Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review

Journals

1. Saxena A, Saxena S, Saxena Y, Jain A, Goyal P, Kumar A, Mishra S, Kumar S. Bipolar Disorder in the Era of Precision Psychiatry: Biomarkers, Digital Innovations, and Global Challenges. Journal for Research in Applied Sciences and Biotechnology 2025;4(5):1 View
2. Diler R, Vahedifard F, Birmaher B, Iyengar S, Wolfe M, Lepore B, Chobany M, Abdul-Waalee H, Malgireddy G, Hart J, Bertocci M. Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity. JMIR Mental Health 2025;12:e78163 View
3. Morelli S, Giansanti D. Recent Advances in AI-Driven Mobile Health Enhancing Healthcare—Narrative Insights into Latest Progress. Bioengineering 2025;13(1):54 View
4. Simon L, Steubl L, Terhorst Y, Cohrdes C, Pryss R, Baumeister H. Digital Phenotyping – Lessons Learned, Challenges, and Future Perspectives for Psychodiagnostics. Diagnostica 2026;72(1):29 View
5. Zhong R, Zhang Y, Wu X, Cui L, Lin J, Lv X, Chen J, Chen S, Folarin A, Dobson R, Chen J, Fang Y. Differentiating unipolar and bipolar depression using multi-task eye-movement features. Psychiatry Research 2026;360:117067 View
6. Trinhammer M, Graßhof S, Kessing L, Kjærstad H, Miskowiak K, Brandt S. Don’t predict if you cannot interpret: investigating the clinical viability of facial movements for machine-learning assisted diagnostics of bipolar disorder. Nordic Journal of Psychiatry 2026;80(3):198 View
7. Ryu V. Trends in pharmacotherapy and guideline comparisons in bipolar disorder: a narrative review. Journal of the Korean Medical Association 2026;69(3):217 View