AJCN North Carolina Research Campus
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental data
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Google Scholar
Right arrow Articles by Weijs, P. J.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Weijs, P. J.
Agricola
Right arrow Articles by Weijs, P. J.
American Journal of Clinical Nutrition, Vol. 88, No. 4, 959-970, October 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18–65 y1,2,3

Peter JM Weijs

1 From the Department of Nutrition and Dietetics, Hogeschool van Amsterdam, University of Applied Science, and the Department of Nutrition and Dietetics, VU University Medical Center, Amsterdam, Netherlands

2 Supported by the Hogeschool van Amsterdam, Amsterdam, Netherlands.

3 Address correspondence to PJM Weijs, Department of Nutrition and Dietetics, Hogeschool van Amsterdam, University of Applied Science, Dr Meurerlaan 8, 1067 SM Amsterdam, Netherlands. E-mail: p.j.m.weijs{at}hva.nl.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry.

Objective: The objective was to analyze which resting energy expenditure (REE) predictive equation was the best alternative to indirect calorimetry in US and Dutch adults aged 18–65 y with a body mass index (in kg/m2) of 25 to 40.

Design: Predictive equations based on weight, height, sex, age, fat-free mass, and fat mass were tested. REE in Dutch adults was measured with indirect calorimetry, and published data from the Institute of Medicine were used for US adults. The accuracy of the equations was evaluated on the basis of the percentage of subjects predicted within 10% of the REE measured, the root mean squared prediction error (RMSE), and the mean percentage difference (bias) between predicted and measured REE.

Results: Twenty-seven predictive equations (9 of which were based on FFM) were included. Validation was based on 180 women and 158 men from the United States and on 154 women and 54 men from the Netherlands aged <65 y with a body mass index (in kg/m2) of 25 to 40. Most accurate and precise for the US adults was the Mifflin equation (prediction accuracy: 79%; bias: –1.0%; RMSE: 136 kcal/d), for overweight Dutch adults was the FAO/WHO/UNU weight equation (prediction accuracy: 68%; bias: –2.5%; RMSE: 178), and for obese Dutch adults was the Lazzer equation (prediction accuracy: 69%; bias: –3.0%; RMSE: 215 kcal/d).

Conclusions: For US adults aged 18–65 y with a body mass index of 25 to 40, the REE can best be estimated with the Mifflin equation. For overweight and obese Dutch adults, there appears to be no accurate equation.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The prevalence of overweight and obesity is high and increasing (1, 2). Any weight-reduction program will try to establish a reachable goal for weight loss and a reachable goal for dietary intake. This requires knowledge of individual energy requirements and relies on accurate methods of assessment. Because the gold standard, indirect calorimetry, is hardly feasible in most dietetic settings, it remains important to use the most accurate predictive equation to determine resting energy expenditure (REE) in overweight and obese persons (3).

Predictive equations have generally been developed in healthy subjects on the basis of regression analysis of body weight, height, sex, and age as independent variables and measured REE by indirect calorimetry as a dependent variable. On the basis of a comparison of published evidence from Harris and Benedict (4), FAO/WHO/UNU weight or weight and height equations (5), and the equations of Mifflin (6) and Owen (7, 8), Frankenfield et al (9) have advised the use of the Mifflin equation for overweight and obese subjects. However, this expert panel also acknowledges that there are limited data to support the use of the Mifflin equation in overweight and obese subjects.

The level of overweight might be an important factor in the accuracy of the predictive equation, but the level of overweight varies among studies. For most equations, overweight and obese subjects were included, but their relative contribution to the final equation often remains unclear. Therefore, validation of predictive equations should be performed in specific overweight and obese groups of subjects. Recent evaluations of the validity of REE predictive equations have been published for overweight and obese subjects (10–13) and for extremely obese subjects with a body mass index (BMI; in kg/m2) >40 (14–18). Only a few studies have validated equations for a clearly defined overweight group (BMI = 25–30) (12) or obese group (BMI = 30–40) (10).

