Recent epidemiological studies have shown that chronic heart failure (CHF), a complex clinical syndrome, affects >56 million individuals globally. This figure is estimated to rise by 46% by 2030 (1), presenting significant challenges not only for healthcare systems but also for affected individuals, especially in terms of quality of life and mental well-being. This projected increase in CHF prevalence represents a critical challenge in contemporary public health governance. Sleep disorders are defined as persistent abnormalities in sleep patterns or physiological dysfunction related to sleep, usually manifested as poor sleep quality. These impact adversely on normal physiological requirements in terms of sleep quality, sleep duration, and maintenance of the circadian rhythm, leading to functional impairment and subjective distress during daytime (2). It has been reported that approximately 85.8% of patients with CHF experience poor sleep quality (3). Studies (4, 5) have also shown that poor sleep quality in patients with CHF was independently associated with adverse clinical outcomes, including significantly impaired quality of life, the development of anxiety and depressive symptoms, a progressive decline in physical functioning, persistent fatigue, and elevated 30-day readmission rates.

Previous studies have demonstrated a relationship between poor sleep quality and the presence of anxiety and depressive symptoms in individuals diagnosed with CHF (6, 7). The prevalence of anxiety and depressive symptoms is significantly higher in the CHF population. It was found that approximately 21.5% of patients with CHF had symptoms of clinical depression, while close on 30% of patients reported anxiety levels reaching clinically significant levels (8). These mental health issues may be related to lifestyle changes associated with CHF, as well as the uncertainty surrounding the progression of heart failure (9). A study by Palagini et al (10) identified a significant correlation between increased severity of anxiety and depressive and the primary clinical outcomes of CHF, including hospitalization due to heart failure, cardiovascular-related mortality, and the need for interventions such as heart transplantation or the implantation of left ventricular assist devices. There is a reciprocal relationship between sleep quality and anxiety-depressive symptoms. Numerous studies have demonstrated that poor sleep quality is a key predictor of the development of anxiety and depressive symptoms. It has been found that individuals experiencing chronic sleep disturbances are predisposed to the development of mental health issues (11, 12). Conversely, the presence of anxiety and depressive symptoms can also lead to or worsen compromised sleep quality, creating a vicious cycle (13). When people are anxious or depressed, their minds may be filled with excessive worry, negative thoughts, or emotional turmoil, which can directly disrupt the normal sleep cycle (14). For instance, these individuals may struggle to fall asleep, experience recurrent awakenings throughout the night, or awaken prematurely in the morning. However, it is noteworthy that effective interventions for poor sleep quality can, to a large extent, prevent the onset of anxiety and depressive symptoms, providing strong support for the maintenance of mental health (15). These strategies include behavioral therapies, such as insomnia-focused cognitive behavioral therapy (CBT-I), which assist individuals in the recognition and modification of detrimental thought patterns and sleep-related behaviors. Lifestyle modifications, such as maintaining a consistent sleep routine, optimizing the sleep environment for comfort, and limiting consumption of caffeine and alcohol, are also recommended (16, 17).

Network analysis is a novel data analysis method that overcomes the limitations of traditional data analysis by its ability to represent features and information in the form of a comprehensive network system (18). The system is composed of a network structure comprising “nodes” and “edges”, which are then used to identify interactions and relationships among factors. Network analysis has three core advantages, namely, its ability to reveal microscopic correlations among variables, visualize dynamic interaction patterns, and quantify the influence of nodes based on centrality indicators (19). Network analysis can determine interactions among symptoms in the symptom clusters of mental disorders, and can also quantify the relationships among individual symptoms (20). It is also able to identify interactions among different symptoms and groups of symptoms and determine core symptoms in the network structure. This approach not only helps to explore the mechanisms underlying internal and mutual influences among mental disorders but can also identify suitable interventions for preventing to treating diseases, thus having significant practical value (21, 22).

Although the co-occurrence of these symptoms is recognized, previous studies (7, 23–25) relying on traditional statistical methods (e.g., correlation or regression) is limited in its ability to model the complex interactions among specific symptoms, as evidenced by studies (25) such as Le Grande et al. (2016) which established their longitudinal links. These methods typically treat symptoms as independent outcomes or aggregate them into total scores, thereby obscuring the fine-grained relational structure (26–30). Network analysis addresses this gap by conceptualizing symptoms as a system of direct interactions, allowing us to identify the most central and interconnected symptoms. Consequently, without this network approach, treatment strategies may lack precision by failing to prioritize these key drivers of comorbidity.

