Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review



      An assessment of energy needs is a necessary component in the development and evaluation of a nutrition care plan. The metabolic rate can be measured or estimated by equations, but estimation is by far the more common method. However, predictive equations might generate errors large enough to impact outcome. Therefore, a systematic review of the literature was undertaken to document the accuracy of predictive equations preliminary to deciding on the imperative to measure metabolic rate.


      As part of a larger project to determine the role of indirect calorimetry in clinical practice, an evidence team identified published articles that examined the validity of various predictive equations for resting metabolic rate (RMR) in nonobese and obese people and also in individuals of various ethnic and age groups. Articles were accepted based on defined criteria and abstracted using evidence analysis tools developed by the American Dietetic Association. Because these equations are applied by dietetics practitioners to individuals, a key inclusion criterion was research reports of individual data. The evidence was systematically evaluated, and a conclusion statement and grade were developed.


      Four prediction equations were identified as the most commonly used in clinical practice (Harris-Benedict, Mifflin-St Jeor, Owen, and World Health Organization/Food and Agriculture Organization/United Nations University [WHO/FAO/UNU]). Of these equations, the Mifflin-St Jeor equation was the most reliable, predicting RMR within 10% of measured in more nonobese and obese individuals than any other equation, and it also had the narrowest error range. No validation work concentrating on individual errors was found for the WHO/FAO/UNU equation. Older adults and US-residing ethnic minorities were underrepresented both in the development of predictive equations and in validation studies.


