{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:43:57Z","timestamp":1767984237883,"version":"3.49.0"},"reference-count":102,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100003130","name":"Research Foundation Flanders","doi-asserted-by":"crossref","award":["G067721N and G0A4923N"],"award-info":[{"award-number":["G067721N and G0A4923N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Interact. Intell. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Recommender systems are increasingly used in mobile health interventions, such as managing Chronic Musculoskeletal Pain (CMP). While researchers have highlighted the importance of explaining health-related recommendations to lay users, with benefits such as increased trust and a higher tendency to follow up on these recommendations, how to design explanations for lay users in critical contexts such as health remains largely unexplored. To address this gap, we develop a mobile health application to support users with CMP through coaching and personalised health recommendations delivered via a conversational rule-based recommender system. This article describes the three-phase iterative development of the RS, involving health experts and end users. In the first iteration, we conduct a preliminary validation study with\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(N=282\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    participants to ensure the app\u2019s validity and improve the initial set of health recommendations. Next, two user studies are conducted centred around designing effective and understandable explanations for these recommendations. First, we design six explanation modalities tailored towards lay users, and through a qualitative study (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(N=11\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), extract initial design guidelines for explaining health recommendations, finding a strong preference towards feature importance explanations and identifying issues with modalities that highlight negative emotions. Given these results, we explore whether extending feature importance explanations with textual information into a \u2018hybrid\u2019 explanation could benefit end users, and whether these benefits depend on a user\u2019s personal characteristics (need for cognition and ease-of-satisfaction). Through a mixed-methods study with\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(N=262\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    participants, we find that the hybrid modality significantly increased user trust, transparency, persuasiveness, usefulness and satisfaction compared to unimodal explanations. However, users with a higher need for cognition rate unimodal explanations more positively than hybrid ones.\n                  <\/jats:p>","DOI":"10.1145\/3772071","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:57:14Z","timestamp":1762167434000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Designing and Personalising Hybrid Health Explanations for Lay Users"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-3198","authenticated-orcid":false,"given":"Maxwell","family":"Szymanski","sequence":"first","affiliation":[{"name":"Augment (an imec Research Group), Department of Computer Science, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6431-092X","authenticated-orcid":false,"given":"Stijn","family":"Keyaerts","sequence":"additional","affiliation":[{"name":"Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-9335","authenticated-orcid":false,"given":"Cristina","family":"Conati","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of British Columbia, Vancouver, British Columbia, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1329-156X","authenticated-orcid":false,"given":"Robin","family":"De Croon","sequence":"additional","affiliation":[{"name":"Augment (an imec Research Group), Department of Computer Science, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3031-9579","authenticated-orcid":false,"given":"Vero","family":"Vanden Abeele","sequence":"additional","affiliation":[{"name":"Augment (an imec Research Group), Department of Computer Science, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6699-7710","authenticated-orcid":false,"given":"Katrien","family":"Verbert","sequence":"additional","affiliation":[{"name":"Augment (an imec Research Group), Department of Computer Science, KU Leuven, Leuven, Belgium"}]}],"member":"320","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.10.014"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.2196\/25384"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.11.013"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12030435"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_3_3_7_2","first-page":"329","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920)","author":"Balog Krisztian","year":"2020","unstructured":"Krisztian Balog and Filip Radlinski. 2020. 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