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Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations

Abstract

Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight.

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Acknowledgements

We thank the editor and three anonymous reviewers for their constructive feedback. Participants in the National Collaborative on Childhood Obesity Research (NCCOR) Envision’s Comparative Modeling Network (CompMod) program provided helpful comments. Financial Support Provided through Envision’s CompMod program and NIH Office of Behavioral and Social Sciences Research (OBSSR) contract: HHSN276201000004C.

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Sabounchi, N., Rahmandad, H. & Ammerman, A. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Int J Obes 37, 1364–1370 (2013). https://doi.org/10.1038/ijo.2012.218

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Keywords

  • basal metabolic rate
  • resting metabolic rate
  • prediction
  • meta-analysis
  • review
  • meta-regression

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