Abstract: Recent research on adolescent internet gaming disorder (IGD) has increasingly emphasized an analytical perspective integrating dynamic, heterogeneous, and holistic approaches. This study combined growth mixture modeling with network analysis methods; it incorporated 15 risk and protective factors across family, school, community, and individual domains, in order to explore the heterogeneous developmental trajectories of adolescent IGD, the causes of heterogeneous development, and their dynamic variation characteristics.
This study used a cluster sampling method to conduct a one-year longitudinal survey with three measurement waves of 1, 852 adolescents from Shandong Province, China. Participants were recruited from three junior high schools (grades 7-9), two general high schools (grades 10-11), and two vocational high schools (grades 10-11). The participants completed a questionnaire assessing 15 risk and protective factors, and then an IGD scale. Preliminary analyses were performed with SPSS, with missing values imputed via the Expectation-Maximization algorithm. After that, Mplus was used to construct growth mixture models and identify heterogeneous trajectory subgroups of IGD development. Next, network analysis and network comparison analysis were performed using R software to construct relational networks of IGD and the multi-domain factors for every subgroup at different time points, and to compare the similarities and differences in network models and properties across subgroups at every time point. Finally, by integrating these comparative results, we examined the underlying mechanisms of heterogeneous IGD development and its dynamic characteristics in adolescents.
This study identified four distinct developmental trajectories of IGD among adolescents. Based on research objectives and sample representativeness considerations, two trajectories characterized by low and stable IGD scores were then merged into a normative development group, and they were subsequently compared with a more representative addictive development group, which was characterized by high initial IGD levels with significant growth and high sample proportion, from the other two trajectories. Comparison results indicated that in early IGD development, no significant differences were observed between the two groups in overall network connectivity patterns or global strength, nor in important factors, namely, proximal factors and high strength centrality nodes. Deviant peer affiliation, peer victimization, parental psychological control, teacher support, community resources, and self-esteem served as proximal factors for both of the groups. School belongingness and self-esteem were consistently high strength centrality nodes in both groups, and no specific factors unique to the addictive development group were identified. The primary difference between the two groups lay in that the addictive development group scored significantly lower than the normative development group on all of the protective factors, while scoring significantly higher on risk factors, thus suggesting that IGD development may be more associated with the accumulation of multi-domain risks characterized by “high risk-low protection.” In the later stage of IGD development, all of the direct connections between factors and IGD were not visible in the addictive development group, possibly indicating a hysteresis phenomenon in adolescent IGD development, namely, that IGD enters a self-sustaining steady state under the cumulative effect of risks, such that even when external risks are diminished, IGD cannot be readily alleviated. However, future research is required to verify this inference based on the present findings.

Key words: adolescents, Internet gaming disorder, risk factors and protective factors, growth mixture modeling, network analysis