REVIEW Open Access
International society of sports nutrition
position stand: diets and body composition
Alan A. Aragon1
, Brad J. Schoenfeld2
, Robert Wildman3
, Susan Kleiner4
, Trisha VanDusseldorp5
, Lem Taylor6
,
Conrad P. Earnest7
, Paul J. Arciero8
, Colin Wilborn6
, Douglas S. Kalman9
, Jeffrey R. Stout10
, Darryn S. Willoughby11
,
Bill Campbell12
, Shawn M. Arent13
, Laurent Bannock14
, Abbie E. Smith-Ryan15
and Jose Antonio16*
Abstract
Position Statement: The International Society of Sports Nutrition (ISSN) bases the following position stand on a critical
analysis of the literature regarding the effects of diet types (macronutrient composition; eating styles) and their
influence on body composition. The ISSN has concluded the following. 1) There is a multitude of diet types and eating
styles, whereby numerous subtypes fall under each major dietary archetype. 2) All body composition assessment
methods have strengths and limitations. 3) Diets primarily focused on fat loss are driven by a sustained caloric deficit.
The higher the baseline body fat level, the more aggressively the caloric deficit may be imposed. Slower rates of weight
loss can better preserve lean mass (LM) in leaner subjects. 4) Diets focused primarily on accruing LM are driven by a
sustained caloric surplus to facilitate anabolic processes and support increasing resistance-training demands. The
composition and magnitude of the surplus, as well as training status of the subjects can influence the nature of the
gains. 5) A wide range of dietary approaches (low-fat to low-carbohydrate/ketogenic, and all points between) can be
similarly effective for improving body composition. 6) Increasing dietary protein to levels significantly beyond current
recommendations for athletic populations may result in improved body composition. Higher protein intakes (2.3–3.1 g/
kg FFM) may be required to maximize muscle retention in lean, resistance-trained subjects under hypocaloric
conditions. Emerging research on very high protein intakes (>3 g/kg) has demonstrated that the known thermic,
satiating, and LM-preserving effects of dietary protein might be amplified in resistance-training subjects. 7) The
collective body of intermittent caloric restriction research demonstrates no significant advantage over daily caloric
restriction for improving body composition. 8) The long-term success of a diet depends upon compliance and
suppression or circumvention of mitigating factors such as adaptive thermogenesis. 9) There is a paucity of research on
women and older populations, as well as a wide range of untapped permutations of feeding frequency and
macronutrient distribution at various energetic balances combined with training. Behavioral and lifestyle modification
strategies are still poorly researched areas of weight management.
Background
There are several major diet types interspersed with a
multitude of subtypes. This creates a maze of conflicting
principles that may be difficult for the general public and
practitioners to navigate. Compounding the confusion is
the continued propagation of fad diets across a range of
media outlets, replete with unfounded practices. Therefore,
it is important to examine the scientific evidence in a
systematic way in order to devise recommendations to
guide healthcare practitioners, coaches (including trainers,
dietitians, and sports nutritionists), athletes, and the general
public regarding all of the above. The purpose of this
position stand is to provide clarity on the effects of various
diets on body composition.
A general definition of “diet” is the sum of energy and
nutrients obtained from foods and beverages consumed
regularly by individuals. Thus, the following dietary archetypes
will be assessed: very-low- and low-energy diets
(VLED and LED), low-fat diets (LFD), low-carbohydrate
diets (LCD), ketogenic diets (KD), high-protein diets
(HPD), and intermittent fasting (IF). Diets with qualitative
themes or commercial brands will inevitably fall under the
* Correspondence: ja839@nova.edu
16
Department of Health and Human Performance, Nova Southeastern
University, Davie, FL, USA
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16
DOI 10.1186/s12970-017-0174-y
umbrella of the classifications above. Therefore, their parent
categories rather than ‘named’ or ‘branded’ diets (e.g.,
Atkins, Ornish, Zone, Paleo, etc.) will receive the majority
of scrutiny in this position stand.
This position stand will further focus on prospective
intervention trials with a duration of at least 4 weeks, as
this can be considered a minimum period of time for
meaningful changes in fat mass (FM) and lean mass (LM,
termed interchangeably with fat-free mass, or FFM), as well
as effects of exercise training on these variables. Studies
and pooled analyses with and without training have been
included, as well as studies across the range of energetic
balances (i.e., hypo-, hyper-, and eucaloric). Studies that did
not measure body composition have not been included, nor
have studies examining dietary effects in clinical contexts –
including disease treatment. Despite the latter topics
breaching the scope of the present article, it is still important
to note that body composition is inextricably tied to
foundational parameters of health. Aside from sports and
fitness applications for improvements in body composition,
a higher proportion of LM reduces the risk of developing
metabolic syndrome [1], bone loss [2], and the multiple
complications associated with sarcopenia [3, 4].
Body composition assessment methods
Body composition assessment is an attempt to simplify a
process that is inherently complex. As such, there are
several methods that attempt to accurately estimate LM
and FM, and their subcomponents. Before outlining the
most common methods used in sports science and
medicine, it should be noted that there is a continuum
of the components measured or estimated. Over 25 years
ago, Wang et al. [5] proposed a five-level model for organizing
body composition research [6]. Each level has
different components, eventually deemed compartments,
and have undergone further organization to include two
(2C), three (3C) and four (4C) compartments [6]:
1) Atomic level: hydrogen, oxygen, nitrogen, carbon,
sodium, potassium, chloride, phosphorus, calcium,
magnesium, sulfur.
2) Molecular level: The 4C model includes FM, total
body water (TBW), total body protein, and bone
mineral content. The 3C model includes FM, TBW,
and nonfat solids. An alternate 3C model includes
FM, bone mineral, and residual mass. The 2C model
includes FM and FFM.
3) Cellular level: The 3C model includes cells,
extracellular fluids, and extracellular solids. The 4C
model includes body cell mass, FM, extracellular
fluids, and extracellular solids.
4) Tissue-organ level: adipose tissue, skeletal muscle,
bone, visceral organs, other tissues.
5) Whole body level: head, trunk, and appendages.
The 4C model has the greatest degree of sensitivity to
interindividual variability of FFM composition. Its comprehensiveness
and accuracy have rendered its reputation
as the “gold standard” to which all other models are
compared, but it is limited to occasional use in primary
research due to its logistical challenges. The 2C model
estimates FM and FFM, and operates under the assumption
that water, protein, and mineral content of FFM are
constant. Thus, the 2C model is the most commonly
used approach for adults. Due to their relatively low
cost, non-invasiveness, and ease of operation, 2C modelbased
methods are common in clinical practice and
sports/fitness settings. Examples of methods based on
the 2C model include hydrodensitometry (underwater
weighing), air displacement plethysmography (ADP or
BOD POD®), skinfold thickness, and bioelectrical impedance
analysis (BIA). Dual energy X-ray absorptiometry
(DXA) is based on a 3C model that measures bone mineral
content, LM, and FM, but it is still subject to confounding
from inter-assessment differences in hydration,
glycogen, and muscle creatine levels, which can be significant
in athletic populations with distinct exercise and
recovery cycles [7, 8].
Body composition methods have been further classified
as direct, indirect, and criterion [9]. Direct methods measure
the specific/targeted aspect or process. Examples include
TBW, isotope dilution, and neutron activation.
Indirect methods provide surrogate measures or proxies
of direct methods and criterion methods. Examples of indirect
methods are anthropometry (e.g., skinfolds), ADP,
BIA, and bioimpedance spectroscopy (BIS). Criterion
methods measure a specific property of the body such as
density or distribution of skeletal muscle and adipose tissue.
Examples include hydrodensitometry, computed tomography,
magnetic resonance imaging (MRI), and DXA. It
should be noted that multi-compartment models have
evolved to be considered criterion methods: standards
against which other methods are judged.
The various methods are often classified in the literature
as either laboratory methods (e.g., DXA, ADP) or field
methods (e.g., skinfolds, ultrasound, BIA, BIS) depending
on their respective use in research and clinical settings as
well as their portability. Laboratory methods – including
multi-compartment models – have traditionally been
viewed as more accurate and valid. BIA and BIS have
evolved to include multiple frequencies. This technology
may more accurately estimate body composition through
multiple frequency-dependent electrical properties of body
tissues, as opposed to traditional single frequency methods
(i.e., handheld BIA or scales). However, higher levels of sophistication
with multi-frequency options are often accompanied
by lower availability and higher cost. Given the
broad range of body composition measurement techniques
and unique challenges involved with measuring athletes
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 2 of 19
(exercise/glycogen depletion, hydration, time availability,
etc.), there is no universally superior method for body composition
assessment in this population [10–12]. An excellent
review by Wagner and Hayward [10] concludes the following:
“There is no single method which is ‘best;’ rather, the
clinician or researcher must weigh the practical considerations
of their assessment needs with the limitations of the
methods.” Table 1 outlines the characteristics of selected
body composition assessment methods [6, 9, 10, 13–20]:
Major diet archetypes
Low-energy diets
Low-energy diets (LED) and very-low-energy diets
(VLED) are characterized by their provision of 800–
1200 kcal/day and 400–800 kcal/day, respectively [21].
Note that LED have also been given a more liberal definition
of providing 800–1800 kcal [22]. Very-low-energy diets
are typically in liquid form and commercially prepared.
The aim of the diet is to induce rapid weight loss (1.0–
2.5 kg/week) while preserving as much LM as possible.
