Alexandre Infanti, Alessandro Giardina, Josip Razum, Daniel L King, Stephanie Baggio, Jeffrey G Snodgrass, Matthew Vowels, Adriano Schimmenti, Orsolya Király, Hans-Juergen Rumpf, Claus Vögele, Joël Billieux
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引用次数: 0
Abstract
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.
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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