DOI

https://doi.org/10.25772/5T93-R266

Defense Date

2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Rehabilitation and Movement Science

First Advisor

Ronald Evans

Abstract

Resting metabolic rate (RMR) accounts for approximately 70% of total daily energy expenditure and is crucial for maintaining bodily functions at rest. Indirect calorimetry, which measures oxygen consumption and carbon dioxide production, is commonly used to assess RMR. However, due to cost and limited availability, RMR prediction equations are often used, though they vary in accuracy. Previous studies have shown lower RMR in African-American women compared to Caucasian women, even when adjusting for factors like body weight and fat-free mass (FFM). This study aimed to evaluate and develop more accurate RMR prediction equations for African-American women. 91 pre-menopausal African-American women underwent assessments of demographics, body composition, and RMR. Initially, measured RMR values were compared to 10 commonly utilized RMR prediction equations to determine their predictive accuracy. Subsequently, multiple linear regression analyses were utilized to determine the best models for predicting RMR within the sample. Lastly, leave-one-out cross validation (LOOCV) was used to evaluate model performance. Overall, the predictive accuracy of the currently available RMR prediction equations evaluated were poor with substantial overestimation observed between measured and predicted RMR values. Multiple linear regression analyses supported the development of two new RMR prediction equations: one equation utilizing the more easily obtained measures of age, weight, and height and a second equation utilizing body composition variables. Both prediction equations were found to provide good estimates of RMR that improved on the currently available options for estimating RMR in pre-menopausal African-American women.

Rights

© Daishan P Johnson

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

5-8-2024

Available for download on Monday, May 07, 2029

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