Total Energy Expenditure, Energy Intake, and Body Composition in Endurance Athletes Across the Training Season: A Systematic Review
Heydenreich et al. Sports Medicine - Open
Total Energy Expenditure, Energy Intake, and Body Composition in Endurance Athletes Across the Training Season: A Systematic Review
Juliane Heydenreich 0 1
Bengt Kayser 0
Yves Schutz 2
Katarina Melzer 1
0 Faculty of Biology and Medicine, University of Lausanne , Lausanne 1015 , Switzerland
1 Swiss Federal Institute of Sport Magglingen SFISM , Hauptstrasse 247, 2532 Magglingen , Switzerland
2 Faculty of Medicine, University of Fribourg , Fribourg 1700 , Switzerland
Background: Endurance athletes perform periodized training in order to prepare for main competitions and maximize performance. However, the coupling between alterations of total energy expenditure (TEE), energy intake, and body composition during different seasonal training phases is unclear. So far, no systematic review has assessed fluctuations in TEE, energy intake, and/or body composition in endurance athletes across the training season. The purpose of this study was to (1) systematically analyze TEE, energy intake, and body composition in highly trained athletes of various endurance disciplines and of both sexes and (2) analyze fluctuations in these parameters across the training season. Methods: An electronic database search was conducted on the SPORTDiscus and MEDLINE (January 1990-31 January 2015) databases using a combination of relevant keywords. Two independent reviewers identified potentially relevant studies. Where a consensus was not reached, a third reviewer was consulted. Original research articles that examined TEE, energy intake, and/or body composition in 1840-year-old endurance athletes and reported the seasonal training phases of data assessment were included in the review. Articles were excluded if body composition was assessed by skinfold measurements, TEE was assessed by questionnaires, or data could not be split between the sexes. Two reviewers assessed the quality of studies independently. Data on subject characteristics, TEE, energy intake, and/or body composition were extracted from the included studies. Subjects were categorized according to their sex and endurance discipline and each study allocated a weight within categories based on the number of subjects assessed. Extracted data were used to calculate weighted means and standard deviations for parameters of TEE, energy intake, and/or body composition. Results: From 3589 citations, 321 articles were identified as potentially relevant, with 82 meeting all of the inclusion criteria. TEE of endurance athletes was significantly higher during the competition phase than during the preparation phase (p < 0.001) and significantly higher than energy intake in both phases (p < 0.001). During the competition phase, both body mass and fat-free mass were significantly higher compared to other seasonal training phases (p < 0.05). (Continued on next page) © 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.
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Conclusions: Limitations of the present study included insufficient data being available for all seasonal training phases
and thus low explanatory power of single parameters. Additionally, the classification of the different seasonal training
phases has to be discussed.
Male and female endurance athletes show important training seasonal fluctuations in TEE, energy intake, and body
composition. Therefore, dietary intake recommendations should take into consideration other factors including the
actual training load, TEE, and body composition goals of the athlete.
Endurance athletes show training seasonal
fluctuations in TEE, energy intake, and body
Dietary recommendations should consider the actual
training load, TEE, and body composition goals.
Total energy expenditure (TEE) is composed of the energy
costs of the processes essential for life (basal metabolic
rate (BMR), 60–80% of TEE), of the energy expended in
order to digest, absorb, and convert food (diet-induced
thermogenesis, ~10%), and the energy expended during
physical activities (activity energy expenditure, ~15–30%)
[1, 2]. Elite endurance athletes are characterized by high
fluctuations of TEE, mainly due to the variability of the
energy expended during sporting activities. Among elite
senior endurance athletes, training loads from 500 h/year
[3, 4] up to 1000 h/year [5–7] have been reported,
depending on the specific muscular loading
characteristic of the sport. During heavy sustained exercise (e.g.,
during the Tour de France), TEE can be as high as
fivefold the BMR over several weeks . On the other
hand, during recovery days, pre-competition tapers, or
during the off-season, the energy expended in activities
is far less. Therefore, TEE is expected to be much lower
and may even reach levels comparable to that of
An appropriate energy intake supports optimal body
function, determines the capacity for intake of
macronutrients and micronutrients, and assists in manipulating
body composition in athletes . It is a challenge for
each endurance athlete to appropriately match energy
intake and TEE in order to achieve energy balance and
thus, weight stability, both on a micro level (i.e., over
1 day or several days) and through the training and
competitive season. Furthermore, endurance athletes in
general strive for a low body mass and/or body fat level
for various advantages in their sports, specifically during
the competition season . This allows runners and
cyclists to reach greater economy of movement and better
thermoregulatory capacity from a favorable ratio of weight
to surface area and less insulation from subcutaneous fat
tissue. Elite endurance athletes are therefore characterized
by low body mass and body fat content. For example, in
elite Kenyan endurance runners, the body fat percentage
was 7.1% , which is only marginally above the
recommended 5% minimum for males . In the same athletes,
body mass index (BMI) was 18.3 kg/m2 , which is
generally classified as being underweight . However, these
athletes were in peak physical conditions as the
investigations were undertaken and a low body fat percentage and
body weight might be an advantage for competition.
Achieving a negative energy balance and a concomitant
loss of body and fat masses in preparation for competition
can be accomplished in phases with high daily TEE solely
by the reduction of energy intake, since any further
training load increases could cause overtraining .
Therefore, the nutritional goals and requirements of
endurance athletes are not static over the training year.
Since endurance athletes undertake a periodized training
program and follow periodized body composition goals,
the nutritional support also needs to be periodized .
Usually, the annual training schedule of an elite
endurance athlete is divided into distinct phases, each with
very specific objectives. This is necessary to maximize
physiological adaptations for improved performance,
usually scheduled to peak around the main competitions
of the year . The principle of training periodization
was first introduced in the 1960s by the Soviet trainer
Leo Matveyev  and has not fundamentally changed
since then . The basis of this model is to prepare the
athlete for one or more major competitions during the
year by separating the training into the following three
main phases (macrocycles): preparatory, competitive,
and transition phases . An example for a “one-peak
annual plan” for a runner is shown in Fig. 1. The
preparatory phase is characterized by predominantly
highvolume training at moderate intensities, which improves
endurance capacity and provides a more efficient use of
fuel substrates. During the late preparatory phase, training
volume is reduced while intensity is gradually increased.
The goal of this phase is to reach peak performance and
to transfer the training effects into the competitive phase,
where exercise intensity is the highest. In the week before
an important competition, volume and intensity are
typically decreased (taper phase) to allow the body to optimally
recover for competition. The days and weeks after a main
competition are characterized by low-intensity and
lowvolume training, with goals to induce regeneration and to
prepare the athlete mentally and physically for the next
training cycle (transition phase) [14, 16].
Although the concept of training periodization in elite
endurance sports has been established for a long time,
the coupling of periodized training with nutrition and
body composition has gained scientific awareness only
recently . Stellingwerff ’s group was one of the first to
publish periodized nutrition guidelines for
middledistance athletes , they then expanded these
recommendations for a multitude of power sports .
Nowadays, there are guidelines for carbohydrate,
protein, and fat intake during training and competition
phases, not exclusively focusing on endurance sports
[19–21]. Meanwhile, for endurance athletes,
sportspecific dietary intake recommendations were developed
only for a few endurance disciplines (e.g., swimmers
[22–25], distance runners , marathon/triathlon/road
cycling ). But it remains unclear whether endurance
athletes are actually following these nutrient guidelines
across all seasonal training phases.
The validity of either body composition, energy intake,
or TEE-determination in athletes strongly depends on
the methods used. The measurement of body
composition in general is prone to error. It has been shown that
acute food or fluid ingestion , subject positioning
, previous physical activity , and hydration status
 have an impact on reliability of body composition
measurement. Since endurance athletes often train
several times per day, it might be difficult to assure best
conditions for body composition assessment. According
to a recent methodology review performed by Nana et
al., only few of the studies, where body composition of
athletes was measured with dual X-ray absorptiometry
(DXA), provided details about their subject and device
standardization . However, other methods like skinfold
measurements require highly experienced investigators
 and strongly depend on the number of measurement
sites and the formula used to calculate the percentage of
body fat . Therefore, it is important to report
standardization protocols in order to evaluate the quality
of data assessment. One main issue in assessing energy
intake in athletes is the magnitude of under-reporting, which
can amount to 10–45% of TEE . It was shown that the
magnitude of under-reporting increases as energy
requirements increase . Since endurance athletes are often
characterized by high TEE, we must assume that these
athletes are very prone to a high percentage of
underreporting. For determination of TEE objective methods
such as doubly labelled water (DLW) or heart frequency
measurements are available. However, in many studies
subjective methods such as activity records and activity
questionnaires are used in order to assess the activity level
and TEE of subjects. These methods estimate TEE or
activity level and their validity strongly depends on the
breadth of the activity dimensions analyzed.
There exist some longitudinal studies that have
assessed fluctuations in body composition, dietary intake,
and/or TEE of endurance athletes across the training
seasons [35–52], but no systematic reviews have been
performed. Therefore, the purpose of this study was to (1)
systematically analyze TEE, energy intake, and body
composition in highly trained athletes of various endurance
disciplines and of both sexes with focusing on objective
assessment methods and (2) analyze fluctuations in these
parameters across the training season. We hypothesized
that endurance athletes show large fluctuations of TEE
during different seasonal training phases due to differing
exercise loads, and concomitant alterations in energy
intake and body composition.