VLED are designed to replace all regular food consumption,
and therefore should not be confused with meal replacement
products intended to replace one or two meals
per day. As such, VLED are fortified with the full
spectrum of essential micronutrients. The macronutrient
content of VLED is approximately 70–100 g/day, 15 g/day
and 30–80 g/day of protein, fat and carbohydrate, respectively.
A protein-sparing modified fast can be considered
the higher-protein variant of a VLED, with protein intakes
of approximately 1.2–1.5 g/kg/d [23]. However, even at
protein intakes as low as 50 g/day, the proportion of LM
loss from VLED has been reported to be 25% of total
weight loss, with 75% as fat loss [24].
Resistance training has shown an impressive ability to
augment the preservation of muscle and even increase it
during VLED – at least in untrained/obese subjects. A
12-week trial by Bryner et al. [25] found that resistance
training while consuming 800 kcal resulted in the preservation
of LM in untrained obese subjects. There was
actually a slight gain, but it did not reach statistical significance.
Resting metabolic rate (RMR) significantly increased
in the training group, but it decreased in the
control group. Donnelly et al. [26] reported a significant
increase in cross-sectional area of both slow- and fasttwitch
muscle fibers in untrained obese subjects after
12 weeks on an 800 kcal diet with resistance training.
While these results cannot necessarily be extrapolated to
lean, trained subjects, they are nevertheless intriguing.
In obese populations, aggressive caloric restriction is a
potentially powerful intervention since a greater initial
weight loss is associated with greater long-term success
in weight loss maintenance [27]. However, a metaanalysis
by Tsai and Wadden [22] found that VLED did
not result in greater long-term (1 year or more) weight
loss than LED. Eight to 12 week VLED are common in
clinical practice before transitioning to less severe caloric
restriction; however, there is an ongoing debate regarding
the duration that can be safely sustained for VLED.
Multiple deaths have been reported due to low-quality
protein intake, excessive loss of lean mass, and inadequate
medical supervision [28]. Adverse effects of
VLED include cold intolerance, fatigue, headache, dizziness,
muscle cramps, and constipation. Hair loss was reported
to be the most common complaint of extended
VLED use [22]. It should be noted that VLED use has
limited relevance to healthy and athletic populations.
Low-Fat diets
Low-fat diets (LFD) have been defined as providing 20–
35% fat [29]. This is based on the Acceptable Macronutrient
Distribution Ranges (AMDR) for adults, set by the
Food and Nutrition Board of the Institute of Medicine
[30]. The AMDR set protein at 10–35%, carbohydrate at
45–65%, and fat at 20–35% of total energy. Although the
classification of LFD is based on the AMDR, it might be
more accurate to call them high-carbohydrate diets,
given the dominance of this macronutrient in the ranges.
As such, the definition of LFD is inherently subjective.
Scientists and physicians have promoted decreased fat
intake since the 1950s [31]. The 1977 publication of the
Dietary Goals for the United States, and the 1980 publication
of the inaugural Dietary Guidelines for Americans
(DGA) reinforced a reduction in total fat intake with the
aim of improving public health [32]. Although the
AMDR were published in 2005, their staying power is
apparent since the recently updated DGA adheres to
these ranges [33], as do major health organizations such
as the American Heart Association, American Diabetes
Association and Academy of Nutrition and Dietetics.
A recent systematic review by Hooper et al. [34] analyzed
32 randomized controlled trials (RCTs) containing
~54,000 subjects, with a minimum duration of 6 months.
Reducing the proportion of dietary fat compared to usual
intake modestly but consistently reduced body weight,
body fat, and waist circumference. Excluded from the analysis
were RCTs where subjects in either the control or experimental
groups had the intention to reduce weight.
The implication of these findings is that reducing the proportion
of dietary fat can cause a de facto reduction of
total energy intake, thereby reducing body fat over time.
The premise of dietary fat reduction for weight loss is
to target the most energy-dense macronutrient to impose
hypocaloric conditions. Tightly controlled experiments
have covertly manipulated the fat content of diets
similar in appearance and palatability, and the higher energy
density of the higher-fat diets resulted in greater
weight gain and/or less weight loss [35, 36]. However,
over the long-term, diets with lower energy density have
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 3 of 19
Table 1 Body composition methods
Method Components measured/estimated Strengths Limitations
Skinfold thickness Subcutaneous fat thickness in specific
sites of the body
Reliable method for assessing regional
fatness. Useful for monitoring fat changes
in children due to their small body size,
and their fat stores are primarily
subcutaneous, even in obese children
(though increasing degrees of obesity
lower the viability of this method).
Most skinfold calipers have an upper
limit of 45–60 mm, limiting their use
to moderately overweight or thin
subjects. Measurement reliability
depends on the skill and experience
level of the technician, which varies,
and type/brand of caliper used. The
best use of this method is the
monitoring of raw values, rather than
assuming an accurate representation
of body composition.
Bioelectrical impedance
analysis (BIA) and
bioelectrical impedance
spectroscopy (BIS)
Total body water (TBW), which is
converted to FFM via the assumption
that 73% of the body’s FFM is water
Economical, safe, quick, minimal
participant participation and technician
expertise. Capable of determining body
composition of groups and monitoring
changes within individuals over time. BIS
or multi-frequency BIA, is capable of delineating
TBW into intracellular water
(ICW) and extracellular water (ECW),
which allows for an estimation of body
cell mass.
Validity of BIA and BIS is populationspecific;
it’s influenced by sex, age,
height, disease state, and race. BIA/BIS
underestimates FFM in normal-weight
individuals and overestimates FFM in
obese individuals compared to DXA.
Validity of single-frequency BIA and
multifrequency BIA may be limited to
healthy, young, euhydrated adults.
Hydrodensitometry
(also called hydrostatic
weighing or
underwater weighing)
Body weight on land and weight in
water, body volume, body density, and
residual lung volume
Good test-retest reliability, accurate in determining
body density, lengthy history
and track record of consistent use in
sports and clinical settings.
Relies upon subject performance
(completely exhaled, submerged).
Errors in measurement of residual
lung volume can confound the
assessment of body composition. The
density of FFM is an assumed
constant but can vary with age, sex,
race, and training status.
Air displacement
plethysmography (ADP)
Total body volume, and total body fat
(FFM and FM)
High reliability for body fat percentage,
body density, and residual lung volume
in adults. Non-invasive, quick, no radiation
exposure or subject performance
demands. Same-day test-retest reliability
has been reported to be slightly better
than hydrodensitometry
Tends to over-estimate fat mass compared
to DXA and the 4C model. Disease
states can reduce accuracy. Inconsistency
of clothing and facial/body hair and exercise
prior to testing can alter repeatability.
Expensive apparatus.
Dual energy X-ray absorptiometry
(DXA)
Total and regional body fat, LM, bone
mineral density
High accuracy and reproducibility for all
age groups. Non-invasive, quick, no subject
performance needed. Measurements
are not confounded by disease states or
growth disorders. Gold standard for diagnosing
osteopenia and osteoporosis.
Small amount of radiation exposure.
Fat mass estimates are confounded
by trunk thickness (error increases
alongside degree of trunk thickness).
Compared to 4C, DXA may be
unreliable for longitudinal studies of
subjects who undergo major changes
in glycogen or hydration status
between measurements. Expensive
apparatus.
Ultrasound Tissue layer thickness (skin, adipose,
muscle)
Highly repeatable, readily available,
widely used, portable, quick. Noninvasive
and no ionizing radiation. Accurate and
precise estimates of fat thickness in
multiple sites of the body, capable of
measuring the thickness of muscle and
bone.
Requires a skilled, experienced
technician. Measurement procedures
and techniques are not yet
standardized. Inherent confounders
such as fascia can complicate the
interpretation of results. Higher cost
than field methods.
Magnetic resonance
imaging (MRI) and
computed tomography
Total and regional fat (including
subcutaneous and visceral), skeletal
muscle, organs and other internal
tissues, lipid content in muscle and liver
High accuracy and reproducibility. MRI
does not involve exposure to radiation.
Expensive, lengthy procedure. Limited
to accommodating normal to
moderately overweight individuals, but
not very large body sizes do not fit in
the field of view. High radiation
exposure with computed tomography.
Near-infrared
interactance (NIR)
Fat, protein, and water – based on
assumptions of optical density
Good test-retest and day-to-day reliability.
Quick, non-invasive.
Large standard errors of estimation
(SEE > 3.5% BF). Percent body fat is
systematically underestimated, and
this error increases alongside larger
body frames.
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 4 of 19
not consistently yielded greater weight loss than energy
restriction alone [37, 38]. Reasons for the disparity between
short- and long-term effects of energy density reduction
include speculation that learned compensation
is occurring. In addition, postprandial factors may increase
sensory-specific satiety that over time can reduce
the initial palatability of energy-dense foods [39].
