Effect of gas exchange data selection methods on resting metabolic rate estimation in young athletes.

This cross-sectional study analysed the effect of the gas exchange data selection methods on the resting metabolic rate (RMR) estimation and proposed a protocol shortening providing a suitable RMR estimation for young athletes. Sixty-six healthy young Brazilian athletes performed a 30-minute RMR assessment. Different methods of gas exchange data selection were applied: short and long-time intervals, steady-state (SSt), and filtering. A mixed one-way ANOVA was used to analyse the mean differences in gas exchange, RMR, respiratory exchange ratio (RER), and coefficients of variation across all methods. Additionally, paired Student's t-test were used to compare the first and best SSt RMR values for each SSt method (3, 4, and 5-min). The 5-min SSt method provided the lowest RMR estimate (1454 kcal.day-1). There was a statistical difference between methods (F = 2.607, p = 0.04), but they presented a clinically irrelevant absolute difference (~36 kcal.day-1). There were no differences in RER among methods. In addition, using the SSt method, 12 minutes of assessment were enough to obtain a valid estimation of RMR. The 5-min SSt method should be employed for assessing the RMR among young athletes, considering the possibility of obtaining a shortened assessment (~12 min) with an acceptable and low coefficient of variation.

New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-Validation Study.

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, whereas 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 (RMSE), 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 regression 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 Jagim's equation showed the worst performance for males (mean difference = -335 kcal·d -1 ; RMSE = 386 kcal·d -1 ). Compared with 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 Jagim's equation should not be used in high-level male athletes.

Deciphering the constrained total energy expenditure model in humans by associating accelerometer-measured physical activity from wrist and hip.

The constrained total energy expenditure (TEE) model posits that progressive increases in physical activity (PA) lead to increases in TEE; but after certain PA threshold, TEE plateaus. Then, a compensatory reduction in the expenditure of non-essential activities constrains the TEE. We hypothesized that high PA levels as locomotion associate with a compensatory attenuation in arm movements. We included 209 adults (64% females, mean [SD] age 32.1 [15.0] years) and 105 children (40% females, age 10.0 [1.1] years). Subjects wore, simultaneously, one accelerometer in the non-dominant wrist and another in the hip for ≥ 4 days. We analyzed the association between wrist-measured (arm movements plus locomotion) and hip-measured PA (locomotion). We also analyzed how the capacity to dissociate arm movements from locomotion influences total PA. In adults, the association between wrist-measured and hip-measured PA was better described by a quadratic than a linear model (Quadratic-R2 = 0.54 vs. Linear-R2 = 0.52; P = 0.003). Above the 80th percentile of hip-measured PA, wrist-measured PA plateaued. In children, there was no evidence that a quadratic model fitted the association between wrist-measured and hip-measured PA better than a linear model (R2 = 0.58 in both models, P = 0.25). In adults and children, those with the highest capacity to dissociate arm movements from locomotion-i.e. higher arm movements for a given locomotion-reached the highest total PA. We conclude that, in adults, elevated locomotion associates with a compensatory reduction in arm movements (probably non-essential fidgeting) that partially explains the constrained TEE model. Subjects with the lowest arm compensation reach the highest total PA.

The estimation of the resting metabolic rate is affected by the method of gas exchange data selection in high-level athletes.

BACKGROUND & AIMS: there is no consensus in the literature about the best method to estimate the RMR in a high-level athlete's cohort. Additionally, a shortening protocol may allow researchers, nutritionists, and clinicians to follow the RMR across the season and to propose better nutritional interventions, but this kind of protocol was not proposed in this cohort yet. Thus, this study aims to analyze the effect of the method of gas exchange data selection upon the RMR estimate and, possibly propose a shortening protocol with a valid and accurate RMR value. METHODS: Eighty-three healthy high-level athletes underwent a 30-minute RMR measurement with no rest period before the test. Different methods of gas exchange data selection were used: short and long time intervals (TI) [6-10, 11-15, 16-20, 21-25, 26-30, 6-25, or 6-30], Steady State (SS) with 3, 4, 5, or 10 min period length, and Filtering (low, medium, and high). Single and multiple linear regressions were used to examine the variance in the RMR provided by each method of gas exchange data selection. RESULTS: The High Filter method provided the lowest RMR estimate (1854 kcal.day-1), and most methods presented a mean absolute difference of ~43 kcal.day-1 from the High Filter method. There were no differences in RER among methods (F = 2.01, p = 0.10). Besides, twenty minutes of gas exchange measurement was necessary to obtain a valid and accurate RMR with no rest period before the test. The linear regression model that included sex, lean body mass, and fat mass better explained the variance in the RMR using the high filter method (~88%). CONCLUSIONS: The High Filter provided the lowest RMR value. Furthermore, a 20-minute protocol estimated a valid and accurate RMR value with no acclimation period before the measurement in high-level athletes.