Energy Balance over One Athletic Season

The Prevalence of Low Energy Availability in Cross-Country Skiers during the Annual Cycle

BACKGROUND AND OBJECTIVES

A sustained mismatch between energy intake (EI) and exercise energy expenditure (EEE) can lead to Low Energy Availability (LEA), as well as health and performance impairments characteristic of Relative Energy Deficiency in Sport (RED-S). Research in females has identified specific LEA cut-points for the risks of developing physiological and performance disturbances. Cut-points in males have yet to be evaluated; therefore, this study examined the prevalence of LEA in highly trained male cross-country skiers. The key purpose of this study was to analyze EI, resting energy expenditure (REE), EEE, and energy availability (EA) in highly trained cross-country skiers during the preparation and competition periods. The secondary objective of our study was to evaluate the relative contribution of fats and carbohydrates to EI, REE, and EEE.

MATERIALS AND METHODS

EI was determined by an estimated 24 h diet recall method, REE was assessed by indirect calorimetry, and EEE was estimated from heart rate in 27 cross-country skiers.

RESULTS

EI amounted to 4050 ± 797 kcal/day on a typical training day (TD) and 5986 ± 924 kcal/day (p < 0.001) on a typical competition day (CD). REE on TDs (2111 ± 294 kcal/day or 30 ± 6 kcal/day/kg) was higher (p < 0.05) than on CDs (1891 ± 504 kcal/day or 27 ± 7 kcal/day/kg). The EA in the athletes was <15 kcal∙kg FFM-1·d-1 on TDs and <65 kcal∙kg FFM-1·d-1 on CDs. EI was not optimal, as indicated by low EA throughout TDs (June). This could be associated with insufficient EI along with a high amount of EEE (3690.7 ± 485.2 kcal/day). During the transition from TD to CD, an increase in the contribution of fats to EI and EEE was observed in cross-country skiers.

CONCLUSION

The conception of LEA and REDs and their potential implication for performance is underestimated among coaches and athletes. The importance of appropriate dietary strategies is essential to ensure that enough calories are consumed to support efficient training.

Evaluation of Nutrition and Performance Parameters in Division 1 Collegiate Athletes

BACKGROUND

Testing and evaluating athletes is necessary and should include performance, body composition, and nutrition. The purpose of this study was to report assessments of dietary intake, V˙O2max, and body composition in D1 collegiate athletes and examine relationships between these assessments.

METHODS

Dietary intake was assessed with 3-day recalls and compared to recommendations, and body composition was assessed via bioelectrical impedance analysis (BIA) (n = 48). V˙O2max was evaluated using a graded exercise test (GXT) with a verification bout (n = 35). Reliability between "true" V˙O2max and verification was determined. Correlations and regressions were performed.

RESULTS

Energy, carbohydrate, and micronutrient intake was lower than recommendations. Mean V˙O2max was 47.3 and 47.4 mL·kg-1·min-1 for GXT and verification, respectively. While correlations were apparent among dietary intake, V˙O2max, and body composition, percent fat-free mass (%FFM) predicted 36% of V˙O2max.

CONCLUSIONS

Collegiate athletes are not meeting energy and carbohydrate recommendations and exceed fat recommendations. Vitamin D and magnesium were low in all sports, and iron and calcium were low in females. V˙O2max ranged from 35.6 to 63.0 mL·kg-1·min-1, with females below average and males meeting typical values for their designated sport. Assessing D1 athletes can provide guidance for sports dietitians, coaches, and strength and conditioning specialists to track and monitor nutrition in athletes.

Characteristics of non-exercise activity thermogenesis in male collegiate athletes under real-life conditions

