Predicting the resting metabolic rate of young Australian males

Objectives: The aims of this study were: (a) to generate regression equatio ns for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Austra lian males from age, height, mass and fat-free mass (FFM); and (b) cross-va lidate RMR prediction equations, which are frequently used in Australia, ag ainst our measured and predicted values. Design: A power analysis demonstrated that 38 subjects would enable us to d etect (alpha = 0.05, power = 0.80) statistically and physiologically signif icant differences of 8% between our predicted/measured RMRs and those predi cted from the equations of other investigators. Subjects: Thirty-eight males ((X) over bar+/-s.d.: 24.3+/-3.3y; 85.04+/-13. 82kg; 180.6+/-8.3cm) were recruited from advertisements placed in a univers ity newsletter and on community centre noticeboards. Interventions: The following measurements were conducted: skinfold thicknes ses, RMR using open circuit indirect calorimetry and FFM via a four-compart ment (fat mass, total body water, bone mineral mass and residual) body comp osition model. Results: A multiple regression equation using the easily measured predictor s of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/ day. Inclusion of FFM as a predictor increased both the RMR and the precisi on of prediction, but there was virtually no difference between FFM via the four-compartment model (R = 0.893, SEE = 433 kJ/day) and that predicted fr om skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equat ions of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518-600 kJ/day (P < 0.001) and these errors we re relatively constant across the range of measured RMR. The equations of H ayter & Henry (1994) and Piers ct al (1997) only produced physiologically s ignificant errors at the lower end of our range of measurement. Conclusions: Equations need to be generated from a large database for the p rediction of the RMR of 18 to 30-yold Australian males and FFM estimated fr om the regression of the sum of skinfold thicknesses on FFM via the four co mpartment body composition model needs to be further explored as an expedie nt RMR predictor.