Network analysis, by modeling these interactions as a system of interconnected nodes, offers a more comprehensive and nuanced understanding of how symptoms influence one another, and can thus lead to more targeted interventions (31). Therefore, this study applied network analysis to examine the interplay between sleep quality, anxiety, and depressive symptoms in individuals suffering from CHF. By identifying core and bridge symptoms within the symptom network, we aim to elucidate the complex relationships between sleep quality and anxiety-depressive comorbidity in this population. The findings may inform integrated the use of treatment strategies to ultimately improve the sleep quality, mental health, and overall quality of life of patients with CHF.

This study found a high co-occurrence of poor sleep quality (57.39%), anxiety symptoms (60.34%), and depressive symptoms (73.15%) in patients with CHF. These prevalence rates are substantially higher than the well-documented range in the literature, where anxiety affects approximately 16% to 52.4% and depression28.0% to 53.4% of CHF patients (28, 42–44). The disparity likely stems from the unique profile of our study population, which was characterized by a high prevalence of severe heart failure (total 87.7% in NYHA class III and class IV) and a prominent proportion of low-income individuals (69.0%). While the reliance on self-reported measures may introduce bias (e.g., PSQI and HADS) (45), and symptom overlap can complicate assessment, the primary contribution of this study lies in moving beyond prevalence. Through network analysis, we elucidate the specific interrelationships among these symptoms. These high levels of sleep impairment, anxiety, and depressive symptoms are strongly associated with poorer well-being in patients with CHF and, in line with existing literature (10, 46, 47), may be linked to adverse disease outcomes, suggesting the potential for a reinforcing interplay. Consequently, it is imperative for clinicians to look beyond cardiac function and prioritize the assessment and management of psychological health and sleep quality. Implementing comprehensive interventions that address this complex symptom burden is essential for improving prognosis, enhancing overall health outcomes (48, 49), and restoring patients’ capacity for self-management—a fundamental step toward improving their quality of life (50).

This study investigated the interrelationships among sleep quality and anxiety/depressive symptoms in Chinese patients with CHF using a network perspective, thus offering a new theoretical structure for understanding the complexity of mental well-being in this patient population. Research has shown that sleep, guilt, restlessness, irritability, and feeling afraid were the primary core symptoms in sleep quality-anxiety/depressive symptoms network of Chinese college students (51). The present study highlights that HADS13 (sudden panic) and HADS12 (not looking forward with enjoyment to things) emerged as central nodes within the overall symptom network. Emerging findings in psychocardiology show symptom clusters in CHF, with Bian et al. (52) identifying fatigue, sleep disturbance, and emotional distress, paralleling our findings of lack of enjoyment and panic. Zhao et al. (53) noted anxiety and depressive symptoms, highlighting “loss of interest” and “fear/panic” as key nodes, emphasizing threat- and loss-related feelings in heart failure. He et al. (54) found anhedonia, agitation, and somatic anxiety central in coronary heart disease, indicating psychological distress often revolves around mood depletion and anticipatory fear across cardiac populations. The identification of ‘not looking forward with enjoyment to things’ and ‘sudden panic’ as the most central symptoms is clinically insightful. In CHF, ‘not looking forward with enjoyment to things’ likely transcends a general loss of interest; it may be directly fueled by the grief of functional loss, social role contraction, and the perceived burden of a chronic illness, which can erode future-oriented pleasure and motivation. Conversely, ‘sudden panic feelings’ could represent the somatic manifestation of a very specific and pervasive fear in CHF—the fear of acute dyspnea, sudden deterioration, or imminent mortality. Their centrality suggests that in CHF, the emotional burden may be organized around themes of loss and threat. These symptoms could represent putative leverage points for psychological intervention, a possibility that requires confirmation in prospective studies. Having identified these central emotional drivers, elucidating the bridge symptoms that connect the sleep and psychological symptom clusters is equally crucial to understanding the integrated network.

The bridge function of ‘subjective sleep quality’ and ‘daytime dysfunction’ helps explain the observed correlations between the broader illness burden and psychological distress in CHF. ‘subjective sleep quality’ can be understood as a perceptual link: long-term physical discomfort and symptoms (e.g., nocturnal dyspnea) are strongly correlated with sleep disturbances, which are subjectively reported as poor sleep quality. This perception of poor sleep shows a notable correlation with heightened anxieties about health and decline. Similarly, ‘daytime dysfunction’ serves as a phenomenological link: the pervasive fatigue intrinsic to advanced CHF and its socioeconomic burden overlaps phenomenologically with the anergia core to depression and is also a commonly co-occurring feature of poor sleep.

The unpredictable progression of CHF is linked to heightened patient concerns about organ damage and mortality (55), a specific fear that corresponds to the core symptom ‘sudden panic feelings’ and is intertwined with the overall psychological stress that accompanies sleep disturbances. This creates a dense web of interconnections. Literature suggests that poor sleep is correlated with imbalances in neurotransmitters and compromised emotional regulation, which are linked to higher levels of anxiety and depressive symptoms (56). Correspondingly, elevated psychological stress shows a strong association with sleep disturbances. These observed interconnections point to a potential reinforcing interplay among sleep disturbances, emotional symptoms, and core physical symptoms.

