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

mHealth for Wellness, Behavior Change and Prevention (2277) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (368) Fitness Trackers and Smart Pedometers/Accelerometers (493)

Published on in Vol 7 , No 10 (2019) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11603, first published .
Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study

Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study

Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study

Authors of this article:

Amanda Jayne Centi 1 Author Orcid Image ;   Mursal Atif 1 Author Orcid Image ;   Sara Bersche Golas 1 Author Orcid Image ;   Ramin Mohammadi 2 Author Orcid Image ;   Sagar Kamarthi 2 Author Orcid Image ;   Stephen Agboola 1, 3, 4 Author Orcid Image ;   Joseph C Kvedar 1, 3, 4 Author Orcid Image ;   Kamal Jethwani 1, 3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Mohammadi R, Atif M, Centi A, Agboola S, Jethwani K, Kvedar J, Kamarthi S. Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study. JMIR mHealth and uHealth 2020;8(9):e18142 View
  2. Kendrick J, Duffy L, Ye L, Burke P. College students’ motivations for using fitness related technology. Journal of American College Health 2022:1 View