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. 2016 May-Jun;10(3):304-14.
doi: 10.1016/j.orcp.2015.07.002. Epub 2015 Jul 22.

Predicting resting energy expenditure in young adults

Erik A Willis  1 , Stephen D Herrmann  2 , Lauren T Ptomey  3 , Jeffery J Honas  3 , Christopher T Bessmer  3 , Joseph E Donnelly  3 , Richard A Washburn  3
Affiliations

Predicting resting energy expenditure in young adults

Erik A Willis et al. Obes Res Clin Pract. 2016 May-Jun.

Abstract

Purpose: To develop and validate a REE prediction equation for young adults.

Methods: Baseline data from two studies were pooled (N=318; women=52%) and randomly divided into development (n=159) and validation samples (n=159). REE was measured by indirect calorimetry. Stepwise regression was used to develop an equation to predict REE (University of Kansas (KU) equation). The KU equation and 5 additional REE prediction equations used in clinical practice (Mifflin-St. Jeor, Harris-Benedict, Owens, Frankenfield (2 equations)) were evaluated in the validation sample.

Results: There were no significant differences between predicted and measured REE using the KU equation for either men or women. The Mifflin-St. Jeor equation showed a non-significant mean bias in men; however, mean bias was statistically significant in women. The Harris-Benedict equation significantly over-predicted REE in both men and women. The Owens equation showed a significant mean bias in both men and women. Frankenfield equations #1 and #2 both significantly over-predicted REE in non-obese men and women. We found no significant differences between measured REE and REE predicted by the Frankenfield #2 equations in obese men and women.

Conclusion: The KU equation, which uses easily assessed characteristics (age, sex, weight) may offer better estimates of REE in young adults compared with the 5 other equations. The KU equation demonstrated adequate prediction accuracy, with approximately equal rates of over and under-prediction. However, enthusiasm for recommending any REE prediction equations evaluated for use in clinical weight management is damped by the highly variable individual prediction error evident with all these equations.

Keywords: Indirect calorimetry; Resting energy expenditure; Weight management; Young adults.

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Conflict of interest statement

Conflict of interest

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Figures

Figure 1
Figure 1
Comparison of resting energy expenditure (REE) estimated by REE prediction equations and assessed by indirect calorimetry. (A) University of Kansas equation, (B) Mifflin–St. Jeor, (C) Harris–Benedict, (D) Owen, (E) Frankenfield #1 non-obese, (F) Frankenfield #1 obese, (G) Frankenfield #2 non-obese, (H) Frankenfield #2 obese.
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