Very-low-fat diets (VLFD) have been defined as providing
10–20% fat [29]. Diets fitting this profile have a limited
amount of research. The body of controlled intervention
data on VLFD mainly consists of trials examining the
health effects of vegetarian and vegan diets that aggressively
minimize fat intake. These diets have shown consistently
positive effects on weight loss [40], but this literature lacks
body composition data. Among the few studies that did,
the A TO Z Weight Loss Study by Gardner et al. [41], did
not show any significant between-group differences in body
fat reduction among the diets (Atkins, Zone, LEARN, and
Ornish). However, despite the Ornish group’s assigned fat
intake of ≤10% of total calories, actual intake progressed
from 21.1 to 29.8% by the end of the 12-month trial. Similar
results were seen by de Souza et al. [42] in the POUNDS
LOST trial. Four groups were assigned high-protein (25%)
and average-protein (15%) versions of high-fat (40%) and
low-fat (20%) diets. No significant between-group differences
were seen in the loss of total abdominal, subcutaneous,
or visceral fat at either six months or two years. A
mean loss of 2.1 kg LM and 4.2 kg FM occurred in both
groups at 6 months. No LM-retentive advantage was seen
in the higher-protein diets, but this could have been due to
both protein intake levels being sub-optimal (1.1 and 0.7 g/
kg). As seen in previous LFD research, the targeted restriction
to 20% fat was apparently difficult to attain since actual
intakes ranged 26–28%.
Low-carbohydrate diets
Similar to LFD, low-carbohydrate diets (LCD) are a broad
category lacking an objective definition. There is no universal
agreement on what quantitatively characterizes an LCD.
The AMDR lists 45–65% of total energy as the appropriate
carbohydrate intake for adults [33]. Therefore, diets with
intakes below 45% fall short of the ‘official’ guidelines and
can be viewed as LCD. However, other published definitions
of LCD disregard the limits set in the AMDR. LCD
have been defined as having an upper limit of 40% of total
energy from carbohydrate [43, 44]. In absolute rather than
proportional terms, LCD have been defined as having less
than 200 g of carbohydrate [43]. Some investigators have
taken issue with this liberal definition of LCD, preferring to
delineate non-ketogenic LCD as containing 50–150 g, and
KD as having a maximum of 50 g [45].
Meta-analyses comparing the effects of LFD with LCD
have yielded mixed results across a wide range of parameters.
Liberal operational definitions of LCD (e.g., ≤45%)
have led to a lack of significant differences in body weight
and waist circumference [46], while lower carbohydrate
classification thresholds (<20%) have favored LCD for
weight loss and other cardiovascular risk factors [47]. Recently,
Hashimoto et al. [48] conducted the first-ever metaanalysis
on the effect of LCD on fat mass (FM) and body
weight. The analysis, limited to trials involving overweight/
obese subjects, had a total of 1416 subjects, stratifying the
diets as “mild LCD” (~40% CHO) or “very LCD” (~50 g
CHO or 10% of total energy). Eight RCTs included a very
LCD treatment, and 7 RCTs included a mild LCD treatment.
With all groups considered, FM decrease was significantly
greater in the LCD than the control diets. However,
sub-analysis showed that fat mass decrease in very LCD
was greater than the controls, while the difference in FM
decrease between mild LCD and controls was not significant.
A separate sub-analysis of short- versus long-term effects
found that both types of LCD yielded significantly
greater fat loss than controls in trials less than, as well as
longer than 12 months. A further sub-analysis of found that
BIA failed to detect significant between-group differences
in FM reduction, while DXA showed significantly greater
decreases in LCD than controls. It should be noted that
despite reaching statistical significance, mean differences in
FM reduction between LCD and control groups were small
(range = 0.57–1.46 kg). Practical relevance is questionable
given the obese nature of the subjects. The authors speculated
that the advantage of the LCD over the control diets
could have been due to their higher protein content.
Ketogenic diets
Despite being a subtype of LCD, the ketogenic diet (KD)
deserves a separate discussion. Whereas non-ketogenic
LCD is subjectively defined, KD is objectively defined by its
ability to elevate circulating ketone bodies measurably – a
state called ketosis, also known as physiological or nutritional
ketosis. Aside from completely fasting, this condition
is attained by restricting carbohydrate to a maximum of
~50 g or ~10% of total energy [45], while keeping protein
moderate (1.2–1.5 g/kg/d) [49], with the remaining predominance
of energy intake from fat (~60–80% or more,
depending on degree protein and carbohydrate displacement).
Ketosis is a relatively benign state not to be confused
with ketoacidosis, which is a pathological state seen in type
1 diabetics, where a dangerous overproduction of ketones
occurs in the absence of exogenous insulin. The primary
ketone produced hepatically is acetoacetate, and the primary
circulating ketone is β-hydroxybutyrate [50]. Under
normal, non-dieting conditions, circulating ketone levels
are low (<3 mmol/l). Depending on the degree of restriction
of carbohydrate or total energy, KD can raise circulating
ketone levels to a range of ~0.5–3 mmol/l, with
physiological ketosis levels reaching a maximum of 7–
8 mmol/l [49].
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 5 of 19
The proposed fat loss advantage of carbohydrate reduction
beyond a mere reduction in total energy is based
largely on insulin-mediated inhibition of lipolysis and presumably
enhanced fat oxidation. However, a single-arm
study by Hall et al. [51] examined the effect of 4 weeks on
a low fat diet (300 g CHO) followed by 4 weeks on a KD
(31 g CHO). Blood ketone levels plateaued at ~1.5 mmol/l
within two weeks into the KD. A transient increase in energy
expenditure (~100 kcal/day) lasting a little over a
week occurred upon switching to the KD. This was accompanied
by a transient increase in nitrogen loss, potentially
suggesting a stress response including the ramping
up of gluconeogenesis. Although insulin levels dropped
rapidly and substantially during the KD (consisting of 80%
fat, 5% CHO), an actual slowing of body fat loss was seen
during the first half of the KD phase.
It has been postulated that the production and
utilization of ketone bodies impart a unique metabolic
state that, in theory, should outperform non-ketogenic
conditions for the goal of fat loss [45]. However, this claim
is largely based on research involving higher protein intakes
in the LCD/KD groups. Even small differences in
protein can result in significant advantages to the higher
intake. A meta-analysis by Clifton et al. [52] found that a
5% or greater protein intake difference between diets at
12 months was associated with a threefold greater effect
size for fat loss. Soenen et al. [53] systematically demonstrated
that the higher protein content of lowcarbohydrate
diets, rather than their lower CHO content,
was the crucial factor in promoting greater weight loss
during controlled hypocaloric conditions. This is not too
surprising, considering that protein is known to be the
most satiating macronutrient [54]. A prime example of
protein’s satiating effect is a study by Weigle et al. [55]
showing that in ad libitum conditions, increasing protein
intake from 15 to 30% of total energy resulted in a spontaneous
drop in energy intake by 441 kcal/day. This led to
a body weight decrease of 4.9 kg in 12 weeks.
With scant exception [56], all controlled interventions
to date that matched protein and energy intake between
KD and non-KD conditions have failed to show a fat loss
advantage of the KD [51, 53, 57–60]. A recent review by
Hall [61] states, “There has never been an inpatient controlled
feeding study testing the effects of isocaloric diets
with equal protein that has reported significantly increased
energy expenditure or greater loss of body fat
with lower carbohydrate diets.” In light of this and the
previously discussed research, the ‘special effects’ of
LCD and KD are not due to their alleged metabolic advantage,
but their higher protein content. Perhaps the
strongest evidence against the alleged metabolic advantage
of carbohydrate restriction is a recent pair of metaanalyses
by Hall and Guo [60], which included only isocaloric,
protein-matched controlled feeding studies
where all food intake was provided to the subjects (as
opposed to self-selected and self-reported intake). A
total of 32 studies were included in the analysis. Carbohydrate
ranged from 1 to 83% and dietary fat ranged
from 4 to 84% of total energy. No thermic or fat loss advantage
was seen in the lower-CHO conditions. In fact,
the opposite was revealed. Both energy expenditure (EE)
and fat loss were slightly greater in the higher-CHO/
lower-fat conditions (EE by 26 kcal/day, fat loss by 16 g/
d); however, the authors conceded that these differences
were too small to be considered practically meaningful.
A common criticism of the existing literature is that trials
need to run longer (several months instead of several
weeks) to allow sufficient “ketoadaptation,” which is a
physiological shift toward increased fat oxidation and decreased
glycogen utilization [62]. The problem with this
claim is that the rise in fat oxidation – objectively measured
via decreased respiratory quotient – reaches a plateau
within the first week of a KD [51]. Increased oxidation
of free fatty acids, plasma triacylglycerol, and intramuscular
triacylglycerol during exercise is a well-established response
to fat-rich diets [63]. However, this rise in fat
oxidation is often misconstrued as a greater rate of net
FM reduction. This assumption ignores the concomitant
increase in fat intake and storage. As a result of fatadaptation,
increased intramuscular triacylglycerol levels
indicate increased fat synthesis over degradation during
the rest periods between exercise bouts [64]. To reiterate
a previous point, rigorously controlled isocaloric, proteinmatched
studies have consistently demonstrated that
ketoadaptation does not necessarily amount to a net decrease
in fat balance, which is ultimately what matters.
If there is any advantage to KD over non-KD for fat loss,
it is potentially in the realm of appetite regulation. Under
non-calorically restricted conditions, KD has consistently
resulted in body fat and/or body weight reduction [65–69].
This occurs via spontaneous energy intake reduction, which
could be due to increased satiety through a suppression of
ghrelin production [70]. Moreover, KD has demonstrated
hunger-suppressive effects independent of protein content.
