ArticleNature and science of sleep2025
The Sleep Patterns and Influencing Factors of Chronic Heart Failure Patients in China: A Latent Profile Analysis.
Article in Nature and science of sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Latent profile analysis of quality of life in patients undergoing on-pump cardiac surgery and its perioperative risk factors.BMC cardiovascular disorders · 2026Article
- The development and validation of a clinical measurement tool for fear of recurrence and progression in cardiac patients.Scientific reports · 2026Article
- Digital health literacy and associated factors in older adult patients with chronic obstructive pulmonary disease: a latent profile analysis.Frontiers in public health · 2026Article
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5 authors.
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Abstract
Purpose: Sleep problems such as reduced sleep efficiency, difficulty initiating sleep, and increased sleep disturbances significantly affect the quality of life and health status of patients with chronic heart failure (CHF). However, the sleep patterns of CHF patients and their influencing factors need to be further studied. Therefore, this study aimed to explore the latent sleep patterns in patients with CHF and to analyze the factors influencing different sleep patterns. Patients and Methods: A convenience sampling method was adopted to select 290 patients with CHF who were hospitalized in the Department of Cardiology of a tertiary hospital in Yangzhou City, Jiangsu Province, China, from January to August 2024. The investigation utilized a general information questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Fear of Progression Questionnaire-Short Form (FoP-Q-SF). Utilizing Mplus version 8.3 for potential profile analysis, the influences on potential categorization were examined through univariate and multivariate logistic regression analyses. Results: The sleep quality score of 290 patients with CHF was (12.00±3.95). The findings from latent profile analysis indicated that the sleep quality patterns of patients with CHF were categorized into three distinct profiles: relatively good sleep group (n=87, 30.3%), low sleep efficiency-low medication use group (n=160, 54.9%), and sleep disorder-substance dependence group (n=43, 14.8%). Multiple logistic regression analysis showed that age, monthly income, number of hospitalizations for heart failure in a year, number of comorbidities, and fear of progression were influential factors (P < 0.05). Conclusion: Sleep quality among patients with CHF exhibits distinct distributional profiles. Healthcare providers should implement tailored sleep management strategies and psychological interventions, aligning with the sleep patterns and influencing factors specific to patients with CHF. However, it is necessary to note that this study employed a cross-sectional design, and future research could benefit from a longitudinal design.
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