PDF(Pubmed)
Abstract：
Limited awareness, social stigma, and access to mental health professionals hinder early detection and intervention of internet gaming disorder (IGD), which has emerged as a significant concern among young individuals. Prevalence estimates vary between 0.7% and 15.6%, and its recognition in the International Classification of Diseases, 11th Revision and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition underscores its impact on academic functioning, social isolation, and mental health challenges.
This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases.
A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes.
A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=-0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (β=.33; P<.001), time interval between keys (β=-0.26; P=.01), word spacing (β=-0.25; P<.001), deletion (β=-0.24; P<.001), and horizontal length of strokes (β=0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD.
The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.
This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases.
A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes.
A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=-0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (β=.33; P<.001), time interval between keys (β=-0.26; P=.01), word spacing (β=-0.25; P<.001), deletion (β=-0.24; P<.001), and horizontal length of strokes (β=0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD.
The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.
摘要：
背景：意识有限，社会耻辱，接触心理健康专业人员会阻碍网络游戏障碍(IGD)的早期发现和干预，这在年轻人中已经成为一个重要的问题。患病率估计在0.7%到15.6%之间，以及它在国际疾病分类中的认可，《精神障碍诊断和统计手册》第11次修订，第5版强调了它对学术功能的影响，社会孤立,和心理健康挑战。
目的：本研究旨在发现数字表型，以便在学习环境中的青少年中早期发现IGD。通过利用从学生平板电脑收集的传感器数据，总体目标是将这些数字指标纳入日常学校活动，以建立这些标记作为心理健康筛查工具，促进IGD病例的早期识别和干预。
方法：共有168名自愿参与者参与，由85名IGD学生和83名没有IGD的学生组成。有53％（89/168）的女性和47％（79/168）的男性，都在13-14岁的年龄范围内。个别学生在个人平板电脑上学习了韩国文学和数学课，自动收集传感器数据。使用自举多元回归和多变量方差分析，将可解释性优先于可预测性，用于交叉验证目的。
结果：IGD量表（IGDS）得分与学习成果之间呈负相关（r166=-0.15；P=.047），提示较高的IGDS评分与较低的学习结果相关.多元回归确定了与IGD相关的5个关键指标，解释23%的IGDS分数方差：冲程加速度(β=.33；P<.001)，键之间的时间间隔（β=-0.26；P=0.01），字间距(β=-0.25；P<.001)，缺失(β=-0.24；P<.001)，和笔划的水平长度(β=-0.21;P=.02)。多变量方差分析交叉验证了这些发现，揭示潜在IGD组和非IGD组之间数字表型的显着差异。平均效果大小，由科恩D衡量，所有指标为0.40，表明效果中等。值得注意的区别包括更快的冲程加速度（Cohend=0.68；P=<.001），减少字距(科恩d=.57；P=<.001)，缺失行为减少(科恩d=0.33；P=.04)，与没有IGD的学生相比，具有潜在IGD的学生的水平笔划更长（Cohend=0.34；P=.03）。
结论：汇总发现显示IGD与学习表现之间呈负相关，突出数字标记在检测IGD中的有效性。这强调了数字表型在教育环境中推进精神卫生保健的重要性。随着学校采用每个学生1台设备的框架，数字表型成为一种有前途的早期检测IGD的方法。这种转变可以将临床方法从被动措施转变为主动措施。
目的：本研究旨在发现数字表型，以便在学习环境中的青少年中早期发现IGD。通过利用从学生平板电脑收集的传感器数据，总体目标是将这些数字指标纳入日常学校活动，以建立这些标记作为心理健康筛查工具，促进IGD病例的早期识别和干预。
方法：共有168名自愿参与者参与，由85名IGD学生和83名没有IGD的学生组成。有53％（89/168）的女性和47％（79/168）的男性，都在13-14岁的年龄范围内。个别学生在个人平板电脑上学习了韩国文学和数学课，自动收集传感器数据。使用自举多元回归和多变量方差分析，将可解释性优先于可预测性，用于交叉验证目的。
结果：IGD量表（IGDS）得分与学习成果之间呈负相关（r166=-0.15；P=.047），提示较高的IGDS评分与较低的学习结果相关.多元回归确定了与IGD相关的5个关键指标，解释23%的IGDS分数方差：冲程加速度(β=.33；P<.001)，键之间的时间间隔（β=-0.26；P=0.01），字间距(β=-0.25；P<.001)，缺失(β=-0.24；P<.001)，和笔划的水平长度(β=-0.21;P=.02)。多变量方差分析交叉验证了这些发现，揭示潜在IGD组和非IGD组之间数字表型的显着差异。平均效果大小，由科恩D衡量，所有指标为0.40，表明效果中等。值得注意的区别包括更快的冲程加速度（Cohend=0.68；P=<.001），减少字距(科恩d=.57；P=<.001)，缺失行为减少(科恩d=0.33；P=.04)，与没有IGD的学生相比，具有潜在IGD的学生的水平笔划更长（Cohend=0.34；P=.03）。
结论：汇总发现显示IGD与学习表现之间呈负相关，突出数字标记在检测IGD中的有效性。这强调了数字表型在教育环境中推进精神卫生保健的重要性。随着学校采用每个学生1台设备的框架，数字表型成为一种有前途的早期检测IGD的方法。这种转变可以将临床方法从被动措施转变为主动措施。
