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Using mobile meditation app data to predict future app engagement: an observational study.

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Predicting meditation app abandonment requires approximately 64 days of user data. This insight helps target interventions to support long-term engagement with digital mental health tools.

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Area of Science:

  • Digital health
  • Behavioral science
  • Machine learning

Background:

  • Mobile meditation apps offer mental and physical health benefits.
  • Sustained engagement is crucial for long-term health improvements.
  • High dropout rates limit the effectiveness of meditation apps.

Purpose of the Study:

  • To identify the minimum data collection period for accurately predicting future meditation app abandonment.
  • To enable targeted behavioral support for users at risk of disengaging.

Main Methods:

  • Utilized 365 days of usage data from 2600 Calm app subscribers.
  • Engineered features: daily sessions, daily duration, and temporal similarity (DTW).
  • Applied random forest models and exponential growth curves to predict future engagement.

Main Results:

  • 83.1% of users engaged initially, but 58.0% abandoned by day 350.
  • Accurate prediction of future app abandonment required an average of 64 days of user data.
  • Identified key user engagement metrics for predictive modeling.

Conclusions:

  • Predicting meditation app abandonment is feasible with approximately 64 days of usage data.
  • This predictive capability allows for timely, targeted interventions to improve user retention.
  • Findings align with habit formation timelines, supporting the development of digital health interventions.