Addiction symptoms network of young Internet users: A network analysis (Preprint)

Abstract

BACKGROUND An increasing number of people are becoming addicted to the Internet as a result of overuse. Internet Addiction Test (IAT) is a popular tool for evaluating Internet usage behaviors. The interaction between different symptoms and the relationship between IAT and clinical diagnostic criteria is not well understood. OBJECTIVE We recruited 3584 Internet users (14-24 years old) and had them complete the IAT. The final analysis included 2845 participants after screening the submitted questionnaires. Participants were classified into Internet Addiction (IA) group and Non-Internet Addiction (NIA)group. METHODS Using partial correlation with LASSO regularization networks, we identified the core symptoms of IA in each group and compare the group differences in network properties (strength, closeness, and betweenness). Then we analyzed the symptom networks of the DSM-5 diagnostic criteria and IAT scale for Internet addiction. RESULTS There were 355 in the IA group and 2490 in the NIA group. IAT_06 (school work suffers, strength = 0.511), IAT_08 (job performance suffers, strength = 0.531), IAT_15 (fantasiaze about online, strength = 0.474), IAT_17 (fail to stop online, strength = 0.526), and IAT_12 (fear of boring if offline, strength = 0.502). IA groups have a stronger edge between IAT_09 (defensive or secretive about online) and IAT_18 (hidden online time) than NIA groups. The items in DSM-5 have a stronger association with IAT_12 (weight=-0.066), IAT_15 (weight=-0.081), IAT_17 (weight=-0.106), IAT_9 (weight=-0.198), and IAT_18 (weight=-0.052). CONCLUSIONS The Internet use symptoms network of IA group is significantly different from that of NIA group. Nodes IAT_06 (school work affected) and IAT_08 (work performance affected) are the resulting symptoms affected by other symptoms, node IAT_12 (fear of boredom if offline), IAT_17 (inability to stop online), and IAT_15 (fantasy online) are key symptoms that activate other symptoms of Internet addiction and are strongly linked to inability to control the intention to play games in the DSM-5.