Journal of Nutritional Therapeutics

Accuracy and Applicability of Resting Metabolic Rate Prediction Equations Differ for Women Across the Lifespan
Pages 50-63
Kathleen Woolf, Shirley Miller, Christine Reese, Leah Beaird and Maureen Mason


Published: 31 July 2015


Abstract: Background: Nutrition clinicians need accurate and reliable resting metabolic rate (RMR) assessments to determine energy needs and an appropriate nutrition care plan.

Material/Methods:This cross-sectional study compared the accuracy of eight RMR prediction equations (Harris-Benedict, Robertson and Reid, Cunningham 1980, FAO/WHO/UNU, Owen, Mifflin-St. Jeor, Cunningham 1991, and Nelson) to measured RMR by indirect calorimetry among young (n=57; age: 25±3 years), midlife (n=57; age: 44±3 years), and older (n=46; age: 68±5 years) women. Paired t-tests examined differences between predicted and measured RMR. Statistical analyses were conducted using SPSS (version 21), with significance defined as p<0.05. Bland-Altman plots displayed prediction bias and agreement. Prediction accuracy was defined when predicted RMR was ±10% of measured RMR. Serum thyroid stimulating hormone and follicle stimulating hormone concentrations were measured to assess thyroid function and ovarian reserve, respectively.

Results:The difference between predicted and measured RMR ranged from +0.6% (Owen) to +17.7% (Cunningham 1980) for the young, -2.8% (Nelson) to +18.1% (Cunningham 1980) midlife, and +2.8 (Nelson) to +26.7% (Cunningham 1980) older women. For the young women, only the Owen equation predicted RMR similar to measured RMR (p=0.905). For the older women, only the Nelson equation predicted RMR similar to measured RMR (p=0.051). All estimates using prediction equations were significantly different from measured values for midlife women.

Conclusion: Many RMR prediction equations have limited applicability for women at difference stages of the lifespan, thus impacting patient outcomes. Additional research is necessary to determine the appropriateness of RMR prediction equations among women of all ages.

Keywords: Resting metabolic rate (RMR), indirect calorimetry, RMR prediction equation, women, lifespan.

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