Athletes experience high total energy expenditure; therefore, it is important to understand the characteristics of the components contributing to this expenditure. To date, few studies have examined particularly the volume and activity intensity of non-exercise activity thermogenesis (NEAT) in athletes compared to non-athletes under real-life conditions. This study aimed to determine the volume and intensity of NEAT in collegiate athletes. Highly trained Japanese male collegiate athletes (n = 21) and healthy sedentary male students (n = 12) participated in this study. All measurements were obtained during the athletes' regular training season under real-life conditions. NEAT was calculated using metabolic equivalent (MET) data using an accelerometer. The participants were asked to wear a validated triaxial accelerometer for 7 consecutive days. Physical activity intensity in NEAT was classified into sedentary (1.0-1.5 METs), light (1.6-2.9 METs), moderate (3.0-5.9 METs), and vigorous (≥6 METs) intensity. NEAT was significantly higher in athletes than in non-athletes (821 ± 185 kcal/day vs. 643 ± 164 kcal/day, p = 0.009). Although there was no significant difference in NEAT values relative to body weight (BW) between the groups (athletes: 10.5 ± 1.7 kcal/kg BW/day, non-athletes: 10.4 ± 2.2 kcal/kg BW/day, p = 0.939), NEAT to BW per hour was significantly higher in athletes than in non-athletes (0.81 ± 0.16 kcal/kg BW/h vs. 0.66 ± 0.12 kcal/kg BW/h, p = 0.013). Athletes spent less time in sedentary and light-intensity activities and more time in vigorous-intensity activities than non-athletes (p < 0.001, p = 0.019, and p = 0.030, respectively). Athletes expended more energy on vigorous- and moderate-intensity activities than non-athletes (p = 0.009 and p = 0.011, respectively). This study suggests that athletes' NEAT relative to BW per day is similar to that of non-athletes, but athletes spend less time on NEAT, which makes them more active in their daily lives when not exercising and sleeping.

Energy balance and energy availability of female basketball players during the preparation period

OBJECTIVE

To determine the level of energy balance and energy availability (EA) in female basketball players during the preparation period.

METHODS

Fifteen basketball players (age: 19.53 ± 1.3 years; height: 173.6 ± 8.95 cm; weight: 67.55 ± 14.34 kg; training experience: 9.6 ± 2.7 years) and 15 age and body mass index-matched controls (age: 19.53 ± 1.1 years; height: 169.4 ± 5.06 cm; weight: 63.10 ± 6.14 kg) participated in the study. Resting metabolic rate (RMR) and body composition were measured by the indirect calorimetric method and dual-energy x-ray absorptiometry, respectively. A 3-day food diary was used to determine macronutrients and energy intake while a 3-day physical activity log was used to determine energy expenditure. Independent Samples t-test was used for data analysis.

RESULTS

The daily energy intake and expenditure of female basketball players were 2136.5 ± 594.9 kcal·day-1 and 2953.8 ± 614.5 kcal·day-1, respectively, indicating 817 ± 779 kcal·day-1 of negative energy balance. One hundred percent and 66.6% of the athletes failed to meet the carbohydrate and protein intake recommendations, respectively. EA of female basketball players was 33.04 ± 15.69 kcal·fat free mass-1·day-1 and the percentages of athletes who had negative energy balance, low EA, and reduced EA were 80%, 40%, and 46.7%, respectively. However, despite the low and decreased EA, the measured RMR to predicted RMR ratio (RMRratio) was 1.31 ± 0.17 and the body fat percentage (BF%) was 31.00 ± 5.21%.

CONCLUSION

This study shows that female basketball players have a negative energy balance during the preparation period which can be partly explained by insufficient carbohydrate intake. Although most of the athletes experienced low or reduced EA during the preparation period, the physiologically normal RMRratio and relatively high BF% indicate that this is a transient situation. In this regard, strategies to prevent the development of low EA and negative energy balance during the preparation period will contribute to positive training adaptations throughout the competition period.

Comparison of Bioelectrical Impedance Indices for Skeletal Muscle Mass and Intracellular Water Measurements of Physically Active Young Men and Athletes

BACKGROUND

Bioelectrical impedance analysis (BIA) is a minimally invasive, safe, easy, and quick technology used to determine body composition.

OBJECTIVES

We compared the relationship among impedance indices obtained using single-frequency BIA, multi-frequency BIA, bioelectrical impedance spectroscopy (BIS), and skeletal muscle mass (SMM) of physically active young men and athletes using the creatine (methyl-d3) dilution method. We also compared the SMM and intracellular water (ICW) of athletes and active young men measured using a reference stable isotope dilution and BIS method, respectively.

METHODS

We analyzed data from 28 men (mean age, 20 ± 2 y) who exercised regularly. Single-frequency BIA at 5 kHz and 50 kHz (R5 and R50), multi-frequency BIA (R250-5), and BIS (RICW) methods of determining the SMM were compared. The deuterium and sodium bromide dilution methods of obtaining the total body water, ICW, and extracellular water measurements were also used, and the results were compared to those acquired using bioimpedance methods.

