Stanford Screenomics: An Open-source Platform for Unobtrusive Multimodal Digital Trace Data Collection from Android...

Area of Science:

• Digital Health
• Behavioral Science
• Human-Computer Interaction

Background:

• Smartphone digital trace data offers valuable insights into behavioral patterns and health risks.
• Current data collection tools often lack scalability, customizability, transparency, and accessibility.
• There is a need for a robust, adaptable platform for comprehensive digital phenotyping.

Purpose of the Study:

• To develop and present an open-source platform for in-situ capture of multimodal digital traces from smartphones.
• To provide researchers with a customizable and scalable solution for digital data collection.
• To demonstrate the platform's utility in expanding the scope of digital phenotyping research.

Main Methods:

• Developed an open-source platform featuring the Stanford Screenomics Data Collection application for tailored data capture (screenshots, app usage, sensor data).
• Integrated a Dashboard application for real-time participant monitoring, data issue identification, and automated communication.
• Utilized a NoSQL database for secure, HIPAA-compliant data storage and management.

Main Results:

• The platform enables in-situ capture of diverse digital traces, including screenshots, application usage, and sensor data.
• Researchers can customize data types, quality, transfer methods, and upload frequency.
• The system facilitates real-time monitoring and issue resolution, ensuring data integrity.

Conclusions:

• The developed open-source platform addresses limitations in existing digital trace data collection tools.
• It enhances the scalability, customizability, transparency, and accessibility of digital phenotyping research.
• The platform expands the possibilities for studying behavioral patterns and health risks using smartphone data.