ArticleJMIR formative research2020
Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study.
Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Barriers and Enablers to Integrating Patient-Generated Health Data in Shared Decision-Making From Health Care Professional and Patient Perspectives: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Assessing the Use of Wearable Mobile-Monitoring Devices Among Individuals With Serious Mental Illness: Qualitative Acceptability and Feasibility Study.Journal of medical Internet research · 2026Article
- Mobile health applications for parents of children with Hirschsprung disease: a requirement analysis.European journal of pediatrics · 2026Article
- Preferences for an Experience Sampling Method-Based Tool as an Adjunct to Usual Treatment in Patients With Problem Substance Use: Qualitative Study.JMIR formative research · 2026Article
- Navigating the Digital Landscape for Potential Use of Mental Health Apps in Clinical Practice: Scoping Review.JMIR mental health · 2026Article
- Visualization of Experience Sampling Method Data in Mental Health: Qualitative Study of the Physicians' Perspective in Germany.Journal of medical Internet research · 2025Article
- Veteran Preferences and Willingness to Share Patient-Generated Health Data.Journal of general internal medicine · 2025Article
- Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.Journal of personalized medicine · 2024Review
- Improving the design of patient-generated health data visualizations: design considerations from a Fitbit sleep study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.JMIR formative research · 2023Article
- Remote Cognitive Screening Of Healthy Older Adults for Primary Care With the MyCog Mobile App: Iterative Design and Usability Evaluation.JMIR formative research · 2023Article
- Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence.Sensors (Basel, Switzerland) · 2022Article
- Making remote measurement technology work in multiple sclerosis, epilepsy and depression: survey of healthcare professionals.BMC medical informatics and decision making · 2022Article
- Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making.Applied clinical informatics · 2022Article
- Monitoring Older Adult Blood Pressure Trends at Home as a Proxy for Brain Health.Online journal of public health informatics · 2021Article
- Article
- Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients' illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health-related PGHD is unknown.
objectiveA mixed methods study was conducted to understand clinicians' perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians' perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?
methodsThe study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.
resultsThe results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.
conclusionsThis study investigates clinicians' perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.
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