Measurement of resting metabolic rate (RMR) is an important f actor f or weight management. Previous research has reported several variables to estimate RMR such as body siz e, percent f at (% BF), age, and sex; however, little is known regarding the effect of circumference measures in estimating RMR. PURPOSE: The purpose of this study was to develop a model to estimate RMR using waist circumf erence (WC), an easily obtainable measure, and cross-validate it to previously published models. METHODS:Subjects were 14 0 adult men and women, ages 18-6 5 ye ars. RMR was measured through indirect calorimetry, % BF was measured through air displacement plethysmography, and f at mass and f at-f ree mass were determined f rom % BF and weight. O ther variables collected were: weight, height, age, sex, ethnicity, body mass index, WC, hip circumf erence, waist-to-hip ratio, waist-to-height ratio, and % BF estimated f rom bioelectrical impedance analysis. Subjects were randomly divided into derivation and cross-validation samples. A multiple regression model was developed to determine the most accurate estimation of RMR in the derivation sample. The crossvalidation sample was used to confirm the accuracy of the model and to compare the accuracy to published models. RESULTS:The best predictors f or estimating RMR were body weight, r = 0.7 0, p= 0.031, age, r =-0.30, p= 0.012, and sex, r = 0.5 1, p= 0.018. Other factors failed to account for significant variation in the model. The derived eq ua tion f or estimating RMR is: RMR (kcal/ day) = 8 4 3.1 1 + 8 .7 7 (weight)-4 .23(age) + 228 .5 4 (sex, M = 1, F = 0), R 2 = 0.6 8 , S EE = 17 3 kc al/ day. Cross-validation statistics were: R 2 = 0.54, p ≤0.05, SEE = 199 kcal/day, and total error = 198 kcal/ day. I n published models, R 2 ranged f rom 0.4 7 t o 0.5 7 , S EE ranged f rom 19 2 t o 213 kcal/ day, and total error ranged f rom 212 to 1311 kcal/ day. CONCLUSIONS:Crossvalidation to published models f or estimating RMR were similar to those of the derived model; however, the total error in the derived eq ua tion was lower than any of the previously published models. Several published models considerably overestimate RMR compared to the current model. The results of this study suggest that RMR can be reasonably estimated with easily obtainable measures which allow f or estimation and implementation of RMR f or weight management in clinical practice.