User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution
User-avatar bond as diagnostic indicator for gaming disorder: A word on the side of caution
Author(s): ALEXANDRE INFANTI, Alessandro Giardina, Josip Razum, Daniel L. King, Stéphanie Baggio, JEFFREY G. SNODGRASS, MATTHEW VOWELS2, Adriano Schimmenti, Orsolya Király, Hans-Jürgen Rumpf, Claus Vögele, Joël BillieuxSubject(s): Media studies, Individual Psychology, Cognitive Psychology, Neuropsychology, Behaviorism, Substance abuse and addiction
Published by: Akadémiai Kiadó
Keywords: gaming disorder; addiction; behaviorism; psychology;
Summary/Abstract: We commend Stavropoulos et al. (2023) for their study which aimed to test whether Gaming Disorder (GD) risk cases could be accurately detected based on Machine Learning (ML) algorithms trained with, among other variables, information regarding the User-Avatar Bond (UAB) (Blinka, 2008). Using longitudinal data, they claimed that the UAB has the potential to detect GD risk with implications for treatment and assessment. Specifically, the authors concluded that capitalizing on their method would permit the use of the UAB as a potential diagnostic indicator of GD risk. This kind of study is particularly relevant at this time, given the limited number of longitudinal studies, and the need to refine and improve the assessment and screening of GD. However, given the novelty of this approach and its potential impact on the field, we believe that some of the claims made by the authors warrant caution, both at the theoretical and methodological level.
Journal: Journal of Behavioral Addictions
- Issue Year: 13/2024
- Issue No: 4
- Page Range: 885-893
- Page Count: 9
- Language: English
