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Compensatory Changes in Energy Balance Regulation over One Athletic Season


Medicine & Science in Sports & Exercise: June 2017 - Volume 49 - Issue 6 - p 1229–1235
doi: 10.1249/MSS.0000000000001216

Purpose Mechanisms in energy balance (EB) regulation may include compensatory changes in energy intake (EI) and metabolic adaption (MA), but information is unavailable in athletes who often change EB components. We aim to investigate EB regulation compensatory mechanisms over one athletic season.

Methods Fifty-seven athletes (39 males/18 females; handball, volleyball, basketball, triathlon, and swimming) were evaluated from the beginning to the competitive phase of the season. Resting and total energy expenditure (REE and TEE, respectively) were assessed by indirect calorimetry and doubly labeled water, respectively, and physical activity energy expenditure was determined as TEE − 0.1(TEE) − REE. Fat mass (FM) and fat-free mass (FFM) were evaluated by dual-energy x-ray absorptiometry and changed body energy stores was determined by 1.0(ΔFFM/Δtime) + 9.5(ΔFM/Δtime). EI was derived as TEE + EB. REE was predicted from baseline FFM, FM, sex, and sports. %MA was calculated as 100(measured REE/predicted REE-1) and MA (kcal) as %MA/100 multiplied by baseline measured REE. Average EI minus average physical activity energy expenditure was computed as a proxy of average energy availability, assuming that a constant nonexercise EE occurred over the season.

Results Body mass increased by 0.8 ± 2.5 kg (P < 0.05), but a large individual variability was found ranging from −6.1 to 5.2 kg. The TEE raise (16.8% ± 11.7%) was compensated by an increase EI change (16.3% ± 12.0%) for the whole group (P < 0.05). MA was found in triathletes, sparing 128 ± 168 kcal·d−1, and basketball players, dissipating 168 ± 205 kcal·d−1 (P < 0.05). MA was associated (P < 0.05) with EB and energy availability (r = 0.356 and r = 0.0644, respectively).

Conclusion TEE increased over the season without relevant mean changes in weight, suggesting that EI compensation likely occurred. The thrifty or spendthrift phenotypes observed among sports and the demanding workloads these athletes are exposed to highlight the need for sport-specific energy requirements.

1Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada, PORTUGAL; 2Department of Mathematical Sciences, United States Military Academy, West Point, NY; 3Institute of Nutritional Medicine, University of Hohenheim, Stuttgart, GERMANY; 4Department of Human Nutrition and Food Science, Christian-Albrechts-University of Kiel, Kiel, GERMANY; and 5Pennington Biomedical Research Center, Baton Rouge, LA

Address for correspondence: Analiza Mónica Silva, Ph.D., Exercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, 1499-002 Cruz-Quebrada, Portugal; E-mail:

Submitted for publication June 2016.

Accepted for publication January 2017.

Energy imbalance resulting from exercise training demands and overall energy expenditure (EE) may occur if energy intake (EI) does not match the energy requirements over a season, resulting in weight gain or loss (12). During long-duration training periods, it is crucial to maintain body mass and health and to enhance training effects (24). The energy balance (EB) components; that is, EI, EE, and rate of body energy stores used needs to be accurately quantified over time.

Doubly labeled water (DLW) is the reference method for estimating total EE (TEE) with a precision of ~5% (34). TEE is equal to EI during a period of EB, but during periods of positive or negative EB, DLW alone cannot estimate EI as TEE may be smaller or greater. To overcome this issue and accurately estimate EI, DLW must be combined with changes in body energy stores. Using the DLW/dual-energy x-ray absorptiometry (DXA) approach, changes in body energy stores, fat mass (FM), and fat-free mass (FFM) through DXA are summed with TEE measures by DLW, and valid estimates of EI can be provided during negative or positive EB (5,25,32,38).

An overlooked issue within the EB regulation of a highly trained athlete is the compensatory mechanisms that may occur over a season as a result of changes in weight and composition. Some of the mechanisms in EB regulation include compensatory changes in EI (32), spontaneous physical activity (16), and metabolic adaptation (MA) (8). Compensation in EI has been pointed out as the major determinant for the lack of success in attempted exercise-induced weight loss (32). The compensatory mechanism of EB recognized as MA represents changes in mass-adjusted resting EE (REE) (i.e., changes in REE beyond that predicted by FFM and FM changes) in response to over or undernutrition; such deviations from predicted values are considered to reflect changes in metabolic efficiency and hence in MA.

