{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:09Z","timestamp":1755219849850,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Postpartum depression (PPD) affects approximately 20% of pregnant individuals, yet half of these cases remain under-treated despite the availability of educational interventions. To address this gap, the Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL) project leverages Artificial Intelligence (AI) to deliver personalized health education materials for PPD prevention and management. This study evaluated the acceptability of a web-based prototype in SPECIAL, developed with patient input, using the Unified Theory of Acceptance of Use of Technology (UTAUT) framework. Survey data from 41 participants indicated high acceptance of this tool. Regression analysis showed that Social Influence (SI) is positively associated with the Behavioral Intention (BI) to use SPECIAL. Patient feedbacks informed further personalization of this prototype, and enhancement of peer-to-peer support features in patient-centered design process.<\/jats:p>","DOI":"10.3233\/shti251044","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:39:14Z","timestamp":1754566754000},"source":"Crossref","is-referenced-by-count":0,"title":["Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL): An Acceptability Study of a Personalized Educational Platform"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5360-9954","authenticated-orcid":false,"given":"Ziwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA"}]},{"given":"Haijing","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Bentley University, Waltham, MA, USA"}]},{"given":"Xiaotong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA"}]},{"given":"Rochelle","family":"Joly","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA"}]},{"given":"Yiye","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251044","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:39:15Z","timestamp":1754566755000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251044","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}