Concurrent Associations of Gaming Time and Problematic Gaming with Depressive Symptoms in Children and Adolescents: A Three-Wave Cohort Study with Complementary Machine Learning Classification.

IF 3.4 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Yeseo Lee, Jun Seok Her, Hyo Jung Kim
{"title":"Concurrent Associations of Gaming Time and Problematic Gaming with Depressive Symptoms in Children and Adolescents: A Three-Wave Cohort Study with Complementary Machine Learning Classification.","authors":"Yeseo Lee, Jun Seok Her, Hyo Jung Kim","doi":"10.3390/healthcare14193315","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background/Objectives</b>: Digital media use is integral to the daily lives of children and adolescents, intensifying concerns regarding potential associations with mental health. However, much of the literature linking gaming and depression is based on cross-sectional designs and does not adequately distinguish within-person change from between-person differences. This study examined concurrent associations between gaming use patterns, problematic gaming behavior, and depressive symptoms across three repeated assessment waves and, as a complementary analysis, evaluated the concurrent classification performance of gaming-related indicators for elevated depressive symptoms. <b>Methods</b>: Three-wave data collected at baseline, 12 months, and 24 months were obtained from the Internet, Game, and Smartphone User Cohort established by the National Center for Mental Health. Depressive symptoms were assessed using the Children's Depression Inventory (CDI), and problematic gaming behavior was assessed using the Internet Game Use-Elicited Symptom Screen (IGUESS). Linear mixed-effects models, including within-between decomposition and change-score models, were applied. Logistic Regression, Random Forest, and XGBoost were used to classify concurrent elevated depressive symptoms (CDI ≥ 22). <b>Results</b>: The primary mixed-effects analyses included 2194 of 2294 enrolled participants. Across models, higher IGUESS scores were consistently associated with greater concurrent depressive symptoms, whereas gaming time showed weak or inconsistent associations. Machine learning models yielded ROC-AUC values of 0.85-0.88; however, precision and F1-scores were modest, indicating a substantial false-positive burden in this low-prevalence setting. <b>Conclusions</b>: Overall, problematic gaming behavior, rather than gaming time alone, may be more closely associated with depressive symptoms in children and adolescents.</p>","PeriodicalId":12977,"journal":{"name":"Healthcare","volume":"14 19","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13664882/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/healthcare14193315","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background/Objectives: Digital media use is integral to the daily lives of children and adolescents, intensifying concerns regarding potential associations with mental health. However, much of the literature linking gaming and depression is based on cross-sectional designs and does not adequately distinguish within-person change from between-person differences. This study examined concurrent associations between gaming use patterns, problematic gaming behavior, and depressive symptoms across three repeated assessment waves and, as a complementary analysis, evaluated the concurrent classification performance of gaming-related indicators for elevated depressive symptoms. Methods: Three-wave data collected at baseline, 12 months, and 24 months were obtained from the Internet, Game, and Smartphone User Cohort established by the National Center for Mental Health. Depressive symptoms were assessed using the Children's Depression Inventory (CDI), and problematic gaming behavior was assessed using the Internet Game Use-Elicited Symptom Screen (IGUESS). Linear mixed-effects models, including within-between decomposition and change-score models, were applied. Logistic Regression, Random Forest, and XGBoost were used to classify concurrent elevated depressive symptoms (CDI ≥ 22). Results: The primary mixed-effects analyses included 2194 of 2294 enrolled participants. Across models, higher IGUESS scores were consistently associated with greater concurrent depressive symptoms, whereas gaming time showed weak or inconsistent associations. Machine learning models yielded ROC-AUC values of 0.85-0.88; however, precision and F1-scores were modest, indicating a substantial false-positive burden in this low-prevalence setting. Conclusions: Overall, problematic gaming behavior, rather than gaming time alone, may be more closely associated with depressive symptoms in children and adolescents.

游戏时间和问题游戏与儿童和青少年抑郁症状的并发关联:一项采用互补机器学习分类的三波队列研究
背景/目标:数字媒体的使用是儿童和青少年日常生活不可或缺的一部分,这加剧了人们对其与心理健康潜在关联的担忧。然而,许多将游戏与抑郁联系起来的文献都是基于横截面设计,并没有充分区分人与人之间的差异和人与人之间的变化。本研究通过三个重复评估波检验了游戏使用模式、问题游戏行为和抑郁症状之间的并发关联,并作为补充分析,评估了游戏相关指标对抑郁症状升高的并发分类表现。方法:从国家心理健康中心建立的互联网、游戏和智能手机用户队列中收集基线、12个月和24个月的三波数据。使用儿童抑郁量表(CDI)评估抑郁症状,使用网络游戏使用引发症状屏幕(IGUESS)评估问题游戏行为。采用了线性混合效应模型,包括between分解和变化评分模型。采用Logistic回归、随机森林和XGBoost对并发抑郁症状升高(CDI≥22)进行分类。结果:主要混合效应分析包括2294名入组参与者中的2194名。在所有模型中,更高的猜测得分与更严重的并发抑郁症状一致相关,而游戏时间则表现出微弱或不一致的关联。机器学习模型的ROC-AUC值为0.85-0.88;然而,准确性和f1评分不高,表明在这种低患病率环境中存在大量假阳性负担。结论:总的来说,问题游戏行为,而不是单独的游戏时间,可能与儿童和青少年的抑郁症状有更密切的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Healthcare
Healthcare Medicine-Health Policy
CiteScore
3.50
自引率
7.10%
发文量
0
审稿时长
47 days
期刊介绍: Healthcare (ISSN 2227-9032) is an international, peer-reviewed, open access journal (free for readers), which publishes original theoretical and empirical work in the interdisciplinary area of all aspects of medicine and health care research. Healthcare publishes Original Research Articles, Reviews, Case Reports, Research Notes and Short Communications. We encourage researchers to publish their experimental and theoretical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be provided so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书