A Biometric Study of Human Basal Metabolism

Harris, J. Arthur; Benedict, Fräncis G.

doi:10.1073/pnas.4.12.370

Cited by 2,231 publications

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“…Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R 0.893, SEE 433 kJaday) and that predicted from skinfold thicknesses (R 0.886, SEE 440 kJaday). The regression equations of Harris & Benedict (1919) and Scho®eld (1985) all overestimated the mean RMR of our subjects by 518 ± 600 kJaday (P`0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically signi®cant errors at the lower end of our range of measurement.…”

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confidence: 56%

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Predicting the resting metabolic rate of young Australian males

et al. 2001

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Objectives: The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) crossvalidate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values.Design: A power analysis demonstrated that 38 subjects would enable us to detect (a 0.05, power 0.80) statistically and physiologically signi®cant differences of 8% between our predictedameasured RMRs and those predicted from the equations of other investigators. Subjects: Thirty-eight males (X AE s.d.: 24.3 AE 3.3 y; 85.04 AE 13.82 kg; 180.6 AE 8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. Interventions: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A multiple regression equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJaday. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R 0.893, SEE 433 kJaday) and that predicted from skinfold thicknesses (R 0.886, SEE 440 kJaday). The regression equations of Harris & Benedict (1919) and Scho®eld (1985) all overestimated the mean RMR of our subjects by 518 ± 600 kJaday (P`0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically signi®cant errors at the lower end of our range of measurement. Conclusions: Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-yold Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.

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confidence: 56%

“…The two most frequently used RMR prediction equations in Australia are those of Harris & Benedict (1919) and Scho®eld (1985). The former were generated on 239 adult American males and females and published in 1919.…”

Section: Introductionmentioning

confidence: 99%

Predicting the resting metabolic rate of young Australian males

et al. 2001

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“…The importance of estimating energy requirements has been well documented and many researchers have attempted to develop prediction equations for use when direct measurement is not possible (Harris & Benedict, 1919;Schofield, 1985;Owen et al, 1986Owen et al, , 1987Mifflin et al, 1990;Vinken et al, 1999). This study aimed to determine current methods used by Australian dietitians for estimating energy requirements of patients and the variability of the outcomes of the calculations.…”

Section: Discussionmentioning

confidence: 99%

Variation in the application of methods used for predicting energy requirements in acutely ill adult patients: a survey of practice

Reeves

Capra

2003

Eur J Clin Nutr

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Objective: To determine current methods used by dietitians for estimating the energy requirement of a chronically and acutely ill adult patient and the variation in the application of methods. Design: A cross-sectional survey including a case study. Setting: Acute care adult hospitals in Australia. Subjects: A total of 307 dietitians (66.2% response rate). Intervention: Surveys were posted to hospitals. A reminder letter was sent to all hospitals 2 weeks after the initial posting and a follow-up survey was sent 6 weeks after the initial posting to non-respondents. Results: Respondents calculated a mean energy requirement for the case study of 978071410 kJ/day. One-third of respondents calculated energy requirement within 7500 kJ of the mean. Respondents primarily used the Schofield equations (67.4%) followed by the Harris-Benedict equations (25.9%) to estimate energy requirement. Estimates using the Schofield equations calculated the highest mean energy requirement. The median injury factor used in the calculations was 1.3 (1.0-1.5). The values and reasons for the selection of injury factors varied widely. Calculated energy requirement did not differ with the aims of nutritional careFmaintaining current weight (970071370 kJ/day) or increasing weight (979071380 kJ/day). Conclusion: There was considerable variation in the methods and factors used for estimating energy requirement, resulting in a wide range of calculated requirements. The application of prediction methods to individuals in acute care does not appear to be universally understood among dietitians. Dietitians require an understanding of the correct application, appropriate use, and limitations of these prediction methods.

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“…Subjects were placed on an isocaloric diet consisting of 15% protein, 55% carbohydrate, and 30% fat, with daily intake of 3500 mg sodium, 800 to 1200 mg calcium, and at least 25 g of dietary fiber (1 g fiber/100 kcal). Initially, energy expenditure was estimated using the Harris-Benedict equation [20] times an activity factor based on the individual's reported activity level. Energy was provided at a level designed to maintain body weight, which was monitored daily, within 1 kg of pre-bed rest weight.…”

Section: Diet and Dietary Assessmentsmentioning

confidence: 99%

Lower body negative pressure treadmill exercise as a countermeasure for bed rest-induced bone loss in female identical twins

Zwart

Hargens

Lee

et al. 2007

Bone

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Supine weight-bearing exercise within lower body negative pressure (LBNP) alleviates some of the skeletal deconditioning induced by simulated weightlessness in men. We examined the potential beneficial effect in women. Because dietary acid load affected the degree of bone resorption in men during bed rest, we also investigated this variable in women. Subjects were 7 pairs of female identical twins assigned at random to 2 groups, sedentary bed rest (control) or bed rest with supine treadmill exercise within LBNP. Dietary intake was controlled and monitored. Urinary calcium and markers of bone resorption were measured before bed rest (BR) and on BR days 5/6, 12/13, 19/20, and 26/27. Bone mineral content was assessed by dual-energy X-ray absorptiometry before and after bed rest. Data were analyzed by repeated measures two-way analysis of variance. Pearson correlation coefficients were used to define the relationships between diet and markers of bone metabolism, and to estimate heritability of markers. During bed rest, all markers of bone resorption and urinary calcium and phosphorus increased (P < 0.001); parathyroid hormone (P = 0.06), bone-specific alkaline phosphatase (P = 0.06), and 1,25-dihydroxyvitamin D (P = 0.09) tended to decrease. LBNP exercise tended to mitigate bone density loss. The ratio of dietary animal protein to potassium was positively correlated with urinary calcium excretion for all weeks of bed rest in the control group, but only during weeks 1 and 3 for the exercise group. Pre-bed rest data suggested that many markers of bone metabolism have strong genetic determinants. Treadmill exercise within LBNP had less of a protective effect on bone resorption during bed rest in women than previously-published results had Sources of support: This study was funded by NASA through grant NAG 9-1425 and by NIH through grant GCRC M01-RR00827 to UCSD.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. shown for its effect in men, but the same trends were observed for both sexes. Dietary acid load of these female subjects was significantly correlated with calcium excretion but not with other bone resorption markers. NIH Public Access

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A Biometric Study of Human Basal Metabolism

Cited by 2,231 publications

References 0 publications

Predicting the resting metabolic rate of young Australian males

Predicting the resting metabolic rate of young Australian males

Variation in the application of methods used for predicting energy requirements in acutely ill adult patients: a survey of practice

Lower body negative pressure treadmill exercise as a countermeasure for bed rest-induced bone loss in female identical twins

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