The review protocol was developed according to the
Meta-analysis of Observational Studies in Epidemiology
Guidelines for meta-analyses and systematic reviews of
observational studies .
A systematic literature search was performed to retrieve
articles pertaining to body composition, energy intake,
and TEE in endurance athletes across the training
season. One researcher (JH) conducted the search for
publications on 31 January 2015 in the electronic databases
MEDLINE (via PubMed) and SPORTDiscus with Full
Text (via EBSCOHost). A hand search of relevant
reviews was performed to obtain additional articles missed
by the database search. No individual or organization
was contacted to receive further publications. To identify
the population of endurance athletes, the following
keywords connected with the Boolean operator “OR” were
searched: endurance athletes, endurance-trained,
endurance trained, aerobically trained, runners, swimmers,
triathletes, skiers, cyclists, and rowers. To identify the
outcome of body composition, TEE, and energy intake,
the following keywords connected with the Boolean
operator “OR” were searched: body composition, fat mass,
fat-mass, fat free mass, fat-free mass, body fat, metabolic
rate, energy expenditure, dietary intake, food intake,
energy intake, food consumption, and macronutrient*.
Terms for the study population and outcomes were
combined by the use of the Boolean operator “AND”.
Limits included articles published in the English
language, human studies, and publishing date limits
between 1990 and January 2015. Keywords were searched
as free text in the title, abstract, and subject heading. A
detailed overview of search strategies in the two
databases can be obtained in Additional file 1: Table S1.
Two researchers independently assessed the eligibility of
the records by screening the title, abstract, and keywords
for inclusion and exclusion criteria. An agreement
between the two researchers was quantified by kappa
statistics . The full texts of all abstracts meeting the
eligibility criteria were retrieved and subjected to a second
assessment for relevance performed by one author (JH).
The inclusion criteria included (1) articles reporting
original data in peer-reviewed journals; (2) in vivo,
human analyses; (3) adult endurance athletes (highly
aerobically trained individuals who were engaged in a competitive
endurance sport) with a mean age of 18–40 years; (4)
reporting of training seasonal phase of data assessment;
and (5) assessment of body composition and/or ad libitum
daily energy intake and/or daily TEE. Articles were
excluded from the review if (1) the article was only in
abstract form or a case report, (2) data could not be split
between the sexes (where both male and female subjects
were analyzed), (3) body composition was assessed by
skinfold measurements, (4) daily TEE was assessed by the
use of questionnaires, and (5) descriptive quantitative
results were not reported in a text or tabular form. Any
difference in assessments between the two researchers
was discussed in the first instance or resolved by a third
Methodological Quality Assessment
All relevant articles were examined for full
methodological quality using a modified version of the Downs
and Black  checklist for the assessment of the
methodological quality of randomized and non-randomized
studies of health care interventions. According to Fox et
al. , 10 of the 27 criteria that logically applied to all
of the types of studies included in this review were used.
The maximum possible total score was 10. Two
researchers assessed the study quality independently, with
differences resolved by consensus or by a third author
(KM). The agreement between the two researchers was
quantified by kappa statistics . Based on the
assessment of the methodological study quality, no studies
were excluded and no additional analyses were
undertaken. The methodological quality of the included studies
is shown in Additional file 2: Table S2.
Body composition, energy intake, and/or TEE data were
extracted from all studies included in the review by the
first author (JH). Demographic and methodological data
were also extracted for the following confounding factors:
age, sex, sports discipline, competition level, seasonal
phase, and methods for assessing body composition,
energy intake, and/or TEE. If the same subjects were
analyzed during different time points in the same seasonal
phase (e.g., energy intake before three different races, or
assessment of energy intake at three time points during
the training period), the first time point was chosen for
data analysis to facilitate data entry and to avoid selection
bias. If studies reported any intervention leading to a
nonhabitual behavior of athletes’ nutrient intakes (e.g., dietary
supplementation), the baseline and/or control group data
were used. To enable comparisons between studies,
reported units were converted into standard units. These
conversions were performed by using the reported mean
values of the outcomes. Energy intake and TEE were
reported in either absolute (kcal/day) or relative values
(energy intake or TEE in relation to body weight [kcal/
kg·day]). Body composition was converted into fat mass
(%, kg) and fat-free mass (kg). According to the definition
by Wang et al. , the terms lean body mass and fat-free
mass (FFM) were considered synonymous. Duplicate
publications from the same data set were identified
according to the criteria published in the Cochrane
Handbook for Systematic Reviews of Intervention . The
most complete record was then used for data extraction.
According to the traditional periodization model, the
reported seasonal training phases of data assessment
were clustered into three groups that included the
preparation phase, the competition phase, and the transition
phase [14–16]. A detailed overview of the clustering can
be obtained in Table 1.
The main outcome measures were body composition
(fat mass, FFM), energy intake, and TEE of endurance
athletes across the season. Once all of the relevant data
were extracted, the weighted mean and standard
deviation of the weighted mean were calculated for the main
outcome variables. Based on the number of subjects
examined within the study, relative to the total number
of subjects examined for the specific variable, a
percentage weight (w) was allocated to each result within each
outcome variable and used for the calculation of the
overall weighted mean (Xw) and standard deviation of
the weighted mean (SDw) for each variable . A
capital “N” denotes the number of separate studies, while a
small “n” denotes the number of included individual
Statistical analyses were performed using the statistical
software SPSS statistics version 22 for Windows (IBM
Corp., Chicago, IL, USA). p values < 0.05 were
considered statistically significant. Kolmogorov-Smirnov tests
were performed to check for normal distributions. All
parameters were normally distributed except body mass,
fat mass, and FFM. To test for comparisons of
subgroups, one-factorial analyses of variance (ANOVAs)
with Scheffé post hoc tests (parametric) and
KruskalWallis tests (H-test) with Mann-Whitney U post hoc
tests (non-parametric) were performed. When multiple
non-parametric post hoc tests were applied,
Bonferroniadjusted alpha levels were applied. Since parameters for
body composition were not normally distributed, we
abstained from multiple statistical comparisons between
Training/preparation/conditioning/peak training period
Beginning/early/middle/ end of training season
Beginning of season
High/low volume weeks
Before/during/after high intensity/exhaustive training
End of preparatory training phase
Habitual/basic/normal training phase
seasonal training phases and endurance disciplines to
reduce the risk of type I errors. For comparisons of energy
intake and TEE during different seasonal training
phases, paired t-tests were used. The separate analysis
of studies, where energy intake and TEE were assessed
in parallel, and longitudinal studies that reported
energy intake during different training season phases,
were performed using the free software for
metaanalysis Review Manager 5 version 5.3.5 for Windows
(Cochrane Collaboration, Copenhagen, Denmark). The
results were then presented as means and 95%
confidence intervals (95% CI).
Description of Studies and Assessment Methods
The flow chart for the study selection process is shown
in Fig. 2. Data were extracted from 82 studies in
endurance athletes, with 53 studies assessing body
composition, 48 energy intake, and 14 TEE. The kappa value of
0.47 for the agreement between the two researchers who
assessed the eligibility of records was considered to
reflect a “fair agreement”, whereas “excellent agreement”
(kappa value of 0.96) was obtained for the assessment of
the methodological quality of included studies .
The characteristics of the included studies for body
composition, energy intake, and TEE are shown in Table 2.
In Additional file 3: Table S3, an overview of excluded
studies and the reasons for their exclusion can be found.
The cumulative number of subjects included in the
analysis was 1674 (71.4% male). Runners (27.8%), cyclists
(18.7%), and swimmers (16.4%) comprised the largest
proportion of subjects. All athletes for whom an
endurance sports discipline was not described or for whom
multiple endurance disciplines were mentioned were
grouped into “other endurance athletes” (13.5%). On
average, the mean age, VO2max, and training volume of
study estimates were 26.3 ± 6.7 years, 61.8 ± 6.0 mL/
kg min, and 12.0 ± 6.9 h/week, respectively (Xw ± SDw).
A detailed overview of physical characteristics of
included study estimates is shown in Table 3.
Body composition was assessed by DXA in 32.1% of
studies, by bioelectrical impedance analysis (BIA) in
N = 226
- Inadequate assessment methods (N = 2)
- Seasonal training phase NR (N = 164)
- Age limit/ age NR (N = 11)
- No sex differentiation/ sex NR (N = 26)
- Multiple publications (N = 4)
- Data extraction not possible (N = 6)
- Inadequate study design (N = 1)
- Inadequate subjects (N = 1)
- No habitual 24h energy intake or TEE (N = 11)
Systematic review protocol
Systematic database search
N = 3,583 (+6 articles snowball)
Inclusion/exclusion of studies based on
Fig. 2 Flow chart for the present systematic review. NR = not reported. *Sum of studies not equal to total as multiple parameters were assessed
in certain studies. N = number of studies
25.6% of studies, and by hydrostatic weighing in 25.6%
of studies. In 71.7% of the studies, where body composition
was measured, no details of standardization were provided.
Ten studies (18.9%) reported some standardization details,
whereas only three studies (5.7%) reported satisfactory
details about their standardization. For determination
of energy intake, dietary records (95.1%) with a mean
observation time of 4.7 ± 4.1 days were most often utilized.
