Introduction
For athletes, chronic energy deficiency termed low energy availability (EA) is a significant
issue in both female and male athletes (1, 2). EA is defined as the amount of dietary
energy remaining for other body functions after the energy cost of exercise is covered
and normalized to fat-free mass (FFM) (or lean body mass) (3). The conventional EA
equation is as follows:
E
A
(
k
c
a
l
/
k
g
F
F
M
/
d
a
y
)
=
[
E
I
(
k
c
a
l
/
d
a
y
)
-
E
E
E
(
k
c
a
l
/
d
a
y
)
]
/
F
F
M
(
k
g
)
EI = energy intake; EEE = exercise energy expenditure.
An EA of < 30 kcal/kg FFM/day is typically defined as clinically low EA (4). Since
the introduction of EA in 2007 (5), numerous researchers have assessed EA in athletes
with equivocal results, due in part to no clear methodological guidelines for calculating
EA, including techniques used to measure each component of the EA equation (6, 7).
For example, female athletes with similar EI had different menstrual conditions (eumenorrheic
or amenorrheic) (8, 9), while in males, EI is similar between cross-country athletes
and sedentary controls (10). Conversely, the mean EEE in female and male athletes
at risk for low EA was significantly higher than moderate or no-risk athletes (11,
12), suggesting that in athletes, high EEE affects EA values. Athletes participating
in high levels of exercise do not appear to be eating adequately to cover EEE. This
inconsistency may be attributed to the difficulty in accurately measuring EI and EEE.
The assessment difficulties of EI are well documented (6), but the components to be
included in EEE are less frequently examined. To date, only one study has attempted
to calculate EA based on different methods for estimating EEE (8). Therefore, the
goal of this opinion piece is to outline the rationale for including non-exercise
activity thermogenesis (NEAT) as part of EEE in the EA equation to improve estimates
of EA.
Calculation of EEE in free-living athletes
Assessing of EEE in the field is challenging and typically only includes exercise
expended in sport training. To demonstrate the difficulty in determining EEE, Guebels
et al. (8) measured EA in female college athletes, with or without menstrual dysfunction,
using different methods for quantifying EEE. They measured total energy expenditure
(TEE) using 7-day activity logs, accelerometers, and running energy expenditure on
the treadmill to assess more accurately “planned EEE” and then EEE calculated using
four different methods. Method 1 comprised of all planned exercise that included exercise
training and all purposeful physical activity (PA) regardless of intensity but did
not include PA that resulted from social games, hobbies, leisure pastimes, or transport-related
activity (< 30 consecutive min). Method 2 included all planned exercise plus bicycle
commuting and all walking. This method also added transport-related activities as
planned PA. For consistency, bicycle commuting was entered as general/leisure bicycling
of 4.0 metabolic equivalent (METs) and all walking was entered as moderate-intensity
walking (3.3 METs). No other activities were identified as being equal to 4.0 or 3.3
METs; walking (lasting for ≥30 consecutive min or within an exercise workout) was
included in Method 1. Method 3 included all exercise at ≥4 METs. This method quantified
EEE more objectively using a 4.0 MET cut-off, which incorporates the bicycle commutes
but excluded walking of ≥3.3 METs. Method 4 included all exercises of >4 METs and
included all the activities from Method 3, except for the bicycle commutes (4.0 METs).
As expected, the more activities were included in EEE, the lower the EA value. This
means that EA values varied widely depending on how EEE was qualified.
Alternative method for calculating EEE in free-living athletes
TEE comprises four components: resting metabolic rate (RMR), diet-induced thermogenesis
(DIT), NEAT, and EEE (13). Activity-induced energy expenditure (AEE) refers to the
energy obtained by subtracting DIT and RMR from TEE (14), that is, the sum of planned
exercise or sport exercise training and NEAT. Athletes often perform spontaneous exercises
such as swimming and running, in addition to their scheduled training. Almost all
previous EA studies have included only the energy expenditure of planned training
as EEE and do not include PA performed in their daily lives. In addition, some athletes
may spend more than an hour commuting to school/work by bicycle over the intensity
of 4.0 METs. For endurance runners, the mean AEE was 1,688 kcal/day (47% of TEE) in
males (15), and 1,585 kcal/day (52% of TEE) in females (16), accounting for approximately
half of the TEE. Since energy used to support one process cannot be used for others
(17), accurate measurement of EA depends on how accurately and realistically EEE is
assessed. A method that includes NEAT and planned exercise in EEE is more suitable
for free-living athletes than the conventional method. Reassessing how EEE is calculated
will allow for more accurate predictions of EA and the ability to detect energy-deficient
athletes earlier. Therefore, we propose that the EA calculation in free-living athletes
should be as follows:
Improved EA (kcal/kg FFM/day) = [EI (kcal/day) – AEE (kcal/day)]/FFM (kg), where AEE
includes programmed EEE and NEAT.
Alternative methods for detecting low EA without measurement of EI or EEE
Early detection of athletes at risk of energy deficiency is essential, regardless
of gender, age, or sports events. Owing to difficulties and errors in measuring EI,
EEE and FFM, which are the components of EA, other potential surrogate markers for
low EA have been investigated (7). The RMR ratio, measured RMR divided by predicted
RMR, is an acceptable indicator of low EA regardless of race and sex (18–22). The
“field method” would allow for identification of athletes at risk for low EA without
assessing EI or EEE. To calculate this ratio, it is necessary to both measure and
estimate the RMR. Thompson and Manore (23) showed that FFM should be used to calculate
RMR estimates for athletes and that the Cunningham equation was the most suitable
for RMR estimation in male and female athletes. The Cunningham equation is also widely
used to estimate the RMR in White individuals (19, 24). The tissues and organs that
are components of FFM are not energetically equal and have specific metabolic rates.
