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

Security and Privacy of mHealth and uHealth (124) Anxiety and Stress Disorders (1053) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 7, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12578, first published .
The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study

The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study

The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study

Authors of this article:

Jennifer Nicholas1 Author Orcid Image ;   Katie Shilton2 Author Orcid Image ;   Stephen M Schueller1, 3 Author Orcid Image ;   Elizabeth L Gray4 Author Orcid Image ;   Mary J Kwasny4 Author Orcid Image ;   David C Mohr1 Author Orcid Image
Article Authors Cited by (46) Tweetations (42) Metrics

Journals

  1. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine 2019;2(1) View
  2. Becker T, Torous J. Recent Developments in Digital Mental Health Interventions for College and University Students. Current Treatment Options in Psychiatry 2019;6(3):210 View
  3. Barnett S, Huckvale K, Christensen H, Venkatesh S, Mouzakis K, Vasa R. Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications. Journal of Medical Internet Research 2019;21(11):e16399 View
  4. Burgess E, Ringland K, Nicholas J, Knapp A, Eschler J, Mohr D, Reddy M. "I think people are powerful". Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1 View
  5. Zhang R, Nicholas J, Knapp A, Graham A, Gray E, Kwasny M, Reddy M, Mohr D. Clinically Meaningful Use of Mental Health Apps and its Effects on Depression: Mixed Methods Study. Journal of Medical Internet Research 2019;21(12):e15644 View
  6. Galvin H, DeMuro P. Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019. Yearbook of Medical Informatics 2020;29(01):032 View
  7. Borghouts J, Eikey E, Mark G, De Leon C, Schueller S, Schneider M, Stadnick N, Zheng K, Mukamel D, Sorkin D. Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review. Journal of Medical Internet Research 2021;23(3):e24387 View
  8. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  9. Suruliraj B, Bessenyei K, Bagnell A, McGrath P, Wozney L, Orji R, Meier S. Mobile Sensing Apps and Self-management of Mental Health During the COVID-19 Pandemic: Web-Based Survey. JMIR Formative Research 2021;5(4):e24180 View
  10. Drissi N, Ouhbi S, Serhani M, Marques G, de la Torre Díez I. Connected Mental Health Solutions: Global Attitudes, Preferences, and Concerns. Telemedicine and e-Health 2023;29(3):315 View
  11. Dolan E, Shiells K, Goulding J, Skatova A. Public attitudes towards sharing loyalty card data for academic health research: a qualitative study. BMC Medical Ethics 2022;23(1) View
  12. Bessenyei K, Suruliraj B, Bagnell A, McGrath P, Wozney L, Huguet A, Elger B, Meier S, Orji R. Comfortability with the passive collection of smartphone data for monitoring of mental health: An online survey. Computers in Human Behavior Reports 2021;4:100134 View
  13. Kruzan K, Meyerhoff J, Biernesser C, Goldstein T, Reddy M, Mohr D. Centering Lived Experience in Developing Digital Interventions for Suicide and Self-injurious Behaviors: User-Centered Design Approach. JMIR Mental Health 2021;8(12):e31367 View
  14. Buhr L, Schicktanz S, Nordmeyer E. Attitudes Toward Mobile Apps for Pandemic Research Among Smartphone Users in Germany: National Survey. JMIR mHealth and uHealth 2022;10(1):e31857 View
  15. Bougeard A, Guay Hottin1 R, Houde V, Jean T, Piront T, Potvin S, Bernard P, Tourjman V, De Benedictis L, Orban P. Le phénotypage digital pour une pratique clinique en santé mentale mieux informée. Santé mentale au Québec 2021;46(1):135 View
  16. Kowalewski M, Herbert F, Schnitzler T, Dürmuth M. Proof-of-Vax: Studying User Preferences and Perception of Covid Vaccination Certificates. Proceedings on Privacy Enhancing Technologies 2022;2022(1):317 View
  17. Martens M, De Wolf R, Vadendriessche K, Evens T, De Marez L. Applying contextual integrity to digital contact tracing and automated triage for hospitals during COVID-19. Technology in Society 2021;67:101748 View
