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Abstract

Despite the lack of standardized terminology, building muscle and losing fat concomitantly has been referred to as body recomposition by practitioners. Although many suggest that this only occurs in untrained/novice and overweight/obese populations, there is a substantial amount of literature demonstrating this body recomposition phenomenon in resistance-trained individuals. Moreover, 2 key factors influencing these adaptations are progressive resistance training coupled with evidence-based nutritional strategies. This review examines some of the current literature demonstrating body recomposition in various trained populations, the aforementioned key factors, nontraining/nutrition variables (i.e., sleep, hormones), and potential limitations due to body composition assessments. In addition, this review points out the areas where more research is warranted.
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Body Recomposition: Can
Trained Individuals Build
Muscle and Lose Fat at
the Same Time?
Christopher Barakat, MS, ATC, CISSN,
1
Jeremy Pearson, MS,
1
Guillermo Escalante, DSc, MBA, ATC, CSCS, CISSN,
2
Bill Campbell, PhD, CSCS, FISSN,
3
and Eduardo O. De
Souza, PhD
1
1
Department of Health Sciences and Human Performance, The University of Tampa, Tampa, Florida;
2
Department of
Kinesiology, California State University, San Bernardino, California; and
3
Performance & Physique Enhancement
Laboratory, University of South Florida, Tampa, Florida
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ABSTRACT
Despite the lack of standardized ter-
minology, building muscle and losing
fat concomitantly has been referred to
as body recomposition by practi-
tioners. Although many suggest that
this only occurs in untrained/novice
and overweight/obese populations,
there is a substantial amount of litera-
ture demonstrating this body recom-
position phenomenon in resistance-
trained individuals. Moreover, 2 key
factors influencing these adaptations
are progressive resistance training
coupled with evidence-based nutri-
tional strategies. This review examines
some of the current literature demon-
strating body recomposition in various
trained populations, the aforemen-
tioned key factors, nontraining/nutri-
tion variables (i.e., sleep, hormones),
and potential limitations due to body
composition assessments. In addition,
this review points out the areas where
more research is warranted.
INTRODUCTION
Acommon goal among active
individuals is to improve their
body composition by increas-
ing skeletal muscle mass and decreas-
ing fat mass (FM). It is well understood
that these positive body composition
changes have a multitude of health
benefits (2,45,66) and have also been
shown to improve athletic perfor-
mance (12,60). Among physique com-
petitors (e.g., individuals who compete
in bodybuilding, figure, bikini, etc.),
increasing muscle and losing body fat
is also of critical importance to be suc-
cessful in their sport. Despite the lack
of standardized terminology, practi-
tioners have described this adaptive
phenomenon in which muscle mass
is gained and FM is lost concomitantly
as body recomposition.
It is generally thought that body
recomposition occurs mainly in both
the untrained/novice and over-
weight/obese populations. When
examining the literature, this dogma
seems logical because training age
and also the novelty of initiating a
resistance training (RT) program have
been shown to directly impact the rate
of muscle mass accrual (30,49,67).
Within RT programs, practitioners
can manipulate training variables
(e.g., intensity, volume, exercise selec-
tion, etc.) as a means to enhance the
muscle hypertrophic stimulus. More-
over, aerobic exercise is commonly im-
plemented within training regimens to
decrease FM (5). In addition, research
has shown that the combination of
both RT and aerobic exercise (i.e., con-
current training) can be an effective
approach to optimize body recompo-
sition (5,57). Thus, practitioners,
coaches, and trainers commonly rec-
ommend concurrent training for indi-
viduals aiming to gain muscle and lose
fat (24). Most importantly, despite the
zeitgeist that well-trained individuals
cannot gain muscle mass and lose fat
simultaneously, there have been many
chronic randomized controlled trials
Address correspondence to Dr. Eduardo O.
De Souza, edesouza@ut.edu.
KEY WORDS:
fat loss; fat-free mass; muscle
hypertrophy; aesthetics; bodybuilding;
body composition; physique
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conducted in resistance-trained indi-
viduals that have demonstrated body
recomposition (3,13,16,21,36,52,62,72).
In addition, dietary intake (i.e., energy
balance, macronutrients, etc.) has been
shown to influence body composition
alone (6,11,23,25,28,31,54). Moreover,
when combined with RT, body recom-
position is potentiated to a greater
degree (3,13,21). For example, protein
intake is commonly manipulated
among individuals seeking to maximize
RToutcomes. There is evidence exhib-
iting recomposition effects when indi-
viduals are engaged in RT and are
consuming a high dietary protein
intake (i.e., .2.0 g/kg/d) (3,13,21).
Interestingly, there are also data dem-
onstrating that reductions in FM can
occur in well-trained subjects with hy-
percaloric intakes, specifically when
the surplus is due to an increase in pro-
tein (13,22). Collectively, those studies
suggest that evidence-based nutritional
strategies can further enhance body
recomposition in trained individuals.
Physique competitors carefully manipu-
late both their training and nutritional
programs to maximize muscle mass and
decrease FM to present their most aes-
thetic physique. During the “off-season,”
they strive to accumulate as much muscle
mass as possible, while minimizing FM
gained in a hypercaloric state (24). How-
ever, most case studies on physique com-
petitors through contest preparation do
not demonstrate a recomposition effect
(33,48,56). During this phase, competitors
restrict their caloric intake, and increase
energy expenditure to attain extremely
low levels of body fat. This hypoenergetic
state has been shown to negatively impact
many variables that can affect body
recomposition such as sleep, hormones,
and metabolism (35,46,69,71). Therefore,
these intense physical demands signifi-
cantly stress the body and make recom-
position difficult for this population.
When analyzing the current literature
demonstrating body recomposition in
trained individuals, it is important to
consider contextual differences between
studies (i.e., body composition assess-
ments, training design, duration,
nutritional control, etc.) and their
potential to impact the outcomes
(Tables 1–4). Therefore, the purpose
of this review is to discuss the existing
literature that has reported body recom-
position among resistance-trained indi-
viduals. Second, we will address the
contrasting results reported in the liter-
ature among a majority of competitive
physique athletes during contest
preparation.
ESTIMATING BODY COMPOSITION
WITH DIFFERENT ASSESSMENTS
To draw conclusions from each study’s
results, it is important to understand the
methods for assessing body composition,
their strengths, weaknesses, and reliabil-
ity. These assessments rely on different
assumptions and vary based on how
many compartments it divides an indi-
vidual’s total mass (i.e., 4C, 3C, 2C). Typ-
ically, body composition is divided into
bone content, lean mass (i.e., muscle,
connective tissue, internal organs, etc.),
and FM. In addition, it is important to
consider that there can be a significant
variability/error rate depending on the
mode of body composition assessment
(8,22,68). Furthermore, external factors
(for example, hydration status [intracel-
lular versus extracellular], nutritional sta-
tus [fasted versus fed], etc.) can influence
the accuracy of how these assessments
quantify fat-free mass (FFM) and FM. It
is also important to note that precisely
quantifying gains in skeletal muscle tissue
can be difficult due to its composition
(i.e., ;75% water, ;15–25% protein,
;2–3% glycogen, and ;5% intramus-
cular triglycerides) (19,22,34). Therefore,
the goal of this section is to provide a
simple overview of the body composi-
tion methods used in the studies, pre-
sented in Tables 2–4.
The 4-compartment model (4C) that
has been considered the gold-
standard assessment divides the body
into FM, water, bone mineral content,
and residual content (63,68). More-
over, the 4C model also allows for esti-
mation of protein content (74).
However, it is costly and very time-
consuming because 4C uses a variety
of laboratorial assessments. Two of
these laboratory assessments include
magnetic-resonance imaging (MRI)
and computed tomography. Recently,
the combination of different tools (i.e.,
DEXA + BIA) has been used to quan-
tify total body volume into 4C.
Dual-energy x-ray absorptiometry
(DEXA) is a 3-compartment model
commonly used to monitor and assess
changes in body composition. It can
distinguish between bone mineral con-
tent, FFM, and FM. Furthermore, it
can compartmentalize different
regions of the body (i.e., trunk, leg,
arm), but is unable to discern between
specific muscle groups (e.g., quadri-
ceps/hamstrings, biceps/triceps, etc.)
(22). A recent validation study demon-
strated a lower error rate using DEXA
for measuring intraindividual, concom-
itant changes in FFM and FM (68). In
addition, newer models have been
shown to have strong test-retest intra-
class correlation coefficients while esti-
mating FFM during whole-body scans
(e.g., .0.99) (32). The standard error
rate when comparing the criterion
MRI to DEXA for estimating body
fat percentage is ;1.6%. It is important
to note that some of the recomposition
results demonstrated in the training/
nutrition literature may be within the
standard error rate. Thus, these results
must be taken with caution because the
magnitude of change in these studies
may be due to inherent variation from
the measurement method.
Body composition can also be separated
into 2 compartments (e.g., FFM and
FM) using BodPod, skinfold calipers,
bioelectrical impedance, underwater
weighing, and ultrasound techniques.
Air-displacement plethysmography
(BodPod) is an apparatus that estimates
body composition based on the inverse
relationship between volume and pres-
sure. It measures the amount of air dis-
placed by an individual’s body
considering thoracic gas volume. A few
studies have shown slight discrepancies
in accurately determining body fat per-
centages (range 51.8–3.6 %BF) when
examining air-displacement plethysmog-
raphy (39,43). However, BodPod does
seem to have a strong test-retest reliability
(e.g., .0.99) (70). In addition, a recent
Body Recomposition
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study comparing DEXA to BodPod in
collegiate hockey players demonstrated
that BodPod significantly overestimated
FFM (2.93 62.06 kg) and underesti-
mated FM (3.27 61.92 kg) (18). Regard-
ing the training/nutrition studies using
BodPod at both baseline and posttesting,
theabsolutevaluesshouldbetakenwith
caution. However, given the relatively
high test-retest reliability for BodPod,
more confidence can be given regarding
the reported delta changes in FM
and FFM.