The range of published REE predictive equations has not been validated, including equations based on body composition [fat-free mass (FFM) and fat mass (FM)]. As part of evidence-based practice, the literature was systematically searched for REE predictive equations, and subsequently included REE equations were validated with indirect calorimetry data from adults aged 18–65 y with a BMI of 25 to 40 to find the most accurate and precise REE predictive equation. To prevent an overgeneralization of our conclusions, all equations were applied to both US (published) and Dutch (new) data.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
For the US group, indirect calorimetry data were obtained from the National Academy of Sciences report on Dietary Reference Intakes (19). Only adults aged 18–65 y with a BMI of 25 to 40 were included.

The Dutch subjects were included from different weight-loss studies at the Nutrition Lab of the Department of Nutrition and Dietetics, Hogeschool van Amsterdam, University of Applied Science, Amsterdam, Netherlands between 3 April 2006 and 27 March 2008. Inclusion criteria for the original weight-loss studies were a BMI > 25 and an age of 18–65 y. Data were only included when the subject's BMI at the time of indirect calorimetry was between 25 and 40. All participants gave informed consent. All procedures were in accordance with the ethical standards of the institution.

Indirect calorimetry and anthropometric measures
The indirect calorimetry measurements were performed with a ventilated hood system (Vmax Encore n29; Viasys Healthcare, Houten, Netherlands), which was calibrated for volume and with 2 standard gases every day before use. Additionally, the ventilated hood system was automatically recalibrated every 5 min. Measurements were standardized by internal guidelines. The subjects were in a supine position and awake and had fasted overnight or for ≥4 h before the measurement was made if the measurements could not be performed before noon. Subjects had not been physically active. Oxygen consumption and carbon dioxide production were measured, and energy expenditure was calculated by using the Weir formula (20). The measurements took 30 min, and only steady state periods of measurement were selected according to the procedures for the ventilated hood system. The first 5 min of the measurements were discarded. An acceptable CV was 10%. Body weight, FFM, and FM were assessed by using the BodPod system (Life Measurement Inc, Concord, CA). BodPod was calibrated immediately before each measurement. Height was measured by using a stadiometer (Seca 222; Seca, Hamburg, Germany).

REE predictive equations
PubMed was used for a systematic search for publications on Mesh-derived keys ‘Energy metabolism', ‘Basal metabolism’, and ‘Indirect calorimetry’ and additional terms ('predict*’, ‘estimat*', ‘equation*’, and ‘formula*’) in every possible combination. Applied limitations were 'english language’ and ‘humans’ and age of ≥18 y. More references were obtained by screening publications cited. Only equations developed in adults were retrieved.

Inclusion criteria were as follows: equations based on body weight, height, age, sex and/or FFM and FM. Exclusion criteria were as follows: age range (only young adults or only elderly), (critically ill) patients, mean BMI < 25 (indication of small proportion of overweight and obese; not applicable to large databases of Harris and Benedict, Schofield, and Oxford), insufficient information, specific ethnic group, small sample size (n < 50), impractical or suspect body composition as variable (including percentage ideal body weight), plasma values of glucose or insulin or diabetes as variable, suspect indirect calorimetry, total energy expenditure, athletes, duplicate publications.

From each included study the best performing equations, based on the highest value for explained variance (R2), were included. However, extra equations were included when based on weight and height (versus weight only) or FFM and also when equations were BMI group specific (different equations for BMI = 25–30 and BMI = 30–40). After this selection, the studies were judged for the methodologic quality of the calorimetry procedure as recently described by Frankenfield et al (21) and Compher et al (22).

For each patient the REE was predicted for all equations in kilocalories per day and compared with measured REE. The actual body weight or FFM at the time of the indirect calorimetry measurement was used for this calculation.

