Identification of central symptoms in Internet addictions and depression among adolescents in Macau: A network analysis

Hong Cai, Wei Bai, Sha Sha, Ling Zhang, Ines H.I. Chow, Si Man Lei, Grace K.I. Lok, Teris Cheung, Zhaohui Su, Brian J. Hall, Robert D. Smith, Yu Tao Xiang

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Background: Internet addiction (IA) and depression are common among adolescents and often are co-occurring. This study examined the network structures of IA and depressive symptoms (depression hereafter) in adolescents. Methods: A total of 1,009 adolescents were recruited. IA and depression were measured using the Internet Addiction Test (IAT) and the 9 items-Patient Health Questionnaire (PHQ-9), respectively. A network analysis was conducted to identify central symptoms and bridge symptoms using centrality indices. Network stability was evaluated using the case-dropping procedure. The Network Comparison Test (NCT) was conducted to examine whether network characteristics differed by gender. Results: Network analysis revealed that nodes IAT-15 (“Preoccupation with the Internet”), IAT-2 (“Neglect chores to spend more time online”), PHQ-6 (“Guilty”), and IAT-16 (“Request an extension for longer time spent online”) were the most central symptoms within the model of coexisting IA and depression. The most important bridge symptom was node IAT-11 (“Anticipation for future online activities”), followed by IAT-12 (“Fear that life is boring and empty without the Internet”) and IAT-19 (“Spend more time online over going out with others”). Gender did not significantly influence the network structure. The IA and depression network model showed a high degree of stability. Conclusion: The central symptoms along with key bridge symptoms identified could be potentially targeted when treating and preventing IA and depression among adolescents.

Original languageEnglish
Pages (from-to)415-423
Number of pages9
JournalJournal of Affective Disorders
Publication statusPublished - 1 Apr 2022
Externally publishedYes


  • Adolescents
  • Depression
  • Internet addiction
  • Network analysis


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