Exploring digital health user engagement: General app usage patterns from a clinical trial with the mLab App

Area of Science:

• Digital Health
• Health Informatics
• Mobile Health Interventions

Background:

• Digital health applications are rapidly expanding but often face sustainability and scalability challenges.
• Paradata, or user interaction data, offers a method to understand user behavior within software without disrupting the user experience.

Purpose of the Study:

• To analyze paradata from a mobile health application designed for at-home HIV testing.
• To identify user behavior patterns, feature usage, and navigational paths within the application.

Main Methods:

• Analysis of longitudinal, session-level interaction logs from the mLab App.
• Evaluation of application-level usage statistics and temporal patterns in user activity.
• Study conducted within a multi-site randomized clinical trial involving at-risk populations.

Main Results:

• Observed distinct temporal patterns in login events and HIV testing behaviors.
• Identified feature gaps and common user navigational pathways within the application.
• Demonstrated the value of paradata in understanding user engagement with digital health tools.

Conclusions:

• Paradata, though often underutilized, provides crucial insights into user behavior for digital health applications.
• Analyzing paradata can guide targeted revisions, enhance feature development, and improve the long-term impact and scalability of digital health interventions.
• This approach deepens the understanding of user engagement, enabling the creation of more effective and sustainable digital health solutions for at-risk populations.