Statistics
Subject characteristics were analyzed with an independent-samples t test. A prediction between 90% and 110% of the REE measured was considered an accurate prediction, a prediction <90% of the REE measured was classified as an underprediction, and a prediction >110% of the REE measured was classified as an overprediction. The percentage of patients that had an REE predicted within ±10% of the REE measured was considered a measure of accuracy on an individual level (21). The mean percentage difference between the REE predicted and that measured (bias) was considered a measure of accuracy on a group level. The root mean squared prediction error (RMSE) was used to indicate how well the model predicted in our data set (23–25). The concordance correlation coefficient (CCC) was used to show the precision and bias of the predictive equations (26). The CCC is calculated by multiplying precision (Pearson correlation coefficient) by accuracy (deviation from line of identity). Data were analyzed by using SPSS 14.0 (SPSS Inc, Chicago, IL), CCC, and Bland-Altman with MedCalc software (version 8.0.2.0; Mariakerke Belgium).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The subject characteristics for the 239 US and 208 Dutch adults with a BMI of 25–40 are shown in Table 1Go. The Dutch adults were slightly younger than the US adults (women: P = 0.06; men: P = 0.09). Weight and height were significantly higher in the Dutch than in the US adults, even within female and male overweight (BMI = 25–30) and obese (BMI = 30–40) subgroups. REE (kcal/d) was {approx}200 kcal higher in the Dutch than in the US adults. Unfortunately, no body composition data for the US adults were available for further evaluation of REE (in kcal /kg FFM).


View this table:
[in this window]
[in a new window]

  		TABLE 1 Subject characteristics1

 
A total of 63 scientific papers or reports were retrieved for adult REE predictive equations. Forty-eight papers were excluded (see Table under "Supplemental data" in the online issue): age range, 10; patients, 10; insufficient information, 5; ethnic group, 5; small sample size (n < 50); impractical or suspect body composition as variable, 4; glucose, insulin, or diabetes as a variable, 3; suspect indirect calorimetry, 3; total energy expenditure, 1; athletes, 1; mean BMI < 25, 1; and duplicate publications with same data and same equation, 1. Fifteen papers or reports were included with 27 equations, 18 weight-based equations and 9 FFM-based equations (Table 2Go. The studies included had >100 subjects.


View this table:
[in this window]
[in a new window]

  		TABLE 2 Predictive equations for resting energy expenditure1

 
The quality of these studies according to the procedure of Frankenfield et al (21) resulted in no further exclusion. Studies based on the Harris and Benedict, Schofield, and Oxford databases were used as published, but, for smaller studies, information on standardization, calibration, and steady state is provided (Table 3Go). When judged by evidence-based guidelines for REE measurement with indirect calorimetry (22), reported criteria were always met but unfortunately were not always reported. Mifflin et al (6) reported the shortest measuring time (20 min) and steady state time used (3 min). The absence of bias in subject selection and subject training were rarely found. None of the included equations were based on Dutch adults.


View this table:
[in this window]
[in a new window]

  		TABLE 3 Indirect calorimetry conditions for the included studies

 
REE data are provided as kcal/d, the percentage bias, the maximum values found for negative error (underprediction) and positive error (overprediction), the RMSE (in kcal/d), the percentage of accurate predictions, the percentage of underpredictions, and the percentage of overpredictions (Table 4Go and Table 5Go).


View this table:
[in this window]
[in a new window]

  		TABLE 4 Evaluation of resting energy expenditure (REE) predictive equations in 239 US adults based on bias, root mean squared prediction error (RMSE), and percentage accurate prediction

 

View this table:
[in this window]
[in a new window]

  		TABLE 5 Evaluation of resting energy expenditure (REE) predictive equations in 208 Dutch adults based on bias, root mean squared prediction error (RMSE), and percentage accurate prediction

 
The percentage of accurate predictions, percentage bias, and RMSE for overweight women, obese women, overweight men, and obese men (further referred to as sex and BMI subgroups) for US and Dutch adults are shown in Figure 1Go.