Finally, another assessment to examine
changes in body composition is A-
mode ultrasonography. Specifically, this
technique can measure muscle thick-
ness and subcutaneous fat. Recently,
this method has also been used to cal-
culate total body FFM, FM, and body
fat percentage in conjunction with the
7-site Jackson-Pollock formula (9). This
assessment has been reported to be
similar to DEXA for estimates of body
composition (9,51). Importantly, train-
ing/nutrition studies using A-mode
ultrasonography need to consider intra-
individual variability when performing
body composition assessments.
Due to the potential limitations for each
assessment, practitioners need to be
aware that minor changes in body com-
position demonstrated with these tools
may be due to inherent variability and/
or covariates that were not quantified
(e.g., hydration and nutritional status).
With that said, when these methods are
appropriately used and strictly standard-
ized, there is a stronger likelihood that the
results observed are accurate and reliable.
TRAINING STATUS
It is well accepted that training status
significantly impacts the rate of pro-
gress in body composition. Novice
trainees tend to experience greater
muscular adaptations compared to
advanced lifters. For example, Cribb
et al. (16) reported significant gains in
FFM (+5 kg) and reductions in FM
(21.4 kg) in a group of recreationally
trained individuals over 10 weeks. How-
ever, Antonio et al. (4) reported that
highly trained subjects gained 1.9 kg
of FFM and did not demonstrate signif-
icant reductions in FM over an 8-week
period. Many high-level athletes often
take time away from their training reg-
imen (i.e., off-season) or have a period
with substantially less work performed
(i.e., detraining). This detraining period
will likely lead to a temporary reduction
in training status, performance, and
body composition profile. However,
once training resumes, these individuals
typically regain their body composition
adaptations rapidly (47). For example,
Zemski et al. (76) reported significant
gains in FFM (+1.8 kg) and reductions
Table 1
Summary of advantages and disadvantages using various techniques for measuring body composition
Method Advantages Disadvantages
4C It is considered the gold standard
One of the best methods for estimating body
composition
It enables the calculation of FFM hydration
It measures total body water
It is time-consuming
It is not accessible for most practitioners and
coaches
DEXA 3-compartment model: Able to measure FFM,
FM, and bone mineral content
Able to measure a region of interest
specifically in extremities
Nutritional/hydration status can significantly
alter body composition and decrease
accuracy
Limited use for measuring baseline FM or
longitudinal changes in FM with weight loss
because measurements are known to be
biased by body size (thickness)
BodPod Could provide longitudinal data on both FFM
and FM mass because its accuracy is less
likely to be affected by changes in fatness.
Cannot provide regional data and can only
distinguish between FM and FFM (i.e., bone,
muscle, connective tissue).
It assumes fixed densities of FM and FFM
Hydrostatic weighing Valid estimate for body density Expensive, not easily accessible
Uncomfortable
It assumes fixed densities of FM and FFM
A-mode ultrasound Capable of regional and segmental
measurements
Valid in the hands of an experienced
technician
Portability
Depending on technician, there can be a high
possibility of intrarater reliability
Most of the equations/formulas
were based on caliper validation
4C 5four compartments; DEXA 5dual-energy x-ray absorptiometry; FFM 5fat-free mass; FM 5fat mass.
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Table 2
Summary of study designs that have demonstrated recomposition with resistance training without nutrition reported in trained individuals
Study Training status/demographic Study design Training intervention BC assessment Nutrition Conclusions
Alcaraz
et al. (1)
Resistance-trained men with
at least 1 y of RT experience
and can produce a force
equal to twice their body
mass during an isometric
squat
Counterbalanced repeated-
measures design.
Participants were randomly
assigned to high-resistance
circuit (HRC) training
or
Traditional strength training
(TST ).
8-wk intervention
Both groups performed 6RM
sets to failure for 6 total
compound and isolation
exercises 3 d/wk
DEXA NR Both groups increased FFM,
and lost a non-significant
amount of FM.
HRC
(FFM +1.5,
a
FM 21.1)
TST
(FFM + 1.2,
a
FM 20.8)
Colquhoun
et al. (15)
RT college males with $6mo
experience.
1RM squat: BM ratio-low
frequency 1.7 high
frequency 1.6
1RM bench: BM ratio-low
frequency 1.3 high
frequency 1.2
1RM deadlift: BM ratio-low
frequency 2.0 high
frequency 2.0
Counterbalanced, parallel-
groups repeated-measures
design. Participants were
randomly assigned to low
frequency (33/wk) or high
frequency (63/wk). 6-wk
intervention
Daily undulating
periodization program
designed to target the
powerlifts (squat, bench
press, and deadlift) while
equating intensity and
volume
A-mode
ultrasound
NR Both groups increased FFM,
and lost a non-significant
amount of FM.
Low frequency (FFM +1.7,
a
FM
20.3)
High frequency (FFM +2.6,
a
FM 20.1)
Wilborn
et al. (72)
NCAA Division III female
basketball players (at least 1
y RT experience)
Parallel-group repeated-
measures design.
Participants were randomly
assigned to whey (W ) or
casein (C). 8-wk training
period
Full-body undulating
periodized program 4 d/wk.
Sport-specific conditioning
3 d/wk
DEXA NR Both groups increased FFM
and lost FM.
W
(FFM +1.5
a
,FM21.3
a
)
C
(FFM +1.4
a
,FM20.6
a
)
Yue et al.
(75)
Recreationally trained men
with average 3 y RT
experience
1RM squat: BM ratio
LV-HF 1.3 60.3
HV-LF 1.1 60.1
1RM bench: BM ratio
LV-HF 1.0 60.2
HV-LF 0.9 60.2
Parallel-group repeated-
measures design.
Participants were randomly
assigned to low training
volume-high frequency (LV-
HF)
or
High training volume-low
frequency group
(HV-LF)
6-wk hypertrophy/strength
program
BodPod NR Both groups increased FFM,
and lost a non-significant
amount of FM.
LV-HF (FFM +1.2,
a
FM 20.6)
HV-LF (FFM +1.4,
a
FM 22.4)
a
Statistical significance.
BM 5body mass; DEXA 5dual-energy x-ray absorptiometry; RT 5resistance training; FFM 5fat-free mass; FM 5fat mass; HP 5high protein intake; HV-LF 5high volume-low frequency;
NP 5normal protein intake; NR 5not recorded; PRO 5protein intake; 1RM 5one repetition maximum.
Body Recomposition
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on behalf of the
Table 3
Summary of study designs that have demonstrated recomposition with resistance training and nutrition data provided in trained individuals
Study Training status/
demographic
Study design Training intervention BC
assessment
Nutrition Conclusions
Antonio
et al. (3)
Resistance-trained men
and women who had
been weight training
regularly
Avg
Normal pro: 2.4 61.7
High pro: 4.9 64.1
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to normal
protein (NP) or high
protein (HP) groups.
8-wk heavy resistance
training program
Hypertrophy-oriented
upper and lower split
routine program 5 d/
wk
BodPod NP group maintained the
same dietary habits (2.3
g PRO/kg/d)
HP group consumed (3.4 g
PRO/kg/d)
Total calories-NP: 2,119
HP: 2,614
Both groups increased FFM
and lost FM.
NP (FFM +1.5
a
,FM20.3
a
)
HP (FFM +1.5
a
,FM21.6
a
)
Antonio
et al. (4)
Resistance-trained men
and women who had
been weight training
regularly
(8.9 66.7 y and an
average of 8.5 63.3 h
per wk)
Counterbalanced-
group repeated-
measures design.
Participants were
randomly assigned
to NP or HP groups.
Subjects performed
training outside of
laboratory and
reported total
volume load at
baseline and
posttesting.
8-wk intervention
Participants exercised
outside of the
laboratory and were
asked to track their
total volume load
BodPod NP group maintained the
same dietary habits (1.8
g PRO/kg/d)
HP group consumed 4.4 g
PRO/kg/d.
Total Cal-
NP: 2,052
HP: 2,835
Both groups increased FFM
and reduced body fat
percentage to a non-
significant degree. The HP
group lost a non-significant
amount of FM and the NP
group gained a trivial
amount of FM.
NP (FFM +1.3, FM +0.3)
HP (FFM +1.9, FM 20.2)
Campbell
et al.
(13)
Aspiring female physique
athletes able to deadlift
1.53BM and $3moRT
1RM squat: BM ratio-High
protein group 1.1
Low protein group 1.2
1RM deadlift: BM ratio-
high protein group 1.4
Low protein group 1.6
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to HP or
low protein (LP).
8-wk intervention
Hypertrophy-oriented
upper and lower split
routine program.
4 d/wk
A-mode
ultrasound
LP group consumed (0.9 g
PRO/kg/d)
HP group consumed (2.5 g
PRO/kg/d)
Total Cal-
HP: 1,839
LP: 1,416
Both groups increased FFM,
however only the HP group
lost a significant amount of
FM.
HP (FFM +2.1
a
,FM21.1
a
)
LP (FFM +0.6
a
,FM20.8)
(continued)
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Table 3
(continued)
Cribb et al.
(16)
Recreational bodybuilders
with at least 2 y of RT
experience
1RM squat: BM ratio-
Both groups 0.9 60.1
1RM bench: BM ratio-whey
group 1.0 60.1
Casein group 1.1 60.1
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to whey
protein (W) or casein
protein (C) groups.