It is important to understand the complex interrelationships in regulating EB in the athletic population as an identified problem is the low energy availability (EA), defined as EI minus the energy expended in exercise (EEE) with the remaining energy used for other physiological processes (31). It has been observed in female athletes that a low EA is associated with a decline in REE and menstrual dysfunction (14,20). In fact, amenorrheic athletes can remain with a stable body weight (and thus in a neutral EB) under low values of EA (20,39) uncovering an apparent disconnection between EB regulation and EA that requires further clarification.

Therefore, the aim of the current study was to analyze the presence and magnitude of compensatory mechanisms in EB regulation of highly trained athletes from different sports and over one season. We further anticipate a link between EA and MA, never explored in diverse sports of varying energy demands over a season, including triathlon, a gravitational sport group classified as a weight sensitive sports, as athletes may reduce body mass or maintain a low weight to gain a competitive advantage (1).

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A total of 57 athletes, 39 males and 18 females, participating in national and international championships were recruited from the following sports: basketball (n = 24), handball (n = 6), volleyball (n = 6), triathlon (n = 10), and swimming (n = 11).

Subject's inclusion criteria were 1) >10 h training per week; 2) negative test outcomes for performance enhancing drugs; and 3) not taking any medications or dietary supplements. Female athletes self-reported taking oral contraceptives were excluded. Female athletes reporting a regular menstrual cycle were assessed during the luteal phase. All participants were informed about the possible risks of the investigation before giving their written informed consent to participate. The study was approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon, and conducted in accordance with the Declaration of Helsinki (40).

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Experimental design

This was an observational study with a follow-up over one season ranging from 5 to 10 months, depending on each sport's competitive timetable (~5 months for volleyball, ~8 months for basketball, ~7 months for handball, ~7 months for swimmers, and ~10 months for triathletes). The first period of evaluation was assessed in the beginning of the season, whereas the second period corresponded to the competitive phase, which occurred just before the main stage of the competition.

Basketball, handball, and volleyball player's training regimens consisted of five sessions per week with a total of 2 h each, divided in technical–tactical training (which included endurance running, drills with the ball in hand, sprint running, and practice game). Special attention was also given to footwork exercises, jumping and running abilities, and hand–eye coordination. Twice a week, athletes performed resistance training for nearly 1 h before the sport practices. In general, resistance training progressed from general conditioning (weeks 1–4), to hypertrophy (weeks 5–8), then to maximal strength and power (weeks 9–16) and concluded with specific strength training (weeks 17–32).

Swimmer's training regimens consisted of five sessions per week with a total of 150 min each, from which 90 min of general preparation training was conducted in the water (with a total volume of ~23.4 km·wk−1) and an additional 60 min of general strength training performed out of the water.

Triathlete's training regimens consisted of sessions divided in swimming, cycling, running, and general resistance training. In the beginning of the preparatory period, the training regimens consisted of five swimming sessions in the pool (volume = 19 km·wk−1), three sessions of cycling (volume = 150 km·wk−1), and five sessions of running (volume = 60 km·wk−1). In the main stage of the competition, the training regimens consisted of five swimming sessions in the pool and one in the ocean (pool + ocean volume = 30 km·wk−1), four sessions of cycling (volume = 240 km·wk−1), and six sessions of running (volume = 60 km·wk−1). In addition, during both assessments, triathletes were performing three sessions per week of general resistance training (volume = 90 min·wk−1).

Competitions were relegated to weekends during the competitive phase with at least one game per weekend integrated in the National Championships.

During both measurement periods, simultaneous assessments of body composition were conducted in the morning after a 12-h fast with no caffeine and alcohol during the preceding 24 h, and no vigorous exercise within 15 h.

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Body composition

Body weight and height were measured to the nearest 0.01 kg and 0.1 cm. DXA (Hologic Explorer-W, software QDR for Windows version 12.4, Waltham, MA) was used to estimate FM and FFM. The coefficients of variation (CV) in our laboratory for FM and FFM are 1.7% and 0.8%, respectively (27).

