Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal study

Abstract

Background: There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time.

Methods: A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis.

Results: The internet addiction symptoms "escape" and "irritable," and depression symptoms "energy" and "guilty" were the central symptoms for both waves. At the same time, "guilty" and "escape" were identified as bridge symptoms. Notably, the correlation between "anhedonia" and "withdrawal" significantly increased, and that between "guilty" and "escape" significantly decreased over time.

Conclusions: This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, "guilty" and "escape," two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students.

Keywords: Bridge symptoms; Central symptoms; Depression; Internet addiction; Longitudinal data; Network analysis.

Fig. 1

The comorbidity network structure of internet addiction and depression (N = 852). Blue nodes indicate depression symptoms, and orange nodes indicate IA symptoms. Thicker edges between symptoms denote stronger associations. All edges denote positive interconnections; T1 = 2020.8, T2 = 2020.11.

Fig. 2

Strength and bridge strength of individual symptoms of IA and depression (N = 852). Blue value-nodes denote strength and bridge strength at T1 (2020.8), and orange value-nodes denote strength and bridge strength at T2 (2020.11). PHQ1: Anhedonia; PHQ2: Sad Mood; PHQ3: Sleep; PHQ4: Energy; PHQ5: Appetite; PHQ6: Guilty; PHQ7: Concentration; PHQ8: Motor; PHQ9: Suicide; IA1: Salience; IA2: Satisfaction; IA3: Relapse; IA4: Irritable; IA5: Overuse; IA6: Conflict; IA7: Lie; IA8: Escape; IA9: Withdrawal; IA10: Money Problem.

Fig. A1

The difference test results of strength using the non-parametric bootstrapping method (N = 852). The black grid indicates a significant difference between the two corresponding edge weights, and the gray grid indicates no significant difference. The left two are the results of the strength and bridge strength difference test at T1, and the right ones are at T2. PHQ1: Anhedonia; PHQ2: Sad Mood; PHQ3: Sleep; PHQ4: Energy; PHQ5: Appetite; PHQ6: Guilty; PHQ7: Concentration; PHQ8: Motor; PHQ9: Suicide; IA1: Salience; IA2: Satisfaction; IA3: Relapse; IA4: Irritable; IA5: Overuse; IA6: Conflict; IA7: Lie; IA8: Escape; IA9: Withdrawal; IA10: Money Problem.

Fig. A2

The difference test results of bridge strength using the non-parametric bootstrapping method (N = 852). The black grid indicates a significant difference between the two corresponding edge weights, and the gray grid indicates no significant difference. The left two are the results of the strength and bridge strength difference test at T1, and the right ones are at T2. PHQ1: Anhedonia; PHQ2: Sad Mood; PHQ3: Sleep; PHQ4: Energy; PHQ5: Appetite; PHQ6: Guilty; PHQ7: Concentration; PHQ8: Motor; PHQ9: Suicide; IA1: Salience; IA2: Satisfaction; IA3: Relapse; IA4: Irritable; IA5: Overuse; IA6: Conflict; IA7: Lie; IA8: Escape; IA9: Withdrawal; IA10: Money Problem.

Fig. A3

Accuracy estimations of all edge weights using the non-parametric bootstrapping method (N = 852). Narrower CIs indicate reliable accuracy. The left one is the result of the accuracy test at T1, and the right one is at T2. PHQ1: Anhedonia; PHQ2: Sad Mood; PHQ3: Sleep; PHQ4: Energy; PHQ5: Appetite; PHQ6: Guilty; PHQ7: Concentration; PHQ8: Motor; PHQ9: Suicide; IA1: Salience; IA2: Satisfaction; IA3: Relapse; IA4: Irritable; IA5: Overuse; IA6: Conflict; IA7: Lie; IA8: Escape; IA9: Withdrawal; IA10: Money Problem.

Fig. A4

Stability estimations of centrality indices using the case-drop bootstrapping method (N = 852). The left one is the result of the stability test at T1, and the right one is at T2.

Fig. A5

The plot of centrality indices betweenness and closeness of every symptom (N = 852). The left two are betweenness and closeness at T1, and the right two are at T2.

Fig. A6

The plot of bridge betweenness and bridge closeness of every symptom (N = 852). The left two are betweenness and closeness at T1, and the right two are at T2.