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Prediction meets time series with gaps: User clusters with specific usage behavior patterns.

Miro Schleicher1, Vishnu Unnikrishnan1, Rüdiger Pryss2

  • 1Knowledge Management & Discovery Lab, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.

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Summary
This summary is machine-generated.

This study introduces a new method to analyze user engagement in mobile health (mHealth) apps. It helps predict user dropout rates and identify adherence patterns, improving data analysis for treatments.

Keywords:
AdherenceChronic diseasesLaw of attritionTime series with gapsmHealth

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Area of Science:

  • Digital Health
  • Machine Learning
  • Time Series Analysis

Background:

  • Mobile health (mHealth) apps collect real-world data for treatments but suffer from fluctuating engagement and high user dropout rates.
  • These data challenges hinder machine learning (ML) analysis and understanding of user adherence.
  • Identifying user disengagement is crucial for effective mHealth interventions.

Purpose of the Study:

  • To develop a method for identifying and predicting varying dropout rates in mHealth app datasets.
  • To predict periods of user inactivity based on their current engagement state.
  • To analyze the evolution of adherence within different user clusters.

Main Methods:

  • Utilized change point detection to identify distinct phases of user engagement and dropout.
  • Employed time series classification to predict user phases based on their activity.
  • Addressed challenges of uneven, misaligned time series with missing values.
  • Evaluated the method on an mHealth app dataset for tinnitus management.

Main Results:

  • Successfully identified phases with different dropout rates and predicted future user behavior.
  • Demonstrated the ability to predict expected inactivity periods for individual users.
  • Showcased the evolution of adherence across different user clusters.
  • Validated the approach's effectiveness on real-world mHealth data.

Conclusions:

  • The proposed method effectively handles uneven, unaligned time series data common in mHealth apps.
  • This approach is suitable for studying user adherence in datasets with missing values and variable lengths.
  • The findings can improve the reliability of data analysis and intervention strategies in mHealth.
  • Accurate prediction of user dropout and adherence is vital for optimizing digital health tools.