10-wk training period
Linear progressive
overload program.
Designed for
maximizing strength
and hypertrophy was
divided into 3
phases; preparatory
(70–75% of 1RM),
overload phase-1
(80–85% of 1RM),
and overload phase-
2 (90–95% of 1RM).
Upper and lower split
routine
DEXA Both groups on average
consumed 2.1 g PRO/
kg/d during the study.
Both groups increased FFM.
However, only the W group
lost FM.
W (FFM +5.0,
a
FM 21.4
a
)C
(FFM +0.8,
a
FM +0.1)
Haun et al.
(21)
Resistance-trained young
men with minimum
estimated 1.5 3BM
squat
3RM squat: BM ratio-1.6
3RM bench: BM ratio-1.2
Parallel-group
repeated-measures
design. Participants
were partitioned to
maltodextrin (M),
whey protein (WP),
or graded whey
protein (GWP)
groups. 6-wk training
period
Linear progressive
overload program.
Full-body 33/wk.
Sets would increase
each wk but
repetitions remained
at a goal of 10 per
exercise.
DEXA All groups aimed for a 500
calorie surplus and 1.6
g/PRO/kg/d during the
first wk of the study. The
groups on average
consumed 2.2 g/PRO/
kg/d throughout the
study.
All groups increased FFM but
only the W and GWP lost
FM.
M (FFM +2.3,
a
FM +0.2)
WP (FFM +1.7,
a
FM 20.7
a
)
GWP (FFM +2.9,
a
FM 21.0
a
)
Kreipke
et al.
(36)
Resistance-trained young
men ($1 y training in
the squat, bench, and
deadlift)
1RM squat: BM ratio-both
groups 1.6
1RM bench: BM ratio-
Placebo 1.2 Preworkout
1.3
1RM deadlift: BM ratio-
Placebo 2.0 Preworkout
2.1
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to placebo
(PL)
or
Preworkout
supplement (SUP)
4-wk training period
4 d/wk progressive,
strength-oriented
powerlifting
regimen. 5 35 and 3
310 of compound
exercises performed
to volitional fatigue
DEXA No differences in PRO or
caloric intake.
Avg PRO intake: 2.1 g/kg/d
Both groups increased FFM
but only the PL group lost a
significant amount of FM.
PL (FFM +1.1,
a
FM 20.7
a
)
SUP (FFM +1.3,
a
FM 20.2)
Body Recomposition
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Table 3
(continued)
Slater et al.
(62)
Elite male water polo and
rowers
Avg RT experience
PL: 7.1 61.7
HMB: 7.4 62.0
trHMB: 6.9 60.8
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to placebo
(PL), HMB, or time
released HMB
(trHMB) groups. 6-wk
training period
Full-body strength-
oriented program
composed of mainly
compound exercises
with 24–32 sets per
session
DEXA All groups on average
consumed 2.4 g PRO/
kg/d and increased
mean energy intake 224
kJ/kg/d during the study
All groups gained FFM.
However, reductions in FM
were non-significant.
PL (LBM +0.9,
a
FM 20.4)
HMB (LBM +1.2,
a
FM 21.0)
trHMB (LBM +3.5,
a
FM 22.5)
Rauch
et al.
(52)
NCAA Division II female
volleyball players
1RM squat: BM ratio
1.1
Parallel-group
repeated-measures
design. Participants
were randomly
assigned to optimal
training load (OTL)
or
Progressive velocity-
based training (PVBT)
7-wk (3 d/wk) power-
oriented full-body
program
DEXA No differences in PRO or
caloric intake.
Avg PRO intake: 1.6 g/kg/d
Both groups increased FFM
and lost FM.
OTL (FFM +2.7,
a
FM 22.7
a
)
PVBT (FFM +2.7,
a
FM 22.1
a
)
a
Statistical significance.
BM 5body mass; DEXA 5dual-energy x-ray absorptiometry; FFM 5fat-free mass; FM 5fat mass; HP 5high protein intake; NP 5normal protein intake; PRO 5protein intake; 1RM 5one
repetition maximum; OTL 5optimal training load.
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Table 4
Summary of case studies that investigate body composition changes in response to exercise, nutrition, and supplementation of competitive physique
athletes
Study Competitor demographic Resistance training Aerobic BC assessment Nutrition supplements Conclusions
Halliday
et al.
(20)
27-y-old drug-free
amateur female figure
competitor
20-wk prep + 20-wk
recovery
Prep: 4–5 d/wk
High-volume program
training each muscle
group 2–33/wk
Recovery: 3–4 d/wk high-
volume program
Prep: (10–30) min HIIT 1–2 d/
wk and (45–120 min) aerobic
exercise 1 d/wk
Recovery: (10–30 min) HIIT
1–2 d/wk and (45–60 min)
aerobic exercise 1 d/wk
DEXA $2.2 g/kg PRO daily
throughout prep and
recovery
Supplements used: whey
and casein protein and
5 g/d of creatine
monohydrate
Body fat decreased
from 15.1% (8.3
kg) at baseline to
8.6% (4.3 kg) one
wk out of
competition.
FFM was maintained
at 44.3 kg
throughout 20-wk
prep 20-wk
postcomp
showed BF%
returned to
baseline at 14.8%.
Kistler
et al.
(33)
26-y-old drug-free,
amateur male
bodybuilder with 10 y
RT experience
26-wk prep
5 d/wk
60–90 min sessions
Each muscle group trained
23/wk
Day 1: 3–8 reps
Day 2: 8–15 reps
Beginning contest prep, two
40-min sessions of high-
intensity interval training
(HIIT) per wk.
End of contest prep, four 60-
min sessions of HIIT and two
30-min sessions of low-
intensity steady-state (LISS)
per wk
DEXA 250 g PRO daily for all
prep
Supplements used: 30 g
BCAA, 3 g HMB, 2 g fish
oil, 5 g creatine mono,
6 g beta alanine,
multivitamin
FFM decreased 6.6
kg
FM decreased
10.4 kg
Pardue
et al.
(48)
21-y-old drug-free,
amateur male
bodybuilder with 8 y
RT experience
32-wk prep + 20-wk
recovery
5–6 d/wk
Each muscle group trained
23/wk
Variety of repetition ranges
(4–25 repetitions) and
intensities
No aerobic exercise was
performed at baseline, but
cardio was incrementally
increased until reaching a
weekly load of two 20-min
HIIT sessions and four 30-min
medium-intensity steady
state (MISS) sessions.
BodPod and
DEXA
At baseline, the
competitor consumed
3,860 cal (28% protein
[3.2 g/kg], 52%
carbohydrate [5.9 g/
kg], 20% fat [1.0 g/kg])
End of prep, the
competitor consumed
1,724 kilocalories (52%
protein [2.9 g/kg], 19%
carbohydrate [1.1 g/
kg], 29% fat [0.7 g/kg])
Supplements used: whey
protein, BCAA, creatine
monohydrate, beta-
alanine, and
preworkout
Prep: BF% decreased
from 13.4 to 9.6%
recovery: BF%
increased to
17.2%
Body Recomposition
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8
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Table 4
(continued)
Petrizzo
et al.
(50)
29-y-old drug-free
amateur female figure
competitor with 8-y RT
experience
32-wk prep
Phase 1: 4–5 d/wk for the
first 22 wk; phase 2: 6 d/wk
for the final 10 wk)
High-volume program
performing 33sets to
failure each exercise
Phase 1 (20–60): min HIIT 3 d/
wk
Phase 2 (30–40): min HIIT 4 d/
wk
DEXA .3.2 g/kg PRO daily
throughout prep
Supplements used:
BCAA, whey protein,
beta alanine, citrulline
malate, alpha-
hydroxyisocaproic
acid, creatine
monohydrate, vitamin
B-6
FFM increased 0.7 kg
FM decreased
8.0 kg
Rohrig
et al.
(55)
24-y-old drug-free female
competitor with 5-y RT
experience 24-wk prep
5 d/wk
Each muscle group trained
23/wk; one with
moderate intensity (60–
80% 1RM) and volume
and one with high
intensity (85% + 1RM) and
lower volume
Weekly adjustments of HIIT and
MISS based on discretion of
coach.
At end of prep 185 min of MISS
with HIIT 3d/wk
Hydrostatic
weighing
$2.0 g/kg PRO daily
throughout prep
Supplements used:
creatine monohydrate,
fish oil, and
multivitamin
BF% was reduced
from 30.45 to
15.85%
FFM increased 1.3 kg
FM decreased 11.4
kg
Rossow
et al.
(56)
27-y-old drug-free
professional male
bodybuilder with 2 y
pro status
24-wk prep + 24-wk
recovery
4 d/wk
Each muscle group trained
23/wk during the 48-wk
Prep: 1 d/wk of HIIT and 1 d/wk
of LISS
Recovery: 1 d/wk of HIIT
BodPod and
DEXA
Prep period macros:
;36% PRO, ;36%
carbohydrate (CHO),
and ;28% fat for 5 d/
wk and ;30% PRO,
;48%
CHO, and ;22% fat for 2
d/wk.
Recovery period macros:
;25–30% PRO, 35–
40% CHO, and 30% to
35% fat.
Supplements used: whey
protein and 5 g/d of
creatine monohydrate
Prep: (FFM 22.8 kg)
BF% decreased from
14.8 to 4.5%
Recovery: (FFM 20.2
kg)
BF% increased to
14.6%
BF% 5body fat percentage; CHO 5carbohydrate intake; RT 5resistance training; DEXA 5dual-energy x-ray absorptiometry; FFM 5fat-free mass; FM 5fat mass; HIIT 5high-intensity
interval training; LISS 5low-intensity steady state; MISS 5medium-intensity steady state; Prep 5weeks leading into a competition; PRO 5protein intake; Recovery 5weeks after a
competition; 1RM 5one repetition maximum.