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Measurements were performed between 7:00 and 9:00 a.m. Before the REE measurements, participants laid supine for 10 min covered with a blanket in a quiet room at an environmental temperature and humidity of ±22°C and 40%–50%, respectively. REE measurements were performed by an open-circuit indirect calorimetry through a portable gas analyzer (K4b2; Cosmed, Rome, Italy) while participants laid supine wearing a mask for 30 min of data collection, as described elsewhere (28). For data analysis, a steady state was defined as a 5-min period with a CV less than 10% for V˙O2 and for V˙CO2, as proposed by Compher et al. (3). The mean V˙O2 and V˙CO2 values of a 5-min steady state were used in the Weir equation (37), and the period with the lowest REE was considered. On the basis of a test–retest in eight participants, the CV for REE in our laboratory is 7.8%.

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The DLW technique was used to assess TEE during a 7-d period by an established procedure using deuterium oxide and 18-oxygen. An oral dose of 0.8 g·kg−1 of total body water (TBW) of ≈10 atom% (AP) H2 18O (Taiyo Nippon Sanso Corporation, Tokyo, Japan), assuming TBW is 61% of body mass, and 0.16 g·kg−1 of TBW of 99.9 AP 2H2O (Sigma-Aldrich, Co., St Louis, MO), diluted in 50 mL of water was administered to the subjects. The analytical procedures used to estimate TEE are described elsewhere (26). The CV for TEE is 4.3% (27).

Physical activity EE (PAEE) was calculated assuming the thermic effect of food is ∼10% of TEE, as follows:

where TEE is assessed using the DLW technique and REE is from the respiratory gas exchange measurements.

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Calculation of EB

The general EB equation is described as follows:

If EB is negative, then EE is larger in magnitude than EI. Likewise, if EB is positive, then EE is smaller in magnitude than EI.

EB represents the average energy deficit or surplus and can be calculated from the changed body energy stores from the beginning to the competitive phase of the season. Using the established energy densities of 1.0 kcal·g−1 for FFM (7) and 9.5 kcal·g−1 for FM (19), we applied equation 3 to quantify the average rate of changed body energy stored or lost in kcal d−1:

where ΔFFM and ΔFM represent the change in g of FFM and FM from the beginning to the end of the follow-up and Δt is the time length of the season in days. Determining EB from the change in body energy stores (equation 3) has been validated experimentally in several studies (5,23).

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Calculation of average EI

Average EI was estimated with DXA and DLW from week 1, the preparatory phase after the beginning of the season (M1) and the main stage of the competition (M2). Simultaneous DXA and DLW measurements were collected during a 1-wk interval at both M1 and M2, and average EI (kcal·d−1) over the period between M1 and M2 was computed. This computation was performed by calculating the average energy change in body stores between M1 and M2 using the discretization of the instantaneous rate of change of body energy stores, as described in equation 3 and by adding DLW measured EE (kcal·d−1) at M2. The right end point was used as justified by approximation of the derivative measured at the right end of the interval. Others investigators have applied EE averages at both end points (known as the centered difference formula) (25), and theoretically, one could apply the left end point as well (32).

The final average EI is given by rearranging equation 2:

where EI represents the average intake over the time interval (M1 and M2), EB represents the rate of change in energy stores from M1 to M2, and EE denotes energy expenditure at M2.

We have used the differential equation (10,33) to model EB = EI − EE, which presents all rates measured in kilocalories per day, as EB is equal to the body energy stores rate of change and EE is already a rate measured by DLW.

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Average EA estimation

EA is defined as dietary EI minus the energy expended in the particular metabolic demand of interest (17). In exercise physiology, therefore, EA is defined as dietary EI minus the energy expended in exercise (EA = EI − EEE). EA is an input to the body's physiological systems, as it represents the amount of EI remaining for all other metabolic processes after ensuring the energy required for the exercise training.

