User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution.
Details
Download: Infanti_JBA_2024.pdf (1995.53 [Ko])
State: Public
Version: Final published version
License: CC BY-NC 4.0
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
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
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