Symptoms of atrial fibrillation and sleep quality in atrial fibrillation: A network analysis

Symptoms of atrial fibrillation and sleep quality in atrial fibrillation: A network analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptoms of atrial fibrillation and sleep quality in atrial fibrillation: A network analysis Yunxia Li, Jing Lu, Zhanhong You, Xiu Tao, Jie Wang, Zhipeng Bao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6510506/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Symptoms of Atrial fibrillation (AF) seriously affect sleep quality and well-being. Understanding their interconnections is crucial for targeted management. Methods : A cohort of 292 patients with AF was recruited through convenience sampling. Data collection included a general information questionnaire, the Chinese Version of the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia (ASTA), and the Pittsburgh Sleep Quality Index (PSQI). Descriptive statistical analyses and network modeling were performed using SPSS and R. An ASTA-PSQI network was developed to identify core and bridge symptoms. Spearman's rankcorrelation was utilized to assess relationships between symptoms, while the Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator (EBICglasso) method was employed to mitigate Type I errors. Results : In 292 patients with AF, fatigue was the most common (80.1%) and severe symptom, with a score of 1.26±0.87. Sleep disturbances affected 97.9% of patients, with daytime dysfunction being the most severe (1.53±0.96). Poor sleep quality (PSQI>7) was found in 54.1% of patients. Strong links were noted between weakness/fatigue and fatigue (edge weight=0.63), sleep duration and efficiency (edge weight=0.52), and dyspnea during activity and rest (edge weight=0.35). Weakness/fatigue had the highest centrality and sleep disturbances exhibited the highest bridge centrality. Conclusion : Effective management of AF should focus on controlling disease progression, improving treatment adherence, and reducing weakness/fatigue through exercise, with particular attention to worry/anxiety and sleep disturbances. Clinical trial number: not applicable. atrial fibrillation symptoms sleep quality network analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Atrial fibrillation (AF) is one of the most prevalent persistent arrhythmias globally. According to the 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation, approximately 50 million individuals worldwide are currently affected by AF, with the associated morbidity and mortality rates on the rise [ 1 ]. Projections suggest that by 2050, the prevalence of AF will increase to 72 million individuals [ 2 ]. AF episodes are frequently accompanied by a wide range of symptoms, including fatigue, dyspnea, palpitations, and anxiety, which can substantially impair daily functioning and reduce patients' quality of life [ 3 ]. The broad spectrum of symptom presentations, intricate interactions, and individual variability in AF collectively pose significant challenges to the effective management of symptoms [ 4 ]. Among the symptoms exhibited by patients with AF, poor sleep quality is particularly prevalent issue [ 5 ]. In China, 71.2% of patients with AF report experiencing poor sleep quality, emphasizing the pervasiveness of sleep disorders in this population [ 6 ]. Research has indicated that the physiological mechanisms underlying AF symptoms, including autonomic dysfunction, nocturnal palpitations, and dyspnea, can result in sleep disturbances, leading to frequent awakenings and sleep fragmentation [ 7 ]. This interaction suggests that sleep disturbances in patients with AF may not be an isolated problem but rather a reflection of the underlying symptom burden. Furthermore, poor sleep quality has been associated with increased severity of AF symptoms, potentially leading to a vicious cycle of worsening symptoms [ 8 ]. Despite the growing recognition of these associations, conventional research methodologies often fail to capture the intricate interactions between AF symptoms and sleep quality. Symptom network analysis offers a novel approach to elucidating these relationships by visualizing and quantifying the interconnections among symptoms [ 9 ]. This method enables the identification of core symptom and bridge symptoms [ 10 ], offering insights that may inform personalised clinical decision-making and symptom management strategies [ 11 ]. This study seeks to accomplish two primary objectives: firstly, to investigate the core and bridge symptoms that connect AF symptoms with sleep quality using network analysis; and secondly, to identify the symptoms most closely related to these bridge symptoms, thereby deepening our understanding of the feedback mechanisms among these symptoms. 2. Methods 2.1. Participants A cohort of 292 patients diagnosed with AF was recruited from the First Affiliated Hospital of Nanjing Medical University between October and December 2024. The inclusion criteria comprised: (i) patients were required to meet the 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation [ 1 ]; (ii) they had to be able to understand and complete a questionnaire; (iii) they had to be over the age of 18. The exclusion criteria included: (i) valvular AF; (ii) a history of mental illness; (iii) severe organic diseases; (iv) participation in other studies. As a network scales, the number of estimated parameters increases rapidly [ 12 ]. Within the context of 16-node networks, the estimation of 120 parameters (i.e., 16 threshold parameters and 16*15/2 = 120 pairwise association parameters) was necessary [ 12 ]. 2.2. Measures 2.2.1. The Chinese Version of the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia (ASTA) ASTA is employed to evaluate the current symptom burden in patients with AF [ 13 ], which has been previously developed and localized for use in China [ 14 ]. The scale consists of three subscales. We employed a subset of the second subscale, which is specifically designed to evaluate symptom burden. This subset comprises nine items that assess symptoms associated with AF such as breathlessness during activity, breathlessness even at rest, dizziness, cold sweats, weakness/fatigue, tiredness, chest pain, pressure/discomfort in chest and worry/anxiety. Each symptom is rated on a scale ranging from 0 (absence of symptoms) to 3 (severe symptoms), with higher scores denoting a greater symptom burden. The items have been meticulously crafted to be concise and precise, thereby facilitating efficient data collection regarding the patient's symptom burden. The reduction in the number of items has been implemented with the objective of minimising the probability of patients experiencing feelings of overwhelm, anxiety, or irritation, which could potentially compromise the validity of the questionnaire. The scale demonstrates Cronbach’s α coefficient of 0.826, indicating good internal consistency. 2.2.2. Pittsburgh Sleep Quality Index (PSQI) The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. in 1989, is a widely employed tool for the assessment of individuals’ overall sleep quality over the preceding month [ 15 ]. The PSQI consists of 19 self-report items and 5 additional evaluation items; although only the first 18 self-report items contribute to the score. These items are categorized into seven dimensions: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, daytime dysfunction, and use of sleep medication. Each dimension is scored on a scale from 0 (no difficulty) to 3 (severe difficulty). The total score ranges from 0 (indicative of better sleep) to 21 (indicative of poorer sleep), with scores exceeding 7 suggesting poor sleep quality. The Chinese version of the PSQI has demonstrated satisfactory reliability, supporting its application in research settings [ 6 ]. 