Cross-sectional and cross-lagged network analyses of Internet addiction among university students
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摘要:
目的 了解大学生网络成瘾的动态时序演化路径并筛选出核心驱动节点，为精准实施靶向干预提供理论依据。 方法 采用方便整群抽样方法，从贵州、江西和广东省的3所高校抽取1 066名大一、大二年级的全日制在校本科生展开追踪调查(T1:2024年1—3月；T2：2025年1—3月)。运用网络成瘾量表(CIAS-R)对大学生网络成瘾状况进行测量，构建横断面与交叉滞后面板网络模型对大学生网络成瘾及其多维影响因素进行分析。 结果 T1网络有19个节点和114条非零边，T2网络有19个节点和126条非零边；横断面网络分析显示，“睡眠不足”与“白天疲乏”之间关联性最强；T1时期的核心节点为“早晨第一念头是上网”与“断网后情绪低落”(心理依赖特征)，T2时期的核心节点为“健康受损”与“上网亢奋”(功能损害与成瘾性精神动力特征)。交叉滞后网络分析揭示，“休闲减少”可直接预测“压缩睡眠”，“需更多时间满足”与“学业下滑”之间存在双向影响。 结论 大学生网络成瘾呈现动态演化的特征。需要分阶段对核心驱动节点进行靶向干预，结合行为调控以及学业支持阻断恶性循环，提升现实需求契合能力。 Abstract:Objective To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods Using a convenient cluster sampling method, a total of 1 066 full-time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow-up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross-sectional as well as cross-lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results The T1 network comprised 19 nodes and 114 non-zero edges, while the T2 network comprised 19 nodes and 126 non-zero edges. Cross-sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross-lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage-specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real-life demands. -
Key words:
- Internet /
- Behavior, addictive /
- Mental health /
- Cross-over studies /
- Students
1) 利益冲突声明 所有作者声明无利益冲突。 -
图 1 大学生T1、T2网络成瘾题项网络结构
注： 1~6为强迫症状，分别表示早晨第一念头是上网、无网络则生活无趣、断网怕错过信息、断网后情绪低落、上网亢奋、下网后又忍不住再连；7~10为耐受症状，分别表示上网时长递增、每周上网时间暴增、计划短时间但难停下、需更多时间满足；11~15为人际与健康问题，分别表示健康受损、社交疏离、学业下滑、休闲减少、身体不适；16~19为时间管理问题，分别表示睡眠不足、压缩睡眠、被劝时长、白天疲乏。
Figure 1. Network structure of T1 and T2 Internet addiction item
图 2 大学生T1、T2网络成瘾网络中心性指标
Figure 2. Centrality indices of the T1 and T2 Internet addiction network
图 3 大学生网络成瘾交叉滞后网络
注：线条越粗表示相关性越强。1~6为强迫症状，分别表示早晨第一念头是上网、无网络则生活无趣、断网怕错过信息、断网后情绪低落、上网亢奋、下网后又忍不住再连；7~10为耐受症状，分别表示上网时长递增、每周上网时间暴增、计划短时间但难停下、需更多时间满足；11~15为人际与健康问题，分别表示健康受损、社交疏离、学业下滑、休闲减少、身体不适；16~19为时间管理问题，分别表示睡眠不足、压缩睡眠、被劝时长、白天疲乏。
Figure 3. Cross-lagged network of Internet addiction among university students
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