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in FM (22.2 kg) in elite rugby players
after detraining for 4 weeks and then
returning for an 11-week high-volume,
high-intensity training program during
their preseason.
When exploring the literature on phy-
sique athletes, most of the data are
demonstrated in case studies examining
competitors during contest preparation
(For details, Table 4). Contrary to what
has been observed in the aforemen-
tioned trained populations, most phy-
sique athlete case studies do not
demonstrate a body recomposition
effect (33,48,56). This is likely due to
the extreme demands of this sport
(i.e., energy restriction, high energy
expenditure, severely low body fat, neg-
ative hormonal adaptations, poor sleep,
etc.), which will be discussed later in this
review. Interestingly, there is some con-
flicting evidence demonstrating body
recomposition in female physique com-
petitors during their contest preparation
phase (50,55). One potential explana-
tion for the differences between males
and females might be associated with
hormonal profile. For example, signifi-
cant reductions in testosterone levels
have been observed in males while in
a hypoenergetic state, dieting for com-
petition purposes (26,44,48,64). There-
fore, the data on physique athletes are
difficult to reconcile due to the unique
hypoenergetic demands of their sport in
season when compared to other trained
populations. Although training status/
age seems to impact the magnitude of
changes in FFM and FM, more
research is warranted to understand
how training status can impact body
recomposition over time in different
trained populations.
TRAINING PRACTICES IN STUDIES
WITH TRAINED INDIVIDUALS
DEMONSTRATING BODY
RECOMPOSITION
Several studies among trained individ-
uals have reported body recomposi-
tion where nutritional intake was not
reported or was similar between the
interventions (1,36,52,62,72,75). For
example, Alcaraz et al. (1) recruited
participants who were able to produce
a force equal to twice their body mass
during an isometric squat at the begin-
ning of the intervention. The subjects
performed 8 weeks of either a high-
resistance circuit (HRC) or a tradi-
tional strength training (TST) pro-
gram. Both groups performed 3–6
supervised sets of 6 exercises (3 com-
pound and 3 isolation) using a 6 repe-
tition maximum (RM) to failure. The
HRC group used a 35-second interset
recovery between exercises and per-
formed the exercises in circuit fashion,
whereas the TST group rested
3 minutes between each set of each
exercise before moving to the next
exercise. Only the HRC group signifi-
cantly decreased body fat percentage
by 21.5%, whereas the TST group
did not (21.1%). However, both
groups demonstrated a significant
increase in FFM of 1.5 and 1.2 kg,
respectively. In addition, in another
investigation, researchers examined
recreationally trained males with 3
years of RT experience. This six-week
study randomized subjects into either a
low volume-high frequency (LV-HF)
group where participants RT 4 days
per week or a high volume-low fre-
quency (HV-LF) group where partici-
pants RT 2 days per week (75). All
participants were instructed not to alter
their normal nutritional habits. How-
ever, the researchers did not report
nutritional intake between the groups.
Both groups performed the same
weekly volume, but the RT volume dif-
fered between the sessions. Regarding
body composition, both LV-HF and
HV-LF groups significantly gained
FFM (1.2 and 1.4 kg, respectively).
However, the reductions in FM only
reached statistical significance in the
HV-LF group (22.4 kg) compared to
LV- H F ( 20.6 kg). In another recent
study, Colquhoun et al. (15) investi-
gated the effects of training frequency
(33/week versus 63/week) using a
volume-matched design in well-
trained subjects undergoing a power-
lifting program. Both groups gained a
significant amount of FFM (33/week:
1.7 kg, 63/week: 2.6 kg) and although
they both lost FM (20.3 and 20.1 kg,
respectively), these reductions were
not statistically significant.
Collectively, these studies indicate that
body recomposition can occur in
trained individuals using a variety of
RT programs that are geared to
develop muscular strength and hyper-
trophy. In addition, adjusting nutri-
tional intake is common in
individuals attempting to maximize
RT gains in strength and hypertrophy
(54). In the next section, we will discuss
RT studies that either monitored, con-
trolled, or manipulated the subjects’
nutritional approach.
NUTRITIONAL INFLUENCE ON
BODY RECOMPOSITION WHEN
COUPLED WITH RESISTANCE
TRAINING
The combination of RT and specific
nutritional strategies can significantly
impact training performance (52),
recovery (7), and body composition
(14,28,61). Generally, caloric deficits
are prescribed for individuals seeking
to lose FM and caloric surpluses are
recommended for those seeking to
maximize muscle mass accrual
(23,54,61,73). Although this is com-
mon practice, there is evidence that
challenges this approach and suggests
there may be alternative strategies to
improve body composition
(3,4,40,42). For instance, there are data
showing significant gains in FFM and
reductions in FM while in a caloric
surplus (21). In addition, significant
body recomposition has been demon-
strated in hypocaloric studies (40,42).
Recently, Slater et al. (61) questioned
the necessity of a hypercaloric intake to
maximize skeletal muscle hypertrophy
in conjunction with RT. The mecha-
nisms that may explain the body
recomposition phenomena are not well
understood. For example, the precise
energy cost of skeletal muscle growth
is not fully known. In addition, we are
unsure how the magnitude of energy
supply, specifically endogenous sour-
ces (i.e., internal fat stores/body fat lev-
els) and exogenous fuel (i.e., diet),
pertain to this process (61). With that
said, body composition changes seem
to be more complex than energy bal-
ance alone because research has shown
that different nutritional strategies (i.e.,
Body Recomposition
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high-protein diets, hypocaloric diets,
etc.) may elicit body recomposition
(13,21,40).
In fact, RT studies have demonstrated
body recomposition in which nutrition
was controlled and/or manipulated.
More specifically, some of these stud-
ies increased the participant’s caloric
intake, primarily from dietary protein
(3,13,21). For example, Antonio et al.
(3) investigated the effects of a very
high-protein diet (HP 3.4 g/kg) com-
pared to a “normal protein” diet (NP
2.3 g/kg) on body composition in well-
trained men and women in conjunc-
tion with heavy RT. The participants
underwent an RT program (upper-
lower split) 5 days per week for 8
weeks and both groups gained a signif-
icant, yet equal amount of FFM (1.5
kg). Interestingly, the HP group, which
was consuming an additional ;495
calories per day, lost significantly more
FM than the NP group (21.6 versus
20.3 kg). The authors highlighted
the large interindividual variability,
which is important for practitioners
to be aware of. For instance, in both
groups, some subjects gained up to 7
kg of FFM while losing 4 kg of FM
concomitantly. However, some sub-
jects actually lost FFM and gained
FM. Their data suggest that ;70% of
subjects improve their overall body
composition when implementing
high-protein diets.
Body recomposition effects of a larger
magnitude that reached statistical sig-
nificance were observed by Haun et al.
(21) in their extreme-volume RT study
that investigated the effects of graded
whey protein (WP) supplementation in
well-trained males. Subjects underwent
full-body RT sessions 3 times per week,
and volume was progressed from 10
weekly sets per exercise to 32 sets over
the 6-week intervention. Participants
were randomized into 3 groups: malto-
dextrin group (MALTO) consuming 30
g per day, WP group receiving 25 g per
day, and graded WP (GWP) group
receiving an additional 25 g WP each
week during the 6-week study (25–150
g WP/day). Furthermore, all groups
were instructed by a registered
dietician to consume specific macronu-
trients guidelines equating to a ;500
calorie surplus. All groups demon-
strated a significant increase in FFM
from pre to post (MALTO: 2.35 kg,
WP: 1.22 kg, GWP: 2.93 kg). However,
only the WP and GWP groups dis-
played a significant reduction in FM
simultaneously (20.65 and 21.0 kg,
respectively). Although the WP and
GWP groups were using different post-
workout nutrition interventions, when
looking at their daily protein intake, no
significant differences were observed
(2.3 versus 2.2 g/kg, respectively).
Notably, although the MALTO group
was not receiving WP supplementa-
tion, their relative daily protein intake
(2.3 g/kg) was not different compared
to the WP and GWP groups. These
results may suggest potential benefits
of specific nutrient timing (i.e., post-
workout) versus total daily intakes in
highly trained individuals performing
extreme volume progressions. How-
ever, nutrient timing and its effects on
body recomposition in trained popula-
tions warrant further investigations.
Additional evidence from Campbell et al.
(13) reported similar positive effects on
body composition when aspiring female
physique athletes increased their total cal-
orie intake (;250 kcal) from dietary pro-
tein alone. These subjects were split into 2
groups, low protein (LP 0.9 g/kg) and
high protein (HP 2.5 g/kg), while under-
going an upper-lower RT split, 43/week.
The HP group demonstrated a significant
body recomposition effect, gaining 2.1 kg
FFM and losing 21.1 kg of FM despite
consuming an additional 423 kcals daily.
However, the LP group only gained a
statistically significant, yet relatively small
amount of FFM (0.6 kg) and did not
demonstrate significant reductions in
FM (20.8kg).Whenevaluatingtheindi-
vidual data from this study, all subjects in
the HP group gained FFM, whereas
some subjects (3 of 9) in the LP group
actually lost FFM. These data further sup-
port the importance of dietary protein
intake for those undergoing RT and trying
to improve body composition. However,
body recomposition effects of even larger
magnitudes have been reported with a
moderate protein intake and a more bal-
anced nutritional approach (52). The var-
iability between studies makes it difficult
for researchers, coaches, and practitioners
to make evidence-based suggestions as
we continue to investigate which
approach is the most advantageous for
trained individuals.
