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

Depression and Mood Disorders; Suicide Prevention (1273) Wearable Devices and Sensors (812)

Published on in Vol 7, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21283, first published .
Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

Authors of this article:

Chul-Hyun Cho1, 2, 3 Author Orcid Image ;   Taek Lee4 Author Orcid Image ;   Jung-Been Lee5 Author Orcid Image ;   Ju Yeon Seo3, 6 Author Orcid Image ;   Hee-Jung Jee7 Author Orcid Image ;   Serhim Son7 Author Orcid Image ;   Hyonggin An7 Author Orcid Image ;   Leen Kim3 Author Orcid Image ;   Heon-Jeong Lee3, 6 Author Orcid Image
Article Authors Cited by (23) Tweetations (4) Metrics

Journals

  1. Balcombe L, De Leo D. Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review. JMIR Formative Research 2020;4(12):e22755 View
  2. Yunusova A, Lai J, Rivera A, Hu S, Labbaf S, Rahmani A, Dutt N, Jain R, Borelli J. Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study. JMIR Research Protocols 2021;10(3):e25775 View
  3. Cho C, Lee H. Applying Circadian Rhythm Concepts in Digital Healthcare. Chronobiology in Medicine 2021;3(1):1 View
  4. Dang A, Dang D, Rane P. The Expanding Role of Digital Therapeutics in the Post-COVID-19 Era. The Open COVID Journal 2021;1(1):32 View
  5. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  6. Kang Y, Sun T, Kim G, Jung H, Kim H, Lee S, Park Y, Tu J, Lee J, Choi K, Cho C. Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., 心) Project. Psychiatry Investigation 2022;19(7):588 View
  7. Ortiz A, Maslej M, Husain M, Daskalakis Z, Mulsant B. Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction. Journal of Affective Disorders 2021;295:1190 View
  8. Gual-Montolio P, Jaén I, Martínez-Borba V, Castilla D, Suso-Ribera C. Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. International Journal of Environmental Research and Public Health 2022;19(13):7737 View
  9. Smit A, Snippe E. Real-time monitoring of increases in restlessness to assess idiographic risk of recurrence of depressive symptoms. Psychological Medicine 2023;53(11):5060 View
  10. Lee J, Choi H. Effects of Using Mobile Apps for Mental Health Care in Korea: A Systematic Review. Journal of Korean Academy of psychiatric and Mental Health Nursing 2022;31(1):88 View
  11. Kwon M, Jung Y, Lee D, Ahn J. Mental Health Problems During COVID-19 and Attitudes Toward Digital Therapeutics. Psychiatry Investigation 2023;20(1):52 View
  12. Saccaro L, Amatori G, Cappelli A, Mazziotti R, Dell'Osso L, Rutigliano G. Portable technologies for digital phenotyping of bipolar disorder: A systematic review. Journal of Affective Disorders 2021;295:323 View
  13. Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E, Narasimhan P. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digital Health 2022;1(8):e0000079 View
  14. Sun T, Yeom J, Choi K, Kim J, Lee H, Kim H, Cho C. Potential effectiveness of digital therapeutics specialized in executive functions as adjunctive treatment for clinical symptoms of attention-deficit/hyperactivity disorder: a feasibility study. Frontiers in Psychiatry 2023;14 View
  15. Xi N, Buruk O, Chen J, Jabari S, Hamari J. Wearable gaming technology: A study on the relationships between wearable features and gameful experiences. International Journal of Human-Computer Studies 2024;181:103157 View
  16. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778 View
  17. Orsolini L, Longo G, Volpe U. Practical application of digital therapeutics in people with mood disorders. Current Opinion in Psychiatry 2024;37(1):9 View
  18. Johnson N, Venturo-Conerly K, Rusch T. Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya. Cambridge Prisms: Global Mental Health 2023;10 View
  19. Sung S, Kim S, Kim Y, Bae Y, Chie E. Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis. Frontiers in Public Health 2024;12 View
  20. Huang H, Tian X, Lam B, Lu W, Li X, He S, Xu X, Zhang R, Wang R, Li D, Gao Y, Chen N, Wu S, Xu G, Lin K. The validity and reliability of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry in the community: a large Chinese college student population. Frontiers in Psychiatry 2024;15 View
  21. Bucher A, Blazek E, Symons C. How Are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024 View

Books/Policy Documents

  1. Wilson C, Crouse J, Carpenter J, Hickie I. Encyclopedia of Sleep and Circadian Rhythms. View
  2. Mondragón-González S, Burguière E, N’diaye K. Machine Learning for Brain Disorders. View