Li Yuan, Shi Jing, Luo Biru, Xiong Anqi, Xiong Siqi, Wang Jing, Liao Shujuan
Department of Nursing, West China Second University Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu 610041, China.
Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University, Chengdu 610041, China.
Depress Anxiety. 2025 Jul 12;2025:4591408. doi: 10.1155/da/4591408. eCollection 2025.
Internet addiction and depression frequently co-occur among university students, resulting in amplified functional deterioration and treatment resistance. Despite established bidirectional relationships, existing research has predominantly examined linear associations and treated these conditions as single global constructs. This study integrated person-centered and network-based approaches to identify distinct symptom profiles of Internet addiction and depressive symptoms, examine sociodemographic predictors of profile membership, and uncover interconnected symptom networks within high-risk populations among Chinese university students. A multicenter cross-sectional study was conducted from April to July 2024. Data were collected through a web-based survey incorporating validated instruments for Internet addiction, depression, and suicide risk assessment. Latent profile analysis was employed to identify distinct symptom profiles, followed by multivariate logistic regression to examine sociodemographic predictors. Network analysis was performed within the high-risk profile to unveil symptom interactions, central symptoms, bridge symptoms, and symptomatic pathways to suicide risk. Among 30,992 participants, latent profile analysis identified three distinct groups: Healthy profile (59.31%), at-risk profile (35.06%), and comorbidity profile (5.63%). Students who were female, ethnic minorities, in higher grade levels, and had prolonged Internet use showed increased risks of problematic profiles. Conversely, enrollment in bachelor's programs, science and medical majors, higher household income, and regular physical activity demonstrated protective effects. Network analysis revealed Internet preoccupation and fatigue as central symptoms, identified key bridge symptoms (e.g., offline negative affect, difficulty concentrating) linking the symptom clusters, and highlighted Internet withdrawal symptoms and depressed mood as critical pathways to suicide risk within the comorbidity profile. This study identified distinct profiles of Internet addiction and depression comorbidity, with specific sociodemographic and lifestyle predictors informing targeted screening strategies. Network analysis revealed central symptoms and specific bridge symptoms connecting the conditions, while also identifying critical pathways to suicide risk in the Comorbidity profile, providing empirical evidence for developing precise and effective interventions.
网络成瘾和抑郁症在大学生中经常同时出现，导致功能恶化加剧和治疗抵抗。尽管已确定存在双向关系，但现有研究主要考察线性关联，并将这些情况视为单一的整体结构。本研究整合了以个体为中心和基于网络的方法，以识别网络成瘾和抑郁症状的不同症状模式，检查模式成员的社会人口学预测因素，并揭示中国大学生高危人群中相互关联的症状网络。2024年4月至7月进行了一项多中心横断面研究。通过基于网络的调查收集数据，该调查纳入了经过验证的网络成瘾、抑郁症和自杀风险评估工具。采用潜在剖面分析来识别不同的症状模式，随后进行多变量逻辑回归以检查社会人口学预测因素。在高危模式内进行网络分析，以揭示症状相互作用、核心症状、桥梁症状以及通往自杀风险的症状途径。在30992名参与者中，潜在剖面分析确定了三个不同的组：健康模式（59.31%）、高危模式（35.06%）和共病模式（5.63%）。女性、少数民族、年级较高且上网时间较长的学生出现问题模式的风险增加。相反，攻读学士学位、理科和医学专业、家庭收入较高以及经常进行体育活动则显示出保护作用。网络分析显示，网络沉迷和疲劳是核心症状，确定了连接症状群的关键桥梁症状（如线下负面影响、注意力不集中），并突出了网络戒断症状和抑郁情绪是共病模式中通往自杀风险的关键途径。本研究确定了网络成瘾和抑郁症共病的不同模式，特定的社会人口学和生活方式预测因素为有针对性的筛查策略提供了依据。网络分析揭示了连接这些情况的核心症状和特定桥梁症状，同时还确定了共病模式中通往自杀风险的关键途径，为制定精确有效的干预措施提供了实证依据。