In a 4-week crossover design, Johnstone et al. [66] found
that a KD consumed ad libitum (without purposeful caloric
restriction) resulted in an energy intake reduction of
294 kcal/day. The latter results were seen despite a relatively
high protein intake (30% of energy) matched between
KD (4% CHO) and non-KD (35% CHO) conditions. In further
support of this idea, a meta-analysis by Gibson et al.
[71] found that KD suppresses appetite more than VLED.
However, it remains unclear whether the appetite suppression
is due to ketosis or other factors such as an increased
protein or fat intake, or restriction of carbohydrate.
An area of growing interest is the effect of KD on athletic
performance. Since training capacity has the potential
to affect body composition, the effect of KD on
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 6 of 19
exercise performance warrants discussion. Carbohydrate
restriction combined with high fat intake to become fatadapted
(or ketoadapted) is a tactic that attempts to improve
performance by increasing the body’s reliance on
fat as fuel, thereby sparing/decreasing glycogen use,
which ostensibly could improve athletic performance.
However, in contrast to the proposed benefits of fatadaptation
on performance, Havemann et al. [72] found
that 7 days of a high-fat diet (68%) followed by 1 day of
high-CHO diet (90%) expectedly increased fat oxidation,
but decreased 1-km sprint power output in well-trained
cyclists. Stellingwerff et al. [73] compared substrate
utilization, glycogenolysis, and enzymatic activity from
either 5 days of a high-fat diet (67%) or high-CHO (70%)
followed by one day of high-CHO with no training,
followed by experimental trials on the seventh day. The
high-fat diet increased fat oxidation, but also lowered
pyruvate dehydrogenase activity and decreased glycogenolysis.
These results provide a mechanistic explanation
for the impairment in high-intensity work output as a
result of high-fat, CHO-restricted diets [62, 65, 67]. Recently,
an ergolytic effect from ketoadaptation has been
observed at lower intensities as well. Burke et al. [74] reported
that after 3 weeks on a KD at a slight energy deficit,
elite race walkers showed increased fat oxidation
and aerobic capacity. However, this was accompanied by
a reduction in exercise economy (increased oxygen demand
for a given speed). The linear and non-linear highCHO
diets in the comparison both caused significant
performance improvements, while no significant improvement
was seen in the KD (there was a nonsignificant
performance decrease). It is notable that Paoli et al.
[75] found no decrease in bodyweight-based strength
performance in elite artistic gymnasts during 30 days of
KD. Furthermore, the KD resulted in significant loss of
FM (1.9 kg) and non-significant gain of LM (0.3 kg).
However, unlike Burke et al.’s study, which equated protein
between groups (~2.2 g/kg), Paoli et al.’s protein intakes
were skewed in favor of the KD (2.9 vs. 1.2 g/kg).
Wilson et al. [56] recently reported similar increases in
strength and power in a protein and calorie-matched
comparison of a KD and a Western diet model, suggesting
that KD might have less ergolytic potential for
strength training than it does for endurance training.
High-protein diets
A common thread among high-protein diets (HPD) is
that they have their various and subjective definitions.
High-protein diets have been more generally defined as
intakes reaching [76] or exceeding 25% of total energy
[29]. High-protein diets have also been identified as ranging
from 1.2–1.6 g/kg [54]. Classic work by Lemon et
al. showed that protein consumed at double the RDA
(1.6 g/kg) repeatedly outperformed the RDA (0.8 g/kg)
for preserving LM and reducing FM [77, 78]. However,
Pasiakos et al. [79] found that triple the RDA (2.4 g/kg)
did not preserve lean mass to a significantly greater extent
than double the RDA. More recently, Longland et
al. [80] found that in dieting conditions involving highintensity
interval sprints and resistance training, protein
intake at 2.4 g/kg caused LM gains (1.2 kg) and fat loss
(4.8 kg), while 1.2 g/kg resulted in preservation of lean
mass (0.1 kg), and less fat loss (3.5 kg). A unique methodological
strength in Longland et al.’s design was the
use of the 4C model to assess body composition. Subjects
were also provided all food and beverage intake,
which added an extra layer of control and strengthened
the findings. Augmenting this body of literature is
Arciero et al.’s work on “protein-pacing” (4–6 meals/day,
>30% protein per meal resulting in >1.4 g/kg/d), which
has demonstrated this method’s superiority over conventional,
lower-protein/lower-frequency diets for improving
body composition in hypocaloric conditions [81, 82].
Of the macronutrients, protein has the highest thermic
effect and is the most metabolically expensive. Given
this, it is not surprising that higher protein intakes have
been seen to preserve resting energy expenditure while
dieting [54]. Also, protein is the most satiating macronutrient,
followed by carbohydrate, and fat being the least
[83]. With just one exception [84], a succession of recent
meta-analyses [52, 85–87] supports the benefit of higher
protein intakes for reducing body weight, FM, and waist
circumference, and preserving LM in an energy deficit.
A systematic review by Helms et al. [88] suggested that
protein intakes of 2.3–3.1 g/kg FFM was appropriate for
lean, resistance trained athletes in hypocaloric conditions.
This is one of the rare pieces of literature that report
protein requirements on the basis of FFM rather
than total body weight.
Antonio et al. [89–92] recently began a series of investigations
of which can be considered super-HPD. First in
the series, the addition of dietary protein amounting to
4.4 g/kg for eight weeks in resistance-trained subjects
did not significantly change body composition compared
to control conditions of maintenance intake with habitual
protein at 1.8 g/kg. Remarkably, the additional protein
amounted to an ~800 kcal/day increase, and did not
result in additional weight gain. A subsequent 8-week
investigation involved resistance-trained subjects on a
formally administered, periodized resistance training
protocol [90]. The high-protein group (HP) consumed
3.4 g/kg, while the normal-protein group (NP) consumed
2.3 g/kg. HP and NP showed significant gains in
LM (1.5 kg in both groups). A significantly greater fat
mass decrease occurred in HP compared to NP (1.6 and
0.3 kg, respectively). This is intriguing, since HP reported
a significant increase caloric intake compared to
baseline (374 kcal), while NP’s caloric increase was not
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 7 of 19
statistically significant (103 kcal). A subsequent 8-week
crossover trial [91] in resistance-trained subjects compared
protein intakes of 3.3 versus 2.6 g/kg/d. A lack of
significant differences in body composition and strength
performance were seen despite a significantly higher caloric
intake in HP vs. NP (an increase of 450 vs. 81 kcal
above baseline). Antonio et al.’s most recent investigation
[92] was a 1-year crossover trial using resistancetrained
subjects, comparing protein intakes of 3.3 vs.
2.5 g/kg. In agreement with previous findings, there
were no differences in body composition (importantly,
no significant increase in fat mass), despite a significantly
higher caloric intake in HP vs. NP (an increase of
450 vs. 81 kcal above baseline). This study also addressed
health concerns about long-term high protein
intakes (3–4 times the RDA) by demonstrating no adverse
effects on a comprehensive list of measured clinical
markers, including a complete metabolic panel and
blood lipid profile.
An in-patient, metabolic ward study by Bray et al. [76]
compared 8 weeks of hypercaloric conditions with protein
at 5 (LP), 15 (NP), and 25% of total energy (HP). All
three groups gained total body weight, but LP lost 0.7 kg
LM. Moreover, the NP and HP groups gained 2.87 and
3.98 kg LM, respectively. All three groups gained body
fat (3.51 kg) with no significant difference between
groups. These results are seemingly at odds with Antonio
et al.’s observations [89–92]. However, aside from
the tighter control and surveillance inherent with metabolic
ward conditions, Bray et al.’s subjects were untrained
and remained sedentary throughout the study.
Antonio et al.’s well-trained subjects were undergoing
intensive resistance training and could have had an advantage
regarding fuel oxidation and preferential nutrient
partitioning toward lean body mass.
Speculation over the fate of the extra protein consumed
in the Antonio et al. studies [89–92] may include a higher
thermic effect of feeding, increased non-exercise activity
thermogenesis (NEAT), increased thermic effect of exercise
(TEE), increased fecal energy excretion, reduced intake
of the other macronutrients via increased satiety and
suppressed hepatic lipogenesis. It should be noted as well
that there might have been a misreporting of energy intake.
Antonio et al.’s findings collectively suggest that the
known thermic, satiating, and LM-preserving effects of
dietary protein might be amplified in trained subjects
undergoing progressive resistance exercise.
Intermittent fasting
Intermittent fasting (IF) can be divided into three subclasses:
alternate-day fasting (ADF), whole-day fasting
(WDF), and time-restricted feeding (TRF) [93]. The
most extensively studied IF variant is ADF, which typically
involves a 24-hour fasting period alternated with a
24-hour feeding period. Complete compensatory intake
on the feeding days (to offset the fasting days’ deficit)
does not occur, and thus total weight loss and fat loss
occurs on ADF. Lean mass retention has been a surprisingly
positive effect of ADF [94–97]. However, lean mass
loss in ADF conditions has also been observed by other
investigators [98–100]. The latter effect might be attributable
to more severe energy deficits. The more lean
mass-friendly is an energy-restricted period (~25% of
maintenance requirements, typically in the form of a single
meal at lunchtime) alternated with a 24-hour ad libitum
(as desired) feeding period. Recently, Catenacci et
al. [97] reported that ADF with zero caloric intake on
the fasting days alternated with ad libitum feeding days
showed similar results to daily caloric restriction on
body composition, and slightly outperformed daily caloric
restriction after 6-months of unsupervised weight
loss maintenance. On the note of alternating fasting and
feeding periods of the same length, alternate-week energy
restriction (1 week on ~1300 kcal/day, one week on
the usual diet) has only a single study to date, but is
worth mentioning since it was as effective as continuous
energy restriction for reducing body weight and waist
girth at 8 weeks and 1 year [101].
