Nutr Res Pract.  2018 Aug;12(4):283-290. 10.4162/nrp.2018.12.4.283.

Validity of predictive equations for resting energy expenditure in Korean non-obese adults

• Ndahimana
• Choi YJ
• Park JH
• Ju MJ
• Kim EK

• KMID: 2420993
• DOI: http://doi.org/10.4162/nrp.2018.12.4.283

Abstract

BACKGROUND/OBJECTIVES
Indirect calorimetry is the gold-standard method for the measurement of resting energy expenditure. However, this method is time consuming, expensive, and requires highly trained personnel. To overcome these limitations, various predictive equations have been developed. The objective of this study was to assess the validity of predictive equations for resting energy expenditure (REE) in Korean non-obese adults.
SUBJECTS/METHODS
The present study involved 109 participants (54 men and 55 women) aged between 20 and 64 years. The REE was measured by indirect calorimetry. Nineteen REE equations were evaluated for validity, by comparing predicted and measured REE results. Predictive equation accuracy was assessed by determining percent bias, root mean squared prediction error (RMSE), and percentage of accurate predictions.
RESULTS
The measured REE was significantly higher in men than in women (P < 0.001), but the difference was not significant after adjusting for body weight (P > 0.05). The equation developed in this study had an accuracy rate of 71%, a bias of 0%, and an RMSE of 155 kcal/day. Among published equations, the FAO(weight) equation gave the highest accuracy rate (70%), along with a bias of −4.4% and an RMSE of 184 kcal/day.
CONCLUSIONS
The newly developed equation provided the best accuracy in predicting REE for Korean non-obese adults. Among the previously published equations, the FAO(weight) equation showed the highest overall accuracy. Regardless, at an individual level, the equations could lead to inaccuracies in a considerable number of subjects.

Keyword

Energy metabolism; indirect calorimetry; adult

MeSH Terms

Adult*
Bias (Epidemiology)
Body Weight
Calorimetry, Indirect
Energy Metabolism*
Female
Humans
Male
Methods

Figure

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  Fig. 1 Percentage of accurate predictions (A), percentage bias (B), and root mean squared prediction error (RMSE) (C) for the study participants obtained from 19 published resting energy expenditure predictive equations and the equation developed in this study.

• 

  Fig. 2 Bland-Altman plots for the FAOWeight equation (A) and the equation developed in this study (B). REEpred, predicted REE; REEmeas, measured REE.

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