Central and Bridge Symptoms of Internet Gaming Disorder, Social Jet Lag, and Insomnia Among College Students: Cross-Sectional Network Analysis.

Abstract

Background: Internet gaming disorder (IGD) has become increasingly prevalent among college students, while the prevalence of insomnia and social jet lag has also gradually increased in this population. Comorbidity among these conditions is common in young adults. Nevertheless, the internal correlational architecture underlying individual symptom indicators remains unclear.

Objective: This study aimed to investigate the central and bridge symptoms of IGD, social jet lag, and insomnia among college students; to explore the network structure and cross-symptom connectivity among them; and to provide evidence for targeted interventions for comorbid insomnia and IGD.

Methods: This cross-sectional study targeted a national sample of Chinese college students. A total of 1346 participants were included in this study, with a mean age of 19.35 (SD 1.35) years, including 668 (49.6%) male participants and 678 (50.4%) female participants. All participants completed a general information questionnaire, the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), the Munich Chronotype Questionnaire (MCTQ), and the Pittsburgh Sleep Quality Index (PSQI). Network analysis was used to investigate the network structure, identify central and bridge symptoms, quantify the correlation intensity between indicators, and assess network stability. Expected influence (EI) and bridge expected influence (BEI) were used to measure centrality.

Results: Three distinct variable clusters were extracted from the overall network: the insomnia factor cluster, the IGD symptom cluster, and social jet lag. The insomnia symptom "subjective sleep quality" (P1; strength=0.903) and the IGD symptom "loss" (I9; strength=2.463) were the most central nodes in the network, constituting the key indicators of this study. The bridge strength of all nodes ranged from 0.029 to 1.295. "Withdrawal" (I2) exhibited markedly higher bridge strength than all other nodes (1.295, 95% CI 1.162-1.428), followed by "social jet lag" (S1; 0.522, 95% CI 0.384-0.690) and "preoccupation" (I1; 0.519, 95% CI 0.364-0.718). As the key bridging symptom between the insomnia and IGD clusters, I2 had the strongest edge connection with "daytime dysfunction" (P7), with an edge weight of 0.329 (95% CI 0.246-0.450). Connections involving the social jet lag cluster (S1) were concentrated on "continue despite problems" (I6; 0.182, 95% CI 0.012-0.364), while the connections between the insomnia symptom cluster and S1 were all low.

Conclusions: Unlike previous studies that focused on bivariate associations or single disorders, our findings reveal that I2 serves as the key bridging symptom connecting insomnia and IGD, while P1 and I9 are central nodes within their respective structural communities. These findings provide a novel perspective on the comorbidity of insomnia and IGD. In real-world practice, interventions targeting withdrawal symptoms should be prioritized, as they may alleviate comorbid symptoms simultaneously. The role of S1 was relatively limited, suggesting that managing circadian rhythm disruption may indirectly reduce the risk of IGD. These findings provide actionable symptom targets for prevention and treatment programs in university settings.