Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.

IF 6.6 1区 医学 Q1 PSYCHIATRY
Journal of Behavioral Addictions Pub Date : 2023-11-09 Print Date: 2023-12-22 DOI:10.1556/2006.2023.00062
Vasileios Stavropoulos, Daniel Zarate, Maria Prokofieva, Noirin Van de Berg, Leila Karimi, Angela Gorman Alesi, Michaella Richards, Soula Bennet, Mark D Griffiths
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引用次数: 0

Abstract

Background and aims: 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 their 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.

Methods: To contribute to the paucity of knowledge in this area, 565 gamers (Mage = 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 series of tuned and untuned artificial intelligence [AI] classifiers analysed concurrently and prospectively their responses.

Results: 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.

Conclusion: 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.

通过用户-化身结合在游戏障碍中的深度学习:一项使用机器学习的纵向研究。
背景和目的:游戏障碍风险与游戏玩家在游戏世界中与视觉表现(即化身)的联系方式有关。更具体地说,玩家与其化身的关系已被证明可以在离线生活中提供关于用户的可靠心理健康信息,例如,如果适当解码,他们当前和未来的GD风险。方法:为了解决该领域知识匮乏的问题,565名游戏玩家(Mage=29.3岁;SD=10.6)使用用户化身债券量表(UABS)和游戏障碍测试进行了两次评估,间隔六个月。一系列调整和未调整的人工智能分类器同时并前瞻性地分析了它们的反应。结果:研究结果表明,人工智能模型学会了根据玩家报告的UABS分数、年龄和参与游戏的时间,同时和纵向(即六个月后),准确、自动地识别GD风险案例。随机森林的表现优于所有其他AI,而化身沉浸感被证明是最强的训练预测因素。结论:研究结果表明,使用经过训练的人工智能分类器,用户化身纽带可以转化为准确、并发和未来的GD风险预测。根据这些发现讨论了评估、预防和实践的影响。
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来源期刊
CiteScore
12.30
自引率
7.70%
发文量
91
审稿时长
20 weeks
期刊介绍: The aim of Journal of Behavioral Addictions is to create a forum for the scientific information exchange with regard to behavioral addictions. The journal is a broad focused interdisciplinary one that publishes manuscripts on different approaches of non-substance addictions, research reports focusing on the addictive patterns of various behaviors, especially disorders of the impulsive-compulsive spectrum, and also publishes reviews in these topics. Coverage ranges from genetic and neurobiological research through psychological and clinical psychiatric approaches to epidemiological, sociological and anthropological aspects.
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