ArticleFrontiers in psychiatry2026
Network analysis of sleep quality-anxiety-depressive symptoms among patients with chronic heart failure.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Network Analysis of Sleep, Mental Health, and Stress in Korean Dentists.International dental journal · 2026Article
- Occupational and psychosocial correlates of sleep disturbance among Chinese expatriate employees in Iraq's Maysan oilfields: a cross-sectional study using regression and network analysis.Frontiers in psychiatry · 2026Article
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5 authors.
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Abstract
Objective: While recent studies in related populations have confirmed the co-occurrence of sleep and mental symptoms in cardiac conditions, the specific network structure connecting these symptoms in a dedicated CHF cohort has not been fully elucidated. This study employed network analysis to identify central and bridging symptoms within this network. Methods: A cross-sectional study was conducted among 406 patients with chronic heart failure (CHF) at a hospital in Nanning, Guangxi, China, between August 2024 and March 2025. All participants completed a questionnaire assessing general and disease-specific characteristics. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, while anxiety and depressive symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS). A network analysis was conducted using Gaussian Graphical Models (GGM) in R software to explore the interrelationships among symptoms. Centrality indices and bridge expected influence (BEI) were used to identify central and bridge symptoms, respectively. Results: Analysis of the network centrality indicated that the HADS12 (not looking forward with enjoyment to things) and HADS13 (sudden panic) categories showed the highest centrality, with values of 1.205 and 1.208, respectively, showing their strong linkage with other symptoms and functions as central nodes in the network. Furthermore, PSQI-A (subjective sleep quality) was identified as the key bridge symptom (bridge expected influence=0.541), indicating its primary role in connecting the sleep and psychological symptom clusters. Conclusions: The identification of core and bridging symptoms in the CHF-specific symptom network underscores the potential for targeted interventions. Addressing these critical symptoms may prove beneficial in improving treatment outcomes and enhancing the quality of life for CHF patients, particularly those with comorbid anxiety and depressive symptoms.
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