Dietary recall (3.3%) and food frequency questionnaires
(FFQs, 1.6%) played secondary roles in energy intake
assessments. Half of the studies (50.0%) used DLW for
determination of TEE. Other methods included heart
rate monitoring (33.3%) and accelerometers (16.7%).
The studies using heart rate monitoring for estimation
of TEE used individual derived linear relationships
between heart rate and oxygen consumption (HR–VO2)
during different tasks to estimate the oxygen cost and
energy expenditure during the observation period. Two
third of the studies used the 24-h heart rate recordings
and the individual HR–VO2 relationship to estimate
TEE (gross calculation). Two studies calculated TEE by
summation of activity energy expenditure (based on
individual HR–VO2 relationship) and resting metabolic
rate (RMR; net calculation).
Total Energy Expenditure and Energy Intake
In total, 14 studies where TEE was assessed during
various seasonal training phases were identified by the
literature search. Since no study assessed TEE during the
transition phase, only data during the preparation phase
(N = 8) and the competition phase (N = 6) are shown. In
addition, due to limited data, no separations between the
sexes and endurance disciplines of TEE were performed.
Absolute and relative TEE were significantly higher
during the competition phase than during the
preparation phase (9869 ± 4129 vs. 4345 ± 1062 kcal/day, and
98.9 ± 46.5 vs. 68.5 ± 11.4 kcal/kg·day, respectively, all
p < 0.001). Most of the studies assessing TEE during
the competitive phase were conducted during an
ultra-endurance competition (N = 5), such as during a
24-h team relay cycling race , during a 6-day cycling
stage race , or during a 4851-km team relay cycling
race . The maximum TEE amounted to 13,862 kcal/
day and 156.0 kcal/kg·day, respectively, observed in male
ultra-endurance runners during a 24-h ultra-marathon
. The absolute and relative TEE were significantly
higher than the energy intake in the preparation phase
(4345 ± 1062 vs. 2915 ± 761 kcal/day, and 68.5 ± 11.4 vs.
42.8 ± 10.5 kcal/kg·day, respectively, all p < 0.001) and
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competition phase (9869 ± 4129 vs. 3156 ± 967 kcal/day,
and 98.9 ± 46.5 vs. 43.5 ± 11.3 kcal/kg·day, respectively, all
p < 0.001).
Absolute and relative energy intake was higher in
males compared to females in the preparation phase
(3111 ± 717 vs. 2291 ± 525 kcal/day, and 44.0 ± 10.6 vs.
39.0 ± 9.1 kcal/kg·day, respectively, all p < 0.001) and
competition phase (3405 ± 940 vs. 2337 ± 483 kcal/day,
and 44.8 ± 11.9 vs. 39.3 ± 7.9 kcal/kg·day, respectively, all
p < 0.001, Figs. 3 and 4).
In males, the absolute energy intake was higher during
the competition phase compared to the preparation
phase (p < 0.001), whereas relative energy intake was
unchanged (p = 0.553). In females, neither the absolute
(p = 0.735) nor relative (p = 0.951) energy intake was
different between the two seasonal training phases.
Table 4 provides a detailed overview of the absolute
and relative energy intakes differentiated by sex,
endurance discipline, and seasonal training phase. Energy
intake was significantly higher in male runners, swimmers,
VO2max [mL/kg min]
Train load [h/week]b
Fig. 3 Energy intake (EI) and total energy expenditure (TEE) in kcal/day of endurance athletes. Data are shown in weighted mean and standard
deviation of the weighted mean (X̅w ± SDw). n = number of cumulative subjects
and rowers compared to their female counterparts
during both the preparation and competition phases (all
p < 0.01). In male and female runners, male endurance
athletes, and combined male and female rowers and
cross-country skiers, the energy intake was higher
during the competition phase compared to the
preparation phase, whereas for male and female swimmers,
energy intake was higher during the preparation phase (all
p < 0.01). The energy intake of female runners and rowers
during the preparation phase was significantly lower
than that of all other endurance athletes (all p < 0.05).
Reasons for the lower energy intake in female rowers might be
that during preparation phase the athletes often reduce
their energy intake in order to reduce concomitantly their
body weight to start in the lightweight category. During
pre-season, body mass may reduce by as much as 8%
among lightweight rowers . Runners, in general, profit
from a low body mass since greater economy of movement
and better thermoregulatory capacity from a favorable ratio
of weight to surface area and less insulation from
subcutaneous fat tissue is reached .
A separate analysis of energy balance was performed
by including only studies where both energy intake and
expenditure were assessed in parallel. Male endurance
athletes showed a significant energy deficit of 304 kcal/
day (95% CI −549, −58, p = 0.02) during the
preparation phase and 2177 kcal/day (95% CI −2772, −1582,
p < 0.0001) during the competition phase (Fig. 5). In female
endurance athletes, a negative energy balance was also
observed during the preparation phase (−1145 kcal/day,
asignificantly different from competition phase (p < 0.001)
bsignificantly different from females in the same seasonal training phase (p < 0.001)
csignificantly different from EI of the same sex and seasonal training phase (p < 0.01)
Energy intake [kcal/day]
Energy intake [kcal/kg·day]
Energy intake [kcal/day]
Energy intake [kcal/kg·day]
3600 ± 1102d
3298 ± 713b
2769 ± 681g,h
3462 ± 341b
3405 ± 940b
46.9 ± 17.7d,f
46.2 ± 6.5b
44.8 ± 11.9b
Other endurance athletes
3789 ± 764d,e,f
3789 ± 764d,e
2489 ± 425a
2640 ± 366a,b,f
2046 ± 230a
3366 ± 902a,d,e,g
3963 ± 762a,b
2683 ± 450a,d,e
2426 ± 448a
2921 ± 326b,f
3224 ± 917a,d,e,g
3287 ± 876d,f,g
2663 ± 1107d,e
3162 ± 159d,e
3162 ± 159f,g
3261 ± 282a,d,e,g
3274 ± 286a,d,f,g
2915 ± 761a
3111 ± 717a,b
52.3 ± 13.3d,e
52.3 ± 13.3d,e
48.7 ± 9.6a,d,e
53.2 ± 9.5a,b,d,e
43.6 ± 6.9a,e
36.0 ± 0.1b
48.3 ± 12.7a,d,e
48.3 ± 11.6d,e
46.5 ± 5.1a,d,e
46.3 ± 5.2a,d,e,f
44.0 ± 10.6b
2091 ± 53.2d,e,f,g
Note. Data are shown in weighted mean and standard deviation of the weighted mean (X̅w ± SDw)
n = cumulative number of subjects, – = insufficient data
aSignificantly different from athletes of the same endurance discipline and sex during competition phase (p < 0.01)
bSignificantly different from females of the same endurance discipline and seasonal training phase (p < 0.01)
cSignificantly different from all other endurance disciplines of the same sex and seasonal training phase (p < 0.05)
dSignificantly different to runners of the same sex and seasonal training phase (p < 0.05)
eSignificantly different to rowers of the same sex and seasonal training phase (p < 0.05)
fSignificantly different to swimmers of the same sex and seasonal training phase (p < 0.05)
gSignificantly different to cyclists of the same sex and seasonal training phase (p < 0.05)
hSignificantly different to cross-country skiers of the same sex and seasonal training phase (p < 0.05)
95% CI −1404, −887, p < 0.0001) and the competition phase
(−1252 kcal/day, 95% CI −1778, −727, p < 0.0001, Fig. 6).
The relative energy deficit was 6.6% of TEE during the
preparation phase and 18.9% during the competition phase in
males, and 29.0% of TEE during the preparation phase and
22.0% during the competition phase in females. When
comparing energy intake during the preparation and
competition phases by solely including studies where energy intake
1.1.1 Preparation phase
1.1.2 Competition phase
Energy intake (kcal/d)
Total (95% CI) 55
Heterogenity: Chi2 = 111.80, df= 6 (p < 0.00001); I2 = 95%
Test for overall effect: Z = 4.97 (p < 0.00001)
Test for subgroup differences: Chi2 = 32.50, df= 1 (p < 0.00001); I2 = 96.9%
IV, Fixed, 95% CI
0 [-1,459, 1,459]
-191 [-954, 572]
-327 [-591, -63]
-304 [-549, -58]
-4,704 [-6,559, -2,849]
-8,365 [-10,044, -6,686]
-24 [-1,030, 982]
-1,502 [-2,420, 584]
-2,177 [-2,772, -1,582]
Fig. 5 Energy balance (EB) of male endurance athletes during preparation and competition phase
mass was lowest during the transition phase (p < 0.05)
higher during the competition phase, being significant in
and absolute and relative fat mass were highest
during the competition phase (all p < 0.05). FFM was
lowest during the transition phase (p < 0.001, Fig. 8).
