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

Usability of Apps and User Perceptions of mHealth (1264) Mobile Health (mhealth) (3747) Quality/Credibility of eHealth Information and Trust Issues (685) Qualitative Methods (157) Artificial Intelligence (2460)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64715, first published .
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Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study

Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study

Authors of this article:

Katie Ryan1 Author Orcid Image ;   Justin Hogg1 Author Orcid Image ;   Max Kasun1 Author Orcid Image ;   Jane Paik Kim1 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Journals

  1. Lāma G, Lastovska A. AI competence and sentiment: a mixed-methods study of attitudes and open-ended reflections. Frontiers in Artificial Intelligence 2025;8 View
  2. Panfil A, Tamasan S, Vasilian C, Horhat R, Lungeanu D. Neuroception of Psychological Safety and Attitude Towards General AI in uHealth Context. Multimodal Technologies and Interaction 2025;10(1):4 View
  3. Agrawal S, Agbeyangi A. Adoption of AI-enabled mental health wearables in India: The roles of psychological assurance and algorithmic credibility. Computers in Human Behavior: Artificial Humans 2026;7:100259 View
  4. Khan M, Gul Z, Yasmeen Z, Akram I. From Doctors to Chatbots: Effect of Disease Threat and Sigma on AI Health Information Seeking Behavior. Qlantic Journal of Social Sciences and Humanities 2026;VII(I):58 View
  5. Subedi R, Chakraborty S, He Z, Pang Y, Zhang S, Lustria M, Charness N, Boot W. Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults Using Source-Free Domain Adaptation: Algorithm Development and Validation. JMIR Aging 2026;9:e79123 View
  6. Deeney C, Sonig A, Hurley M, Tunç B, Storch E, Herrington J, Blumenthal-Barby J, Kostick-Quenet K. Perceived Sensitivity of Sensor-Based Digital Health Data: A Qualitative Interview Study (Preprint). JMIR mHealth and uHealth 2025 View
  7. Sun H, Shi Z, Xia Y, Bai Y, Yu M, Liu R, He P. Patient preferences and willingness-to-pay for AI-enabled blended type 2 diabetes care by digital experience and socioeconomic status: a discrete-choice experiment in China. BMJ Open 2026;16(5):e115755 View

Conference Proceedings

  1. Lee J, Jung K, Lee C, MacDonnell L, Kim J, Otterson D, Newman E, Chow E, Chen Y. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. From Efficiency to Meaning: Adolescents’ Envisioned Role of AI in Health Management View