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Predicting the resting metabolic rate of 30-60-year-old Australian males.
Eur J Clin Nutr. 2002 Aug; 56(8):701-8.EJ

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.

Authors+Show Affiliations

Exercise Physiology Laboratory, School of Education, Flinders University, Adelaide, South Australia, Australia.No affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

12122544

Citation

van der Ploeg, G E., and R T. Withers. "Predicting the Resting Metabolic Rate of 30-60-year-old Australian Males." European Journal of Clinical Nutrition, vol. 56, no. 8, 2002, pp. 701-8.
van der Ploeg GE, Withers RT. Predicting the resting metabolic rate of 30-60-year-old Australian males. Eur J Clin Nutr. 2002;56(8):701-8.
van der Ploeg, G. E., & Withers, R. T. (2002). Predicting the resting metabolic rate of 30-60-year-old Australian males. European Journal of Clinical Nutrition, 56(8), 701-8.
van der Ploeg GE, Withers RT. Predicting the Resting Metabolic Rate of 30-60-year-old Australian Males. Eur J Clin Nutr. 2002;56(8):701-8. PubMed PMID: 12122544.
* Article titles in AMA citation format should be in sentence-case
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 -