A Systematic Review of the Accuracy of Estimated and Measured Resting Metabolic Rate in Chronic Spinal Cord Injury

• Gary J. Farkas ^[1] ; Marika A. Pitot Pitot ^[2] ; David R. Gater ^[3]
  1. [1] University of California, San Francisco
  2. [2] Penn State College of Medicine
  3. [3] Penn State Hershey Medical Center and Penn State College of Medicine

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• Localización: International journal of sport nutrition and exercise metabolism, ISSN 1526-484X, ISSN-e 1543-2742, Vol. 29, Nº. 5, 2019, págs. 548-558
• Idioma: inglés
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• Resumen

  • The objective of this systematic review was to examine resting metabolic rate (RMR) measured through indirect calorimetry in adults with chronic spinal cord injury (SCI) and to evaluate the predication equations used for the estimation of RMR in this population. The authors additionally aimed to review the impact of sex and level of injury on RMR. A MEDLINE/PubMed, Google Scholar, Scopus, and Web of Science search was performed for studies published from the database inception to January 2018, identifying 326 articles. On the basis of the inclusion criteria, 22 studies remained for review. All articles that were identified were full-text, English language articles examining adults with chronic SCI who were fasted for a minimum of 8 hr before undergoing RMR through indirect calorimetry for at least 20 min. The measured RMR ranged from 1,256.0 to 1,854.0 kcal/day, whereas the estimated RMR ranged from 1,276.8 to 1,808.0 kcal/day in the chronic SCI population. Seven studies overestimated RMR from 4% to 15%, whereas two studies underestimated RMR from 2% to 17% using prediction equations established for the able-bodied population. Two studies produced SCI-specific equations to estimate RMR. With regard to sex and level of injury, RMR does not differ between individuals with tetraplegia and paraplegia, whereas sex-based differences remain inconclusive, given limited results. These data provide evidence for the use of indirect calorimetry to assess RMR and the need to validate SCI-specific prediction equations in the estimation of RMR.