as well in the bench press as in the squat. For longterm strength development, benching through a longer range of motion than your
competition-style setup may be worth a shot if you plateau.
Finally, I’d just like to touch on something the authors of the present study
mention in their discussion (1). They
propose that powerlifters are actually
training the bench press through a partial range of motion, due to arching, taking a wide grip, and specifically aiming
to minimize range of motion, and that
they may be able to gain more strength
if they did more of their training through
a purposefully longer range of motion. I
think that’s an idea worth at least considering. Based on competition definition,
a “full” range of motion is any range
of motion that allows the bar to touch
your chest and lock out, as long as your
grip width doesn’t exceed 81cm. But
is that REALLY a full range of motion
biomechanically? I’d argue that it isn’t.
For example, a close grip bench with a
smaller arch involves more elbow flexion, and a greater combination of shoulder extension and horizontal abduction
than a typical competition-style bench,
so your prime movers clearly aren’t going through their full range of motion
with a wide-grip, arched bench press. Is
it possible that optimizing technique for
short-term performance could actually
limit long-term development, assuming
you do most of your bench press training
with a competition-style set-up? I certainly think it’s possible. For what it’s
worth, that matches my experience (I
always tend to make better bench progress when I’m doing a lot of cambered
bar bench or close-grip bench with a
smaller arch). It matches the anecdote of
Mike MacDonald, who may be the most
successful bench presser of all time; he
simultaneously held the bench press records in four different weight classes at
one point and swore by cambered bar
bench press. More recently, Josh Bryant’s lifters have been very successful on the bench press, while primarily
benching with pretty narrow grip widths
(at least by powerlifting standards). Jeremy Hoornstra and Julius Maddox are
his two most successful lifters, owning
the all-time bench press records at 242,
275, and superheavyweight. At the very
least, if your bench press is plateaued, I
think it’s worth considering doing some
of your weekly bench press training
with a technique that allows for a longer
range of motion.
46
Next Steps
I’d like to see more research looking
at range of motion specificity in a wider
array of exercises and in more advanced
lifters. I’d also like to see a training study
in powerlifters comparing a training
program consisting solely of wide grip
bench against a training program with
pressing volume split evenly between
wide-grip bench and close-grip bench.
47
References
1. Martínez-Cava A, Hernández-Belmonte A, Courel-Ibáñez J, Morán-Navarro R, González-Badillo JJ, Pallarés JG. Bench Press at Full Range of Motion Produces Greater Neuromuscular Adaptations Than Partial Executions After Prolonged Resistance Training. J Strength Cond Res. 2019
Sep 26.
2. Mean propulsive velocity is very similar to mean concentric velocity. The difference is that
mean propulsive velocity trims off the end of each concentric when the bar decelerates prior
to lockout before calculating average velocity, whereas mean concentric velocity is a measure of velocity for the entire concentric portion of each rep. At high loads, mean propulsive
velocity and mean concentric velocity are similar. At low loads, mean propulsive velocity
is a bit faster than mean concentric velocity since the bar needs more deceleration prior to
lockout. For the purposes of this study, the differences don’t really matter, since both are
valid and reliable measures and convey nearly identical information.
3. Bloomquist K, Langberg H, Karlsen S, Madsgaard S, Boesen M, Raastad T. Effect of range
of motion in heavy load squatting on muscle and tendon adaptations. Eur J Appl Physiol.
2013 Aug;113(8):2133-42.
4. Valamatos MJ, Tavares F, Santos RM, Veloso AP, Mil-Homens P. Influence of full range of
motion vs. equalized partial range of motion training on muscle architecture and mechanical
properties. Eur J Appl Physiol. 2018 Sep;118(9):1969-1983.
5. Clark RA, Humphries B, Hohmann E, Bryant AL. The influence of variable range of motion
training on neuromuscular performance and control of external loads. J Strength Cond Res.
2011 Mar;25(3):704-11.
6. Massey CD, Vincent J, Maneval M, Moore M, Johnson JT. An analysis of full range of motion vs. partial range of motion training in the development of strength in untrained men. J
Strength Cond Res. 2004 Aug;18(3):518-21.
7. Massey CD, Vincent J, Maneval M, Johnson JT. Influence of range of motion in resistance
training in women: early phase adaptations. J Strength Cond Res. 2005 May;19(2):409-11.
8. McMahon GE, Morse CI, Burden A, Winwood K, Onambélé GL. Impact of range of motion
during ecologically valid resistance training protocols on muscle size, subcutaneous fat, and
strength. J Strength Cond Res. 2014 Jan;28(1):245-55.
9. Rhea, M., Kenn, J., Peterson, M., et al. Joint-Angle Specific Strength Adaptations Influence
Improvements in Power in Highly Trained Athletes. Human Movement, 2016, 17(1), pp. 4349.
█
48
Study Reviewed: Heart Rate Variability, Neuromuscular and Perceptual
Recovery Following Resistance Training. Flatt et al. (2019)
The Usefulness of Heart
Rate Variability in Resistance
Training is Tenuous
BY MIC HAE L C . ZO URD O S
The potential benefit of heart rate variability in resistance training is its
ability to track recovery and be used as a readiness indicator. However,
does heart rate variability actually correlate with performance? This
article reviews a recent study and examines the totality of the literature
to provide some answers.
49
KEY POINTS
1. This study examined if the recovery pattern of heart rate variability (HRV) was
related to the recovery of perceived recovery, soreness, movement velocity, and
vertical jump power following a damaging training session.
2. Recovery of HRV did not correlate with any other metric. Therefore, it does not
seem wise to use these recovery metrics as proxies for each other.
3. Before we can confidently implement HRV as a recovery metric or readiness
indicator, there needs to be more data showing that changes in HRV are actually
correlated with resistance training performance.
M
any different metrics are thrown
around to track recovery; however, the metrics are often conflicting, making it hard to choose one.
It is also often accepted that the more
technical a metric is, the better it is at
gauging recovery. An example of this is
heart rate variability (HRV), which measures the variability of the time between
heart beats. As reviewed by Eric Helms
in a previous MASS issue, when used
as a readiness indicator to implement a
flexible training template, HRV-guided
training did not yield greater strength or
hypertrophy compared to a fixed training template (2). That study doesn’t
negate the possibility that HRV could
have some benefit, but even if HRV is
useful, it has some practical limitations.
Thus, if it can be reliably correlated with
a more practical metric, then that practical metric could be used as an HRV
proxy. This observational study (1) had
10 trained men perform 6 sets to failure
with 90% of a 10-repetition maximum
(RM) on the squat, bench press, and lat
pulldown. Then, recovery was tracked
immediately post-training and 24 and
48 hours post-training with HRV and
various other metrics (vertical jump
power, squat and bench press velocity,
perceived recovery, and perceived soreness). Recovery of HRV did not correlate
with any other metrics. Further, the rate
of recovery for each metric varied considerably between individuals. Based on
these findings, it does not seem that any
practical recovery metric can be used
interchangeably with HRV. This article
will not only examine the present findings, but will also discuss why a recovery metric must actually correlate with
performance to be used as a readiness
indicator, and what metrics fit these criteria in the literature.
Purpose and Hypotheses
Purpose
The purpose of this study was to examine if the time course of HRV recov-
50
Table 1
Subject characteristics
Subjects
Age (years)
Body Mass (kg)
Height (cm)
Training experience
10 men
24.4 ± 4.5
94.8 ± 21.4
180.7 ± 6.7
> 1 year
Data shown are group means ± SD
Subject characteristics from Flatt et al. 2019 (1)
ery was correlated with various other
recovery metrics following a damaging
training session.
Hypotheses
No hypotheses were provided.
Subjects and Methods
Subjects
10 men with at least 1 year of training experience participated. All subjects
played rugby in the surrounding area
(Savannah, Georgia, USA). The available descriptive details of the subjects
are in Table 1.
Study Protocol
This study was conducted over four
visits to the lab. Each visit is detailed
below.
Visit 1: Baseline HRV and 10RM testing on the squat, bench press, and lat
pulldown.
Visit 2: Occurred 5 days after visit 1.
This visit involved a damaging training
session with recovery metrics taken immediately before and after training with
the exception of HRV, which was as-
sessed immediately upon waking up in
the morning. The recovery metrics used
were the perceived recovery status and
soreness scales (0-10 scales), vertical
jump, and average concentric velocity against a load that corresponded to a
velocity of 1.0 m/s when the lifter was
fresh. The training session, which was 6
sets to failure on the squat, bench press,
and lat pulldown, was performed at a
load of 90% of a 10RM. If we assume
that a 10RM is generally about 75% of
1RM, then the load used in this study
would have been ~67.5% of 1RM.
Visits 3 and 4: All recovery metrics
were tested to assess the time course of
recovery on each day. HRV was again
tested after waking in the morning, and
the other recovery metrics were tested
at the same time as they were tested at
pre-training in visit 2.
Heart Rate Variability (HRV)
Eric has covered HRV before in depth,
but before we move onto the findings
and interpretation, I want to provide a
refresher. In short, HRV is the variability of the time between each heartbeat. In
general, a lower HRV will be seen when
someone is under-recovered. In the reviewed study, HRV was measured in
51
Table 2
Load used and reps performed
Exercise
Load used (90% of 10RM)
(kg)
Average total reps
performed per subject
Squat
94.7 ± 12.7
62.7 ± 10.3
Bench press
81.1 ± 11.6
38.8 ± 4.5
Lat Pulldown
50.5 ± 6.6
42.2 ± 7.6
Data shown are group means ± SD
Subject characteristics from Flatt et al. 2019 (1)
both the standing and supine (lying on
your back) position at each time point.
Findings
Table 2 shows the average load used
on each exercise and the average reps
performed by each subject.
Time Course of Recovery
All metrics demonstrated significant
fatigue immediately post-training. At 24
hours post-training, both HRV metrics
were no longer significantly different
from baseline (p>0.05). At 48 hours, all
metrics except for perceived recovery
and soreness were no longer significantly different when compared to pre-training levels. The time course of recovery
for each recovery metric is shown in Table 3.
Correlations Between Recovery Metrics
There was no significant correlation
between the change in either HRV metric and the change in any other recovery
metric at any time point. The specific
r-values of the correlations are in Table
4.
Interpretation
On the surface, these results don’t tell
us much other than the fact that all recovery metrics don’t track along the
same exact time course. The premise of
this study, however, is that HRV is an
acceptable proxy for readiness to train.
In other words, the rationale for this
study is that when implementing a flexible template (read more here), HRV
can be used to guide what training day
to do, or when to train again following a damaging session. The problem
is that little data exist showing HRV as
an effective readiness indicator for resistance training, or even showing that
52
Table 3
Time course of recovery for each metric
Recovery metric
Pre
IP
24h
48h
Supine HRV
4.38 ± 0.74
2.32 ± 0.48*
4.18 ± 0.81
4.31 ± 0.59
Standing HRV
3.45 ± 0.32
1.83 ± 0.56*
3.38 ± 0.47
3.40 ± 0.48
VJ peak power (W)
4877 ± 432
4375 ± 404*
4636 ± 321*
4754 ± 427
Squat V1.0 (m•s-1)
1.00 (0.00)
0.90 (0.07)*
0.95 (0.07)*
0.93 (0.11)
Bench press V1.0 (m•s-1)
1.00 (0.00)
0.90 (0.13)*
0.94 (0.05)*
0.95 (0.09)
Perceived soreness (au)
1.00 (1.25)
5.50 (4.00)*
5.50 (3.25)*
6.50 (3.00)*
Perceived recovery (au)
8.50 (2.00)
4.00 (3.00)*
5.00 (1.50)*
6.50 (3.25)*
From Flatt et al. (1)
*Significantly worse (more fatigue) than Pre (pre-training); IP = Immediately post-training; 24 h = 24 hours post-training; 48 h = 48 hours post-training
V1.0 = A load that corresponded to a velocity of 1.0 m/s at pre-training; AU = Arbitrary units; HRV = heart rate variability; VJ = Vertical Jump.
