Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems

Area of Science:

• Digital psychiatry
• Biomarker research
• Mental health technology

Background:

• Current major depressive disorder diagnosis relies on subjective methods like interviews and self-reports, prone to bias.
• These traditional approaches fail to capture real-time symptom fluctuations.
• There's a need for objective, dynamic measures in depression assessment.

Purpose of the Study:

• To review the current landscape of digital biomarkers for depression.
• To critically evaluate their clinical relevance and real-world application.
• To assess alignment with Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision (DSM-5-TR) and Research Domain Criteria (RDoC) frameworks.

Main Methods:

• Systematic review of studies on passive digital biomarkers.
• Analysis of correlations between digital data and depression symptom domains.
• Evaluation of biomarker alignment with established diagnostic and research criteria.

Main Results:

• Passive digital biomarkers (activity, sleep, speech, communication) correlate with key depression symptoms like anhedonia, psychomotor changes, and social withdrawal.
• Combining DSM-5-TR symptoms with RDoC constructs enhances digital phenotyping interpretability.
• Digital phenotyping offers potential for individualized and sensitive depression monitoring.

Conclusions:

• Digital biomarkers offer objective insights into depression, complementing traditional assessments.
• Integrating digital phenotyping with clinical frameworks is crucial for practical application.
• Future digital psychiatry should prioritize multimodal data, construct alignment, and clinician feedback for holistic patient care.