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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review
Rongrong Zhong1, XiaoHui Wu1, Jun Chen1
1Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Journal of Medical Internet Research
|May 23, 2025
Summary
Digital phenotyping shows promise in differentiating bipolar disorder (BD) from unipolar depression (UD) by analyzing activity patterns and speech. Further research is needed to address privacy and access challenges for clinical use.
Area of Science:
- Digital Health
- Mental Health Technology
- Computational Psychiatry
Background:
- Distinguishing bipolar disorder (BD) from unipolar depression (UD) is critical due to differing prognoses and treatments.
- Digital phenotyping, utilizing data from digital devices, offers a novel approach for mental health assessment.
- This review focuses on digital phenotyping's role in differentiating BD and UD.
Purpose of the Study:
- To systematically review literature on digital phenotyping for distinguishing between UD and BD.
- To review studies classifying UD, BD, and healthy controls (HC) using digital phenotyping.
- To identify research gaps and suggest future directions in this field.
Main Methods:
- Systematic search of multiple databases (Scopus, PubMed, etc.) up to March 20, 2025.
- Inclusion of original studies using portable/wearable digital tools for UD/BD distinction or classification.
- Exclusion of reviews and studies not based on professional medical evaluation or relying solely on EHR/clinical data.
Main Results:
- 21 studies were included, with 11 distinguishing UD/BD and 10 classifying UD/BD/HC.
- Smartphone apps and wearable devices analyzed activity levels, with BD patients showing lower activity and different daily patterns than UD patients.
- Audiovisual recordings and multimodal approaches, particularly speech, improved classification accuracy for UD, BD, and HC groups.
Conclusions:
- Digital phenotyping demonstrates potential for differentiating BD from UD.
- Key challenges include data privacy, security, and ensuring equitable access.
- Future research should focus on overcoming these obstacles to enhance clinical applicability.
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