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Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven

Shihan Wang^1,2, Chao Zhang^3,4, Ben Kröse^5,6

¹Informatics Institute, University of Amsterdam, Amsterdam, Netherlands. s.wang2@uu.nl.

Journal of Medical Systems

|October 19, 2021

Summary

This summary is machine-generated.

This study introduces a reinforcement learning (RL) method for mobile health (mHealth) interventions to optimize notification delivery. The approach reduces user burden by learning adaptive strategies from data, improving behavioral impact.

Keywords:

Adaptive agent Human simulator Just-in-time adaptive intervention Mobile health intervention Reinforcement learning

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Area of Science:

Digital Health
Artificial Intelligence
Behavioral Science

Background:

Mobile health (mHealth) systems can use adaptive strategies for user interaction.
Reinforcement learning (RL) offers a way to optimize these strategies based on user context.
Designing effective adaptive strategies manually is complex and challenging.

Purpose of the Study:

To address the issue of overwhelming interactions in RL-based mHealth intervention agents.
To develop a data-driven approach for optimizing context-aware notification strategies.
To reduce user interaction burden while enhancing the behavioral impact of mHealth interventions.

Main Methods:

Integrated psychological insights and historical data for a data-driven approach.
Utilized RL to optimize context-aware notification delivery strategies.
Incorporated a constraint on notification frequency to minimize user burden.
Handled missing counterfactual information (user responses) during the learning process.

Main Results:

The proposed RL agent demonstrated an ability to optimize notification delivery strategies.
The approach effectively learned from empirical data, even with missing counterfactual information.
Notification frequency constraints successfully reduced the interaction burden on users.
Evaluations in simulation scenarios using real-world data showed improved behavioral impact compared to context-blind strategies.

Conclusions:

The developed RL approach provides an effective method for optimizing mHealth intervention strategies.
This data-driven technique enhances user engagement and intervention effectiveness by delivering timely, context-aware notifications.
The findings suggest a promising direction for creating more personalized and less intrusive digital health solutions.