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

Formative Evaluation of Digital Health Interventions (3558) Web-based and Mobile Health Interventions (4300) Mobile Health (mhealth) (3732) Usability of Apps and User Perceptions of mHealth (1260) User-Centered Design Case Studies (765) mHealth for Health Administration (189) E-Health / Health Services Research and New Models of Care (823) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (709)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47813, first published .
Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners

Authors of this article:

Xinghui Yan1 Author Orcid Image ;   Mark W Newman1 Author Orcid Image ;   Sun Young Park1, 2 Author Orcid Image ;   Angelle Sander3 Author Orcid Image ;   Sung Won Choi4 Author Orcid Image ;   Jennifer Miner5 Author Orcid Image ;   Zhenke Wu5 Author Orcid Image ;   Noelle Carlozzi5 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Bei R, Graves C, Choi S, Sander A, Fansher M, Miner J, Wu Z, Klasnja P, Newman M, Carlozzi N. Qualitative Analysis of User Experiences of a mHealth Self-Care Intervention for Care Partners of Individuals with Traumatic Brain Injury. Archives of Rehabilitation Research and Clinical Translation 2026:100608 View
  2. Ulloa M, Kamali N, Fernandes G, Soyemi E, Beltzer M, Alshurafa N, Jacobs M. “I Don’t Like Being Told Just What to Do; I Need to Know Why”: Patient Expectations of Machine Learning-Driven Just-in-Time-Adaptive Interventions (JITAIs) for Prenatal Stress Management. ACM Transactions on Computing for Healthcare 2026 View

Conference Proceedings

  1. Kumakawa E, Takahashi M, Sano T, Fujimura K, Sato T, Katagiri Y. 2025 IEEE International Conference on Digital Health (ICDH). Predicting Action Plan Adherence: A Machine Learning Approach Using Dialogue Data in Health Guidance Interviews View