Background An increasing number of people are becoming addicted to the internet as a result of overuse. The Internet Addiction Test (IAT) is a popular tool for evaluating internet use behaviors. The interaction between different symptoms and the relationship between IAT and clinical diagnostic criteria are not well understood. Objective This study aimed to explore the core symptoms of internet addiction (IA) and the correlation between different symptoms of the IA symptom network. Network analysis was also conducted to explore the association between the IAT scale and the Diagnostic and Statistical Manual of Mental Disorders–5th edition (DSM-5) criteria for IA. Methods We recruited 4480 internet users (aged 14-24 years), and they completed the IAT. The final analysis included 63.50% (2845/4480) of the participants after screening the submitted questionnaires. Participants were classified into IA group and non-IA (NIA) group. By using partial correlation with Lasso regularization networks, we identified the core symptoms of IA in each group and compared 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 IA. Results A total of 12.47% (355/2845) of the patients were in the IA group and 87.52% (2490/2845) of the patients were in the NIA group, and both groups were evaluated for the following nodes: IAT_06 (school work suffers; strength=0.511), IAT_08 (job performance suffers; strength=0.531), IAT_15 (fantasize about being on the web; strength=0.474), IAT_17 (fail to stop being on the web; strength=0.526), and IAT_12 (fear about boredom if offline; strength=0.502). The IA groups had a stronger edge between IAT_09 (defensive or secretive about being on the web) and IAT_18 (hidden web time) than the NIA groups. The items in DSM-5 had a strong association with IAT_12 (weight=−0.066), IAT_15 (weight=−0.081), IAT_17 (weight=−0.106), IAT_09 (weight=−0.198), and IAT_18 (weight=−0.052). Conclusions The internet use symptom network of the IA group is significantly different from that of the NIA group. Nodes IAT_06 (school work affected) and IAT_08 (work performance affected) are the resulting symptoms affected by other symptoms, whereas nodes IAT_12 (fear about boredom if offline), IAT_17 (inability to stop being on the web), and IAT_15 (fantasize about being on the web) are key symptoms that activate other symptoms of IA and are strongly linked to the inability to control the intention to play games in the DSM-5.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.