HRV correlates with resistance training
performance. Indeed, using HRV as a
readiness metric to implement a flexible
template has been evaluated only once,
in a study previously reviewed by Eric.
In that study from de Oliveira et al (2),
one group of untrained men performed
20 total training sessions in a fixed strategy of three times per week (Monday,
Wednesday, Friday), and one group performed 20 total training sessions and
only performed the next session when
HRV had recovered to baseline. Hypertrophy and strength adaptations after
the 20 sessions were similar between
groups, with the only difference being
that the HRV group took only ~5 weeks
to complete all 20 sessions compared
to the 7 weeks in the fixed group. On
the surface, that looks positive, in that
HRV allowed subjects to gain the same
strength in a shorter time frame. However, I would argue that we can’t actually infer much from that study. I would
bet that untrained individuals in the de
Oliveira study simply progressed at a
rate in which they didn’t need that much
rest; thus, the fixed group could have
probably performed the sessions within
5 weeks in a fixed time frame without
issue. In fact, Greg previously reviewed
a study in MASS that found that a group
of untrained subjects who did progressive resistance training 3 days in a row
each week for 12 weeks versus a group
who trained on 3 nonconsecutive days
per week experienced the same strength
gains (3). Further, when implementing
a flexible template, it is probably important to have one of these two factors
present to see a greater benefit than a
normal fixed order training protocol: 1)
A very busy lifestyle in which stress or
sleep is probably suboptimal, 2) A really
demanding training block in which high
levels of muscle damage and/or fatigue
are consistently present, or both. In the
de Oliveira study, the training was pret-
53
Table 4
All results
Recovery metric
ΔHRV Supine (%)
ΔHRV Standing (%)
24h
48h
24h
48h
ΔSquat V1.0 (%)
r = 0.58
r = 0.04
r = 0.63
r = -0.38
ΔBench Press V1.0 (%)
r = -0.06
r = 0.45
r = -0.08
r = -0.23
ΔVJ Peak Power (%)
r = 0.04
r = 0.39
r = 0.35
r = 0.36
ΔPerceived recovery (au)
r = -0.01
r = -0.36
r = 0.50
r = 0.06
ΔPerceived soreness (au)
r = -0.01
r = -0.37
r = -0.58
r = -0.47
From Flatt et al. (1)
All values are r values derived from bivariate correlations examining the percentage ∆ (change) in HRV (heart rate variability) metrics with all other recovery metrics.
None of the relationships in this table were statistically significant (p values ranged from 0.052 to 0.978).
24 h = 24 hours post-training; 48 h = 48 hours post-training; V1.0 = A load that corresponded to a velocity of 1.0 m/s at pre-training; AU = Arbitrary units; VJ = Vertical Jump.
ty standard and probably didn’t warrant
flexibility, especially since each training
session was the same. In a flexible template, training sessions typically differ
throughout the week (i.e. hypertrophy,
power, strength or heavy, moderate, and
light), which makes more sense to use
flexibility than when sessions are the
same. Another question to ask before using a flexible template: How much does
readiness really matter? I think it matters
if you feel absolutely terrible, but probably not so much if you feel decent (compared to amazing). For example, if you
take the 0-10 perceived recovery status
scale (4), where 0 = “Very poorly recovered / Extremely tired” and 10 = “Very
well recovered / Highly energetic,” it
likely matters if you are a 0 or 1 versus
a 9 or 10. However, if you rate your recovery a 6 versus an 8, does that really
matter? Probably not, especially if your
training sessions are pretty standard. So,
I’m not sure the design in the de Oliveira
study allowed for a lot of insight into the
usefulness of HRV in resistance training.
In research, it is customary to go from
point A to point B rather than from A to
C. However, with HRV, I think we have
gone from A to C in the resistance training literature. For example, if HRV is
to be used as a readiness indicator, we
should first examine if changes in HRV
are indeed indicative of changes in performance. Watkins et al did this a few
years ago with vertical jump performance. Specifically, in 2017, Watkins
et al (5) tested vertical jump height on
trained men and women and then had
them perform 4 sets to failure at 80% on
the squat. Then, 48 hours later, they retested vertical jump performance and had all
lifters perform the squat workout again.
The researchers found a significant decrease in both vertical jump height (-2.5
cm and -8.4%) and squat reps performed
(-5.6 reps and -28.3%%) and a significant
correlation between decreases in vertical
jump height and squat volume (r=0.65).
54
Now that’s going from point A to point
B. To go to point C with vertical jump,
the next step would be to carry out a flexible training study and use vertical jump
as the readiness indicator. With HRV, we
simply don’t have data showing that it is
truly an effective readiness indicator for
the main lifts. The closest we’ve come
to going from point A to B with HRV in
resistance training is a 2011 study from
Chen and colleagues (6). Chen did observe that both HRV and squat performance declined in the days following
a damaging training session in weightlifters. However, in the Chen study, the
authors did not conduct correlations between HRV changes and performance
declines. Further, Chen and colleagues
also found that muscle pain and creatine
kinase recovery also followed the same
pattern as HRV, so it would be inappropriate to pick out HRV as the best metric
of the bunch in the absence of a statistical analysis demonstrating its superiority
over the other measures. We’ll get back
to this in the “Next Steps,” but it would
be nice to see a replication of the Watkins
study with HRV added as a readiness indicator.
Although there were no significant
correlations between HRV and other recovery metrics in the reviewed study (1),
there were some r-values that represented moderate correlations and a p-value
of 0.052 (just shy of significance) for the
relationship between squat velocity and
standing HRV at 24 hours post-training
I WOULD URGE YOU TO BE
CAUTIOUS ABOUT USING HRV
AS YOUR GOLD STANDARD FOR
RECOVERY FROM LIFTING.
(r=0.63, Table 4). With only 10 subjects
in this study, it is very possible that this
correlation could be significant with
more subjects (although the p-value
could also increase); however, the problem still remains that we don’t know if
either squat velocity at such a light load
or standing HRV are actually indicative
of true performance changes. Although
vertical jump power and HRV statistically recovered at different times (HRV at
24 hours and vertical jump at 48 hours),
the percentage changes weren’t that different. Vertical jump power was 4.9%
lower than baseline at 24 hours, while supine HRV and standing HRV were 4.6%
and 2.0% lower respectively, at 24 hours.
However, it’s important to reiterate that
although mean changes were similar,
these metrics were not correlated on an
individual level. Therefore, at the individual level, it is possible that if HRV was
used to implement a flexible template, it
would have stipulated more training at 24
hours; however, that would have likely
been at odds with vertical jump for some
individuals.
55
APPLICATION AND TAKEAWAYS
1. Following a damaging training session, recovery of HRV does not correlate with
recovery of various neuromuscular performance metrics or simple Likert scales.
2. Importantly, the overall picture surrounding HRV is quite thin in regards to resistance
training. It is necessary to see a study examining if HRV is truly indicative of
performance on the main lifts before recommending it as a readiness indicator.
3. Vertical jump has more support than HRV in the existing literature as a readiness
indicator. Further, vertical jump is quick and easy to test and likely useful in this
regard. However, there is still no longitudinal data examining vertical jump to
dictate training in a flexible template.
Overall, this study provides more questions than answers. However, I would
urge you to be cautious about using HRV
as your gold standard for recovery from
lifting and would be even more cautious
about letting it dictate what you actually
do. Might HRV eventually be good in this
regard? Maybe. It has been an effective
autoregulatory tool in endurance training
(7), but we are years into HRV research
now and the evidence for it in resistance
training just isn’t that strong. It is possible
that some metrics are more indicative of
recovery and performance in some people than in others, so perhaps some trial
and error on what metrics seem to be useful for you are in order. Lastly, I would
also urge you to not go overboard with recovery metrics and readiness, in general.
For example, as discussed above, don’t
go crazy over minor differences in your
readiness. Rather, keep your threshold
for changing the day’s workout between
feeling absolutely terrible and amazing.
If you have minor fluctuations in readi-
ness, just use RPE or velocity to autoregulate the load on the barbell and get your
workout in.
Next Steps
As discussed above, I’d love to see the
Watkins et al study (5) replicated with
HRV as a readiness indicator alongside
vertical jump. With this design, we could
see if HRV correlated with squat performance and if HRV and vertical jump
height are correlated with each other. If
HRV holds up to vertical jump, then a
longitudinal flexible training study could
be conducted with one fixed order group,
one group using vertical jump to determine the specific training day, and another group using HRV as a readiness indicator.
56
References
1. Flatt AA, Globensky L, Bass E, Sapp BL, Riemann BL. Heart Rate Variability, Neuromuscular and Perceptual Recovery Following Resistance Training. Sports. 2019 Oct;7(10):225.
2. de Oliveira RM, Ugrinowitsch C, Kingsley JD, da Silva DG, Bittencourt D, Caruso FR,
Borghi-Silva A, Libardi CA. Effect of individualized resistance training prescription with
heart rate variability on individual muscle hypertrophy and strength responses. European
journal of sport science. 2019 Jan 30:1-9.
3. Yang Y, Bay PB, Wang YR, Huang J, Teo HW, Goh J. Effects of consecutive versus non-consecutive days of resistance training on strength, body composition, and red blood cells. Frontiers in physiology. 2018 Jun 18;9:725.
4. Laurent CM, Green JM, Bishop PA, Sjökvist J, Schumacker RE, Richardson MT, Curtner-Smith M. A practical approach to monitoring recovery: development of a perceived recovery status scale. The Journal of Strength & Conditioning Research. 2011 Mar 1;25(3):620-8.
5. Watkins CM, Barillas SR, Wong MA, Archer DC, Dobbs IJ, Lockie RG, Coburn JW, Tran
TT, Brown LE. Determination of vertical jump as a measure of neuromuscular readiness and
fatigue. The Journal of Strength & Conditioning Research. 2017 Dec 1;31(12):3305-10.
6. Chen JL, Yeh DP, Lee JP, Chen CY, Huang CY, Lee SD, Chen CC, Kuo TB, Kao CL, Kuo
CH. Parasympathetic nervous activity mirrors recovery status in weightlifting performance
after training. The Journal of Strength & Conditioning Research. 2011 Jun 1;25(6):1546-52.
7. Vesterinen V, Nummela A, Heikura I, Laine T, Hynynen E, Botella J, Häkkinen K. Individual
endurance training prescription with heart rate variability. Medicine and science in sports
and exercise. 2016;48.
█
57
Concept Review
How the Brain Controls
Eating Behavior
BY ANNE-K AT HRI N E I S E LT
Many people find it difficult to follow a calorie-restricted diet. Is your brain
working against you? This review of neuroscience research helps explain why
dieting makes you feel lousy, why it is so easy to overeat hyperpalatable food,
and how you can use this knowledge to your advantage.
58
KEY POINTS
1. Your brain has a finely tuned system to keep a healthy body weight, but this does
not mean a shredded physique. To achieve that, you’ll most likely have to fight your
internal hunger signals.
2. Hunger signals are generated by the activation of AGRP neurons, which will induce
a negative feeling and initiate food-seeking activities.
3. Eating highly palatable food makes you feel good because of the connection
between the consumption and reward systems, but this “high” is only short-lived.
