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游戏热情有助于对问题性游戏进行定义和识别。

Gaming passion contributes to the definition and identification of problematic gaming.

机构信息

Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.

Department of Psychology, Catholic University of Murcia, Murcia, Spain.

出版信息

Addict Behav. 2023 Dec;147:107805. doi: 10.1016/j.addbeh.2023.107805. Epub 2023 Jul 16.

DOI:10.1016/j.addbeh.2023.107805
PMID:37523871
Abstract

Even if for most people playing video games is a healthy leisure activity, a minority of vulnerable users present an excessive use associated to negative consequences (e.g., psychosocial maladjustment, sleep interference) and functional impairment. The current study first aims to identify psychological factors that contribute to discriminate highly involved (but healthy) gamers from problematic gamers. For that purpose, we used a cluster analysis approach to identify different groups of gamers based on their profiles of passion towards gaming (using the Dualistic Model of Passion). Another objective of the present study is to explore, using supervised machine-learning, how gaming disorder symptoms, assessed within the substance use disorder framework (e.g., tolerance, withdrawal), might be linked to harmonious and/or an obsessive passion for gaming. Three distinct clusters of gamers were identified based on their passion profiles, including risky gamers, engaged gamers, and casual gamers. Supervised machine-learning algorithms identified that specific gaming disorder symptoms (salience, mood modification, tolerance, low level of conflict) were predominantly related to harmonious passion, whereas others (withdrawal, high level of conflict, relapse) were more directly related to obsessive passion. Our results support the relevance of person-centered approaches to the treatment of problematic gaming.

摘要

即使对于大多数人来说,玩电子游戏是一种健康的休闲活动,但仍有少数易受影响的用户存在过度使用游戏的问题,这可能导致负面后果(例如,心理社会适应不良、睡眠干扰)和功能障碍。本研究首先旨在确定有助于区分高度投入(但健康)游戏者和有问题游戏者的心理因素。为此,我们使用聚类分析方法根据他们对游戏的热情程度(使用双因素激情模型)来确定不同的游戏者群体。本研究的另一个目的是探索,使用监督机器学习,游戏障碍症状(例如,耐受性、戒断)如何与对游戏的和谐和/或痴迷激情相关联。根据他们的激情特征,确定了三个不同的游戏者群体,包括风险游戏者、参与游戏者和休闲游戏者。监督机器学习算法确定了特定的游戏障碍症状(凸显、情绪调节、耐受性、低冲突水平)与和谐激情更为相关,而其他症状(戒断、高冲突水平、复发)与痴迷激情更为直接相关。我们的研究结果支持针对有问题的游戏行为采用以个体为中心的治疗方法的相关性。

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