Determining a Resting Metabolic Rate Prediction Equation for Collegiate Female Athletes
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Alicia D. Watson [1] ; Hannah A. Zabriskie [2] ; Kyle E. Witherbee ; Andrea Sulavik [3] ; Bradley T. Gieske [2] ; Chad M. Kerksick
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[1] Washington University in St. Louis
Washington University in St. Louis
Estados Unidos
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[2] Lindenwood University
Lindenwood University
Township of Rivers, Estados Unidos
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[3] Amherst College
Amherst College
Town of Amherst, Estados Unidos
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- Localización: Journal of strength and conditioning research: the research journal of the NSCA, ISSN 1064-8011, Vol. 33, Nº. 9, 2019, págs. 2426-2432
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
A lack of evidence exists regarding the accuracy of common resting metabolic rate (RMR) prediction equations in athletic female populations. The purpose of this research was to measure RMR in a large cohort of NCAA Division II female athletes and use regression techniques to develop new prediction equations. Sixty-six female athletes from 11 different sports completed this protocol, which included skinfold measurements followed by an RMR assessment using indirect calorimetry. The average RMR was 1,466 +/- 150 kcal[middle dot]d-1. Many between-sport differences in body composition were identified, with gymnastics athletes having the lowest body fat percentage (p < 0.05) and basketball athletes having the greatest absolute fat-free mass (p < 0.05). Resting metabolic rate was moderately correlated (p < 0.05) with height (r = 0.52), total mass (r = 0.59), and fat-free mass (r = 0.54). Two equations were developed, both of which were more accurate for this population than other RMR prediction equations. One of the new equations, which used height and body mass as covariates (equation 1), was slightly more accurate than the equation using body composition parameters (equation 2). The new equations were cross-validated using a randomly selected subset (n = 22) of the original sample. The subset did not show statistically different results from the remainder of the sample (n = 44) between equation 1 (p = 0.083) and equation 2 (p = 0.22). Equation 1, which had more easily measurable parameters, exhibited heightened accuracy, which has important implications for implementation among athletes, coaches, and athletic support staff.
