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The estimation of the resting metabolic rate is affected by the method of gas exchange data selection in high-level athletes
- Source :
- Clinical Nutrition ESPEN. 41:234-241
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Summary 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.
- Subjects :
- 0301 basic medicine
Rest
Endocrinology, Diabetes and Metabolism
030209 endocrinology & metabolism
Fat mass
03 medical and health sciences
0302 clinical medicine
Linear regression
Statistics
Humans
Medicine
Resting energy expenditure
Estimation
030109 nutrition & dietetics
Nutrition and Dietetics
biology
business.industry
Athletes
Calorimetry, Indirect
biology.organism_classification
Basal metabolic rate
Body Composition
Lean body mass
Basal Metabolism
business
Data selection
Subjects
Details
- ISSN :
- 24054577
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Clinical Nutrition ESPEN
- Accession number :
- edsair.doi.dedup.....74377ed6c50b58b23bbf91e2ad8b93ea
- Full Text :
- https://doi.org/10.1016/j.clnesp.2020.12.008