RESULTS

The correlation coefficients between SMM and L2/R5, L2/R50, L2/R250-5, and L2/RICW were 0.738, 0.762, 0.790, and 0.790, respectively (P < 0.01). The correlation coefficients between ICW and L2/R5, L2/R50, L2/R250-5, and L2/RICW were 0.660, 0.687, 0.758, and 0.730, respectively (P < 0.001). However, the correlation coefficients of L2/R50, L2/R250-5, and L2/RICW for SMM and ICW were not significantly different.

CONCLUSIONS

Our findings suggest that single-frequency BIA at L2/R50, multi-frequency BIA, and BIS are valid for assessing the SMM of athletes and active young men. Additionally, we confirmed that the SMM and ICW were correlated with single-frequency BIA, multi-frequency BIA, and BIS. Bioimpedance technologies may be dependable and practical means for assessing SMM and hydration compartment status of active young adult males; however, cross-validation is needed.

Athletes with different habitual fluid intakes differ in hydration status but not in body water compartments

Physiological differences have been reported between individuals who have habitual low (LOW) and high (HIGH) water intake (WI). The aims of this study were to explore body water compartments, hydration status, and fat-free mass (FFM) hydration of elite athletes exposed to different habitual WI. A total of 68 athletes (20.6 ± 5.3 years, 23 females) participated in this observational cross-sectional study. Total WI was assessed by seven-day food diaries and through WI, athletes were categorized as HIGH (n = 28, WI≥40.0 mL/kg/d) and LOW (n = 40, WI≤35.0 mL/kg/d). Total body water (TBW) and extracellular water (ECW) were determined by dilution techniques and intracellular water (ICW) as TBW-ECW. Hydration status was assessed by urine-specific gravity (USG) using a refractometer. Fat (FM) and FFM were assessed by dual-energy X-ray absorptiometry (DXA). The FFM hydration was calculated by TBW/FFM. The USG was statistically different between groups for females (LOW: 1.024 ± 0.003; HIGH: 1.015 ± 0.006; p = 0.005) and males (LOW: 1.024 ± 0.002; HIGH: 1.018 ± 0.005; p < 0.001). No differences between groups were detected in body water compartments and FFM hydration in both sexes (p > 0.05). Multiple regression showed that WI remains a predictor of USG regardless of FFM, age, and sex (β = -0.0004, p < 0.01). We concluded that LOW athletes were classified as dehydrated through USG although their water compartments were not different from HIGH athletes. These results suggest that LOW athletes may expectedly maintain the body water compartments' homeostasis through endocrine mechanisms. Interventions should be taken to encourage athletes to have sufficient WI to maintain optimal hydration.

Measurement of Energy Intake Using the Principle of Energy Balance Overcomes a Critical Limitation in the Assessment of Energy Availability

Prolonged low energy availability, which is the underpinning aetiology of the Relative Energy Deficiency in Sport and the Female and Male Athlete Triad frameworks, can have unfavourable impacts on both health and performance in athletes. Energy availability is calculated as energy intake minus exercise energy expenditure, expressed relative to fat free mass. The current measurement of energy intake is recognized as a major limitation for assessing energy availability due to its reliance on self-report methods, in addition to its short-term nature. This article introduces the application of the energy balance method for the measurement of energy intake, within the context of energy availability. The energy balance method requires quantification of the change in body energy stores over time, with concurrent measurement of total energy expenditure. This provides an objective calculation of energy intake, which can then be used for the assessment of energy availability. This approach, the Energy Availability - Energy Balance (EA_EB) method, increases the reliance on objective measurements, provides an indication of energy availability status over longer periods and removes athlete burden to self-report energy intake. Implementation of the EA_EB method could be used to objectively identify and detect low energy availability, with implications for the diagnosis and management of Relative Energy Deficiency in Sport and the Female and Male Athlete Triad.

Predicting energy intake in adults who are dieting and exercising

BACKGROUND

When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.

OBJECTIVE

The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE.

DESIGN

The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE.

RESULTS

Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d.

