An open source mobile platform for psychophysiological self tracking

Area of Science:

• Digital Health
• e-Health
• Mental Health Technology

Background:

• Self-tracking is an emerging e-health trend utilizing ubiquitous computing for personal health data management.
• Mobile devices and wearable sensors are key tools for collecting and visualizing health information.
• Existing platforms may not be optimized for the specific needs of mental health research.

Purpose of the Study:

• To design and describe a mobile self-tracking platform tailored for mental health clinical and research applications.
• To enable the collection of diverse personal health data streams through a single smartphone application.
• To foster wider adoption within the research community by providing an open-source solution.

Main Methods:

• Development of a smartphone-based application integrating multiple data collection functionalities.
• Incorporation of pre-programmed questionnaires for self-reported feelings and activities.
• Integration of wireless sensor platforms for collecting electrocardiographic (ECG) data and tri-axis accelerometer data for movement activity.

Main Results:

• The platform successfully integrates self-reported data, ECG, and activity metrics on a smartphone.
• Physiological signals are processed and stored directly on the user's smartphone.
• The mobile data collection platform is released as free, open-source software.

Conclusions:

• The developed mobile platform offers a comprehensive solution for self-tracking in mental health research.
• Its open-source nature facilitates accessibility and encourages collaborative research efforts.
• This tool has the potential to advance the collection and analysis of digital phenotypes in mental health.