Adults

  • 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

  • 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

  • Brandes, 2012- activity-specific linear regression equations for predicting energy expenditure during walking, cycling, and stair walking in children and adults using acceleration measured by a Dynaport on the lower back

  • Chuang, 2013- single regression, activity-specific regression, and a mono-exponential equation to predict energy expenditure in adults wearing accelerometers on the wrist and ankle

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

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

  • Crouter, 2007- a 2-regression model for hip-worn Actical counts in adults

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

  • Crouter, 2011- an updated 2-regression model for hip-worn Actical counts in adults

  • Curone, 2010- cut-points for signal magnitude area using ADXL330 on upper part of trunk

  • 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

  • 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

  • Duclos, 2015- equation to predict total energy expenditure from smartphone accelerometers in adults

  • 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

  • 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 (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

  • Hiremath, 2012- a general equation and a set of four activity-specific regression equations for predicting energy expenditure of manual wheelchair users using a Sensewear armband

  • Horner, 2012- models for predicting total energy expenditure and physical activity energy expenditure for males and females during free-living using a 3dnx on the lower back

  • Jang, 2006- combination of 7 accelerometers (15 total axes) is used to predict energy expenditure in adults using a simple calculation converting acceleration into velocity and work

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

  • Kim, 2008- three linear regression models to predict energy expenditure in adults using waist, ankle, and wrist accelerometers

  • 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

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

  • Mehta, 2013- cut-points for activity intensity from raw acceleration

  • 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, 2017 (2)- an artificial neural network to predict energy expenditure of adults wearing ActivPal on right thigh

  • Montoye, 2018- four machine learning models to predict energy expenditure of adults wearing GENEActiv on left wrist

  • Nguyen, 2013- data from three monitors - a Lifecorder accelerometer at the waist, Step Watch 3 at the ankle, and a GPS unit - are combined to predict energy expenditure in adults

  • Nolan, 2014- acceleration measured by an iPod touch worn on the lower back is used to determine speed and grade of walking or running which are used to calculate energy expenditure based on pre-existing equations in adults

  • O'Brien, 2021- logarithmic equation to predict METs from thigh-worn activPAL data in adults

  • Ohkawara, 2011- magnitude of filtered acceleration and the ratio of unfiltered to filtered acceleration is used to determine activity type (sedentary, household, or locomotive), then activity-specific equations are used to predict energy expenditure in adults wearing a waist-worn Omron accelerometer

  • 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

  • Vaha-Ypya, 2015- equations to predict energy expenditure and activity intensity cut-points for mean amplitude deviation were determined from adults wearing a hip-worn Hookie monitor

  • Vaha-Ypya, 2015 (2)- cut-points for mean amplitude deviation (MAD) that can be used for any accelerometer brand were determined from adults wearing three monitors at the hip

  • 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

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

  • Weippert, 2013- four models to predict energy expenditure in adults wearing a chest-worn accelerometer

  • Yamazaki, 2009- model to predict energy expenditure during walking in adults wearing a waist-worn accelerometer