In more than half (53.7%) of the female study
populaFor females, absolute and relative body fat were
tions, where TEE was assessed, the menstrual status was
higher during the preparation phase compared to
not reported. 24.4% of the female study populations
those during the transition phase (p < 0.01, Fig. 8).
were eumenorrheic, whereas in 22.0% menstrual
irreguNeither body mass nor FFM differences between
sealarities were reported. However, a separate statistical
sonal training phases were observed (all p > 0.05). When
analysis assessing seasonal training phase differences of
separately analyzing the few studies where body mass and
TEE between eumenorrheic and amenorrheic athletes
composition were assessed during both the preparation
could not be performed, since the cumulative number of
subjects was too low in the single training phases.
ance athletes showed a significantly lower percentage of
body fat and higher absolute FFM during the competition
phase compared to the preparation phase (18.2 ± 5.0% vs.
For the total sample during the competition phase, both
19.6 ± 5.0%, and 56.6 ± 8.7 kg vs. 54.0 ± 8.7 kg,
rebody mass and FFM were significantly higher compared
spectively, all p < 0.0001).
to the preparation and transition phases (p < 0.05,
In more than one third (34.5%) of the female study
Table 5). For the percentage of fat mass, no differences
populations, where body composition was assessed, the
were detected between the seasonal training phases
menstrual status was not reported. 39.7% of the female
(p > 0.05). Since the percentage of female data on
study populations were eumenorrheic, whereas 16.4%
total data varies between the seasonal training phases,
menstrual irregularities were reported. However, a separate
we further split the data by sex. In males, the body
analysis between eumenorrheic and amenorrheic athletes
Total energy expenditure (kcal/d)
Mean SD Total
IV, Fixed, 95% CI
IV, Fixed, 95% CI
IV, Fixed, 95% CI
6.2% -1,743 [-2,675, -811]
11.6% -24 [-705, 657]
22.6% -2,462 [-2,949, -1,975]
40.1% -633 [-999, -267]
80.5% -1,145 [-1,404, -887]
1.7% -7,648 [-,9452, -5,844]
17.8% -660 [-1,209, -111]
19.5% -1,252 [-1,778, -727]
1.2.1 Preparation phase
1.2.2 Competition phase
Total (95% CI)
Heterogenity: Chi2 = 100.43, df= 5 (p < 0.00001); I2 = 95%
Test for overall effect: Z = 9.85 (p < 0.00001)
Test for subgroup differences: Chi2 = 0.13, df= 1 (p =0.72); I2 = 0%
Fig. 6 Energy balance (EB) of female endurance athletes during preparation and competition phase
could not be performed, since the cumulative number of
subjects during the different seasonal training phases was
In this systematic review, we examined fluctuations in
TEE, energy intake, and/or body composition in
endurance athletes across the training season. We found that
some, but not all, of the investigated outcomes depended
on the time point of data assessment during seasonal
training. TEE was highest during the competition phase
and higher than energy intake in all seasonal training
phases. Alterations in TEE did not lead to adaptations of
energy intake in females, whereas in males, a higher
absolute energy intake during the competition phase was
observed. The finding that male endurance athletes
demonstrated the highest fat mass values during the
competition phase and the lowest FFM during the
transition phase seems to be an anomaly from the pooling
Our systematic search initially yielded many studies
where TEE, energy intake, or body composition in
endurance athletes were investigated. Only a few (2%)
reported the time point of data collection with regard to
the training season and could thus be included in this
review. This is unfortunate since our analysis clearly
illustrates how training volume and related TEE vary
importantly with seasonal training phases. Specifically and
expectedly, both absolute and relative TEEs were
significantly higher during the competition phase compared to
the preparation phase. Interestingly, these differences
were only partly in agreement with alterations in energy
intake and/or body composition of endurance athletes.
During the transition phase, limited data for TEE and
energy intake of endurance athletes was available. Only
for body composition, it was possible to compare with
other seasonal training phases, although the number of
study estimates and therefore, explanatory power, was
weak. Future research on elite athletes should focus on
the effects of a sudden stop or reduction in TEE on body
composition (e.g., because of injury). There exist only a
few studies (with conflicting results) where this question
has been examined. Ormsbee and Arciero investigated
the effects of 5 weeks of detraining on body composition
and RMR in eight male and female swimmers . RMR
decreased, whereas fat mass and body weight increased
with detraining. In contrast, LaForgia et al. showed that
after 3 weeks of detraining, no differences in RMR and
percentage of fat mass occurred in male endurance
athletes . Unfortunately, energy intake was not reported
in either of these studies. Thus, it remains unclear when,
whether, and to what extent the body adapts (through
changes in energy intake and/or body composition) for
the decrease in TEE caused by detraining.
Our analysis highlights an important apparent negative
energy balance in endurance athletes, both in the
preparation and competition phases, when separately
examining the energy balance in articles where both energy
intake and TEE were assessed (N = 11). Negative energy
balance was reported during the preparation phase in
male [66, 67] and female  cross-country skiers, male
 and female  runners, and female lightweight
rowers  and swimmers , and amounted to a
mean of 304 kcal/day (4.7% of TEE) for males and
1145 kcal/day (27.8%) for females. During the
competition phase, a negative energy balance was reported in
male cyclists and triathletes , male  and female
[63, 71] runners, and male cyclists [61, 62], averaging
2177 kcal/day (32.5%) for male and 1252 kcal/day
(47.9%) for female endurance athletes. The most obvious
explanation for these energy deficits is likely the classical
issue of under-reporting energy intake through
assessment in human studies. A review of nine studies
using DLW to validate self-reported energy intake in
athletes revealed that under-reporting can amount to
10–45% of TEE . Since under-reporting increases in
magnitude as energy requirements increase , we
must assume that under-reporting in the present study
estimates was more important during the competition
phase. Even when 45% was added to the energy intake of
all athletes included in our review, there still remained a
negative energy balance of 118 kcal (2.7% of TEE) in the
preparation and 5293 kcal (53.6%) in the competition
phase. Another explanation for the negative energy
balance might be the low accuracy and precision of
methods used to estimate energy intake in athletes in
the articles included in our review. For example, mostly
dietary records with a mean observation time of 4.7 ±
4.1 days were used. According to Magkos and Yannakoulia,
for athletes, a 3–7-day diet-monitoring period would be
enough for reasonably accurate and precise estimations of
habitual energy and macronutrient consumption .
However, other methods like FFQs and dietary recalls were
also used for energy intake estimations. These
methods are both memory-dependent and show lower
accuracy and precision than prospective methods like
dietary records . However, even when only articles
were considered where energy intake was assessed by
the use of dietary records, the error remained high
(2.5% of TEE during the preparation phase and 54.9%
during the competition phase). Finally, the high
negative energy balance during the competition phase may
also be explained by the fact that, apart from one
study, all included studies investigated the TEE during the
days with actual competition and not during habitual
training days in the competition phase. Thus, it is likely
that the TEE during this phase was over-estimated. During
the preparation phase, a negative energy balance leading
to increased energy store utilization might be desirable by
coaches and athletes to reach a sport-specific body
composition, but during the competition phase, body
composition should not be modified anymore since it is
typically already at its optimum. There was one study in
which dietary intake was strictly controlled since the
subjects were in confinement. Brouns et al. simulated a Tour
de France race in a metabolic chamber and calculated the
daily energy balance from the energy expended and energy
intake as calculated from daily food and fluid
consumption . They found a positive energy balance during
active rest days whereas during the exercise days, a
significant negative energy balance was observed. The authors
concluded that if prolonged intensive cycling increases
energy expenditure to levels above a certain threshold
(probably around 20 MJ or 4780 kcal), athletes are unable
to consume enough conventional food to provide
adequate energy to compensate for the increased energy
expenditure. The authors of a recent review
addressing the criticisms regarding the value of self-reported
dietary intake data reasoned that these should not be
used as a measure of energy intake . Our analysis
supports this statement since, for athletes, relative
energy deficits amounted up to 48% of TEE in female
athletes and 33% in male athletes during the
competition phase. Thus, there is an urgent need for better
methods of dietary intake quantification, such as
dietary biomarkers and automated image analysis of food
and drink consumption . The classical concept of
energy balance, defined as dietary energy intake
minus TEE, has been criticized, since according to this
definition energy balance is the amount of dietary energy
added to or lost from the body’s energy stores after the
body’s physiological systems have done their work for the
day . Thus, energy balance is an output from those
systems. In contrast, energy availability, defined as the
dietary energy intake minus the energy expended during
exercise, is an input to the body’s physiological systems,
since energy availability is the amount of dietary energy
remaining for all other metabolic processes .
Endurance athletes, especially female athletes, show low energy
availability (<30 kcal/kg FFM/day)  and increased risk
for changes of the endocrine system affecting energy
and bone metabolism, as well as in the cardiovascular
and reproductive systems . In healthy young adults,
energy balance = 0 kcal/day when energy availability =
45 kcal/kg FFM/day . Since the results of the
present study indicate a high negative energy balance in
endurance athletes, we must assume that the athletes
also demonstrate low energy availability. However, due to
the limited data, it was not possible to account for other
clinical markers (e.g., bone mineral density), menstrual
status, or prevalence of eating disorders in the athletes.
We recommend that energy balance-related studies in
endurance athletes should also assess and report clinical
markers, such as bone mineral density and menstrual
status, in order to assess the clinical consequences of the
mismatch of TEE and energy intake.