2.3. Data Collection Methodology The data were collected using questionnaires. Prior to the administration of the survey, the researchers meticulously explained the purpose and content of the study to the participants and ensured that informed consent was obtained. Participants were then provided with the questionnaires and instructed to complete them independently. In instances where participants encountered difficulties with reading or writing, the researchers provided a neutral reading of each question and recorded the participant's responses on their behalf. Throughout the data collection process, researchers addressed any queries raised by participants and meticulously scrutinised each completed questionnaire to ensure data integrity and validity. A total of 300 questionnaires were distributed, and 292 valid questionnaires were returned yielding an effective response rate of 97.3%. 2.4. Statistical methods Statistical analyses were performed utilizing SPSS version 27.0. Descriptive statistics, including frequency, percentage, mean, and standard deviation, were employed to characterise general demographic characteristics and the frequency and severity of symptoms. Network models were developed and examined using the qgraph package within the R software environment (version 4.4.1). Spearman’s rank correlation analysis was utilized to assess the relationships among symptoms and to estimate the symptom network. The EBICglasso procedure in qgraph was applied to mitigate the risk of Type I errors, thereby ensuring a more accurate network structure, reducing false connections, and enhancing both model accuracy and interpretability [ 16 ]. The nine symptoms from the ASTA and the seven sleep dimensions from the PSQI were designated as network nodes to construct the ASTA-PSQI network. The outer ring of a node signifies its predictability: higher predictability indicates that the symptom can be more readily controlled through its adjacent nodes. The edges connecting nodes denote the strength of their relationships, with blue edges indicating positive correlations and red edges indicating negative correlations. The thickness and saturation of an edge correspond to the strength of the relationship. In this study, centrality measures, including strength, closeness, betweenness, and expected influence, were employed to quantify node centrality. Strength centrality denotes the aggregate weight of a node's connections to other nodes. Closeness centrality is defined as the inverse of the sum of the shortest path lengths from a given node to all other nodes in the network. Betweenness centrality quantifies the frequency with which a node appears on the shortest path between two other nodes, thereby demonstrating the bridging role of the node in the network. Expected influence is a metric that evaluates both the nature and strength of edges, thus assessing the overall influence of a node on the network. In addition to node centrality, this research also examined bridge centrality, which was assessed by measuring bridge strength, bridge closeness, and bridge betweenness. The accuracy of the edge weights and the stability of the node centrality were evaluated using the bootstrap algorithm available in the bootnet package. The 95% Confidence Intervals (CIs) of the edge weights were calculated by the bootstrap method in order to estimate edge precision. Narrower confidence intervals are indicative of higher accuracy of the edge weight estimates. The stability of node centrality was evaluated using the case-drop bootstrap method. Centrality Stability coefficients (CS coefficients) were deemed acceptable if they were greater than or equal to 0.25, and considered good if they were greater than or equal to 0.5. A p-value of less than 0.05 was regarded as statistically significant. 3. Results 3.1. AF Symptoms and sleep quality in Patients with AF A total of 292 patients with AF (age = 60.75 ± 11.19 years, 112 males) were included in this study. Table 1 presents the symptom burden and sleep problem scores of these patients. The average total symptom burden score was 9.40 ± 5.44, with symptom occurrence rates varying between 54.4% and 80.1%. The most prevalent symptom reported was tiredness, affecting 80.1% of the patients, which also had the highest severity score of 1.26 ± 0.87. The mean PSQI score was 8.38 ± 4.04, with the prevalence of sleep quality dimensions ranging from 11.0–97.9%. Sleep disturbances were the most commonly reported problem, affecting 97.9% of the patients. Daytime dysfunction was the dimension with the highest severity score, recorded at 1.53 ± 0.96. Notably, poor sleep quality, defined as a PSQI score greater than 7, was observed in 54.1% of the patients with AF. Table 1 AF symptoms and sleep quality in AF Patients(n = 292) Item n (%) Score Item n (%) Score Total Symptom Burden Score - 9.40 ± 5.44 PSQI Score - 8.38 ± 4.04 Breathlessness during activity 227 (77.7) 1.20 ± 0.88 Subjective sleep quality 246 (84.2) 1.28 ± 0.80 Breathlessness even at rest 159 (54.4) 0.73 ± 0.77 Sleep latency 244 (83.6) 1.52 ± 1.02 Dizziness 201 (68.8) 1.00 ± 0.86 Sleep duration 225 (77.1) 1.25 ± 0.95 Cold Sweats 175 (59.9) 0.93 ± 0.91 Sleep efficiency 174 (59.6) 1.09 ± 1.10 Weakness/Fatigue 227 (77.7) 1.17 ± 0.84 Sleep disturbances 286 (97.9) 1.51 ± 0.62 Tiredness 234 (80.1) 1.26 ± 0.87 Use of sleep medication 32 (11.0) 0.21 ± 0.66 Chest Pain 161 (55.1) 0.73 ± 0.76 Daytime dysfunction 249 (85.3) 1.53 ± 0.96 Pressure/discomfort in chest 225 (77.1) 1.14 ± 0.82 Poor Sleep Quality (PSQI > 7) 158 (54.1) Worry/anxiety 227 (77.7) 1.25 ± 0.91 3.2. Network centrality and bridge centrality of the ASTA-PSQI network The ASTA-PSQI network is illustrated in Fig. 1 . This network reveals two distinct clusters: one for AF symptoms and the other for sleep quality. The thickness of the edges between nodes indicates the strength of the relationship between them. The strongest associations were identified between weakness/fatigue and tiredness (S5-S6, edge weight = 0.63), sleep duration and sleep efficiency (P3-P4, edge weight = 0.52), and breathlessness during activity and breathlessness even at rest (S1-S2, edge weight = 0.35). The centrality and bridge centrality metrics for all nodes are shown in Fig. 2 . Based on centrality indicators, weakness/fatigue (S5) was found to have the highest strength centrality and the highest Expected Influence coefficient (EI), indicating that it is the core symptom of the ASTA-PSQI network. The network structure containing bridge symptoms is depicted in Fig. 3 , which illustrates the bridge strength centrality of the top 10% of nodes. Notably, sleep disturbances (P5) and worry/anxiety (S9) are prominent in the bridge centrality metrics, suggesting that these two nodes function as bridge symptoms in the network. A mediation analysis was performed to examine the mediating role of sleep disturbances (P5) in the relationship between AF symptoms and sleep quality. The mediation model and corresponding effect size analyses are detailed in the Supplementary Materials Figure S1 and Table S1 . The results of the mediation analyses further substantiate that sleep disturbances act as intermediary symptoms in the ASTA-PSQI network. 3.4. Accuracy and stability of the ASTA-PSQI network The results of the edge weight bootstrapping are displayed in Supplementary Materials Figure S2. The average edge weights evaluated are basically consistent with the edge weights of the sample in this study. It showed that the edge weight has sufficient stability via edge weight boot strapping. The results of the node centrality bootstrap are shown in Supplementary Material Figure S3. The CS coefficient index is 0.596, which exceeds the threshold of 0.5, indicating that the ASTA-PSQI network has robust stability. 