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

Mobile Health (mhealth) (3464) New Methods (1134) Wearable Devices and Sensors (1133) Design and Usability of Consumer Health Tech and Home Monitoring Devices (326) Design and Formative Evaluation of Mobile Apps (809) Ecological Momentary Assessment (EMA) (405) mHealth for Data Collection and Research (1196) Development and Evaluation of Research Methods, Instruments and Tools (744)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55694, first published .
Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study

Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study

Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study

Authors of this article:

Christopher Slade1, 2 Author Orcid Image ;   Roberto M Benzo3, 4 Author Orcid Image ;   Peter Washington2 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Slade C, Sun Y, Chao W, Chen C, Benzo R, Washington P. Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review. JAMIA Open 2025;8(4) View
  2. Washington P. Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics. JMIR AI 2025;4:e55530 View
  3. Carlozzi N, Troost J, Lombard W, Miner J, Graves C, Choi S, Wu Z, Sen S, Sander A. Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial. JMIR mHealth and uHealth 2025;13:e73772 View