CONCLUSION

The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Energy Availability Over One Athletic Season: An Observational Study Among Athletes From Different Sports

During the athletic season, changes in body composition occur due to fluctuations in energy expenditure and energy intake. Literature regarding changes of energy availability (EA) is still scarce. The aim was to estimate EA of athletes from nonweight and weight-sensitive sports during the athletic season (i.e., preparatory and competitive phase). Eighty-eight athletes (19.1 ± 4.2 years, 21.8 ± 2.0 kg/m2, 27% females, self-reported eumenorrheic) from five sports (basketball [n = 29]; handball [n = 7]; volleyball [n = 9]; swimming [n = 18]; and triathlon [n = 25]) were included in this observational study. Energy intake and exercise energy expenditure were measured through doubly labeled water (over 7 days and considering neutral energy balance) and metabolic equivalents of tasks, respectively. Fat-free mass (FFM) was assessed through a four-compartment model. EA was calculated as EA = (energy intake - exercise energy expenditure)/FFM. Linear mixed models, adjusted for sex, were performed to assess EA for the impact of time by sport interaction. Among all sports, EA increased over the season: basketball, estimated mean (SE): 7.2 (1.5) kcal/kg FFM, p < .001; handball, 14.8 (2.9) kcal/kg FFM, p < .001; volleyball, 7.9 (2.8) kcal/kg FFM, p = .006; swimming, 8.7 (2.0) kcal/kg FFM, p < .001; and triathlon, 9.6 (2.0) kcal/kg FFM, p < .001. Eleven athletes (12.5%) had clinical low EA at the preparatory phase and none during the competitive phase. During both assessments, triathletes' EA was below optimal, being lower than basketballers (p < .001), volleyballers (p < .05), and swimmers (p < .001). Although EA increased in all sports, triathlon's EA was below optimal during both assessments. Risk of low EA might be seasonal and resolved throughout the season, with higher risk during the preparatory phase. However, in weight-sensitive sports, namely triathlon, low EA is still present.

Tracking changes in body composition: comparison of methods and influence of pre-assessment standardisation

The present study reports the validity of multiple assessment methods for tracking changes in body composition over time and quantifies the influence of unstandardised pre-assessment procedures. Resistance-trained males underwent 6 weeks of structured resistance training alongside a hyperenergetic diet, with four total body composition evaluations. Pre-intervention, body composition was estimated in standardised (i.e. overnight fasted and rested) and unstandardised (i.e. no control over pre-assessment activities) conditions within a single day. The same assessments were repeated post-intervention, and body composition changes were estimated from all possible combinations of pre-intervention and post-intervention data. Assessment methods included dual-energy X-ray absorptiometry (DXA), air displacement plethysmography, three-dimensional optical imaging, single- and multi-frequency bioelectrical impedance analysis, bioimpedance spectroscopy and multi-component models. Data were analysed using equivalence testing, Bland-Altman analysis, Friedman tests and validity metrics. Most methods demonstrated meaningful errors when unstandardised conditions were present pre- and/or post-intervention, resulting in blunted or exaggerated changes relative to true body composition changes. However, some methods - particularly DXA and select digital anthropometry techniques - were more robust to a lack of standardisation. In standardised conditions, methods exhibiting the highest overall agreement with the four-component model were other multi-component models, select bioimpedance technologies, DXA and select digital anthropometry techniques. Although specific methods varied, the present study broadly demonstrates the importance of controlling and documenting standardisation procedures prior to body composition assessments across distinct assessment technologies, particularly for longitudinal investigations. Additionally, there are meaningful differences in the ability of common methods to track longitudinal body composition changes.

Contributing Factors to Low Energy Availability in Female Athletes: A Narrative Review of Energy Availability, Training Demands, Nutrition Barriers, Body Image, and Disordered Eating

Relative Energy Deficiency in sport is experiencing remarkable popularity of late, particularly among female athletes. This condition is underpinned by low energy availability, which is a byproduct of high energy expenditure, inadequate energy intake, or a combination of the two. Several contributing factors exist that may predispose an athlete to low energy availability, and therefore a holistic and comprehensive assessment may be required to identify the root causes. The focus of the current narrative review is to discuss the primary contributing factors as well as known risk factors for low energy availability among female athletes to help practitioners increase awareness on the topic and identify future areas of focus.

Association of energy availability with resting metabolic rates in competitive female teenage runners: a cross-sectional study

Background

Resting metabolic rate (RMR) has been examined as a proxy for low energy availability (EA). Previous studies have been limited to adult athletes, despite the serious health consequences of low EA, particularly during adolescence. This study aimed to explore the relationship between RMR and EA in competitive teenage girl runners.

