1National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.
2School of Psychology, The University of Queensland, St Lucia, Australia.
J Behav Addict. 2024 Aug 1;13(3):729-741. doi: 10.1556/2006.2024.00038. Print 2024 Oct 4.
Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment.
We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder.
Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets.
The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.
游戏障碍被纳入 2022 年最新版《国际疾病分类》（ICD-11）中的成瘾障碍。本研究旨在根据 ICD-11 的四项诊断标准：失控、优先级提升、尽管有伤害仍继续游戏以及功能损害，开发一种游戏障碍筛查工具，即游戏障碍识别测试（GADIT）。
我们回顾了来自 48 种现有游戏成瘾量表的 297 个问卷条目，并根据内容效度选择了 68 个条目。收集了两个数据集：1）来自澳大利亚、美国、英国和加拿大的在线小组（N=803），分为开发集（N=589）和验证数据集（N=214）；2）来自澳大利亚的大学样本（N=408）。使用项目反应理论和验证性因素分析，选择八项条目组成 GADIT。通过将 GADIT 回归到游戏障碍的已知相关因素来确定有效性。
GADIT 的验证性因素分析显示出良好的模型拟合度（RMSEA=<0.001-0.108；CFI=0.98-1.00），内部一致性非常好（Cronbach's alphas=0.77-0.92）。GADIT 得分与互联网游戏障碍测试（IGDT-10）高度相关，与游戏强度、眼疲劳、手部疼痛、手腕疼痛、背部或颈部疼痛以及过度游戏内购买显著相关，在验证数据集和大学样本数据集中均如此。
GADIT 在来自四个英语国家的两个独立样本中具有良好的心理测量特性，通过不同的渠道收集，并与游戏障碍相关的现有量表和变量具有有效性。建议暂定 5 分作为筛查游戏障碍的切点。