Whole-day fasting involves one to two 24-hour fasting
periods throughout the week of otherwise maintenance
intake to achieve an energy deficit. Of note, not all WDF
studies involve zero energy intake during the ‘fasting’
days. Although WDF has been consistently effective for
weight loss, Harvie et al. [102] saw no difference in body
weight or body fat reduction between the WDF (2 ‘fasting’
days of ~647 kcal) group and controls when the
weekly energy deficit was equated over a 6-month
period. A subsequent study by Harvie et al. [103] compared
daily energy restriction (DER) with two separate
WDF diets: one with two structured energy-restricted
‘fasting’ days per week, and one whose 2 ‘fasting’ days
consisted of ad libitum protein and unsaturated fat. Both
WDF diets caused greater 3-month fat loss than DER
(3.7 vs. 2.0 kg). An important detail here is that at 3
months, 70% of the fasting days were completed in the
WDF groups while the DER group achieved their targeted
caloric deficit only 39% of the trial.
Time-restricted feeding typically involves a fasting
period of 16–20 hours and a feeding period of 4–8 hours
daily. The most widely studied form of TRF is Ramadan
fasting, which involves approximately 1 month of
complete fasting (both food and fluid) from sunrise to
sunset. Unsurprisingly, significant weight loss occurs,
and this includes a reduction in lean mass as well as fat
mass [104, 105]. Aside from Ramadan fasting studies,
dedicated TRF research has been scarce until recently.
An 8-week trial by Tinsley et al. [106] examined the effect
of a 20-hour fasting/4-hour feeding protocol (20/4)
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 8 of 19
done 4 days per week on recreationally active, but untrained
subjects. No limitations were placed on the
amounts and types of food consumed in the 4-hour eating
window. A standardized resistance training program
was administered 3 days per week. The TRF group lost
body weight, due to a significantly lower energy intake
(667 kcal less on fasting compared to non-fasting days).
Cross sectional area of the biceps brachii and rectus
femoris increased similarly in both the TRF and normal
diet (ND) group. No significant changes in body composition
(via DXA) were seen between groups. Despite a
lack of statistical significance, there were notable effect
size differences in lean soft tissue (ND gained 2.3 kg,
while TRF lost 0.2 kg). Although both groups increased
strength without significant between-group differences,
effect sizes were greater in the TRF group for bench
press endurance, hip sled endurance, and maximal hip
sled strength. This finding should be viewed cautiously
given the potential for greater and more variable neurological
gains in untrained subjects.
A subsequent study by Moro et al. [107] found that in
resistance-trained subjects on a standardized training
protocol, a 16-hour fasting/8-hour feeding cycle (16/8)
resulted in significantly greater FM loss in TRF vs. normal
diet control group (ND) (1.62 vs. 0.31 kg), with no
significant changes in LM in either group. TRF’s meals
were consumed at 1 pm, 4 pm, and 8 pm. ND’s meals
were consumed at 8 am, 1 pm, and 8 pm. Macronutrient
intake between the TRF and ND groups was matched,
unlike the aforementioned Tinsley et al. study [106]
whereby protein intake was disparate and sub-optimal
(1.0 g/kg in the TRF group and 1.4 g/kg in the ND control
group). Subjects in the present study’s TRF and ND
group consumed 1.93 and 1.89 g/kg, respectively. The
mechanisms underlying these results are not clear. The
authors speculated that increased adiponectin levels in
the TRF group could have stimulated mitochondrial biogenesis
via interacting with PPAR-gamma, in addition to
adiponectin acting centrally to increase energy expenditure.
However, the TRF group also experienced unfavorable
changes such as decreased testosterone and
triiodothyronine levels.
Seimon et al. [108] recently published the largest systematic
review of IF research to date, comparing the
effects of intermittent energy restriction (IER) to continuous
energy restriction (CER) on body weight, body composition,
and other clinical parameters. Their review
included 40 studies in total, 12 of which directly compared
an IER with a CER condition. They found that overall, the
two diet types resulted in “apparently equivalent outcomes”
in terms of body weight reduction and body composition
change. Interestingly, IER was found to be
superior at suppressing hunger. The authors speculated
that this might be attributable to ketone production in the
fasting phases. However, this effect was immaterial since
on the whole, IF failed to result in superior improvements
in body composition or greater weight loss compared to
CER. Table 2 outlines the characteristics of the major diet
archetypes.
Mechanisms governing changes in body
composition vis a vis diet alterations
Calories in/calories Out (CICO)
In its simplest form, CICO is an acronym for the idea
that weight loss or gain is determined by a caloric deficit
or surplus, regardless of diet composition. While this
technically is true, it fails to account for the composition
of the weight gained or lost, as well as the multitude of
factors that drive eating behaviors that dictate caloric intake.
Both voluntary and involuntary factors govern the
“calories out” side of the equation, beginning with the
varying metabolic cost of processing the macronutrients.
As reported by Jéquier, the thermic effect of protein
(expressed as a percentage of energy content) is 25–30%,
carbohydrate is 6–8%, and fat is 2–3% [109]. However,
Halton and Hu [110] reported greater variability, with
the thermic effect of protein being 20–35%, carbohydrate
at 5–15%, and fat being subject to debate since
some investigators found a lower thermic effect than
carbohydrate while others found no difference.
Variability in the thermic effect of fat can be attributed
to differences in molecular structure that significantly
alter its metabolism. For example, Seaton et al. [111]
found that medium chain triglycerides (MCTs) produced
a significantly greater thermic effect than long chain triglycerides
during a 6-hour postprandial period (12 vs.
4% higher than basal oxygen consumption). Differences
in the TEF of protein have also been observed in direct
comparisons. Acheson et al. [112] demonstrated that
within mixed-macronutrient meals (50% protein, 40%
CHO, 10% fat) meals, whey had a higher thermic effect
than casein, which had a higher thermic effect than soy
protein. All protein sources had a higher thermic effect
than an all-CHO meal. Importantly, the thermic effect of
each macronutrient can vary within and across individuals
[113]. In any case, protein has consistently shown a
higher thermic effect than carbohydrate or fat. Alcohol
has been reported to have a similar thermic effect to
protein but with a wider range of 10–30% [114].
The thermic effect of food (TEF), also called diet-induced
thermogenesis, is one of several components of EE. TEF
represents approximately 8–15% of total daily energy expenditure
(TDEE) [115]. The largest component of TDEE,
at least among individuals not involved in extremely high
volumes of exercise, is resting energy expenditure (REE),
which is often mentioned interchangeably with resting
metabolic rate (RMR) or basal metabolic rate (BMR). Basal
metabolic rate is the energetic cost of the biological
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 9 of 19
processes required for survival at rest. As a matter of technical
trivia, BMR is measured in an overnight fasted state,
lying supine at complete rest, in the postabsorptive state
(the condition in which the gastrointestinal tract is empty
of nutrients and body stores must supply required energy).
REE/RMR represents fasted-state energy expenditure at
rest at any time of the day, and can range 3–10% higher
than BMR due to the residual influence of TEF and physical
activity [116].
Basal metabolic rate typically amounts to 60–70% of
TDEE. The other main component of TDEE is nonresting
energy expenditure, which is comprised of 3 subcomponents:
non-exercise activity thermogenesis (NEAT),
exercise activity thermogenesis (EAT), and finally, TEF.
NEAT encompasses the energy expenditure of occupation,
leisure, basic activities of daily living, and unconscious/
spontaneous activity such as fidgeting. While BMR and
TEF are relatively static, NEAT and EAT vary widely
within and across individuals. EAT has been reported to
range from 15 to 30% of TDEE [115], but the role of
NEAT is more easily overlooked. NEAT comprises ~15%
of TDEE in sedentary individuals and perhaps 50% or
more in highly active individuals [117]. The impact of
NEAT can be substantial since it can vary by as much as
2000 kcals between individuals of similar size [118].
Table 3 outlines the components of TDEE, with examples
of low, moderate, and high TDEE [115–117].
The oversimplification of the CICO concept has led to
a call to “eat less, move more” as a solution to the obesity
pandemic. While this advice technically is the answer,
the challenge lies in programming the variables so that
the desired energy balance is sustained over the longterm,
and the targeted body composition is reached and
maintained while preventing or minimizing REE losses.
Involuntary adaptive shifts separate humans from machines.
We differ from bomb calorimeters primarily due
to our dynamic nature, which is based on the homeostatic
drive toward survival. When hypocaloric conditions
are imposed, energy expenditure has a tendency to
decrease. Conversely, when a caloric surplus is imposed,
Table 2 Diet categories
Diet Composition Strengths Limitations
Low-energy
diets (LED)
LED: 800–1200 kcal/day
VLED: 400–800 kcal/day
Rapid weight loss (1.0–2.5 kg/week, diets involve
premade products that eliminate or minimize the
need for cooking and planning.
VLED have a higher risk for more severe side-effects,
but do not necessary outperform LED in the long-
term
Low-fat diets
(LFD)
LFD: 25–30% fat
VLFD: 10–20% fat
LFD have the support of the major health
organizations due to their large evidence basis in the
literature on health effects. Flexible macronutrient
range. Does not indiscriminately vilify foods based
on CHO content.