4. The simplest way to prevent overeating is to avoid highly rewarding food, as these
foods override your satiety signals.
5. Low-reward (i.e. non-hyperpalatable) foods are hard to overeat and will make it
easier to maintain or lose body fat.
E
volutionarily conserved brain circuits control our eating behavior
– especially when and how much
we eat. This control is exerted by three
distinct but interconnected circuits: the
hunger circuit, the consumption circuit, and the satiety circuit. Dieting and
weight loss cause constant activation of
the hunger circuit, which is also associated with negative feelings (aka feeling
“hangry”) to motivate an individual to
consume more food. Eating highly pleasurable foods that are high in fat and
sugar will over-activate the reward system that is coupled to the consumption
circuit, making it easy to overeat. At the
same time, eating these foods will override satiety signals, which is why you
can become uncomfortably stuffed after a Thanksgiving dinner. Relying on
mostly non-hyperpalatable food will appropriately engage all three circuits to
keep a stable and healthy body weight
without feeling hangry; for most individuals, though, more effort is needed to
achieve a six-pack physique.
Guest Reviewer: Anne-Kathrin Eiselt, PhD, CPT
Anne-Kathrin Eiselt is a neuroscientist at the Howard Hughes Medical Institute with a
research focus on how nutrition and exercise affect the brain. She studied psychology with
a focus on human decision-making at the University of Potsdam, Germany and received her
Ph.D. in neurobiology in 2013 from the University of Tuebingen, Germany where she studied
how the brain computes decisions. Anne-Kathrin competed in powerlifting and CrossFit and
has more than 10 years of experience in coaching clients as a certified fitness professional
with a special focus on behavioral change, lifestyle modification, sports nutrition, and preand postnatal fitness.
59
Background
Eating is a very complex behavior that
is influenced by many different factors
like what time of day it is, where you
are, if you are alone or with others, your
emotional state, the time and macronutrient profile of your last meal, and so
forth. Eating behavior is hard to study
because it’s so individualized and variable based on your genetics, how you
grew up, your lifestyle, and your surroundings. There are a couple of things
that are pretty much the same for all of
us, though – not only as humans, but as
animals in general. Eating is a behavior that is controlled by different brain
processes that are highly conserved
and similar across mammalian species.
Thanks to tremendous progress in the
development of new technologies and
tools over the last 10 years, neuroscientists can finally answer more questions
about what is going on in the brain when
we reach for that cookie.
The Three Brain Circuits
That Control Eating
The control of eating can be separated into three distinct but interconnected
neural circuits that are anatomically located in different brain regions: the hunger circuit, the consumption circuit, and
the satiety circuit (1).
The Hunger Circuit
What’s a concept review?
A written concept review is similar
to our signature video reviews. The
aim of this article type is to review a
cornerstone topic in physiology or
applied science research.
The whole process of looking for food
starts with the hunger (or more accurately called food-seeking) circuit. This neural circuit is mainly centered on neurons
in the arcuate nucleus of the hypothalamus, a very unique brain structure at the
bottom of your brain that is anatomically
positioned to get access to peptides and
hormones that can cross the blood brain
barrier (see Figure 1). What kind of hormones and peptides? This is where the
familiar names of leptin, ghrelin, and
insulin, among others, come into play.
These hormones constantly circulate
in your blood and the arcuate nucleus
tightly monitors their levels, whether
they are high or low (2). If you haven’t
eaten anything all day, then your insulin levels will be moderately low (since
your pancreas does not produce a lot of
insulin when you are fasting), but your
ghrelin levels – a hormone that is secreted by your stomach in response to
fasting – will be high. The arcuate nucleus, specifically neurons called AGRP
(agouti-related peptide) and POMC
(pro-opiomelanocortin) neurons, will
notice and respond to these levels of
different hormones and peptides. These
60
neurons detect short- and long-term energy stores, as well as other hormonal
signals and work via a yin-yang principle to encourage or discourage eating
behavior. POMC neurons work on a longer timescale and don’t have immediate
influence over behavior, whereas AGRP
neurons do. Therefore, we will now focus on AGRP neurons.
AGRP neurons are generally pretty
quiet, but they become active when they
detect signs of low energy stores. When
these neurons are active, they start a
whole cascade of responses that make
you active and motivated to find food
(hence food-seeking circuit). This is why
it is really hard to just sit on the couch
and do nothing when you are hungry.
AGRP neurons make you want to get
up and move around, preferably toward
the fridge or pantry. It also explains why
it is much easier to stick to a diet when
you can distract yourself throughout the
day or when you are running around to
get things done. The activation of this
hunger circuit corresponds to the typical
feeling of hunger you experience when
you haven’t eaten for a while. Interestingly, we recently discovered that these
food-seeking neurons also make you
feel lousy and unhappy (3). For example, when you are on a calorie-restricted
diet or haven’t eaten in a while, the activity of the AGRP neurons will cause
an unpleasant feeling. How do we know
this? In the lab, we can specifically and
artificially activate AGRP neurons in
Figure 1
The hunger circuit
Arcuate nucleus
Ghrelin
Insulin
Leptin
PYY
The hunger circuit is mainly controlled by neurons in the arcuate nucleus
of the hypothalamus that regulate short- and long-term appetite and
metabolism through peripheral hormones and peptides produced by
the body.
the brains of mice and monitor how the
animals behave. Not only do these mice
actively seek and voraciously consume
food (even if they just ate and shouldn’t
be hungry at all), but they also avoid the
areas and places that are associated with
this artificial neuron activation. We call
this “place avoidance.” Mice do not like
to have these neurons activated. It is un-
61
pleasant – a feeling we want to get rid of
as soon as we can.
Evolutionarily, this makes a lot of
sense. The brain needed some kind of
mechanism to motivate an animal to
get up and find food, especially since
finding food is often risky compared to
staying put in a secure cave. This unpleasant feeling of hunger ensured that
the animal would get active to find some
calories, instead of lazily sitting in his
cave and accidentally starving to death.
It also explains the common feeling of
being “hangry” – it’s literally no fun to
be on a diet. Hence, this hunger circuit
makes you very motivated to stop this
unpleasant feeling by finding and eating
food. As soon as you eat something (and
even before, just as you have the food
right in front of you), these neurons will
become silent within a couple of seconds
– also something we can test and visualize in the lab – and the “hangry state”
is over. At this point, the consumption
circuit takes over. But before we leave
the hunger circuit, a word about leptin.
Leptin
Leptin, a hormone secreted by your
adipose tissue (i.e. fat cells) informs
the arcuate nucleus (and other brain areas) about your energy reserves in the
form of fat. Lots of body fat means lots
of circulating leptin, whereas very little
fat stores means lower circulating leptin
levels. It is of course more complicated
than this, since circulating leptin levels
are also influenced by a host of other
factors and hormones, but for simplicity, you could say that high leptin levels
signal that energy reserves are high (4).
High leptin levels reduce the activity of
ARGP neurons while activating POMC
neurons, making you less hungry and
less motivated to find food, while increasing your metabolic rate. This is an
evolutionary mechanism to keep your
body weight somewhat stable. Enough
energy stores in the form of fat means
no need to find more food. In lay terms:
if you are a little chubby, your brain
will make you less hungry and increase
the amount of calories you burn just
by sitting around, so you are less likely to get any fatter. On the other hand,
when fat energy stores get low – for example, while you are dieting to reach a
very lean body composition – those low
leptin levels will constantly activate your
AGRP neurons, making you hangry and
motivated to get up and find something
to eat, while at the same time reducing
your energy expenditure (5). It’s a pretty
nifty system to ensure that your bodyweight stays in homeostasis – not too fat
and not too lean. But of course, this evolutionary system doesn’t care that you
want visible six-pack abs; it only cares
about maximizing your survivability.
The tools it employs are increasing hunger, increasing food reward (increasing
how good something tastes), and slowing your metabolic rate. No wonder it is
so hard to stay lean and shredded, right?
Your brain is working against you.
62
Figure 2
The consumption circuit
Lateral
hypothalamus
Dorsal
Striatum
Ventral tegmental
area
Insulia
Substantia
Nigra
Amygdala
Nucleus
Accumbens
The consumption circuit. Simplified schematic of the brain areas activated in response to palatable food.
At the center of the consumption circuit is the lateral hypothalamus, which regulates the rewarding responses
to palatable food to drive and sustain food consumption. The lateral hypothalamus is highly interconnected
with the reward system of the brain.
Another obvious question then is:
Why do people become obese when
you should have a mechanism that theoretically prevents you from becoming
too fat? Since its discovery in 1994 (6),
researchers were hoping leptin was the
holy grail for solving the obesity epidemic. However, only in some rare
genetic cases do obese patients bene-
fit from leptin supplementation, while
most other obese patients don’t lose a lot
of weight in clinical studies (7). The reason? Beyond a certain point, increased
leptin production does little to curb your
appetite or increase your metabolism. In
fact, obesity is often coupled with leptin
resistance (similar to insulin resistance)
(4,8). These obese individuals have
63
enough leptin in their system; the problem is that the brain is not able to sense
and detect all that leptin.
Where leptin treatment might play a
potential role is in weight loss maintenance to counteract the effect of decreased leptin levels with less body fat,
which will cause increased hunger and
energy intake and decreased energy expenditure (7). It is hypothesized that it
could prevent “yo-yo” dieting by restoring the leptin balance; however, this is
still under investigation and needs to be
proven. Overall, the hunger circuit and
various changes in hormones and peptides help explain what kicks off your
hunger and motivates you to find food.
But what happens after you start eating?
That brings us to the consumption circuit.
The Consumption Circuit
The onset of eating, and even the mere
sight of food in front of you, will silence
the hunger circuit by reducing AGRP
neuron activity. It will stay inactive until it detects another energy deficit. Now
it is time for the consumption circuit to
take the lead. This neural circuit is mainly based in the lateral hypothalamus, another small but evolutionarily preserved
region in the middle of the brain. It is
strongly connected to the reward centers
of the brain, including the ventral tegmental area, ventral striatum, substantia nigra, and nucleus accumbens (see
Figure 2) (9). These are areas that are
well-known for playing a fundamental
role in reward and pleasure signaling.
When you consume food, neurons in the
lateral hypothalamus will be activated
and signal to those reward centers that
good stuff is happening. Of course, the
more palatable (aka tasty) the food is,
the higher the reward response will be
– especially if it is high in carbs and
fat (think cookies or pizza). Interestingly, artificial sweeteners like sucralose
do not activate the reward system to the
same extent as sugar.
Palatable, caloric food is so powerful
that rodents prefer it even over cocaine
and are willing to expose themselves
to extreme or painful conditions just
to get access to shortcake, Coca-Cola,
or M&M’s, even if they are not hungry
(9). The main reason is how these overly tasty items stimulate the brain reward
centers, especially the dopamine and
opioid systems. Some researchers argue
that overeating in obesity is similar to
excessive drug use in addiction and that
obesity should be considered a brain disorder. The concept of food addiction is
still highly debated (10, 11), but there is
mounting evidence supporting the idea
that obesity and a high-fat-high-sugar
diet change the brain and might make it
harder for individuals to stop binge eating.
The consumption of food, especially highly palatable food, has also been
shown to enhance your mood, which is
another feature of activating the lateral
64
hypothalamus and the connected reward
system. And here is the double whammy: high-calorie, palatable food activates these reward centers much more
when you are hungry or dieting (12). So,
the hungrier you are, the more you will
like and want that tasty cookie. This is
encapsulated in the old adage “Hunger is
the best sauce.” Thus, it is not surprising
that periods of dieting increase self-reported ratings of the power of palatable
food and cravings for ‘‘tempting’’ foods
(13). This is another way your brain is
seemingly working against you on your
path to a ripped physique.
