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Dynamic Modeling and System Identification of User Engagement in mHealth Interventions using a Bayesian Approach for

Mohamed El Mistiri1, Steven De La Torre2, Benjamin M Marlin3

  • 1Control Systems Engineering Laboratory in the Chemical Engineering Department, School for Engineering of Matter, Transport at Arizona State University, Tempe, 85282, Arizona, USA.

Control Engineering Practice
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

Digital behavior change interventions (DBCIs) improve health behaviors but require user engagement. This study introduces a Bayesian imputation method to accurately model engagement dynamics and handle missing data in DBCIs.

Keywords:
Bayesian methodsControl-oriented behavioral interventionsdynamic modeling for social science applicationseHealthmissing data

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Area of Science:

  • Control Systems Engineering
  • Digital Health
  • Behavioral Science

Background:

  • Digital behavior change interventions (DBCIs) show promise for improving health behaviors.
  • User engagement with digital tools and interventions is crucial for DBCI effectiveness.
  • Engagement patterns evolve based on individual context and psychological state.

Purpose of the Study:

  • To model user engagement in DBCIs as a dynamical system.
  • To address missing data challenges in engagement tracking using a novel Bayesian imputation method.
  • To quantify imputation uncertainty for robust closed-loop intervention design.

Main Methods:

  • Modeling engagement data from the HeartSteps II study as a dynamical system.
  • Applying prediction-error methods from system identification.
  • Utilizing a novel Bayesian imputation technique to handle missing engagement data.

Main Results:

  • The Bayesian imputation method provides more accurate data imputation than traditional approaches.
  • The approach quantifies uncertainty arising from imputation and data scarcity.
  • Insights into factors influencing engagement behavior over time and context were gained.

Conclusions:

  • Accurate modeling of engagement dynamics is essential for effective DBCIs.
  • Bayesian imputation offers a robust solution for handling missing data in engagement studies.
  • This work supports the development of control engineering-based digital health interventions.