User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution.

Details

Ressource 1Download: Infanti_JBA_2024.pdf (1995.53 [Ko])
State: Public
Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_AA08BA234DFC
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution.
Journal
Journal of behavioral addictions
Author(s)
Infanti A., Giardina A., Razum J., King D.L., Baggio S., Snodgrass J.G., Vowels M., Schimmenti A., Király O., Rumpf H.J., Vögele C., Billieux J.
ISSN
2063-5303 (Electronic)
ISSN-L
2062-5871
Publication state
In Press
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
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.
Keywords
classification, diagnosis, gaming disorder, machine learning, user-avatar bond
Pubmed
Open Access
Yes
Create date
25/11/2024 16:31
Last modification date
30/11/2024 7:16
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