The aggregate analysis yielded a surprising finding. In
male endurance athletes, the absolute and relative fat
mass was highest during the competition phase. In
contrast, during the transition phase, FFM was lowest,
which goes along with our expectations with a decrease
in exercise volume and intensity. For the female athletes,
we did not find these fluctuations in body composition,
except for a higher body fat content during the
preparation phase compared to the transition phase. We
believe that these findings are due to the paucity of data
and to the fact that the number and type of athletes
varied between seasonal training phases. Indeed, when
separately analyzing the few studies where body mass and
composition were assessed during both the preparation
and competition phases (N = 5), both male and female
endurance athletes showed a significantly lower percentage
of body fat and higher FFM during the competition phase.
Further studies with longitudinal assessments of body
composition are required to support these findings.
However, in only 5.7% of the studies, where body composition
was assessed, satisfactory details about standardization
were provided. According to Nana et al., studies involving
DXA scans of body composition should report details of
the DXA machine and software, subject presentation and
positioning protocols, and analysis protocols . It has
been shown that the use of a non-standardized protocol
increased the variability for total and fat-free soft tissue
mass compared to a standard protocol, which might
include a loss in ability to detect an effect of an intervention
that might have relevance for sports performance .
The use of non-standardized protocols and the
concomitant higher variability might explain some of the
unexpected findings of body composition changes in athletes
of the present study.
In male endurance athletes, absolute energy intake was
higher during the competition phase compared to the
preparation phase. The relative energy intake was not
different, which can be explained by the apparent
significant increase of body mass during the competition
phase, and is likely an artifact of the aggregation of data
from various studies. In female athletes, neither absolute
nor relative energy intake was different between seasonal
phases. When focusing on longitudinal studies that
assessed energy intake during different training seasons
in the same cohort, there was a tendency for male
athletes to show greater fluctuations in energy intake. In
female cross-country skiers, the energy intake was higher
during the preparation phase , whereas in female
runners and swimmers, the energy intake was higher during
the competition phase . However, summing up both
studies, no significant differences between training season
phases were found. In contrast, male endurance athletes
showed a significantly higher energy intake during the
competition phase, as seen in male runners ,
crosscountry skiers , swimmers , and triathletes .
Although some of the included studies showed greater
energy intake in male endurance athletes during the
preparation phase (cyclists [46, 48], swimmers ), the power
of these studies was too low to change the results.
However, since energy intake varies in male endurance athletes
depending on the training season phase, it indeed seems
appropriate to adapt dietary recommendations according
to the different training season phases, as proposed by
Stellingwerff et al. [17, 18].
Strengths and Limitations
This is, to our knowledge, the first systematic review
focusing on fluctuations in TEE, energy intake, and body
composition in endurance athletes. To increase the
robustness of the outcomes of our systematic review, we
excluded articles where body composition was estimated
by skinfold measurements and equations. The accuracy
of skinfold measurements depends on the number of
measurement sites and the formula used to calculate the
percentage of body fat . Since there are many
different techniques , it is impossible to compare results
accurately between studies. Furthermore, skinfold
measurements cannot be used to assess intra-abdominal
adipose tissue and are highly variable when assessors with
limited training and experience perform the
measurements . Of course, since skinfolds are very often
used for body composition assessments, the exclusion of
these articles reduced the total number of articles
measuring body composition, which were included in the
present systematic review. The inclusion of articles with
skinfold body composition determination would have
led to a higher number of study estimates and
comparisons of different seasonal training phases would have a
higher explanatory power. The same is true for
estimations of TEE. We included only articles measuring TEE
in a more objective way (such as DLW) and excluded
articles where TEE was assessed by questionnaires or
activity records. This led to the inclusion of a limited
number of high-quality studies.
Limitations of the present study relate to the limited
cumulative number of subjects, which provided a low
explanatory power, and the classification of the different
seasonal training phases. In the literature, several
similar-sounding terms have been used to describe time
points of data collection in athletes. However, assigning
the appropriate classification into one of the three
seasonal training phases is essential and has a great impact
on the final analysis. Furthermore, if articles reported
several time points of data collection within one
seasonal training phase, we included only the first time
point into the analysis in order to assure standardization
and avoid selection bias. The exclusion of other time
points might have led to the loss of interesting data.
Our analysis highlights the important seasonal
fluctuations in TEE, energy intake, and body composition in
male and female endurance athletes across the training
season. Therefore, dietary intake recommendations
should take into consideration other factors including
the actual training load, TEE, and body composition
goals of the athlete. The present review supports the
statement of the current position stand of the American
College of Sports Medicine (ACSM) that energy and
nutrient requirements are not static and that periodized
dietary recommendations should be developed .
Importantly, our analysis again shows the uselessness of
self-reported dietary intake, a well-known limitation to
energy balance studies, in endurance athletes. The
important underreporting suggested by our analysis again
raises the question of whether self-reported energy
intake data should be used for the determination of energy
intake and illustrates the need for more valid and
applicable energy intake assessment methods in free-living
humans . Since we observed a lack of data during
the transition phase, future research should focus on the
assessment of TEE, energy intake, and body composition
on a reduction in training intensity and volume, such as
at the end of the competitive season. In addition, future
studies dealing with energy balance and nutrient intake in
elite endurance athletes should always mention the time
point of data assessments (e.g., seasonal training phase).
Additional file 1: Search strategies in SPORTDiscus and MEDLINE. (PDF
Additional file 2: Results of methodological quality assessment
undertaken on included studies. (PDF 276 kb)
Additional file 3: List of excluded references and reason for exclusion.
(PDF 490 kb)
The authors thank Elena Hartmann (M.Sc. Human Movement Sciences) and
Laura Oberholzer (B.Sc. Health Science and Technology) for their valuable
assistance during the literature selection process and quality assessment of
relevant articles. Furthermore, the authors thank the team from the
“Sportmediathek” of the Swiss Federal Institute of Sport Magglingen SFISM
who provided relevant articles.
JH participated in the design of the study; carried out the data acquisition,
analysis and interpretation of the results; and drafted the manuscript. BK, YS,
and KM participated in the conception and design; analysis and
interpretation of the results; drafting and revisions of the manuscript for
important intellectual content. All authors read and approved the final
Juliane Heydenreich, Bengt Kayser, Yves Schutz, and Katarina Melzer declare
that there are no conflicts of interests regarding the publication of this
1. Ravussin E , Bogardus C. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization . Am J Clin Nutr . 1989 ; 49 ( 5 Suppl): 968 - 75 .
2. Westerterp KR . Physical activity and physical activity induced energy expenditure in humans: measurement , determinants, and effects. Front Physiol . 2013 ; 4 : 90 .
3. Billat VL , Demarle A , Slawinski J , Paiva M , Koralsztein JP . Physical and training characteristics of top-class marathon runners . Med Sci Sports Exerc . 2001 ; 33 ( 12 ): 2089 - 97 .
4. Stellingwerf T. Case study: Nutrition and training periodization in three elite marathon runners . Int J Sport Nutr Exerc Metab . 2012 ; 22 ( 5 ): 392 - 400 .
5. Zapico AG , Calderon FJ , Benito PJ , Gonzalez CB , Parisi A , Pigozzi F , et al. Evolution of physiological and haematological parameters with training load in elite male road cyclists: a longitudinal study . J Sports Med Phys Fitness . 2007 ; 47 ( 2 ): 191 - 6 .
6. Fiskerstrand A , Seiler KS . Training and performance characteristics among Norwegian international rowers 1970-2001 . Scand J Med Sci Sports . 2004 ; 14 ( 5 ): 303 - 10 .
7. Neal CM , Hunter AM , Galloway SD . A 6-month analysis of training-intensity distribution and physiological adaptation in Ironman triathletes . J Sports Sci . 2011 ; 29 ( 14 ): 1515 - 23 .
8. Westerterp KR , Saris WH , van Es M , ten Hoor F. Use of the doubly labeled water technique in humans during heavy sustained exercise . J Appl Physiol ( 1985 ). 1986 ; 61 ( 6 ): 2162 - 7 .
9. Thomas DT , Erdman KA , Burke LM . American College of Sports Medicine Joint Position Statement . Nutrition and Athletic Performance . Med Sci Sports Exerc . 2016 ; 48 ( 3 ): 543 - 68 .
10. O'Connor H , Slater G. Losing, gaining and making weight for athletes . In: Lanham-New S, Stear S , Sherriffs M , Collins A , editors. Sport and exercise nutrition. West Sussex: Wiley-Blackwell ; 2011 . p. 210 - 32 .
11. Fudge BW , Westerterp KR , Kiplamai FK , Onywera VO , Boit MK , Kayser B , et al. Evidence of negative energy balance using doubly labelled water in elite Kenyan endurance runners prior to competition . Br J Nutr . 2006 ; 95 ( 1 ): 59 - 66 .
12. Sundgot-Borgen J , Meyer NL , Lohman TG , Ackland TR , Maughan RJ , Stewart AD , et al. How to minimise the health risks to athletes who compete in weight-sensitive sports review and position statement on behalf of the Ad Hoc Research Working Group on Body Composition, Health and Performance, under the auspices of the IOC Medical Commission . Br J Sports Med . 2013 ; 47 ( 16 ): 1012 - 22 .
13. World Health Organization (WHO). Obesity: preventing and managing the global epidemic . Report of a WHO Consultation, WHO Technical Report Series 894. Geneva: World Health Organization ; 2000 .
