Predicting the resting metabolic rate of young Australian males.
OBJECTIVE
The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values.


METHODS
A power analysis demonstrated that 38 subjects would enable us to detect (alpha = 0.05, power = 0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the equations of other investigators.


METHODS
Thirty-eight males (chi +/- s.d.: 24.3+/-3.3y; 85.04+/-13.82 kg; 180.6+/-8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards.


METHODS
The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model.


RESULTS
A multiple regression equation using the easily measured predictors 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 R and the precision 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 from skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equations 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 were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically significant errors at the lower end of our range of measurement.


CONCLUSIONS
Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-y-old Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.
van der Ploeg
G E
GE
Exercise Physiology Laboratory, School of Education, Flinders University, Adelaide, South Australia.
Gunn
S M
SM
Withers
R T
RT
Modra
A C
AC
Keeves
J P
JP
Chatterton
B E
BE
D015502:Absorptiometry, Photon; D000273:Adipose Tissue; D000293:Adolescent; D000328:Adult; D001315:Australia; D001481:Basal Metabolism; D001823:Body Composition; D002153:Calorimetry, Indirect; D006801:Humans; D008297:Male; D008954:Models, Biological; D010101:Oxygen Consumption; D010865:Pilot Projects; D011865:Radioisotope Dilution Technique; D012044:Regression Analysis; D012885:Skinfold Thickness