| dc.description.abstract | Background: Resting energy expenditure (REE) accounts for the largest portion of total energy expenditure (TEE) and is a valuable tool for clinicians to provide individualized energy needs recommendations. Accurate recommendations are important in light of the current childhood obesity epidemic. Highly accurate measures of REE, such as indirect calorimetry, are costly and impractical for use in a clinical setting. Prediction equations have been developed for quick and easy use by clinicians. While various prediction equations exist, few have been validated for use in overweight and obese adolescent populations. Objective: The objective of this thesis project is to investigate the accuracy of prediction equations against resting metabolic rate (RMR) measured by indirect calorimetry in overweight and obese adolescents in comparison to normal weight adolescents. In addition, this project will examine the factors of measured RMR that account for the largest amount of variance. Methods: Data from 111 adolescent male and female subjects across all body mass index (BMI) groups from three previous Energy Balance Research studies was used. Measured RMR was compared to REE predicted by equations. Age, sex, race, fat mass, and fat-free mass were used as variables in linear regression modeling to determine factors accounting for the most variance in measured RMR. Results: In terms of significant differences from measured RMR, the Muller-1 equation was the most accurate prediction equation, (p0.05 for overweight, obese, and combined overweight and obese groups), followed by the Molnar equation (p0.05 for overweight and combined overweight and obese groups). The addition of age, sex, race, fat mass, and fat-free mass to the unadjusted model of measured RMR resulted in non-significant differences (p0.05) between all BMI groups, accounting for 75% of variance. The addition of fat-free mass to the linear regression model resulted in the largest increase in RMR variance explanation, accounting for an additional 23%. Conclusions: This data confirms that prediction equations overestimate measured RMR in overweight and obese adolescents. Equations developed with the specific addition of overweight and obese subjects, such as the Muller-1 and Molnar equation, prove to be better tools to accurately estimate RMR. | |
