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已发表论文
维持性血液透析患者不同的睡眠质量特征及其不同程度的污名化：一项横断面研究
Authors Tan H, Li L , Zhang Y , Zhang H
Received 14 August 2025
Accepted for publication 28 October 2025
Published 5 November 2025 Volume 2025:18 Pages 7285—7298
DOI https://doi.org/10.2147/JMDH.S557424
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Professor Linda Yoder
Hong Tan,1 Li Li,2,3 YiPei Zhang,1 HuiZe Zhang1
1School of Nursing, Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 2Department of Urology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 3Health Care Research Center for Xinjiang Regional Population, Urumqi, Xinjiang, People’s Republic of China
Correspondence: Li Li, Email 1311016216@qq.com
Objective: To identify distinct sleep quality profiles among patients undergoing maintenance hemodialysis (MHD) using latent profile analysis (LPA), and examine differences in perceived stigma across these sleep quality subtypes.
Methods: From December 2024 to March 2025, a total of 334 MHD patients were recruited via convenience sampling from the nephrology departments of two tertiary hospitals in Xinjiang, China. Data were collected using structured questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Impact Scale (SIS), along with sociodemographic and clinical information. LPA was employed to identify latent subgroups of sleep quality based on PSQI components. Multinomial logistic regression was used to determine predictors of sleep profile membership. Differences in stigma scores across sleep profiles were analyzed using non-parametric equivalents.
Results: Three distinct sleep profiles were identified: Class 1 – “overall better sleep”, Class 2 – “short sleep duration and low efficiency”, and Class 3 – “poor sleep quality with high medication use”. Multinomial logistic regression identified comorbid heart failure (OR=2.867, P=0.001 for Class 2), pruritus (OR=2.715, P< 0.001 for Class 2), depressive symptoms (OR=2.568, P=0.001 for Class 2; OR=4.823, P< 0.001 for Class 3), and elevated C-reactive protein (OR=1.044, P< 0.001 for Class 2; OR=1.035, P=0.008 for Class 3) as significant predictors of poorer sleep profiles. Stigma scores differed significantly across all sleep profiles (Class 1 vs 2: P=0.039; Class 1 vs 3: P< 0.001; Class 2 vs 3: P=0.005), with Class 3 exhibiting the highest median SIS score.
Conclusion: Patients with MHD exhibit heterogeneous patterns of sleep disturbance, which are associated with varying levels of perceived stigma. Those with the poorest sleep quality and highest reliance on medication experience the most pronounced stigma. Tailored interventions addressing sleep-related issues and psychosocial factors may help reduce stigma and improve patient well-being.
Keywords: maintenance hemodialysis, sleep quality, stigma, latent profile analysis, depression, cross-sectional study
1School of Nursing, Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 2Department of Urology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 3Health Care Research Center for Xinjiang Regional Population, Urumqi, Xinjiang, People’s Republic of China
Correspondence: Li Li, Email 1311016216@qq.com
Objective: To identify distinct sleep quality profiles among patients undergoing maintenance hemodialysis (MHD) using latent profile analysis (LPA), and examine differences in perceived stigma across these sleep quality subtypes.
Methods: From December 2024 to March 2025, a total of 334 MHD patients were recruited via convenience sampling from the nephrology departments of two tertiary hospitals in Xinjiang, China. Data were collected using structured questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Impact Scale (SIS), along with sociodemographic and clinical information. LPA was employed to identify latent subgroups of sleep quality based on PSQI components. Multinomial logistic regression was used to determine predictors of sleep profile membership. Differences in stigma scores across sleep profiles were analyzed using non-parametric equivalents.
Results: Three distinct sleep profiles were identified: Class 1 – “overall better sleep”, Class 2 – “short sleep duration and low efficiency”, and Class 3 – “poor sleep quality with high medication use”. Multinomial logistic regression identified comorbid heart failure (OR=2.867, P=0.001 for Class 2), pruritus (OR=2.715, P< 0.001 for Class 2), depressive symptoms (OR=2.568, P=0.001 for Class 2; OR=4.823, P< 0.001 for Class 3), and elevated C-reactive protein (OR=1.044, P< 0.001 for Class 2; OR=1.035, P=0.008 for Class 3) as significant predictors of poorer sleep profiles. Stigma scores differed significantly across all sleep profiles (Class 1 vs 2: P=0.039; Class 1 vs 3: P< 0.001; Class 2 vs 3: P=0.005), with Class 3 exhibiting the highest median SIS score.
Conclusion: Patients with MHD exhibit heterogeneous patterns of sleep disturbance, which are associated with varying levels of perceived stigma. Those with the poorest sleep quality and highest reliance on medication experience the most pronounced stigma. Tailored interventions addressing sleep-related issues and psychosocial factors may help reduce stigma and improve patient well-being.
Keywords: maintenance hemodialysis, sleep quality, stigma, latent profile analysis, depression, cross-sectional study