Due to the rewarding nature of this circuit, its activity becomes self-sustaining
and operates almost like an eating loop.
The main molecular players causing the
positive feelings associated with eating
are dopamine, serotonin, opioids, and
cannabinoids. In short, eating releases
chemicals that make us feel good and
happy, which is also the reason why people often eat when stressed or to cope
with negative feelings or emotions (14,
15, 16, 17). This comfort/emotional eating is typically directed toward sweet,
fatty, or highly processed food and is
thought to help, at least transiently, with
mood improvement or to distract from
negative emotions. If you catch yourself snacking when you are not hungry,
the “feel-good” characteristics of the
consumption and reward circuits are to
blame. In fact, you would not stop eating unless the last circuit (the satiety cir-
Figure 3
The satiety circuit
PBN
LH
NTS
Blood
circulation
N. Vagus
Adipose tissue
Leptin
Stomach
Ghrelin
Leptin
Pancreas
Amylin
Insulin
PP
Small intestine
Large intestine
PYY
CCK
GLP-1
The satiety circuit is activated by caloric and volume feedback from ingested
food. Peripheral peptides and hormones as well as signals from the intestines
will be transmitted via the vagus nerve and processed in the hindbrain (first
in the NTS, then in the PBN). The hindbrain also receives information from
circulating blood levels. Once information that enough calories have been
ingested is received, the hindbrain will send the stop signal to the
consumption circuit.
LH = lateral hypothalamus; NTS= nucleus of the solitary tract;
PBN = parabrachial nucleus.
cuit) stepped in, as it conveys the stop
signal to cease eating.
Satiety Circuit
The satiety circuit is activated by the
caloric and volume feedback from the
ingested food. A variety of signals will
65
communicate the current state of energy availability (mainly via the vagus
nerve) to the hindbrain, where the satiety circuit is located (Figure 3, 18).
These signals include ghrelin, intestinal peptides (like peptide tyrosine tyrosine-PYY, cholecystokinin-CCK, and
glucagon-like peptide-1-GLP-1), insulin, amylin (an enzyme that helps with
protein digestion), and leptin. Recently,
scientists discovered exactly what these
satiety circuit neurons are (19). They are
called CGRP (calcitonin-gene-related
peptide) neurons and are located in the
parabrachial nucleus in the hindbrain.
These CGRP neurons transmit the stop
signal to the consumption circuit to stop
eating. Interestingly, some experiments
unintentionally revealed that if you get
rid of these satiety neurons, mice will
consume food until it comes out of their
nose or until their stomach bursts. Without these powerful neurons, you would
not know when enough volume and calories are consumed to stop eating (20).
CGRP neurons are also active when you
are nauseated and sick, which is why
you don’t want to eat even the most delicious meal in these situations. A related concept is taste aversion; if you eat
something and get sick from it, these
neurons will “remember” that taste and
associate it with feeling sick. The next
time you encounter that specific food
or taste, you might feel disgusted or repelled by it (21). Powerful neurons, indeed.
There are, of course, many other factors that play a role when it comes to
optimizing your satiety signals, like the
amount of fiber you ingest, the macronutrient content of the meal, the water content in your stomach, how lean you are,
and so forth. In general, eating a meal
that is high in fiber, with a sufficient
amount of water content, and a balanced
mix of macronutrients will swiftly activate the satiety circuit to send the appropriate “stop eating” signal. It should
also leave you satisfied for hours until
your hunger circuit detects the next energy deficit or bout of prolonged fasting.
These three interconnected systems
(the hunger, consumption, and satiety circuits) work best when you eat a
well-balanced diet with lower-reward
foods. Once your stomach is filled and
some nutrients and calories are absorbed,
you stop eating and feel satisfied. Overall, your brain is monitoring your energy
stores and hormones to make sure you
are fit and healthy with enough energy
reserves for a potential famine.
The Problem: An Obesogenic
Environment
This finely balanced system is not
adapted to an environment of readily available high-reward, high-calorie
foods. Unfortunately, most of us are living in such an obesogenic environment
where we are surrounded by easily accessible food options, many of which are
66
Figure 4
Cat
The prefontal cortex (PFC)
Dog
Man
In humans, the prefrontal cortex covers almost 30% of the cortex, making us the
most cognitive and goal-directed species.
processed or engineered to circumvent
our body’s normal satiety response. Why
does it affect our brain so much to promote overeating? After we are born, our
brain circuits learn the cues and stimuli
in our environment (like the smell and
sight of bacon) and predict the positive
survival-enhancing effect of ingesting
those calories and the good feelings we
get from eating tasty food. The connection between eating specific foods and
the amount of energy it will provide is
learned, and cues that predict the availability of these foods become able to activate the same brain reward system as
the eaten food itself (22). This happens
through the postingestive consequences
of consuming: your brain learns to associate the good feeling of eating pizza with the sight, smell, and location of
where you ate that pizza. Next time you
drive by that pizza place or smell pizza, you might feel a craving or desire to
eat. Interestingly, after learning such an
association, the smell or sight of pizza
can influence, for example, the release
of hormones like ghrelin (23). This can
trigger an almost automatic response to
crave the pizza, even though you might
not need the calories at all.
Another problem with consuming
high calorie foods is that they not only
over-activate your reward systems, but
67
they also overwrite the incoming satiety signals, leading you to massively
overeat beyond any homeostatic needs.
Evolutionarily, your brain wants to ensure that those precious calorie resources are consumed and saved for future
famines; this was especially relevant in
times when high calorie food was not an
everyday thing. In modern-day, affluent
societies, this system now contributes
to our increasing waistlines, and famines are almost nonexistent. We are surrounded by high-calorie food cues and
stimuli 24/7, and our brain is constantly
trying to make us eat those precious calories to prepare for a potential, but improbable, famine.
Prefrontal Cortex – Control
Over Goal-Directed Behavior
In situations where we have to actively
fight the urge to eat pizza and cookies, we
have to engage yet another brain region.
It’s called the prefrontal cortex. This is
the area located right behind your forehead. The prefrontal cortex is the area
that is most developed in humans compared to all other animal species (see
Figure 4) (24). Not surprisingly, we are
the only species able to plan our diet and
behavior in accordance with goals like
a powerlifting or bodybuilding competition. This brain region also takes the
longest to completely mature in humans
and won’t be fully wired up until adolescence ends. If you have ever heard
of impulse control and the marshmallow test (where toddlers can either have
one marshmallow immediately or they
can wait a couple of minutes to receive
two), it is exactly this region supporting those functions to delay immediate
reward and focus on long-term goals.
An athlete dieting for a competition
needs to engage their prefrontal cortex
a lot to delay the immediate reward of
a tasty and satisfying treat for the longterm goal of having a specific amount of
body fat on competition day. There are
considerable amounts of interesting research and insights about the prefrontal
cortex (so much that we’ll have to save
them for a future review), but it’s worth
mentioning that you need this brain region to stick to your diet (25), especially
when things are starting to get harder and
distractions to derail your diet are everywhere. To make it easier on you and
your prefrontal cortex, building healthy
habits that you don’t need to think about
is your best bet for long-lasting diet success.
Conclusion
With weight management in mind,
we can use the knowledge about how
the brain controls appetite and eating
behavior to our advantage and trick the
consumption and satiety circuit to function in our favor for fat loss. The secret:
eating simple, low-reward foods that
contain enough fiber and water. You
68
APPLICATION AND TAKEAWAYS
1. Highly rewarding food will over-activate the reward system and overwrite satiety
signals.
2. Dieting and weight loss will be easier if you focus on mostly non-hyperpalatable
food, which will appropriately activate the satiety circuit, so you feel full without
eating too many calories.
3. If you have trouble gaining weight, include food items that are less bland and
higher in palatability to be able to consume a larger amount of calories.
have probably heard it before and it may
not be an exciting revelation, but neuroscience research supports this notion.
There are many ways to lose fat, but if
you mainly consume meals that don’t
over-activate the reward system, you
will be more likely to succeed and stick
with it. Your brain circuits will work in
accordance with your goals – eat only
when hungry, and stop when you’ve ingested enough. The less rewarding the
diet, the sooner your satiety circuit will
tell you to stop eating. It’s really hard to
overeat on chicken and broccoli. French
fries and strawberry cheesecake, on the
other hand, not so much.
69
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systems towards high-calorie foods. Eur J Neurosci 30:1625-1635.
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71
Study Reviewed: Short-Term Impact of Sucralose Consumption on the Metabolic
Response and Gut Microbiome of Healthy Adults. Thomson et al. (2019)
More Good News for Artificial Sweeteners:
No Effect of Short-Term Sucralose Ingestion
on Glycemic Control or Gut Microbiome
BY E RI C T RE X LE R
Some view artificial sweetener consumption as a safe and healthy way to
cut calories, while others suspect that they’re too good to be true. A recent
study found that one week of consuming high-dose sucralose, also known as
SPLENDA®, had no effect on glycemic control or the gut microbiome. Read on to
get the scoop on the potential health effects of artificial sweeteners.
72
KEY POINTS
1. The current study (1) sought to determine if sucralose (also known as SPLENDA®),
a particularly common non-nutritive sweetener, affects glycemic control and the
gut microbiome.
2. Compared to placebo, seven days of consuming 780mg/day of sucralose
(equivalent to about 20 diet sodas per day) did not significantly affect glycemic
control or the gut microbiome.
3. Current literature has failed to consistently show unfavorable effects of sucralose
on glycemic control or the gut microbiome, and has also failed to demonstrate that
sucralose increases body weight, energy intake, or cancer risk. More research is
needed, particularly on its effects on the gut microbiome, but sucralose appears
to be a suitable sugar replacement that can be safely used to facilitate weight
management.
T
he term “non-nutritive sweetener” describes a group of diverse
sweetening agents that have one
thing in common: they can deliver the
same sweetness as a typical sugar dose,
while contributing virtually no calories. They accomplish this task by being
sweeter than sugar — way sweeter. For
example, saccharin is about 500x sweeter than sugar, aspartame and acesulfame
potassium are about 200x sweeter, and
sucralose is about 600x sweeter. There
are some naturally occurring non-nutritive sweeteners, such as stevia, but
many of them, including sucralose, are
synthetically produced. When people
hear that their diet soda tastes the same,
has zero calories, and that an “unnatural” substance made it possible, many
assume that it’s too good to be true, resulting in skepticism and concerns about
potential downsides. Common concerns relate to the effects of non-nutri-
tive sweeteners on cancer risk, appetite,
glycemic control, and the gut microbiome. The current study (1) evaluated
the effects of sucralose (also known as
SPLENDA®), a particularly common
non-nutritive sweetener, on glycemic
control and the gut microbiome. Results
of this study indicated that seven days
of high-dose sucralose ingestion did not
significantly alter the gut microbiome
or glycemic control, which contradicts
some previous findings. Read on to find
out what these new results mean for our
growing understanding of sucralose and
other non-nutritive sweeteners.
