RESUMO
PURPOSE: The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. METHODS: The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, while the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, Root Mean Square Error, Simple Linear Regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ± 10%, respectively) of measured RMR. Multiple linear regressions models were employed to develop the new predictive equations based on traditional predictors (Equation 1) and the stepwise method (Equation 2). RESULTS: The new Equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal.d-1), RMSE (200 and 192 kcal.d-1), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Harris & Benedict's equation showed the worst performance for males (mean difference = 406 kcal.d-1; RMSE = 473 kcal.d-1). Compared to measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; p ≤ 0.05), mainly in males. CONCLUSIONS: The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Harris & Benedict's equation should not be used in high-level male athletes.
RESUMO
The aim of this study was to comprehensively describe the physiological responses to an acute bout of mild cold in young lean men (n = 11, age: 23 ± 2 years, body mass index: 23.1 ± 1.2 kg/m2) to better understand the underlying mechanisms of non-shivering thermogenesis and how it is regulated. Resting energy expenditure, substrate metabolism, skin temperature, thermal comfort perception, superficial muscle activity, hemodynamics of the forearm and abdominal regions, and heart rate variability were measured under warm conditions (22.7 ± 0.2°C) and during an individualized cooling protocol (air-conditioning and water cooling vest) in a cold room (19.4 ± 0.1°C). The temperature of the cooling vest started at 16.6°C and decreased ~ 1.4°C every 10 minutes until participants shivered (93.5 ± 26.3 min). All measurements were analysed across 4 periods: warm period, at 31% and at 64% of individual´s cold exposure time until shivering occurred, and at the shivering threshold. Energy expenditure increased from warm period to 31% of cold exposure by 16.7% (P = 0.078) and to the shivering threshold by 31.7% (P = 0.023). Fat oxidation increased by 72.6% from warm period to 31% of cold exposure (P = 0.004), whereas no changes occurred in carbohydrates oxidation. As shivering came closer, the skin temperature and thermal comfort perception decreased (all P<0.05), except in the supraclavicular skin temperature, which did not change (P>0.05). Furthermore, the superficial muscle activation increased at the shivering threshold. It is noteworthy that the largest physiological changes occurred during the first 30 minutes of cold exposure, when the participants felt less discomfort.