      The Mifflin-St Jeor equation is more likely than the other equations tested to estimate RMR to within 10% of that measured, but noteworthy errors and limitations exist when it is applied to individuals and possibly when it is generalized to certain age and ethnic groups. RMR estimation errors would be eliminated by valid measurement of RMR with indirect calorimetry, using an evidence-based protocol to minimize measurement error. The Expert Panel advises clinical judgment regarding when to accept estimated RMR using predictive equations in any given individual. Indirect calorimetry may be an important tool when, in the judgment of the clinician, the predictive methods fail an individual in a clinically relevant way. For members of groups that are greatly underrepresented by existing validation studies of predictive equations, a high level of suspicion regarding the accuracy of the equations is warranted.
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        • Frankenfield D.C.
        • Muth E.R.
        • Rowe W.A.
        The Harris-Benedict studies of human basal metabolism.
        J Am Diet Assoc. 1998; 98: 439-445
        • Phang P.T.
        • Rich T.
        • Ronco J.
        A validation and comparison study of two metabolic monitors.
        J Parenter Enteral Nutr. 1990; 14: 259-264
        • Foltz M.B.
        • Schiller M.R.
        • Ryan A.S.
        Nutrition screening and assessment.
        J Am Diet Assoc. 1993; 93: 1388-1395
        • Scientific Affairs and Research Committee
        ADA evidence analysis guide. 1st ed. American Dietetic Association, Chicago, IL2003
        • Myers E.F.
        • Pritchett E.
        • Johnson E.Q.
        Evidence-based practice guides vs. protocols.
        J Am Diet Assoc. 2001; 101: 1085-1090
        • West S.
        • King V.
        • Carey T.S.
        • Lohr K.N.
        • McKoy N.
        • Sutton S.F.
        • Lux L.
        Systems to rate the strength of scientific evidence. Evidence Report/Technology Assessment No. 47 (prepared by the Research Triangle Institute-University of North Carolina Evidence-based Practice Center under Contract No. 290-97-0011). AHRQ Publication no. 02-E016. Agency for Healthcare Research and Quality, Rockville, MDApril 2002
        • Greer N.
        • Mosser G.
        • Logan G.
        • Halaas G.W.
        A practical approach to evidence grading.
        Jt Comm J Qual Improvement. 2000; 26: 700-712
        • National Heart, Lung, and Blood Institute. NHLBI Obesity Education Initiative Expert Panel
        Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. The evidence report. National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MDJune 1998 (Available at: Accessed August 14, 2004.)
        • Heshka S.
        • Feld K.
        • Yang M.U.
        • Allison D.B.
        • Heymsfield S.B.
        Resting energy expenditure in the obese: A cross-validation in the obese: A cross-validation and comparison of prediction equations.
        J Am Diet Assoc. 1993; 93: 1031-1036
        • Harris J.A.
        • Benedict F.G.
        A Biometric Study of Basal Metabolism in Man. Publication no. 279. Carnegie Institute of Washington, Washington, DC1919
        • Owen O.E.
        • Holup J.L.
        • Dalessio D.A.
        • Craig E.S.
        • Polansky M.
        • Smalley J.K.
        • Kavle E.C.
        • Bushman M.C.
        • Owen L.R.
        • Mozzoli M.A.
        • Kendrick Z.
        • Boden G.H.
        A reappraisal of the caloric requirements of men.
        Am J Clin Nutr. 1987; 46: 875-885
        • Owen O.E.
        • Kavle E.
        • Owen R.S.
        • Polansky M.
        • Caprio S.
        • Mozzoli M.A.
        • Kendrick Z.V.
        • Bushman M.C.
        • Boden G.
        A reappraisal of caloric requirements in healthy women.
        Am J Clin Nutr. 1986; 44: 1-19
        • Food and Agricultural Organization/World Health Organization/United Nations University
        Energy and Protein Requirements. Report of a Joint FAO/WHO/UNU Expert Consultation World Health Organization Technical Report Series 724. WHO, Geneva, Switzerland1985
        • Mifflin M.D.
        • St Jeor S.T.
        • Hill L.A.
        • Scott B.J.
        • Daugherty S.A.
        • Koh Y.O.
        A new predictive equation for resting energy expenditure in healthy individuals.
        Am J Clin Nutr. 1990; 51: 241-247
        • Arciero P.J.
        • Goran M.I.
        • Gardner A.M.
        • Ades P.A.
        • Tyzbir R.S.
        • Poehlman E.T.
        A practical equation to predict resting metabolic rate in older men.
        Metabolism. 1993; 42: 950-957
        • Arciero P.J.
        • Goran M.I.
        • Gardner A.M.
        • Ades P.A.
        • Tyzbir R.S.
        • Poehlman E.T.
        A practical equation to predict resting metabolic rate in older females.
        J Am Geriatr Soc. 1993; 41: 389-395
        • DeLorenzo A.
        • Tabliabue A.
        • Andreoli A.
        • Testolin G.
        • Comelli M.
        • Deurenberg P.
        Measured and predicted resting metabolic rate in Italian males and females, aged 18–59 y.
        Eur J Clin Nutr. 2001; 55: 208-214
        • Frankenfield D.C.
        • Rowe W.A.
        • Smith J.S.
        • Cooney R.N.
        Validation of several established equations for resting metabolic rate in obese and nonobese people.
        J Am Diet Assoc. 2003; 103: 1152-1159
        • Garrel D.R.
        • Jobin N.
        • DeJonge L.H.M.
        Should we still use the Harris and Benedict equations?.
        Nutr Clin Pract. 1996; 11: 99-103
        • Liu H.Y.
        • Lu Y.F.
        • Chen W.J.
        Predictive equations for basal metabolic rate in Chinese adults.
        J Am Diet Assoc. 1995; 95: 1403-1408
        • Scalfi L.
