Literature DB >> 16614432

Normalization of energy expenditure data for differences in body mass or composition in children and adolescents.

Issa Zakeri1, Maurice R Puyau, Anne L Adolph, Firoz A Vohra, Nancy F Butte.   

Abstract

The most appropriate model for normalization of energy expenditure (EE) data for body mass or composition in growing children and adolescents has not been studied extensively. In this study, we investigated allometric modeling for the normalization of EE data for body mass or composition in a large cohort of children (n = 833), ages 5-19 y for a wide range of physical activities. Anthropometry was performed by standard techniques, and total body fat-free mass (FFM) and fat mass (FM) were determined by dual-energy X-ray absorptiometry (DXA). Weight status was defined as nonoverweight or overweight based on the 95th percentile for BMI. Total energy expenditure (TEE), basal energy expenditure (BEE), sleeping energy expenditure (SEE), and cycling EE were measured during 24-h room respiration calorimetry. Walking and maximal EE (MaxEE) were measured according to a treadmill protocol. Allometric or power function models were used to identify appropriate scaling parameters for EE. For BEE and lower levels of EE, weight scaled to 0.5. For cycling and treadmill walking/running, the weight exponent approached 0.7. Scaling EE for FFM resulted in exponents of 0.6 for lower rates of EE and 0.8-1.0 for higher rates of EE. Appropriate scaling of EE for body weight and composition of children and adolescents varied primarily as a function of the level of EE. In some instances, the exponents for scaling EE by body weight or composition were influenced by gender and weight status, but not by age.

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Year:  2006        PMID: 16614432     DOI: 10.1093/jn/136.5.1371

Source DB:  PubMed          Journal:  J Nutr        ISSN: 0022-3166            Impact factor:   4.798


  13 in total

1.  Twelve weeks of moderate aerobic exercise without dietary intervention or weight loss does not affect 24-h energy expenditure in lean and obese adolescents.

Authors:  Gert-Jan van der Heijden; Pieter Jj Sauer; Agneta L Sunehag
Journal:  Am J Clin Nutr       Date:  2010-01-27       Impact factor: 7.045

2.  High energy expenditure is not protective against increased adiposity in children.

Authors:  S R J Zinkel; R I Berkowitz; A J Stunkard; V A Stallings; M Faith; D Thomas; D A Schoeller
Journal:  Pediatr Obes       Date:  2016-02-22       Impact factor: 4.000

3.  Hierarchical Linear Models for Energy Prediction using Inertial Sensors: A Comparative Study for Treadmill Walking.

Authors:  Harshvardhan Vathsangam; B Adar Emken; E Todd Schroeder; Donna Spruijt-Metz; Gaurav S Sukhatme
Journal:  J Ambient Intell Humaniz Comput       Date:  2013-12-01

4.  Energy cost of common activities in children and adolescents.

Authors:  Kate Lyden; Sarah Kozey Keadle; John Staudenmayer; Patty Freedson; Sofiya Alhassan
Journal:  J Phys Act Health       Date:  2012-02-29

5.  Energy prediction equations are inadequate for obese Hispanic youth.

Authors:  Catherine J Klein; Stephan A Villavicencio; Amy Schweitzer; Joel S Bethepu; Heather J Hoffman; Nazrat M Mirza
Journal:  J Am Diet Assoc       Date:  2011-08

6.  Revisit to functional data analysis of sleeping energy expenditure.

Authors:  Seungchul Baek; Yewon Kim; Junyong Park; Jong Soo Lee
Journal:  J Appl Stat       Date:  2020-10-27       Impact factor: 1.416

7.  Youth Metabolic Equivalents Differ Depending on Operational Definitions.

Authors:  Paul R Hibbing; David R Bassett; Dawn P Coe; Samuel R Lamunion; Scott E Crouter
Journal:  Med Sci Sports Exerc       Date:  2020-08

8.  Modeling energy expenditure in children and adolescents using quantile regression.

Authors:  Yunwen Yang; Anne L Adolph; Maurice R Puyau; Firoz A Vohra; Nancy F Butte; Issa F Zakeri
Journal:  J Appl Physiol (1985)       Date:  2013-05-02

9.  A Simple Model Predicting Individual Weight Change in Humans.

Authors:  Diana M Thomas; Corby K Martin; Steven Heymsfield; Leanne M Redman; Dale A Schoeller; James A Levine
Journal:  J Biol Dyn       Date:  2011-11       Impact factor: 2.179

Review 10.  Size Exponents for Scaling Maximal Oxygen Uptake in Over 6500 Humans: A Systematic Review and Meta-Analysis.

Authors:  Lorenzo Lolli; Alan M Batterham; Kathryn L Weston; Greg Atkinson
Journal:  Sports Med       Date:  2017-07       Impact factor: 11.136

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