Olympic Laboratory, Brazil Olympic Committee, Rio de Janeiro, Brazil.
PROFITH "PROmoting FITness and Health Through Physical Activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada, 18011, Granada, Spain.
Clin Nutr ESPEN. 2021 Feb;41:234-241. doi: 10.1016/j.clnesp.2020.12.008. Epub 2020 Dec 30.
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.
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.
The High Filter method provided the lowest RMR estimate (1854 kcal.day), and most methods presented a mean absolute difference of 43 kcal.day 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%).
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.
目前,对于高水平运动员群体,文献中尚未就最佳静息代谢率(RMR)估算方法达成共识。此外,缩短协议可能使研究人员、营养师和临床医生能够在整个赛季中跟踪 RMR,并提出更好的营养干预措施,但这种协议尚未在该队列中提出。因此,本研究旨在分析气体交换数据选择方法对 RMR 估计的影响,并可能提出一种具有有效且准确 RMR 值的缩短协议。
83 名健康的高水平运动员在测试前没有休息期的情况下进行了 30 分钟的 RMR 测量。使用了不同的气体交换数据选择方法:短时间间隔(TI)[6-10、11-15、16-20、21-25、26-30、6-25 或 6-30]和长时间间隔(6-30);3、4、5 或 10 分钟的稳定状态(SS)以及低、中、高过滤。使用单因素和多因素线性回归来检查每种气体交换数据选择方法提供的 RMR 方差。
高过滤方法提供的 RMR 估计值最低(1854kcal.day),并且大多数方法与高过滤方法的平均绝对差异约为 43kcal.day。方法之间的呼吸商(RER)没有差异(F=2.01,p=0.10)。此外,在没有测试前休息期的情况下,进行 20 分钟的气体交换测量是获得有效且准确的 RMR 的必要条件。使用高过滤方法,包含性别、瘦体重和体脂肪量的线性回归模型更好地解释了 RMR 的方差(~88%)。
高过滤提供了最低的 RMR 值。此外,在高水平运动员中,20 分钟的协议可以在没有适应期的情况下估计有效且准确的 RMR 值。