Literature DB >> 22143114

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

Scott E Crouter1, Magdalene Horton, David R Bassett.   

Abstract

PURPOSE: The purpose of this study was to develop two new two-regression models (2RM), for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts. The new 2RMs were also compared with existing ActiGraph equations for children.
METHODS: Fifty-seven boys and 52 girls (mean ± SD: age = 11 ± 1.7 yr, body mass index = 21.4 ± 5.5 kg·m(-2)) performed 30-min supine rest and 8 min of six different activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were split into three routines with each routine performed by 38-39 participants. Seventy-seven participants were used for the development group, and 39 participants were used for the cross-validation group. During all testing, activity data were collected using an ActiGraph GT3X, worn on the right hip, and oxygen consumption was measured using a Cosmed K4b. All energy expenditure values are expressed as MET(RMR) (activity VO(2)/resting VO(2)).
RESULTS: For each activity, a coefficient of variation was calculated using 10-s epochs for the VA and VM to determine whether the activity was continuous walking/running or an intermittent lifestyle activity. Separate regression equations were developed for walking/running and intermittent lifestyle activity. In the cross-validation group, the VM and VA 2RMs were within 0.8 MET(RMR) of measured MET(RMR) for all activities except Sportwall and running (all P > 0.05). The other existing ActiGraph equations had mean errors ranging from 0.0 to 2.6 MET(RMR) for the activities.
CONCLUSIONS: The new 2RMs for use in children with the ActiGraph GT3X provide a closer estimate of mean measured MET(RMR) than other currently available prediction equations. In addition, they improve the individual prediction errors across a wide range of activity intensities.

Entities:  

Mesh:

Year:  2012        PMID: 22143114      PMCID: PMC3324667          DOI: 10.1249/MSS.0b013e3182447825

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


  27 in total

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2.  Development of novel techniques to classify physical activity mode using accelerometers.

Authors:  David M Pober; John Staudenmayer; Christopher Raphael; Patty S Freedson
Journal:  Med Sci Sports Exerc       Date:  2006-09       Impact factor: 5.411

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

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

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5.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
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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.  Assessment of energy expenditure for physical activity using a triaxial accelerometer.

Authors:  C V Bouten; K R Westerterp; M Verduin; J D Janssen
Journal:  Med Sci Sports Exerc       Date:  1994-12       Impact factor: 5.411

8.  Validation of the RT3 triaxial accelerometer for the assessment of physical activity.

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Journal:  Med Sci Sports Exerc       Date:  2004-03       Impact factor: 5.411

9.  Physical activity in the United States measured by accelerometer.

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10.  The level and tempo of children's physical activities: an observational study.

Authors:  R C Bailey; J Olson; S L Pepper; J Porszasz; T J Barstow; D M Cooper
Journal:  Med Sci Sports Exerc       Date:  1995-07       Impact factor: 5.411

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  26 in total

1.  Validity of ActiGraph child-specific equations during various physical activities.

Authors:  Scott E Crouter; Magdalene Horton; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2013-07       Impact factor: 5.411

2.  Healthy families study: design of a childhood obesity prevention trial for Hispanic families.

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3.  Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.

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Journal:  Med Sci Sports Exerc       Date:  2017-04       Impact factor: 5.411

4.  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

5.  Bipart: Learning Block Structure for Activity Detection.

Authors:  Yang Mu; Henry Z Lo; Wei Ding; Kevin Amaral; Scott E Crouter
Journal:  IEEE Trans Knowl Data Eng       Date:  2014-10-01       Impact factor: 6.977

6.  Rationale and protocol for translating basic habituation research into family-based childhood obesity treatment: Families becoming healthy together study.

Authors:  Steve M Douglas; Grace M Hawkins; Kristoffer S Berlin; Scott E Crouter; Leonard H Epstein; John G Thomas; Hollie A Raynor
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7.  Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility.

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Review 8.  Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges.

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Journal:  Br J Sports Med       Date:  2013-12-02       Impact factor: 13.800

9.  Estimating physical activity in youth using a wrist accelerometer.

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Journal:  Med Sci Sports Exerc       Date:  2015-05       Impact factor: 5.411

10.  Comparison of accelerometer cut points to estimate physical activity in US adults.

Authors:  Kathleen B Watson; Susan A Carlson; Dianna D Carroll; Janet E Fulton
Journal:  J Sports Sci       Date:  2013-11-05       Impact factor: 3.337

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