Purpose and Hypotheses
Purpose
The purpose of this study was to determine if short-term (one week), daily
73
Table 1
Baseline subject characteristics from Thomson et al
Placebo
(n=14)
Sucralose
(n=16)
Age (years)
23.5 ± 2.9
[18.2 - 29.3]
22.8 ± 3.0
[18.7 - 30.2]
0.51
Weight (kg)
77.0 ± 8.3
[57.9 - 88.0]
73.2 ± 6.9
[60.9 - 83.7]
0.19
Height (m)
1.73 ± 0.04
[1.67 - 1.80]
1.75 ± 0.07
[1.63 - 1.90]
0.31
Body mass index (kg/m2)
25.7 ± 2.9
[20.8 - 28.9]
23.8 ± 1.7
[21.1 - 26.6]
0.04
Glycemia (mg/dl)
84 ± 8
[67 - 95]
85 ± 6
[72 - 94]
0.50
Cholesterol (mg/dl)
173 ± 23
[134 - 219]
147 ± 22
[106 - 184]
<0.01
Despite using standard randomization protocols, the placebo and sucralose groups had significantly different body mass index
and cholesterol levels at baseline. However, these differences probably didn’t have a meaningful effect on the study results.
sucralose intake alters glycemic control
or the gut microbiome. Just to clarify,
“glycemic control” refers to the body’s
ability to efficiently manage blood glucose levels throughout the day; a person
with poorly controlled diabetes has poor
glycemic control, and a person with
high insulin sensitivity has excellent
glycemic control. Common research
outcomes related to glycemic control include fasting glucose and insulin levels,
glucose and insulin responses to glucose
or food ingestion, glycated hemoglobin
(HbA1c), and more.
has been shown to adversely affect glycemic control and that the effect could
feasibly be related to sucralose-induced
changes in the gut microbiome. However, they also presented some human
evidence suggesting that sucralose does
not influence glycemic control. They
were clearly testing the hypothesis that
daily sucralose ingestion alters glycemic control by causing gut microbiome
changes, but it’s unclear if they suspected (or doubted) that this effect would be
observed.
Hypotheses
Subjects and Methods
The authors did not clearly state a hypothesis. In the introduction, they presented evidence indicating that sucralose
Subjects
The
researchers
recruited
34
74
weight-stable, healthy men between 18
and 50 years of age, with a body mass
index (BMI) of 20-30kg/m2. Participants
were not trained and did not regularly
participate in intense physical activity
for three months prior to the study. Four
subjects dropped out, so the study finished with 16 subjects in the sucralose
group and 14 in the placebo group. Subject characteristics are presented in Table 1.
day for a 70kg individual. The daily dose
used in the current study is equivalent to
about 20 diet sodas per day.
Methods
Throughout the study, body weight
remained stable; the sucralose group
and placebo group both experienced
non-significant, negligible increases in
body mass (+0.21 ± 1.17kg and +0.16 ±
0.74kg, respectively).
Participants arrived at the first visit to
get their glycemic control tested using
an oral glucose tolerance test. A baseline
blood sample was taken, followed by ingestion of 75g of glucose, then additional blood draws at 30, 60, 90, and 120
minutes after glucose ingestion. At each
time point, glucose and insulin were
measured, which allows the researchers to understand how efficiently each
subject clears glucose from their bloodstream. Subjects completed this test in a
fasted state and brought a fecal sample
with them (or collected one in the lab) to
allow for gut microbiome profiling.
After this visit, the seven-day supplementation period began. Subjects either
took a sucralose capsule, or a placebo
capsule of similar appearance, three
times per day. The total daily dose of sucralose was 780mg/day. To put that into
perspective, the European Union lists 15
mg/kg/day as the acceptable daily intake
for sucralose, which would be 1,050mg/
Following the seven-day supplementation period, the oral glucose tolerance
test was repeated, and another fecal
sample was collected to allow for gut
microbiome profiling.
Findings
The researchers observed and calculated many different indices pertaining
to blood levels of glucose and insulin
and their changes in response to glucose
intake. The results of these outcomes are
presented in Table 2. If the treatments
(placebo versus sucralose) caused differential responses, this would be reflected by a significant group × time interaction; as can be seen in Table 2, none
of these interaction effects were statistically significant.
In addition to the outcomes presented in Table 2, the authors calculated the
area under the curve for glucose and insulin responses to the oral glucose tolerance test in each group, both before and
after the seven-day intervention period.
As can be seen in Figure 1, these val-
75
Table 2
Glycemic control variables before and after intervention (mean ± SD)
Placebo (n=14)
Sucralose (n=16)
Before
After
Change
Before
Glycemia (mg/dl)
82 ± 5
79 ± 4
-2.2 ± 5.0
82 ± 5
Insulinemia (μU/ml)
12 ± 5
11 ± 4
-1.0 ± 3.3
9±4
HOMA-IR
2.4 ± 1.1
2.1 ± 0.8
-0.3 ± 0.7
1.9 ± 0.9
After
p
Change
Group
Time
Group x Time
82 ± 5
0.0 ± 6.1
0.22
0.31
0.29
8±4
-0.9 ± 4.6
0.07
0.22
0.96
1.7 ± 0.9
-0.2 ± 1.1
0.13
0.21
0.77
Fasting
After oral glucose
Glycemia (mg/dl)**
115 ± 17
112 ± 21
-3.0 ± 17.4
107 ± 21
113 ± 21
6.2 ± 18.6
0.65
0.63
0.17
Insulinemia (μU/ml)**
81 ± 38
87 ± 51
5.6 ± 38.1
63 ± 40
78 ± 41
15.4 ± 30.5
0.35
0.11
0.44
ISI-Composite
4.1 ± 2.1
4.7 ± 3.0
0.5 ± 2.4
7.8 ± 10.0
5.9 ± 4.2
-1.9 ± 7.7
0.21
0.52
0.29
** = Mean of responses over the 2-hr time period following glucose ingestion
ues were not significantly altered by sucralose or placebo.
In terms of the gut microbiome, a
baseline difference was observed; the
placebo group had a relatively higher
abundance of phylum Firmicutes before the study began, and this persisted
throughout the intervention (Figure 2).
However, treatment (sucralose versus
placebo) did not induce any meaningful
changes throughout the study in either
group. The researchers ran a few more
in-depth analyses pertaining to the gut
microbiome, which assessed indices of
intra-individual microbiome alterations.
These additional analyses also indicated
that the gut microbiome was largely unaffected by the intervention in the current study.
Finally, the researchers split the sample
into “responders” and “non-responders”
based on whether their values increased
or decreased following the intervention,
regardless of which treatment they received. These exploratory analyses are
not central to the research question, so
I won’t spend a lot of time focusing on
them, but they generally found that responders and non-responders had different gut microbiome compositions.
While this doesn’t suggest that sucralose
induced any changes in gut microbiome
composition or glycemic control, it does
reinforce the general relationship between the gut microbiome and glycemic
control.
Interpretation
The current study provides more data
to help answer an important (and valid)
question: Do artificial sweeteners unfavorably affect the gut microbiome or
glycemic control? As reviewed by Pepino (2), there are legitimate reasons to
investigate this question. Previous studies have suggested that non-nutritive
sweeteners (including sucralose) can
significantly alter learned responses that
relate to glycemic control and energy
intake, significantly alter the gut microbiome, and significantly alter glycemic
76
Figure 1 Changes in metabolic response to glucose consumption before
and after the intervention
Glycemic response
(AUC, x 103)
A
After
16
14
12
10
B
Insulinemic response
(AUC, x 103)
Before
18
n = 14
n = 16
Placebo
Sucralose
Treatment
p-value = 0.57
20
Before
After
15
10
5
0
n = 14
n = 16
Placebo
Sucralose
Treatment
p-value = 0.73
AUC = Total area under curve for each group, both before and after the intervention period
and insulinemic responses to glucose
intake (2, 3). In fact, one rodent study
found that while sucralose impaired
oral glucose tolerance, this effect was
blocked by antibiotics and was transferable to other mice via fecal transplant
(4). These observations would appear to
imply (but not prove) that the microbiome might play a causative role in the
observed changes in glycemic responses. While the overwhelming majority of
this evidence comes from either in vitro
models or rodent studies, there are also
some human trials lending support to
some of these theoretical downsides of
sucralose. For example, one study suggested that sucralose elicited a cephalic phase insulin response in a subset of
77
Figure 2
Gut microbiome compositions before and after intervention
Mean relative abundance
p = 0.05
Actinobacteria
p = 0.02
1.0
Bacteroidetes
Firmicutes
0.8
Proteobacteria
0.6
0.4
0.2
0.0
Before
After
Placebo (14)
“responders,” particularly when ingested as a food rather than a beverage (5). A
separate trial suggested that consuming
sucralose (48mg) 10 minutes before an
oral glucose tolerance test led to unfavorable effects on glycemic responses
(6), and another found that 14 days of
sucralose ingestion (36mg/day) led to a
significant reduction in insulin sensitivity (7). In contrast, the current study (1)
did not identify meaningful effects of
sucralose ingestion on glycemic control
or the gut microbiome. So, what gives?
In a fairly recent study, Grotz et al (8)
summarized the existing literature pertaining to the effects of sucralose on
glycemic responses. They presented a
big table containing 16 studies; some
involved single-dose sucralose inges-
Before
After
Sucralose (14)
tion, while others included repeated sucralose ingestion over a period of weeks
or months. All 16 studies included some
measure of blood glucose or blood insulin levels, and only one of the 16 studies
reported a statistically significant effect
on either parameter. A couple of additional studies have come out in the time
since Grotz et al published this summary table, but the general idea remains the
same: the majority of human trials find
no significant impact of sucralose ingestion on glycemic control.
When we look at the studies suggesting that sucralose impairs various outcomes related to glycemic control, a few
patterns emerge. For example, a couple
of them are unblinded designs lacking
a placebo condition (6, 7). A couple of
78
them openly questioned the real-world
impact of their own results, as one
study’s results were dependent upon
responder/nonresponder stratification
and distinguishing between solid versus liquid sources of sucralose (5), and
the other used a relatively small sample
and did not collect baseline data, which
would have facilitated interpretation of
the study results (9). All four of these
studies recruited participants with relatively low habitual intake of non-nutritive sweeteners, and also utilized samples that were mostly (64-88%) female,
with mean ages suggesting that the majority of the participants were premenopausal. This is important, because it
has been speculated that habitual users
and non-users of non-nutritive sweeteners may have differential responses to
short-term interventions, and outcomes
related to glycemic control and insulin
release can be influenced by menstrual
cycle phase (10).
As the literature currently stands, it
would appear that the majority of studies fail to observe significant effects of
acute or chronic sucralose consumption
on outcomes related to glycemic control. Of the studies that do report significant differences, there may be some key
design limitations and sample characteristics underlying the observed effects. In
addition, it is important to distinguish
between statistically significant effects
and clinically or practically significant
effects; if an intervention slightly alters
AS THE LITERATURE CURRENTLY
STANDS, IT WOULD APPEAR
THAT THE MAJORITY OF STUDIES
FAIL TO OBSERVE SIGNIFICANT
EFFECTS OF ACUTE OR CHRONIC
SUCRALOSE CONSUMPTION
ON OUTCOMES RELATED
TO GLYCEMIC CONTROL.
insulin or glucose kinetics after glucose
consumption but doesn’t appear to have
a meaningful impact on an individual’s
overall health or body composition, it’s
hard to view that as a good enough reason to avoid sucralose altogether. Given that previous studies have identified
some characteristics that may alter glycemic responses to sucralose and stratified between responders and non-responders, it would seem that individual
responses may vary. If you’re concerned
about your glycemic response to acute
or chronic sucralose consumption, you
could easily do a little experiment with
a home glucose monitor (thanks to Greg
for suggesting this practical recommendation).