Upper limits of fat allowance may falsely convey the
message that dietary fat is inherently antagonistic to
body fat reduction. VLFD have a scarce evidence
basis in terms of comparative effects on body
composition, and extremes can challenge
adherence.
Low-
carbohydrate
diets (LCD)
50–150 g CHO, or up
to 40% of kcals from
CHO
Defaults to higher protein intake. Large amount of
flexibility in macronutrient proportion, and by
extension, flexibility in food choices. Does not
indiscriminately prohibit foods based on fat content.
Upper limits of CHO allowance may falsely convey
the message that CHO is inherently antagonistic to
body fat reduction.
Ketogenic
diets (KD)
Maximum of ~50 g
CHO
Maximum of ~10%
CHO
Defaults to higher protein intake. Suppresses
appetite/controls hunger, causes spontaneous
reductions in kcal intake under non-calorically restricted
conditions. Simplifies the diet planning and
decision-making process.
Excludes/minimizes high-CHO foods which can be
nutrient dense and disease-preventive. Can compromise
high-intensity training output. Has not
shown superior effects on body composition compared
to non-KD when protein and kcals are
matched. Dietary extremes can challenge long-term
adherence.
High-protein
diets (HPD)
HPD: ≥ 25% of total
kcals, or 1.2–1.6 g/kg
(or more)
Super HPD: > 3 g/kg
HPD have a substantial evidence basis for improving
body composition compared to RDA levels (0.8 g/
kg), especially when combined with training. SuperHPD
have an emerging evidence basis for use in
trained subjects seeking to maximize intake with
minimal-to-positive impacts on body composition.
May cause spontaneous reductions in total energy
intake that can antagonize the goal of weight gain.
Potentially an economical challenge, depending on
the sources. High protein intakes could potentially
displace intake of other macronutrients, leading to
sub-optimal intakes (especially CHO) for athletic performance
goals.
Intermittent
fasting (IF)
Alternate-day fasting
(ADF): alternating 24-h
fast, 24-h feed.
Whole-day fasting
(WDF): 1–2 complete
days of fasting per
week.
Time-restricted feeding
(TRF): 16–20-h fast, 4–
8-h feed, daily.
ADF, WDF, and TRF have a relatively strong evidence
basis for performing equally and sometimes
outperforming daily caloric restriction for improving
body composition. ADF and WDF have ad libitum
feeding cycles and thus do not involve precise
tracking of intake. TRF combined with training has an
emerging evidence basis for the fat loss while
maintaining strength.
Questions remain about whether IF could
outperform daily linear or evenly distributed intakes
for the goal of maximizing muscle strength and
hypertrophy. IF warrants caution and careful
planning in programs that require optimal athletic
performance.
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 10 of 19
EE has a tendency to increase. However, human energy
balance has been called an asymmetric control system
[119], because it tends to be lopsided in favor of more
easily gaining weight but less easily losing weight. This
asymmetry has been attributed to evolutionary pressures
that selected the survival of “metabolically thrifty” individuals
who more easily stored body fat during times of
famine [120].
The degree of processing or refinement of foods can influence
their thermic effect. Barr and Wright [121] found a
diet-induced thermogenesis of 137 kcal for a ‘whole food’
meal, and 73 kcal for the processed food meal. The ‘whole
food’ meal had 5% more protein, and 2.5 g more fiber, but
these factors are too small to account for the substantial
difference in postprandial energy expenditure. The authors
speculated that the greater mechanized preparation of the
processed food caused less peristalsis and a greater loss of
bioactive compounds, resulting in fewer metabolites, thus
requiring less enzyme activity. This would lead to more energetically
efficient absorption and metabolism. It is important
to note that this was not a comparison of a highly
processed food versus a whole food. Both of the meals in
the comparison were cheese sandwiches. One just happened
to have less mechanical refinement, and slightly
more fiber and protein. The results of this study imply that
processed foods are more fattening or less effective for
weight management. However, the contrary has been demonstrated.
Meal replacement products (powders, shakes,
and bars) have matched or outperformed the effectiveness
of whole food-based diets for weight loss and weight loss
maintenance [82, 122, 123].
An awareness of tissue-specific metabolism can be
helpful in understanding the resting metabolic benefits
of improving body composition. It can also serve to clarify
the widely misunderstood and often overestimated
contribution of muscle to REE. McClave and Snider
[124] reported that the greatest contributors to REE, per
unit of mass, are the heart and kidneys, each spending
approximately 400 kcal/kg/day. Next in the hierarchy
are the brain and the liver, at 240 and 200 kcal/kg/day,
respectively. These four organs constitute up to 70–80%
of REE. In contrast, muscle and adipose tissue expend
13 and 4.5 kcal/kg/day, respectively. This should debunk
the notion that increases in muscle mass give individuals
the license to reduce dietary discretion. Even a relatively
significant muscular gain of 5 kg would increase REE by
only ~65 kcal/day. However, on a net basis (accounting
for the total mass of each tissue in the body), muscle,
brain, and liver are the top-3 contributors to overall
REE. Thus, substantial losses in LM – including muscle
– can meaningfully impact REE. Finally, it should be
noted that tissue-specific EE can vary according to obese
vs. non-obese status, advanced age, and to a lesser degree,
sex [125]. Table 4 outlines the contribution of organs
and tissues to REE in healthy adult humans [124].
Adaptations to underfeeding
Humans have a remarkable ability to maintain a relatively
constant body weight through adult life despite wide variations
in daily energy intake and expenditure. This indicates
a highly sophisticated integration of systems that
tirelessly auto-regulate homeostasis. In the case of hypocaloric
conditions, the body up-regulates hunger and downregulates
energy expenditure. The integration of physiological
factors regulating the body’s defense against weight
loss (and also weight gain) is symphonic. The central nervous
system ‘communicates’ with the adipose tissue,
gastrointestinal tract and other organs in an effort to defend
against homeostatic changes. This regulatory system
is influenced by nutritional, behavioral, autonomic, and
endocrine factors [126].
The changes in EE are not always completely
accounted for by changes in lean mass and fat mass.
Therefore, in the context of hypocaloric diets, adaptive
thermogenesis (AT) is a term used to describe the gray
area where losses in metabolic tissue cannot simply explain
reduced EE. In lean and obese subjects, maintaining
a drop of ≥10% of total body weight results in a
~20–25% decrease in TDEE [127]. AT is a 10–15% drop
in TDEE beyond what is predicted by losses in LM and
FM as a result of maintaining a loss of ≥10% of total
body weight. In weight-reduced subjects, the vast majority
of (85–90%) of AT is due to decreased non-resting
energy expenditure. The mechanisms underlying AT are
unclear, but speculations include increased sympathetic
drive and decreased thyroid activity. A classic experiment
by Leibel et al. [128] demonstrated that in obese
subjects, a 10% or greater weight loss resulted in a 15%
greater EE reduction than predicted by body composition
change. However, these subjects were put on an
Table 3 Components of total daily energy expenditure
Component of TDEE Percent of TDEE Example:
1600 kcal TDEE
Example:
2600 kcal TDEE
Example:
3600 kcal TDEE
Thermic effect of food (TEF) 8–15 128–240 208–390 288–540
Exercise activity thermogenesis (EAT) 15–30 240–480 390–780 540–1080
Non-exercise activity thermogenesis (NEAT) 15–50 240–800 390–1300 540–1800
Basal metabolic rate (BMR) 60–70 960–1120 1560–1820 2160–2520
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 11 of 19
800 kcal liquid diet composed of 15% protein, 45%
CHO, and 40% fat. Imposed reductions in EE via lowprotein
VLED do not necessarily reflect what is possible
under conditions involving better macronutrient targets
and proper training.
In contrast to Leibel et al.’s findings [128] and a recent
study by Rosenbaum and Leibel [129] using the same
low-protein VLED, Bryner et al. [25] observed an increased
RMR by the end of 12 weeks in subjects on an
800 liquid kcal diet. The discrepancy between Bryner et
al.’s results and those of Leibel et al. can be explained by
better macronutrient distribution and the implementation
of resistance exercise. Bryner et al.’s VLED was
composed of 40% protein, while Leibel et al.’s was 15%
(30 g protein). Bryner’s subjects underwent full-body resistance
training three times per week, while Leibel’s design
neglected exercise programming altogether.
More recently, Camps et al. [130] found that after significant
weight loss resulting from 8 weeks on a VLED,
reduced EE beyond what was predicted was still present
after a year. While this can be viewed as the unfortunate
persistence of weight loss-induced AT, the actual difference
in RMR at baseline versus 52 weeks was a reduction
of 81 kcal, where total weight loss was 5.4 kg
(5.0 kg of which was FM). However, it is worth reiterating
that higher protein alongside resistance training has
been shown to prevent this impairment despite severe
caloric restriction [25]. As it stands, the subjects were
not engaged in structured exercise at any point (let alone
a resistance training program that would support the
metabolic activity of lean mass), and the details of their
maintenance diet were not reported. In all likelihood, it
was not optimized in terms of macronutrition.
