Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study
BackgroundThe increasing use of direct-to-consumer artificial intelligence (AI)–enabled mobile health (AI-mHealth) apps presents an opportunity for more effective health management and monitoring and expanded mobile health (mHealth) capabilities. However, AI’s early developme...
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JMIR Publications
2025-05-01
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| Series: | JMIR mHealth and uHealth |
| Online Access: | https://mhealth.jmir.org/2025/1/e64715 |
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| author | Katie Ryan Justin Hogg Max Kasun Jane Paik Kim |
| author_facet | Katie Ryan Justin Hogg Max Kasun Jane Paik Kim |
| author_sort | Katie Ryan |
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BackgroundThe increasing use of direct-to-consumer artificial intelligence (AI)–enabled mobile health (AI-mHealth) apps presents an opportunity for more effective health management and monitoring and expanded mobile health (mHealth) capabilities. However, AI’s early developmental stage has prompted concerns related to trust, privacy, informed consent, and bias, among others. While some of these concerns have been explored in early stakeholder research related to AI-mHealth, the broader landscape of considerations that hold ethical significance to users remains underexplored.
ObjectiveOur aim was to document and explore the perspectives of individuals who reported previous experience using mHealth apps and their attitudes and ethically salient considerations regarding direct-to-consumer AI-mHealth apps.
MethodsAs part of a larger study, we conducted semistructured interviews via Zoom with self-reported users of mHealth apps (N=21). Interviews consisted of a series of open-ended questions concerning participants’ experiences, attitudes, and values relating to AI-mHealth apps and were conducted until topic saturation was reached. We collaboratively reviewed the interview transcripts and developed a codebook consisting of 37 codes describing recurring or otherwise noteworthy sentiments that inductively arose from the data. A single coder coded all transcripts, and the entire team contributed to conventional qualitative analysis.
ResultsOur qualitative analysis yielded 3 major categories and 9 subcategories encompassing participants’ perspectives. Participants described attitudes toward the impact of AI-mHealth on users’ health and personal data (ie, influences on health awareness and management, value for mental vs physical health use cases, and the inevitability of data sharing), influences on their trust in AI-mHealth (ie, endorsements and guidance from health professionals or health or regulatory organizations, attitudes toward technology companies, and reasonable but not necessarily explainable output), and their preferences relating to the amount and type of information that is shared by AI-mHealth apps (ie, the types of data that are collected, future uses of user data, and the accessibility of information).
ConclusionsThis paper provides additional context relating to a number of concerns previously posited or identified in the AI-mHealth literature, including trust, explainability, and information sharing, and revealed additional considerations that have not been previously documented, that is, users’ differentiation between the value of AI-mHealth for physical and mental health use cases and their willingness to extend empathy to nonexplainable AI. To the best of our knowledge, this study is the first to apply an open-ended, qualitative descriptive approach to explore the perspectives of end users of direct-to-consumer AI-mHealth apps. |
| format | Article |
| id | doaj-art-ec446bd81b6f42c2957ba4b4c9ab3d8c |
| institution | OA Journals |
| issn | 2291-5222 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
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| series | JMIR mHealth and uHealth |
| spelling | doaj-art-ec446bd81b6f42c2957ba4b4c9ab3d8c2025-08-20T01:54:41ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222025-05-0113e6471510.2196/64715Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview StudyKatie Ryanhttps://orcid.org/0000-0003-1437-3126Justin Hogghttps://orcid.org/0009-0005-9329-606XMax Kasunhttps://orcid.org/0000-0002-6364-6234Jane Paik Kimhttps://orcid.org/0000-0002-1475-9822 BackgroundThe increasing use of direct-to-consumer artificial intelligence (AI)–enabled mobile health (AI-mHealth) apps presents an opportunity for more effective health management and monitoring and expanded mobile health (mHealth) capabilities. However, AI’s early developmental stage has prompted concerns related to trust, privacy, informed consent, and bias, among others. While some of these concerns have been explored in early stakeholder research related to AI-mHealth, the broader landscape of considerations that hold ethical significance to users remains underexplored. ObjectiveOur aim was to document and explore the perspectives of individuals who reported previous experience using mHealth apps and their attitudes and ethically salient considerations regarding direct-to-consumer AI-mHealth apps. MethodsAs part of a larger study, we conducted semistructured interviews via Zoom with self-reported users of mHealth apps (N=21). Interviews consisted of a series of open-ended questions concerning participants’ experiences, attitudes, and values relating to AI-mHealth apps and were conducted until topic saturation was reached. We collaboratively reviewed the interview transcripts and developed a codebook consisting of 37 codes describing recurring or otherwise noteworthy sentiments that inductively arose from the data. A single coder coded all transcripts, and the entire team contributed to conventional qualitative analysis. ResultsOur qualitative analysis yielded 3 major categories and 9 subcategories encompassing participants’ perspectives. Participants described attitudes toward the impact of AI-mHealth on users’ health and personal data (ie, influences on health awareness and management, value for mental vs physical health use cases, and the inevitability of data sharing), influences on their trust in AI-mHealth (ie, endorsements and guidance from health professionals or health or regulatory organizations, attitudes toward technology companies, and reasonable but not necessarily explainable output), and their preferences relating to the amount and type of information that is shared by AI-mHealth apps (ie, the types of data that are collected, future uses of user data, and the accessibility of information). ConclusionsThis paper provides additional context relating to a number of concerns previously posited or identified in the AI-mHealth literature, including trust, explainability, and information sharing, and revealed additional considerations that have not been previously documented, that is, users’ differentiation between the value of AI-mHealth for physical and mental health use cases and their willingness to extend empathy to nonexplainable AI. To the best of our knowledge, this study is the first to apply an open-ended, qualitative descriptive approach to explore the perspectives of end users of direct-to-consumer AI-mHealth apps.https://mhealth.jmir.org/2025/1/e64715 |
| spellingShingle | Katie Ryan Justin Hogg Max Kasun Jane Paik Kim Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study JMIR mHealth and uHealth |
| title | Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study |
| title_full | Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study |
| title_fullStr | Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study |
| title_full_unstemmed | Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study |
| title_short | Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study |
| title_sort | users perceptions and trust in ai in direct to consumer mhealth qualitative interview study |
| url | https://mhealth.jmir.org/2025/1/e64715 |
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