4. Discussion This study employed network analysis to construct the ASTA-PSQI network, thereby providing a visual representation of the interconnections among symptoms. The findings of this study indicated that tiredness and weakness/fatigue are the most common and severe symptoms in AF patients. As is shown in Fig. 1 , two symptom pairs exhibit a high degree of interconnectedness: tiredness and weakness/fatigue, and breathlessness during activity and breathlessness even at rest. These symptom pairs offer a nuanced depiction of the varying severity of AF symptoms. The network analysis reveals that these symptoms are closely connected to a progressive decline in cardiac function and overall health status. This finding is consistent with the existing literature, which suggests that these symptoms may interact and collectively impact patients’ quality of life and functional status [ 17 – 19 ]. Poor sleep quality was prevalent among patients with AF. The study's findings revealed that sleep disturbances were the most common sleep-related issue, with daytime dysfunction being regarded as the most severe sleep-related complication. The results indicated that 54.1% of patients with AF exhibited poor sleep quality (PSQI > 7). This proportion is slightly lower than the results of previous studies [ 20 , 21 ]. The reason for this discrepancy may be the composition of the study sample, which included 61.6% male participants. It has been observed that female patients with AF tend to have poorer sleep quality than their male counterparts [ 6 ]. Given the high prevalence of poor sleep quality among patients with AF, it is imperative to prioritize the assessment and management of sleep quality in this population. The network analysis revealed a significant interaction between sleep duration and habitual sleep efficiency in sleep quality dimensions. Prior studies have demonstrated that inadequate sleep duration has a detrimental effect on sleep efficiency, resulting in an elevated frequency of nocturnal awakenings and contributing to sleep fragmentation and, consequently, a reduction in overall sleep quality [ 22 ]. Therefore, individuals with low sleep efficiency frequently have difficulty maintaining consistent sleep, which may further reduce their overall sleep duration. Weakness/fatigue emerged as the central symptom in the ASTA-PSQI network, demonstrating the strongest correlation with other nodes and playing a pivotal role in maintaining the network as a whole. Our findings align with previous research that identified weakness/fatigue as a prevalent complaint among patients with AF [ 23 ]. The manifestation of AF symptoms is frequently associated with the onset of weakness, which can adversely impact sleep quality. Persistent weakness has the potential to disrupt circadian rhythms, thereby hindering the initiation and maintenance of sleep. Conversely, impaired sleep quality may further exacerbate the sensation of weakness. Patients with AF often experience sleep fragmentation and reduced sleep efficiency, which may further intensify weakness [ 24 ]. This bidirectional relationship may create a vicious cycle, that further compromises patients’ quality of life and complicates disease management. Currently, numerous studies have demonstrated that cardiac rehabilitation, particularly exercise-based programs, can effectively reduce fatigue, improve physical resilience, and enhance overall quality of life in patients with AF [ 25 – 28 ]. Thus, the incorporation of structured exercise rehabilitation into the management of AF has been shown not only to reduce fatigue, but also to have a broader impact on associated symptoms, including sleep disturbances. The bridge symptoms were worry/anxiety and sleep disturbances. Among these, sleep disturbances exhibited the highest bridge centrality, indicating that they play a critical role in the comorbidity mechanism linking AF symptoms and sleep quality. Results from the simple mediation analysis revealed that sleep disturbances partially mediated the relationship between AF symptoms and sleep quality. This further underscores the role of sleep disturbances as a connecting factor between AF symptoms and sleep quality. This finding aligns with previous research suggesting that sleep disturbances may be both a cause and an exacerbating factor of AF symptoms [ 8 , 29 ]. As shown in Fig. 1 , worry/anxiety and sleep disturbances exhibit strongest correlations, suggesting a synergistic relationship between these factors. Patients with AF who suffer from anxiety or anxiety-related disorders frequently experience poor sleep quality and exhibit sleep disturbances [ 30 ]. In network analysis, bridge symptoms serve as connectors between AF symptoms and sleep quality, potentially acting as pivotal factors in the progression and chronicity of the disease [ 31 ]. Therefore, addressing worry/anxiety and sleep disturbances is crucial for effective symptom management and enhancement of overall quality of life. Interventions such as Cognitive-Behavioral Therapy (CBT), mindfulness-based stress reduction, and pharmacological treatment for severe cases may be beneficial in breaking the cycle among worry/anxiety, sleep disturbances, and AF symptom [ 32 – 34 ]. 5. Study limitations The network analysis conducted in this study reveals a complex relationship between symptoms and sleep quality in patients with AF. Nevertheless, several limitations warrant consideration. Firstly, patients with AF exhibits a wide spectrum of symptoms, both in terms of frequency and severity. Furthermore, as patients progress through various phases of treatment, the symptom network may demonstrate distinct developmental patterns. Future research should focus on dynamic symptom networks to identify both core and bridge symptoms across various stages. This will facilitate the development of more targeted and stage-specific management strategies for AF symptoms. The representativeness of the sample is constrained, underscoring the need for larger and more diverse cohorts. Additionally, unmeasured confounding variables, such as medication use, may affect the findings. Future studies should aim to address these limitations to improve the understanding of the networks between AF symptoms and sleep quality, as well as the effects of potential interventions. 6. Conclusions This study employed network analysis to investigate the interactions between AF symptoms and sleep quality, identifying weakness/fatigue as central symptoms, while sleep disturbances and worry/anxiety emerged as bridge symptoms. The findings underscore the importance of managing AF progression, controlling risk factors, enhancing treatment adherence, and mitigating frailty through rehabilitation. Furthermore, addressing worry/anxiety and sleep disturbances through psychological interventions or pharmacotherapy may disrupt the cycle of symptom exacerbation and enhance patients' quality of life. Abbreviations AF: Atrial fibrillation ACC/AHA/ACCP/HRS: American College of Cardiology/American Heart Association/American College of Chest Physicians/Heart Rhythm Society ASTA: Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia PSQI: Pittsburgh Sleep Quality Index EBICglasso: Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator CIs: Confidence Intervals CS coefficients: Centrality Stability coefficients Declarations Ethics approval and consent to participate The study was conducted in accordance with the Helsinki Declaration. Ethical approval was obtained from Nanjing Medical University (No. 2024-807), and written informed consent was obtained from all participants prior to data collection. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they do not have any financial or non-financial competing interests to declare. Funding This work was supported by the General Program of National Natural Science Foundation of China(72074124) and Project of “Nursing Science” Funded by the 4th Priority Discipline Development Program of Jiangsu Higher Education Institutions(Jiangsu Education Department〔2023〕No.11). Authors' contributions Yunxia Li was responsible for the development of the methodology, data curation, formal analysis, visualisation, and the composition of the original draft. Jie Wang, Zhijie Tang and Zhipeng Bao managed project administration, resources, and software development. Jing Lu, Zhanhong You, Xiu Tao, and Kaixin Miao participated in the data collection, survey, review and editing process. Guozhen Sun provided financial support and guidance.. 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Epidemic Risk Perception, Perceived Stress, and Mental Health During COVID-19 Pandemic: A Moderated Mediating Model. Front Psychol. 2020;11:563741. Garcia-Mondragon L, Konac D, Newbury JB, Young KS, Ing A, Fürtjes AE, et al. Role of polygenic and environmental factors in the co-occurrence of depression and psychosis symptoms: a network analysis. Transl Psychiatry. 2022;12:259. Li F, He CJ, Ding CH, Wang RX, Li H. Continuous positive airway pressure therapy might be an effective strategy on reduction of atrial fibrillation recurrence after ablation in patients with obstructive sleep apnea: insights from the pooled studies. Front Neurol. 2023;14:1269945. Särnholm J, Skúladóttir H, Rück C, Axelsson E, Bonnert M, Bragesjö M, et al. Cognitive Behavioral Therapy Improves Quality of Life in Patients With Symptomatic Paroxysmal Atrial Fibrillation. J Am Coll Cardiol. 2023;82:46-56. Ski CF, Taylor RS, McGuigan K, Long L, Lambert JD, Richards SH, et al. Psychological interventions for depression and anxiety in patients with coronary heart disease, heart failure or atrial fibrillation. Cochrane Database Syst Rev. 2024;4:Cd013508. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6510506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463489021,"identity":"3a47d545-0c8d-4efd-b220-4b55f2b99a08","order_by":0,"name":"Yunxia Li","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunxia","middleName":"","lastName":"Li","suffix":""},{"id":463489022,"identity":"f81541a5-3a88-44f4-89c4-51c7b9257fec","order_by":1,"name":"Jing Lu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Lu","suffix":""},{"id":463489023,"identity":"0c0a047d-8bd5-4136-9362-126ed8140a5c","order_by":2,"name":"Zhanhong You","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhanhong","middleName":"","lastName":"You","suffix":""},{"id":463489025,"identity":"b096562a-f7dc-442f-b083-878d3a41827b","order_by":3,"name":"Xiu Tao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiu","middleName":"","lastName":"Tao","suffix":""},{"id":463489027,"identity":"6974f2ef-d22e-451a-96ab-d4a955a1e58a","order_by":4,"name":"Jie Wang","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wang","suffix":""},{"id":463489029,"identity":"9456b40e-6021-42a1-9e13-c8c0f676ca4a","order_by":5,"name":"Zhipeng Bao","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Bao","suffix":""},{"id":463489030,"identity":"d789b19b-bb70-4822-a450-5c0a684d51cb","order_by":6,"name":"Kaixin Miao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaixin","middleName":"","lastName":"Miao","suffix":""},{"id":463489032,"identity":"95ffa9ab-0bcc-438f-a5f5-8e07ec98769f","order_by":7,"name":"Zhijie Tang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Tang","suffix":""},{"id":463489033,"identity":"ea90d109-42fc-4da4-866e-fd2b56790490","order_by":8,"name":"Guozhen Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYNACAwkefmbmgw9I0FJgIyfZzpZsQIKWD2nGBud5zASIUizvf/yZdIHB4cTNhxnMGBhqbKIJajE8cMZMegZQy7bDDGkPGI6l5TYQ1NLYwybNA9Fy3ICx4TARWprZn4G1bG5mbJMgSos8G4MZUAvQ+8zMbMRpMeDhMbbmMbCRkzjMxmyQQIxf5PuPP7zN8wcYlf3nPz74UGNDhC0HkHkJhJSDbSFo6CgYBaNgFIwCAGJkOb3DhYnJAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guozhen","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-04-23 08:23:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6510506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6510506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83682450,"identity":"c964e97f-d3c5-47bd-9d9d-56f9b27ff01f","added_by":"auto","created_at":"2025-05-30 16:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":814260,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork constructed by AF symptoms and sleep quality.\u003c/p\u003e\n\u003cp\u003eS1: Breathlessness during activity; S2: Breathlessness even at rest; S3: Dizziness; S4: Cold sweats; S5: Weakness/Fatigue; S6: Tiredness; S7: Chest Pain; S8: Pressure/discomfort in chest; S9: Worry/anxiety; P1: Subjective sleep quality; P2: Sleep latency; P3: Sleep duration; P4: Sleep efficiency; P5: Sleep disturbances; P6: Use of sleep medication; P7: Daytime dysfunction.\u003c/p\u003e\n\u003cp\u003eThe network graph illustrates the associations and predictability estimates between AF symptoms and sleep quality. AF symptoms are depicted in green, while sleep quality dimensions are shown in red. Each circle within the graph denotes a node corresponding to a specific item within the network, with the outer ring of each circle indicating the level of predictability.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6510506/v1/de159d4d63817cc36986e279.png"},{"id":83681604,"identity":"610efa43-5fcd-4658-929b-249b5fb3943d","added_by":"auto","created_at":"2025-05-30 16:18:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":943354,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork centrality and bridge centrality measures for the network representing each node.\u003c/p\u003e\n\u003cp\u003eThe figure illustrates plots that depict the centrality indices of 16 nodes within the ASTA-PSQI network. The nine AF symptoms are represented in red, while the seven sleep quality dimensions are depicted in blue. Network centrality is quantified using strength, closeness, betweenness, and expected influence (EI), whereas bridge centrality is assessed through bridge betweenness, bridge closeness, and bridge strength.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6510506/v1/70110abed4cb676a369862d4.png"},{"id":83681607,"identity":"7f9a19f4-3783-4892-8f07-8962a420b38b","added_by":"auto","created_at":"2025-05-30 16:18:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":826978,"visible":true,"origin":"","legend":"\u003cp\u003eBridge group in the ASTA-PSQInetwork.\u003c/p\u003e\n\u003cp\u003eNodes with bridge strength values ranking in the top 10% were designated as the bridge group. Symptoms within this bridge group were depicted in purple, while other atrial fibrillation symptoms were illustrated in green, and other dimensions of sleep quality were shown in red.