Notably, there are a couple of fairly
long, well-designed studies that can give
79
us some confidence in sucralose. In one
such study, 128 diabetic subjects were
randomly assigned to receive either sucralose (667mg/day) or a placebo for 13
weeks. The study measured a number
of outcomes related to glycemic control
and general safety, with no significant
effects observed. Similarly, the same
research group randomly assigned 47
healthy male volunteers to receive either
sucralose (1000mg/day) or a placebo
for 12 weeks. Again, the study found no
change in outcomes related to glycemic
control, such as fasting glucose, fasting
insulin, c-peptide, glycated hemoglobin
(HbA1c), or responses to an oral glucose tolerance test. Taken together, it
would seem that there is insufficient evidence to warrant major concern about
sucralose inducing unfavorable effects
on glycemic control.
When it comes to effects on the microbiome, we really don’t have much
human evidence to go off of. A recent
review from 2019 sought to describe the
effects of several non-nutritive sweeteners on the gut microbiome (3). While
some sweeteners, such as aspartame
and saccharin, had several studies documenting their effects on the gut microbiome, only a few rodent studies (and zero
human studies) were reviewed for sucralose. It’s way too early to determine
if sucralose significantly alters the gut
microbiome in humans. However, several studies have provided large, daily
doses of sucralose for weeks at a time,
and these studies have failed to consistently yield serious adverse effects,
weight gain, or clinically meaningful alterations in glycemic control. We can’t
discount the possibility that long-term
sucralose ingestion may alter the human
gut microbiome, or that this alteration
could have physiologically or clinically
meaningful effects. However, the current evidence doesn’t seem to hint at any
particularly disastrous effects, even with
fairly high doses of sucralose ingested
for up to three months (8, 11).
Effects of non-nutritive sweeteners on
other outcomes
While we’re on the subject of non-nutritive sweeteners, it makes sense to
briefly address some of the other common concerns that people worry about.
As MASS readers will remember from
Volume 3, Issue 10, Dr. Helms covered
a recent study indicating that artificially
sweetened diet soda has a neutral, if not
positive, effect on cravings and food intake. Along the same lines, a meta-analysis found that non-nutritive sweeteners
tend to reduce energy intake and body
weight when they’re used to replace
sugar calories and have a positive or
neutral effect when compared to plain
water (12). While many folks fear that
some non-nutritive sweeteners may increase cancer risk, a recent review found
no evidence linking sucralose to cancer,
even at intake levels substantially higher than would be anticipated (13). Taken
80
WE CAN’T DISCOUNT THE
POSSIBILITY THAT LONGTERM SUCRALOSE INGESTION
MAY ALTER THE HUMAN
GUT MICROBIOME, BUT
THE CURRENT EVIDENCE
DOESN’T SEEM TO HINT
AT ANY PARTICULARLY
DISASTROUS EFFECTS, EVEN
WITH FAIRLY HIGH DOSES.
together, there’s insufficient evidence to
conclude that sucralose has a negative
impact on the gut microbiome or glycemic control, and plenty of evidence
suggesting that regular consumption of
sucralose is not linked to weight gain,
appetite alterations that result in overeating, or elevated cancer risk. It’s not
uncommon for people to distrust organizations that provide oversight, and
I wouldn’t be shocked if some people
doubted that such organizations would
actually apply bans or change policy in
response to unfavorable data. However,
when it comes to non-nutritive sweeteners, there is actually precedent that lends
evidence to the contrary. Cyclamate
used to be an approved sweetener in the
United States, but it was banned in 1969
in response to some rodent data linking
cyclamate to bladder cancer (14). Other
countries were less convinced by this evidence and opted not to ban cyclamate,
but national and international agencies
put a great deal of thought into which
food additives and sweetening agents are
safe to consume, and these decisions are
updated (and sometimes reversed) when
new evidence becomes available. For
now, the sucralose data seem to range
from neutral to positive, and sucralose
is regarded as a safe sweetening agent
by organizations all over the world.
Non-nutritive sweeteners: a very broad
term
Before I wrap things up, I want to emphasize a point that sometimes seems
to get lost in the artificial sweetener
conversation. I’ll frequently get asked
questions like, “Do artificial sweeteners
increase cancer risk?” or “Do artificial
sweeteners mess up your gut microbiome?” In reality, the underlying premise of these questions is flawed; non-nutritive sweeteners are just a bunch of
things that are really, really sweet, with
extremely low caloric content per serving. They can be natural or synthetic
in origin, and they are wildly different
compounds with totally different structures and properties. We can’t lump
them together as one cohesive category,
81
APPLICATION AND TAKEAWAYS
A lot of people are wary of non-nutritive sweeteners, possibly because they seem
too good to be true. However, the current evidence does not suggest that sucralose
has meaningful effects on glycemic control or the human gut microbiome, although
more data on the gut microbiome are certainly needed. Overall, sucralose appears
to be a safe sugar replacement and can be used as a tool to support successful
weight management.
which means we can’t project positive
attributes of sweetener X onto sweetener Y, and we can’t assume that sweetener Z is harmful because a new study
reported some unfavorable effects of
sweetener Y. Unfortunately, this means
we need a ton of research to evaluate all
of the outcomes we’re concerned about,
using all of the sweeteners that find their
way into our diets.
Conclusions
The results of the current study indicate that one week of ingesting high
doses of sucralose, equivalent to about
20 diet sodas per day, does not significantly affect glycemic control or the
gut microbiome. While there isn’t much
evidence about the human microbiome
to compare these results to, the findings are in line with a number of studies showing no effect or negligible effects of sucralose on glycemic control.
Of course, it’s important to note that the
current results do not conclusively rule
out the possibility that sucralose might
influence these outcomes in some cir-
cumstances. Limitations of the current
study include the use of a fairly small
sample size and an intervention with a
relatively short duration. The studies
that do report statistically significant effects tend to indicate that responses vary
substantially from person to person, and
the exact reasons for this variability are
not currently known. Based on the limited data available, sucralose does not
significantly affect glycemic control or
the gut microbiome, nor does it appear
to increase body weight, energy intake,
or cancer risk. Overall, sucralose looks
like a suitable sugar replacement that
can facilitate weight management within the context of a well-constructed diet.
Next Steps
Right now, we simply don’t have
much research examining the effect of
sucralose on the human gut microbiome. In addition, much of the research
evaluating glycemic control has utilized
pretty short study durations, ranging
from single-dose designs to only a week
82
or two of daily consumption in many
cases. Finally, studies on both glycemic
responses and microbiome changes in
response to sucralose intake have suggested that inter-individual responses
vary greatly. A great next step would
be to design a randomized controlled
trial evaluating longitudinal changes in
response to sucralose intake. Ideally,
such a study would be long enough in
duration to ensure that any sucralose-induced changes would be observed by
the time of post-testing, and it would
have a large enough sample to identify
characteristics that might reliably separate responders from nonresponders. It
seems that menstrual cycle phase and
habitual intake of non-nutritive sweeteners might possibly be influential characteristics, but many other possibilities
exist. Finally, this area of research will
eventually have to perform similar trials
with other common non-nutritive sweeteners, as the results of sucralose would
not necessarily generalize to all other
non-nutritive sweeteners.
83
References
1. Thomson P, Santibañez R, Aguirre C, Galgani JE, Garrido D. Short-term impact of sucralose
consumption on the metabolic response and gut microbiome of healthy adults. Br J Nutr.
2019 Oct 28;122(8):856–62.
2. Pepino MY. Metabolic effects of non-nutritive sweeteners. Physiol Behav. 2015 Dec
1;152:450–5.
3. Ruiz-Ojeda FJ, Plaza-Díaz J, Sáez-Lara MJ, Gil A. Effects of Sweeteners on the Gut Microbiota: A Review of Experimental Studies and Clinical Trials. Adv Nutr Bethesda Md. 2019
Jan 1;10:S31–48.
4. Suez J, Korem T, Zeevi D, Zilberman-Schapira G, Thaiss CA, Maza O, et al. Artificial
sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014 Oct
9;514(7521):181–6.
5. Dhillon J, Lee JY, Mattes RD. The cephalic phase insulin response to nutritive and low-calorie sweeteners in solid and beverage form. Physiol Behav. 2017 Nov 1;181:100–9.
6. Pepino MY, Tiemann CD, Patterson BW, Wice BM, Klein S. Sucralose affects glycemic and
hormonal responses to an oral glucose load. Diabetes Care. 2013 Sep;36(9):2530–5.
7. Romo-Romo A, Aguilar-Salinas CA, Brito-Córdova GX, Gómez-Díaz RA, Almeda-Valdes
P. Sucralose decreases insulin sensitivity in healthy subjects: a randomized controlled trial.
Am J Clin Nutr. 2018 Sep 1;108(3):485–91.
8. Grotz VL, Pi-Sunyer X, Porte D, Roberts A, Richard Trout J. A 12-week randomized clinical
trial investigating the potential for sucralose to affect glucose homeostasis. Regul Toxicol
Pharmacol. 2017 Aug 1;88:22–33.
9. Lertrit A, Srimachai S, Saetung S, Chanprasertyothin S, Chailurkit L-O, Areevut C, et al.
Effects of sucralose on insulin and glucagon-like peptide-1 secretion in healthy subjects: a
randomized, double-blind, placebo-controlled trial. Nutrition. 2018;55–56:125–30.
10. Sheu WH. Alteration of insulin sensitivity by sex hormones during the menstrual cycle. J
Diabetes Investig. 2011 Aug 2;2(4):258–9.
11. Grotz VL, Henry RR, McGill JB, Prince MJ, Shamoon H, Trout JR, et al. Lack of effect of
sucralose on glucose homeostasis in subjects with type 2 diabetes. J Am Diet Assoc. 2003
Dec;103(12):1607–12.
12. Rogers PJ, Hogenkamp PS, de Graaf C, Higgs S, Lluch A, Ness AR, et al. Does low-energy
sweetener consumption affect energy intake and body weight? A systematic review, including meta-analyses, of the evidence from human and animal studies. Int J Obes 2005. 2016
Mar;40(3):381–94.
13. Berry C, Brusick D, Cohen SM, Hardisty JF, Grotz VL, Williams GM. Sucralose Non-Carcinogenicity: A Review of the Scientific and Regulatory Rationale. Nutr Cancer. 2016
Dec;68(8):1247–61.
84
14. Yang Q. Gain weight by “going diet?” Artificial sweeteners and the neurobiology of sugar
cravings. Yale J Biol Med. 2010 Jun;83(2):101–8.
█
85
Study Reviewed: Peak Age and Performance Progression in World-Class
Weightlifting and Powerliting Athletes. Solberg et al. (2019)
What’s the Best Age to
Dominate Strength Sports?
BY G RE G NUC KO LS
If you want to maximize your competitiveness in powerlifting or
weightlifting, at what age should you anticipate being at the peak of
your prowess? It seems that weightlifting is a young person’s game, but
many powerlifters are still improving well into their 30s (or even 40s).
86
KEY POINTS
1. When analyzing competition results from 4000+ athletes at world championships
and the Olympics, it appears that powerlifters peak at an age of 35 ± 7, while
weightlifters attain their best performances at 26 ± 3 years old.
2. Notice the standard deviations: if you want to be a world-class weightlifter, you
realistically need to start training young enough that you can peak sometime in
your 20s. However, while some powerlifters also reach their peak performance at a
young age, there are some world-class lifters who are still improving into their 40s.
3. These differences are probably driven by the differing demands of the sports
(weightlifting is more based on power, and powerlifting is more based on sheer force
production), along with differences in overall competitiveness and talent pools.
A
fter genetics, training age may
be the most important factor
explaining strength differences
between individuals. However, biological age clearly plays a role as well. For
every sport, there’s an age at which the
best of the best are the most competitive.
