Comparison of Measured versus Predicted Resting Metabolic Rate in Taiwanese Adults with Excess Weight

碩士 === 臺北醫學大學 === 保健營養學研究所 === 96 === Objectives: To compare the values of resting metabolic rate (RMR) derived from six common prediction equations with the measured RMR using an indirect calorimetry in adults with excess weight. Methods: A total of 250 overweight and obese adults, aged 20-86 (40+...

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Bibliographic Details
Main Authors: Hui-Hsin Hsia, 夏慧欣
Other Authors: Ming-Jer Shieh
Format: Others
Language:zh-TW
Published: 2008
Online Access:http://ndltd.ncl.edu.tw/handle/53913411253396974064
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Summary:碩士 === 臺北醫學大學 === 保健營養學研究所 === 96 === Objectives: To compare the values of resting metabolic rate (RMR) derived from six common prediction equations with the measured RMR using an indirect calorimetry in adults with excess weight. Methods: A total of 250 overweight and obese adults, aged 20-86 (40+14.5) years and BMI 24.1-51.7 (32 + 5.6) kg/m2, were recruited from the obesity clinic in a medical center, and their RMR was measured using an indirect calorimetry (MetaMax 3B,Cortex Germany). These measured RMR values were compared with values from six prediction equations (Harris and Benedict, Owen, Mifflin, WHO, Bernstein, and Liu) using a statistical analysis. Results: A significant but moderate correlation (P < 0.001) adjusted for age and gender was found between measured RMR and RMR derived from the Harris-Benedict (R2= 0.63), Owen (R2= 0.61), Mifflin (R2= 0.63), WHO (R2= 0.62), Bernstein (R2=0.62), and Liu’s equation (R2= 0.62). Furthermore, the measured RMR values were significantly lower than RMR values calculated using the six prediction equations. Among these, the Bernstein equation has the smallest difference (176.2 + 339.8 kcal/day). Several predictive models showed progressively poor prediction in the groups with BMI. Conclusions: The present prediction equations seem to overestimate RMR of the overweight and obese Taiwanese adults. Factors having influence on variations in resting metabolic rate are weight、height、fat-free mass 、waist、 gender and age. The better suggested predictive model was : -577.02 + 5.15× weight(kg) + 426.98 × height(m) + 6.87 × waist(cm)-2.72 × age(years)+ 184.28 × gerder (male=1,female=0)