Addressing people's current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active.

Autor: Albers N; Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands., Neerincx MA; Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands.; Department of Perceptual and Cognitive Systems, Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO), Soesterberg, The Netherlands., Brinkman WP; Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Dec 01; Vol. 17 (12), pp. e0277295. Date of Electronic Publication: 2022 Dec 01 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0277295
Abstrakt: Behavior change applications often assign their users activities such as tracking the number of smoked cigarettes or planning a running route. To help a user complete these activities, an application can persuade them in many ways. For example, it may help the user create a plan or mention the experience of peers. Intuitively, the application should thereby pick the message that is most likely to be motivating. In the simplest case, this could be the message that has been most effective in the past. However, one could consider several other elements in an algorithm to choose a message. Possible elements include the user's current state (e.g., self-efficacy), the user's future state after reading a message, and the user's similarity to the users on which data has been gathered. To test the added value of subsequently incorporating these elements into an algorithm that selects persuasive messages, we conducted an experiment in which more than 500 people in four conditions interacted with a text-based virtual coach. The experiment consisted of five sessions, in each of which participants were suggested a preparatory activity for quitting smoking or increasing physical activity together with a persuasive message. Our findings suggest that adding more elements to the algorithm is effective, especially in later sessions and for people who thought the activities were useful. Moreover, while we found some support for transferring knowledge between the two activity types, there was rather low agreement between the optimal policies computed separately for the two activity types. This suggests limited policy generalizability between activities for quitting smoking and those for increasing physical activity. We see our results as supporting the idea of constructing more complex persuasion algorithms. Our dataset on 2,366 persuasive messages sent to 671 people is published together with this article for researchers to build on our algorithm.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2022 Albers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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