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These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings.<\/jats:p>","DOI":"10.1145\/3411837","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T21:39:45Z","timestamp":1599255585000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence"],"prefix":"10.1145","volume":"4","author":[{"given":"Jittrapol","family":"Intarasirisawat","sequence":"first","affiliation":[{"name":"School of Engineering and Digital Arts, University of Kent, Jennison Building, Canterbury, Kent, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chee Siang","family":"Ang","sequence":"additional","affiliation":[{"name":"School of Engineering and Digital Arts, University of Kent, Jennison Building, Canterbury, Kent, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Efstratiou","sequence":"additional","affiliation":[{"name":"School of Engineering and Digital Arts, University of Kent, Jennison Building, Canterbury, Kent, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luke","family":"Dickens","sequence":"additional","affiliation":[{"name":"Department of Information Studies, University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naranchaya","family":"Sriburapar","sequence":"additional","affiliation":[{"name":"School of Psychology, University of Kent, Keynes College, Canterbury, Kent, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinkar","family":"Sharma","sequence":"additional","affiliation":[{"name":"School of Psychology, University of Kent, Keynes College, Canterbury, Kent, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Burachai","family":"Asawathaweeboon","sequence":"additional","affiliation":[{"name":"Department of Psychology, Faculty of Liberal Arts, Thammasat University, Pathumthani, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"url: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/alcohol;. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/alcohol","year":"2020","unstructured":"2018. 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