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

Fitness Trackers and Smart Pedometers/Accelerometers (784) mHealth for Wellness, Behavior Change and Prevention (4219) Machine Learning (3082) Innovations and Technology for Physical Activity Education (1055) Artificial Intelligence (4591)

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 .
Woman checking fitness tracker and smartphone after workout

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 (3) Tweetations (8) Metrics

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  1. Jiang J, Qin L, Shen Y. Comparative effects of exercise modalities on ectopic fat reduction in overweight and obese children and adolescents: a narrative review. European Journal of Pediatrics 2026;185(6) View

Books/Policy Documents

  1. Davids J, Ashrafian H. Artificial Intelligence in Medicine. View
  2. Davids J, Ashrafian H. Artificial Intelligence in Medicine. View