This paper is in the following e-collection/theme issue:

Fitness Trackers and Smart Pedometers/Accelerometers (561) Artificial Intelligence (1377) Innovations and Technology for Physical Activity Education (743) Machine Learning (1471) mHealth for Wellness, Behavior Change and Prevention (2875)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18142, first published .
Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

Authors of this article:

Ramin Mohammadi1, 2 Author Orcid Image ;   Mursal Atif2 Author Orcid Image ;   Amanda Jayne Centi2 Author Orcid Image ;   Stephen Agboola2, 3, 4 Author Orcid Image ;   Kamal Jethwani2, 3, 4 Author Orcid Image ;   Joseph Kvedar2, 3, 4 Author Orcid Image ;   Sagar Kamarthi1, 2 Author Orcid Image
Article Authors Cited by (2) Tweetations (7) Metrics

Ramin Mohammadi   1, 2 , PhD ;   Mursal Atif   2 , BA ;   Amanda Jayne Centi   2 , PhD ;   Stephen Agboola   2, 3, 4 , MD, PhD ;   Kamal Jethwani   2, 3, 4 , MD, PhD ;   Joseph Kvedar   2, 3, 4 , MD ;   Sagar Kamarthi   1, 2 , PhD

1 Northeastern University, Boston, MA, United States

2 Partners connected for health, Boston, MA, United States

3 Massachusetts General Hospital, Department of Dermatology, Boston, MA, United States

4 Harvard University, Harvard Medical School, Boston, MA, United States

Corresponding Author: