Date of Award

5-2015

Document Type

Thesis

Degree Name

Master of Science in Human Performance

Department

Kinesiology

Abstract

Dietary requirements in athletes are critical to overall health and performance, yet no formula to predict dietary needs is available for female athletes. This study's purpose was to develop a validated prediction equation to predict the resting metabolic needs of female athletes. 63 NCAA Division II female athletes (19.7 ± 1.1 years, 169.3 ± 8.6 cm, 67.5 ± 9.1 kg) were recruited as participants in this study. Upon providing their informed consent, study participants arrived for testing in the morning between the hours of 0600 - 1000 after observing a ~10 hour fast and within 30 - 60 minutes of waking up. Demographic information, dietary intake and daily activity were recorded before having height and weight determined. Subjects then had their resting metabolic rate determined using indirect calorimetry. Using one-way ANOVA, significant differences in height, weight, body surface area, body density, fat mass, body fat % and fat-free mass were found (p< 0.05). Multiple regression statistical techniques were used to develop prediction equations for resting metabolic rate. Both equations found to have excellent agreement (r = 0.55 - 62, p< 0.01) and were not significantly different from measured resting metabolic rate values using paired samples t-tests (p < 0.05). Crossvalidation techniques using a random sample from the study cohort yielded excellent agreement (r = 0.99, p < 0.001) and, again, was not significantly different from measured resting metabolic rate values (p > 0.05). Further validation was made by additional dependent t-tests using previously validated prediction equations. Findings from this study and future development of this research could enhance our understanding and ability to determine caloric needs in competitive, collegiate female athletes, which will help all athletes, coaches and support staff better understand the dietary requirements of female athletes.

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