Hyunsuk Jeong1, Hyeon Woo Yim2, Sun-Jin Jo1, Seung-Yup Lee3, Hae Kook Lee3, Douglas A Gentile4, Hye Jung Son1, Hyun-Ho Han1, Yong-Sil Kweon3, Soo-Young Bhang5, Jung-Seok Choi6. 1. Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 2. Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. Electronic address: y1693@catholic.ac.kr. 3. Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 4. Department of Psychology, Iowa State University, Iowa, USA. 5. Department of Psychiatry, College of Medicine, Eulji University, Seoul, Republic of Korea. 6. Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.
Abstract
PURPOSE: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. METHODS: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. RESULTS: Three different clusters were identified. Cluster 1 (19.2%) was users with combined potential psychological and social issues. Cluster 2 (32.3%) was users with potential social but no psychological issues. Cluster 3 (45.6%) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95%CI:7.5-19.9), smartphone addiction (aOR:5.4, 95%CI:4.0-7.2), musculoskeletal discomfort (aOR:2.6, 95%CI:2.1-7.4), and dry eye symptoms (aOR:3.8, 95%CI:3.0-4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95%CI:2.8-7.6; aOR:2.8, 95%CI:2.1-3.7; aOR:1.6, 95%CI:1.3-1.9; and aOR:1.9, 95%CI:1.6-2.4, respectively). CONCLUSIONS: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.
PURPOSE: The risk and protective factors of Internet gaming disorder (IGD) could vary by individual. The identification of more homogeneous subgroups may lead to better understanding of gaming behaviors and their consequences in adolescents. The purpose of this study was to investigate the prevalence of IGD among the subgroups defined by cluster analysis in adolescents. METHODS: A total of 2319 adolescents were enrolled in the Internet User Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study at baseline. Self-reported IGD was assessed with a DMS-5 adapted measurement. Smartphone addiction, musculoskeletal discomfort, and dry eye symptoms were evaluated by self-administered questionnaires. Cluster analysis was performed using risk and protective factors of IGD after considering multicollinearity. RESULTS: Three different clusters were identified. Cluster 1 (19.2%) was users with combined potential psychological and social issues. Cluster 2 (32.3%) was users with potential social but no psychological issues. Cluster 3 (45.6%) was users with no potential issues of either a social or psychological nature. Adolescents from both clusters 1 and 2 showed higher degrees of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms than did those from cluster 3. Also compared with adolescents in cluster 3, those in cluster 1 showed statistically higher risks of IGD (aOR:11.9, 95%CI:7.5-19.9), smartphone addiction (aOR:5.4, 95%CI:4.0-7.2), musculoskeletal discomfort (aOR:2.6, 95%CI:2.1-7.4), and dry eye symptoms (aOR:3.8, 95%CI:3.0-4.9). Those in cluster 2 also showed statistically higher risk of IGD, smartphone addiction, musculoskeletal discomfort, and dry eye symptoms compared with cluster 3 (aOR:4.5, 95%CI:2.8-7.6; aOR:2.8, 95%CI:2.1-3.7; aOR:1.6, 95%CI:1.3-1.9; and aOR:1.9, 95%CI:1.6-2.4, respectively). CONCLUSIONS: Clustering based on the risk and preventive factors of IGD may be suitable for determination of high risk of IGD in adolescents. However, we need to confirm the usefulness and clinical application of the classifications by observing their longitudinal changes.