D R Taaffe1, J Thompson, G Butterfield, R Marcus. 1. Aging Study Unit of the Geriatric Research, Education and Clinical Center, Veterans Affairs Medical Center, Palo Alto, Calif 94304, USA.
Abstract
OBJECTIVE: To assess the accuracy of several published equations for predicting basal metabolic rate (BMR) in older women. DESIGN: BMR was assessed in 116 healthy, older white women, aged 60 to 82 years, on three successive mornings by indirect calorimetry. Body composition was determined by dual energy X-ray absorptiometry or hydrostatic weighing. The measured BMRs were compared with values obtained from eight published prediction equations that used solely, or in various combinations, measures of height, weight, fat-free mass, age, and menopausal status. STATISTICAL ANALYSES PERFORMED: The root mean squared prediction error (RMSPE) was used to determine how accurately predicted BMR matched actual BMR for each subject. In addition, regression analysis was used to evaluate accuracy of predicted BMR vs directly measured BMR. RESULTS: Predicted mean BMR determined using all eight equations was significantly correlated to measured BMR (P = .0001), accounting for 30% to 52% of the variance of measured BMR. When analyzed by RMSPE, however, the equations of Owen et al (1986), Fredrix et al (1990), and Harris-Benedict (1919) predicted actual BMR for each subject within an average of 116 kcal/day, and the equation of Cunningham (1980) resulted in the largest prediction error at 208 kcal/day. APPLICATIONS/ CONCLUSIONS: The regression equations of Owen et al (1986), which used body weight, Fredrix et al (1990), which used body weight and age, and Harris-Benedict (1919), which used age, weight, and height as variables, were most accurate in predicting BMR in our sample of healthy older women.
OBJECTIVE: To assess the accuracy of several published equations for predicting basal metabolic rate (BMR) in older women. DESIGN: BMR was assessed in 116 healthy, older white women, aged 60 to 82 years, on three successive mornings by indirect calorimetry. Body composition was determined by dual energy X-ray absorptiometry or hydrostatic weighing. The measured BMRs were compared with values obtained from eight published prediction equations that used solely, or in various combinations, measures of height, weight, fat-free mass, age, and menopausal status. STATISTICAL ANALYSES PERFORMED: The root mean squared prediction error (RMSPE) was used to determine how accurately predicted BMR matched actual BMR for each subject. In addition, regression analysis was used to evaluate accuracy of predicted BMR vs directly measured BMR. RESULTS: Predicted mean BMR determined using all eight equations was significantly correlated to measured BMR (P = .0001), accounting for 30% to 52% of the variance of measured BMR. When analyzed by RMSPE, however, the equations of Owen et al (1986), Fredrix et al (1990), and Harris-Benedict (1919) predicted actual BMR for each subject within an average of 116 kcal/day, and the equation of Cunningham (1980) resulted in the largest prediction error at 208 kcal/day. APPLICATIONS/ CONCLUSIONS: The regression equations of Owen et al (1986), which used body weight, Fredrix et al (1990), which used body weight and age, and Harris-Benedict (1919), which used age, weight, and height as variables, were most accurate in predicting BMR in our sample of healthy older women.