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.
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.
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
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
Authors: Richard P Troiano; David Berrigan; Kevin W Dodd; Louise C Mâsse; Timothy Tilert; Margaret McDowell Journal: Med Sci Sports Exerc Date: 2008-01 Impact factor: 5.411
Authors: Roger Zoorob; Maciej S Buchowski; Bettina M Beech; Juan R Canedo; Rameela Chandrasekhar; Sylvie Akohoue; Pamela C Hull Journal: Contemp Clin Trials Date: 2013-04-26 Impact factor: 2.226
Authors: Andrea Mannini; Mary Rosenberger; William L Haskell; Angelo M Sabatini; Stephen S Intille Journal: Med Sci Sports Exerc Date: 2017-04 Impact factor: 5.411
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
Authors: Steve M Douglas; Grace M Hawkins; Kristoffer S Berlin; Scott E Crouter; Leonard H Epstein; John G Thomas; Hollie A Raynor Journal: Contemp Clin Trials Date: 2020-09-17 Impact factor: 2.226
Authors: Erin R Hager; Margarita S Treuth; Candice Gormely; LaShawna Epps; Soren Snitker; Maureen M Black Journal: Res Q Exerc Sport Date: 2015-08-19 Impact factor: 2.500