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However, for goals to be effective, they need to be tailored to a user\u2019s 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\u2019s expertise and the client\u2019s 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\u2019s 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\u2019 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.<\/jats:p>","DOI":"10.1007\/978-3-031-58226-4_9","type":"book-chapter","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T19:02:25Z","timestamp":1712689345000},"page":"100-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Collaboratively Setting Daily Step Goals with\u00a0a\u00a0Virtual Coach: Using Reinforcement Learning to\u00a0Personalize Initial Proposals"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3708-639X","authenticated-orcid":false,"given":"Martin","family":"Dierikx","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0502-6176","authenticated-orcid":false,"given":"Nele","family":"Albers","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4748-2321","authenticated-orcid":false,"given":"Bouke L.","family":"Scheltinga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8485-7092","authenticated-orcid":false,"given":"Willem-Paul","family":"Brinkman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"issue":"12","key":"9_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0082901","volume":"8","author":"MA Adams","year":"2013","unstructured":"Adams, M.A., Sallis, J.F., Norman, G.J., Hovell, M.F., Hekler, E.B., Perata, E.: An adaptive physical activity intervention for overweight adults: a randomized controlled trial. 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