title: Predictors and patterns of problematic Internet game use using a decision tree model.
abstract: Background and aims Problematic Internet game use is an important social issue that increases social expenditures for both individuals and nations. This study identified predictors and patterns of problematic Internet game use. Methods Data were collected from online surveys between November 26 and December 26, 2014. We identified 3,881 Internet game users from a total of 5,003 respondents. A total of 511 participants were assigned to the problematic Internet game user group according to the Diagnostic and Statistical Manual of Mental Disorders Internet gaming disorder criteria. From the remaining 3,370 participants, we used propensity score matching to develop a normal comparison group of 511 participants. In all, 1,022 participants were analyzed using the chi-square automatic interaction detector (CHAID) algorithm. Results According to the CHAID algorithm, six important predictors were found: gaming costs (50%), average weekday gaming time (23%), offline Internet gaming community meeting attendance (13%), average weekend and holiday gaming time (7%), marital status (4%), and self-perceptions of addiction to Internet game use (3%). In addition, three patterns out of six classification rules were explored: cost-consuming, socializing, and solitary gamers. Conclusion This study provides direction for future work on the screening of problematic Internet game use in adults.
authors: 1 Department of Medical Informatics, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.|2 Catholic Institute for Healthcare Management and Graduate School of Healthcare Management and Policy, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Mi Jung
authors: MJ
authors: Rho
authors: 3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Jo-Eun
authors: JE
authors: Jeong
authors: 3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Ji-Won
authors: JW
authors: Chun
authors: 3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Hyun
authors: H
authors: Cho
authors: 3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Dong Jin
authors: DJ
authors: Jung
authors: 1 Department of Medical Informatics, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.|2 Catholic Institute for Healthcare Management and Graduate School of Healthcare Management and Policy, The Catholic University of Korea , Seoul, Republic of Korea.
authors: In Young
authors: IY
authors: Choi
authors: 3 Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.
authors: Dai-Jin
authors: DJ
authors: Kim
mesh_terms: D000328:Adult; D000465:Algorithms; D016739:Behavior, Addictive / Q000175:diagnosis* / Q000523:psychology*; D003663:Decision Trees; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D020407:Internet; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D011379:Prognosis; D056910:Republic of Korea; D012649:Self Concept; D012919:Social Behavior; D013997:Time Factors; D018910:Video Games / Q000523:psychology*; D055815:Young Adult
keywords: chi-square automatic interaction detector; decision tree analysis; pattern; predictors; problematic Internet game use