Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals

Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals

Dierikx, M. (Student TU Delft)
Albers, N. (TU Delft Interactive Intelligence)
Scheltinga, Bouke (University of Twente)
Brinkman, W.P. (TU Delft Interactive Intelligence)

Baghaei, Nilufar (editor)
Ali, Raian (editor)
Win, Khin (editor)
Oyibo, Kiemute (editor)

2024

Goal-setting is commonly used in behavior change applications for physical activity. However, for goals to be effective, they need to be tailored to a user’s situation (e.g., motivation, progress). One way to obtain such goals is a collaborative process in which a healthcare professional and client set a goal together, thus making use of the professional’s expertise and the client’s knowledge about their own situation. As healthcare professionals are not always available, we created a dialog with the virtual coach Steph to collaboratively set daily step goals. Since judgments in human decision-making processes are adjusted based on the starting point or anchor, the first step goal proposal Steph makes is likely to influence the user’s final goal and self-efficacy. Situational factors impacting physical activity (e.g., motivation, self-efficacy, available time) or how users process information (e.g., mood) may determine which initial proposals are most effective in getting users to reach their underlying previous activity-based recommended step goals. Using data from 117 people interacting with Steph for up to five days, we designed a reinforcement learning algorithm that considers users’ current and future situations when choosing an initial step goal proposal. Our simulations show that initial step goal proposals matter: choosing optimal ones based on this algorithm could make it more likely that people move to a situation with high motivation, high self-efficacy, and a favorable daily context. Then, they are more likely to achieve, but also to overachieve, their underlying recommended step goals. Our dataset is publicly available.

Physical activity
Behavior change
Reinforcement learning
Conversational agent
Goal-setting

http://resolver.tudelft.nl/uuid:e188246d-f1c2-4062-b20c-9aea1badb84b

https://doi.org/10.1007/978-3-031-58226-4_9

2024-09-30

9783031582257

19th International Conference, PERSUASIVE 2024, Wollongong, NSW, Australia, April 10–12, 2024, Proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 14636 LNCS

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2024 M. Dierikx, N. Albers, Bouke Scheltinga, W.P. Brinkman