{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:59:32Z","timestamp":1785459572486,"version":"3.56.0"},"reference-count":23,"publisher":"Wiley","issue":"S5","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":37,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 111\u20102636\u2010B\u2010006\u2010011"],"award-info":[{"award-number":["MOST 111\u20102636\u2010B\u2010006\u2010011"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["NSTC 112\u20102314\u2010B\u2010006\u2010081\u2010MY3"],"award-info":[{"award-number":["NSTC 112\u20102314\u2010B\u2010006\u2010081\u2010MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["NSTC 112\u20102636\u2010B\u2010006\u2010007"],"award-info":[{"award-number":["NSTC 112\u20102636\u2010B\u2010006\u2010007"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Int AIDS Soc."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Pre\u2010exposure prophylaxis (PrEP) is an effective HIV prevention tool that relies on good adherence in high\u2010risk scenarios. To understand the factors that predict adherence, technology such as mobile applications like UPrEPU\u2014allowing for logging users\u2019 daily behaviours at close to the time they have sex or PrEP intake\u2014can be used as a person\u2010centred, self\u2010care intervention. This study aims to develop a machine learning model using logs of sexual activities and user attributes recorded in the UPrEPU mobile application in Taiwan to predict whether a sexual event was protected by oral PrEP among gay, bisexual and other men who have sex with men (GBMSM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used data from the UPrEPU app collected between January 2022 and May 2023 in Taiwan. The dataset included information on users\u2019 sex events, such as the timing and users\u2019 sex roles (e.g. versatile, receptive or insertive partner), and the dynamic user\u2010based attributes related to sexual behaviours and PrEP use. Various subsets of these features were employed in CatBoost models to predict whether the sex events were associated with correct PrEP use. We evaluated the models\u2019 performance using five\u2010fold cross\u2010validation. The influential features were identified through feature importance analysis and Shapley Additive Explanations (SHAP) values to explain the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 198 users recorded 2356 anal sex events on UPrEPU. The model with dynamic user\u2010based attributes outperformed those without them. The most parsimonious model had a good prediction performance (accuracy = 75%, precision = 78%, recall = 90%, F1\u2010score = 83%) and identified the key features of PrEP protection. The model with five dynamic user\u2010based attributes\u2014age, cumulative PrEP use, condom use and the proportion of anal sex events with HIV\u2010negative partners not on PrEP\u2014significantly outperformed the model based on event\u2010level attributes alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Behavioural patterns significantly influence PrEP adherence among GBMSM. Person\u2010centred mobile applications such as UPrEPU provide valuable data for tailored, just\u2010in\u2010time interventions, enhancing adherence. Recognizing these patterns can guide person\u2010centred interventions. Incorporating these insights into clinical care or digital tools may improve consultations and support timely, informed HIV prevention decisions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jia2.70033","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:01:27Z","timestamp":1759917687000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing PrEP adherence through person\u2010centred mobile app interventions: a real\u2010world data and machine learning approach using UPrEPU among gay, bisexual and other men who have sex with men in Taiwan"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1759-4971","authenticated-orcid":false,"given":"Jay Chiehen","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Public Health College of Medicine National Cheng Kung University  Tainan Taiwan"},{"name":"Department of 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