User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution [commentary].
Year: 2024
Type: article
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.
DOI: 10.48620/77294
Authors: Infanti, Alexandre, Giardina, Alessandro, Razum, Josip, King, Daniel L, Baggio, Stéphanie, Snodgrass, Jeffrey G, Vowels, Matthew, Schimmenti, Adriano, Király, Orsolya, Rumpf, Hans-Juergen, Vögele, Claus, Billieux, Joël
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6929394964
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