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The Practical Use of Charts to Estimate Resting Energy Expenditure in Adults

St Jeor, Sachiko T. PhD, RD; Cutter, Gary R. PhD; Perumean-Chaney, Suzanne E. PhD; Hall, Scott J. MPH; Herzog, Holly MS, RD; Bovee, Vicki MS, RD

Clinical Nutrition Issues

Numerous predictive equations have evolved for the estimation of resting energy expenditure (REE) since it is not generally practical or feasible to use indirect calorimetry in practice settings. Recently, the Mifflin-St Jeor Equation (MSJE), derived from a population of 498 healthy subjects and published in 1990, was recommended as the standard for calculating REE by the Application of Indirect Calorimetry in ADA Evidence-Based Guides for Practice Expert Consensus Panel of the American Dietetic Association. Although no equation can replace the accuracy of a personalized indirect calorimetry measurement, this equation reportedly explained a substantial amount of the variation of the measured REE (R2 = 0.71) and provided the most accurate estimate (78% within ±10% limit) of actual REE in the largest percentage of nonobese and obese in a defined test population. Importantly, the MSJE was derived on a diverse sample of healthy adults (N = 498), including females (n = 247) and males (n = 251), subjects aged 19 to 78 (45 ± 14) years, and nonobese (n = 264) and obese (n = 234) individuals. This article presents 2 charts by gender from which an estimated REE can be easily derived from known height (inches) and weight (pounds) with an individual adjustment of −5 kcal/year of age. The charts are limited to the source sample from which the data were derived since the estimated REE for individuals falling within these parameters will be more accurate. Extrapolated REEs beyond this population are available and may be useful, but will require further study.

From the Division of Medical Nutrition, Department of Internal Medicine, University of Nevada School of Medicine, Reno, Nev (Jeor, Herzog, Bovee)

The Center for Research Design and Statistical Methods, University of Nevada, Reno, Nev. (Cutter, Perumean-Chaney, Hall)

The authors acknowledge the support of their teams involved in the development, testing, and completion of this work. These individuals included Mark D. Mifflin, MD, SLC, Utah; Barbara Scott, MPH, RD, Jessica Krenkel, MS, RD, and Jolyn Wirshing, RD from the Medical Nutrition Division; and Anapulaki Ragavan, MS from the Center for Research Design and Statistical Methods.

This work was partially supported by Grants # R01 34589 and K07 03972 from the National Heart Lung and Blood Institute, National Institutes of Health.

Corresponding author: Sachiko T. St Jeor, PhD, RD, Division of Medical Nutrition, Department of Internal Medicine, University of Nevada School of Medicine, Redfield Building, Mailstop 153, Reno, NV 89557 (e-mail:

THE measurement of energy expenditure is most accurately obtained by indirect calorimetry using a ventilated hood. 1 Direct measurements of oxygen consumed (VO2) and carbon dioxide exhaled (VCO2) are obtained and energy expended at rest is calculated employing the Weir equation [3.9 (mL/minVO2) + 1.1 (mL/minVCO2) 1.44] to arrive at energy expended at rest in kcal/d. 2 Basal metabolic rate (BMR) or basal energy expenditure (BEE) is the energy expended by the body in the resting state under basal conditions and is usually obtained in an inpatient setting in the morning after a night's rest and a 12 hour fast. 1 The resting energy expenditure (REE) or resting metabolic rate or (RMR) is generally determined under outpatient conditions and is reportedly 7% to 10% higher than the BMR/BEE. 3 Confusion arises, because all of these terms are used interchangeably in the literature to describe energy expenditure under resting conditions. The REE or RMR is currently the practical method of choice since most measurements for the assessment of weight are taken in the outpatient setting.

Numerous predictive equations have evolved for the estimation of energy expenditure since it has not been practical or feasible to utilize indirect calorimetry in general practice settings. The Harris-Benedict Equation (HBE), established in 1919, has been the most widely used predicted BEE, 4 although many applications were referred to as REE or RMR instead. Importantly, it should be noted that this equation was developed approximately 85 years ago on a population of 136 men (64 ± 10.3 kg; 27 ± 9 years) and 103 women (56.5 ± 11.5 kg; 31 ± 14 years) who were generally younger and leaner than the current population on which the equation is applied. Thus, the HBE has been known to overpredict basal energy requirements by 5%–15%. 5,6 However, it is also important to recognize that any predictive formula has limitations when applied to individuals in different settings. Thus, formulas specific to gender, 7,8 age, 9 weight, 10,11 and lean body mass, 12,13), as well as those who are critically ill 14 have been recommended. Further, the variability of methods and instruments that are used 15 pose further questions regarding generalizability of the predictive equations and introduction of systematic errors to other populations and individuals. 16 Additionally, new techniques using doubly labeled water to measure total energy expenditure (TEE) show new promise. 17

