Wrist

  • 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

  • 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

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

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

  • Dibben, 2020- cut-points for two acceleration-based metrics (sum of vector magnitudes and mean amplitude deviation) for GENEActiv monitors worn at the hip and each wrist in patients with heart failure

  • Dillon, 2016- cut-points for signal vector magnitude from GENEActiv worn on each wrist in adults

  • 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

  • Esliger, 2011- cut-points for signal magnitude vector from GENEA worn on right hip and each wrist in adults

  • Fraysse, 2021- cut-points for signal vector magnitude from GENEActiv worn on each wrist in older adults

  • Heil, 2006- single and 2-regression models for predicting activity energy expenditure in children and adults wearing an Actical accelerometer on the right hip or non-dominant ankle or 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

  • Kiuchi, 2014- twelve total models (three feature sets at four wear locations including both wrists and upper arms) were developed for predicting energy expenditure using acceleration and gyroscopic angular velocity in manual wheelchair users

  • 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, 2016 (3)- six acceleration-based machine learning models to predict energy expenditure of adults wearing 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, 2018- four machine learning models to predict energy expenditure of adults wearing GENEActiv on left 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

  • Phillips, 2013- cut-points for signal magnitude vector from GENEA worn on right hip and each wrist in children

  • Roscoe, 2017- cut-points for signal magnitude vector from GENEActiv worn on non-dominant wrist in preschool-aged 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

  • Schaefer, 2014- cut-points for signal magnitude vector divided by sampling frequency from GENEActiv worn on non-dominant wrist in children

  • 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

  • van Hees, 2011- prediction equations for energy expenditure for pregnant women and non-pregnant women wearing a GENEA on the wrist or non-dominant hip

  • van Hees, 2013- prediction equations for energy expenditure of women wearing a wrist-worn GENEA