Figure 1
View larger version (23K):
[in this window]
[in a new window]

  		FIGURE 1. Percentage of accurate predictions, percentage bias, and root mean squared prediction error (RMSE) for US (left panels) and Dutch (right panels) adults (with 95% CIs), overweight women ({circ}), obese women (Figure 1), overweight men ({square}), and obese men (Figure 1) for 18 resting energy expenditure predictive equations. For each panel the data are sorted by values for all adults combined (line). See Table 2Go for predictive equations.

 
For US adults, the equation of Mifflin et (6) provided 79% accurate predictions, 11% underpredictions, and 9% overpredictions and performed well across sex and BMI subgroups; the RMSE was 136 kcal/d, and the percentage bias was –1.0%. For overweight Dutch adults, the FAO/WHO/UNU weight equation (5) provided 64% and 80% accurate predictions for women and men, respectively. For obese Dutch adults, the Lazzer equation (16, 34) provided 68% and 72% accurate predictions for women and men, respectively. Only the De Lorenzo equation (33) provided >60% accurate predictions for all sex and BMI subgroups (overall, 65% accurate predictions). The percentage of accurate predictions varied from 79% to 23% for US adults and from 64% to 13% for Dutch adults. The bias for equations varied from –15% to 9% for the US adults and from –20% to 3% for the Dutch adults, and RMSE varied from 136 to 298 kcal/d for US adults and from 193 to 471 kcal/d for Dutch adults (Figure 1Go).

FFM provided no benefit to REE prediction (Figure 2Go and Table 5Go). Body-composition methods were very different among the studies (Table 2Go). However, even the use of air-displacement plethysmography (32), consistent with the present Dutch study group, did not improve REE prediction. The inclusion of height or BMI-specific equations did not improve the percentage of accurate predictions (Figure 2Go).


Figure 2
View larger version (22K):
[in this window]
[in a new window]

  		FIGURE 2. Comparison of percentage accurate predictions for weight-based compared with fat-free mass (FFM)–based resting energy expenditure predictive equations and for weight-based or BMI-independent compared with weight- and height-based or BMI-specific REE predictive equations for US and Dutch adults.

 
Bland-Altman plots for 6 selected equations are shown in Figure 3Go.


Figure 3
View larger version (18K):
[in this window]
[in a new window]

  		FIGURE 3. Bland-Altman plots for 6 selected resting energy expenditure (REE) predictive equations (Mifflin, Owen, Lazzer, De Lorenzo, FAO/WHO/UNU, HB1919) for overweight women ({circ}), obese women (•), overweight men ({square}), and obese men ({blacksquare}).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
From this study it appears that REE for US overweight and obese class I and II adults can best be predicted with the Mifflin equation (6). For Dutch overweight adults, the FAO/WHO/UNU weight equation (5) can be used with reasonable accuracy up to a BMI of 30. However, for Dutch obese adults with a BMI of 30–40, the Lazzer equation (16, 34) provides improved accuracy. Whereas the Mifflin equation provides almost 80% accurate equations for US adults, this level of accuracy cannot be reached with presently available equations for Dutch adults. Assessment of FFM and FM did not improve the accuracy of REE prediction. Because any choice is less than optimal for the Dutch, the best way to proceed would be to produce a new equation.

This extends a previous observation in Dutch patients, which showed that the FAO/WHO/UNU weight and height (FAOwh) equations (5) was most accurate (23), although only {approx}50% of patients had accurate predictions. In this previous validation study, there were not enough overweight and obese patients available to establish the accuracy for a BMI of 25–40.

