Abstract
OBJECTIVES
This study: (a) generated regression equations for predicting the resting metabolic rate (RMR) of 30-60-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validated RMR prediction equations which are currently used in Australia against our measured and predicted values.
DESIGN
A power analysis demonstrated that 41 subjects would enable the detection of (alpha=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the equations of other investigators.
SUBJECTS
Forty-one males ([X]+/-s.d.:, 44.8+/-8.6 y; 83.50+/-11.32 kg; 179.1+/-5.0 cm) were recruited for this study.
INTERVENTIONS
The following variables were measured: 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 mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model (r=0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses (r=0.805, s.e.e.=441 kJ/day).
CONCLUSIONS
Cross-validation analyses emphasised that equations need to be generated from a large database for the prediction of the RMR of 30-60-y-old Australian males.
TY - JOUR
T1 - Predicting the resting metabolic rate of 30-60-year-old Australian males.
AU - van der Ploeg,G E,
AU - Withers,R T,
PY - 2001/03/09/received
PY - 2001/10/17/revised
PY - 2001/10/24/accepted
PY - 2002/7/18/pubmed
PY - 2002/11/26/medline
PY - 2002/7/18/entrez
SP - 701
EP - 8
JF - European journal of clinical nutrition
JO - Eur J Clin Nutr
VL - 56
IS - 8
N2 - OBJECTIVES: This study: (a) generated regression equations for predicting the resting metabolic rate (RMR) of 30-60-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validated RMR prediction equations which are currently used in Australia against our measured and predicted values. DESIGN: A power analysis demonstrated that 41 subjects would enable the detection of (alpha=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the equations of other investigators. SUBJECTS: Forty-one males ([X]+/-s.d.:, 44.8+/-8.6 y; 83.50+/-11.32 kg; 179.1+/-5.0 cm) were recruited for this study. INTERVENTIONS: The following variables were measured: 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 mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model (r=0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses (r=0.805, s.e.e.=441 kJ/day). CONCLUSIONS: Cross-validation analyses emphasised that equations need to be generated from a large database for the prediction of the RMR of 30-60-y-old Australian males.
SN - 0954-3007
UR - https://www.unboundmedicine.com/medline/citation/12122544/Predicting_the_resting_metabolic_rate_of_30_60_year_old_Australian_males_
L2 - https://doi.org/10.1038/sj.ejcn.1601369
DB - PRIME
DP - Unbound Medicine
ER -
