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Addressing people's current and future states in a reinforcement learning algorithm for persuading to quit smoking
Nele Albers1, Mark A Neerincx1,2, Willem-Paul Brinkman1
1Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands.
Plos One
|December 1, 2022
Summary
This study found that more complex algorithms, incorporating user state and peer similarity, are more effective for persuasive messages in behavior change apps. These advanced methods particularly benefit users in later sessions and those finding activities helpful.
Area of Science:
- Behavioral Science
- Human-Computer Interaction
- Computational Social Science
Background:
- Behavior change applications utilize persuasive messages to encourage user engagement in activities like smoking cessation or physical activity.
- Current methods for selecting persuasive messages often rely on simple historical effectiveness, potentially missing opportunities for optimization.
Purpose of the Study:
- To investigate the added value of incorporating user-specific factors into algorithms for selecting persuasive messages.
- To compare the effectiveness of simple versus complex persuasion algorithms in a virtual coaching context.
Main Methods:
- An experiment was conducted with over 500 participants interacting with a text-based virtual coach over five sessions.
- Participants received preparatory activities for smoking cessation or physical activity, paired with persuasive messages selected by algorithms with varying complexity.
- Data on user state, future state, and peer similarity were considered in more complex algorithms.
Main Results:
- Algorithms incorporating additional elements (user state, peer similarity) demonstrated increased effectiveness in message selection.
- The benefits of complex algorithms were more pronounced in later intervention sessions and for users who perceived the activities as useful.
- Limited generalizability of optimal policies was observed between smoking cessation and physical activity interventions.
Conclusions:
- Constructing more complex persuasion algorithms that consider a wider range of user-specific factors is supported by these findings.
- Personalized persuasion strategies can enhance the efficacy of digital health interventions.
- The study provides a dataset of persuasive messages for future research in algorithm development.
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