A recent review by an expert panel (9) advised that the Mifflin equation be used for overweight and obese subjects. However, this expert panel acknowledges that there are limited data to support the use of the Mifflin equation in overweight and obese subjects. In this review, the evidence for the accuracy of the Mifflin and Owen equations in overweight and obese subjects is based on one study by Frankenfield et al (10). Six studies were used to validate the original Harris and Benedict equation, of which 2 studies were of suboptimal quality, 1 had an unclear BMI range, and the other 3 were from Owen et al (7, 8) and Frankenfield et al (10). No individual accuracy studies for overweight and obese subjects were used to validate the FAOw and FAOwh equations (5). The present study essentially confirms the conclusion by Frankenfield et al (10) for US adults with a BMI of 25–40 and an age <65 y and now provides the strongest evidence for use of the Mifflin equation in the United States. Because African American females were found to have lower REE values than European American females (36, 37), ethnicity should be addressed. The higher REE values for the Dutch than for the US adults are probably due to the higher weight and height values, even within sex and BMI subgroups. If the higher values are due to the Dutch being taller, the low accuracy level might be true for other countries with "tall" adults.

Recent evaluations of the validity of REE predictive equations have been published for overweight and obese subjects (10–13) and for extremely obese subjects (14–18). There is some support for using the Mifflin equation in European American females (36), males (29), and extremely obese females (17). The FAOw equation has been acceptable in females (29) and in extremely obese subjects (14, 16). Also, the HB1919 equation has been found to be acceptable in persons with a broad weight range (12) and in extremely obese persons (14, 16). However, there seems to be no consensus concerning the use of one preferred REE equation. This might be explained by differences in subject group composition, methods used, or the statistics used, for example.

Different statistical methods may have been used to evaluate how well an equation fits the data. Bias, correlation, and regression analysis are not preferred methods for validating equations (25). Suitable methods include the RMSE and an individual measure of accuracy, such as the percentage of subjects predicted within 10% of the measured value. Additionally, the bias obviously has to be small in order for a predictive equation to perform well. However, a statistical comparison (t test or ANOVA with a post hoc test) indicating a nonsignificant difference between group means is not the same as an accurate fit, because high positive errors might counterbalance high negative errors.

The methods used for the equation development studies might also have differed (Table 3Go). Differences in subject selection, subject training, and measurement conditions should be considered. Subject selection refers to the representativeness of the study group relative to the population for which the equation should be used. In the present study, data from Dutch adults that intended to lose weight and agreed to the study procedures were included. This group is not representative of the whole Dutch population and certainly not of the different ethnic groups. However, it seems reasonable to use this group as an approximation for the (white) Dutch adults with a BMI of 25–40 and <65 y of age. The US data might be more representative of the US population with a BMI of 25–40 and <65 y of age. Subject training was not reported in most studies, as in the present study, although the procedures had been explained to the subjects beforehand. The effect of stress (activation of the sympathetic nervous system) might be minimized by collecting steady state data, although this might not fully account for the problem. Because Mifflin had the shortest time of measurement and of steady state duration, it might be preferable not to extend the measurement time beyond 20 min because some subjects get restless. Another factor that might affect REE data is the time of day. However, when measurement conditions as defined by Compher et al (22) are adhered to, this should not be of major concern (38).

Inclusion of height into the equation does not systematically improve REE prediction for overweight and obese adults (Table 5Go), although this has been observed for a group of patients of whom 50% were underweight (BMI < 18.5) (23). However, the Mifflin and Lazzer equations both include height as a variable. The FAO/WHO/UNU report (5) showed no statistical advantage of height inclusion; however, there was no evaluation of BMI groups in this 1985 report. Furthermore, there was no improvement in REE prediction by FFM assessment. Müller et al (12) showed that, even for BMI groups, there is no advantage to using FFM-based equations. Korth et al (32) showed that although weight explains less variance in REE than FFM does, inclusion of weight, height, age, and sex together explain a similar amount of variance in REE. This observation is important because weight-based equations are more likely to be used in clinical practice than are FFM-based equations, although there might be other sound reasons for body-composition analysis in weight treatment.

