Publication · Review

Estimation of energy expenditure using prediction equations in overweight and obese adults: a systematic review

Angela M. Madden, Hilda Mary Mulrooney, Surbhi Shah

Journal of Human Nutrition and DieteticsFeb 29, 2016
Abstract

BackgroundEstimates of energy requirements are needed in weight management and are usually determined using prediction equations. The objective of these two systematic reviews was to identify which equations based on simple anthropometric and demographic variables provide the most accurate and precise estimates of (1) resting energy expenditure (REE) and (2) total energy expenditure (TEE) in healthy obese adults.

MethodsSystematic searches for relevant studies in healthy adults with body mass index (BMI) ≥25 kg m(-2) and published in English were undertaken using Cinahl, Cochrane Library, OpenGrey, PubMed and Web of Science (completed March 2014). Search terms included metabolism, calorimetry, obesity and prediction equations. Data extraction, study appraisal and synthesis followed guidelines from PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses).

ResultsFrom 243 REE papers and 254 TEE papers identified, 21 and four studies, respectively, met the inclusion criteria. (1) The most accurate REE predictions varied with BMI subgroup: WHO (weight and height) ≥25 and ≥30 kg m(-2) ; Mifflin 30-39.9 kg m(-2) ; Henry ≥40 kg m(-2) . The most precise REE predictions were obtained using Mifflin in BMI 30-39.9 and ≥40 kg m(-2) , where approximately 75% of predictions were within 10% of measured REE. (2) No accurate or precise predictions of TEE were identified.

ConclusionsNo single prediction equation provides accurate and precise REE estimates in all obese adults. Mifflin equations are recommended in this population, although errors exceed 10% in 25% of those assessed. There is no evidence to support the use of prediction equations in estimating TEE in obesity.

1Authors

AuthorAffiliationh-indexCitations
Angela M. MaddenUniversity of Hertfordshire262,279
Hilda Mary MulrooneyKingston University London16912
Surbhi ShahUniversity of Hertfordshire9400

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