» Articles » PMID: 36910812

Predictive Power of the DSM-5 Criteria for Internet Use Disorder: A CHAID Decision-tree Analysis

Overview
Journal Front Psychol
Date 2023 Mar 13
PMID 36910812
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: Although the majority of internet users enjoy the internet as a recreational activity, some individuals report problematic internet use behaviors causing negative psychosocial consequences. Therefore, it is important to have precise and valid diagnostic criteria to ensure suitable treatment for those affected and avoid over-pathologization.

Methods: The aim of the present study was to determine which of the nine DSM-5 criteria of internet gaming disorder (IGD) are crucial in distinguish pathological from non-pathological internet use based on the questionnaire-based response behavior of the participants by applying the Chi-squared automatic interaction detection (CHAID) decision tree analysis. Under consideration of the nine DSM-5 criteria for IGD and according to the short-form scale to assess Internet Gaming Disorder (IGDS-SF9) the DSM-5 criteria were formulated as questions and applied to the broader concept of Internet Use Disorder (IUD). The nine questions were answered on a 5-point Likert scale from "never" to "very often." In accordance with the IGDS-SF9 participants were assigned to IUD-5plus if at least 5 of the 9 criteria were answered with "very often." The study was conducted in Germany ( = 37,008; : 32 years,  = 13.18, 73.8% male).

Results: Although "loss of control," "continued overuse" and "mood regulation" were the most endorsed criteria, the analysis indicated that the criterion "jeopardizing" was found as the best predictor for IUD-5plus, followed by "loss of interest" and "continued overuse." Overall 64.9% of all participants who were in the IUD-5plus, could been identified by the fulfillment of the three criteria mentioned above.

Discussion: The results found support for adjustment of the DSM-5 criteria of IGD in accordance to ICD-11. If the predictive power of the three criteria can be replicated in future representative studies, such a decision tree can be used as guidance for diagnostics to capture the particularly relevant criteria.

Citing Articles

Exploring the association between internet addiction and time management among undergraduate nursing students.

Ali H, Mousa M, Atta M, Morsy S BMC Nurs. 2024; 23(1):632.

PMID: 39256720 PMC: 11389558. DOI: 10.1186/s12912-024-02273-5.

References
1.
Muller K, Beutel M, Dreier M, Wolfling K . A clinical evaluation of the DSM-5 criteria for Internet Gaming Disorder and a pilot study on their applicability to further Internet-related disorders. J Behav Addict. 2019; 8(1):16-24. PMC: 7044592. DOI: 10.1556/2006.7.2018.140. View

2.
Stevens M, Dorstyn D, Delfabbro P, King D . Global prevalence of gaming disorder: A systematic review and meta-analysis. Aust N Z J Psychiatry. 2020; 55(6):553-568. DOI: 10.1177/0004867420962851. View

3.
Brand M . Can internet use become addictive?. Science. 2022; 376(6595):798-799. DOI: 10.1126/science.abn4189. View

4.
Song Y, Lu Y . Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry. 2015; 27(2):130-5. PMC: 4466856. DOI: 10.11919/j.issn.1002-0829.215044. View

5.
Jo Y, Bhang S, Choi J, Lee H, Lee S, Kweon Y . Clinical Characteristics of Diagnosis for Internet Gaming Disorder: Comparison of DSM-5 IGD and ICD-11 GD Diagnosis. J Clin Med. 2019; 8(7). PMC: 6678371. DOI: 10.3390/jcm8070945. View