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6510506/v1/167f0759c60b5afeac2a3610.png"},{"id":97324630,"identity":"14b80b5e-dad8-4daa-b8b6-a96c515abf27","added_by":"auto","created_at":"2025-12-03 08:25:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2902955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6510506/v1/c4cba2d8-7a74-40ae-b884-aa85567f88ce.pdf"},{"id":83681608,"identity":"f2fda758-2144-4bbb-82ba-90b7572c32c7","added_by":"auto","created_at":"2025-05-30 16:18:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":312623,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6510506/v1/aee2533cc29322c26ef648ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Symptoms of atrial fibrillation and sleep quality in atrial fibrillation: A network analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is one of the most prevalent persistent arrhythmias globally. According to the 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation, approximately 50\u0026nbsp;million individuals worldwide are currently affected by AF, with the associated morbidity and mortality rates on the rise [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Projections suggest that by 2050, the prevalence of AF will increase to 72\u0026nbsp;million individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AF episodes are frequently accompanied by a wide range of symptoms, including fatigue, dyspnea, palpitations, and anxiety, which can substantially impair daily functioning and reduce patients' quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The broad spectrum of symptom presentations, intricate interactions, and individual variability in AF collectively pose significant challenges to the effective management of symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the symptoms exhibited by patients with AF, poor sleep quality is particularly prevalent issue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In China, 71.2% of patients with AF report experiencing poor sleep quality, emphasizing the pervasiveness of sleep disorders in this population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Research has indicated that the physiological mechanisms underlying AF symptoms, including autonomic dysfunction, nocturnal palpitations, and dyspnea, can result in sleep disturbances, leading to frequent awakenings and sleep fragmentation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This interaction suggests that sleep disturbances in patients with AF may not be an isolated problem but rather a reflection of the underlying symptom burden. Furthermore, poor sleep quality has been associated with increased severity of AF symptoms, potentially leading to a vicious cycle of worsening symptoms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing recognition of these associations, conventional research methodologies often fail to capture the intricate interactions between AF symptoms and sleep quality. Symptom network analysis offers a novel approach to elucidating these relationships by visualizing and quantifying the interconnections among symptoms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This method enables the identification of core symptom and bridge symptoms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], offering insights that may inform personalised clinical decision-making and symptom management strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study seeks to accomplish two primary objectives: firstly, to investigate the core and bridge symptoms that connect AF symptoms with sleep quality using network analysis; and secondly, to identify the symptoms most closely related to these bridge symptoms, thereby deepening our understanding of the feedback mechanisms among these symptoms.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eA cohort of 292 patients diagnosed with AF was recruited from the First Affiliated Hospital of Nanjing Medical University between October and December 2024. The inclusion criteria comprised: (i) patients were required to meet the 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; (ii) they had to be able to understand and complete a questionnaire; (iii) they had to be over the age of 18. The exclusion criteria included: (i) valvular AF; (ii) a history of mental illness; (iii) severe organic diseases; (iv) participation in other studies.\u003c/p\u003e \u003cp\u003eAs a network scales, the number of estimated parameters increases rapidly [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within the context of 16-node networks, the estimation of 120 parameters (i.e., 16 threshold parameters and 16*15/2\u0026thinsp;=\u0026thinsp;120 pairwise association parameters) was necessary [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. The Chinese Version of the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia (ASTA)\u003c/h2\u003e \u003cp\u003eASTA is employed to evaluate the current symptom burden in patients with AF [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which has been previously developed and localized for use in China [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The scale consists of three subscales. We employed a subset of the second subscale, which is specifically designed to evaluate symptom burden. This subset comprises nine items that assess symptoms associated with AF such as breathlessness during activity, breathlessness even at rest, dizziness, cold sweats, weakness/fatigue, tiredness, chest pain, pressure/discomfort in chest and worry/anxiety. Each symptom is rated on a scale ranging from 0 (absence of symptoms) to 3 (severe symptoms), with higher scores denoting a greater symptom burden. The items have been meticulously crafted to be concise and precise, thereby facilitating efficient data collection regarding the patient's symptom burden. The reduction in the number of items has been implemented with the objective of minimising the probability of patients experiencing feelings of overwhelm, anxiety, or irritation, which could potentially compromise the validity of the questionnaire. The scale demonstrates Cronbach\u0026rsquo;s α coefficient of 0.826, indicating good internal consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Pittsburgh Sleep Quality Index (PSQI)\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. in 1989, is a widely employed tool for the assessment of individuals\u0026rsquo; overall sleep quality over the preceding month [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The PSQI consists of 19 self-report items and 5 additional evaluation items; although only the first 18 self-report items contribute to the score. These items are categorized into seven dimensions: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, daytime dysfunction, and use of sleep medication. Each dimension is scored on a scale from 0 (no difficulty) to 3 (severe difficulty). The total score ranges from 0 (indicative of better sleep) to 21 (indicative of poorer sleep), with scores exceeding 7 suggesting poor sleep quality. The Chinese version of the PSQI has demonstrated satisfactory reliability, supporting its application in research settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Collection Methodology\u003c/h2\u003e \u003cp\u003eThe data were collected using questionnaires. Prior to the administration of the survey, the researchers meticulously explained the purpose and content of the study to the participants and ensured that informed consent was obtained. Participants were then provided with the questionnaires and instructed to complete them independently. In instances where participants encountered difficulties with reading or writing, the researchers provided a neutral reading of each question and recorded the participant's responses on their behalf. Throughout the data collection process, researchers addressed any queries raised by participants and meticulously scrutinised each completed questionnaire to ensure data integrity and validity. A total of 300 questionnaires were distributed, and 292 valid questionnaires were returned yielding an effective response rate of 97.3%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical methods\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed utilizing SPSS version 27.0. Descriptive statistics, including frequency, percentage, mean, and