Building the foundations for global data banking in digital phenotyping for mental health

Area of Science:

• Digital phenotyping and mental health research.
• Leveraging mobile and wearable sensor data for psychiatric insights.

Background:

• Mental illness is a major global disability, necessitating scalable, data-driven early identification and intervention strategies.
• Passive sensing technologies offer continuous, unobtrusive measurement of behavioral and physiological signals relevant to mental health.

Purpose of the Study:

• To outline four key priorities for advancing digital phenotyping databanks in depression and anxiety research.
• To propose a framework for building equitable, transparent, and scientifically robust digital mental health databanks.

Main Methods:

• Informed by international initiatives, academic literature, and expertise in digital phenotyping.
• Outlined priorities focus on data quality, ethical data stewardship, privacy-preserving governance, and promoting data reuse.

Main Results:

• Ensuring data quality via standardization and harmonization is essential for comparability and preventing fragmentation.
• Ethical data stewardship requires hybrid consent models for participant control.
• Privacy-preserving governance, co-created with lived experience, and technical pathways like federated learning are vital.
• Reforming incentives and investing in infrastructure are needed to promote data reuse.

Conclusions:

• These priorities offer a pragmatic framework for equitable, transparent, and robust digital mental health databanks.
• Sustained international collaboration is required for implementation.
• Digital phenotyping databanks can be transformative tools for understanding and treating mental illness when scientific rigor aligns with ethical responsibility.