Basal metabolic rate and body composition of elite Japanese male athletes

Sex Differences in Resting Metabolic Rate among Athletes and Association with Body Composition Parameters: A Follow-Up Investigation

The purpose of this study was to examine sex differences in resting metabolic rate (RMR) and associations between measured RMR and body composition parameters in athletes. One-hundred and ninety collegiate men (n = 98; age: 20.1 ± 1.6 yr.; body mass: 92.7 ± 17.5 kg; height: 181.6 ± 6.2 cm, body mass index: 28.0 ± 4.7 kg/m2) and women (n = 92; age: 19.4 ± 1.1 yr.; body mass: 65.2 ± 11.0 kg; height: 168.0 ± 6.6 cm, body mass index: 23.0 ± 3.6 kg/m2) athletes volunteered to participate in this study. Athletes completed a body composition assessment using air displacement plethysmography and RMR using indirect calorimetry. Assessments were completed in a fasted state and after refraining from intense physical activity > 24 h prior to testing. Data were collected during the 2016-2019 seasons. Men had a higher RMR compared to women (2595 ± 433 vs. 1709 ± 308 kcals; p < 0.001); however, when adjusted for body mass (p = 0.064) and fat-free mass (p = 0.084), the observed differences were not significant. Height, body mass, body mass index, fat-free mass, and fat mass were positively associated with RMR in both men and women athletes (r = 0.4-0.8; p < 0.001). Body mass (men: β = 0.784; women: β = 0.832)) was the strongest predictor of RMR. Men athletes have a higher absolute RMR compared to their women counterparts, which is influenced by greater body mass and fat-free mass. Body mass is the strongest predictor of RMR in both men and women athletes.

Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes

High accuracy in estimating energy expenditure is essential for enhancing sports performance. The resting metabolic rate (RMR), as a primary component of total energy expenditure (TEE), is commonly estimated using predictive equations. However, these references may not be applicable to adolescent athletes. The purpose of this cross-sectional study was to analyse the differences between predicted RMR in relation to energy expenditure measured by indirect calorimetry (IC) among 45 Brazilian male adolescent football athletes. Indirect calorimetry (IC) and anthropometric (bioimpedance) measurements were recorded at a single visit to the laboratory after fasting overnight. The mean age was 15.6 ± 1.14 years, body mass was 63.05 ± 7.8 kg, and height was 172 ± 7.5 cm. The RMR values predicted by equations proposed by the Food and Agriculture Organization (FAO) (United Nations), Henry and Rees (HR), Harris Benedict (HB), and Cunningham (CUN) were compared with IC RMR values, by correlation analysis. The FAO and HR predictive equations yielded different values from IC (IC: 1716.26 ± 202.58, HR: 1864.87 ± 147.78, FAO: 1854.28 ± 130.19, p = 0.001). A moderate correlation of 0.504 was found between the results of HB and IC. In the survival-agreement model, the CUN equation showed low disagreement with the IC RMR, with error values between 200 and 300 kcal/day. The results showed that HB and CUN yielded similar values as IC, with the CUN equation showing low disagreement with IC; hence, adolescent athletes should undergo evaluation with precise laboratory methods to ensure that accurate information about RMR is recorded.

The Effects of Sports Drinks During High-Intensity Exercise on the Carbohydrate Oxidation Rate Among Athletes: A Systematic Review and Meta-Analysis

Background: This study examines the effects of sports drinks ingestion during high-intensity exercise for carbohydrate oxidation rate (CHO-O) among athletes.

Methods: PubMed, Embase, and the Cochrane library were searched for available papers published up to November 2019. The primary outcome is the carbohydrate oxidation rate (CHO-O), and the secondary outcome is the fat oxidation rate (Fat-O). Statistical heterogeneity among the included studies was evaluated using Cochran's Q test and the I² index. The random-effects model was used for all analyses, regardless of the I² index.

Results: Five studies are included, with a total of 58 participants (range, 8–14/study). All five studies are randomized crossover trials. Compared to the control beverages, sports drinks have no impact on the CHO-O of athletes [weighted mean difference (WMD) = 0.29; 95% CI, −0.06 to 0.65, P = 0.106; I² = 97.4%, P < 0.001] and on the Fat-O of athletes (WMD = −0.074; 95% CI, −0.19 to 0.06, P = 0.297; I² = 97.5%, P < 0.001). Carbohydrate–electrolyte solutions increase CHO-O (WMD = 0.47; 95% CI, 0.08–0.87, P = 0.020; I² = 97.8%, P < 0.001) but not Fat-O (WMD = −0.14; 95% CI, −0.31 to 0.03, P = 0.103; I² = 98.2%, P < 0.001). Caffeine has a borderline effect on Fat-O (WMD = 0.05; 95% CI, 0.00–0.10, P = 0.050).

