Infanti Alexandre, Giardina Alessandro, Razum Josip, King Daniel L, Baggio Stephanie, Snodgrass Jeffrey G, Vowels Matthew, Schimmenti Adriano, Király Orsolya, Rumpf Hans-Juergen, Vögele Claus, Billieux Joël
1Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
2Institute of Psychology, University of Lausanne, Lausanne, Switzerland.
J Behav Addict. 2024 Nov 22;13(4):885-893. doi: 10.1556/2006.2024.00032. Print 2024 Dec 30.
In their study, Stavropoulos et al. (2023) capitalized on supervised machine learning and a longitudinal design and reported that the User-Avatar Bond could be accurately employed to detect Gaming Disorder (GD) risk in a community sample of gamers. The authors suggested that the User-Avatar Bond is a "digital phenotype" that could be used as a diagnostic indicator for GD risk. In this commentary, our objectives are twofold: (1) to underscore the conceptual challenges of employing User-Avatar Bond for conceptualizing and diagnosing GD risk, and (2) to expound upon what we perceive as a misguided application of supervised machine learning techniques by the authors from a methodological standpoint.
在他们的研究中，斯塔夫罗普洛斯等人（2023年）利用监督式机器学习和纵向设计，并报告称用户-虚拟形象纽带可准确用于在游戏玩家社区样本中检测游戏障碍（GD）风险。作者们认为用户-虚拟形象纽带是一种“数字表型”，可作为GD风险的诊断指标。在本评论中，我们有两个目标：（1）强调将用户-虚拟形象纽带用于概念化和诊断GD风险所面临的概念挑战，以及（2）从方法论角度阐述我们认为作者对监督式机器学习技术的错误应用。