Recently, the Mifflin-St Jeor Equation (MSJE), derived from a population of 498 healthy subjects and published in 1990, 13 was recommended as the standard for calculating REE by the Application of Indirect Calorimetry in ADA Evidence-Based Guides for Practice Expert Consensus Panel of the American Dietetic Association. 18,19 Criteria for this recommendation included limiting the percentage of subjects whose calculated REE was outside a ±10% limit (error rate) from the measured values. The MSJE had the lowest error rate of 22% compared to the HBE at 33% and Owen equations at 35%. 18,19 Thus, the purpose of this article is to describe the derivation of the MSJE and present 2 charts (women and men) created from the database to simplify the use of the MSJE in practice. Applications for practice and future directions are also discussed.

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The MSJE equations were developed from data obtained from 498 healthy, free-living subjects participating in an 8-year, longitudinal investigation of the relationships of energy, nutrition, and obesity to cardiovascular disease risk (RENO Diet-Heart Study). 13 The sample consisted of measurements taken at baseline of 247 women, ranging in age from 20 to 76 years (44.6 ± 14.0 years) and 251 men ranging in age from 19 to 78 years (44.4 ± 14.3 years). Of the population, 264 (135 women and 129 men) were classified as nonobese and 234 (112 women and 122 men) were classified as obese. REE was obtained by indirect calorimetry taken with a metabolic measurement cart with a canopy hood (Metabolic Measurement Cart Horizons System, Sensor Medics, Anaheim, Calif) by trained and certified nutritionists using a standardized protocol. Subjects were instructed to fast and refrain from exercise for 12 hours before the test and to refrain from smoking before the test. Subjects were placed under the canopy hood in a relaxed, supine position and a standardized relaxation tape was played. Measurements were repeated on all subjects until a 3-minute steady state was achieved. The entire test took approximately 20 minutes to complete. Standard computer programs converted measured O2 and CO2 gas exchange into REE (kcal/d). Body weight to the nearest 0.5 kg was determined before the REE measurement on a standard physician's beam scale with the subject in street clothes and without shoes. Height was measured to the nearest 0.5 cm on a standardized, wall-mounted height board according to established protocol (without shoes; heels together; subject's heels, buttocks, shoulders, and head touching the vertical wall service and with the line of sight aligned horizontally).

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Multiple regression analyses were employed to derive the relationships between REE and weight, height, age, and gender. The derived predictive equation (MSJE) accounted for a substantial portion of the variation in measured REE (R2 = 0.71):

These formulas were then used to create Table 1: Women and Table 2: Men using the mean of 2 in and 10 lb increments. The tables report the mean REE for that height/weight specific range by gender. As age was not included, each value must be age adjusted by −5 kcal/y of age to derive the estimated REE.

Table 1

Table 1

Table 2

Table 2


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The applicability of the charts was tested during the normal clinical routine for 59 patients including 46 women (aged 22–77 years, weight 142 to 406 lb, and height 62 to 71 in) and 13 men (aged 34 to 73 years, weight 185 to 310 lb, and height 68 to 75 in). The accuracy of the charts in predicting REE was compared to the REE derived from the MSJE. The difference in REE ranged from −26 to +41 kcal/d for women and −15 to +35 kcal/d for men. This reflects an overall potential difference between the 2 methods of 0.04% for women and 0.02% for men of the mean kcal/d and is considered to be insignificant. It is also important to recognize that in this clinical population, extrapolations for weight needed to be made, as the weight of 8 women exceeded the upper limit of 269 lb.

The individualized assessment of energy balance offers new directions for the treatment of obesity and weight-related problems. New methods for the estimation of total energy intake (TEI) and TEE have demonstrated utility in the practice setting. Dietary recalls, food records, and food frequencies have been traditionally used by experienced interviewers to estimate food intake (kcal/d) or TEI. 1 Data input and analyses have been burdensome, but computerized programs have encouraged interest and use both at the professional and consumer level. Although, the accuracy of estimates of TEI continue to be debated, estimates within a ±10% limit have been generally expected and useful in clinical applications. The estimation of TEE has been more problematic. Of the TEE, the BMR/BEE comprises approximately 60% to 75%, physical activity (PA) comprises approximately 30%, and the thermic effect of food (TEF) or specific dynamic action of food (SDA) comprises approximately 10%. 13,18 Because PA is difficult to accurately measure, the REE can be multiplied by an activity factor depending on the individual's level of activity.