In another study, Rauch et al. (50) re-
ported significant body recomposition in
female collegiate volleyball players
undergoing 7 weeks of power-oriented,
full-body RT with similar relative lower-
body strength. All dietary information
was recorded and analyzed to quantify
total and relative (i.e., g/kg) calories and
macronutrient intake. Moreover, all par-
ticipants consumed 25 grams of WP
immediately after each exercise session,
consumed the same relative quantity of
protein per day (1.6 g/kg), and con-
sumed a similar caloric intake through-
out the study. Participants were assigned
to either an optimal training load (OTL)
where participants worked at velocities
that maximized power output, or a pro-
gressive velocity-based training (PVBT)
group, where participants worked at
slower velocities (geared at strength),
the first training block, and then pro-
gressed to OTL velocities, the last train-
ing block. The investigators reported
that the OTL group increased 2.7 kg
of lean body mass and lost 2.7 kg of
FM, whereas the PVBT group gained
2.7 kg of lean body mass and lost 2.1
kg of FM. These substantial recomposi-
tion findings may have been amplified
due to multiple factors. For example,
the starting body fat percentage in these
volleyball players was higher compared
to the leaner, aspiring female physique
competitors in the aforementioned study
(;29 versus ;22%). This may have
influenced the greater reductions in
FM and gains in lean body mass. In
addition, these athletes received nutri-
tional guidance from a sports nutrition-
ist/registered dietician. Finally, this
investigation was conducted during their
conditioning phase (i.e., off-season) after
a detraining period.
Taken together, these reports document
the process of body recomposition with
moderate to high dietary protein intakes
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coupled with progressive RT across a
wide spectrum of trained populations.
Moreover, having higher levels of body
fat may affect the magnitude of body
recomposition becausethesefatstores
may provide endogenous energy to sup-
port muscle mass accrual (61). However,
the impact of initial body fat levels, train-
ing status, RT programs, and nutritional
intake on body recomposition are not
yet fully elucidated and warrants further
investigation.
NONTRAINING/NUTRITION-
RELATED FACTORS THAT MAY
INFLUENCE BODY
RECOMPOSITION
Although it is not fully understood,
additional factors such as sleep (i.e.,
quality and quantity), stress hormones
(e.g., cortisol), androgenic hormones
(e.g., testosterone), and metabolic rate
may influence changes in body com-
position (38,46,53,71). Unfortunately,
many training and nutrition studies
do not take into account these impor-
tant covariates. However, when exam-
ining the body of literature that has
investigated the effects of these factors
on body composition, it is clear they
can impact how each individual is re-
sponding to the interventions.
For example, Wang et al. (71) examined
the effects of sleep restriction (;1hour
reduction, 53/week) on weight loss
outcomes in overweight adults in a hypo-
caloric environment. They demonstrated
that both groups in an equated caloric
deficit lost a similar amount of total body
weight (23.2 kg). However, when ana-
lyzing the percentage of FFM within total
mass lost, the sleep-restricted group lost
significantly more FFM than they did
FM (84.8 versus 16.9%), respectively.
However, their counterparts who were
not sleep-restricted better preserved
FFM and lost a significant amount of
FM (17.3 versus 80.7%) of the total mass
lost, respectively. It is important to note
that these subjects were not undergoing
RT. They also observed that the sleep-
restricted group had a significant increase
in ghrelin (71). Ghrelin is commonly
referred to as the “hunger hormone”
and has been shown to increase the likeli-
hood of weight regain (specifically fat)
and is one component (of many) why
some individuals fail to maintain their
weight loss (65,69).
Additional data investigating sleep dep-
rivation have demonstrated negative
effects on multiple athletic performance
variables and recovery capabilities
(17,41,53). For example, Reilly and Piercy
(53) observed significant reductions in
strength-endurance performance and
total volume load on compound exer-
cises such as the bench press, deadlift,
and leg press when subjects were in a
sleep-restricted state. Furthermore, they
reported that the subject’s rating of per-
ceived exertion was significantly greater
when performing the same RT task in a
sleep-deprived state. These negative
effects are important to note because
training volume is a critical variable for
muscle hypertrophy (59).
Sleep deprivation is also associated with
negative hormonal adaptations through
the hypothalamic-pituitary-adrenal axis
—leading to an increase in cortisol, glu-
cose, and insulin, and a decrease in tes-
tosterone, adiponectin, and growth
hormone (27,37,38). This dysregulation
seems to create an “anti” body recom-
position environment, where building
muscle mass and losing FM would be
less likely. More specifically, in athletic
populations, hypocaloric intakes and
significant reductions in body weight
and FM have been shown to negatively
impact testosterone (48,64,69). For
example, Bhasin et al. (10) have demon-
strated that there is a direct relationship
between serum testosterone levels and
gains in FFM. This may partially
explain why the case studies in natural
bodybuilders have demonstrated a loss
in FFM while preparing for their com-
petition despite their RT and high pro-
tein intake. More recently, a study also
demonstrated that sleep restriction had
a detrimental acute effect on myofibril-
lar protein synthesis rates, which may
be associated with loss of muscle mass
negatively impacting body composition.
This study also reported that protein
synthesis rates can be maintained by
performing high-intensity exercise even
under the sleep-restriction scenario (58).
Although studies have focused on
describing the negative effects of sleep
restriction on several different parame-
ters including body composition, there
is a paucity of data on how improving
sleep quality would specifically impact
body composition. To date, only one
study investigated the effects of a sleep
intervention combined with chronic
RT on body composition. Jabekk et al.
(29), designed a very practical study in
which 23 untrained individuals were
analyzed after undergoing a sleep edu-
cation intervention on how to improve
both sleep quantity and quality (ExS
group) compared to exercise only (Ex
group). Both groups performed a full-
body workout routine for 10 weeks, and
body composition was assessed using
DEXA. After 10 weeks, both groups
similarly increased FFM (ExS: 1.7 kg
and Ex: 1.3 kg). However, only ExS
significantly reduced FM, whereas Ex
did not (ExS: 21.8 and Ex 0.8 kg). Inter-
estingly, sleep questionnaire scores were
not different from pretesting to posttest-
ing between groups.
Although the last study suggests that
optimizing sleep may potentiate body
recomposition in people RT, it was con-
ducted in untrained individuals. Thus,
the impact sleep quality and quantity
may have on body recomposition in
trained individuals needs to be deter-
mined. In addition, when investigating
many of the previous studies referenced
in this review, these nontraining/nutri-
tion factors were not monitored or con-
trolled in trained populations. Therefore,
one may argue they may have impacted
the results of the studies and partially
explain differences in the body recompo-
sition outcomes between subjects and
groups. However, more research is
required to better understand if these
negative outcomes in body composition
can be prevented or minimized when
trained participants have adequate sleep
and a more favorable hormonal profile.
CONCLUSION
Despite the common belief that building
muscle and losing fat at the same
time is only plausible in novice/obese
individuals, the literature provided
Body Recomposition
VOLUME 00 | NUMBER 00 | JULY 2020
12 Copyright © 20 The Author(s). Published by Wolters Kluwer Health, Inc. 20 National Strength and Conditioning Association.
on behalf of the
supports that trained individuals can
also experience body recomposition.
Individuals’ training status, the exercise
interventions, and their baseline body
composition can influence the magni-
tude of muscle gained and fat lost. Resis-
tance training coupled with dietary
strategies has been shown to augment
this phenomenon. In addition, there
seems to be confounding nontraining/
nutrition variables such as sleep, hor-
mones, and metabolism that can signif-
icantly influence these adaptations.
Thus, coaches and practitioners must
self-audit their current approach, deter-
mine how they can improve their train-
ing and nutritional regimen on an
individual basis, and implement
evidence-based strategies to optimize
body recomposition.
PRACTICAL APPLICATIONS
The literature demonstrating a body
recomposition effect consist of a highly
heterogeneous set of designs, methods,
and outcomes. These discrepancies in
methodology make specific guidelines
to optimize body recomposition difficult
to reconcile. Nevertheless, the following
recommendations can be drawn from
the methods used and results reported
within the studies discussed in this
review.
Implement a progressive RT regimen
with a minimum of 3 sessions
per week.
Tracking rate of progress, and paying
attention to performance and recov-
ery can be important tools to appro-
priately adjust training over time.
Consuming 2.6–3.5 g/kg of FFM
may increase the likelihood or mag-
nitude of recomposition (3,25,28,61).
Protein supplements (i.e., whey and
casein) may be used as a means to
increase daily dietary protein intake as
well as a tool to maximize muscle pro-
tein synthesis. This may be of greater
importance postworkout as a means to
maximize the recomposition effect (21).
Prioritizing sleep quality and quan-
tity may be an additional variable
that can significantly impact changes
in performance, recovery, and body
composition (41,46,53,71).
Conflicts of Interest and Source of Funding:
The authors report no conflicts of interest
and no source of funding.
ACKNOWLEDGMENT
The authors thank Competitive Breed
LLC & SchoolOfGainz.com for paying
the open access fee.
Christopher
Barakat is a
human perfor-
mance researcher
at the University
of Tampa and the
owner of Com-
petitive Breed
LLC., specializ-
ing in bodybuil-
ding/physique coaching.
Jeremy
Pearson is a
PhD student in
the Applied
Physiology Labo-
ratory at The
University of
Kansas.
Guillermo
Escalante is an
associate profes-
sor of Kinesiology
at California
State University
San Bernardino.
