Addict Behav. 2025 Aug 25;171:108457. doi: 10.1016/j.addbeh.2025.108457. Online ahead of print.
ABSTRACT
INTRODUCTION: Existing diagnosis systems, such as DSM-5 and ICD-11, predominantly rely on a dichotomous approach, flat, cross-sectional definitions of Internet Gaming Disorder (IGD) that fail to capture the persistent, evolving nature and may contribute to heterogeneity. Applying a clinical staging approach, we propose that IGD spans a continuum with early stages marked by euphoria from gaming and later stages by compulsive gaming. This study aimed to empirically identify distinct IGD stages.
METHODS: Employing a mixed design with three Chinese gamer samples (adolescent: N = 2,061, Mage = 16.90 for main study; longitudinal cohort: N = 662, Mage = 17.03 for transition analysis; young adult: N = 741, Mage = 23.68 for validation), participants completed the Stages of Internet Gaming Disorder Scale (S-IGDS), a self-developed tool assessing core phenotypes, plus measures of craving, IGD symptoms, gaming experiences and life satisfaction. Latent Profile Analysis and k-means clustering identified distinct stages; Latent Transition Analysis tested stage progression. Psychometric properties of the S-IGDS were evaluated.
RESULTS: Three stages were identified: Stage 0 (Non-IGD), Stage 1 (Early Risk-IGD), and Stage 2 (Later IGD), progressing from a “high euphoria” phase to a “high compulsivity” phase. The results from LPA and k-means clustering were highly consistent. The S-IGDS exhibited a stable factor structure and robust psychometric properties. Stage progression showed moderate one-year stability (56.83%), with a high probability (74.2% to 98.2%) of transition to adjacent stages. Patterns were similar in young adults.
CONCLUSIONS: This study provides novel evidence for distinct, empirically-derived IGD stages, highlighting the progression defined by the interplay of euphoria and compulsivity. The S-IGDS offers a reliable and valid tool for assessing an individual’s status along the IGD continuum. This provides a more nuanced understanding of IGD and paves the way for personalized and stratified interventions.
PMID:40902308 | DOI:10.1016/j.addbeh.2025.108457
