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已发表论文
慢性病患者自我污名、失眠与心理健康共病网络：一项网络分析
Authors Zhang X, Lin R , Zhang Z , Hu Q, Li P, Fei X, Jiang Z, Zhang Q, Deng Q, Wang G, Zhou J, Zhao Y, Zhang X
Received 19 April 2025
Accepted for publication 19 November 2025
Published 28 November 2025 Volume 2025:18 Pages 2333—2345
DOI https://doi.org/10.2147/PRBM.S529940
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
Editor who approved publication: Dr Bao-Liang Zhong
Xiangbin Zhang,1– 3 Ruoheng Lin,4 Zheng Zhang,5– 7 Qing Hu,8 Peiting Li,9 Xiyun Fei,10 Zhangliang Jiang,11 Qi Zhang,12 Que Deng,4 Guibin Wang,4 Jianda Zhou,9 Yixin Zhao,5,6 Xiangyan Zhang13
1Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 2National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 3Multi-Modal Monitoring Technology for Severe Cerebrovascular Disease of Human Engineering Research Center Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 4Department of Neurology, 921st Hospital of PLA (Second Affiliated Hospital of Hunan Normal University), Changsha, Hunan, 410003, People’s Republic of China; 5Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China; 6Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China; 7School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, People’s Republic of China; 8Neuro-ICU, Chenzhou First People’s Hospital, Chenzhou, Hunan, 423000, People’s Republic of China; 9Department of Plastic Surgery, the Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People’s Republic of China; 10Changsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital), Changsha, Hunan, 410100, People’s Republic of China; 11Neurosurgery, Zhangjiajie People’s Hospital, Zhangjiajie, Hunan, 427000, People’s Republic of China; 12Hunan Children’s Hospital, Changsha, Hunan, 410007, People’s Republic of China; 13Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China
Correspondence: Xiangyan Zhang, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China, Email zhangxiangyan25@csu.edu.cn Yixin Zhao, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China, Email 1033154728@qq.com
Background: Patients with chronic illnesses frequently exhibit symptoms including self-stigma, insomnia, depression, and anxiety. While previous research has primarily focused on the effects of individual symptoms, a comprehensive analysis of the complex interactions among these symptoms remains lacking. The present study investigates these interactions using network analysis.
Methods: The study collected data on the psychological status of 406 patients using self-assessment scales (sleep/anxiety/depression scales). We conducted network analyses with the R packages botnet and qgraph to evaluate the bridging relationships between symptom networks and the strength of these networks. Additionally, we analyzed the interrelationships among the various symptoms of self-stigma, insomnia, depression, and anxiety, and explored the core and bridging symptoms within the symptom networks.
Results: Network analyses identified self-stigma emotions and daytime conditions as the core symptoms of self-stigma and insomnia within the dimensional network models of self-stigma, depression, anxiety, and insomnia. The most significant bridging symptoms in these models were anxiety, depression, self-stigma, emotions, and daytime conditions. In contrast, the prominent bridging symptoms in the self-stigma, depression, anxiety, and insomnia dimensional network models were SD6 (Bad mood or unstable mood during the day), AN2 (Unable to stop or control worrying), DP2 (Feeling down, depressed, hopeless), and SS1 (Patient identity is burdens). Additionally, SS9 (Illness-concealed social avoidance) and SD7 (Poor or unstable mental state during daytime physical activities) emerged as the core symptoms of self-stigma and insomnia symptoms, respectively.
Conclusion: This network analysis identified self-stigma cognition and sleep quality as central symptoms within the self-stigma-insomnia network structure. It pinpointed a lack of interest and pleasure in activities, along with the inability to stop or control worrying, as bridge symptoms in the self-stigma-insomnia-depression and self-stigma-insomnia-anxiety network structures.
Keywords: self-stigma, insomnia, anxiety, depressive symptoms, chronic disease states, network analysis, centrality
1Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 2National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 3Multi-Modal Monitoring Technology for Severe Cerebrovascular Disease of Human Engineering Research Center Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 4Department of Neurology, 921st Hospital of PLA (Second Affiliated Hospital of Hunan Normal University), Changsha, Hunan, 410003, People’s Republic of China; 5Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China; 6Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China; 7School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, People’s Republic of China; 8Neuro-ICU, Chenzhou First People’s Hospital, Chenzhou, Hunan, 423000, People’s Republic of China; 9Department of Plastic Surgery, the Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People’s Republic of China; 10Changsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital), Changsha, Hunan, 410100, People’s Republic of China; 11Neurosurgery, Zhangjiajie People’s Hospital, Zhangjiajie, Hunan, 427000, People’s Republic of China; 12Hunan Children’s Hospital, Changsha, Hunan, 410007, People’s Republic of China; 13Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China
Correspondence: Xiangyan Zhang, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China, Email zhangxiangyan25@csu.edu.cn Yixin Zhao, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, People’s Republic of China, Email 1033154728@qq.com
Background: Patients with chronic illnesses frequently exhibit symptoms including self-stigma, insomnia, depression, and anxiety. While previous research has primarily focused on the effects of individual symptoms, a comprehensive analysis of the complex interactions among these symptoms remains lacking. The present study investigates these interactions using network analysis.
Methods: The study collected data on the psychological status of 406 patients using self-assessment scales (sleep/anxiety/depression scales). We conducted network analyses with the R packages botnet and qgraph to evaluate the bridging relationships between symptom networks and the strength of these networks. Additionally, we analyzed the interrelationships among the various symptoms of self-stigma, insomnia, depression, and anxiety, and explored the core and bridging symptoms within the symptom networks.
Results: Network analyses identified self-stigma emotions and daytime conditions as the core symptoms of self-stigma and insomnia within the dimensional network models of self-stigma, depression, anxiety, and insomnia. The most significant bridging symptoms in these models were anxiety, depression, self-stigma, emotions, and daytime conditions. In contrast, the prominent bridging symptoms in the self-stigma, depression, anxiety, and insomnia dimensional network models were SD6 (Bad mood or unstable mood during the day), AN2 (Unable to stop or control worrying), DP2 (Feeling down, depressed, hopeless), and SS1 (Patient identity is burdens). Additionally, SS9 (Illness-concealed social avoidance) and SD7 (Poor or unstable mental state during daytime physical activities) emerged as the core symptoms of self-stigma and insomnia symptoms, respectively.
Conclusion: This network analysis identified self-stigma cognition and sleep quality as central symptoms within the self-stigma-insomnia network structure. It pinpointed a lack of interest and pleasure in activities, along with the inability to stop or control worrying, as bridge symptoms in the self-stigma-insomnia-depression and self-stigma-insomnia-anxiety network structures.
Keywords: self-stigma, insomnia, anxiety, depressive symptoms, chronic disease states, network analysis, centrality