Published on in Vol 4, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18123, first published .
Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Journals

  1. Oh E, Kearns W, Laine M, Demiris G, Thompson H. Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence. Sensors 2022;22(10):3621 View
  2. Young S, Lattie E, Berry A, Bui L, Byrne G, Yoshino Benavente J, Bass M, Gershon R, Wolf M, Nowinski C. Remote Cognitive Screening Of Healthy Older Adults for Primary Care With the MyCog Mobile App: Iterative Design and Usability Evaluation. JMIR Formative Research 2023;7:e42416 View
  3. Antonio M, Davis S, Smith M, Burgener P, Price M, Lavallee D, Fletcher S, Lau F. Advancing digital patient-centered measurement methods for team-based care. DIGITAL HEALTH 2022;8:205520762211454 View
  4. Andrews J, Craven M, Lang A, Guo B, Morriss R, Hollis C. Making remote measurement technology work in multiple sclerosis, epilepsy and depression: survey of healthcare professionals. BMC Medical Informatics and Decision Making 2022;22(1) View
  5. Ozkaynak M, Voida S, Dunn E. Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making. Applied Clinical Informatics 2022;13(01):252 View
  6. Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T. 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 2023;7:e47380 View
  7. Tsai C, Rajput G, Gao A, Wu Y, Wu D. Improving the design of patient-generated health data visualizations: design considerations from a Fitbit sleep study. Journal of the American Medical Informatics Association 2024;31(2):465 View
  8. Chang S, Gray L, Alon N, Torous J. Patient and Clinician Experiences with Sharing Data Visualizations Integrated into Mental Health Treatment. Social Sciences 2023;12(12):648 View
  9. Kim H, Cho B, Jung J, Kim J. Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data. DIGITAL HEALTH 2023;9 View
  10. Bertelsen P, Bossen C, Knudsen C, Pedersen A. Data work and practices in healthcare: A scoping review. International Journal of Medical Informatics 2024;184:105348 View
  11. Ullman A, Larsen E, Gibson V, Binnewies S, Ohira R, Marsh N, Mcbride C, Winterbourn K, Boyte F, Cunninghame J, Dufficy M, Plummer K, Roberts N, Takashima M, Cooke M, Byrnes J, Rickard C, Kleidon T. An mHealth application for chronic vascular access: A multi‐method evaluation. Journal of Clinical Nursing 2024;33(5):1762 View
  12. Khatiwada P, Yang B, Lin J, Blobel B. Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy. Journal of Personalized Medicine 2024;14(3):282 View
  13. Guardado S, Karampela M, Isomursu M, Grundstrom C. Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review. Journal of Medical Internet Research 2024;26:e49320 View
  14. Hogan T, Etingen B, Zocchi M, Bixler F, McMahon N, Patrianakos J, Robinson S, Newton T, Shah N, Frisbee K, Shimada S, Lipschitz J, Smith B. Veteran Preferences and Willingness to Share Patient-Generated Health Data. Journal of General Internal Medicine 2024 View

Books/Policy Documents

  1. Edberg D. Clinical Health Psychology in Military and Veteran Settings. View
  2. Reindl-Spanner P, Prommegger B, Ikonomi T, Gensichen J, Krcmar H. Human-Computer Interaction. View