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.
PLoS One. 2022 Dec 1;17(12):e0277295. doi: 10.1371/journal.pone.0277295. eCollection 2022.
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.
行为改变应用程序通常会为用户分配活动，例如跟踪吸烟数量或规划跑步路线。为了帮助用户完成这些活动，应用程序可以通过多种方式说服他们。例如，它可以帮助用户制定计划或提及同行的经验。直观地说，应用程序应该选择最有可能激励用户的消息。在最简单的情况下，这可能是过去最有效的消息。但是，在算法中可以考虑其他几个元素来选择消息。可能的元素包括用户的当前状态（例如，自我效能感）、用户阅读消息后的未来状态以及用户与收集数据的用户的相似性。为了测试随后将这些元素纳入选择有说服力的消息的算法中是否有附加值，我们进行了一项实验，在该实验中，有超过 500 人在四种条件下与基于文本的虚拟教练进行了互动。该实验包括五个会话，在每个会话中，参与者都被建议进行戒烟或增加体育活动的预备活动，并附有一条有说服力的信息。我们的研究结果表明，向算法中添加更多元素是有效的，尤其是在后续会话中，对于那些认为活动有用的人来说更是如此。此外，虽然我们发现了在两种活动类型之间转移知识的一些支持，但为两种活动类型分别计算的最优策略之间的一致性相当低。这表明戒烟和增加体育活动之间的活动策略的通用性有限。我们认为我们的结果支持构建更复杂的说服算法的想法。我们的关于发送给 671 人的 2366 条有说服力的消息的数据集与本文一起发布，供研究人员在我们的算法基础上进行构建。
