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

Depression and Mood Disorders; Suicide Prevention (1691) Wearable Devices and Sensors (1133)

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 (46) Tweetations (4) Metrics

Journals

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  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
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  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
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  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
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  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;2(3):375 View
  22. Lo H, Ho F, Yeung J, Ng S, Wong E, Chung K. Self-help interventions for the prevention of relapse in mood disorder: a systematic review and meta-analysis. Family Practice 2024;41(5):662 View
  23. Yeom J, Yoon Y, Seo J, Cho C, Lee T, Lee J, Jeon S, Kim L, Lee H. Daily Self-Monitoring and Feedback of Circadian Rhythm Measures in Major Depression and Bipolar Disorder Using Wearable Devices and Smartphones–The Circadian Rhythm for Mood (CRM®) Trial Protocol: A Randomized Sham Controlled Double-Blind Trial. Psychiatry Investigation 2024;21(8):918 View
  24. Yao H, Liao Z, Zhang X, Zhang X, Li M, You L, Liu Y. A comprehensive survey of the clinical trial Landscape on digital therapeutics. Heliyon 2024;10(16):e36115 View
  25. Olfermann R, Schlegel S, Vogelsang A, Ebner‐Priemer U, Zeeck A, Reichert M. Relationship between nonexercise activity and mood in patients with eating disorders. Acta Psychiatrica Scandinavica 2025;151(3):448 View
  26. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View
  27. Weingott S, Parkinson J. The application of artificial intelligence in health communication development: A scoping review. Health Marketing Quarterly 2025;42(1):67 View
  28. Jang S, Sun T, Shin S, Lee H, Shin Y, Yeom J, Park Y, Cho C. A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study. Scientific Data 2024;11(1) View
  29. Cho C. Why Behavioral Medicine Is Critical for Circadian Health in the Digital Era?. Chronobiology in Medicine 2024;6(4):143 View
  30. Eisner E, Faulkner S, Allan S, Ball H, Di Basilio D, Nicholas J, Priyam A, Wilson P, Zhang X, Bucci S. Barriers and Facilitators of User Engagement With Digital Mental Health Interventions for People With Psychosis or Bipolar Disorder: Systematic Review and Best-Fit Framework Synthesis. JMIR Mental Health 2025;12:e65246 View
  31. Yang L, Zhang M, Jia L, Yan Z, Yin Q. Understanding digital therapeutics in disease self-management: A systematic literature review. Technology in Society 2025;81:102831 View
  32. Yeom J, Lee J, Kim H, Park S, Cho C, Kim L, Lee T, Lee H. Development and Validation of a Wearable-Based Monitoring System for Predicting Psychiatric Crises in Inpatients: Study Protocol. Chronobiology in Medicine 2025;7(1):40 View
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  34. Piotr Łapiński , Mateusz Świątko , Anna Tokarska , Marcin Grzebyk , Aleksandra Arnista , Joanna Rybak , Katarzyna Gawrońska , Agnieszka Waszczuk , Aleksandra Kołodziejczyk , Paweł Sosnowski . PREDICTING AFFECTIVE EPISODES THROUGH DIGITAL MONITORING OF CIRCADIAN RHYTHM DISTURBANCES: A SYSTEMATIC REVIEW OF MODERN TECHNOLOGY APPLICATIONS. International Journal of Innovative Technologies in Social Science 2025;(2(46)) View
  35. Choi W, Ahn B, Moorthy G, Do K. AI-assisted care for older adults: a review of practical and ethical areas of concern. AI and Ethics 2025 View
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  37. Amin R, Schreynemackers S, Oppenheimer H, Petrovic M, Hegerl U, Reich H. Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review. Journal of Medical Internet Research 2025;27:e57418 View
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  39. Jung H, Kim D, Lee I, Kim O, Lee S, Lee S, Chung U, Kim J, Kim S, Kim J, Shin A, Lee J. Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2025;27:e77331 View
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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
  3. Cho C, Lee H, Kim Y. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View
  4. Cho C, Lee H, Kim Y. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View