| Title: |
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). |
| Authors: |
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 |
| Source: |
Journal of Behavioral Addictions; Dec2024, Vol. 13 Issue 4, p885-893, 9p |
| Subject Terms: |
SUPERVISED learning, GAMING disorder, DEEP learning, MACHINE learning, WORD games |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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