standard deviation, were employed to characterise general demographic characteristics and the frequency and severity of symptoms. Network models were developed and examined using the qgraph package within the R software environment (version 4.4.1). Spearman\u0026rsquo;s rank correlation analysis was utilized to assess the relationships among symptoms and to estimate the symptom network. The EBICglasso procedure in qgraph was applied to mitigate the risk of Type I errors, thereby ensuring a more accurate network structure, reducing false connections, and enhancing both model accuracy and interpretability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The nine symptoms from the ASTA and the seven sleep dimensions from the PSQI were designated as network nodes to construct the ASTA-PSQI network. The outer ring of a node signifies its predictability: higher predictability indicates that the symptom can be more readily controlled through its adjacent nodes. The edges connecting nodes denote the strength of their relationships, with blue edges indicating positive correlations and red edges indicating negative correlations. The thickness and saturation of an edge correspond to the strength of the relationship. In this study, centrality measures, including strength, closeness, betweenness, and expected influence, were employed to quantify node centrality. Strength centrality denotes the aggregate weight of a node's connections to other nodes. Closeness centrality is defined as the inverse of the sum of the shortest path lengths from a given node to all other nodes in the network. Betweenness centrality quantifies the frequency with which a node appears on the shortest path between two other nodes, thereby demonstrating the bridging role of the node in the network. Expected influence is a metric that evaluates both the nature and strength of edges, thus assessing the overall influence of a node on the network. In addition to node centrality, this research also examined bridge centrality, which was assessed by measuring bridge strength, bridge closeness, and bridge betweenness. The accuracy of the edge weights and the stability of the node centrality were evaluated using the bootstrap algorithm available in the bootnet package. The 95% Confidence Intervals (CIs) of the edge weights were calculated by the bootstrap method in order to estimate edge precision. Narrower confidence intervals are indicative of higher accuracy of the edge weight estimates. The stability of node centrality was evaluated using the case-drop bootstrap method. Centrality Stability coefficients (CS coefficients) were deemed acceptable if they were greater than or equal to 0.25, and considered good if they were greater than or equal to 0.5. A p-value of less than 0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. AF Symptoms and sleep quality in Patients with AF\u003c/h2\u003e \u003cp\u003eA total of 292 patients with AF (age\u0026thinsp;=\u0026thinsp;60.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.19 years, 112 males) were included in this study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the symptom burden and sleep problem scores of these patients. The average total symptom burden score was 9.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44, with symptom occurrence rates varying between 54.4% and 80.1%. The most prevalent symptom reported was tiredness, affecting 80.1% of the patients, which also had the highest severity score of 1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87. The mean PSQI score was 8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04, with the prevalence of sleep quality dimensions ranging from 11.0\u0026ndash;97.9%. Sleep disturbances were the most commonly reported problem, affecting 97.9% of the patients. Daytime dysfunction was the dimension with the highest severity score, recorded at 1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96. Notably, poor sleep quality, defined as a PSQI score greater than 7, was observed in 54.1% of the patients with AF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAF symptoms and sleep quality in AF Patients(n\u0026thinsp;=\u0026thinsp;292)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Symptom Burden Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePSQI Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreathlessness during activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubjective sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreathlessness even at rest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep latency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDizziness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold Sweats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness/Fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep disturbances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiredness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of sleep medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaytime dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure/discomfort in chest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoor Sleep Quality (PSQI\u0026thinsp;\u0026gt;\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorry/anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Network centrality and bridge centrality of the ASTA-PSQI network\u003c/h2\u003e \u003cp\u003eThe ASTA-PSQI network is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This network reveals two distinct clusters: one for AF symptoms and the other for sleep quality. The thickness of the edges between nodes indicates the strength of the relationship between them. The strongest associations were identified between weakness/fatigue and tiredness (S5-S6, edge weight\u0026thinsp;=\u0026thinsp;0.63), sleep duration and sleep efficiency (P3-P4, edge weight\u0026thinsp;=\u0026thinsp;0.52), and breathlessness during activity and breathlessness even at rest (S1-S2, edge weight\u0026thinsp;=\u0026thinsp;0.35).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe centrality and bridge centrality metrics for all nodes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on centrality indicators, weakness/fatigue (S5) was found to have the highest strength centrality and the highest Expected Influence coefficient (EI), indicating that it is the core symptom of the ASTA-PSQI network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe network structure containing bridge symptoms is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which illustrates the bridge strength centrality of the top 10% of nodes. Notably, sleep disturbances (P5) and worry/anxiety (S9) are prominent in the bridge centrality metrics, suggesting that these two nodes function as bridge symptoms in the network. A mediation analysis was performed to examine the mediating role of sleep disturbances (P5) in the relationship between AF symptoms and sleep quality. The mediation model and corresponding effect size analyses are detailed in the Supplementary Materials Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The results of the mediation analyses further substantiate that sleep disturbances act as intermediary symptoms in the ASTA-PSQI network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Accuracy and stability of the ASTA-PSQI network\u003c/h2\u003e \u003cp\u003eThe results of the edge weight bootstrapping are displayed in Supplementary Materials Figure S2. The average edge weights evaluated are basically consistent with the edge weights of the sample in this study. It showed that the edge weight has sufficient stability via edge weight boot strapping. The results of the node centrality bootstrap are shown in Supplementary Material Figure S3. The CS coefficient index is 0.596, which exceeds the threshold of 0.5, indicating that the ASTA-PSQI network has robust stability.