Models Provided as
R Code

For these methods, there is R code, sample data, and instructions based on the information provided by the original authors. As the purpose of this repository is to simply make these models easier to find (by putting them all in one place), the provided code is not optimized-meaning there is certainly more efficient code, particularly for implementing the models for data from more than one participant. Feel free to reach out if you need help with how to use this code more efficiently/easily for your project or have questions. All of these models use R, so more information about R is found here.

• 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

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

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

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

• 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

• 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

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

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

• 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