Literature DB >> 22776880

Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

Joshua Warolin1, Amanda R Carrico, Lauren E Whitaker, Li Wang, Kong Y Chen, Sari Acra, Maciej S Buchowski.   

Abstract

PURPOSE: The objective of this study is to determine the effect of body mass index (BMI) on level of agreement between six previously established prediction equations for three commonly used accelerometers to predict summary measures of energy expenditure (EE) in youth.
METHODS: One hundred and thirty-one youth between the ages of 10-17 yr and BMI from 15 to 44 kg·m were outfitted with hip-worn ActiGraph GT1M (Pensacola, FL), Actical (MiniMiter/Respironics, Bend, OR), and RT3 (StayHealthy, Monrovia, CA) accelerometers and spent approximately 24 h in a whole-room indirect calorimeter while performing structured and self-selected activities. Five commonly used regression and one propriety equations for each device were used to predict the minute-to-minute EE (normalized to METs), daily physical activity level (PAL), and time spent in sedentary, light, moderate, and vigorous physical activity intensity categories. The calculated values were compared with criterion measurements obtained from the room calorimeter.
RESULTS: All predictive equations, except RT3, significantly over- or underpredicted daily PAL (P < 0.001), with large discrepancies observed in the estimate of sedentary and light activity. Discrepancies between actual and estimated PAL ranged from 0.05 to 0.68. In addition, BMI represented a modifier for two ActiGraph predictive equations (AG1 and AG2), affecting the accuracy of physical activity-related EE predictions.
CONCLUSION: ActiGraph (AG3) and the RT3 closely predicted overall PAL (within 4.2% and 6.8%, respectively) as a group. When adjusting for age, sex, and ethnicity, Actical (AC1 and AC2) and ActiGraph (AG3) were not influenced by BMI. However, a gap between some hip-worn accelerometer predictive and regression equations was demonstrated compared with both criterion measurement and each other, which poses a potential difficulty for interstudy (e.g., different accelerometers) and intrastudy (e.g., BMI and adiposity) comparisons.

Entities:  

Mesh:

Year:  2012        PMID: 22776880      PMCID: PMC3501581          DOI: 10.1249/MSS.0b013e318267b8f1

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  39 in total

1.  Defining accelerometer thresholds for activity intensities in adolescent girls.

Authors:  Margarita S Treuth; Kathryn Schmitz; Diane J Catellier; Robert G McMurray; David M Murray; M Joao Almeida; Scott Going; James E Norman; Russell Pate
Journal:  Med Sci Sports Exerc       Date:  2004-07       Impact factor: 5.411

2.  Validation and calibration of the Actical accelerometer in preschool children.

Authors:  Karin A Pfeiffer; Kerry L McIver; Marsha Dowda; Maria J C A Almeida; Russell R Pate
Journal:  Med Sci Sports Exerc       Date:  2006-01       Impact factor: 5.411

3.  Validation of the RT3 in the measurement of physical activity in children.

Authors:  Juliette Hussey; Kathleen Bennett; Jamie O Dwyer; Sinead Langford; Christopher Bell; John Gormley
Journal:  J Sci Med Sport       Date:  2008-02-20       Impact factor: 4.319

Review 4.  A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: a systematic review.

Authors:  Kristi B Adamo; Stéphanie A Prince; Andrea C Tricco; Sarah Connor-Gorber; Mark Tremblay
Journal:  Int J Pediatr Obes       Date:  2009

5.  Physical activity levels of high school students --- United States, 2010.

Authors: 
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2011-06-17       Impact factor: 17.586

6.  Predicting activity energy expenditure using the Actical activity monitor.

Authors:  Daniel P Heil
Journal:  Res Q Exerc Sport       Date:  2006-03       Impact factor: 2.500

7.  Validation of the ActiGraph two-regression model for predicting energy expenditure.

Authors:  Megan P Rothney; Robert J Brychta; Natalie N Meade; Kong Y Chen; Maciej S Buchowski
Journal:  Med Sci Sports Exerc       Date:  2010-09       Impact factor: 5.411

Review 8.  Assessment of free-living physical activity in humans: an overview of currently available and proposed new measures.

Authors:  Y Schutz; R L Weinsier; G R Hunter
Journal:  Obes Res       Date:  2001-06

9.  A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.

Authors:  Kate Lyden; Sarah L Kozey; John W Staudenmeyer; Patty S Freedson
Journal:  Eur J Appl Physiol       Date:  2010-09-15       Impact factor: 3.078

10.  How children move: activity pattern characteristics in lean and obese chinese children.

Authors:  Alison M McManus; Eva Y W Chu; Clare C W Yu; Yong Hu
Journal:  J Obes       Date:  2010-12-28
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  3 in total

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Authors:  Laureen H Smith; Devin Laurent; Erica Baumker; Rick L Petosa
Journal:  J Phys Act Health       Date:  2018-10-13

2.  Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.

Authors:  Carmen D Tekwe; Mengli Zhang; Raymond J Carroll; Yuanyuan Luan; Lan Xue; Roger S Zoh; Stephen J Carter; David B Allison; Marco Geraci
Journal:  Biostatistics       Date:  2022-10-14       Impact factor: 5.279

3.  Peer mentor versus teacher delivery of a physical activity program on the effects of BMI and daily activity: protocol of a school-based group randomized controlled trial in Appalachia.

Authors:  Laureen H Smith; Rick L Petosa; Abigail Shoben
Journal:  BMC Public Health       Date:  2018-05-16       Impact factor: 3.295

  3 in total

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