Article Title



Adam H. Ibrahim1, Alexander Brooks1, Traci Smith1, Cary Springer2, Bill Campbell1. 1University of South Florida, Tampa, FL. 2University of Tennessee, Knoxville, TN.

BACKGROUND: Estimating resting metabolic rate (RMR) is useful when determining an individual's energy needs. Indirect calorimetry is the gold standard for estimating RMR, however, this procedure is not available to the general population. As such, prediction equations are a useful alternative. It has been recognized that the accuracy of prediction equations may be population-specific. Individuals who resistance train are expected to have a higher RMR due to an increase in metabolically active tissue. Due to this, previously developed prediction equations often underestimate RMR in resistance-trained individuals. Therefore, a prediction equation specific to this population may be required. METHODS: Forty-two apparently healthy non-obese, individuals (74% female, 24.6 ± 7.3 years, 167.3 ± 9.2cm, 67.2kg ± 11.6kg 21.5 ± 7.4% BF) who engaged in muscle strength training for a minimum of twice per week for the previous 6 months were included. Reference RMR was estimated utilizing indirect calorimetry. Height, weight, and body composition (body fat percentage [BF%], fat mass [FM], fat-free mass [FFM], total body water [TBW], and dry FFM [dFFM: FFM-TBW]) were measured using A-mode ultrasound, skinfold calipers, and bioelectrical impedance. The results of these three methods were averaged and then used for analyses. Multiple stepwise linear regression analyses were performed to develop FFM and weight-based RMR prediction equations. Accuracy of the equations was examined using values for, R, R squared, standard error of the estimate, total error, and 95% limits of agreement. RESULTS: The resultant equations are as follows: FFM-based equation [RMR= 460 + (27 * FFM (kg)) - (10 *age) - (184*sex (1=M, 0=F))], weight-based equation [RMR= -850 + (15*weight (kg)) - (10*age) + (9.9*height (cm))]. A significant correlation was observed between the FFM and weight-based prediction equations when compared to the reference RMR (r =.91, p <.001 & r=.88, p<.001 respectively). CONCLUSION: Estimated RMR values from both equations were strongly related to measured RMR within resistance-trained individuals. If a person is unaware of their body composition, the weight-based equation may be a reasonable option. These results require replication using a fully powered and more diverse sample. Future research should provide validation of this newly developed equation to refine the ability to predict RMR in resistance-trained individuals.

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