Bill Campbell is
a professor of
Exercise Science
and the Director
of the Perfor-
mance and Phy-
sique Enhance-
ment Laboratory
at the University
of South Florida.
Eduardo O. De
Souza is an
assistant profes-
sor in the Health
Sciences and
Human Perfor-
mance Depart-
ment at The
University
of Tampa.
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... Another commonly utilized training outcome measure is body composition. Favorable training adaptations include increasing lean [20] and bone [21] mass while decreasing fat mass [20] across a competitive season. Unhealthy adaptations in response to over-training, inadequate nutrition, or relative energy deficiency in sport (RED-S), often manifest as decreased lean [22] or bone [23] mass which, in turn, precedes injury and illness [22][23][24][25]. ...
... Another commonly utilized training outcome measure is body composition. Favorable training adaptations include increasing lean [20] and bone [21] mass while decreasing fat mass [20] across a competitive season. Unhealthy adaptations in response to over-training, inadequate nutrition, or relative energy deficiency in sport (RED-S), often manifest as decreased lean [22] or bone [23] mass which, in turn, precedes injury and illness [22][23][24][25]. ...
... The directionality of these associations was also paradoxical, as increased serum ferritin levels across the competitive season were associated with decreased lean mass ( Figure 4a) and increased body fat percent (Figure 4b). Decreased muscle mass and increased fat usually represent unfavorable training adaptations which signify overtraining, undertraining, under-recovery, or even under-nutrition with or without RED-S [20,[22][23][24]. Although published studies clearly suggest that increased serum ferritin levels (from iron supplementation) enhance both endurance performance [3,16] and strength gains [3], the physiological effects of serum ferritin levels on body composition markers requires more controlled investigations -including dietary and caloric intake data -particularly in female athletes participating in aesthetic and gravitational sports. ...
Preprint
Adequate serum vitamin D and iron levels are thought to positively influence physical training adaptations and mood. The purpose of this prospective, observational, study was to investigate relationships between serum 25-OH vitamin D and serum ferritin levels with body composition and athlete burnout symptoms. Seventy-three collegiate athletes (49 female) from 7 indoor and outdoor sports were tested pre-season and post-season for: nutrient biomarkers (serum 25-OH vitamin D and serum ferritin) via venipuncture; body composition (total lean mass, bone mineral densi-ty/BMD, and % body fat) via dual energy x-ray absorptiometry (DXA) scans; and athlete burnout symptoms (post-season) via the athlete burnout questionnaire (ABQ). When male and female co-horts were combined, significant relationships were noted between pre-season serum 25-OH vit-amin D versus the change (∆: post-season minus pre-season) in both BMD (r=-0.34;p=0.0003) and % body fat (r=-0.28;p=0.015). Serum ferritin ∆ was significantly associated with lean mass ∆ (r=-0.34;p=0.003). For burnout symptoms, serum 25-OH vitamin D ∆ significantly explained 20.6% of the variance for devaluation of sport in the male cohort only. Across time, serum 25-OH vitamin D levels increased while serum ferritin levels decreased, non-significantly, in both males and fe-males. Relationships between nutrient biomarkers and body composition were opposite of physio-logical expectations.
... Another commonly utilized training outcome measure is body composition. Favorable training adaptations include increasing lean [21] and bone [22] mass while decreasing fat mass [21] across a competitive season. Unhealthy adaptations in response to over-training, inadequate nutrition, or relative energy deficiency in the sport (RED-S) often manifest as decreased lean [23] or bone [24] mass which, in turn, precedes injury and illness [23][24][25][26]. ...
... Another commonly utilized training outcome measure is body composition. Favorable training adaptations include increasing lean [21] and bone [22] mass while decreasing fat mass [21] across a competitive season. Unhealthy adaptations in response to over-training, inadequate nutrition, or relative energy deficiency in the sport (RED-S) often manifest as decreased lean [23] or bone [24] mass which, in turn, precedes injury and illness [23][24][25][26]. ...
... The directionality of these associations was also paradoxical, as increased serum ferritin levels across the competitive season were associated with decreased lean mass ( Figure 4a) and increased body fat percent (Figure 4b). Decreased muscle mass and increased fat usually represent unfavorable training adaptations which signify overtraining, undertraining, under-recovery, or even under-nutrition with or without RED-S [21,[23][24][25]. Although published studies clearly suggest that increased serum ferritin levels (from iron supplementation) enhance both endurance performance [3,17] and strength gains [3], the physiological effects of serum ferritin levels on body composition markers require more controlled investigations-including dietary and caloric intake data-particularly in female athletes participating in aesthetic and gravitational sports. ...
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Adequate serum vitamin D and iron levels are thought to influence physical training adaptations and mood positively. The primary purpose of this prospective, observational study was to investigate relationships between serum 25-OH vitamin D/25(OH)D and serum ferritin levels with body composition and athlete burnout symptoms. Seventy-three collegiate athletes (female: n = 49; male: n = 24) from indoor (swimming, basketball) and outdoor (soccer, cross-country) sports were tested pre-season and post-season for serum 25(OH)D and serum ferritin (nutrient biomarkers) via venipuncture; body composition (total lean mass, bone mineral density/BMD, and % body fat) via dual energy X-ray absorptiometry (DXA) scans; and athlete burnout symptoms (post-season) via the athlete burnout questionnaire (ABQ). When male and female cohorts were combined, significant correlations (Pearson’s r) were noted between pre-season serum 25(OH)D versus the change (∆: post-season minus pre-season) in both BMD (r = −0.34; p = 0.0003) and % body fat (r = −0.28; p = 0.015). Serum ferritin ∆ was significantly associated with lean mass ∆ (r = −0.34; p = 0.003). For burnout symptoms, serum 25(OH)D ∆ significantly explained 20.6% of the variance for devaluation of the sport in the male cohort only. Across time, serum 25(OH)D levels decreased while serum ferritin levels increased, non-significantly, in both males and females. Relationships between nutrient biomarkers and body composition were opposite of physiological expectations.
... The duration spend during training in uences the fatty acids and carbohydrates oxidation 13 . Furthermore, in the review article by Barakat et al. (2020), it is emphasised that the extent of muscle gain and fat loss among individuals may be in uenced by several things, among others, the training status, exercise interventions, and the individual's body composition status at baseline 14 . ...
Preprint
Full-text available
Background: There is a growing number of athletes participating in bodybuilding sport around the Limpopo Province. However, little is known about the nutrition information sources used by these athletes to guide their decisions during sports performance. Therefore, the researcher aimed at closing this gap through the investigation of nutrition information sources used by the bodybuilding athletes around one of the largest municipalities (Polokwane) in Limpopo Province. Methods: A quantitative descriptive study design was adopted to purposively sample 51 out of 60 amateur bodybuilding athletes in gyms around Polokwane municipality. Ethical approval and permission were obtained from the MEDUNSA Research and Ethics Committee (MREC) and coaches respectively. Athletes signed informed consent forms after the purpose of the study was explained. Data were collected at gyms in the evenings using self-designed questionnaires. The athletes’ demography, training information, and nutrition sources were collected. The SPSS (23) was used to analyse data using descriptive tests. Results: A Few athletes (11.8%) trained as bodybuilders for ≤6 months, while most athletes (66.7%) had been training for >7 months to 2 years. The majority (86.3%) trained for ≥1hour during weekdays. Most of the athletes (37.3%) relied on coaches as their source of information, while 29.4% and 29.3% relied on social media and teammates respectively. Only 4.0% used a professional for nutrition information. Conclusion: The bodybuilding athletes around Polokwane municipality mostly used coaches as sources for nutritional information. Trial registration: Not Applicable.
... It is generally accepted in the industry, but less so in the scientific literature, that RT may promote body composition changes in addition to accrual of lean mass. The concept of concomitantly reducing body fat mass and gaining lean mass has been termed body 'recomposition' [13]. Given that higher levels of lean mass are associated with a lower risk of all-cause mortality [14], body recomposition is arguably more important than simply reducing body mass in healthy individuals. ...
Article
Full-text available
Background Resistance training is the gold standard exercise mode for accrual of lean muscle mass, but the isolated effect of resistance training on body fat is unknown.Objectives This systematic review and meta-analysis evaluated resistance training for body composition outcomes in healthy adults. Our primary outcome was body fat percentage; secondary outcomes were body fat mass and visceral fat.DesignSystematic review with meta-analysis.Data SourcesWe searched five electronic databases up to January 2021.Eligibility CriteriaWe included randomised trials that compared full-body resistance training for at least 4 weeks to no-exercise control in healthy adults.AnalysisWe assessed study quality with the TESTEX tool and conducted a random-effects meta-analysis, with a subgroup analysis based on measurement type (scan or non-scan) and sex (male or female), and a meta-regression for volume of resistance training and training components.ResultsFrom 11,981 records, we included 58 studies in the review, with 54 providing data for a meta-analysis. Mean study quality was 9/15 (range 6–15). Compared to the control, resistance training reduced body fat percentage by − 1.46% (95% confidence interval − 1.78 to − 1.14, p < 0.0001), body fat mass by − 0.55 kg (95% confidence interval − 0.75 to − 0.34, p < 0.0001) and visceral fat by a standardised mean difference of − 0.49 (95% confidence interval − 0.87 to − 0.11, p = 0.0114). Measurement type was a significant moderator in body fat percentage and body fat mass, but sex was not. Training volume and training components were not associated with effect size.Summary/Conclusions Resistance training reduces body fat percentage, body fat mass and visceral fat in healthy adults.Study Registrationosf.io/hsk32.