14. Issurin VB . New horizons for the methodology and physiology of training periodization . Sports Med . 2010 ; 40 ( 3 ): 189 - 206 .
15. Matveyev L. Periodisierung des sportlichen Trainings . 2nd ed. Berlin: Bartels & Wernitz; 1975 .
16. Bompa T , Haff G. Periodization . Theory and methodology of training . 5th ed. Champaign: Human Kinetics ; 2009 .
17. Stellingwerff T , Boit MK , Res PT . Nutritional strategies to optimize training and racing in middle-distance athletes . J Sports Sci . 2007 ;25 Suppl 1: S17 - 28 .
18. Stellingwerff T , Maughan RJ , Burke LM . Nutrition for power sports: middledistance running, track cycling , rowing, canoeing/kayaking, and swimming. J Sports Sci . 2011 ;29 Suppl 1: S79 - 89 .
19. Burke LM , Hawley JA , Wong SH , Jeukendrup AE . Carbohydrates for training and competition . J Sports Sci . 2011 ;29 Suppl 1: S17 - 27 .
20. Maughan RJ , Burke LM . Practical nutritional recommendations for the athlete . Nestle Nutr Inst Workshop Ser . 2011 ; 69 : 131 - 49 .
21. Rodriguez NR , Di Marco NM , Langley S. American College of Sports Medicine position stand. Nutrition and athletic performance . Med Sci Sports Exerc . 2009 ; 41 ( 3 ): 709 - 31 .
22. Burke LM , Mujika I. Nutrition for recovery in aquatic sports . Int J Sport Nutr Exerc Metab . 2014 ; 24 ( 4 ): 425 - 36 .
23. Mujika I , Stellingwerff T , Tipton K. Nutrition and training adaptations in aquatic sports . Int J Sport Nutr Exerc Metab . 2014 ; 24 ( 4 ): 414 - 24 .
24. Shaw G , Koivisto A , Gerrard D , Burke LM . Nutrition considerations for openwater swimming . Int J Sport Nutr Exerc Metab . 2014 ; 24 ( 4 ): 373 - 81 .
25. Shaw G , Boyd KT , Burke LM , Koivisto A. Nutrition for swimming . Int J Sport Nutr Exerc Metab . 2014 ; 24 ( 4 ): 360 - 72 .
26. Burke LM , Millet G , Tarnopolsky MA . Nutrition for distance events . J Sports Sci . 2007 ;25 Suppl 1: S29 - 38 .
27. Jeukendrup AE . Nutrition for endurance sports: marathon, triathlon, and road cycling . J Sports Sci . 2011 ;29 Suppl 1: S91 - 9 .
28. Vilaca KH , Ferriolli E , Lima NK , Paula FJ , Moriguti JC . Effect of fluid and food intake on the body composition evaluation of elderly persons . J Nutr Health Aging . 2009 ; 13 ( 3 ): 183 - 6 .
29. Lohman M , Tallroth K , Kettunen JA , Marttinen MT . Reproducibility of dualenergy x-ray absorptiometry total and regional body composition measurements using different scanning positions and definitions of regions . Metabolism . 2009 ; 58 ( 11 ): 1663 - 8 .
30. Nana A , Slater GJ , Stewart AD , Burke LM . Methodology review: using dual-energy X-ray absorptiometry (DXA) for the assessment of body composition in athletes and active people . Int J Sport Nutr Exerc Metab . 2015 ; 25 ( 2 ): 198 - 215 .
31. Saunders MJ , Blevins JE , Broeder CE . Effects of hydration changes on bioelectrical impedance in endurance trained individuals . Med Sci Sports Exerc . 1998 ; 30 ( 6 ): 885 - 92 .
32. Madden AM , Smith S. Body composition and morphological assessment of nutritional status in adults: a review of anthropometric variables . J Hum Nutr Diet . 2016 ; 29 ( 1 ): 7 - 25 .
33. Temple D , Denis R , Walsh MC , Dicker P , Byrne AT. Comparison of anthropometric-based equations for estimation of body fat percentage in a normal-weight and overweight female cohort: validation via airdisplacement plethysmography . Public Health Nutr . 2015 ; 18 ( 3 ): 446 - 52 .
34. Magkos F , Yannakoulia M. Methodology of dietary assessment in athletes: concepts and pitfalls . Curr Opin Clin Nutr Metab Care . 2003 ; 6 ( 5 ): 539 - 49 .
35. Bemben DA , Buchanan TD , Bemben MG , Knehans AW . Influence of type of mechanical loading, menstrual status, and training season on bone density in young women athletes . J Strength Cond Res . 2004 ; 18 ( 2 ): 220 - 6 .
36. Carbuhn AF , Fernandez TE , Bragg AF , Green JS , Crouse SF . Sport and training influence bone and body composition in women collegiate athletes . J Strength Cond Res . 2010 ; 24 ( 7 ): 1710 - 7 .
37. Kabasakalis A , Kalitsis K , Tsalis G , Mougios V. Imbalanced nutrition of top-level swimmers . Int J Sports Med . 2007 ; 28 ( 9 ): 780 - 6 .
38. LaForgia J , Withers RT , Williams AD , Murch BJ , Chatterton BE , Schultz CG , et al. Effect of 3 weeks of detraining on the resting metabolic rate and body composition of trained males . Eur J Clin Nutr . 1999 ; 53 ( 2 ): 126 - 33 .
39. Loftin M , Warren B , Mayhew J. Comparison of physiologic and performance variables in male and female cross-country runners during a competitive season . Sports Med Train Rehabil . 1992 ; 3 ( 4 ): 281 - 8 .
40. Noland RC , Baker JT , Boudreau SR , Kobe RW , Tanner CJ , Hickner RC , et al. Effect of intense training on plasma leptin in male and female swimmers . Med Sci Sports Exerc . 2001 ; 33 ( 2 ): 227 - 31 .
41. Siders WA , Bolonchuk WW , Lukaski HC . Effects of participation in a collegiate sport season on body composition . J Sports Med Phys Fitness . 1991 ; 31 ( 4 ): 571 - 6 .
42. Siders WA , Lukaski HC , Bolonchuk WW . Relationships among swimming performance, body composition and somatotype in competitive collegiate swimmers . J Sports Med Phys Fitness . 1993 ; 33 ( 2 ): 166 - 71 .
43. Barr SI , Costill DL . Effect of increased training volume on nutrient intake of male collegiate swimmers . Int J Sports Med . 1992 ; 13 ( 1 ): 47 - 51 .
44. Couzy F , Lafargue P , Guezennec CY . Zinc metabolism in the athlete: influence of training, nutrition and other factors . Int J Sports Med . 1990 ; 11 ( 4 ): 263 - 6 .
45. Desgorces FD , Chennaoui M , Gomez-Merino D , Drogou C , Guezennec CY . Leptin response to acute prolonged exercise after training in rowers . Eur J Appl Physiol . 2004 ; 91 ( 5-6 ): 677 - 81 .
46. Garcia-Roves PM , Terrados N , Fernandez S , Patterson AM . Comparison of dietary intake and eating behavior of professional road cyclists during training and competition . Int J Sport Nutr Exerc Metab . 2000 ; 10 ( 1 ): 82 - 98 .
47. Hassapidou MN , Manstrantoni A. Dietary intakes of elite female athletes in Greece . J Hum Nutr Dietetics . 2001 ; 14 ( 5 ): 391 - 6 .
48. Jensen CD , Zaltas ES , Whittam JH . Dietary intakes of male endurance cyclists during training and racing . J Am Diet Assoc . 1992 ; 92 ( 8 ): 986 - 8 .
49. Margaritis I , Palazzetti S , Rousseau AS , Richard MJ , Favier A. Antioxidant supplementation and tapering exercise improve exercise-induced antioxidant response . J Am Coll Nutr . 2003 ; 22 ( 2 ): 147 - 56 .
50. Papadopoulou SK , Gouvianaki A , Grammatikopoulou MG , Maraki Z , Pagkalos IG , Malliaropoulos N , et al. Body composition and dietary intake of elite cross-country skiers members of the greek national team . Asian J Sports Med . 2012 ; 3 ( 4 ): 257 - 66 .
51. Peters EM , Goetzsche JM . Dietary practices of South African ultradistance runners . Int J Sport Nutr . 1997 ; 7 ( 2 ): 80 - 103 .
52. Taylor SR , Rogers GG , Driver HS . Effects of training volume on sleep, psychological, and selected physiological profiles of elite female swimmers . Med Sci Sports Exerc . 1997 ; 29 ( 5 ): 688 - 93 .
53. Stroup DF , Berlin JA , Morton SC , Olkin I , Williamson GD , Rennie D , et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group . JAMA . 2000 ; 283 ( 15 ): 2008 - 12 .
54. Orwin R. Evaluating coding decisions . In: Cooper H, Hedges L, editors. The handbook of research synthesis . New York : Russel Sage Foundation ; 1994 .
55. Downs SH , Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions . J Epidemiol Community Health . 1998 ; 52 ( 6 ): 377 - 84 .
56. Fox AS , Bonacci J , McLean SG , Spittle M , Saunders N. What is normal? Female lower limb kinematic profiles during athletic tasks used to examine anterior cruciate ligament injury risk: a systematic review . Sports Med . 2014 ; 44 ( 6 ): 815 - 32 .
