{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T23:59:52Z","timestamp":1788566392711,"version":"build-2803163510"},"reference-count":70,"publisher":"Akademiai Kiado Zrt.","issue":"4","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"RMIT University, Early Career Researcher Fund ECR 2020","award":["68761601"],"award-info":[{"award-number":["68761601"]}]},{"name":"Australian Research Council, Discovery Early Career Researcher Award, 2021","award":["DE210101107"],"award-info":[{"award-number":["DE210101107"]}]}],"content-domain":{"domain":["akjournals.com"],"crossmark-restriction":true},"short-container-title":["JBA"],"published-print":{"date-parts":[[2023,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with\u00a0their avatar has been shown to provide reliable mental health information about the user in their offline life, such as their current and prospective GD risk, if appropriately decoded.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      To\u00a0contribute to the paucity of knowledge in this area, 565 gamers (\n                      <jats:italic>M<\/jats:italic>\n                      <jats:sub>age<\/jats:sub>\n                      = 29.3 years; SD =10.6) were assessed twice, six months apart, using the User-Avatar-Bond Scale (UABS) and the Gaming Disorder Test. A\u00a0series of tuned and untuned artificial intelligence [AI] classifiers analysed concurrently and prospectively their responses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Findings showed that AI models learned to accurately and automatically identify GD risk cases, based on gamers' reported UABS score, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Study outcomes demonstrated that the user-avatar bond can be translated into accurate, concurrent and future GD risk predictions using trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these findings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1556\/2006.2023.00062","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T08:30:32Z","timestamp":1699518632000},"page":"878-894","update-policy":"https:\/\/doi.org\/10.1556\/policypage.crossmark.crossref.2","source":"Crossref","is-referenced-by-count":18,"title":["Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning"],"prefix":"10.1556","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6964-4662","authenticated-orcid":true,"given":"Vasileios","family":"Stavropoulos","sequence":"first","affiliation":[{"name":"Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT\u00a0University, Australia"},{"name":"National and Kapodistrian University of Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1508-8637","authenticated-orcid":true,"given":"Daniel","family":"Zarate","sequence":"additional","affiliation":[{"name":"Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT\u00a0University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1974-3827","authenticated-orcid":true,"given":"Maria","family":"Prokofieva","sequence":"additional","affiliation":[{"name":"Victoria University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noirin","family":"Van de Berg","sequence":"additional","affiliation":[{"name":"The Three Seas Psychology, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2364-504X","authenticated-orcid":true,"given":"Leila","family":"Karimi","sequence":"additional","affiliation":[{"name":"Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT\u00a0University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela","family":"Gorman Alesi","sequence":"additional","affiliation":[{"name":"Catholic Care Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michaella","family":"Richards","sequence":"additional","affiliation":[{"name":"Mighty Serious, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soula","family":"Bennet","sequence":"additional","affiliation":[{"name":"Quantum Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-6524","authenticated-orcid":true,"given":"Mark D.","family":"Griffiths","sequence":"additional","affiliation":[{"name":"International Gaming Research Unit, Psychology Department, Nottingham Trent University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"425","reference":[{"key":"B1","series-title":"What is a good accuracy score in machine learning?","author":"Allwright, S.","year":"2022, May 14","unstructured":"Allwright, S. 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