Therefore, the dual-energy x-ray absorptiometry (DXA) equation, which is obtained
by measuring body composition with high accuracy using DXA and multiplying it by the
value of the RMR of each tissue, has been utilized (20, 24). Race was found to be
a significant predictor of RMR after adjusting for age, sex, body mass index, fat
mass, and FFM, and it is appropriate to use an RMR equation that matches the population's
characteristics (22). In addition, there is a cut-off value suitable for each RMR
estimation method to determine the RMR ratio (25).
In response to periods of low EA, the hypothalamic-pituitary-thyroid axis adapts to
reduce energy expenditure (26). Athletes with menstrual disorders have demonstrated
consistently decreased triiodothyronine (T3) levels (9, 27), therefore, a low T3 level
is one objective blood marker that could be used to identify female athletes with
low EA. In exercising men, it has been reported that leptin and insulin are reduced,
independent of whether low EA had originally occurred with or without exercise; however,
low EA did not significantly impact ghrelin, T3, testosterone, and insulin-like growth
factor-1 (IGF-1) levels (28). Another study indicated a significant positive association
between IGF-1 and the RMR ratio in highly-trained male soccer players (29). Further
research regarding male athletes' endocrine adaptive processes to exercise training
and response to reduced EA is necessary.
Discussion
Better method for EEE
For athletes, EA is the residual energy available to support physiological functions
after covering the costs of physical activity. However, the EA equation and low cut-off
value was derived in a metabolic laboratory-based study based on the impairment of
hormones related to the female reproductive cycle in eumenorrheic, weight stable,
and sedentary women (4, 6). This EA concept only accounts for EEE of planned exercise
in the laboratory setting and NEAT was low. NEAT varies with environmental factors,
activity status, physiological factors, and occupation, and can vary up to 2,000 kcal/day
in individuals (30), even with similar body sizes (31). While the lack of consideration
of NEAT in the calculation of EA outside the laboratory provides simplicity of EA
calculation, it poses a potential “noise” factor for the comparison of EA between
studies or for using universal EA threshold values (13). This may skew the true EA
for physiological functionality in active populations (32). Therefore, we suggest
an improved EA calculation that includes NEAT (Figure 1). NEAT should include the
energy expenditure outside of planned sport training such as voluntary exercise training,
strength training, cycling exercise using a bicycle ergometer, swimming, and biking
to school/work. Methods available in the field to measure NEAT are accelerometer (29),
multisensor armband (32), or calculation from activity logs using METs (8). If the
doubly labeled water (DLW) technique is available, there is a laboratory method of
calculating NEAT by subtracting RMR, DIT (0.1TEE), and EEE from TEE (33).
Figure 1
Components of TEE, components of conventional EA equation, and components of improved
EA equation. TEE, total energy expenditure; EA, energy availability; RMR, resting
metabolic rate; DIT, diet-induced thermogenesis; NEAT, non-exercise activity thermogenesis;
EEE, exercise energy expenditure; AEE, activity-induced energy expenditure. Conventional
EA (kcal/kg FFM/day) = [EI (kcal/day) – EEE (kcal/day)] / FFM (kg). Improved EA (kcal/kg
FFM/day) = [EI (kcal/day) – AEE (kcal/day)] / FFM (kg).
Lee et al. (29) reported that the EA of collegiate soccer players calculated by the
conventional equation was 31.9 ± 9.8 kcal/kg FFM/day. A recalculation of EA for the
same participants using the AEE approach resulted in an EA of 19.7 ± 8.5 kcal/kg FFM/day.
The number of participants with LEA (< 30 kcal) increased from 5 to 10 using AEE instead
of EEE with the improved equation. All five participants with newly classified LEA
had lower testosterone levels, and higher bone resorption markers than the reference
value. Thus, these participants would be considered at risk for future health issues
caused by LEA, making early detection of at-risk athletes more realistic by improved
EA equation.
Better methods for EI
As mentioned earlier, EI is a critical component of EA and is known to be underestimated
(6). DLW is the gold standard for measuring TEE under free-living conditions, and
the TEE measured by DLW can be considered an EI if body weight is stable (34). To
eliminate the underestimation of EI by participants in EA studies, research assessing
EI using DLW could be used in the EA calculation. It is also necessary to measure
body composition in relation to FFM with high accuracy using DXA. So far, only one
study (35) has combined DLW and DXA to determine EA (kcal/day) of athletes. In this
study, the EA at the beginning of the season was ~39.1 kcal/kg FFM/day in male athletes
and 42.9 kcal/kg FFM/day in female athletes. These values are higher than those reported
in previous studies of both sexes using EI values obtained from dietary records (36,
37). The Food Frequency Questionnaire (FFQ) is often used to calculate EI in EA studies
because it is less burdensome and more cost-effective. However, the FFQ tends to overestimate
EI in low-energy consumers and underestimate EI in large eaters (38); thus, researchers
and dietitians should be careful in EA evaluation using FFQ.
Taken together, it is crucial to build evidence for the physiological effects of low
EA by facilitating studies that can more accurately measure the components of EA,
including using the DLW surrogate for EI, adding NEAT in EEE, and accurately measuring
FFM. Better laboratory-based measurements will help researchers develop a more accurate,
cheaper, and simpler field method for calculating EA. A better field method for EA
will standardize and improve the identification of free-living athletes at risk for
energy deficiency and associated health issues that occur if chronic energy deficiency
persists. Furthermore, knowing an athlete's EA can help in developing diet plans that
more accurately help an athlete meet their needs.
Author contributions
MT and MM conceived the idea for this manuscript, developed the outline, and compiled
the manuscript. Both authors contributed to the article and approved the submitted
version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.
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