EEE was not measured in this sample at the preparatory phase of the season and at the main stage of the competition although PAEE and average EI were accurately assessed. PAEE involves both EEE and nonexercise PAEE. Because EEE was not determined at both moments, we were not able to differentiate EEE from nonexercise PAEE. However, as the majority of these athletes lived and trained in training center facilities, the changes observed in PAEE would reflect changes in EEE, assuming that nonexercise PAEE was similar in both moments. Because nonexercise PAEE is assumed to be constant, average EI minus average PAEE would reflect any demands in EEE. Thus, if PAEE would increase significantly, the average value would be higher because of any increase in EEE over the season. Based on this assumption, average EA was calculated as the difference between average EI and average PAEE.

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Metabolic adaptation (MA)

To predict REE at the beginning of the season (M1), a regression equation using baseline FFM and FM, sex, and sports as the independent predictors was generated as follows:

where females = 0 and males = 1, whereas basketball players = 0, handball players = 1, volleyball players = 2, swimmers = 3, and triathletes = 4.

This equation was then used to predict REE at the main stage of the competition (M2), using the measured values of FM and FFM at that time point.

After deriving predicted REE, the linear equation was then used to calculate expected values of REE (predicted REE) at the competitive period from observed values of FFM and FM. To disclose any adaptations in REE not accounted for by changes in FFM and FM, percent metabolic adaptation (%MA) was calculated, as described by Thomas et al. (32). Thus, the ratio between the differences of measured and predicted REE with the predicted REE was used, which could be simplified as measured REE/predicted REE − 1.

Negative values indicate a decrease in REE beyond that expected by changes in body composition (REE below predicted REE at the competitive phase of the season), whereas positive values represent an increase in REE beyond that expected from body composition changes (REE higher than predicted REE at the main stage of the competition). Percent MA was also converted to kilocalories by multiplying the values, as a fraction, with baseline measured REE.

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Statistical analysis

Statistical analysis was performed using PASW Statistics for Windows version 22.0, 2013 (SPSS Inc., an IBM Company, Chicago, IL). Descriptive statistics including means ± SD were calculated for all the variables. Comparison of means were performed using one-sample t-tests (mean difference from zero or a given value), paired-sample t-tests (mean difference between moments), and independent-sample t-tests (mean difference between gender). Changes were expressed as a percentage of the baseline value. ANCOVA with Bonferroni corrections was used to test differences between sports. Bivariate and partial correlations were used to test the association between metabolic adaption (MA) with weight changes, EB, and average EA. Multiple regression analysis was conducted to predict REE.

Considering a mean response of 16.3% within individuals for average EI with a standard deviation of 12.0%, this study is able to reject the null hypothesis that a true difference is zero, with a power of 99% and a type I error probability >0.1%. Also, considering a mean response of 10.2% and −10.6% of matched pairs for MA with a standard deviation of 12.2 and 11.4, correspondingly for basketball players and triathletes, this study is able to reject the null hypothesis that a true difference is zero with a power of 98% and 76% (for basketball and triathletes, respectively) and a type I error probability of 5%.

For all tests, statistical significance was set at P < 0.05.

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Summary of measured EB components

Although body mass significantly increased by 0.8 ± 2.5 kg (P < 0.05), a large individual variability was observed ranging from −6.1 to 5.2 kg (−7.0% to 6.6% body mass change).

For the whole sample, TEE, PAEE, REE, and FFM significantly increased by 16.8% ± 11.7%, 34.6% ± 39.3%, 8.8% ± 21.8%, and 2.1% ± 2.8% (P < 0.05), whereas FM and percent change in EA reduced by 2.7% ± 13.1% and 18.3% ± 32.1% (P < 0.05), correspondingly. Average EI over the season was 4228 ± 627 kcal·d−1, and no mean changes in EB and metabolic adaptation were found (P > 0.05), as observed in Table 1. Mean values of 0.15 ± 0.73 kg at M1 and 0.12 ± 0.68 kg at M2 for body weight change from day 0 to day 7 of the DLW measurements were observed. These changes were within ±0.5 kg, which means that if TEE significantly increased and no mean changes in EB were observed (i.e., athletes were in an average neutral EB), then average EI has likely increased over the season.



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MA, EB, and EA

A significant MA was observed across sports, with basketball players displaying significant positive values of MA (P < 0.05), whereas triathletes showed significant negative values (P < 0.05), even after adjusting for sex and age (Fig. 1).