Methods

Eighteen girl runners (mean ± standard-deviation; age, 16.8 ± 0.9 years; body mass, 45.6 ± 5.2 kg, %fat, 13.5 ± 4.2 %) in the same competitive high-school team were evaluated. Each runner was asked to report dietary records with photos and training logs for seven days. Energy intake (EI) was assessed by Registered Dietitian Nutritionists. The runners were evaluated on a treadmill with an indirect calorimeter to yield individual prediction equations for oxygen consumption using running velocity and heart rate (HR). Exercise energy expenditure (EEE) was calculated by the equations based on training logs and HR. Daily EA was calculated by subtracting EEE from EI. The daily means of these variables were calculated. RMR was measured early in the morning by whole-room calorimetry after overnight sleep on concluding the final day of the seven-day assessment. The ratio of measured RMR to predicted RMR (RMR ratio) was calculated by race, age, sex-specific formulae, and Cunningham’s equation. Body composition was measured using dual-energy X-ray absorptiometry. Bivariate correlation analyses were used to examine the relationship between variables.

Results

RMR, EI, EEE, and EA were 26.9 ± 2.4, 56.8 ± 15.2, 21.7 ± 5.9, and 35.0 ± 15.0 kcal⋅kg⁻¹ FFM⋅d⁻¹, respectively. RMR reduced linearly with statistical significance, while EA decreased to a threshold level (30 kcal⋅kg⁻¹ FFM⋅d⁻¹) (r= 0.58, p= 0.048). Further reduction in RMR was not observed when EA fell below the threshold. There was no significant correlation between RMR ratios and EA, irrespective of the prediction formulae used.

Conclusions

These results suggest that RMR does not reduce with a decrease in EA among highly competitive and lean teenage girl runners. RMR remains disproportionally higher than expected in low EA states. Free-living teenage girl runners with low EA should be cautiously identified using RMR as a proxy for EA change.

Changes in Energy Expenditure, Dietary Intake, and Energy Availability Across an Entire Collegiate Women's Basketball Season

ABSTRACT

Zanders, BR, Currier, BS, Harty, PS, Zabriskie, HA, Smith, CR, Stecker, RA, Richmond, SR, Jagim, AR, and Kerksick, CM. Changes in energy expenditure, dietary intake, and energy availability across an entire collegiate women's basketball season. J Strength Cond Res 35(3): 804-810, 2021-The purpose of this study was to identify changes in energy expenditure and dietary intake across an entire women's basketball season. On 5 different occasions across the competitive season, female collegiate basketball players (19.8 ± 1.3 years, 173.9 ± 13.6 cm, 74.6 ± 9.1 kg, 27.1 ± 3.2% fat, 53.9 ± 6.4 ml·kg-1·min-1, n = 13) were outfitted with heart rate and activity monitors over 4 consecutive days and completed 4-day food and fluid records to assess changes in energy expenditure and dietary status. Dual-energy x-ray absorptiometry was used to assess baseline body composition and resting energy expenditure (REE) was measured before and after the season. Data were analyzed using 1-factor repeated-measures analysis of variance. Total daily energy expenditure (TDEE, p = 0.059) and physical activity levels (TDEE/REE, p = 0.060) both tended to decrease throughout the season. Energy balance was negative at all time points throughout the season. Absolute and normalized daily protein intake at the end of the season was significantly (p < 0.05) lower than at the beginning of the season. Carbohydrate (3.7 ± 0.4 g·kg-1·d-1) and protein (1.17 ± 0.16 g·kg-1·d-1) intakes were lower than commonly recommended values based on previously published guidelines. These findings suggest that greater education and interventions for collegiate athletes and coaches regarding dietary intake and energy expenditure are warranted.

