Literature DB >> 21131873

Comparison of accelerometer cut points for predicting activity intensity in youth.

Stewart G Trost1, Paul D Loprinzi, Rebecca Moore, Karin A Pfeiffer.   

Abstract

UNLABELLED: The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents.
PURPOSE: This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard.
METHODS: A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities (comfortable walk, brisk walk, brisk treadmill walk, running). During each trial, participants wore an ActiGraph GT1M, and V˙O2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system. Physical activity intensity was estimated using five independently developed cut points: Freedson/Trost (FT), Puyau (PU), Treuth (TR), Mattocks (MT), and Evenson (EV). Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve (ROC-AUC).
RESULTS: Across all four intensity levels, the EV (κ=0.68) and FT (κ=0.66) cut points exhibited significantly better agreement than TR (κ=0.62), MT (κ=0.54), and PU (κ=0.36). The EV and FT cut points exhibited significantly better classification accuracy for moderate- to vigorous-intensity physical activity (ROC-AUC=0.90) than TR, PU, or MT cut points (ROC-AUC=0.77-0.85). Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages. The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy (ROC-AUC=0.90).
CONCLUSIONS: On the basis of these findings, we recommend that researchers use the EV ActiGraph cut points to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents.

Entities:  

Mesh:

Year:  2011        PMID: 21131873     DOI: 10.1249/MSS.0b013e318206476e

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


  481 in total

1.  Use of a two-regression model for estimating energy expenditure in children.

Authors:  Scott E Crouter; Magdalene Horton; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2012-06       Impact factor: 5.411

2.  General versus central adiposity and relationship to pediatric metabolic risk.

Authors:  Jason A Mendoza; Theresa A Nicklas; Yan Liu; Janice Stuff; Tom Baranowski
Journal:  Metab Syndr Relat Disord       Date:  2011-12-13       Impact factor: 1.894

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

Authors:  Joshua Warolin; Amanda R Carrico; Lauren E Whitaker; Li Wang; Kong Y Chen; Sari Acra; Maciej S Buchowski
Journal:  Med Sci Sports Exerc       Date:  2012-12       Impact factor: 5.411

4.  Measuring Physical Activity and Sedentary Behavior in Youth with Type 2 Diabetes.

Authors:  Bonny Rockette-Wagner; Kristi L Storti; Sharon Edelstein; Linda M Delahanty; Bryan Galvin; Alexandra Jackson; Andrea M Kriska
Journal:  Child Obes       Date:  2016-02-09       Impact factor: 2.992

5.  Age-Related Differences in OMNI-RPE Scale Validity in Youth: A Longitudinal Analysis.

Authors:  Catherine Gammon; Karin A Pfeiffer; James M Pivarnik; Rebecca W Moore; Kelly R Rice; Stewart G Trost
Journal:  Med Sci Sports Exerc       Date:  2016-08       Impact factor: 5.411

6.  Physical activity outcomes in afterschool programs: A group randomized controlled trial.

Authors:  Michael W Beets; R Glenn Weaver; Gabrielle Turner-McGrievy; Jennifer Huberty; Dianne S Ward; Russell R Pate; Darcy Freedman; Brent Hutto; Justin B Moore; Matteo Bottai; Jessica Chandler; Keith Brazendale; Aaron Beighle
Journal:  Prev Med       Date:  2016-07-07       Impact factor: 4.018

7.  Physical Activity is Related to Fatty Liver Marker in Obese Youth, Independently of Central Obesity or Cardiorespiratory Fitness.

Authors:  Clarice Martins; Luisa Aires; Ismael Freitas Júnior; Gustavo Silva; Alexandre Silva; Luís Lemos; Jorge Mota
Journal:  J Sports Sci Med       Date:  2015-03-01       Impact factor: 2.988

8.  Physical activity and screen-media-related parenting practices have different associations with children's objectively measured physical activity.

Authors:  Teresia M O'Connor; Tzu-An Chen; Janice Baranowski; Deborah Thompson; Tom Baranowski
Journal:  Child Obes       Date:  2013-09-12       Impact factor: 2.992

9.  Association between Physical Activity and Adiposity in Adolescents with Down Syndrome.

Authors:  E Andrew Pitchford; Chelsea Adkins; Rebecca E Hasson; Joseph E Hornyak; Dale A Ulrich
Journal:  Med Sci Sports Exerc       Date:  2018-04       Impact factor: 5.411

10.  Changes in Moderate-to-Vigorous Physical Activity Among Older Adolescents.

Authors:  Kaigang Li; Denise Haynie; Leah Lipsky; Ronald J Iannotti; Charlotte Pratt; Bruce Simons-Morton
Journal:  Pediatrics       Date:  2016-10       Impact factor: 7.124

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.