... On the contrary, several disadvantages may be also highlighted. These were the high amounts of soy protein supplement, which may reduce the variability of the nutritional plan and compromise adherence, and an insufficient intake of some micronutrients such as calcium, iron, zinc, and magnesium due to a low energy budget [15]. ...
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Powerlifting is a weight-class strength sport where achieving low fat mass (FM) and high fat-free mass (FFM) is desirable to improve performance. Recent studies have evaluated the nutritional considerations of different eating patterns, such as vegan diets (VD), in athlete populations. VD are a challenge for athletes who want to attain body composition changes. The aim of this case study is to report on the body composition changes and subjective feelings of a male professional vegan powerlifter following VD for six weeks. The body mass of the powerlifter decreased from 79.3 to 77.4 kg (2.39%). Along with this, FM decreased from 15.0 to 11.4 kg (24%). Conversely, FFM increased from 64.3 to 66.0 kg (2.64%). Moreover, the powerlifter communicated no subjective feelings of low energy availability during training sessions. The VD might compromise adherence in a nutritional intervention which aims to improve body composition due to the nutritional requirements for fat loss. Therefore, more appropriate health assessments, including blood and psychological tests, are required for professional athletes. This short-term VD intervention was satisfactory for improving body composition and no adverse outcomes were reported.
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In this work, we reflect upon the energy balance hypothesis of obesity. International organizations, the general population and many scientists hold the belief that obesity is indisputably caused by an imbalance between energy intake and energy expenditure. Most of them argue that the laws of thermodynamics support this view. We identify and review the main arguments used to support this belief, and we explain the reasoning mistakes those arguments harbor. We show that the laws of thermodynamics do not support the idea that obesity is an energy problem nor an energy balance problem more than they do in the growth of any other tissue in the human body. We argue that the validity of the energy balance paradigm for obesity must be questioned. Although correction of a wrong belief is laudable per se, in this particular case harm may arise by influencing the way in which obesity prevention is tackled and obese patients are treated.
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Introduction Accurate assessment of total body composition in tall (>1.96m) individuals using dual energy x-ray absorptiometry (DXA) scans is problematic due to current height restrictions of the scan table. The aim of this investigation was to quantify absolute and relative contributions of fat, bone and lean mass, of the feet and head regions, to whole-body composition DXA scan totals. Methods Whole-body DXA scans were performed in collegiate athletes. Athlete DXA scans were included in data analyses if the entire body fit within the confines of scan table area. The feet region of interest (ROI) was delineated at the ankle joint mortise, marked superiorly by the inferior margin of the tibial plafond and encompassing all inferior anatomical structures. The head region was calculated by the DXA scan software. Both absolute (kg) and relative (feet/whole-body x 100 = feet mass %) contributions to body composition were calculated. Data presented as mean±SD. Results 132 National Collegiate Athletic Association (NCAA) athletes (85 female) underwent DXA scans which met the inclusion criteria. The feet region represented: 1.9±0.3kg (2.6±0.3%) of total mass; 0.4±0.3kg (2.6±0.5%) of fat mass; 1.3±0.3kg (2.5±0.3%) of lean mass; and 0.14±0.0kg (5.4±0.6%) of bone mineral content (BMC). The head region represented: 4.8±0.5kg (6.9±0.8%) of total mass; 1.2±0.2kg (8.2±3.0%) of fat mass; 3.2±0.5kg (6.1±0.9%) of lean mass; and 0.48±0.07kg (18.7±2.7%) of BMC. Significant negative relationships were found between head% versus whole-body BMC (r=-0.54;p<0.0001), lean mass (r=-0.57;p<0.0001), and fat mass (r=-0.81;p<0.0001) and between feet% versus fat mass (r=-0.68;p<0.0001). A significant positive relationship was noted between feet% versus whole-body BMC (r=0.18;p=0.04) but not versus lean mass (r=0.15;p=0.09). Conclusions Removing the feet from whole-body composition analyses reduces lean, fat and bone mass compartment totals by 3-5%. Removing the head region reduces body composition compartments by 6-19%, from whole-body DXA scan totals.
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En la actualidad, el fútbol es uno de los deportes con mayor campo de investigación científica. En los últimos tiempos, el entrenamiento de la fuerza ha adquirido un papel fundamental en la planificación y programación del entrenamiento en fútbol, ya que, influye de forma positiva en la mejora de las demás cualidades y, por tanto, en un mayor rendimiento del deportista. Sin embargo, aún existe mucha controversia sobre el desarrollo de trabajos de fuerza en jugadores jóvenes de fútbol y su posible interferen cia con otras cualidades físicas y/o antropométricas. El objetivo de esta tesis doctoral, fue comparar los efectos que producen dos tratamientos de fuerza diferentes, sobre la capacidad de salto, el consumo máximo de oxígeno y la composición corporal, en jugadores jóvenes de fútbol durante una temporada, atendiendo a la categoría (cadete/juvenil) y la demarcación específica (portero, defensa central, lateral, centrocampista, extremo y delantero). Los tratamientos de fuerza fueron: 1. Grupo de fuerza con au tocargas (GAUT) y 2. Grupo de fuerza con sobrecargas (GSOB). Después de 37 semanas, con un total de 74 sesiones de entrenamiento,se produjeron mejoras significativas sobre la capacidad de salto (17.54 % GAUT ; 16.54 % GSOB ) y sobre el consumo máximo de oxígeno (7.58 % GAUT; 6.69 % GSOB). Además, se produjeron descensos del % graso (13.94 %), aumentos significativos desde el 0.5 al 1.84 % en la variable altura (cm) y aumentos en la masa muscular (Kg) (8.30 % GAUT; 4.81 % GSOB), en ambas categorías. Podemos concluir que las diferentes metodologías de entrenamiento de fuerza utilizadas, hicieron mejorar de manera significativa las variables de rendimiento estudiadas. Así pues, podemos indicar que tanto el tratamiento de fuerza con autocargas como el de sobrecargas, son métodos de entrenamiento válidos y óptimos para producir cambios a niveles neuromusculares, cardiorrespiratorios y antropométricos. Por último, se recomienda tanto a los entrenadores y/o preparadores físicos, qu e implementar un programa de entrenamiento de fuerza durante la temporada en jugadores jóvenes de fútbol, es efectivo para mejorar el rendimiento de sus jugadores.
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Key points: Sleep restriction has previously been associated with the loss of muscle mass in both human and animal models. The rate of myofibrillar protein synthesis (MyoPS) is a key variable in regulating skeletal muscle mass and can be increased by performing high-intensity interval exercise (HIIE), but the effect of sleep restriction on MyoPS is unknown. In this study, we demonstrate that participants undergoing a sleep restriction protocol (5 nights, with 4 h time in bed each night) had lower rates of skeletal muscle MyoPS; however, rates of MyoPS were maintained at control levels by performing HIIE during this period. Our data suggest that the lower rates of MyoPS in the sleep restriction group may contribute to the detrimental effects of sleep loss on muscle mass and that HIIE may be used as an intervention to counteract these effects. Abstract: The aim of this study was to investigate the effect of sleep restriction, with or without high-intensity interval exercise (HIIE), on the potential mechanisms underpinning previously-reported sleep-loss-induced reductions to muscle mass. Twenty-four healthy, young men underwent a protocol consisting of two nights of controlled baseline sleep and a five-night intervention period. Participants were allocated into one of three parallel groups, matched for age, V̇O2peak , BMI, and habitual sleep duration; a normal sleep group (NS, 8 h time in bed (TIB) each night), a sleep restriction group (SR, 4 h TIB each night), and a sleep restriction and exercise group (SR+EX, 4 h TIB each night, with three sessions of HIIE). Deuterium Oxide (D2 O) was ingested prior to commencing the study, and muscle biopsies obtained pre- and post-intervention were used to assess myofibrillar protein synthesis (MyoPS) and molecular markers of protein synthesis and degradation signalling pathways. MyoPS was lower in the SR group (FSR %/day ± SD, 1.24 ± 0.21), compared to both the NS (1.53 ± 0.09) and SR+EX groups (1.61 ± 0.14), (P < 0.05). However, there were no changes in the purported regulators of protein synthesis (i.e., p-AKTser473 and p-mTORser2448 ) and degradation (i.e., Foxo1/3 mRNA and LC3 protein) in any group. These data suggest that MyoPS is acutely reduced by sleep restriction, but that MyoPS can be maintained by performing HIIE. These findings may explain the sleep-loss-induced reductions in muscle mass previously reported, and highlight the potential therapeutic benefit of HIIE to maintain myofibrillar remodelling in this context. This article is protected by copyright. All rights reserved.
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The popularity of physique sports is increasing, yet there are currently few comprehensive nutritional guidelines for these athletes. Physique sport now encompasses more than just a short phase before competition and offseason guidelines have recently been published. Therefore, the goal of this review is to provide an extensive guide for male and female physique athletes in the contest preparation and recovery period. As optimal protein intake is largely related to one’s skeletal muscle mass, current evidence supports a range of 1.8-2.7 g/kg. Furthermore, as a benefit from having adequate carbohydrate to fuel performance and activity, low-end fat intake during contest preparation of 10-25% of calories allows for what calories remain in the “energy budget” to come from carbohydrate to mitigate the negative impact of energy restriction and weight loss on training performance. For nutrient timing, we recommend consuming four or five protein boluses per day with one consumed near training and one prior to sleep. During competition periods, slower rates of weight loss (≤0.5% of body mass per week) are preferable for attenuating the loss of fat-free mass with the use of intermittent energy restriction strategies, such as diet breaks and refeeds, being possibly beneficial. Additionally, physiological and psychological factors are covered, and potential best-practice guidelines are provided for disordered eating and body image concerns since physique athletes present with higher incidences of these issues, which may be potentially exacerbated by certain traditional physique practices. We also review common peaking practices, and the critical transition to the post-competition period.