Misreporting energy intake and output is a common
occurrence that has the potential to be mistaken for
metabolic adaptation. For example, Lichtman et al. [131]
used indirect calorimetry and doubly labeled water to
objectively assess energy intake and output in obese subjects
with a history of diet resistance, and a claimed intake
of less than 1200 kcal/day. In the experimental
group, no subject had a TEE more than 9.6% below the
predicted values (average TEE was 2468 kcal), and no
subject had a RMR more than 10.4% below predicted
values. It was determined that instead of some defect in
thermogenesis, subjects under-reported their intake by
an average of 47% (1053 kcal/day), and over-reported
physical activity by 51% (251 kcal/day). Clearly, the gap
between perceived compliance and actual compliance remains
a major challenge to the goal of improving body
composition.
Adaptations to overfeeding
In hypocaloric conditions, adaptive thermogenesis (AT)
is a misnomer; it would more accurately be called adaptive
thermoreduction due to a reduction in energy expenditure
in response to reductions in energy intake.
However, “adaptive thermogenesis” would be a more appropriate
term for describing the production of heat in
response to reductions in environmental temperature, or
hypercaloric conditions. Joosen and Westerterp [132] examined
the literature (11 studies) to see if AT existed in
overfeeding experiments. No evidence beyond the theoretical
costs of increased body size and TEF were found.
Nevertheless, there is substantial interindividual variability
in the energetic response to overfeeding. Some individuals
appear to be resistant to weight/fat gain, showing
a concurrent increase in expenditure alongside increased
intake. Others show less homeostatic drive and greater
efficiency of energy storage. This interindividual variability
is due, at least in part, to differences in NEAT.
A question relevant to fitness, sports nutrition, and body
composition-oriented goals is whether so-called “hardgainers”
have a metabolic impediment against weight gain or
whether this is a lack of conscious discipline to sustain a
caloric surplus. It is possible that conscious and unconscious
increases in NEAT can pose a significant challenge
to weight gain. A prime illustration of this is a study by
Levine et al. [133], who fed non-obese adults 1000 kcal
above their maintenance needs for eight weeks. On average,
432 kcal were stored, and 531 kcal were burned.
Nearly two-thirds of the latter (336 kcal) was attributable
to NEAT, which on the upper end of the range was
692 kcal/day. This finding explains why some individuals
can purposely increase daily caloric intake and still
Table 4 Energy Expenditure of Different Tissues/Organs
Organ or tissue Metabolic rate (kcal/kg/day) % Overall REE Weight (kg) % of Total body weight
Adipose 4.5 4 15 21.4
Other (bone, skin, intestine, glands) 12 16 23.2 33.1
Muscle 13 22 28 40.0
Liver 200 21 1.8 2.6
Brain 240 22 1.4 2.0
Heart 400 9 0.3 0.5
Kidneys 400 8 0.3 0.5
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 12 of 19
experience a lack of weight gain. Unbeknownst to them,
increased NEAT can negate the targeted caloric surplus.
The partitioning of a chronic energy surplus into the
various tissue compartments is an important yet understudied
area. Rosqvistet al. [134] compared the effects of
hypercaloric diets fortified with polyunsaturated fatty acid
(PUFA) versus saturated fatty acid (SFA). Despite similar
gains in total body weight (1.6 kg, via an additional
750 kcal/day from fat-fortified muffins), the ratio of
LM:FM gained in the PUFA group was 1:1, whereas it was
1:4 in the SFA group, indicating a better LM-partitioning
effect of surplus energy from PUFA. Furthermore, liver fat
and visceral fat deposition were significantly greater in
SFA. The authors speculated that a greater oxidation of
PUFA might have decreased the production of nonesterified
fatty acids, which in turn could have lowered
hepatic triacylglycerol synthesis. Caution is warranted
when attempting to generalize these results beyond the fat
sources used (palm oil for SFA, sunflower oil for PUFA).
Chronic overfeeding adaptations can also vary according
to training status. Garthe et al. [135] compared the 12week
effects of 3585 kcal/day (544 kcal increase from
baseline intake) in a nutritionally counseled group vs.
2964 kcal/day (128 kcal decrease from baseline) in the ad
libitum group, without counseling. Elite athletes in a variety
of sports were used. Lean mass gains were slightly but
not significantly higher in the nutritionally counseled
group (1.7 kg vs. 1.2 kg), but fat gain was also significantly
higher (1.1 kg vs. 0.2 kg). In contrast, Rozenek et al. [136]
compared the 8-week effects of a massive caloric surplus
(2010 kcal/day) consisting of 356 g carbohydrate, 106 g
protein, and 18 g fat (CHO-PRO), or an isocaloric highercarb
treatment (CHO) consisting of 450 g carbohydrate,
24 g protein, and 14 g fat. A non-supplemented control
group was included in the comparison, and this group
underwent the same progressive resistance training protocol
as the treatment groups. In contrast to Garthe et al.’s
findings [135], Roznek et al.’s subjects gained almost exclusively
LM in the CHO-PRO group (2.9 kg) with very
little fat mass gain (0.2 kg). The CHO group showed
slightly better results than CHO-PRO, although not to a
statistically significant degree (3.4 kg LM gain, 0.3 kg FM
loss). It was speculated that both groups consumed adequate
protein at baseline (1.6 g/kg), so the additional protein
in CHO-PRO (which increased protein intake to
2.9 g/kg) did not further enhance LM gains. Garthe et al.
[135] saw a significant amount of fat gain alongside the
lean gain despite a much smaller caloric surplus (544 vs.
2010 kcal above maintenance). However, Garthe et al.’s
subjects were elite athletes, while Rozenek et al.’s subjects
were untrained, so it is possible that they were better
primed for more dramatic progress in both departments
(LM gain with minimal FM gain) despite the massive caloric
surplus.
It can be argued that sustaining a caloric surplus is not
necessary for muscle anabolism since LM gains have been
reported in the literature during hypocaloric conditions
[26, 80, 137, 138]. However, Pasiakos et al. [139] demonstrated
a significant decrease in muscle protein synthesis
and lower phosphorylation of associated intracellular signaling
proteins during 10 days of a moderate energy deficit
(80% of estimated energy requirements). Therefore, it
is likely that diets seeking to optimize rates of LM gain are
compromised by sustained caloric deficits, and optimized
by sustained caloric surpluses to facilitate anabolic processes
and support increasing training demands.
Summary and conclusions
Summary
Understanding how various diet types affect body composition
is of utmost importance to researchers and practitioners.
Ultimately, the interpretation of the data and
implementation of the procedures determine the progress
made by clients, patients, and the public. Fortunately, the
current body of research is rich with information that can
guide evidence-based theory and practice. Body composition
assessment methods vary in their level of precision,
reliability, and availability. Each method has its strengths
and weaknesses. No single approach is ideal for all circumstances.
Rather, the practitioner or researcher must
employ the most practical option for the assessment needs
of the individuals at hand, in order to achieve consistency
in the face of inherent limitations and logistical considerations
such as financial expense and technician skill. The
various diet archetypes are wide-ranging in total energy
and macronutrient distribution. Each type carries varying
degrees of supporting data, and varying degrees of unfounded
claims. Common threads run through the diets
in terms of mechanism of action for weight loss and
weight gain (i.e., sustained hypocaloric versus hypercaloric
conditions), but there are also potentially unique means
by which certain diets achieve their intended objectives
(e.g., factors that facilitate greater satiety, ease of compliance,
support of training demands, etc.).
Conclusions and recommendations
 There is a vast multitude of diets. In addition, there
are numerous subtypes that fall under the major diet
archetypes. Practitioners, clinicians, and researchers
need to maintain a grasp of the claims versus the
evidence underlying each archetype to properly
guide science-based practical and educational objectives
with clients, patients, and the public.
 All body composition assessment methods have
strengths and limitations. Thus, the selection of the
method should weigh practicality and consistency
with the prohibitive potential of cost, invasiveness,
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 13 of 19
availability, reproducibility, and technician skill
requirements. Ultimately, the needs of the client,
patient, or research question should be matched
with the chosen method; individualization and
environmental considerations are essential.
 Diets focused primarily on FM loss (and weight loss
beyond initial reductions in body water) operate
under the fundamental mechanism of a sustained
caloric deficit. This net hypocaloric balance can
either be imposed linearly/daily, or non-linearly over
the course of the week. The higher the baseline FM
level, the more aggressively the caloric deficit may
be imposed [27]. As subjects get leaner, slower rates
of weight loss can better preserve LM, as in Garthe
et al.’s example of a weekly reduction of 0.7% of body
weight outperforming 1.4% [138]. Helms et al. [140]
similarly suggested a weekly rate of 0.5–1.0% of body
weight for bodybuilders in contest preparation.
 Although LM gains have been reported in the
literature during hypocaloric conditions, diets
primarily focused on LM gain are likely optimized
via sustained caloric surplus to facilitate anabolic
processes and support increasing training demands.
The composition and magnitude of the surplus, the
inclusion of an exercise program, as well as training
status of the subjects can influence the nature of the
gains. Larger caloric surpluses are more appropriate
for untrained subjects who are primed for more
dramatic progress in LM gain [136] and for those
with a high level of NEAT [133]. On the other hand,
smaller caloric surpluses are appropriate for more
advanced trainees who may be at a higher risk for
undue FM gain during aggressive hypercaloric
conditions [135]. It should be noted that not all
trainees will fit within this general framework. Some
novices might require smaller surpluses while some
advanced trainees will require larger surpluses in
order to push muscular gains forward. It is the job
of the practitioner to tailor programs to the
inevitable variability of individual response.
