PURPOSE: Equations published in literature for predicting resting metabolic rate (RMR) in older individuals were derived from studies with small samples of this age group or extrapolated from data of younger adults. The aim of the present investigation was therefore to validate various predictive equations by comparing calculated RMR with measured RMR in a large group of elderly subjects. SUBJECTS AND METHODS: RMR was measured by indirect calorimetry after an overnight fast in 225 female (age 67.7 +/- 5.7 y, BMI 26.7 +/- 3.9 kg/m2) and 130 male (age 67.4 +/- 5.4 y, BMI 26.7 +/- 3.2 kg/m2) participants of the longitudinal study on nutrition and health status in an aging population of Giessen, Germany, who were at least 60 years old. RESULTS: In females and males RMR was on average underestimated by 3.3% and 7.5% with the Schofield equation based on body weight, by 2.4% and 4.5% with the Schofield equation based on both weight and height, by 0.7% and 5.0% with the WHO equation based on body weight, and by 2.6% and 4.6% with the Harris-Benedict equation, respectively. RMR calculated with the WHO equation based on body weight and height was 1.8% higher in females and 3.9% lower in males compared to measured RMR. Regarding all predictive equations the difference between predicted and measured RMR were negatively correlated with measured RMR and were partly more pronounced in smokers and obese subjects than in non-smokers and subjects with a BMI < 30 kg/m2. CONCLUSION: At the group level all predictive equations used provide a valid estimation of RMR. However, on an individual basis estimation errors may be high. Thus in individuals RMR should be measured instead of being estimated. If measurements cannot be taken, population specific equations should be used for predicting RMR.
PURPOSE: Equations published in literature for predicting resting metabolic rate (RMR) in older individuals were derived from studies with small samples of this age group or extrapolated from data of younger adults. The aim of the present investigation was therefore to validate various predictive equations by comparing calculated RMR with measured RMR in a large group of elderly subjects. SUBJECTS AND METHODS: RMR was measured by indirect calorimetry after an overnight fast in 225 female (age 67.7 +/- 5.7 y, BMI 26.7 +/- 3.9 kg/m2) and 130 male (age 67.4 +/- 5.4 y, BMI 26.7 +/- 3.2 kg/m2) participants of the longitudinal study on nutrition and health status in an aging population of Giessen, Germany, who were at least 60 years old. RESULTS: In females and males RMR was on average underestimated by 3.3% and 7.5% with the Schofield equation based on body weight, by 2.4% and 4.5% with the Schofield equation based on both weight and height, by 0.7% and 5.0% with the WHO equation based on body weight, and by 2.6% and 4.6% with the Harris-Benedict equation, respectively. RMR calculated with the WHO equation based on body weight and height was 1.8% higher in females and 3.9% lower in males compared to measured RMR. Regarding all predictive equations the difference between predicted and measured RMR were negatively correlated with measured RMR and were partly more pronounced in smokers and obese subjects than in non-smokers and subjects with a BMI < 30 kg/m2. CONCLUSION: At the group level all predictive equations used provide a valid estimation of RMR. However, on an individual basis estimation errors may be high. Thus in individuals RMR should be measured instead of being estimated. If measurements cannot be taken, population specific equations should be used for predicting RMR.