Thus, it seems reasonable to apply REE utilizing charts or predictive equations that fall within the ±10% error rate. In our practice, 20 to arrive at the TEE, we generally employ a factor of 1.3 × REE for sedentary individuals and have individually adjusted the total kcal/d for intentional activity (such as walking at 5 kcal/min or 100 kcal/mile) or exercise (aerobics, swimming, etc from approximately 5 to 12 kcal/min × duration) to arrive at the average added kcal/d from intentional activity. Further, because we use REE/RMR in lieu of the BEE/BMR, we do not further adjust for the TEF/SDA since we assume an additional approximately 10% inherent difference.

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Using the energy balance equation to guide decisions in weight management is long overdue. More personalized and effective diet and activity prescriptions can be made and tailored to individualized needs and preferences. Although, it is recognized that no predictive equation can replace the applicability of a personalized indirect calorimetry measurement, estimates of REE can be routinely used to guide our recommendations and more adequately assess outcomes. The variability of many known and unknown factors which significantly affect both TEE (including the REE) and TEI should not discourage the use of the energy balance equation in nutrition assessment. Alternatively, the limitations of the methods and results should be appreciated and future studies are encouraged to better refine our methodology. In the meantime, charts and predictive equations should encourage the application of REE in general practice. Thus, we have made available Tables 1 and 2 from our source sample from which the MSJE was derived. However, charts are also available that accommodate a larger range of weights and heights for application with our general clinical population. The accuracy and practicality for use will await further studies and reports of application in practice. New predictive equations for REE using height adjustments and BMI are being developed.

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1. Jequier E. Measurement of energy expenditure in clinical nutritional assessment. J Parenter Enternal Nutr. 1987;11:86S–89S.
2. Weir JR. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1–9.
3. Taaffe DR, 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.
4. Harris JA, Benedict FG. A Biometric Study of Basal Metabolism in Man. Washington, DC: Carnegie Institution of Washington; 1919. Carnegie Institute of Washington publication 279.
5. Daly JM, Heymsfield SB, Head A, et al. Human energy requirements: overestimation by widely used prediction equation. Am J Clin Nutr. 1985;42:1170–1174.
6. Frankenfield DC, Muth ER, Rowe WA. The Harris-Benedict studies of human basal metabolism: history and limitations. J Am Diet Assoc. 1998;98:439–445.
7. Owen OE, Kalve E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr. 1986;44:1–19.
8. Owen OE, Holup JL, D'Alessio DA, et al. A reappraisal of caloric requirements of men. Am J Clin Nutr. 1987;46:75–85.
9. Arciero PJ, Goran MI, Gardner AM, Ades PA, Tyzbir RS, Poehlman ET. A practical equation to predict resting metabolic rate in older females. J Am Geriatr Soc. 1993;41:389–395.
10. Heshka S, Feld K, Yang MU, Allison DB, Heymsfield SB. Resting energy expenditure in the obese: a cross-validation and comparison of prediction equations. J Am Diet Assoc. 1993;93:1031–1036.
11. Luhrmann PM, Herbert BM, Krems C, Neuhauser-Berthold M. A new equation especially developed for predicting resting metabolic rate in the elederly for easy use in practice. Eur J Nutr. 2002;41:108–113.
12. Cunningham JJ. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr. 1991;54:963–969.
13. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–247.
14. Flancbaum L, Choban PS, Sambucco S, Verducci J, Burge JC. Comparison of indirect calorimetry, the Fick method, and prediction equations in estimating the energy requirements of critically ill patients. Am J Clin Nutr. 1999;69:461–466.
15. Soares MJ, Sheela ML, Kurpad AV, Kulkarni RN, Shetty PS. The influence of different methods on basal metabolic rate measurements in human subjects. Am J Clin Nutr. 1989;50:731–736.
16. Wang Z, Hesjla S, Zjamg L, Bppzer CM. Resting energy expenditure: systematic organization and critique of prediction methods. Obes Res. 2001;9:331–336.
17. Vinken AG, Bathalon GP, Sawaya AL, Dallal GE, Tucker KL, Roberts SB. Equations for predicting the energy requirements of healthy adults aged 18–81 y. Am J Clin Nutr. 1999;69:920–926.
18. Frankenfield DC, Rowe WA, Smith S, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc. 2003;103:1152–1159.
19. Expert Consensus Panel for the Application of Indirect Calorimetry in ADA Evidence-Based Guides for Practice. Let the Evidence Speak: Indirect Calorimetry and Weight Management Guides. Accuracy of Determining Energy Expenditure in Healthy and Ill Individuals. A systematic Review. Tex: San Antonio, FNCE, American Dietetic Association, October 27, 2003.
20. Plodkowski RA, St Jeor ST. Medical nutrition therapy for the treatment of obesity. Endocrinol Metab Clin North Am. In press.

energy balance; indirect calorimetry; nutrition assessment; obesity; predictive equations; resting energy expenditure

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