A positive association between MA (kcal·d−1) and weight changes of 0.340 (P < 0.05) was observed. A positive association between MA (kcal·d−1) and EB component was also found (r = 0.356, P < 0.01). These results indicate that athletes exposed to a negative EB tend to spare energy, whereas those who show a positive EB tend to dissipate energy, as displayed in the left panel of Figure 2.



We further analyzed the association between a proxy estimation of average EA (kcal·d−1) and MA (kcal·d−1). A positive relation was observed (r = 0.644, P < 0.001), as displayed in the right panel of Figure 2, meaning that athletes with a lower average EA tend to spare energy. This association remained significant even after adjusting for sex, age, and sports (r = 0.612, P < 0.001).

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Analysis by sport

Basketball, handball, volleyball, swimmers, and triathletes increased TEE (14.9% ± 12.9%, 18.8% ± 8.5%, 10.7% ± 7.7%, 15.6% ± 9.5%, and 27.0% ± 9.9%) and PAEE (18.9% ± 42.9%, 34.3% ± 16.1%, 24.2% ± 12.5%, 47.9% ± 30.7%, and 61.2% ± 43.4%), respectively, whereas REE only increase by 20.5% ± 23.6% in basketball players. Values of average EI were 4347 ± 756, 4652 ± 219, 4531 ± 327, 3877 ± 507, 4030 ± 478 kcal·d−1 for basketball, handball, volleyball, swimmers, and triathletes, respectively.

Changes in mean weight were variable, with basketball and handball players gaining 1.9 ± 1.7 and 2.9 ± 0.5 kg, respectively, whereas volleyball players and triathletes lost weight by −2.6 ± 2.7 and −0.7 ± 2.2 kg, respectively. Swimmers did not change mean body weight (0.0 ± 2.3). A mean negative EB was observed for volleyball players (−174.2 ± 78.8 kcal·d−1), swimmers (−20.4 ± 52.1 kcal·d−1), and triathletes (−21.4 ± 49.2 kcal·d−1), whereas a mean positive EB was found for basketball (8.9 ± 43.9 kcal·d−1) and handball players (33.0 ± 52.3 kcal·d−1). A significant MA, with a decrease of REE beyond that expected by changes in body composition, was observed in triathletes (−10.6% ± 11.4%, P < 0.05), representing an energy sparing of 128 ± 168 kcal·d−1 (P < 0.05), whereas for basketball players, significant increases in REE beyond that expected by FFM changes were observed (−10.2% ± 12.2%, P < 0.001, representing an energy dissipation of 168 ± 205 kcal·d−1, P < 0.01). The lower average EA value was found for triathletes (2229 ± 386 kcal·d−1), followed by swimmers (2347 ± 450 kcal·d−1) and team sports (basketball = 2428 ± 397 kcal·d−1, volleyball = 2450 ± 323 kcal·d−1, and handball = 2674 ± 286 kcal·d−1).

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The novel findings from this investigation are as follows: i) the overall increase in EE over the season in the absence of relevant changes in average body weight suggest that EI compensation might have occurred; ii) exercise-specific training may uniquely induce metabolic adaptation in the REE component even if weight is relatively stable, and iii) MA was positively associated with the variability observed in weight changes, EB, and average EA over one athletic season.

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REE and MA

As a group, our highly trained athletes showed no mean changes in REE beyond those expected based on body composition alterations, suggesting no metabolic adaptation over the season. However, among sports, basketball players seemed to increase REE by 10% away from expected based on FFM and FM changes, suggesting energy dissipation and thus MA. These athletes were exposed to a significant positive EB over the season, and their EA remained constant whereas a decrease was observed in the other sports over the season.

Although with different designs and population, this particular finding in basketball players may extend results found in overfeeding studies that examined positive EB and REE (6,11,15,35). Overall, the literature on overfeeding human subjects for 0.2 to 17 wk suggests that the REE response to overfeeding is variable and that REE increases by ~10%, a value we also observed in the basketball players (11).

The explanation used from some studies that support an MA during overfeeding is the fact that weight gain is smaller than expected (36) and the theoretical cost of storing dietary fat is exceeded (4). They show that thermogenesis did increase above obligatory costs (16,21,36), either in diet-induced thermogenesis (21) or in the EE associated with physical activity such as fidgeting, sitting, and standing (16).