57. Wang ZM , Pierson Jr RN , Heymsfield SB . The five-level model: a new approach to organizing body-composition research . Am J Clin Nutr . 1992 ; 56 ( 1 ): 19 - 28 .
58. Higgins , Green, editors. Cochrane Handbook for Systematic Reviews of Interventions . Chichester, West Sussex , England: Wiley-Blackwell 2012
59. Gravetter F , Wallnau L. Essentials of statistics for the behavioral sciences . 8th ed. Belmont: Cengage Learning; 2013 .
60. Bescós R , Rodríguez FA , Iglesias X , Knechtle B , Benítez A , Marina M , et al. Nutritional behavior of cyclists during a 24-hour team relay race: a field study report . Journal of the International Society of Sports Nutrition . 2012 ; 9 ( 1 ): 1 - 11 .
61. Rehrer NJ , Hellemans IJ , Rolleston AK , Rush E , Miller BF. Energy intake and expenditure during a 6-day cycling stage race . Scand J Med Sci Sports . 2010 ; 20 ( 4 ): 609 - 18 .
62. Hulton AT , Lahart I , Williams KL , Godfrey R , Charlesworth S , Wilson M , et al. Energy expenditure in the Race Across America (RAAM). Int J Sports Med . 2010 ; 31 ( 7 ): 463 - 7 .
63. Costa RJ , Gill SK , Hankey J , Wright A , Marczak S. Perturbed energy balance and hydration status in ultra-endurance runners during a 24 h ultramarathon . Br J Nutr . 2014 ; 112 ( 3 ): 428 - 37 .
64. Morris FL , Payne WR . Seasonal variations in the body composition of lightweight rowers . Br J Sports Med . 1996 ; 30 ( 4 ): 301 - 4 .
65. Ormsbee MJ , Arciero PJ . Detraining increases body fat and weight and decreases VO2peak and metabolic rate . J Strength Cond Res . 2012 ; 26 ( 8 ): 2087 - 95 .
66. Boulay MR , Serresse O , Almeras N , Tremblay A. Energy expenditure measurement in male cross-country skiers: comparison of two field methods . Med Sci Sports Exerc . 1994 ; 26 ( 2 ): 248 - 53 .
67. Sjodin AM , Andersson AB , Hogberg JM , Westerterp KR . Energy balance in cross-country skiers: a study using doubly labeled water . Med Sci Sports Exerc . 1994 ; 26 ( 6 ): 720 - 4 .
68. Schulz LO , Alger S , Harper I , Wilmore JH , Ravussin E. Energy expenditure of elite female runners measured by respiratory chamber and doubly labeled water . J Appl Physiol . 1992 ; 72 ( 1 ): 23 - 8 .
69. Hill RJ , Davies PS . Energy intake and energy expenditure in elite lightweight female rowers . Med Sci Sports Exerc . 2002 ; 34 ( 11 ): 1823 - 9 .
70. Trappe TA , Gastaldelli A , Jozsi AC , Troup JP , Wolfe RR . Energy expenditure of swimmers during high volume training . Med Sci Sports Exerc . 1997 ; 29 ( 7 ): 950 - 4 .
71. Winters KM , Adams WC , Meredith CN , Loan MD , Lasley BL . Bone density and cyclic ovarian function in trained runners and active controls . Med Sci Sports Exerc . 1996 ; 28 ( 7 ): 776 - 85 .
72. Thompson FE , Byers T. Dietary assessment resource manual . J Nutr . 1994 ; 124 (11 Suppl): 2245S - 317S .
73. Brouns F , Saris WH , Stroecken J , Beckers E , Thijssen R , Rehrer NJ , et al. Eating , drinking, and cycling. A controlled Tour de France simulation study, Part I. Int J Sports Med . 1989 ;10 Suppl 1: S32 - 40 .
74. Subar AF , Freedman LS , Tooze JA , Kirkpatrick SI , Boushey C , Neuhouser ML , et al. Addressing current criticism regarding the value of self-report dietary data . J Nutr . 2015 ; 145 ( 12 ): 2639 - 45 .
75. Loucks AB , Kiens B , Wright HH . Energy availability in athletes . J Sports Sci . 2011 ;29 Suppl 1: S7 - 15 .
76. Loucks AB . Low energy availability in the marathon and other endurance sports . Sports Med . 2007 ; 37 ( 4-5 ): 348 - 52 .
77. Melin A , Tornberg AB , Skouby S , Moller SS , Sundgot-Borgen J , Faber J , et al. Energy availability and the female athlete triad in elite endurance athletes . Scand J Med Sci Sports . 2015 ; 25 ( 5 ): 610 - 22 .
78. Nana A , Slater GJ , Hopkins WG , Halson SL , Martin DT , West NP , et al. Importance of standardized DXA protocol for assessing physique changes in athletes . Int J Sport Nutr Exerc Metab . 2016 ; 26 ( 3 ): 259 - 67 .
79. Ball SD , Altena TS , Swan PD . Comparison of anthropometry to DXA: a new prediction equation for men . Eur J Clin Nutr . 2004 ; 58 ( 11 ): 1525 - 31 .
80. Armstrong LE , Casa DJ , Emmanuel H , Ganio MS , Klau JF , Lee EC , et al. Nutritional , physiological, and perceptual responses during a summer ultraendurance cycling event . J Strength Cond Res . 2012 ; 26 ( 2 ): 307 - 18 .
81. Berg U , Enqvist JK , Mattsson CM , Carlsson-Skwirut C , Sundberg CJ , Ekblom B , et al. Lack of sex differences in the IGF-IGFBP response to ultra endurance exercise . Scand J Med Sci Sports . 2008 ; 18 ( 6 ): 706 - 14 .
82. Brewer CP , Dawson B , Wallman KE , Guelfi KJ . Effect of repeated sodium phosphate loading on cycling time-trial performance and VO2peak . Int J Sport Nutr Exerc Metab . 2013 ; 23 ( 2 ): 187 - 94 .
83. Brinkworth GD , Buckley JD , Bourdon PC , Gulbin JP , David A. Oral bovine colostrum supplementation enhances buffer capacity but not rowing performance in elite female rowers . Int J Sport Nutr Exerc Metab . 2002 ; 12 ( 3 ): 349 - 65 .
84. Decombaz J , Gmuender B , Sierro G , Cerretelli P. Muscle carnitine after strenuous endurance exercise . J Appl Physiol . 1992 ; 72 ( 2 ): 423 - 7 .
85. Dellavalle DM , Haas JD . Iron supplementation improves energetic efficiency in iron-depleted female rowers . Med Sci Sports Exerc . 2014 ; 46 ( 6 ): 1204 - 15 .
86. Desgorces FD , Chennaoui M , Drogou C , Guezennec CY , Gomez-Merino D. Relationships between leptin levels and carbohydrate intake during rowing training . J Sports Med Phys Fitness . 2008 ; 48 ( 1 ): 83 - 9 .
87. Drenowatz C , Eisenmann JC , Carlson JJ , Pfeiffer KA , Pivarnik JM . Energy expenditure and dietary intake during high-volume and low-volume training periods among male endurance athletes . Appl Physiol Nutr Metab . 2012 ; 37 ( 2 ): 199 - 205 .
88. Drenowatz C , Eisenmann JC , Pivarnik JM , Pfeiffer KA , Carlson JJ . Differences in energy expenditure between high- and low-volume training . Eur J Sport Sci . 2013 ; 13 ( 4 ): 422 - 30 .
89. Emhoff CA , Messonnier LA , Horning MA , Fattor JA , Carlson TJ , Brooks GA . Gluconeogenesis and hepatic glycogenolysis during exercise at the lactate threshold . J Appl Physiol . 2013 ; 114 ( 3 ): 297 - 306 .
90. Enqvist JK , Mattsson CM , Johansson PH , Brink-Elfegoun T , Bakkman L , Ekblom BT . Energy turnover during 24 hours and 6 days of adventure racing . J Sports Sci . 2010 ; 28 ( 9 ): 947 - 55 .
91. Fudge BW , Easton C , Kingsmore D , Kiplamai FK , Onywera VO , Westerterp KR , et al. Elite Kenyan endurance runners are hydrated day-to-day with ad libitum fluid intake . Med Sci Sports Exerc . 2008 ; 40 ( 6 ): 1171 - 9 .
92. Garcia-Roves PM , Terrados N , Fernandez SF , Patterson AM . Macronutrients intake of top level cyclists during continuous competition-change in the feeding pattern . Int J Sports Med . 1998 ; 19 ( 1 ): 61 - 7 .
93. Gorsuch J , Long J , Miller K , Primeau K , Rutledge S , Sossong A , et al. The effect of squat depth on multiarticular muscle activation in collegiate crosscountry runners . J Strength Cond Res . 2013 ; 27 ( 9 ): 2619 - 25 .
94. Griffith RO , Dressendorfer RH , Fullbright GD , Wade CE . Testicular function during exhaustive endurance training. / La fonction testiculaire lors d ' un entrainement epuisant d ' endurance . Phys Sportsmed . 1990 ; 18 ( 5 ): 54 - 6 . 61-2 ; 4 .
95. Havemann L , Goedecke JH . Nutritional practices of male cyclists before and during an ultraendurance event . Int J Sport Nutr Exerc Metab . 2008 ; 18 ( 6 ): 551 - 66 .
