Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity Reply to: User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution (2024)
Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity Reply to: User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution (2024)

Summary/Abstract: In response to our paper (Stavropoulos et al., 2023), the recent commentary by Infanti et al. (2024) highlighted areas requiring further clarification regarding (i) the conceptualization and utility of the user-avatar bond (UAB) as a gaming disorder (GD) risk indicator, and (ii) the optimal application of supervised Machine Learning (ML) techniques in addressing prediction challenges related to imbalanced data, particularly within the context of the UAB-GD association. Infanti et al. noted that: (i) the UAB may not apply to all game genres and differs across individuals, and (ii) digital phenotyping should involve superior objective data (e.g., screen/application monitoring). They also had concerns regarding methodology, including the (iii) use of synthetic minority oversampling technique (SMOTE) in algorithm development, (iv) the non-use of ML regression methods, (v) how the Gaming Disorder Test (GDT-4) was used, and (vi) the lack of access to the original data.

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