User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution : Commentary on: Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning (Stavropoulos et al., 2023)

Infanti, Alexandre and Giardina, Alessandro and Razum, Josip and King, Daniel L. and Baggio, Stephanie and Snodgrass, Jeffrey G. and Vowels, Matthew and Schimmenti, Adriano and Király, Orsolya and Rumpf, Hans-Juergen and Vögele, Claus and Billieux, Joël (2024) User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution : Commentary on: Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning (Stavropoulos et al., 2023). JOURNAL OF BEHAVIORAL ADDICTIONS, 13 (4). pp. 885-893. ISSN 2062-5871

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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.

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