96. Heinonen A , Oja P , Kannus P , Sievanen H , Manttari A , Vuori I. Bone mineral density of female athletes in different sports . Bone Miner . 1993 ; 23 ( 1 ): 1 - 14 .
97. Herring JL , Mole PA , Meredith CN , Stern JS . Effect of suspending exercise training on resting metabolic rate in women . Med Sci Sports Exerc . 1992 ; 24 ( 1 ): 59 - 65 .
98. Jones PJ , Leitch CA. Validation of doubly labeled water for measurement of caloric expenditure in collegiate swimmers . J Appl Physiol . 1993 ; 74 ( 6 ): 2909 - 14 .
99. Jurimae J , Jurimae T , Pihl E. Rowing ergometer performance and anaerobic capacity in college rowers . Kinesiology . 1999 ; 31 ( 2 ): 13 - 8 .
100. Jurimae J , Hofmann P , Jurimae T , Maestu J , Purge P , Wonisch M , et al. Plasma adiponectin response to sculling exercise at individual anaerobic threshold in college level male rowers . Int J Sports Med . 2006 ; 27 ( 4 ): 272 - 7 .
101. Jurimae J , Jurimae T. Plasma leptin responses to prolonged sculling in female rowers . J Sports Med Phys Fitness . 2004 ; 44 ( 1 ): 104 - 9 .
102. Jurimae J , Purge P , Jurimae T. Effect of prolonged training period on plasma adiponectin in elite male rowers . Horm Metab Res . 2007 ; 39 ( 7 ): 519 - 23 .
103. Jurimae J , Ramson R , Maestu J , Jurimae T , Arciero PJ , Braun WA , et al. Interactions between adipose, bone, and muscle tissue markers during acute negative energy balance in male rowers . J Sports Med Phys Fitness . 2011 ; 51 ( 2 ): 347 - 54 .
104. Koshimizu T , Matsushima Y , Yokota Y , Yanagisawa K , Nagai S , Okamura K , et al. Basal metabolic rate and body composition of elite Japanese male athletes . J Med Invest . 2012 ; 59 ( 3-4 ): 253 - 60 .
105. Lazzer S , Salvadego D , Rejc E , Buglione A , Antonutto G , di Prampero PE . The energetics of ultra-endurance running . Eur J Appl Physiol . 2012 ; 112 ( 5 ): 1709 - 15 .
106. Maestu J , Jurimae J , Purge P , Ramson R , Jurimae T. Performance improvement is associated with higher postexercise responses in interleukin-6 and tumor necrosis factor concentrations . J Sports Med Phys Fitness . 2010 ; 50 ( 4 ): 524 - 9 .
107. Magkos F , Yannakoulia M , Kavouras SA , Sidossis LS . The type and intensity of exercise have independent and additive effects on bone mineral density . Int J Sports Med . 2007 ; 28 ( 9 ): 773 - 9 .
108. Maïmoun L , Manetta P , Leroux S. Testosterone is significantly reduced in endurance athletes without impact on bone mineral density . Horm Res . 2003 ; 59 ( 6 ): 285 - 92 .
109. Martin MK , Martin DT , Collier GR , Burke LM . Voluntary food intake by elite female cyclists during training and racing: influence of daily energy expenditure and body composition . Int J Sport Nutr Exerc Metab . 2002 ; 12 ( 3 ): 249 .
110. Medelli J , Lounana J , Menuet JJ , Shabani M , Cordero-MacIntyre Z. Is osteopenia a health risk in professional cyclists ? J Clin Densitom . 2009 ; 12 ( 1 ): 28 - 34 .
111. Moses K , Manore MM . Development and testing of a carbohydrate monitoring tool for athletes . J Am Diet Assoc . 1991 ; 91 ( 8 ): 962 - 5 .
112. Motonaga K , Yoshida S , Yamagami F , Kawano T , Takeda E. Estimation of total daily energy expenditure and its components by monitoring the heart rate of Japanese endurance athletes . J Nutr Sci Vitaminol (Tokyo) . 2006 ; 52 ( 5 ): 360 - 7 .
113. Muoio DM , Leddy JJ , Horvath PJ , Awad AB , Pendergast DR . Effect of dietary fat on metabolic adjustments to maximal VO2 and endurance in runners . Med Sci Sports Exerc . 1994 ; 26 ( 1 ): 81 - 8 .
114. Ousley-Pahnke L , Black DR , Gretebeck RJ . Dietary intake and energy expenditure of female collegiate swimmers during decreased training prior to competition . J Am Diet Assoc . 2001 ; 101 ( 3 ): 351 - 4 .
115. Palazzetti S , Rousseau AS , Richard MJ , Favier A , Margaritis I. Antioxidant supplementation preserves antioxidant response in physical training and low antioxidant intake . Br J Nutr . 2004 ; 91 ( 1 ): 91 - 100 .
116. Palm R , Jürimäe J , Mästu J , Purge P , Jürimäe T , Rom K , et al. Relationship between body composition and aerobic capacity values in well-trained male rowers . Acta Kinesiol Universitatis Tartu . 2005 ; 10 : 125 - 32 .
117. Penteado VS , Castro CH , Pinheiro Mde M , Santana M , Bertolino S , de Mello MT , et al. Diet , body composition, and bone mass in well-trained cyclists . J Clin Densitom . 2010 ; 13 ( 1 ): 43 - 50 .
118. Phillips SM , Atkinson SA , Tarnopolsky MA , MacDougall JD . Gender differences in leucine kinetics and nitrogen balance in endurance athletes . J Appl Physiol ( 1985 ). 1993 ; 75 ( 5 ): 2134 - 41 .
119. Roberts D , Smith DJ . Training at moderate altitude: iron status of elite male swimmers . J Lab Clin Med . 1992 ; 120 ( 3 ): 387 - 91 .
120. Santos DA , Dawson JA , Matias CN , Rocha PM , Minderico CS , Allison DB , et al. Reference values for body composition and anthropometric measurements in athletes . PLoS One . 2014 ; 9 ( 5 ): e97846 .
121. Sato A , Shimoyama Y , Ishikawa T , Murayama N. Dietary thiamin and riboflavin intake and blood thiamin and riboflavin concentrations in college swimmers undergoing intensive training . Int J Sport Nutr Exerc Metab . 2011 ; 21 ( 3 ): 195 - 204 .
122. Schena F , Pattini A , Mantovanelli S. Iron status in athletes involved in endurance and in prevalently anaerobic sports . In: Kies CV , Driskell JA , editors. Sports nutrition: minerals and electrolytes . Boca Raton: CRC Press ; 1995 . p. 65 - 80 .
123. Schenk K , Gatterer H , Ferrari M , Ferrari P , Cascio VL , Burtscher M. Bike Transalp 2008 : liquid intake and its effect on the body's fluid homeostasis in the course of a multistage, cross-country , MTB marathon race in the central Alps . Clin J Sport Med . 2010 ; 20 ( 1 ): 47 - 52 .
124. Sherman WM , Doyle JA , Lamb DR , Strauss RH . Dietary carbohydrate, muscle glycogen, and exercise performance during 7 d of training . Am J Clin Nutr . 1993 ; 57 ( 1 ): 27 - 31 .
125. Simsch C , Lormes W , Petersen KG , Baur S , Liu Y , Hackney AC , et al. Training intensity influences leptin and thyroid hormones in highly trained rowers . Int J Sports Med . 2002 ; 23 ( 6 ): 422 - 7 .
126. Sundby OH , Gorelick ML S . Relationship between functional hamstring: quadriceps ratios and running economy in highly trained and recreational female runners . J Strength Cond Res . 2014 ; 28 ( 8 ): 2214 - 27 .
127. Tomten SE , Hostmark AT. Energy balance in weight stable athletes with and without menstrual disorders . Scand J Med Sci Sports . 2006 ; 16 ( 2 ): 127 - 33 .
128. Vaiksaar S , Jurimae J , Maestu J , Purge P , Kalytka S , Shakhlina L , et al. No effect of menstrual cycle phase on fuel oxidation during exercise in rowers . Eur J Appl Physiol . 2011 ; 111 ( 6 ): 1027 - 34 .
129. Witard OC , Jackman SR , Kies AK , Jeukendrup AE , Tipton KD . Effect of increased dietary protein on tolerance to intensified training . Med Sci Sports Exerc . 2011 ; 43 ( 4 ): 598 - 607 .
130. Yeater R , Reed C , Ullrich I , Morise A , Borsch M. Resistance trained athletes using or not using anabolic steroids compared to runners: effects on cardiorespiratory variables, body composition, and plasma lipids . Br J Sports Med . 1996 ; 30 ( 1 ): 11 - 4 .
131. Zajac A , Poprzecki S , Maszczyk A , Czuba M , Michalczyk M , Zydek G. The effects of a ketogenic diet on exercise metabolism and physical performance in off-road cyclists . Nutrients . 2014 ; 6 ( 7 ): 2493 - 508 .
132. Zalcman I , Guarita HV , Juzwiak CR , Crispim CA , Antunes HK , Edwards B , et al. Nutritional status of adventure racers . Nutrition . 2007 ; 23 ( 5 ): 404 - 11 .