JMIR Publications

ABSTRACT

Background:

Mental health disorders are increasingly prevalent globally, leading to a growing reliance on digital health interventions, particularly mental health mobile applications. These apps provide accessible mental health support but their effectiveness largely depends on user engagement and adherence.

Objective:

This study aims to analyze user usage patterns and adherence in mental health mobile applications using simulated data to identify key factors influencing user engagement and adherence.

Methods:

We generated a robust simulated dataset representing real-world usage scenarios, including variables such as weekly session counts, average session durations, and features used. The K-means clustering algorithm was employed to categorize users into three distinct clusters based on their engagement levels. Statistical analyses were conducted to examine the distribution of session counts, session durations, and feature usage.

Results:

The analysis identified three user clusters: low engagement, moderate engagement, and high engagement. Most users engaged with the app on a near-daily basis, with sessions averaging around 20 minutes. Meditation was the most frequently used feature, followed by courses and mood tracking. These findings highlight the diverse engagement levels among users and the importance of tailoring app features to different user needs.

Conclusions:

Understanding user behavior through simulated data provides valuable insights for optimizing mental health app design. By integrating personalized feedback and tailoring features to meet user preferences, developers can enhance user adherence and engagement. Future research should validate these findings with real-world data and leverage advanced analytics to further refine digital mental health interventions.