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Direct linkMontoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A. – Measurement in Physical Education and Exercise Science, 2017
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a…
Descriptors: Validity, Electronic Equipment, Prediction, Physical Activity Level
Zhu, Zheng; Chen, Peijie; Zhuang, Jie – Research Quarterly for Exercise and Sport, 2013
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…
Descriptors: Foreign Countries, Physical Activities, Physical Activity Level, Children
George, James D.; Paul, Samantha L.; Hyde, Annette; Bradshaw, Danielle I.; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G. – Measurement in Physical Education and Exercise Science, 2009
This study sought to develop a regression model to predict maximal oxygen uptake (VO[subscript 2max]) based on submaximal treadmill exercise (EX) and non-exercise (N-EX) data involving 116 participants, ages 18-65 years. The EX data included the participants' self-selected treadmill speed (at a level grade) when exercise heart rate first reached…
Descriptors: Metabolism, Body Composition, Physical Activities, Physical Activity Level