You don’t see many basketball players
or footballers dominating in their 40s,
after all. So, what is the prime age for
strength sports?
A recent study examined competition
results in world championships and the
Olympics and found that world-class
weightlifters tend to peak at around
26 years old, while powerlifters peak
around 35. The standard deviation was
also wider for powerlifters (7 years)
than weightlifters (3 years), giving powerlifters a wider competitive window.
These differences may be driven by several factors, including the age at which
people begin training for each sport, the
different physical requirements of the
sports, and possibly changes in equipment over time.
Purpose and Hypotheses
Purpose
The purpose of this study was to identify the age of peak performance in elite
weightlifters and powerlifters, as well as
the magnitude of the performance improvement in the preceding years.
Hypotheses
No hypotheses were stated.
Subjects and Methods
Subjects
This was retrospective research, meaning the authors analyzed data that already
existed, instead of actually recruiting
87
Figure 1
Theoretical representation of chnages in total over time
850
Total
825
800
775
750
30
32
34
36
Year
These are theoretical totals at different ages for a fictitious athlete, fitted with a polynomial trendline. The trendline peaks
at 33.7 years old. In this example, the athlete hit their best total at age 34. As such, this method for estimating peak age
probably gives a reasonably close approximation, unless an athlete had a ton of random variance in their year-to-year results.
subjects and running a study. As such,
the “subjects” were everyone who had
competed in the IPF powerlifting single
ply world championships between 20032017, and the IWF weightlifting world
championships or Olympics between
1998-2017, totaling 4385 competitors.
Data Analysis
The authors made an interesting decision when determining each athlete’s
age of peak performance. Personally, I
would have just recorded their age when
they hit their highest total. Instead, the
authors fit a quadratic curve to each of
the athletes’ performance trends over
time, with the assumption that peak
performance occurred at the top of the
curve. An example is provided below as
Figure 1. As such, only the athletes with
at least three world championship appearances could be analyzed. This was
a somewhat odd choice, in my opinion,
but as we’ll see, it probably didn’t matter a whole lot.
Rather than just looking at totals, the
authors also split things out by lift, by
sex, and by performance level (medalists
88
Table 1
Predicted peak age (in years) and weight lifted (in kilograms) for powerlifting events
Squat
Event (n)
Age
Bench press
Weight
Age
Deadlift
Weight
Age
Weight
Men
59 kg (8)
30.7 (5.4)
260 (18)
32.1 (4.0)
170 (20)
32.1 (4.2)
243 (19)
66 kg (11)
34.9 (7.0)
281 (27)
34.6 (4.4)
186 (30)
33.5 (6.6)
277 (26)
74 kg (8)
33.9 (3.6)
295 (40)
34.2 (3.6)
196 (30)
33.8 (4.2)
288 (21)
83 kg (10)
33.7 (7.6)
331 (25)
36.4 (6.9)
229 (30)
32.8 (5.8)
307 (21)
93 kg (12)
36.2 (6.1)
339 (21)
35.8 (6.8)
240 (27)
35.0 (6.6)
316 (24)
105 kg (8)
33.8 (5.1)
376 (25)
33.4 (4.8)
269 (25)
31.0 (5.2)
342 (25)
120 kg (5)
34.6 (10.0)
382 (34)
33.7 (10.5)
253 (26)
36.1 (10.1)
355 (31)
>120 kg (8)
35.0 (5.2)
397 (29)
34.1 (7.5)
306 (43)
33.6 (5.3)
327 (23)
Women
47 kg (7)
34.2 (7.9)
169 (25)
35.5 (8.0)
98 (19)
35.4 (7.8)
162 (18)
52 kg (10)
36.3 (7.0)
171 (26)
35.4 (7.3)
99 (17)
36.5 (7.1)
168 (15)
57 kg (8)
35.2 (5.6)
197 (19)
35.5 (5.8)
114 (19)
36.9 (6.2)
189 (8)
63 kg (5)
35.3 (6.6)
220 (15)
34.8 (5.2)
154 (19)
32.7 (5.2)
211 (21)
72 kg (8)
34.5 (9.1)
233 (18)
35.7 (8.4)
160 (16)
33.7 (8.3)
222 (16)
84 kg (5)
38.3 (7.8)
230 (31)
40.2 (6.3)
168 (20)
35.0 (8.5)
202 (22)
>84 kg (5)
37.9 (7.3)
263 (38)
40.6 (5.8)
177 (21)
37.7 (7.3)
223 (20)
Data shown are group means ± SD
vs. non-medalists). Furthermore, they
analyzed the rates at which lifters improved their performance prior to their
peak, and whether moving up or down
weight classes improved athletes’ placing at competitions. Since weight classes changed a few times in both sports
during this time span, the authors treated similar weight classes as if they were
the same weight class for athletes who
competed before and after each weight
class realignment (i.e. if you competed
in the 75kg class before the IPF’s new
weight classes, and the 74kg class after
the new weight classes, you were considered to be in the same class).
Findings
Peak age was 35 ± 7 for powerlifters
and 26 ± 3 for weightlifters. That 9-year
gap was a statistically significant difference. Within each sport, peak age for all
three powerlifts was similar, as was the
peak age for both the snatch and clean
and jerk for weightlifters. The study
89
Table 2
Predicted peak age (in years) and weight lifted (in kilograms) for weightlifting events
Snatch
Event (n)
Age
Clean and jerk
Weight
Age
Weight
Men
56 kg (26)
25.9 (3.6)
122 (9)
26.2 (3.4)
151 (10)
62 kg (21)
27.2 (4.1)
133 (9)
27.6 (5.1)
164 (10)
69 kg (17)
26.4 (2.6)
146 (9)
26.1 (3.1)
177 (9)
77 kg (19)
26.2 (3.1)
159 (7)
25.8 (2.7)
193 (9)
85 kg (19)
26.5 (3.2)
166 (10)
24.7 (2.6)
202 (9)
94 kg (17)
25.1 (2.7)
173 (6)
24.9 (2.7)
212 (8)
105 kg (21)
25.7 (2.5)
185 (8)
24.7 (2.2)
220 (9)
>105 kg (25)
28.0 (3.0)
193 (15)
28.4 (3.5)
232 (15)
Women
48 kg (19)
24.7 (3.4)
82 (6)
24.8 (3.1)
103 (9)
53 kg (13)
25.8 (3.0)
88 (67)
24.3 (2.7)
110 (8)
58 kg (13)
25.6 (3.3)
96 (5)
26.0 (4.2)
123 (8)
63 kg (10)
27.2 (2.8)
103 (8)
26.5 (3.1)
128 (10)
69 kg (12)
24.3 (4.7)
108 (11)
24.4 (4.7)
136 (13)
75 kg (12)
26.7 (3.1)
111 (11)
27.1 (3.4)
136 (14)
>75 kg (21)
25.1 (3.8)
122 (13)
25.7 (3.8)
154 (16)
Data shown are group means ± SD
provides a couple of tables of peak age
for all lifts, all weight classes, and both
sexes, which you can see below in Tables 1 and 2. Personally, I think it’s more
granular than is useful, but it’s here if
you’re interested.
In weightlifting, peak age was slightly
younger for medalists than non-medalists, though the difference wasn’t significant for powerlifting. Peak age was
slightly older for males in weightlift-
ing and for females in powerlifting. All
of those differences were pretty small
(<1.5 years) and likely not meaningful;
when you have almost 4500 subjects,
just about everything will be statistically
significant.
In the five years preceding peak performance, competitors in both sports
improved by ~10%, though there was a
huge range. The upper and lower bounds
aren’t given, but the standard deviations
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Figure 2
Improvements in world-class strength athletes prior to peak performance
Men
Women
Improvement (%)
30
20
***
*
10
0
Weightlifting
Powerlifting
Likelihood of clear substantial sex differences:
* = possibly
*** = very likely
were ~7-10%, meaning that anything
between an incredibly small increase
and a 20% increase in performance in
the five years before peak performance
wouldn’t be outside of the norm. Interestingly, in both sports, female lifters
improved more than male lifters in the
five years preceding peak performance
(2.7 ± 3.8% more in powerlifting, and
3.3 ± 1.6% more in weightlifting). Powerlifters made a bit more progress than
weightlifters in the 5 years prior to peak
performance (12 ± 10% vs. 9 ± 7%), and
within powerlifting, rates of improvement were greater for the squat and
bench press than the deadlift.
Within both sexes, changing weight
classes was generally beneficial. Males
who moved down a weight class improved their placing by three spots, on
average, while males who moved up a
weight class and females who moved
either up or down improved their placing by one spot, on average. However,
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Figure 3
Relative strength gains by age
Monthly gains in allometrically scaled strength
8.00%
7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
7.5
20
32.5
45
57.5
70
-1.00%
-2.00%
10th percentile
25th percentile
50th percentile
75th percentile
90th percentile
Percentiles of strength improvements or decrements as a function of age in all raw, drug-tested lifters in the Open Powerlifting database who competed at least twice.
changing a weight class was relatively
uncommon. Only 733 of the 4385 athletes went up or down a weight class
(16.7%). Of the people who changed
weight classes, moving up a class was
more common than moving down a class
(534 vs. 199; almost 73% of the lifters
who changed weight classes went up a
weight class, while only ~27% moved
down a weight class).
Interpretation
This was a neat article that confirms
what strength sport fans have known for
a long time, or at least strongly suspected: Weightlifting is a young person’s
game, while powerlifters can improve
much deeper into their careers than athletes in most other sports.
On the surface, powerlifting and
weightlifting look like pretty similar
sports, but the make-or-break factors in
elite competition separate them. Namely, in powerlifting, you’re not penalized
(much) by being slow. In weightlifting,
on the other hand, speed is the name of
the game. If you can still produce just
as much force, but you can’t generate
as high of velocities at the end of your
second pull, or you can’t drop under the
bar as fast to receive a clean or snatch,
or you can’t generate as much power in
the blink of an eye on your jerk drive,
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Figure 4
Summary of findings
Effect size range (Cohen’s D)
1
0.5
0
-0.5
Direct
Hypertrophy
Indirect
Hypertrophy
Older strength
gains
Strength gains
20+ weeks
Lower body
strength gains
Overall
strength gains
Young
strength gains
Upper body
strength gains
Positive values mean larger relative gains for women, while negative values mean larger relative gains for men
Diamonds = Effect size 95% CI; Black = not significant; Red = significant difference
The white region represents trivial effects, light green is small effects, darker green is medium effects and darkest green is large effects
your performance suffers. In powerlifting, on the other hand, generating higher
velocities right out of the hole on a squat
could theoretically give you a little more
leeway when grinding out a max, but the
overall impact is MUCH smaller; there
have always been successful powerlifters who don’t move the bar particularly
fast. That distinction helps explain why
weightlifters peak at a younger age than
powerlifters: The ability to generate velocity and power drop off faster due to
age than the ability to generate force (2,
3). There are a couple of reasons for this:
the elastin content of tendons decreases
(which decreases the efficiency of the
stretch shortening cycle), and neural impulses travel at a slightly slower speed
(which decreases active rate of force development; 4). So, while trained lifters
can still add muscle mass and increase
their force output into their 30s or even
40s, power output and velocity are much
more subject to the aging process, and
continued training can only slow down
their decline.