Conclusions: Compared with the control beverages, sports drinks show no significant improvement in CHO-O and Fat-O in athletes. Carbohydrate–electrolyte solutions increase CHO-O in athletes but not Fat-O.

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Background

Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults.

Methods

With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group.

Results

EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930).

Conclusion

Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

Using physical activity levels to estimate energy requirements of female athletes

[Purpose]

The goal of this study was to review data on physical activity level (PAL), a crucial index for determining estimated energy requirement (EER), calculated as total energy expenditure (TEE, assessed with doubly labeled water [DLW]) divided by resting metabolic rate (RMR, PAL = TEE/RMR) in female athletes and to understand the methods of assessing athletes’ EERs in the field.

[Methods]

For the PAL data review among female athletes, we conducted a PubMed search of the available literature related to the DLW method. DLW studies measuring TEE and RMR were included for the present review.

[Results]

Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes’ EERs that can be used in the field.

[Conclusion]

Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes’ EERs that can be used in the field.

Comparison of Conventional and Individualized 1-MET Values for Expressing Maximum Aerobic Metabolic Rate and Habitual Activity Related Energy Expenditure

The maximum aerobic metabolic rate can be expressed in multiple metabolically equivalent tasks (MET), i.e., METmax. The purpose was to quantify the error when the conventional (3.5 mL∙kg⁻¹∙min⁻¹) compared to an individualized 1-MET-value is used for calculating METmax and estimating activity energy expenditure (AEE) in endurance-trained athletes (END) and active healthy controls (CON). The resting metabolic rate (RMR, indirect calorimetry) and aerobic metabolic capacity (spiroergometry) were assessed in 52 END (46% male, 27.9 ± 5.7 years) and 53 CON (45% male, 27.3 ± 4.6 years). METmax was calculated as the ratio of VO₂max over VO₂ during RMR (METmax_ind), and VO₂max over the conventional 1-MET-value (METmax_fix). AEE was estimated by multiplying published MET values with the individual and conventional 1-MET-values. Dependent t-tests were used to compare the different modes for calculating METmax and AEE (α = 0.05). In women and men CON, men END METmax_fix was significantly higher than METmax_ind (p < 0.01), whereas, in women END, no difference was found (p > 0.05). The conventional 1-MET-value significantly underestimated AEE in men and women CON, and men END (p < 0.05), but not in women END (p > 0.05). The conventional 1-MET-value appears inappropriate for determining the aerobic metabolic capacity and AEE in active and endurance-trained persons.

Energy Requirement Assessment and Water Turnover in Japanese College Wrestlers Using the Doubly Labeled Water Method

Estimated energy requirements (EERs) are important for sports based on body weight classifications to aid in weight management. The basis for establishing EERs varies and includes self-reported energy intake (EI), predicted energy expenditure, and measured daily energy expenditure. Currently, however, no studies have been performed with male wrestlers using the highly accurate and precise doubly labeled water (DLW) method to estimate energy and fluid requirement. The primary aim of this study was to compare total energy expenditure (TEE), self-reported EI, and the difference in collegiate wrestlers during a normal training period using the DLW method. The secondary aims were to measure the water turnover and the physical activity level (PAL) of the athletes, and to examine the accuracy of two currently used equations to predict EER. Ten healthy males (age, 20.4±0.5 y) belonging to the East-Japan college league participated in this study. TEE was measured using the DLW method, and EI was assessed with self-reported dietary records for ~1 wk. There was a significant difference between TEE (17.9±2.5 MJ•d^-1 [4,283±590 kcal•d^-1]) and self-reported EI (14.4±3.3 MJ•d^-1 [3,446±799 kcal•d^-1]), a difference of 19%. The water turnover was 4.61±0.73 L•d^-1. The measured PAL (2.6±0.3) was higher than two predicted values during the training season and thus the two EER prediction equations produced underestimated values relative to DLW. We found that previous EERs were underestimating requirements in collegiate wrestlers and that those estimates should be revised.

Total Energy Expenditure, Energy Intake, and Body Composition in Endurance Athletes Across the Training Season: A Systematic Review

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 18-40-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).

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.