 A wide range of dietary approaches (low-fat to lowcarbohydrate/ketogenic,
and all points between) can
be similarly effective for improving body composition,
and this allows flexibility with program design.
To date, no controlled, inpatient isocaloric diet comparison
where protein is matched between groups
has reported a clinically meaningful fat loss or thermic
advantage to the lower-carbohydrate or ketogenic
diet [60]. The collective evidence in this vein
invalidates the carbohydrate-insulin hypothesis of
obesity. However, ketogenic diets have shown
appetite-suppressing potential exemplified by spontaneous
caloric intake reductions in subjects on ketogenic
diets without purposeful caloric restriction.
Athletic performance is a separate goal with varying
demands on carbohydrate availability depending on
the nature of the sport. Carbohydrate restriction can
have an ergolytic potential, particularly for endurance
sports. Effects of carbohydrate restriction on
strength and power warrant further research.
 Increasing dietary protein to levels significantly
beyond current recommendations for athletic
populations may improve body composition. The
ISSN’s original 2007 position stand on protein intake
(1.4–2.0 g/kg) [141] has gained further support from
subsequent investigations arriving at similar
requirements in athletic populations [88, 140, 142–
145]. Higher protein intakes (2.3–3.1 g/kg FFM) may
be required to maximize muscle retention in lean,
resistance-trained subjects in hypocaloric conditions
[88]. Emerging research on very high protein intakes
(>3 g/kg) has demonstrated that the known thermic,
satiating, and LM-preserving effects of dietary protein
might be amplified in resistance-training subjects. It is
possible that protein-targeted caloric surpluses in outpatient
settings have resulted in eucaloric balance via
satiety-mediated decreases in total calories, increased
heat dissipation, and/or LM gain with concurrent FM
loss [89, 90, 92].
 Time-restricted feeding (a variant of IF) combined with
resistance training is an emerging area of research that
has thus far shown mixed results [106, 107]. However,
the body of intermittent caloric restriction research, on
the whole, has indicated no significant advantage over
daily caloric restriction for improving body
composition [108]. Therefore, programming of linear
versus nonlinear caloric deficits should be determined
by individual preference, tolerance, and athletic goals.
Adequate protein, resistance training, and an
appropriate rate of weight loss should be the primary
focus for achieving the objective of LM retention (or
gain) during FM loss.
 The long-term success of the diet depends upon
how effectively the mitigating factors of homeostatic
drive are suppressed or circumvented. Hypocaloric
conditions for fat loss have resulted in adaptive
thermogenesis – a larger than predicted decrease in
energy expenditure (10–15% below the predicted
drop in TDEE after accounting for LM and FM loss).
However, the majority of the existing research showing
AT has involved diets that combine aggressive
caloric restriction with low protein intakes and an
absence of resistance training; therefore, essentially
creating a perfect storm for the slowing of metabolism.
Research that has mindfully included resistance
training and adequate protein has circumvented the
problem of AT [25] and LM loss [26], despite very
low-calorie intakes.
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 14 of 19
Perspectives and future directions
It is important to maintain the proper “big picture” perspective
of the various programming elements to productively
direct the right amount of focus and effort. When
ranking nutritional factors by importance or impact on
body composition, a cake analogy is simple, vivid, and
memorable. The cake is total daily macronutrition (and
micronutrition), the icing is the specific timing and distribution
of nutrient intake through the day, and the sprinkles
are supplements that might help trainees clinch the
competitive edge. An ideal yet not always feasible scenario
is a multidisciplinary team approach to client or patient
care (i.e., dietitian, personal trainer, psychologist, physician).
This makes the most efficient use of expertise in
covering the various facets of lifestyle modification, and
when necessary, medical intervention [146].
Research on dietary effects on body composition has
plenty of gray areas and unbeaten paths ripe for investigation.
There is still a general lack of research on
women and older populations. Studies on the effect of
different within-day meal frequencies and nutrient distributions
in varying energetic balances combined with resistance
or endurance training are still rather scarce.
Linear versus nonlinear macronutrient intakes through
the week, combined with exercise, is still an untapped
area in research despite being widely practiced in the
real-world. Therefore, while a certain amount of our
current knowledge will remain static, scientists both in
the lab and in the field should stay vigilant and openminded
to the modification and falsification of models
and beliefs as the march of research continues.
Abbreviations
2C: Two-compartment model; 3C: Three-compartment model; 4C: Fourcompartment
model; AMDR: Acceptable Macronutrient Distribution Ranges;
AT: Adaptive thermogenesis; BIA: Bioelectrical impedance analysis;
BIS: Bioimpedance spectroscopy; BMR: Basal metabolic rate; CHO: Carbohydrate;
CICO: Calories-in/calories-out; EAT: Exercise activity thermogenesis; EE: Energy
expenditure; FFM: Fat-free mass, used interchangeably with lean mass (LM)
according to how it was reported in the literature; FM: Fat mass; HP: Highprotein;
IER: Intermittent energy restriction; IF: Intermittent fasting;
KD: Ketogenic diet; LCD: Low-carbohydrate diet; LM: Lean mass; LP: Lowprotein;
NEAT: Non-exercise activity thermogenesis; PUFA: Polyunsaturated fatty
acid; RDA: Recommended dietary allowance; REE: Resting energy expenditure;
RMR: Resting metabolic rate; SFA: Saturated fatty acid; SM: Skeletal muscle;
TBW: Total body water; TDEE: Total daily energy expenditure; TEE: Thermic
effect of exercise; TEF: Thermic effect of food; VLED: Very-low-energy diet
Acknowledgements
AAA would like to thank his wife and children for enduring the lengthy
process of writing this position stand. He would also like to thank his
co-authors for providing the constructive criticism and direction resulting
in an immeasurably improved manuscript. JA would like to thank Anya
Ellerbroek and Sérgio Fontinhas for lending an extra pair of eyes in
proofreading the manuscript.
Funding
This Position Paper was not funded.
Availability of data and materials
Not applicable.
Authors’ contributions
AAA was responsible for drafting the manuscript and incorporating revisions
suggested by his co-authors. All co-authors were equally responsible for
reviewing, editing, and providing feedback for submission of the final draft.
All authors read and approved the final manuscript.
Competing interests
ASR has received funding for research from several dietary supplement
companies.
BC writes and is compensated for various media outlets on topics
related to sports nutrition and fitness; has received funding for research
related to dietary supplements; serves on an advisory board for sports
nutrition companies and is compensated for these activities.
CPE is a research scientist at Texas A&M University and Director of Clinical
Science at Nutrabolt, a dietary supplement company. CPE also holds paid
consulting relationships with Naturally Slim (Dallas, TX) and Catapult Health
(Dallas, TX).
CW is a sponsored athlete by True Grit Nutrition and does funded
supplement/nutrition research from dozens of privately owned
companies.
DK works for a Contract Research Organization (QPS). QPS has received
funding from food, beverage and supplement companies.
JA is the co-founder and CEO of the ISSN. The ISSN is supported in part
by grants from raw good suppliers and branded sports nutrition
companies.
JRS has received funding to conduct studies from Natural Alternatives Inc.,
Kemin, Metabolic Technologies and Abbott Nutrition in the past 6 years.
PJA serves on the American Heart Association Advisory Board (Capital
Region); serves on the Scientific Advisory Boards for Dymatize Nutrition and
Isagenix International LLC; serves as a paid consultant to Isagenix
International LLC; Founder and CEO of PRISE LLC a health and wellness
consultant company that owns the GenioFit App.
RW is the Chief Science Officer for Post Active Nutrition.
Consent for publication
Not applicable.
Ethics approval and consent to participate
This paper was reviewed by the International Society of Sports
Nutrition Research Committee and represents the official position of
the Society.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
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Author details
1
Department of Family and Consumer Sciences, California State University,
Northridge, CA, USA. 2
Department of Health Sciences, Lehman College,
Bronx, NY, USA. 3
Dymatize Nutrition, Dallas, TX, USA. 4
High Performance
Nutrition, Mercer Island, WA, USA. 5
Department of Exercise Science and Sport
Management, Kennesaw State University, Kennesaw, GA, USA. 6
Department
of Exercise and Sports Science, University of Mary Hardin-Baylor, Belton, TX,
USA. 7
Exercise and Sports Nutrition Laboratory, Texas A&M University,
College Station, TX, USA. 8
Health and Exercise Science, Skidmore College,
Saratoga Springs, NY, USA. 9
Nutrition Research Division, QPS, Miami, FL, USA.
10
Institute of Exercise Physiology and Wellness, University of Central Florida,
Orlando, FL, USA. 11
Department of Health, Human Performance and
Recreation, Baylor University, Waco, TX, USA. 12
Performance & Physique
Enhancement Laboratory, Exercise Science Program, University of South
Florida, Tampa, FL, USA. 13
Department of Kinesiology & Health, IFNH Center
for Health & Human Performance, Rutgers University, New Brunswick, NJ,
USA. 14
Guru Performance Institute, Norwich, UK. 15
Department of Exercise
and Sport Science, University of North Carolina, Chapel Hill, NC, USA.
16
Department of Health and Human Performance, Nova Southeastern
University, Davie, FL, USA.
Aragon et al. Journal of the International Society of Sports Nutrition (2017) 14:16 Page 15 of 19
Received: 25 May 2017 Accepted: 30 May 2017
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