        • Coltorti A.
        • Sapio C.
        • DiBiase G.
        • Borrelli R.
        • Contaldo F.
        Predicted and measured resting energy expenditure in healthy young women.
        Clin Nutr. 1993; 12: 1-7
        • Siervo M.
        • Boschi V.
        • Falconi C.
        Which REE prediction equation should we use in normal-weight, overweight and obese women?.
        Clin Nutr. 2003; 22: 193-204
        • Taaffe D.R.
        • Thompson J.
        • Butterfield G.
        • Marcus R.
        Accuracy of equations to predict basal metabolic rate in older women.
        J Am Diet Assoc. 1995; 95: 1387-1392
        • Case K.O.
        • Brahler C.J.
        • Heiss C.
        Resting energy expenditures in Asian women measured by indirect calorimetry are lower than expenditures calculated from prediction equations.
        J Am Diet Assoc. 1997; 97: 1288-1292
        • Clark H.D.
        • Hoffer L.J.
        Reappraisal of the resting metabolic rate of normal young men.
        Am J Clin Nutr. 1991; 53 (Comment in: Am J Clin Nutr. 1991; 54:613–614): 21-26
        • Daly J.M.
        • Heymsfield S.B.
        • Head C.A.
        • Harvey L.P.
        • Nixon D.W.
        • Katzeff H.
        • Grossman G.D.
        Human energy requirements.
        Am J Clin Nutr. 1985; 42: 1170-1174
        • Feurer I.D.
        • Crosby L.O.
        • Mullen J.F.
        Measured and predicted resting energy expenditure in clinically stable patients.
        Clin Nutr. 1984; 3: 27-34
        • Feurer I.D.
        • Crosby L.O.
        • Buzby G.P.
        • Rosato E.F.
        • Mullen J.L.
        Resting energy expenditure in morbid obesity.
        Ann Surg. 1983; 197: 17-21
        • Forman J.N.
        • Miller W.C.
        • Szymanski L.M.
        • Fernahll B.
        Differences in resting metabolic rates of inactive obese African-American and Caucasian women.
        Int J Obes Relat Metab Disord. 1998; 22 (Comment in: Am J Clin Nutr. 1998;67:740–741.): 215-221
        • Foster G.D.
        • Wadden T.A.
        • Mullen J.L.
        • Stunkard A.J.
        • Wag J.
        • Feurer I.D.
        • Pierson R.N.
        • Yang M.U.
        • Presta E.
        • Van Itallie T.B.
        Resting energy expenditure, body composition, and excess weight in the obese.
        Metabolism. 1988; 37: 467-472
        • Fredrix E.W.
        • Soeters P.B.
        • Deerenberg I.M.
        • Kester A.D.
        • von Meyenfeldt M.F.
        • Saris W.H.
        Resting and sleeping energy expenditure in the elderly.
        Eur J Clin Nutr. 1990; 44: 741-747
        • Hirano K.M.
        • Heiss C.J.
        • Olson K.E.
        • Beerman K.A.
        • Brahler C.J.
        A comparison of calculated and measured resting energy expenditure in obese women.
        Top Clin Nutr. 2001; 16 (85–88): 61-69
        • Pavlou K.N.
        • Hoefer M.A.
        • Blackburn G.L.
        Resting energy expenditure in moderate obesity. Predicting velocity of weight loss.
        Ann Surg. 1986; 203: 136-141
        • van der Ploeg G.E.
        • Withers R.T.
        Predicting the resting metabolic rate of 30–60-year old Australian males.
        Eur J Clin Nutr. 2002; 56: 701-708
        • Vermeij C.G.
        • Feenstra B.W.
        • Oomen A.M.
        • de Graaf E.J.
        • Zillikens M.C.
        • Swart G.R.
        • Bruining H.A.
        Assessment of energy expenditure by indirect calorimetry in healthy subjects and patients with liver cirrhosis.
        J Parenter Enteral Nutr. 1991; 15: 421-425
        • Schofield C.
        An annotated bibliography of source material for basal metabolic rate data.
        Hum Nutr Clin Nutr. 1985; 39C: 42-91
        • Schofield W.N.
        Predicting basal metabolic rate, new standards and review of previous work.
        Hum Nutr Clin Nutr. 1985; 39C: 5-41
        • James W.P.
        Basal metabolic rate.
        Hum Nutr Clin Nutr. 1985; 39C: 92-96
        • Hayter J.E.
        • Henry C.J.F.
        A re-examination of basal metabolic predictive equations.
        Eur J Clin Nutr. 1994; 48: 702-707
        • Gannon B.
        • DiPietro L.
        • Poehlman E.T.
        Do African Americans have lower energy expenditure than Caucasians?.
        Int J Obes. 2000; 24: 4-13
        • Foster G.D.
        • Wadden T.A.
        • Swain R.M.
        • Anderson D.A.
        • Vogt R.A.
        Changes in resting energy expenditure after weight loss in obese African American and white women.
        Am J Clin Nutr. 1999; 69: 13-17
        • Foster G.D.
        • Wadden T.A.
        • Vogt R.A.
        Resting energy expenditure in obese African American and Caucasian women.
        Obes Res. 1997; 5: 1-8
        • Kumanyika S.K.
        • Espeland M.A.
        • Bahnson J.L.
        • Bottom J.B.
        • Charleston J.B.
        • Folmar S.
        • Wilson A.C.
        • Whelton P.K.
        • TONE Cooperative Research Group
        Ethnic comparison of weight loss in the trial of nonpharmacologic interventions in the elderly.
        Obes Res. 2002; 10: 96-106
        • Kimm S.
        • Glynn N.W.
        • Aston C.E.
        • Damcott C.M.
        • Poehlman E.T.
        • Daniels S.R.
        • Ferrell R.E.
        Racial differences in the relation between uncoupling protein genes and resting energy expenditure.
        Am J Clin Nutr. 2002; 75: 714-719


      D. Frankenfield is chief clinical dietitian and nutrition support dietitian, Department of Clinical Nutrition, Milton S. Hershey Medical Center, Hershey, PA.


      L. Roth-Yousey is president, Lori Roth-Yousey Associates, North Branch, MN.


      C. Compher is assistant professor of nutrition science, Penn Nursing and Hospital of University of Pennsylvania Clinical Nutrition Support Service, Philadelphia.