Available reports show the pivotal function of bridge symptoms in both the maintenance and progression of comorbid mental disorders. These symptoms thus represent a critical focal point for the prevention and management of these diseases (57).The bridging function of sleep-related symptoms has also been underscored in recent cardiac network research. For instance, Bian et al. (52) reported that impaired sleep quality served as a pathway linking somatic discomfort and emotional distress clusters during the vulnerable phase of CHF, implying that bridge nodes may facilitate shifts between physical and psychological burden. Similarly, Zhao et al. (53) identified insomnia and concentration difficulties as central bridging elements connecting anxiety and depressive symptoms in CHF patients. In this study, subjective sleep quality and daytime dysfunction were identified as key bridge symptom which may serve as hubs to integrate core symptoms such as ‘use of sleeping medication’, ‘sudden panic’, and ‘not looking forward with enjoyment to things’. This cluster of interconnected symptoms is closely clustered with poorer mental health and sleep outcomes in patients with CHF. Notably, the bridge symptoms identified here differ from those reported in other conditions, such as ‘trouble relaxing’ and ‘feeling down’ as bridging symptoms between anxiety and depressive in patients with rheumatoid arthritis (58), suggesting the existence of disease-specific symptom pathways. Impaired cardiac function characteristic of CHF is accompanied by blood circulation. Insufficient blood supply to the brain may be linked to neurotransmitter imbalances and consequent alterations in brain function, which could serve as potential correlates of poor sleep quality and fear of injury (59). Thus, effective management in CHF should extend beyond optimizing cardiac function to include targeted attention to bridging symptom clusters identified in the present network analysis. Improving subjective sleep quality, in particular, may represent a practical and potentially impactful entry point for modifying the broader symptom network. Multimodal strategies—such as pharmacotherapy, cognitive-behavioral therapy, stress reduction, and mindfulness-based practices—could be employed to address this target. While these approaches hold promise for alleviating sleep deficits and mitigating associated psychological burdens, their utility as intervention priorities should be regarded as a hypothesis arising from current cross-sectional evidence and thus warrants confirmation in prospective and experimental studies (60, 61), before firm causal inferences or clinical recommendations can be established.

Building on this integrated framework, we propose that specific interventions could be tailored to the central symptoms identified in our network, as hypothetical priorities that warrant prospective testing before clinical application. Cognitive behavioral therapy (CBT) serves as a foundational intervention for both core symptom clusters, effectively addressing the irrational beliefs underlying anxiety (e.g., frightened feelings) and the negative thought patterns characterizing depression (e.g., feeling down) (62). Specifically, for the core symptom frightened feelings (‘sudden panic’), healthcare providers should offer clear explanations of CHF pathophysiology and treatment, which may enhance the patient’s sense of control and help mitigate health-related anxieties (63), alongside implementing targeted CBT strategies (64). Concurrently, for the core depressive symptom of ‘not looking forward with enjoyment to things’, CBT can be usefully supplemented with moderate exercise (65) as well as music (66) and art therapy, among other rehabilitative activities, to help shift focus away from negative feelings. At the same time, communication with family members should be strengthened, emphasizing the importance of cultivating a positive family atmosphere while providing care and companionship (67).

The bridge symptom of subjective sleep quality may serve as a potential intervention target within this symptom network. This hypothesis addresses the interconnectedness between psychological distress and sleep disturbance, and should be evaluated in future longitudinal and interventional studies before firm conclusions are drawn. To effectively address poor subjective sleep quality, a stepped-care approach is recommended. First, healthcare providers should deliver structured sleep education to cultivate healthy sleep habits. For patients experiencing significantly sleep impairment, pharmacological interventions may be considered following a thorough medical evaluation. Furthermore, the integration of relaxation training into the treatment plan can promote better sleep quality, while cognitive behavioral therapy may help modify maladaptive thoughts related to sleep (68).

In conclusion, this study identified the key characteristics of the sleep quality-anxiety/depressive symptoms network in patients with CHF. Beyond identifying HADS12 (not looking forward with enjoyment to things) and HADS13 (sudden panic) as core symptoms within the network, it also revealed PSQI-A (subjective sleep quality) as the most prominent bridge symptom, connecting the sleep quality symptom cluster with the anxiety/depressive symptom cluster. The application of interventional measures simultaneously targeting these core and bridge symptoms may help disrupt the connections within the symptom network, break the observed vicious cycle, and reduce the overall symptom burden in patients. However, these potential intervention priorities should be regarded as preliminary hypotheses, and their effectiveness requires prospective confirmation in multi-center longitudinal and interventional studies to facilitate the precise treatment of related symptoms in patients with CHF.