On the other hand, we also found the presence of negative values of metabolic adaptation (i.e., a significant energy sparing), of approximately 11% in triathletes, indicating an REE decrease further than that expected from changes in FFM and FM. These athletes did not significantly change body weight, composition, and EI, but a trend for a negative EB was observed. This demanding individual sport showed the most pronounced percentage increase in PAEE of 54% ± 40% and the lower average EA (−38.5% ± 35.0%), suggesting a relative energy deficiency over the season. These findings extend previous reports on a lower EA in endurance athletes, specifically in long distance runners (14,20). In fact, stable body weight in amenorrheic athletes (13,20,39) suggests that EB can be restored while EA is low.

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MA, EB, and EA

Using state-of-the art methods, the observed increase in PAEE over the season did not promote a systematic weight loss likely through compensation in EI, as mean TEE increased and an average neutral EB was observed. A large individual variability around the mean weight change was found, and the athletes tended to spare energy if weight was lost and if exposed to a negative EB.

Assuming that nonexercise EE remained similar over the season, PAEE increases were likely expected from a higher volume of exercise during the competitive training season. Using a proxy of EA, our findings reveal a strong positive association with MA, meaning that athletes with higher or lower average EA tend to dissipate or spare energy, respectively, throughout the season, regardless of sex, age, and sports.

Low EA in athletes occurring from either reduced EI or increased exercise load causes adjustments to body systems to reduce EE, leading to the disruption of an array of hormonal, metabolic, and functional characteristics (18). Long-term exercise training, particularly in endurance sports, may lead to the downregulation of thyroid hormone concentrations (22,29,30). Among the candidate factors, this response may be triggered by the exercise-induced negative EB as well as signaling alterations in the hypothalamus–pituitary–thyroid and hypothalamus–adipocyte–leptin axes (30). A decrease in adiposity causes a drop in plasma leptin levels, an adipocyte-secreted hormone that plays an important role as a signal of EA (2). Unfortunately, we do not have measures of thyroid function and leptin levels to discuss the observed sport-specific MA.

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It is important to underline several limitations to the present study. First, the athletes were tested at the beginning of the season and at the main stage of the competitive period; hence, it is not known if these two measurements represent what happened during the entire season.

Second, we did not assess hormonal changes that would likely explain the changes in MA observed in specific sports.

Third, REE was assessed only after 15 h without exercise. The effects of exhaustive exercise on REE have been reported to increase REE up to 48 h, although the effect is small (9).

Fourth, to determine EA, we would require an estimate of the exercise EE cost at both moments of the season. Because this information was unavailable, a proxy estimation of EA was calculated as the average EI minus average PAEE by assuming that nonexercise PAEE remained constant over the season (athletes lived and trained in training center facilities), and the average increase in PAEE would reflect an increase in exercise EE. Nevertheless, average EA would be underestimated as average PAEE is always a higher value compared with exercise EE, as it comprises exercise and nonexercise EE.

Lastly, and more importantly, given that we did not have a control group to compare the results of our athletic population, we were not able to ensure that some of the significant changes observed may have been a result of the effect of the training process over the season.

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Overall, EE increased over the season without meaningful changes in body weight, suggesting that EI compensation likely occurred.

MA was not observed in the whole sample or by sex but occurred within specific sports. In fact, regardless of sex and age, basketball players significantly dissipated energy from the REE compartment, i.e., REE increased beyond expected based on body composition changes, whereas triathletes spared energy.

MA was positively related with the degree of EB and EA, which means that athletes who were exposed to a negative EB or showed lower values of EA tend to spare energy within the REE compartment.

These findings contributed to clarify the complex compensatory mechanisms beyond EB regulation over one season in highly trained athletes, highlighting the need for a sports specific EI adequacy in nutritional prescription.

The current work was supported by National funding from the Portuguese Foundation for Science and Technology within the R&D units 472 (UID/DTP/00447/2013). This work was supported by the Portuguese Foundation for Science and Technology (grant no. PTDC/DES/098963/2008). The authors are grateful to the athletes and coaches for their time and effort.

The authors declare that there are no conflicts of interest. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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