Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Environ Res. 2020 Mar;182:109105. doi: 10.1016/j.envres.2019.109105. Epub 2019 Dec 31.
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.
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.
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).
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.
互联网游戏障碍(IGD)的风险和保护因素可能因个体而异。确定更同质的亚组可能有助于更好地理解青少年的游戏行为及其后果。本研究的目的是调查聚类分析在青少年中定义的亚组中 IGD 的患病率。
共有 2319 名青少年参加了青少年早期无偏认知游戏障碍的互联网用户队列研究(iCURE),在基线时进行了自我报告的 IGD 评估。使用 DMS-5 改编的测量方法评估智能手机成瘾、肌肉骨骼不适和干眼症症状。在考虑多重共线性后,使用 IGD 的风险和保护因素进行聚类分析。
确定了三个不同的亚组。第 1 组(19.2%)是同时存在潜在心理和社会问题的用户。第 2 组(32.3%)是存在潜在社会问题但无心理问题的用户。第 3 组(45.6%)是既无社会问题也无心理问题的用户。第 1 组和第 2 组的青少年比第 3 组的青少年表现出更高程度的 IGD、智能手机成瘾、肌肉骨骼不适和干眼症症状。与第 3 组青少年相比,第 1 组青少年的 IGD(aOR:11.9,95%CI:7.5-19.9)、智能手机成瘾(aOR:5.4,95%CI:4.0-7.2)、肌肉骨骼不适(aOR:2.6,95%CI:2.1-7.4)和干眼症症状(aOR:3.8,95%CI:3.0-4.9)的风险更高。第 2 组青少年的 IGD、智能手机成瘾、肌肉骨骼不适和干眼症症状的风险也高于第 3 组(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;aOR:1.9,95%CI:1.6-2.4)。
基于 IGD 的风险和预防因素的聚类分析可能适合确定青少年的 IGD 高风险。然而,我们需要通过观察其纵向变化来确认分类的有用性和临床应用。