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed network analysis to construct the ASTA-PSQI network, thereby providing a visual representation of the interconnections among symptoms.\u003c/p\u003e \u003cp\u003eThe findings of this study indicated that tiredness and weakness/fatigue are the most common and severe symptoms in AF patients. As is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, two symptom pairs exhibit a high degree of interconnectedness: tiredness and weakness/fatigue, and breathlessness during activity and breathlessness even at rest. These symptom pairs offer a nuanced depiction of the varying severity of AF symptoms. The network analysis reveals that these symptoms are closely connected to a progressive decline in cardiac function and overall health status. This finding is consistent with the existing literature, which suggests that these symptoms may interact and collectively impact patients\u0026rsquo; quality of life and functional status [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePoor sleep quality was prevalent among patients with AF. The study's findings revealed that sleep disturbances were the most common sleep-related issue, with daytime dysfunction being regarded as the most severe sleep-related complication. The results indicated that 54.1% of patients with AF exhibited poor sleep quality (PSQI\u0026thinsp;\u0026gt;\u0026thinsp;7). This proportion is slightly lower than the results of previous studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The reason for this discrepancy may be the composition of the study sample, which included 61.6% male participants. It has been observed that female patients with AF tend to have poorer sleep quality than their male counterparts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given the high prevalence of poor sleep quality among patients with AF, it is imperative to prioritize the assessment and management of sleep quality in this population.\u003c/p\u003e \u003cp\u003eThe network analysis revealed a significant interaction between sleep duration and habitual sleep efficiency in sleep quality dimensions. Prior studies have demonstrated that inadequate sleep duration has a detrimental effect on sleep efficiency, resulting in an elevated frequency of nocturnal awakenings and contributing to sleep fragmentation and, consequently, a reduction in overall sleep quality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, individuals with low sleep efficiency frequently have difficulty maintaining consistent sleep, which may further reduce their overall sleep duration.\u003c/p\u003e \u003cp\u003eWeakness/fatigue emerged as the central symptom in the ASTA-PSQI network, demonstrating the strongest correlation with other nodes and playing a pivotal role in maintaining the network as a whole. Our findings align with previous research that identified weakness/fatigue as a prevalent complaint among patients with AF [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The manifestation of AF symptoms is frequently associated with the onset of weakness, which can adversely impact sleep quality. Persistent weakness has the potential to disrupt circadian rhythms, thereby hindering the initiation and maintenance of sleep. Conversely, impaired sleep quality may further exacerbate the sensation of weakness. Patients with AF often experience sleep fragmentation and reduced sleep efficiency, which may further intensify weakness [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This bidirectional relationship may create a vicious cycle, that further compromises patients\u0026rsquo; quality of life and complicates disease management. Currently, numerous studies have demonstrated that cardiac rehabilitation, particularly exercise-based programs, can effectively reduce fatigue, improve physical resilience, and enhance overall quality of life in patients with AF [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, the incorporation of structured exercise rehabilitation into the management of AF has been shown not only to reduce fatigue, but also to have a broader impact on associated symptoms, including sleep disturbances.\u003c/p\u003e \u003cp\u003eThe bridge symptoms were worry/anxiety and sleep disturbances. Among these, sleep disturbances exhibited the highest bridge centrality, indicating that they play a critical role in the comorbidity mechanism linking AF symptoms and sleep quality. Results from the simple mediation analysis revealed that sleep disturbances partially mediated the relationship between AF symptoms and sleep quality. This further underscores the role of sleep disturbances as a connecting factor between AF symptoms and sleep quality. This finding aligns with previous research suggesting that sleep disturbances may be both a cause and an exacerbating factor of AF symptoms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, worry/anxiety and sleep disturbances exhibit strongest correlations, suggesting a synergistic relationship between these factors. Patients with AF who suffer from anxiety or anxiety-related disorders frequently experience poor sleep quality and exhibit sleep disturbances [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In network analysis, bridge symptoms serve as connectors between AF symptoms and sleep quality, potentially acting as pivotal factors in the progression and chronicity of the disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, addressing worry/anxiety and sleep disturbances is crucial for effective symptom management and enhancement of overall quality of life. Interventions such as Cognitive-Behavioral Therapy (CBT), mindfulness-based stress reduction, and pharmacological treatment for severe cases may be beneficial in breaking the cycle among worry/anxiety, sleep disturbances, and AF symptom [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Study limitations","content":"\u003cp\u003eThe network analysis conducted in this study reveals a complex relationship between symptoms and sleep quality in patients with AF. Nevertheless, several limitations warrant consideration. Firstly, patients with AF exhibits a wide spectrum of symptoms, both in terms of frequency and severity. Furthermore, as patients progress through various phases of treatment, the symptom network may demonstrate distinct developmental patterns. Future research should focus on dynamic symptom networks to identify both core and bridge symptoms across various stages. This will facilitate the development of more targeted and stage-specific management strategies for AF symptoms. The representativeness of the sample is constrained, underscoring the need for larger and more diverse cohorts. Additionally, unmeasured confounding variables, such as medication use, may affect the findings. Future studies should aim to address these limitations to improve the understanding of the networks between AF symptoms and sleep quality, as well as the effects of potential interventions.