Designing a Visual Analytics Tool to Support Data Analysis Tasks of Digital Mental Health Interventions: Case Study.

Area of Science:

• Digital Health
• Health Informatics
• Human-Computer Interaction

Background:

• Digital health interventions (DHIs) generate valuable user data for improving health management.
• Current DHI data analysis practices are fragmented, hindering comprehensive understanding.
• Data-driven insights are crucial for post-deployment intervention improvements.

Purpose of the Study:

• Propose an analysis task model for holistic DHI data analysis.
• Develop an interactive visual analytics tool based on the model.
• Evaluate the model's suitability and the tool's task support for DHI research.

Main Methods:

• Constructed a DHI data analysis model with user characteristics, engagement, and effectiveness components.
• Designed Maum Health Analytics, a visual analytics tool prototype, mapping features to the model.
• Conducted a user study with 15 researchers using real-world DHI data and interviews.

Main Results:

• Researchers found the analysis model and tool positively supported identifying users needing care and informing recommendations.
• The tool facilitated analysis of intervention effectiveness relative to user characteristics and engagement.
• Participants highlighted the tool's role in simplifying analytics and improving multidisciplinary communication.

Conclusions:

• The proposed model and visual analytics tool offer a holistic approach to DHI data analysis.
• The tool simplifies complex analytic tasks and enhances collaboration among researchers.
• This integrated approach meets growing DHI data analysis needs and boosts efficiency.