Andrew Plucker1, Diana M Thomas2, Nick Broskey3, Corby K Martin3, Dale Schoeller4, Robin Shook5, Steven B Heymsfield3, James A Levine6,7, Leanne A Redman3. 1. Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA. 2. Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA. diana.thomas@usma.edu. 3. Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA. 4. University of Wisconsin, Madison, WI, USA. 5. Department of Pediatrics, Children's Mercy, Kansas City, MO, USA. 6. Mayo Clinic, Rochester, MN, USA. 7. Ipsen Foundation, Paris, France.
Abstract
BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.
BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.
Authors: William J Tharion; Harris R Lieberman; Scott J Montain; Andrew J Young; Carol J Baker-Fulco; James P Delany; Reed W Hoyt Journal: Appetite Date: 2004-11-14 Impact factor: 3.868
Authors: K W DeGregory; P Kuiper; T DeSilvio; J D Pleuss; R Miller; J W Roginski; C B Fisher; D Harness; S Viswanath; S B Heymsfield; I Dungan; D M Thomas Journal: Obes Rev Date: 2018-02-09 Impact factor: 9.213