ActiGraph

  • Ahmadi, 2020- four machine learning models to predict energy expenditure of preschoolers from raw acceleration features from hip or non-dominant wrist-worn ActiGraph accelerometers

  • Aittasalo, 2015- cut-points for mean amplitude deviation in adolescents wearing Hookie or ActiGraph monitors at the hip

  • Bai, 2016-calculates the acceleration-based metric Activity Index from a hip-worn ActiGraph accelerometer which was related to energy expenditure in adult women

  • Bammann, 2021- cut-points for Euclidean norm minus one for monitors worn at each hip, wrist, and ankle in older adults

  • Bianchim, 2022- cut-points for Euclidean norm minus one and mean amplitude deviation for monitors worn at the right hip and each wrist in children and adolescents with and without cystic fibrosis

  • Brage, 2003- branched and not branched models to predict energy expenditure from hip-worn CSA accelerometer counts/min and/or heart rate in adult males

  • Brønd, 2019- six sets of cut-points for non-proprietary counts from children wearing an ActiGraph on the right hip

  • Choi, 2010- five prediction models for energy expenditure in adolescents using ActiGraph counts from the hip, wrist, or ankle

  • Clevenger, 2022- consensus method for time spent in moderate-to-vigorous physical activity using hip-worn ActiGraph data in adults

  • Crotti, 2020- cut-points for Euclidean norm minus one from ActiGraph worn on right hip and each wrist in children

  • Crouter, 2006- a 2-regression model for hip-worn ActiGraph counts in adults

  • Crouter, 2010- an updated 2-regression model for hip-worn ActiGraph counts in adults

  • Crouter, 2012- a 2-regression model for hip-worn ActiGraph counts in children

  • Crouter, 2018- a 2-regression model for ankle-worn ActiGraph counts in children

  • Diniz-Sousa, 2020- regression equations and cut-points for Euclidean norm minus one, mean amplitude deviation, and vector magnitude of ActiGraph counts from ActiGraphs worn on the lower back or hip in adults with class 2 or 3 obesity

  • Ellingson, 2016- modified Sojourn to predict energy expenditure of adults from hip-worn ActiGraph counts and thigh-worn ActivPal activity classification data

  • Ellingson, 2017- modification of the Hildebrand et al. (2014) model to predict energy expenditure in adults using Euclidean norm minus one from an ActiGraph worn on the right wrist

  • Hibbing, 2018- four Sojourn models to predict energy expenditure of children using hip- or wrist-worn ActiGraph counts or Euclidean norm minus one

  • Hibbing, 2018 (2)- fifteen 2-regression models to predict energy expenditure in adults for an ActiGraph worn at the hip, each wrist and ankle using Euclidean norm minus one, gyroscope vector magnitude, and direction changes

  • Hildebrand, 2014- calculates Euclidean norm minus one from raw acceleration data of hip or non-dominant wrist-worn ActiGraph or GENEActivs with energy expenditure equations and activity intensity cut-points for children and adults

  • Jimmy, 2013- overall linear regression model, and a cubic 2-regression model and linear 2-regression model with separate equations for locomotor and play activities to predict energy expenditure in children wearing an ActiGraph on the hip

  • Johansson, 2006- branched equation model to predict energy expenditure from heart rate and counts in adults wearing a ActiGraph on the lower back

  • Lyden, 2014- Sojourn model to predict energy expenditure from hip-worn ActiGraph count data in adults

  • Mackintosh, 2016- thirteen machine learning models to predict energy expenditure in children using count data from ActiGraphs on chest, both hips, wrists, ankles, and knees

  • Migueles, 2021- cut-points for activity intensity using Euclidean norm minus measured at the hip or either wrist in older adults

  • Montoye, 2015- sixteen acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on thigh or hip or a GENEActiv on either wrist

  • Montoye, 2016 (2)- four acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on thigh or hip or GENEActiv on either wrist

  • Montoye, 2017- four acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on ankle, hip, or either wrist

  • Montoye, 2019- six machine learning models using either count or raw data from ActiGraphs on right hip or left wrist to predict energy expenditure of children

  • Sanders, 2019- cut-points for Euclidean norm minus one from ActiGraph worn on right hip and GENEActiv worn on non-dominant wrist in older adults

  • Staudenmayer, 2015- linear regression and decision tree to predict energy expenditure or activity intensity, respectively, from dominant wrist-worn ActiGraphs in adults

  • Steenbock, 2019- twenty-four machine learning models to predict energy expenditure of preschoolers using acceleration metrics from an ActiGraph on either hip, GENEActiv right hip, GENEActiv on either wrist, and/or ActivPal right thigh

  • Trost, 2016- two decision tree models for classifying activity intensity from ActiGraph counts in children with cerebral palsy wearing an ActiGraph at the hip

  • Wang, 2022- linear regression and cut-points for accelerometer activity index from a hip-worn ActiGraph monitor in older women

  • Zakeri, 2012- Cross-sectional time series and multivariate adaptive regression splines for ActiHeart, ActiGraph, or ActiGraph plus heart rate inputs, resulting in six total models for predicting total energy expenditure in preschool-aged children