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Resistance training is commonly prescribed to enhance strength/power qualities and is achieved via improved neuromuscular recruitment, fiber type transition, and/ or skeletal muscle hypertrophy. The rate and amount of muscle hypertrophy associated with resistance training is influenced by a wide array of variables including the training program, plus training experience, gender, genetic predisposition, and nutritional status of the individual. Various dietary interventions have been proposed to influence muscle hypertrophy, including manipulation of protein intake, specific supplement prescription, and creation of an energy surplus. While recent research has provided significant insight into optimization of dietary protein intake and application of evidence based supplements, the specific energy surplus required to facilitate muscle hypertrophy is unknown. However, there is clear evidence of an anabolic stimulus possible from an energy surplus, even independent of resistance training. Common textbook recommendations are often based solely on the assumed energy stored within the tissue being assimilated. Unfortunately, such guidance likely fails to account for other energetically expensive processes associated with muscle hypertrophy, the acute metabolic adjustments that occur in response to an energy surplus, or individual nuances like training experience and energy status of the individual. Given the ambiguous nature of these calculations, it is not surprising to see broad ranging guidance on energy needs. These estimates have never been validated in a resistance training population to confirm the “sweet spot” for an energy surplus that facilitates optimal rates of muscle gain relative to fat mass. This review not only addresses the influence of an energy surplus on resistance training outcomes, but also explores other pertinent issues, including “how much should energy intake be increased,” “where should this extra energy come from,” and “when should this extra energy be consumed.” Several gaps in the literature are identified, with the hope this will stimulate further research interest in this area. Having a broader appreciation of these issues will assist practitioners in the establishment of dietary strategies that facilitate resistance training adaptations while also addressing other important nutrition related issues such as optimization of fuelling and recovery goals. Practical issues like the management of satiety when attempting to increase energy intake are also addressed.
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Many nutrition practices often used by bodybuilders lack scientific support and can be detrimental to health. Recommendations during the dieting phase are provided in the scientific literature, but little attention has been devoted to bodybuilders during the off-season phase. During the off-season phase, the goal is to increase muscle mass without adding unnecessary body fat. This review evaluated the scientific literature and provides nutrition and dietary supplement recommendations for natural bodybuilders during the off-season phase. A hyper-energetic diet (~10–20%) should be consumed with a target weight gain of ~0.25–0.5% of bodyweight/week for novice/intermediate bodybuilders. Advanced bodybuilders should be more conservative with the caloric surplus and weekly weight gain. Sufficient protein (1.6–2.2 g/kg/day) should be consumed with optimal amounts 0.40–0.55 g/kg per meal and distributed evenly throughout the day (3–6 meals) including within 1–2 hours pre- and post-training. Fat should be consumed in moderate amounts (0.5–1.5 g/kg/day). Remaining calories should come from carbohydrates with focus on consuming sufficient amounts (≥3–5 g/kg/day) to support energy demands from resistance exercise. Creatine monohydrate (3–5 g/day), caffeine (5–6 mg/kg), beta-alanine (3–5 g/day) and citrulline malate (8 g/day) might yield ergogenic effects that can be beneficial for bodybuilders.
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Skeletal muscle is highly adaptable and has consistently been shown to morphologically respond to exercise training. Skeletal muscle growth during periods of resistance training has traditionally been referred to as skeletal muscle hypertrophy, and this manifests as increases in muscle mass, muscle thickness, muscle area, muscle volume, and muscle fiber cross-sectional area (fCSA). Delicate electron microscopy and biochemical techniques have also been used to demonstrate that resistance exercise promotes ultrastructural adaptations within muscle fibers. Decades of research in this area of exercise physiology have promulgated a widespread hypothetical model of training-induced skeletal muscle hypertrophy; specifically, fCSA increases are accompanied by proportional increases in myofibrillar protein, leading to an expansion in the number of sarcomeres in parallel and/or an increase in myofibril number. However, there is ample evidence to suggest that myofibrillar protein concentration may be diluted through sarcoplasmic expansion as fCSA increases occur. Furthermore, and perhaps more problematic, are numerous investigations reporting that pre-to-post training change scores in macroscopic, microscopic, and molecular variables supporting this model are often poorly associated with one another. The current review first provides a brief description of skeletal muscle composition and structure. We then provide a historical overview of muscle hypertrophy assessment. Next, current-day methods commonly used to assess skeletal muscle hypertrophy at the biochemical, ultramicroscopic, microscopic, macroscopic, and whole-body levels in response to training are examined. Data from our laboratory, and others, demonstrating correlations (or the lack thereof) between these variables are also presented, and reasons for comparative discrepancies are discussed with particular attention directed to studies reporting ultrastructural and muscle protein concentration alterations. Finally, we critically evaluate the biological construct of skeletal muscle hypertrophy, propose potential operational definitions, and provide suggestions for consideration in hopes of guiding future research in this area.
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This study investigated the effects of two different velocity-based training (VBT) regimens on muscular adaptations. Fifteen female college volleyball players were randomly assigned into either progressive velocity-based training (PVBT) or optimum training load (OTL). Both groups trained three times a week for seven weeks. PVBT performed a 4-week strength block (e.g., 0.55-0.70 m·s −1) followed by a 3-week power block (e.g., 0.85-1.0 m·s −1), whereas OTL performed training at ~0.85-0.9 m·s −1. 1RM and peak power output (PP) assessments on the back squat (BS), bench press (BP) and deadlift (DL) exercises were assessed pre and post training. There was a main time effect (p ≤ 0.05) for BS and BP 1RM, (PVBT: 19.
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Background: The purpose of this study was to investigate the effect of advising sleep health optimization on anthropomorphic variables following 10 weeks resistance exercise training. Methods: 30 untrained healthy men were recruited for the study and 23 were included in the final analysis. Participants were randomly assigned to exercise and sleep optimization: ExS (n=10) or exercise only: Ex (n=12). Both groups performed a whole body resistance exercise program twice a week for 10 weeks. The ExS group received sleep health (SH) education on how to improve both sleep quantity and quality. Results: After 10 weeks of training both groups had increased lean body mass by a similar amount. The ExS group experienced an increase of 1.7kg ± 1.1kg while the Ex group experienced an increase of 1.3kg ± 0.8kg (p=0.29 for difference between groups). The men in the ExS group reduced fat mass significantly (-1.8kg ± 0.8kg) while the Ex group did not (0.8kg ± 1.0kg). p=0.02 for difference between groups. Conclusions: This randomized controlled trial suggests that combining regular resistance exercise training with optimization of sleep health provide significant added benefits to body composition. This optimization provides a simple and cheap tool, applicable to the general healthy population.
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The validity of dual-energy x-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analysis (MFBIA) for detecting changes in fat mass (FM), fat-free mass (FFM), and body fat percentage (BF%) was evaluated, as compared to a rapid 4-component (4C) model, in 31 females completing 8 weeks of resistance training. Analyses were performed in all participants (ALL) and in subgroups that gained FFM but lost FM (R subgroup) or gained both FFM and FM (G subgroup). It was hypothesized that methods would comparably detect changes in ALL, but discrepancies would occur in subgroup analysis. Changes in body composition did not significantly differ between 4C, DXA, and MFBIA. Equivalence testing indicated that similar changes were detected by DXA and MFBIA, compared to 4C, for ΔFFM in all analyses and ΔBF% in ALL and R subgroup. ΔFM was equivalent to 4C only in R subgroup for DXA and G subgroup for MFBIA. For ΔFM and ΔBF%, DXA and MFBIA produced similar magnitude errors in ALL. However, DXA exhibited lower error in R subgroup, whereas MFBIA exhibited lower error in G subgroup. For ΔFFM, DXA and MFBIA exhibited relatively similar errors in ALL and R subgroup, although MFBIA displayed proportional bias and weaker correlations with 4C than DXA. In G subgroup, MFBIA exhibited lower errors and a higher correlation with 4C ΔFFM than DXA. Although both DXA and MFBIA may have utility for estimating body composition changes during FFM accretion, DXA may be superior during simultaneous FM loss, whereas MFBIA may produce lower error during simultaneous FM gain.
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We assessed whether body composition changes with 9 months of exercise training predicted changes in cardiometabolic health indices in weight-stable adults. We hypothesized that within ±5% weight change, changes in whole-body fat and lean masses would predict changes in cardiometabolic health indices with exercise training. Using a randomized parallel design, 152 adults (age: 49 ± 8 year; body mass index: 30.0 ± 2.7 kg/m²; mean ± SD) performed resistance exercises 2 d/wk and aerobic exercises 1 d/wk for 9 months. Participants consumed isoenergetic supplements with 0, 10, 20, or 30 g whey protein twice daily and remained weight stable within ±5% of baseline weight. Body weight and composition were measured using dual-energy x-ray absorptiometry pre- and postintervention. Multiple linear regression model was applied for data analyses. Independent of whey protein supplementation, reductions in fat mass predicted increases in high-density lipoprotein cholesterol (unstandardized beta-coefficient [β], −0.03; 95% confidence interval [CI], −0.06 to −0.01; P =.007) and insulin sensitivity index (β, −0.52; 95% CI, −0.95 to −0.09; P =.018) and decreases in waist circumference (β, 0.67; 95% CI, 0.17-1.18; P =.009). In contrast, increases in lean mass did not predict changes in any of the measured cardiometabolic health indices. Health improvements with training that emphasize resistance exercises are typically attributed to increases in lean mass; however, these results underscore reducing body fat to predict cardiometabolic health improvements.