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study employed network analysis to investigate the interactions between AF symptoms and sleep quality, identifying weakness/fatigue as central symptoms, while sleep disturbances and worry/anxiety emerged as bridge symptoms. The findings underscore the importance of managing AF progression, controlling risk factors, enhancing treatment adherence, and mitigating frailty through rehabilitation. Furthermore, addressing worry/anxiety and sleep disturbances through psychological interventions or pharmacotherapy may disrupt the cycle of symptom exacerbation and enhance patients' quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAF: Atrial fibrillation\u003c/p\u003e\n\u003cp\u003eACC/AHA/ACCP/HRS: American College of Cardiology/American Heart Association/American College of Chest Physicians/Heart Rhythm Society\u003c/p\u003e\n\u003cp\u003eASTA: Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia\u003c/p\u003e\n\u003cp\u003ePSQI: Pittsburgh Sleep Quality Index\u003c/p\u003e\n\u003cp\u003eEBICglasso: Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eCIs: Confidence Intervals\u003c/p\u003e\n\u003cp\u003eCS coefficients: Centrality Stability coefficients\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Helsinki Declaration. Ethical approval was obtained from Nanjing Medical University (No. 2024-807), and written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they do not have any financial or non-financial competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the General Program of National Natural Science Foundation of China(72074124) and Project of \u0026ldquo;Nursing Science\u0026rdquo; Funded by the 4th Priority Discipline Development Program of Jiangsu Higher Education Institutions(Jiangsu Education Department〔2023〕No.11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunxia Li was responsible for the development of the methodology, data curation, formal analysis, visualisation, and the composition of the original draft. Jie Wang, Zhijie Tang and Zhipeng Bao managed project administration, resources, and software development. Jing Lu, Zhanhong You, Xiu Tao, and Kaixin Miao participated in the data collection, survey, review and editing process. Guozhen Sun provided financial support and guidance.. All authors were involved in the revision and final approval of the manuscript, and agreed to take responsibility for all aspects of the work to ensure the completeness and accuracy of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJoglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. 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Prog Neuropsychopharmacol Biol Psychiatry. 2023;125:110737.\u003c/li\u003e\n\u003cli\u003eBarmano N, Charitakis E, Karlsson JE, Nystrom FH, Walfridsson H, Walfridsson U. Predictors of improvement in arrhythmia-specific symptoms and health-related quality of life after catheter ablation of atrial fibrillation. Clin Cardiol. 2019;42:247-255.\u003c/li\u003e\n\u003cli\u003eSchnabel RB, Marinelli EA, Arbelo E, Boriani G, Boveda S, Buckley CM, et al. Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference. Europace. 2023;25:6-27.\u003c/li\u003e\n\u003cli\u003eTilz RR, Dagres N, Arbelo E, Blomstr\u0026ouml;m-Lundqvist C, Crijns HJ, Kirchhof P, et al. Which patients with atrial fibrillation undergo an ablation procedure today in Europe? A report from the ESC-EHRA-EORP Atrial Fibrillation Ablation Long-Term and Atrial Fibrillation General Pilot Registries. Europace. 2020;22:250-258.\u003c/li\u003e\n\u003cli\u003eRisom SS, Fevejle Cromhout P, Overgaard D, Hastrup Svendsen J, Kikkenborg Berg S. Effect of Rehabilitation on Sleep Quality After Ablation for Atrial Fibrillation: Data From a Randomized Trial. Journal of Cardiovascular Nursing. 2018;33:261-8.\u003c/li\u003e\n\u003cli\u003eWood KA, Higgins MK, Barnes AH. Self-reported Sleep Quality Before and After Atrial Fibrillation Ablation. J Cardiovasc Nurs. 2023;38:e78-e86.\u003c/li\u003e\n\u003cli\u003eVats V, Kulkarni V, Shafique MA, Haseeb A, Arain M, Armaghan M, et al. Analyzing the impact of sleep duration on atrial fibrillation risk: a comprehensive systematic review and meta-analysis. Ir J Med Sci. 2024;193:1787-1795.\u003c/li\u003e\n\u003cli\u003eWalfridsson U, Hassel J\u0026ouml;nsson A, Karlsson LO, Liuba I, Almroth H, Sandgren E, et al. Symptoms and health-related quality of life 5 years after catheter ablation of atrial fibrillation. 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Role of polygenic and environmental factors in the co-occurrence of depression and psychosis symptoms: a network analysis. Transl Psychiatry. 2022;12:259.\u003c/li\u003e\n\u003cli\u003eLi F, He CJ, Ding CH, Wang RX, Li H. Continuous positive airway pressure therapy might be an effective strategy on reduction of atrial fibrillation recurrence after ablation in patients with obstructive sleep apnea: insights from the pooled studies. Front Neurol. 2023;14:1269945.\u003c/li\u003e\n\u003cli\u003eS\u0026auml;rnholm J, Sk\u0026uacute;lad\u0026oacute;ttir H, R\u0026uuml;ck C, Axelsson E, Bonnert M, Bragesj\u0026ouml; M, et al. Cognitive Behavioral Therapy Improves Quality of Life in Patients With Symptomatic Paroxysmal Atrial Fibrillation. J Am Coll Cardiol. 2023;82:46-56.\u003c/li\u003e\n\u003cli\u003eSki CF, Taylor RS, McGuigan K, Long L, Lambert JD, Richards SH, et al. Psychological interventions for depression and anxiety in patients with coronary heart disease, heart failure or atrial fibrillation. Cochrane Database Syst Rev. 2024;4:Cd013508.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"atrial fibrillation, symptoms, sleep quality, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-6510506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6510506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e: Symptoms of Atrial fibrillation (AF) seriously affect sleep quality and well-being. Understanding their interconnections is crucial for targeted management.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e: A cohort of 292 patients with AF was recruited through convenience sampling. Data collection included a general information questionnaire, the Chinese Version of the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia (ASTA), and the Pittsburgh Sleep Quality Index (PSQI). Descriptive statistical analyses and network modeling were performed using SPSS and R. An ASTA-PSQI network was developed to identify core and bridge symptoms. Spearman's rankcorrelation was utilized to assess relationships between symptoms, while the Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator (EBICglasso) method was employed to mitigate Type I errors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: In 292 patients with AF, fatigue was the most common (80.1%) and severe symptom, with a score of 1.26±0.87. Sleep disturbances affected 97.9% of patients, with daytime dysfunction being the most severe (1.53±0.96). Poor sleep quality (PSQI\u0026gt;7) was found in 54.1% of patients. Strong links were noted between weakness/fatigue and fatigue (edge weight=0.63), sleep duration and efficiency (edge weight=0.52), and dyspnea during activity and rest (edge weight=0.35). Weakness/fatigue had the highest centrality and sleep disturbances exhibited the highest bridge centrality.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e: Effective management of AF should focus on controlling disease progression, improving treatment adherence, and reducing weakness/fatigue through exercise, with particular attention to worry/anxiety and sleep disturbances.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Symptoms of atrial fibrillation and sleep quality in atrial fibrillation: A network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 16:18:40","doi":"10.21203/rs.3.rs-6510506/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5fe21dc-61b1-43b1-8a2d-75222af8582c","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T08:25:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 16:18:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6510506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6510506","identity":"rs-6510506","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}