PURPOSE: The purpose of this study was to develop and cross-validate an equation based on ActiGraph accelerometer GT3X output to predict children and youth's energy expenditure (EE) of physical activity (PA). METHOD: Participants were 367 Chinese children and youth (179 boys and 188 girls, aged 9 to 17 years old) whowore 1 ActiGraph GT3X accelerometeron their right hip during the following tests/activities: resting metabolic rate (RMR), six 5-min treadmill walk/runs (tested at different speeds: 3 km x h(-1), 4 km x h(-1), 5 km x h(-1), 6 km x h(-1), 7 km x h(-1), and 8 km x h(-1)), 1 broadcast gymnastics, and 2 table-tennis exercises. Participants' oxygen consumption was measured using Cosmed K4b(2). The participants were randomly divided into a calibration group (n = 331, 90%) and a cross-validation group (n = 36, 10%). The calibration group's data were used to determine the relationship between EE and triaxial vector magnitude counts (VM) using the Pearson correlation and to derive the equation using a stepwise multiple regression. In the cross-validation group, differences between measured and predicted EE were evaluated using pairwise t tests. RESULTS:VM activity counts had a moderately high correlation with EE (r = .758, p < .01). An EE prediction equation was developed: EE (kcal x min(-1)) = 0.00083 x VM + 0.073 x weight-2.01 (R2 = .72, SEE = 1.45 kcal x min(-1)). According to the cross-validation study results, this equation could predict the EE within the range of known accuracy (i.e., about 20% error). CONCLUSIONS: An equation based on ActiGraph accelerometer VM activity counts was derived to predict EE of PA in Chinese children and youth within the range of known accuracy.
RCT Entities:
PURPOSE: The purpose of this study was to develop and cross-validate an equation based on ActiGraph accelerometer GT3X output to predict children and youth's energy expenditure (EE) of physical activity (PA). METHOD:Participants were 367 Chinese children and youth (179 boys and 188 girls, aged 9 to 17 years old) who wore 1 ActiGraph GT3X accelerometer on their right hip during the following tests/activities: resting metabolic rate (RMR), six 5-min treadmill walk/runs (tested at different speeds: 3 km x h(-1), 4 km x h(-1), 5 km x h(-1), 6 km x h(-1), 7 km x h(-1), and 8 km x h(-1)), 1 broadcast gymnastics, and 2 table-tennis exercises. Participants' oxygen consumption was measured using Cosmed K4b(2). The participants were randomly divided into a calibration group (n = 331, 90%) and a cross-validation group (n = 36, 10%). The calibration group's data were used to determine the relationship between EE and triaxial vector magnitude counts (VM) using the Pearson correlation and to derive the equation using a stepwise multiple regression. In the cross-validation group, differences between measured and predicted EE were evaluated using pairwise t tests. RESULTS: VM activity counts had a moderately high correlation with EE (r = .758, p < .01). An EE prediction equation was developed: EE (kcal x min(-1)) = 0.00083 x VM + 0.073 x weight-2.01 (R2 = .72, SEE = 1.45 kcal x min(-1)). According to the cross-validation study results, this equation could predict the EE within the range of known accuracy (i.e., about 20% error). CONCLUSIONS: An equation based on ActiGraph accelerometer VM activity counts was derived to predict EE of PA in Chinese children and youth within the range of known accuracy.
Authors: Jairo H Migueles; Cristina Cadenas-Sanchez; Ulf Ekelund; Christine Delisle Nyström; Jose Mora-Gonzalez; Marie Löf; Idoia Labayen; Jonatan R Ruiz; Francisco B Ortega Journal: Sports Med Date: 2017-09 Impact factor: 11.136
Authors: Zhixiong Zhou; Shanshan Dong; Jun Yin; Quan Fu; Hong Ren; Zenong Yin Journal: Int J Environ Res Public Health Date: 2018-05-14 Impact factor: 3.390
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