However, I don’t know that the differences between the sports can be chalked
up solely to physiology. Weightlifting
is also a much more competitive sport
worldwide. Most powerlifters don’t
start training for powerlifting until high
school at the earliest, whereas internationally competitive weightlifting programs begin developing weightlifters
from a very young age. Thus, the training age of a 26-year-old internationally
competitive weightlifter is, on average,
much higher than a 26-year-old internationally competitive powerlifter. The
present study (1) had no way of accounting for training age, but I strongly
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suspect that it’s an important factor. If
powerlifters started training for powerlifting at the same age that weightlifters
start training for weightlifting, it’s possible that we’d discover that the physiological peak age for powerlifting is, say,
30 years old rather than 35. Maybe the
average 30-year-old powerlifter simply
isn’t at an advanced enough training age
to be at peak performance, and so they
wind up peaking in the sport a few years
after they “should” have peaked physiologically.
One final factor to consider is that
this study examined world-class single
ply powerlifters. Since the resurgence
of raw lifting is a relatively recent phenomenon, the authors had to focus in
on single ply competition in order to
have enough years of data to analyze.
During the time period the authors analyzed, it’s my understanding that single
ply, IPF-approved powerlifting gear has
improved considerably. Thus, it’s possible that a fair amount of the lifters in
the sample put up their best totals a few
years after their actual strength peaked,
as improved equipment allowed them to
keep improving their competition numbers.
When considering the effects of lifting gear and training age, I think we
can firmly conclude that weightlifters
do likely reach their peak in the sport
younger than powerlifters do, but I think
the average of 35 years old for powerlifters is a little higher than it “should” be.
In other words, that may have been the
average age at which the lifters in their
sample actually attained their best performance, but I’m not fully convinced
that it’s actually the “optimal” age for
powerlifters.
I want to make one thing clear, however: Just because internationally competitive weightlifters peak at 26 and internationally competitive powerlifters
peak at 35, on average, you shouldn’t
assume that’s when you’ll peak. I carried out an analysis of powerlifting meet
results last year (on all raw competitors,
not just internationally competitive lifters) and found that people of all ages
are still making gains, on average. Now,
there’s clearly some self-selection bias
– lifters who started competing in their
20s don’t keep getting stronger into
their 70s, after all. It’s more likely that
people simply stop competing (or competing as often) once their strength starts
waning. However, of the people who do
compete in powerlifting, we can see that
the median rate of progress is positive
well into the Masters divisions (40 years
old and above). One common fear I hear
from middle-aged folks who start lifting is that the progress they’ll be able to
make may be very modest on account of
their age. The data don’t really bear that
out. While it’s true that people in their
teens and early 20s make progress at a
faster rate than Masters and Submasters
athletes, median rates of progress only
drop by about 50% between 30 and 60
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APPLICATION AND TAKEAWAYS
For both powerlifting and weightlifting, getting into the sport at an early age can help
ensure that your competitive peak can coincide with your physiological peak. However,
if you don’t have a time machine, you can still improve powerlifting performance (and,
more generally, the ability to build muscle and increase your capacity to generate
force) well into your 30s and beyond. Time catches up with everyone eventually, but
the impact of aging on force generation starts later and proceeds more gradually than
the impact of aging on velocity and power generation.
years old (Figure 3). So, if you’re just
getting into lifting in your 40s or 50s,
your rate of progress may not be quite as
fast as someone in their late 20s or early
30s, but on the flip side, new lifters in
their late 20s or early 30s can generally
make a LOT of progress pretty quickly. You’ll probably also be able to make
very substantial progress, just at a somewhat slower rate.
An interesting finding of the present
study (1) is that athletes tended to improve their placing by changing weight
classes. However, I’d caution you about
getting too trigger happy on jumping
to a new weight class, assuming it will
improve your competitiveness. Since
this was a study on internationally competitive lifters, most of the lifters shifting weight classes probably did so because they knew they could move into
a less competitive weight class. In other
words, we don’t know that they actually
became more competitive in a vacuum
(i.e. their Wilks or Sinclair scores improved). Rather, it’s likely they moved
into a new weight class because they
knew that the weight class above or
below their own was likely to be less
competitive in the upcoming year due to
injuries, retirements, etc. I do think it’s
worth noting that the majority of athletes
that changed weight classes tended to
move up a weight class, though. In general, you should be the most competitive
in the heaviest weight class you can fill
out while maintaining pretty good body
composition. The fact that way more of
the internationally competitive lifters
in this sample moved up a weight class
rather than down a weight class bears
that out.
There’s one more observation I’d like
to bring your attention to: The female
lifters in this sample made more progress (on a percentage basis) than the
male lifters in the five years prior to
peak performance. That matches trends
I’ve noted in both the published literature (mostly in studies on untrained lifters) and in powerlifters more generally
(not just world-class lifters): On a relative basis, female lifters seem to progress faster than male lifters. I’ll be hon-
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est: I’m not sure why that’s the case. In
untrained lifters, differences in starting
points could certainly play a role (i.e.
“untrained” female lifters may be more
untrained, relatively speaking, than “untrained” male lifters, perhaps due to differences in sport participation or jobs
involving manual labor). However, I’d
assume that any of those baseline differences would be washed out by the time
people felt sufficiently trained to step on
a powerlifting platform, and certainly
before they became world-class lifters.
I’m really not sure why relative rates
of strength progress would be higher in
females than males, but it’s an observation that’s shown up several times now,
in lifters ranging from untrained to internationally competitive. I hope we see
some future research on physiological
factors underpinning this observation.
Next Steps
In general, I’d just like to see a repeat of this study in 10 years with raw
lifters used as the powerlifting sample.
The sport is growing so much, and more
youth lifters are getting into powerlifting, so I’ll be interested to see if peak
age stays in the mid-30s, or if it trends a
bit younger over time.
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References
1. Solberg PA, Hopkins WG, Paulsen G, Haugen TA. Peak Age and Performance Progression
in World-Class Weightlifting and Powerlifting Athletes. Int J Sports Physiol Perform. 2019
Oct 7:1-7.
2. Reid KF, Fielding RA. Skeletal muscle power: a critical determinant of physical functioning
in older adults. Exerc Sport Sci Rev. 2012 Jan;40(1):4-12.
3. It’s almost necessarily true that power will decrease faster than force output. Since power
is force multiplied by velocity, if force output goes down 10%, then power must also
decrease by at least 10%. If any other changes occur that impact velocity, power necessarily
decreases at a faster rate than force does.
4. In sedentary folks, type II fibers also atrophy due to aging at a faster rate than type I fibers,
but that shouldn’t be a major factor in elite strength athletes
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VIDEO: Powerlifting Game Day
BY MIC HAE L C . ZO URD O S
MASS has many articles and videos on programming for powerlifting,
but what can you expect at the actual competition? This video breaks
down everything you need to prepare, and what you need to know about
powerlifting game day as a coach and lifter.
Click to watch Michael's presentation.
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References
1. Pritchard, HJ and Morton, RH. Powerlifting: Success and Failure at the 2012 Oceania and
2013 Classic World Championships. J Aust Strength Cond 23: 67–70, 2015.
2. Gary, M. A Powerlifter’s Guide to Attempt Selection. Maryland Powerlifting, 2009.
3. USA Powerlifting. Lifter’s Handbook. 2016.
4. International Powerlifting Federation. Technical Rules Book. 2019.
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VIDEO: New Perspectives on
Activity and Bodyweight
BY E RI C HE LMS
We are still uncovering the mechanisms of how humans regulate body weight. Typically,
this is viewed from the perspective of our body attempting to maintain a certain level
of adiposity or mass during an energy deficit or surplus. However, there are likely
regulatory mechanisms related to total daily energy expenditure that influence body
weight as well.
Click to watch Eric's presentation.
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References
1. Melby CL, Paris HL, Sayer RD, Bell C, Hill JO. Increasing Energy Flux to Maintain Diet-Induced Weight Loss. Nutrients. 2019 Oct 21;11(10).
2. Speakman JR. The evolution of body fatness: trading off disease and predation risk. The
Journal of experimental biology. 2018 Mar 7;221(Pt Suppl 1).
3. Beaulieu K, Hopkins M, Blundell J, Finlayson G. Homeostatic and non-homeostatic appetite
control along the spectrum of physical activity levels: An updated perspective. Physiology &
behavior. 2018 Aug 1;192:23-9.
4. Pontzer H. Energy Constraint as a Novel Mechanism Linking Exercise and Health. Physiology (Bethesda, Md.). 2018 Nov 1;33(6):384.
5. Mountjoy M, Sundgot-Borgen JK, Burke LM, Ackerman KE, Blauwet C, Constantini N,
Lebrun C, Lundy B, Melin AK, Meyer NL, Sherman RT. IOC consensus statement on relative energy deficiency in sport (RED-S): 2018 update. British journal of sports medicine.
2018 Jun;52(11):687.
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Just Missed the Cut
Every month, we consider hundreds of new papers, and they can’t all be included in MASS.
Therefore, we’re happy to share a few pieces of research that just missed the cut. It’s
our hope that with the knowledge gained from reading MASS, along with our interpreting
research guide, you’ll be able to tackle these on your own.
1. Schwarz et al. A Comparison of Machine versus Free-Weight Squats for the Enhancement of Lower-Body Power, Speed, and Change-of-Direction Ability during an Initial
Training Phase of Recreationally-Active Women
2. Bennett et al. A randomised controlled trial of movement quality-focused exercise
versus traditional resistance exercise for improving movement quality and physical
performance in trained adults
3. Wilke et al. Acute Effects of Foam Rolling on Range of Motion in Healthy Adults: A
Systematic Review with Multilevel Meta-analysis
4. Huebner eet al. Age-associated Performance Decline and Sex Differences in Olympic
Weightlifting
5. Delgado et al. Comparison Between Back Squat, Romanian Deadlift, and Barbell Hip
Thrust for Leg and Hip Muscle Activities During Hip Extension
6. Schoenfeld et al. Does Training to Failure Maximize Muscle Hypertrophy?
7. Mielgo-Ayuso et al. Effect of Caffeine Supplementation on Sports Performance Based
on Differences Between Sexes: A Systematic Review
8. Barrón-Cabrera et al. Epigenetic Modifications as Outcomes of Exercise Interventions
Related to Specific Metabolic Alterations: A Systematic Review.
9. Baptista et al. Exercise Dependence: An Updated Systematic Review
10. Armour et al. Exercise for dysmenorrhoea.
11. Kristiansen et al. Inter- and intra-individual variability in the kinematics of the back
squat
12. Kasovic et al. Kinematic Differences Between the Front and Back Squat and Conventional and Sumo Deadlift
13. Moro et al. Low skeletal muscle capillarization limits muscle adaptation to resistance
exercise training in older adults
14. Hyatt et al. Muscle-Specific Sensitivity to Voluntary Physical Activity and Detraining
15. Tung et al. Physiological and Biochemical Effects of Intrinsically High and Low Exercise Capacities Through Multiomics Approaches
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16. Barreto et al. Protective Effect Conferred by Isometric Preconditioning Against Slowand Fast-Velocity Eccentric Exercise-Induced Muscle Damage
17. Martínez-Cava et al. Range of Motion and Sticking Region Effects on the Bench Press
Load-Velocity Relationship
18. Tungate. The Bench Press: A Comparison Between Flat-Back and Arched-Back Techniques
19. García-Ramos et al. The load-velocity profiles of three upper-body pushing exercises
in men and women
20. Pareja-Blanco et al. Time course of recovery from resistance exercise before and after
a training program
21. Chapman et al. Using Perceptual and Neuromuscular Responses to Estimate Mechanical Changes During Continuous Sets in the Bench Press
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Thanks for
reading MASS.
The next issue will be released to
subscribers on January 1, 2020.
Graphics by Kat Whitfield, and layout design by Lyndsey Nuckols.
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