It is advisable to validate REE prediction equations for any single specific population, because prediction equations are expected to be valid for the original population only (39). This is especially true for individual accuracy: the percentage of accurate predictions.

In conclusion, this study showed that there is a wide variation in the accuracy of REE predictive equations. For overweight and obese class I and II US adults, almost 80% could be accurately predicted with the Mifflin equation. For Dutch adults, however, there is no single accurate REE prediction equation. For now, the FAO/WHO/UNU weight equation can be used for overweight adults, and the Lazzer equation for obese subjects. Body-composition assessment is not needed for REE prediction. Whether these equations will also be best for obese patients remains to be assessed.


   ACKNOWLEDGMENTS
 
Thanks to Marcel Hesseling (data management), Monique Dekker, Judith Huisman, Fiona van Donselaar, Mieke Kramer, Annemieke Schaap, Ankie van Baar, Renske Strikwerda, Linda Sluijmers, Dennis Bast, Ryanne Zomer, Willemijn Wissekerke, Johanna van Zoonen, Ilona Sonke, and Bianca Ooteman for collecting the data and to Aimee van Dijk, Hinke Kruizenga, and Ageeth Hofsteenge for collecting part of the data for the predictive equations. The author's responsibilities were as follows—designed the study, performed the literature search, conducted the data analysis, and wrote the manuscript. The author had no conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA 2006;295:1549–55.[Abstract/Free Full Text]
  2. Schokker DF, Visscher TL, Nooyens AC, van Baak MA, Seidell JC. Prevalence of overweight and obesity in the Netherlands. Obes Rev 2007;8:101–8.[Medline]
  3. Schoeller DA. Making indirect calorimetry a gold standard for predicting energy requirements for institutionalized patients. J Am Diet Assoc 2007;107(3):390–2.[Medline]
  4. Harris JA, Benedict FG. A biometric study of basal metabolism in man. Washington, DC: Carnegie Institute of Washington, 1919.
  5. FAO/WHO/UNU. Energy and protein requirements. Geneva, Switzerland: World Health Organ Tech Rep Ser, 1985.
  6. 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;51:241–7.[Abstract/Free Full Text]
  7. Owen OE, Kavle E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr 1986;44:1–19.[Abstract/Free Full Text]
  8. Owen OE, Holup JL, D'Alessio DA, et al. A reappraisal of the caloric requirements of men. Am J Clin Nutr 1987;46:875–85.[Abstract/Free Full Text]
  9. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc 2005;105:775–89.[Medline]
  10. Frankenfield DC, Rowe WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc 2003;103:1152–9.[Medline]
  11. Siervo M, Boschi V, Falconi C. Which REE prediction equation should we use in normal-weight, overweight and obese women? Clin Nutr 2003;22(2)193–204.[Medline]
  12. Müller MJ, Bosy-Westphal A, Klaus S, et al. World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure. Am J Clin Nutr 2004;80:1379–90.[Abstract/Free Full Text]
  13. De Luis DA, Aller R, Izaola O, Romero E. Prediction equation of resting energy expenditure in an adult Spanish population of obese adult population. Ann Nutr Metab 2006;50:193–6.[Medline]
  14. Das SK, Saltzman E, McCrory MA, et al. Energy expenditure is very high in extremely obese women. J Nutr 2004;134:1412–6.[Abstract/Free Full Text]
  15. Huang KC, Kormas N, Steinbeck K, Loughnan G, Caterson ID. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res 2004;12:840–5.[Medline]
  16. Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A. Prediction of resting energy expenditure in severely obese Italian women. J Endocrinol Invest 2007;30(1):20–7.[Medline]
  17. Dobratz JR, Sibley SD, Beckman TR, et al. Predicting energy expenditure in extremely obese women. JPEN J Parenter Enteral Nutr 2007;31(3):217–27.[Abstract/Free Full Text]