  18. Liu T, Meyerhoff J, Eichstaedt J, Karr C, Kaiser S, Kording K, Mohr D, Ungar L. The relationship between text message sentiment and self-reported depression. Journal of Affective Disorders 2022;302:7 View
  19. Nilsen P, Svedberg P, Nygren J, Frideros M, Johansson J, Schueller S. Accelerating the impact of artificial intelligence in mental healthcare through implementation science. Implementation Research and Practice 2022;3 View
  20. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  21. Thornton L, Osman B, Champion K, Green O, Wescott A, Gardner L, Stewart C, Visontay R, Whife J, Parmenter B, Birrell L, Bryant Z, Chapman C, Lubans D, Slade T, Torous J, Teesson M, Van de Ven P. Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review. JMIR mHealth and uHealth 2022;10(2):e27337 View
  22. Koumpouros Y. User-Centric Design Methodology for mHealth Apps: The PainApp Paradigm for Chronic Pain. Technologies 2022;10(1):25 View
  23. Orr M, MacLeod L, Bagnell A, McGrath P, Wozney L, Meier S. The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. Computers in Human Behavior 2023;140:107603 View
  24. Jacobson N, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1) View
  25. Cooper J, Scarf D, Conner T. University students’ opinions towards mobile sensing data collection: A qualitative analysis. Frontiers in Digital Health 2023;5 View
  26. Martens M, De Wolf R, De Marez L. Datafication and algorithmization of education: How do parents and students evaluate the appropriateness of learning analytics?. Education and Information Technologies 2024;29(7):8151 View
  27. Wyant K, Moshontz H, Ward S, Fronk G, Curtin J. Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study. JMIR mHealth and uHealth 2023;11:e41833 View
  28. Terhorst Y, Weilbacher N, Suda C, Simon L, Messner E, Sander L, Baumeister H. Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial. Frontiers in Digital Health 2023;5 View
  29. Rathbone A, Stumpf S, Claisse C, Sillence E, Coventry L, Brown R, Durrant A, Kocaballi B. People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design. PLOS Digital Health 2023;2(5):e0000264 View
  30. Lang M, McKibbin K, Shabani M, Borry P, Gautrais V, Verbeke K, Zawati M. Crowdsourcing smartphone data for biomedical research: Ethical and legal questions. DIGITAL HEALTH 2023;9 View
  31. Kowalewski M, Utz C, Degeling M, Schnitzler T, Herbert F, Schaewitz L, Farke F, Becker S, Dürmuth M. 52 Weeks Later: Attitudes Towards COVID-19 Apps for Different Purposes Over Time. Proceedings of the ACM on Human-Computer Interaction 2023;7(CSCW2):1 View
  32. Baines R, Stevens S, Austin D, Anil K, Bradwell H, Cooper L, Maramba I, Chatterjee A, Leigh S. Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review. Journal of Medical Internet Research 2024;26:e50421 View
  33. de Azevedo Cardoso T, Kochhar S, Torous J, Morton E. Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions. JMIR Mental Health 2024;11:e58631 View
  34. Rottstädt F, Becker E, Wilz G, Croy I, Baumeister H, Terhorst Y. Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  35. Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  36. O’Neill C, Duckworth E, Shah R, Jayakumar P. Evaluating Patient Perceptions of Smartphone Use for Active and Passive Collection of Health Data. Current Orthopaedic Practice 2024;35(6):250 View
  37. Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e51875 View
  38. Terhorst Y, Messner E, Asare K, Montag C, Kannen C, Baumeister H. Which Smartphone-Based Sensing Features Matter in Depression Severity Prediction? Results from an Observation Study. (Preprint). Journal of Medical Internet Research 2023 View
  39. Ahuja N, Gulabani M, Ahuja N. Factors affecting U.S. adults’ comfort level in sharing social needs information with healthcare providers. Patient Education and Counseling 2025;130:108493 View

Books/Policy Documents

  1. Marvel F, Huynh P, Martin S. Precision Medicine in Cardiovascular Disease Prevention. View
  2. Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  3. Garatva P, Terhorst Y, Messner E, Karlen W, Pryss R, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  4. Flore J. The Artefacts of Digital Mental Health. View