DXA-derived estimates of energy balance and its relationship with changes in body composition across a season in team sport athletes

This study examined the relationship between dual-energy x-ray absorptiometry (DXA)-derived estimates of energy balance (EB) and changes in body composition across various seasonal phases in team sport athletes. Forty-five Australian rules footballers underwent six DXA scans across a 12-month period (off-season [OS, Week 0-13], early [PS1, Week 13-22] and late pre-season [PS2, Week 22-31] and early [IS1, Week 3-42] and late in-season [IS2, Week 42-51]). EB (kcal·day^-1) was estimated from changes in fat free soft tissue mass (FFSTM) and fat mass (FM) between scans according to a validated formula. An EB threshold of ± 123 kcal·day^-1 for >60 days demonstrated a very likely (>95% probability) change in FFSTM (>1.0 kg) and FM (>0.7 kg). There were small to almost perfect relationships between EB and changes in FM (r = 0.97, 95% CI, 0.96-0.98), FFSTM (r = -0.41, -0.92 to -0.52) and body mass (r = 0.27, 0.14-0.40). EB was lowest during PS1 compared to all other phases (range, -265 to -142 kcal·day^-1), with no other changes at any time. Increases in FFSTM were higher during OS compared to PS2 (1.6 ± 0.4 kg), and higher during PS1 compared to PS2, IS1, and IS2 (range, 1.6-2.1 kg). There were no changes during in-season (-0.1-0.05 kg). FM decreased only in PS1 compared to all other seasonal phases (-1.8 to -1.0 kg). Assessments of body composition can be used as a tool to estimate EB, which practically can be used to indicate athlete's training and nutrition behaviours/practices.

Postural Stability in Goalkeepers of the Polish National Junior Handball Team

The aim of the study was to assess postural stability of goalkeepers from the Polish national junior handball team. Eleven juniors of the Polish national handball team (age 16.82 ± 1.6 years, body height 191.27 ± 3.1 cm, body mass 88.41 ± 12.26 kg, BMI 24.18 ± 3.22 kg/m2) were selected for the study. The Biodex Balance System and AccuGait AM¬TI platform were used to evaluate postural stability. The obtained results indicated good postural stability of the subjects. During the Biodex Balance System platform tests, all subjects presented very good postural stability and maintained within Zone A. Postural sway was greater in the sagittal plane compared to the frontal one. Most of the participants demonstrated slight backward tilts, but maintained in Quadrant IV. During the AccuGait AMTI platform trial, Path Length and Average COP Speed significantly increased in the test performed with closed eyes. Furthermore, there were significant positive correlations between the number of variables obtained during the Biodex Balance System and AccuGait AMTI tests. Proper and stable posture are necessary conditions to be met to carry out most free movements and locomotion. They play a significant role in the game of a handball goalkeeper and for that reason, postural stability testing of handball goalkeepers is an important element of coordination training. Thus, the use of postural stability exercises implementing the biofeedback method on stabilo and dynamometric platforms is practical and justifiable.

Adaptive thermogenesis and changes in body composition and physical fitness in army cadets

BACKGROUND

To analyze the association between a 34-week military training on body composition, physical fitness and compensatory changes in resting energy expenditure (REE) recognized as adaptive thermogenesis (AT). We also explored if regional body composition changes were related to AT.

METHODS

Twenty-nine male army cadets, aged 17 to 22 years were tested at baseline (T0) and after 34-weeks military training (T1). Physical training was performed 5 days/week during 90 minutes/day. Measurements included body composition by dual-energy x-ray absorptiometry; physical fitness by 3000-m running, pull-up, 50-m freestyle swimming, push-up and sit-up tests; REE measured by indirect calorimetry (REEm) and predicted from fat-free mass (FFM), fat mass (FM) and ethnicity at T0 (REEp). %AT was calculated using values at T1: 100(REEm/REEp-1); and AT (kcal/day) as %AT/100 multiplied by baseline REEm.

RESULTS

Physical training was associated with increases of lean soft tissue (LST) (∆1.2±1.3 kg), FM (∆1.4±1.3 kg), FFM (∆1.2±1.3 kg) and physical fitness (P<0.01), but no REE changes (∆59.6±168.9 kcal/day) and AT were observed (P>0.05). Though a large variability was found, AT was partially explained by trunk LST (r2=0.17, P=0.027). Individuals showing a higher AT response demonstrated a higher trunk LST increase (∆0.8±0.7 kg, P<0.05).

CONCLUSIONS

The military training increased LST, FM, FFM and physical fitness. Though no mean changes in AT occurred, a large individual variability was observed with some participants increasing REE beyond the expected body composition changes, suggesting a spendthrift phenotype. Changes of trunk LST may play an important role in the AT response observed in these individuals.