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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77331, first published .
Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review

Journals

  1. Yang N, Yang X. Uncovering eHealth Engagement Patterns Through Latent Class Analysis and SHAP: A Data Mining Perspective on Telehealth Access. Information 2026;17(2):215 View
  2. Xu Y, Zhao Z, Zhu H, Lai S, Jia Y, Liu G. Machine Learning-Empowered Depression Detection With Wearable Skin Electronics: A Review. IEEE Sensors Journal 2026;26(6):7949 View
  3. Lee H, Kang S, Lee S. The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2026;28:e76432 View
  4. Ma N, Zhang X, Qu B, Wang W. EAVS-TF: A bio-inspired spiking neural network for energy-efficient multimodal emotion recognition. Current Radiopharmaceuticals 2026;19(3):100041 View
  5. Emanuele E, Minoretti P. Monitoring airline pilot mental health: a 3PM framework utilising digital phenotyping and AI. EPMA Journal 2026 View
  6. Torales J, O’Higgins M, Barrios I, Ventriglio A, Castaldelli-Maia J, Smith A, Liebrenz M. From Mood Episodes to Digital Signatures: Passive and Active Phenotyping of Bipolar Disorder Over Time. International Journal of Social Psychiatry 2026 View
  7. ElBarazi A, Mohamed H, Nasser R. Exploring Students’ Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar. Healthcare 2026;14(9):1247 View