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

Digital Biomarkers and Digital Phenotyping (177) Depression and Mood Disorders; Suicide Prevention (1271) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) Machine Learning (1370) Wearable Devices and Sensors (811) Diagnostic Tools in Mental Health (220) Methods and New Tools in Mental Health Research (424) mHealth for Data Collection and Research (907) Mobile Health in Psychiatry (399) New Methods (901)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24365, first published .
Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Authors of this article:

Ran Bai1, 2 Author Orcid Image ;   Le Xiao3 Author Orcid Image ;   Yu Guo4 Author Orcid Image ;   Xuequan Zhu3 Author Orcid Image ;   Nanxi Li3 Author Orcid Image ;   Yashen Wang2 Author Orcid Image ;   Qinqin Chen2 Author Orcid Image ;   Lei Feng3 Author Orcid Image ;   Yinghua Wang2 Author Orcid Image ;   Xiangyi Yu2 Author Orcid Image ;   Chunxue Wang5 Author Orcid Image ;   Yongdong Hu6 Author Orcid Image ;   Zhandong Liu7 Author Orcid Image ;   Haiyong Xie1, 2 Author Orcid Image ;   Gang Wang1, 3 Author Orcid Image
Article Authors Cited by (23) Tweetations (3) Metrics

Journals

  1. Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatric Disease and Treatment 2021;Volume 17:3415 View
  2. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  3. Mouchabac S, Maatoug R, Conejero I, Adrien V, Bonnot O, Millet B, Ferreri F, Bourla A. In Search of Digital Dopamine: How Apps Can Motivate Depressed Patients, a Review and Conceptual Analysis. Brain Sciences 2021;11(11):1454 View
  4. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  5. Dlima S, Shevade S, Menezes S, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e39618 View
  6. Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Formative Research 2023;7:e42935 View
  7. Abd-alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. Journal of Medical Internet Research 2023;25:e42672 View
  8. Ates H, Nguyen P, Gonzalez-Macia L, Morales-Narváez E, Güder F, Collins J, Dincer C. End-to-end design of wearable sensors. Nature Reviews Materials 2022;7(11):887 View
  9. Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Mental Health 2023;10:e42045 View
  10. Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Frontiers in Psychiatry 2022;13 View
  11. Zou B, Zhang X, Xiao L, Bai R, Li X, Liang H, Ma H, Wang G. Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1786 View
  12. Anmella G, Corponi F, Li B, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young A, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study. JMIR mHealth and uHealth 2023;11:e45405 View
  13. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View
  14. ZhuParris A, de Goede A, Yocarini I, Kraaij W, Groeneveld G, Doll R. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. Sensors 2023;23(11):5243 View
  15. 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
  16. Park D, Lim S, Choi Y, Oh H. Depression Emotion Multi-Label Classification Using Everytime Platform With DSM-5 Diagnostic Criteria. IEEE Access 2023;11:89093 View
  17. El Dahr Y, Perquier F, Moloney M, Woo G, Dobrin-De Grace R, Carvalho D, Addario N, Cameron E, Roos L, Szatmari P, Aitken M. Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study. JMIR Formative Research 2023;7:e42916 View
  18. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
  19. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  20. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  21. Lee J, Kim M, Hwang S, Lee K, Park J, Shin T, Lim H, Urtnasan E, Chung M, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024;14(6):e073290 View

Books/Policy Documents

  1. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  2. Garatva P, Baumeister H. Psychologische Begutachtung. View