BACKGROUND: There is a growing amount of evidence suggesting high rates of co-occurring internet addiction (IA) symptoms and psychological distress in youth. However, the extent to which IA symptoms develop over time, how they interact with psychological distress symptoms dynamically, and how they predict one another remain unclear. Additionally, what specific types of distress, including depression, anxiety, and stress, are more closely associated with IA symptoms remains inconclusive.
OBJECTIVE: This longitudinal study aimed to explore the development of and changes in IA symptoms over time and the directional relationship between IA and various psychological distress symptoms.
METHODS: This study followed a sample of 2497 Chinese college students (mean age 19.14, SD 0.72 years) across 3 waves of a data collection span of 2 years. Their IA and psychological distress symptoms were assessed at baseline (T1), 12-month follow-up (T2), and 24-month follow-up (T3). We used network analysis to examine the network structure of IA symptoms at each wave and cross-lagged panel network (CLPN) analysis to investigate longitudinal associations between IA symptoms and psychological distress, including depressive, anxiety, and stress symptoms.
RESULTS: The cross-sectional networks of IA symptoms at 3 time points showed high similarity in terms of structure, existence of edges, and centrality indices. Nodes A2 (excessive use), A1 (salience), and A5 (lack of control) emerged as nodes with the highest expected influence (EI) centrality in the IA symptom networks across time (A2: EI=1.13 at T1, 1.15 at T2, 1.17 at T3; A1: EI=1.10 at T1, 1.13 at T2, 1.15 at T3; A5: EI=0.86 at T1, 0.88 at T2, 0.92 at T3). CLPN analysis revealed that psychological distress predicts IA symptoms but not the other way around. Depressive symptoms played a key role in predicting various IA-related problems (T1 to T2, edge weight=0.11; T2 to T3, edge weight=0.28; T1 to T3, edge weight=0.22) and served as bridge symptoms connecting IA and psychological distress (T1 to T2: bridge-expected influence [BEI]=0.15; T2 to T3: BEI=0.14; T1 to T3: BEI=0.19).
CONCLUSIONS: Findings revealed a relatively stable network structure of IA symptoms among college students and suggested that psychological distress, especially depressive symptoms, may play a central role in activating IA symptoms over time. These results provide evidence for understanding the directional relationship between the central characteristics of distress symptoms and IA. The study also underscores the importance of depressive symptoms in their co-occurrence with IA, indicating that the key and bridge symptoms identified in this study can be prioritized as targets for preventing and treating IA in Chinese youth. Through identification and early intervention of depressive symptoms, we may avoid the progression of co-occurring issues, leading to more effective treatment outcomes.
