Literature DB >> 25459235

Comparisons of prediction equations for estimating energy expenditure in youth.

Youngwon Kim1, Scott E Crouter2, Jung-Min Lee3, Phillip M Dixon4, Glenn A Gaesser5, Gregory J Welk6.   

Abstract

OBJECTIVES: The purpose of this study was to compare the validity of Actigraph 2-regression models (2RM) and 1-regression models (1RM) for estimation of EE in children.
DESIGN: The study used a cross-sectional design with criterion estimates from a metabolic cart.
METHODS: A total of 59 children (7-13yrs) performed 12 activities (randomly selected from a set of 24 activities) for 5min each, while being concurrently measured with an Actigraph GT3X and indirect calorimetry. METRMR (MET considering one's resting metabolic rate) for the GT3X was estimated applying 2RM with vector magnitude (VM2RM) and vertical axis (VA2RM), and four standard 1RMs. The validity of the 2RMs and 1RMs was evaluated using 95% equivalence testing and mean absolute percent error (MAPE).
RESULTS: For the group-level comparison, equivalence testing revealed that the 90% confidence intervals for all 2RMs and 1RMs were outside of the equivalence zone (range: 3.63, 4.43) for indirect calorimetry. When comparing the individual activities, VM2RM produced smaller MAPEs (range: 14.5-45.3%) than VA2RM (range, 15.5-58.1%) and 1RMs (range, 14.5-75.1%) for most of the light and moderate activities.
CONCLUSIONS: None of the 2RMs and 1RMs were equivalent to indirect calorimetry. The 2RMs showed smaller individual-level errors than the 1RMs.
Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Accelerometer; Calibration; Children; Physical fitness; Public health; Validation studies

Mesh:

Year:  2014        PMID: 25459235      PMCID: PMC4402097          DOI: 10.1016/j.jsams.2014.10.002

Source DB:  PubMed          Journal:  J Sci Med Sport        ISSN: 1878-1861            Impact factor:   4.319


  28 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

Review 2.  Calibration of accelerometer output for children.

Authors:  Patty Freedson; David Pober; Kathleen F Janz
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

Review 3.  Validity and reliability of selected commercially available metabolic analyzer systems.

Authors:  L D Hodges; D A Brodie; P D Bromley
Journal:  Scand J Med Sci Sports       Date:  2005-10       Impact factor: 4.221

4.  A novel method for using accelerometer data to predict energy expenditure.

Authors:  Scott E Crouter; Kurt G Clowers; David R Bassett
Journal:  J Appl Physiol (1985)       Date:  2005-12-01

5.  Validity of the computer science and applications (CSA) activity monitor in children.

Authors:  S G Trost; D S Ward; S M Moorehead; P D Watson; W Riner; J R Burke
Journal:  Med Sci Sports Exerc       Date:  1998-04       Impact factor: 5.411

6.  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

7.  A new statistical procedure for testing equivalence in two-group comparative bioavailability trials.

Authors:  W W Hauck; S Anderson
Journal:  J Pharmacokinet Biopharm       Date:  1984-02

8.  Measured resting energy expenditure in children.

Authors:  S Firouzbakhsh; R K Mathis; W L Dorchester; R S Oseas; P K Groncy; K E Grant; J Z Finklestein
Journal:  J Pediatr Gastroenterol Nutr       Date:  1993-02       Impact factor: 2.839

9.  Calibration of an accelerometer during free-living activities in children.

Authors:  Calum Mattocks; Sam Leary; Andy Ness; Kevin Deere; Joanne Saunders; Kate Tilling; Joanne Kirkby; Steven N Blair; Chris Riddoch
Journal:  Int J Pediatr Obes       Date:  2007

10.  Validity of estimating minute-by-minute energy expenditure of continuous walking bouts by accelerometry.

Authors:  Erin E Kuffel; Scott E Crouter; Jere D Haas; Edward A Frongillo; David R Bassett
Journal:  Int J Behav Nutr Phys Act       Date:  2011-08-24       Impact factor: 6.457

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  10 in total

1.  A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement.

Authors:  Philip M Dixon; Pedro F Saint-Maurice; Youngwon Kim; Paul Hibbing; Yang Bai; Gregory J Welk
Journal:  Med Sci Sports Exerc       Date:  2018-04       Impact factor: 5.411

2.  Physical activity across the curriculum (PAAC3): Testing the application of technology delivered classroom physical activity breaks.

Authors:  Amanda N Szabo-Reed; Richard A Washburn; J Leon Greene; Lauren T Ptomey; Anna Gorczyca; Robert H Lee; Todd D Little; Jaehoon Lee; Jeff Honas; Joseph E Donnelly
Journal:  Contemp Clin Trials       Date:  2020-01-29       Impact factor: 2.226

3.  Modifying Accelerometer Cut-Points Affects Criterion Validity in Simulated Free-Living for Adolescents and Adults.

Authors:  Paul R Hibbing; David R Bassett; Scott E Crouter
Journal:  Res Q Exerc Sport       Date:  2020-02-05       Impact factor: 2.500

4.  The accuracy of the 24-h activity recall method for assessing sedentary behaviour: the physical activity measurement survey (PAMS) project.

Authors:  Youngwon Kim; Gregory J Welk
Journal:  J Sports Sci       Date:  2016-03-28       Impact factor: 3.337

5.  Surveillance of Youth Physical Activity and Sedentary Behavior With Wrist Accelerometry.

Authors:  Youngwon Kim; Paul Hibbing; Pedro F Saint-Maurice; Laura D Ellingson; Erin Hennessy; Dana L Wolff-Hughes; Frank M Perna; Gregory J Welk
Journal:  Am J Prev Med       Date:  2017-06       Impact factor: 5.043

6.  A comparison of accelerometer cut-points for measuring physical activity and sedentary time in adolescents with Down syndrome.

Authors:  Bethany Forseth; Jordan A Carlson; Erik A Willis; Brian C Helsel; Lauren T Ptomey
Journal:  Res Dev Disabil       Date:  2021-11-24

7.  Validity of accelerometry for predicting physical activity and sedentary time in ambulatory children and young adults with cerebral palsy.

Authors:  Ruirui Xing; Wendy Yajun Huang; Cindy Hui-Ping Sit
Journal:  J Exerc Sci Fit       Date:  2020-06-27       Impact factor: 3.103

8.  The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.

Authors:  Sunku Kwon; Neng Wan; Ryan D Burns; Timothy A Brusseau; Youngwon Kim; Santosh Kumar; Emre Ertin; David W Wetter; Cho Y Lam; Ming Wen; Wonwoo Byun
Journal:  Sensors (Basel)       Date:  2021-02-18       Impact factor: 3.576

9.  Self-Reported Physical Activity is Not a Valid Method for Measuring Physical Activity in 15-Year-Old South African Boys and Girls.

Authors:  Makama Andries Monyeki; Sarah J Moss; Han C G Kemper; Jos W R Twisk
Journal:  Children (Basel)       Date:  2018-06-06

10.  Effect of epoch length on intensity classification and on accuracy of measurement under controlled conditions on treadmill: Towards a better understanding of accelerometer measurement.

Authors:  Nicolas Fabre; Léna Lhuisset; Caroline Bernal; Julien Bois
Journal:  PLoS One       Date:  2020-01-24       Impact factor: 3.240

  10 in total

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