  18. Boullata J, Williams J, Cottrell F, Hudson L, Compher C. Accurate determination of energy needs in hospitalized patients. J Am Diet Assoc 2007;107(3):393–401.[Medline]
  19. National Academy of Sciences. Dietary Reference Intakes for energy, carbohydrates, fiber, fat, protein and amino acids (macronutrients). Washington, DC: National Academy of Sciences, 2002.
  20. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949;109:1–9.[Free Full Text]
  21. Frankenfield D, Roth-Yousey L, Compher C, for the Evidence Analysis Working Group. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc 2005;105(5):775–89.[Medline]
  22. Compher C, Frankenfield D, Keim N, Roth-Yousey L, for the Evidence Analysis Working Group. Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. J Am Diet Assoc 2006;106(6):881–903.[Medline]
  23. Weijs PJM, Kruizenga HM, van Dijk AE, et al. Validation of predictive equations for resting energy expenditure in adult outpatients and inpatients. Clin Nutr 2008;27(1):150–7.[Medline]
  24. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th ed. New York, NY: McGraw-Hill/Irwin, 2005.
  25. Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm 1981;9:503–12.[Medline]
  26. Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:255–68.[Medline]
  27. Roza AM, Shizgal HM. The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass. Am J Clin Nutr 1984;40:168–82.[Abstract/Free Full Text]
  28. Bernstein RS, Thornton JC, Yang MU, et al. Prediction of the resting metabolic rate in obese patients. Am J Clin Nutr 1983;37:595–602.[Abstract/Free Full Text]
  29. Livingston EH, Kohlstadt I. Simplified resting metabolic rate—predicting formulas for normal-sized and obese individuals. Obes Res 2005;13:1255–62.[Medline]
  30. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39C:5–41.[Medline]
  31. Henry CJK. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 2005;8:1133–52.[Medline]
  32. Korth O, Bosy-Westphal A, Zschoche P, Glüer CC, Heller M, Müller MJ. Influence of methods used in body composition analysis on the prediction of resting energy expenditure. Eur J Clin Nutr 2007;61(5):582–9.[Medline]
  33. De Lorenzo A, Tagliabue 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;55:208–14.[Medline]
  34. Lazzer S, Agosti F, Resnik M, Marazzi N, Mornati D, Sartorio A. Prediction of resting energy expenditure in severely obese Italian males. J Endocrinol Invest 2007 Oct;30(9):754–61.[Medline]
  35. Johnstone AM, Rance KA, Murison SD, Duncan JS, Speakman JR. Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects. Eur J Clin Nutr 2006;60:1437–44.[Medline]
  36. Vander Weg MW, Watson JM, Klesges RC, Eck Clemens LH, Slawson DL, McClanahan BS. Development and cross-validation of a prediction equation for estimating resting energy expenditure in healthy African-American and European-American women. Eur J Clin Nutr 2004;58:474–80.[Medline]
  37. Douglas CC, Lawrence JC, Bush NC, Oster RA, Gower BA, Darnell BE. Ability of the Harris-Benedict formula to predict energy requirements differs with weight history and ethnicity. Nutr Res 2007;27:194–9.
  38. Weststrate JA, Weys PJM, Poortvliet EJ, Deurenberg P, Hautvast JGAJ. Diurnal variation in postabsorptive resting metabolic rate and diet-induced thermogenesis. Am J Clin Nutr 1989;50:908–14.[Abstract/Free Full Text]
  39. Moreira da Rocha EE, Alves VGF, Silva MHN, Chiesa CA, da Fonseca RBV. Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice? Curr Opin Clin Nutr Metab Care 2005;8:319–28.[Medline]
Received for publication November 28, 2007. Accepted for publication June 25, 2008.





This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental data
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Google Scholar
Right arrow Articles by Weijs, P. J.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Weijs, P. J.
Agricola
Right arrow Articles by Weijs, P. J.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS