Predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma

Predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma | 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 Predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma Emel Bahadır Yılmaz, Arzu Yüksel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7597114/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: Sleep problems lead to physical and psychological health problems in patients with chronic obstructive pulmonary disease and asthma. Aims: This study aimed to determine the predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma. Methods: The sample of this cross-sectional study consisted of 205 patients. Patient Information Form, State-Trait Anxiety Scale (STAS), Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS), Dyspnea-12 Scale (D-12S) and Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS) have been used for data collection. Pearson correlation analysis and multiple linear regression analysis have been used to analyse the data. Results: There is a weak correlation between sleep quality levels and state anxiety (r=0.175) and trait anxiety (r=0.155) levels. There is a moderate correlation between sleep quality and dyspnea (r=0.428) and fatigue (r=0.448) levels. State anxiety and trait anxiety explained 3% of the total variance, dyspnea and its sub-dimensions explained 18% and fatigue level explained 20% (p<0.05). The best model explaining the sleep quality of the patients was dyspnea and its sub-dimensions and fatigue levels, which explained 23% of the total variance. The most significant predictors were the physical sub-dimension of dyspnea (β=0.187, p=0.047) and the COPDFAS (β=0.291, p=0.000). Conclusion: The most important predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma are dyspnea and fatigue. Patients should be supported with psychological therapies and good nursing care in addition to drug treatment to reduce anxiety, dyspnea, and sleep problems. Asthma chronic obstructive pulmonary disease anxiety sleep quality dyspnea Introduction Chronic respiratory diseases (CRD) affect the airways and other structures of the lungs. The two most common diseases are chronic obstructive pulmonary disease (COPD) and asthma [1]. Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide and caused 3.23 million deaths in 2019 [2]. COPD is a preventable disease that is usually progressive and characterised by exacerbations. It is characterised by cough, expectoration, dyspnea, and wheezing [3]. Asthma is a disease that varies in severity and frequency from person to person, manifests itself with recurrent episodes of breathlessness and wheezing due to narrowing of the airways, and worsens during physical activity or at night in some individuals [1]. It causes symptoms such as coughing, wheezing, shortness of breath and chest tightness because of inflammation and narrowing of the small airways in the lungs and affected an estimated 262 million people and caused 455,000 deaths in 2019 [4]. COPD increases somatic complaints and symptoms including wheezing and dyspnea change the sleep structure and lead to complaints including insomnia and deep sleepiness [5]. In these patients, oxygen saturation decreases at night and dyspnea and insomnia are experienced [6]. Furthermore, sleep problems including nocturnal hypoxaemia, sleep respiratory disorders including sleep apnea and sleep-related hypoventilation, insomnia and restless leg syndrome are observed in patients with COPD [7]. In one study, sleep quality worsened in one third of patients with stable COPD [8]. There is no difference between insomnia and sleep quality in patients with COPD and asthma [9]. Poor sleep quality and sleep disorders are also commonly observed in patients with asthma [10]. In addition, obstructive sleep apnea is more common in patients with severe asthma [11]. The two most important factors that lead to sleep disorders and decrease sleep quality in patients with asthma are nocturnal symptoms of the disease and uncontrolled asthma symptoms [12]. Sleep problems lead to physical and psychological health problems in these patients. Fatigue levels of patients with COPD increase as their sleep quality decreases [13]. Sleep disturbance is also associated with depression, low quality of life and poor self-efficacy [8]. It has also been reported that insomnia leads to poor clinical outcomes in asthma patients, including deterioration in quality of life and increased admission to emergency services, hospitals, and intensive care units [11]. Objectives This study aimed to determine the predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma. Research questions: Is there a relation between patients' sleep quality and demographic characteristics? Is there a relation between sleep quality and state and trait anxiety levels of patients? Is there a relation between patients' sleep quality and dyspnea levels? Is there a relation between patients' sleep quality and fatigue levels? What are the most important predictors of patients' sleep quality? Methods Study design This study is across-sectional and correlational study. Population and sample The population of the study consisted of patients over the age of 18 who were hospitalised in the Chest Clinic of Aksaray University Training and Research Hospital between May and August 2019, having a clear consciousness, without any mental problems, without any communication difficulties, and diagnosed with Chronic Obstructive Pulmonary Disease or asthma. The sample consisted of 205 patients who agreed to participate in the study between the specified dates. The mean pretest Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale score (57.14±15.4) obtained by Kütmeç-Yılmaz and Kapucu [14] has been used in sample calculation. With G*Power 3.1.9.7. programme, the sample size has been calculated as 45 with effect size d=0.5, α=0.05 and 95% type 1 error. After the data were collected, the effect size was calculated as 4.08 in the post hoc analysis performed with the scale mean obtained in this study. Data collection tools Patient Information Form, State-Trait Anxiety Scale (STAS), Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS), Dyspnea-12 Scale (D-12S) and Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS) have been used for data collection. Patient Introduction Form , the first part of the form included questions such as the patient's age, educational status, employment status, economic status, and family structure, while the second part included questions such as the duration of hospitalisation, previous hospital experience, and the status of having a chronic disease. It consisted of 21 questions in total. State and Trait Anxiety Scale (STAS ) The scale consists of two sub-dimensions, State Anxiety Scale (SAS) and Trait Anxiety Scale (TAS), with 20 items in each sub-dimension [15]. Increasing scores indicate a high level of anxiety. Turkish validity and reliability have been performed [16]. The reliability coefficient of the scale varies between 0.83 and 0.87, and the test-retest reliability varies between 0.71 and 0.86. In this study, the Cronbach's alpha value of the SAS has been found to be 0.85 and the Cronbach's alpha value of the TAS has been found to be 0.81. Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS) has been developed to determine the effect of COPD and asthma on sleep. The scale, which consists of 7 questions in total, is 5-point Likert type. A high scale score indicates poor sleep quality, and a low scale score indicates good sleep quality [17]. In our country, validity and reliability studies have been conducted and the cronbach alpha coefficient of the scale has been determined as 0.87 [18]. In this study, cronbach alpha coefficient has been found to be 0.86. Dyspnea-12 Scale (D-12S) measures the severity of dyspnea and consists of a total of 12 items. The first seven items of the four-point Likert-type scale question the physical difficulties caused by dyspnea in patients. The remaining five items of the scale focus on the effect of breathing on emotional states [19]. The maximum score that can be obtained from the physical dimension of the scale is 21 and the maximum score that can be obtained from the emotional dimension is 15. An increase in the score obtained from the scale indicates an increase in the severity of dyspnea in the patient. Cronbach alpha value of the scale has been reported as 0.97 [20]. In this study, Cronbach's alpha value has been found to be 0.94. Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS) consists of 12 items. The maximum score from the five-point Likert-type scale is 60. As the scale score increases, the fatigue level of the person increases [21]. The Turkish validity and reliability of the scale has been performed and Cronbach's alpha value has been determined as 0.92 [22]. In this study, Cronbach's alpha value has been found to be 0.77. Ethical consideration Before starting the study, written permissions have been obtained from the Chief Physician's Office of the relevant Training and Research Hospital, Provincial Health Directorate and Aksaray University Human Research Ethics Committee (Date: 19. 04. 2019, No: 2019/03-61). The patients participating in the study have been informed about the research and it has been explained that individual information will remain confidential. Data has been collected in line with the principles of the Declaration of Helsinki. Data analysis IBM SPSS Statistics 25 programme has been used in the evaluation of the data. Descriptive statistics such as frequency, percentage, mean and standard deviation have been used. Cronbach alpha values of the scales used in this study have been calculated. The assessment of whether the data followed a normal distribution or not was conducted through the Kolmogorov-Smirnov test. It has been decided that the data are normally distributed. Pearson correlation test has been used to determine the correlation between the scales. Multiple Linear Regression analysis has been used to determine the predictors of the COPDASS. Adjusted R 2 , standard error, beta and p values have been given to explain the total variance. P<0.05 level has been accepted as statistically significant. Results The mean age of the patients was 65.67 ± 12.42 (min:18, max:78), 59.0% are female, 84.4% have nuclear family structure, 87.8% are married, 96.6% have children and 43.9% live in the province. 60.5% of the patients have completed primary school, 90.2% are employed, 70.2% have social security, and 83.4% have a moderate economic status. Among the patients, 47.3% had quit smoking and 39.0% had never smoked (Table 1 ). Table 1 Sociodemographic characteristics of patients Sociodemographic characteristics N % Patient's age (mean ± SD: 65,6 ± 12,42) ≤ 60 65 31.7 61–70 years old 74 36.1 ≥ 71 66 32.2 Gender Female 84 41.0 Male 121 59.0 Family structure Nuclear family 173 84.4 Extended family 32 15.6 Marital Status Married 180 87.8 Unmarried 25 12.2 Childbearing status Existent 198 96.6 Non-existent 7 3.4 Inhabitence Province 90 43.9 District 41 20.0 Village 74 36.1 Educational Status Illiterate 63 30.7 Primary School 124 60.5 High school and above 18 8.8 Employment status Employed 20 9.8 Unemployed 185 90.2 Health coverage Existent 144 70.2 Non-existent 61 29.8 Economic Status Good 17 8.3 Medium 171 83.4 Bad 17 8.3 Cigarette Smokers 28 13.7 Quitters 97 47.3 Never smoked 80 39.0 Table 2 Information about the disease process Information about the disease N % Diagnosis COPD 130 63.4 Asthma 75 36.6 Duration of diagnosis (mean ± SD: 9.6 ± 9.11) 1–7 years 108 52.7 8–14 years 43 21.0 15 years and over 54 26.3 Form of taking medication Regular 184 89.8 In the presence of complaints 21 10.2 Using a therapeutic device at home Yes 46 22.4 No 159 77.6 Previous hospitalisation due to illness Yes 182 88.8 No 23 11.2 Attending the emergency department due to a disease in the last 6 months Yes 162 79.0 No 43 21.0 Receiving education about his/her disease Yes 113 55.1 No 92 44.9 Level of impact of the disease on life Low level of impact 15 7.3 Moderate impact 83 40.5 High level of impact 107 52.2 Perceived health status Good 10 4.9 Medium 85 41.4 Bad 110 53.7 Respiratory distress Rarely 32 15.6 Mostly 129 62.9 All the time 44 21.5 63.4% of the patients have COPD and have been receiving treatment for a mean of 9.6 ± 9.11 years. 89.8% of the patients regularly used their medication, 22.4% used a device to assist treatment and 88.8% had been hospitalised before. 79.0% of the patients presented to the emergency department in the last 6 months due to their disease; 55.1% received education about their disease; 52.2% reported that the disease has a high impact on their life; 53.7% perceived their health status as poor; 62.9% mostly experienced respiratory distress. The mean score of the patients was 45.77 ± 10.16 for the SAS, 47.46 ± 9.00 for the TAS, 53.71 ± 13.14 for the COPDASS, 24.32 ± 8.70 for the D-12S, 14.94 ± 4.85 for the Dyspnea Physical Subscale, 9.38 ± 4.57 for the Dyspnea Emotional Subscale and 59.36 ± 15.12 for the COPDFAS (Table 3 ). Table 3 The mean scores of the patients on the SAS, TAS, COPDASS, D-12S, and COPDFAS scales Scales Mean ± SD Min-Max scores SAS 45.77 ± 10.16 20–69 TAS 47.46 ± 9.00 24–70 COPDASS 53.71 ± 13.14 10.71–89.29 D-12S 24.32 ± 8.70 1–36 Dyspnea Physical Subscale 14.94 ± 4.85 0–21 Dyspnea Emotional Subscale 9.38 ± 4.57 0–15 COPDFAS 59.36 ± 15.12 8.33–95.83 SAS: State Anxiety Scale, TAS: Trait Anxiety Scale, COPDASS: Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale, D-12S: Dyspnea-12 Scale, COPDFAS: Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale There is a weak but significant correlation (P < 0.05) between the COPDASS and the SAS (r = 0.175) and TAS (r = 0.155). There is a moderately significant correlation (P < 0.01) between the COPDASS and D-12S (r = 0.428), physical sub-dimension (r = 0.418) and emotional sub-dimension (r = 0.370). There is a moderately significant correlation (r = 0.448) between the COPDASS and the COPDFAS (P < 0.01) (Table 4 ). Table 4 Correlations between the SAS, TAS, COPDASS, D-12S, and COPDFAS scales 1 2 3 4 5 6 7 1. SAS r 1 P - 2. TAS r 0.589 1 P 0.000 - 3. COPDASS r 0.175 0.155 1 P 0.012 0.026 - 4. D-12S r 0.363 0.311 0.428 1 P 0.000 0.000 0.000 - 5. Dyspnea Physical Subscale r 0.261 0.242 0.418 0.928 1 P 0.000 0.000 0.000 0.000 - 6. Dyspnea Emotional Subscale r 0.414 0.334 0.370 0.918 0.704 1 P 0.000 0.000 0.000 0.000 0.000 - 7. COPDFAS r 0.365 0.335 0.448 0.638 0.613 0.562 1 P 0.000 0.000 0.000 0.000 0.000 0.000 - SAS: State Anxiety Scale, TAS: Trait Anxiety Scale, COPDASS: Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale, D-12S: Dyspnea-12 Scale, COPDFAS: Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale Variables found to be significant in the preliminary analyses were included in the Multiple Linear Regression analysis (Table 5 ). In Model 1, age and gender explained 2% of the total variance and the most important predictor was age (β = 0.147, P = 0.036). In Model 2, state anxiety and trait anxiety explained 3% of the total variance and neither of them were significant predictors of sleep quality (P > 0.05). In Model 3, D-12S and its subscales explained 18% of the total variance and the most important predictor was dyspnea (β = 0.561, P = 0.001). Table 5 Predictors of sleep quality Model Variables B Std. Error β P 1 Age 0.156 0.074 0.147 0.036 Gender -2.918 1.854 -0.109 0.117 2 SAS 0.167 0.111 0.129 0.134 TAS 0.116 0.125 0.079 0.356 3 Dyspnea Emotional Subscale -0.417 0.460 -0.145 0.367 D-12S 0.847 0.242 0.561 0.001 4 COPDFAS 0.389 0.055 0.448 0.000 5 SAS 0.037 0.107 0.029 0.728 TAS 0.027 0.116 0.018 0.817 Dyspnea Emotional Subscale -0.475 0.474 -0.165 0.317 D-12S 0.851 0.243 0.563 0.001 6 SAS -0.009 0.104 -0.007 0.930 TAS -0.017 0.113 -0.012 0.882 Dyspnea Physical Subscale 0.500 0.256 0.185 0.053 Dyspnea Emotional Subscale 0.231 0.270 0.080 0.393 COPDFAS 0.257 0.072 0.296 0.000 7 Dyspnea Physical Subscale 0.506 0.253 0.187 0.047 Dyspnea Emotional Subscale 0.214 0.256 0.075 0.405 COPDFAS 0.253 0.070 0.291 0.000 Model 1 = Adjusted R 2 : 0.021, F: 3.208, P: 0.043; Model 2 = Adjusted R 2 : 0.025, F: 3.649, P: 0.028; Model 3 = Adjusted R 2 : 0.178, F: 23.096, P: 0.000; Model 4 = Adjusted R 2 : 0.197, F: 50.955, P: 0.000; Model 5 = Adjusted R 2 : 0.171, F: 11.544, P: 0.000; Model 6 = Adjusted R 2 : 0.217, F: 12.325, P: 0.000; Model 7 = Adjusted R 2 : 0.225, F: 20.722, P: 0.000 In Model 4, COPDFAS explained 20% of the total variance (β = 0.448, P = 0.000). In Model 5, state anxiety, trait anxiety, dyspnea and its sub-dimensions explained 17% of the total variance and the most important predictor was D-12S (β = 0.563, P = 0.001). In Model 6, state anxiety, trait anxiety, dyspnea and its sub-dimensions and COPDFAS explained 22% of the total variance and the most important predictor was COPDFAS (β = 0.296, P = 0.000). In Model 7, dyspnea and its sub-dimensions and COPDFAS explained 23% of the total variance. The most significant predictors were the physical sub-dimension of dyspnea (β = 0.187, P = 0.047) and the COPDFAS (β = 0.291, P = 0.000). Discussion The aim of this study has been to determine the predictors of sleep quality in patients with asthma and COPD. It has been determined that there is a significant correlation between dyspnea and fatigue levels and sleep quality of the patients rather than demographic findings. It has been reported that high sleep quality and low anxiety levels mediate the correlation between quality of life and social deprivation in patients with asthma [ 23 ]. In this study, a weak correlation has been found between sleep quality and state and trait anxiety levels of the patients. As the anxiety levels of the patients increased, their sleep quality deteriorated. In one study, anxiety level was not found to be among the important predictors of sleep quality in patients with asthma, whereas it was found to be among the important predictors affecting sleep quality in patients with COPD [ 24 ]. In other studies, a significant correlation has been found between sleep quality and anxiety in patients with asthma [ 25 – 26 ]. However, sleep quality in patients with asthma and COPD may be affected by psychological factors such as anxiety as well as environmental factors. In one study, it has been determined that the noise level in the environment negatively affected the sleep quality and anxiety of the patients [ 27 ]. In the regression analysis performed in this study, state and trait anxiety predicted only 3% of sleep quality, which may be related to these results. This study also found a moderate correlation between sleep quality and severity of dyspnea and physical and emotional difficulties of dyspnea. As the severity of dyspnea increased and caused physical and emotional difficulties, patients' sleep quality deteriorated. In the regression analysis, dyspnea severity and physical and emotional difficulties of dyspnea explained 18% of sleep quality. Similarly, in a study conducted with patients with COPD, a high correlation has been found between the severity of dyspnea and sleep quality [ 28 ]. In another study, sleep duration has been found to be correlated with symptoms such as dyspnea and cough in patients with COPD and asthma [ 29 ]. In patients with lung cancer, it has been found that sleep quality decreased as dyspnea increased [ 30 ]. Hence, it can be said that dyspnea and the physiological and psychological distress caused by dyspnea impair the quality of sleeping in patients. Another finding of this study in patients with asthma and COPD that there was a significant correlation between sleep quality and fatigue level. In the regression analysis, fatigue accounted for 20% of the variance in sleep quality. In one study, it was found that the level of fatigue in asthmatic patients was quite high (approximately 63%) and the most important predictors of fatigue were quality of life, dyspnea, age and disease control status [ 31 ]. In another study, a negative and weak correlation has been found between fatigue level and sleep quality in patients with COPD [ 13 ]. It has been reported that sleep quality is severely impaired in asthmatic patients, and as sleep quality deteriorates, patients feel excessively sleepy and tired during the day [ 32 ]. Fatigue is an important symptom in asthmatic patients, and it has been found that patients with severe fatigue have high levels of depression and anxiety, low asthma control, low quality of life, limited activity level and impaired emotional functioning [ 33 ]. Therefore, fatigue is a symptom that needs to be treated with care. In this study, age and gender explained only 2% of the variance in sleep quality. Furthermore, age has been determined as the most important predictor affecting the sleep quality of patients among demographic variables. In one study, demographic variables affecting sleep quality in patients with asthma were marriage and educational status, whereas variables including age, gender, marriage and educational status were found in patients with COPD [ 24 ]. In a study involving elderly patients, gender and COPD were identified as two significant variables predicting sleep quality [ 34 ]. In another study, the sleep duration and sleep difficulty of patients with COPD were investigated, with gender identified among the associated demographic variables, while age was not included [ 35 ]. As can be seen, different results have been obtained in the studies. At least in this study, it can be said that physiological variables affect and predict sleep quality more than demographic variables in patients with asthma and COPD. Limitations There are some limitations of this study. The research has been conducted in a single centre. Hence, it is not possible to generalize the findings to the entire population of patients with asthma and COPD. In this study, self-report scales have been used. By conducting phenomenological studies, in-depth analyses can also be conducted. Evaluating patients' own perceptions can also make a great contribution to the solution of the problem. Conclusion In this study, the most important predictors of sleep quality in patients with asthma and COPD have been dyspnea and fatigue. The quality of sleep plays a crucial role in the patient's overall quality of life. Therefore, the factors that impair sleep quality should be taken under control and should be supported by both treatment and nursing care. Environmental arrangement, applications for patient comfort, psychological support and mindfulness-based cognitive behavioural therapies can also be supported. In this regard, it has also been suggested to plan and implement intervention studies to solve the problem. Declarations Acknowledgements The authors would like to express their gratitude to all the patients. Declaration of Conflicting Interests The authors declared no potential conflicts of interest. 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Clin Respir J 15:741–752. https://doi.org/10.1111/crj.13356 Wang P, Song L, Wang K et al (2020) Prevalence and associated factors of poor sleep quality among Chinese older adults living in a rural area: A population-based study. Aging Clin Exp Res 32:125–131. https://doi.org/10.1007/s40520-019-01171-0 Park D, Jun J (2023) Factors influencing sleep duration and sleep difficulty in people with chronic obstructive pulmonary disease. Iran J Public Health 52(3):553-562. https://doi.org/10.18502/ijph.v52i3.12138 Additional Declarations The authors declare no competing interests. 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. 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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-7597114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514008403,"identity":"30536727-623f-440b-b129-f4a66ecce1d1","order_by":0,"name":"Emel Bahadır Yılmaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCQY2BgY2CQYGZuYGINcGJEK0FkaQljSitYBYYC2HCWvhl25+9uBHmUW+wXHG1g0//pxP7J/dfPABQ41NNC4tknOOmRv2nJOw3HCYse1mD8/txBl3jiUbMBxLy23AocXgRoKZBG+bhIEBUMsNHonbiQ03cswkGBsO49RifyP9m+RfqJabfwzOJc4npMVAIsdMGmbLbZ6EA4kbCGmRuHOmTFrmnISBJEiLzIFk44030pINEvD4hX92+zbJN2V1BnznDx+7+eaPney8G8kHH3yoscGpBQ4UDkBoR7DKBELKQUAeaqg9MYpHwSgYBaNgZAEAztZf+03LQV8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1785-3539","institution":"Giresun University","correspondingAuthor":true,"prefix":"","firstName":"Emel","middleName":"Bahadır","lastName":"Yılmaz","suffix":""},{"id":514008404,"identity":"5cea3f22-115a-4b60-ae82-f54d2f0b2f55","order_by":1,"name":"Arzu Yüksel","email":"","orcid":"https://orcid.org/0000-0001-7819-2020","institution":"Aksaray University","correspondingAuthor":false,"prefix":"","firstName":"Arzu","middleName":"","lastName":"Yüksel","suffix":""}],"badges":[],"createdAt":"2025-09-12 06:28:30","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7597114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7597114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91317666,"identity":"e4622c12-e12d-404c-8d3b-9c738db467ae","added_by":"auto","created_at":"2025-09-15 08:37:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7597114/v1/0bf48a9b-a3aa-49f9-9835-d8089a30a06b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePredictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic respiratory diseases (CRD) affect the airways and other structures of the lungs. The two most common diseases are chronic obstructive pulmonary disease (COPD) and asthma [1]. Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide and caused 3.23 million deaths in 2019 [2]. COPD is a preventable disease that is usually progressive and characterised by exacerbations. It is characterised by cough, expectoration, dyspnea, and wheezing [3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAsthma is a disease that varies in severity and frequency from person to person, manifests itself with recurrent episodes of breathlessness and wheezing due to narrowing of the airways, and worsens during physical activity or at night in some individuals [1]. It causes symptoms such as coughing, wheezing, shortness of breath and chest tightness because of inflammation and narrowing of the small airways in the lungs and affected an estimated 262 million people and caused 455,000 deaths in 2019 [4].\u003c/p\u003e\n\u003cp\u003eCOPD increases somatic complaints and symptoms including wheezing and dyspnea change the sleep structure and lead to complaints including insomnia and deep sleepiness [5]. In these patients, oxygen saturation decreases at night and dyspnea and insomnia are experienced [6]. Furthermore, sleep problems including nocturnal hypoxaemia, sleep respiratory disorders including sleep apnea and sleep-related hypoventilation, insomnia and restless leg syndrome are observed in patients with COPD [7]. In one study, sleep quality worsened in one third of patients with stable COPD [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is no difference between insomnia and sleep quality in patients with COPD and asthma [9]. Poor sleep quality and sleep disorders are also commonly observed in patients with asthma [10]. In addition, obstructive sleep apnea is more common in patients with severe asthma [11]. The two most important factors that lead to sleep disorders and decrease sleep quality in patients with asthma are nocturnal symptoms of the disease and uncontrolled asthma symptoms [12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSleep problems lead to physical and psychological health problems in these patients. Fatigue levels of patients with COPD increase as their sleep quality decreases [13]. Sleep disturbance is also associated with depression, low quality of life and poor self-efficacy [8]. It has also been reported that insomnia leads to poor clinical outcomes in asthma patients, including deterioration in quality of life and increased admission to emergency services, hospitals, and intensive care units [11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to determine the predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma.\u003c/p\u003e\n\u003cp\u003eResearch questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIs there a relation between patients' sleep quality and demographic characteristics?\u003c/li\u003e\n \u003cli\u003eIs there a relation between sleep quality and state and trait anxiety levels of patients?\u003c/li\u003e\n \u003cli\u003eIs there a relation between patients' sleep quality and dyspnea levels?\u003c/li\u003e\n \u003cli\u003eIs there a relation between patients' sleep quality and fatigue levels?\u003c/li\u003e\n \u003cli\u003eWhat are the most important predictors of patients' sleep quality?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is across-sectional and correlational study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation and sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population of the study consisted of patients over the age of 18 who were hospitalised in the Chest Clinic of Aksaray University Training and Research Hospital between May\u0026nbsp;and\u0026nbsp;August\u0026nbsp;2019, having a clear consciousness, without any mental problems, without any communication difficulties, and diagnosed with Chronic Obstructive Pulmonary Disease or asthma.\u0026nbsp;The sample consisted of 205 patients who agreed to participate in the study between the specified dates.\u003c/p\u003e\n\u003cp\u003eThe mean pretest Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale score (57.14±15.4) obtained by Kütmeç-Yılmaz and Kapucu [14] has been used in sample calculation. With G*Power 3.1.9.7. programme, the sample size has been calculated as 45 with effect size d=0.5, α=0.05 and 95% type 1 error. After the data were collected, the effect size was calculated as 4.08 in the post hoc analysis performed with the scale mean obtained in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient Information Form, State-Trait Anxiety Scale (STAS), Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS), Dyspnea-12 Scale (D-12S) and Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS) have been used for data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient Introduction Form\u003c/em\u003e\u003c/strong\u003e, the first part of the form included questions such as the patient's age, educational status, employment status, economic status, and family structure, while the second part included questions such as the duration of hospitalisation, previous hospital experience, and the status of having a chronic disease. It consisted of 21 questions in total.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eState and Trait Anxiety Scale (STAS\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e The scale consists of two sub-dimensions, State Anxiety Scale (SAS) and Trait Anxiety Scale (TAS), with 20 items in each sub-dimension [15]. Increasing scores indicate a high level of anxiety. Turkish validity and reliability have been performed [16]. The reliability coefficient of the scale varies between 0.83 and 0.87, and the test-retest reliability varies between 0.71 and 0.86. In this study, the Cronbach's alpha value of the SAS has been found to be 0.85 and the Cronbach's alpha value of the TAS has been found to be 0.81.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS)\u003c/em\u003e\u003c/strong\u003e has been developed to determine the effect of COPD and asthma on sleep. The scale, which consists of 7 questions in total, is 5-point Likert type. A high scale score indicates poor sleep quality, and a low scale score indicates good sleep quality [17]. In our country, validity and reliability studies have been conducted and the cronbach alpha coefficient of the scale has been determined as 0.87 [18]. In this study, cronbach alpha coefficient has been found to be 0.86.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDyspnea-12 Scale (D-12S)\u003c/em\u003e\u003c/strong\u003e measures the severity of dyspnea and consists of a total of 12 items. The first seven items of the four-point Likert-type scale question the physical difficulties caused by dyspnea in patients. The remaining five items of the scale focus on the effect of breathing on emotional states [19]. The maximum score that can be obtained from the physical dimension of the scale is 21 and the maximum score that can be obtained from the emotional dimension is 15. An increase in the score obtained from the scale indicates an increase in the severity of dyspnea in the patient. Cronbach alpha value of the scale has been reported as 0.97 [20]. In this study, Cronbach's alpha value has been found to be 0.94.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS)\u003c/em\u003e\u003c/strong\u003e consists of 12 items. The maximum score from the five-point Likert-type scale is 60.\u0026nbsp;As the scale score increases, the fatigue level of the person increases [21]. The Turkish validity and reliability of the scale has been performed and Cronbach's alpha value has been determined as 0.92 [22]. In this study, Cronbach's alpha value has been found to be 0.77.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore starting the study, written permissions have been obtained from the Chief Physician's Office of the relevant Training and Research Hospital, Provincial Health Directorate and Aksaray University Human Research Ethics Committee (Date: 19. 04. 2019, No: 2019/03-61). The patients participating in the study have been informed about the research and it has been explained that individual information will remain confidential. Data has been collected in line with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIBM SPSS Statistics 25 programme has been used in the evaluation of the data. Descriptive statistics such as frequency, percentage, mean and standard deviation have been used. Cronbach alpha values of the scales used in this study have been calculated. The assessment of whether the data followed a normal distribution or not was conducted through the Kolmogorov-Smirnov test. \u0026nbsp; It has been decided that the data are normally distributed. Pearson correlation test has been used to determine the correlation between the scales. Multiple Linear Regression analysis has been used to determine the predictors of the COPDASS. Adjusted R\u003csup\u003e2\u003c/sup\u003e, standard error, beta and p values have been given to explain the total variance. P\u0026lt;0.05 level has been accepted as statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of the patients was 65.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42 (min:18, max:78), 59.0% are female, 84.4% have nuclear family structure, 87.8% are married, 96.6% have children and 43.9% live in the province. 60.5% of the patients have completed primary school, 90.2% are employed, 70.2% have social security, and 83.4% have a moderate economic status. Among the patients, 47.3% had quit smoking and 39.0% had never smoked (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSociodemographic characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSociodemographic characteristics\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\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient's age\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 65,6\u0026thinsp;\u0026plusmn;\u0026thinsp;12,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e61\u0026ndash;70 years old\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily structure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNuclear family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtended family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChildbearing status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExistent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-existent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInhabitence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth coverage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExistent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-existent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEconomic Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCigarette\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmokers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuitters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation about the disease process\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation about the disease\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\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of diagnosis\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;7 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;14 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15 years and over\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForm of taking medication\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn the presence of complaints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUsing a therapeutic device at home\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious hospitalisation due to illness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttending the emergency department due to a disease in the last 6 months\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReceiving education about his/her disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of impact of the disease on life\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level of impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level of impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerceived health status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory distress\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRarely\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMostly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e63.4% of the patients have COPD and have been receiving treatment for a mean of 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11 years. 89.8% of the patients regularly used their medication, 22.4% used a device to assist treatment and 88.8% had been hospitalised before. 79.0% of the patients presented to the emergency department in the last 6 months due to their disease; 55.1% received education about their disease; 52.2% reported that the disease has a high impact on their life; 53.7% perceived their health status as poor; 62.9% mostly experienced respiratory distress.\u003c/p\u003e\u003cp\u003eThe mean score of the patients was 45.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16 for the SAS, 47.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00 for the TAS, 53.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14 for the COPDASS, 24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70 for the D-12S, 14.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85 for the Dyspnea Physical Subscale, 9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57 for the Dyspnea Emotional Subscale and 59.36\u0026thinsp;\u0026plusmn;\u0026thinsp;15.12 for the COPDFAS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean scores of the patients on the SAS, TAS, COPDASS, D-12S, and COPDFAS scales\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin-Max scores\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e45.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e47.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPDASS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e53.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.71\u0026ndash;89.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-12S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnea Physical Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPDFAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e59.36\u0026thinsp;\u0026plusmn;\u0026thinsp;15.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.33\u0026ndash;95.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eSAS: State Anxiety Scale, TAS: Trait Anxiety Scale, COPDASS: Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale, D-12S: Dyspnea-12 Scale, COPDFAS: Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere is a weak but significant correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the COPDASS and the SAS (r\u0026thinsp;=\u0026thinsp;0.175) and TAS (r\u0026thinsp;=\u0026thinsp;0.155). There is a moderately significant correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the COPDASS and D-12S (r\u0026thinsp;=\u0026thinsp;0.428), physical sub-dimension (r\u0026thinsp;=\u0026thinsp;0.418) and emotional sub-dimension (r\u0026thinsp;=\u0026thinsp;0.370). There is a moderately significant correlation (r\u0026thinsp;=\u0026thinsp;0.448) between the COPDASS and the COPDFAS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations between the SAS, TAS, COPDASS, D-12S, and COPDFAS scales\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1. SAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2. TAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3. COPDASS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4. D-12S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5. Dyspnea Physical Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6. Dyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7. COPDFAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eSAS: State Anxiety Scale, TAS: Trait Anxiety Scale, COPDASS: Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale, D-12S: Dyspnea-12 Scale, COPDFAS: Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eVariables found to be significant in the preliminary analyses were included in the Multiple Linear Regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In Model 1, age and gender explained 2% of the total variance and the most important predictor was age (β\u0026thinsp;=\u0026thinsp;0.147, P\u0026thinsp;=\u0026thinsp;0.036). In Model 2, state anxiety and trait anxiety explained 3% of the total variance and neither of them were significant predictors of sleep quality (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In Model 3, D-12S and its subscales explained 18% of the total variance and the most important predictor was dyspnea (β\u0026thinsp;=\u0026thinsp;0.561, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictors of sleep quality\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=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD-12S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPDFAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD-12S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Physical Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPDFAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Physical Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDyspnea Emotional Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPDFAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.021, F: 3.208, P: 0.043; Model 2\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.025, F: 3.649, P: 0.028; Model 3\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.178, F: 23.096, P: 0.000; Model 4\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.197, F: 50.955, P: 0.000; Model 5\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.171, F: 11.544, P: 0.000; Model 6\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.217, F: 12.325, P: 0.000; Model 7\u0026thinsp;=\u0026thinsp;Adjusted R\u003csup\u003e2\u003c/sup\u003e: 0.225, F: 20.722, P: 0.000\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Model 4, COPDFAS explained 20% of the total variance (β\u0026thinsp;=\u0026thinsp;0.448, P\u0026thinsp;=\u0026thinsp;0.000). In Model 5, state anxiety, trait anxiety, dyspnea and its sub-dimensions explained 17% of the total variance and the most important predictor was D-12S (β\u0026thinsp;=\u0026thinsp;0.563, P\u0026thinsp;=\u0026thinsp;0.001). In Model 6, state anxiety, trait anxiety, dyspnea and its sub-dimensions and COPDFAS explained 22% of the total variance and the most important predictor was COPDFAS (β\u0026thinsp;=\u0026thinsp;0.296, P\u0026thinsp;=\u0026thinsp;0.000). In Model 7, dyspnea and its sub-dimensions and COPDFAS explained 23% of the total variance. The most significant predictors were the physical sub-dimension of dyspnea (β\u0026thinsp;=\u0026thinsp;0.187, P\u0026thinsp;=\u0026thinsp;0.047) and the COPDFAS (β\u0026thinsp;=\u0026thinsp;0.291, P\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study has been to determine the predictors of sleep quality in patients with asthma and COPD. It has been determined that there is a significant correlation between dyspnea and fatigue levels and sleep quality of the patients rather than demographic findings.\u003c/p\u003e\u003cp\u003eIt has been reported that high sleep quality and low anxiety levels mediate the correlation between quality of life and social deprivation in patients with asthma [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, a weak correlation has been found between sleep quality and state and trait anxiety levels of the patients. As the anxiety levels of the patients increased, their sleep quality deteriorated. In one study, anxiety level was not found to be among the important predictors of sleep quality in patients with asthma, whereas it was found to be among the important predictors affecting sleep quality in patients with COPD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In other studies, a significant correlation has been found between sleep quality and anxiety in patients with asthma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, sleep quality in patients with asthma and COPD may be affected by psychological factors such as anxiety as well as environmental factors. In one study, it has been determined that the noise level in the environment negatively affected the sleep quality and anxiety of the patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the regression analysis performed in this study, state and trait anxiety predicted only 3% of sleep quality, which may be related to these results.\u003c/p\u003e\u003cp\u003eThis study also found a moderate correlation between sleep quality and severity of dyspnea and physical and emotional difficulties of dyspnea. As the severity of dyspnea increased and caused physical and emotional difficulties, patients' sleep quality deteriorated. In the regression analysis, dyspnea severity and physical and emotional difficulties of dyspnea explained 18% of sleep quality. Similarly, in a study conducted with patients with COPD, a high correlation has been found between the severity of dyspnea and sleep quality [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In another study, sleep duration has been found to be correlated with symptoms such as dyspnea and cough in patients with COPD and asthma [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In patients with lung cancer, it has been found that sleep quality decreased as dyspnea increased [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hence, it can be said that dyspnea and the physiological and psychological distress caused by dyspnea impair the quality of sleeping in patients.\u003c/p\u003e\u003cp\u003eAnother finding of this study in patients with asthma and COPD that there was a significant correlation between sleep quality and fatigue level. In the regression analysis, fatigue accounted for 20% of the variance in sleep quality. In one study, it was found that the level of fatigue in asthmatic patients was quite high (approximately 63%) and the most important predictors of fatigue were quality of life, dyspnea, age and disease control status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In another study, a negative and weak correlation has been found between fatigue level and sleep quality in patients with COPD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It has been reported that sleep quality is severely impaired in asthmatic patients, and as sleep quality deteriorates, patients feel excessively sleepy and tired during the day [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Fatigue is an important symptom in asthmatic patients, and it has been found that patients with severe fatigue have high levels of depression and anxiety, low asthma control, low quality of life, limited activity level and impaired emotional functioning [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, fatigue is a symptom that needs to be treated with care.\u003c/p\u003e\u003cp\u003eIn this study, age and gender explained only 2% of the variance in sleep quality. Furthermore, age has been determined as the most important predictor affecting the sleep quality of patients among demographic variables. In one study, demographic variables affecting sleep quality in patients with asthma were marriage and educational status, whereas variables including age, gender, marriage and educational status were found in patients with COPD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In a study involving elderly patients, gender and COPD were identified as two significant variables predicting sleep quality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In another study, the sleep duration and sleep difficulty of patients with COPD were investigated, with gender identified among the associated demographic variables, while age was not included [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As can be seen, different results have been obtained in the studies. At least in this study, it can be said that physiological variables affect and predict sleep quality more than demographic variables in patients with asthma and COPD.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThere are some limitations of this study. The research has been conducted in a single centre. Hence, it is not possible to generalize the findings to the entire population of patients with asthma and COPD. In this study, self-report scales have been used. By conducting phenomenological studies, in-depth analyses can also be conducted. Evaluating patients' own perceptions can also make a great contribution to the solution of the problem.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the most important predictors of sleep quality in patients with asthma and COPD have been dyspnea and fatigue. The quality of sleep plays a crucial role in the patient's overall quality of life. Therefore, the factors that impair sleep quality should be taken under control and should be supported by both treatment and nursing care. Environmental arrangement, applications for patient comfort, psychological support and mindfulness-based cognitive behavioural therapies can also be supported. In this regard, it has also been suggested to plan and implement intervention studies to solve the problem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to all the patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization (2023) Chronic respiratory diseases. https://www.who.int/health-topics/chronic-respiratory-diseases#tab=tab_2 Accessed in September 30, 2023.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (2023) Chronic obstructive pulmonary disease (COPD) https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd) Accessed in September 30, 2023.\u003c/li\u003e\n \u003cli\u003eBing\u0026ouml;l Z, \u0026Ccedil;ağatay T (2016) Exacerbation causes in patients with chronic obstructive pulmonary disease (COPD) diagnosis, treatment, and awareness of risk group patients. Klinik Tıp Aile Hekimliği Dergisi 8(5):21-23.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (2023) Asthma https://www.who.int/news-room/fact-sheets/detail/asthma Accessed in September 30, 2023.\u003c/li\u003e\n \u003cli\u003eBatum M, Batum \u0026Ouml;, Can H et al (2015) Evaluation of the severity of sleep complaints according to the stages of chronic obstructive pulmonary disease. J Turk Sleep Med 3:59-64. https://doi.org/10.4274/jtsm.75\u003c/li\u003e\n \u003cli\u003eKaya I, Pekcan S, Dursunoğlu N et al (2023) Determination and comparison of sleep quality and sleep disorders in patients with COPD according to GOLD groups. J Turk Sleep Med 10:42-7. https://doi.org/10.4274/jtsm.galenos.2022.28190\u003c/li\u003e\n \u003cli\u003eVanfleteren LEGW, Beghe B, Andersson A et al (2020) Multimorbidity in COPD, does sleep matter? 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J Asthma 56(2):118-29. https://doi.org/10.1080/02770903.2018.1444049\u003c/li\u003e\n \u003cli\u003eDamianaki A, Vagiakis E, Sigala I et al (2019) The co-existence of obstructive sleep apnea and bronchial asthma: Revelation of a new asthma phenotype? J. Clin. Med 8(9):1476. https://doi.org/10.3390/jcm8091476\u003c/li\u003e\n \u003cli\u003eBozkurt C, Akay B, Sınmaz T. The relationship between fatigue level and sleep quality in patients with chronic obstructive pulmonary disease. Osmangazi Journal of Medicine. 2020; 42(6):627-38. https://doi.org/10.20515/otd.655648\u003c/li\u003e\n \u003cli\u003eK\u0026uuml;tme\u0026ccedil;-Yılmaz C, Kapucu S (2017) The effect of progressive relaxation exercises on fatigue and sleep quality in individuals with COPD. Holist Nurs Pract 31(6):369-77. https://doi.org/10.1097/HNP.0000000000000234\u003c/li\u003e\n \u003cli\u003eSpielberger CD, Gonzalez-Reigosa F, Martinez-Urrutia A et al (1971) The State-Trait Anxiety Inventory. Revista Interamericana De Psicolog\u0026iacute;a/Interamerican Journal of Psychology 5(3-4): 145-158. https://doi.org/10.30849/rip/ijp.v5i3 \u0026amp; 4.620\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;ner N, Le Compte A (1983) Durumluk-S\u0026uuml;rekli Kaygı Envanteri El Kitabı. 2. Baskı. İstanbul, Boğazi\u0026ccedil;i \u0026Uuml;niversitesi Yayınları.\u003c/li\u003e\n \u003cli\u003ePokrzywinski RF, Meads DM, McKenna SP et al (2009) Development and psychometric assessment of the COPD and Asthma Sleep Impact Scale (CASIS). Health and Quality of Life Outcomes 7;1-98.\u003c/li\u003e\n \u003cli\u003eAyhan E (2011) The validity and reliability study of chronic obstructive pulmonary disease and asthma sleep impact scale in the patients with chronic obstructive pulmonary disease. Atat\u0026uuml;rk University, Institute of Health Sciences, Master\u0026rsquo;s Thesis, Erzurum.\u003c/li\u003e\n \u003cli\u003eYorke J, Moosavi SH, Shuldham C et al (2010) Quantification of dyspnoea using descriptors: development and initial testing of the Dyspnoea-12. Thorax 65:21-26.\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;k-Metin Z, Helvacı A (2018) Validity and reliability of Turkish version of the dyspnea-12 scale. Journal of Hacettepe University Faculty of Nursing 5(2): 102-115. https://doi.org/10.31125/hunhemsire.454354\u003c/li\u003e\n \u003cli\u003eRevicki DA, Meads DM, McKenna SP et al (2010) COPD and Asthma Fatigue Scale (CAFS): Development and Psychometric Assessment. Health Outcomes Res Med 1(1): e5-e16. https://doi.org/10.1016/j.ehrm.2010.06.001\u003c/li\u003e\n \u003cli\u003eArslan S, \u0026Ouml;ztun\u0026ccedil; G (2013) Validity and Reliability of Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale. Hemşirelikte Araştırma Geliştirme Dergisi 15(1):48-60.\u003c/li\u003e\n \u003cli\u003eMoitra S, Adan A, Akg\u0026uuml;n M et al (2023) Less social deprivation ıs associated with better health-related quality of life in asthma and is mediated by less anxiety and better sleep quality. J Allergy Clin Immunol Pract 11(7):2115-2124. https://doi.org/10.1016/j.jaip.2023.03.052\u003c/li\u003e\n \u003cli\u003eAldabayan YS (2023) Mental health and sleep quality among patients with asthma and COPD. Front Med 10:1181742. https://doi.org/10.3389/fmed.2023.1181742\u003c/li\u003e\n \u003cli\u003eSumarni S (2022) Relationshıp between anxıety levels and sleep quality of asthmatics. Journal of Innovation Research and Knowledge 2(3):811-818. https://doi.org/10.53625/jirk.v2i3.3143\u003c/li\u003e\n \u003cli\u003eSuryadi SM, Simamora RS, Meliyana E (2022) The correlation between anxiety levels and sleep quality in asthma sufferers at the Lemahabang Health Center. Soscience: Journal of Social Science 1(1).\u003c/li\u003e\n \u003cli\u003eAydın-Sayılan A, Kulaka\u0026ccedil; N, Sayılan S (2021) The effects of noise levels on pain, anxiety, and sleep in patients. Nurs Crit Care 26:79\u0026ndash;85. https://doi.org/10.1111/nicc.12525\u003c/li\u003e\n \u003cli\u003eSerin EK, Ister ED, Ozdemir A (2020) The relationship between sleep quality and dyspnoea severity in patients with COPD. Afri Health Sci 20(4):1785-92. https://doi.org/10.4314/ahs.v20i4.32\u003c/li\u003e\n \u003cli\u003eRuan Z, Li D, Cheng X et al (2023) The association between sleep duration, respiratory symptoms, asthma, and COPD in adults. Front Med 10:1108663. https://doi.org/10.3389/fmed.2023.1108663\u003c/li\u003e\n \u003cli\u003eDoğan F, Menekli T (2023) The relationship between dyspnea and sleep quality in lung cancer patients. Med Res Rep 6(2):64-76. https://doi.org/10.55517/mrr.1167792\u003c/li\u003e\n \u003cli\u003eVan Herck M, Spruit MA, Burtin C et al (2018) Fatigue is highly prevalent in patients with asthma and contributes to the burden of disease. J Clin Med 7(12):471. https://doi.org/10.3390/jcm7120471\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;neş A, Yıldız D, Dikiş \u0026Ouml;Ş et al (2019). Association with asthma and restless legs syndrome and sleep quality. J Turk Sleep Med 6:7-9. https://doi.org/10.4274/jtsm.galenos.2019.29392\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;naydın FE, Ediger D, Erbay M (2021) Fatigue: A forgotten symptom of asthma. Clin Respir J 15:741\u0026ndash;752. https://doi.org/10.1111/crj.13356\u003c/li\u003e\n \u003cli\u003eWang P, Song L, Wang K et al (2020) Prevalence and associated factors of poor sleep quality among Chinese older adults living in a rural area: A population-based study. Aging Clin Exp Res 32:125\u0026ndash;131. https://doi.org/10.1007/s40520-019-01171-0\u003c/li\u003e\n \u003cli\u003ePark D, Jun J (2023) Factors influencing sleep duration and sleep difficulty in people with chronic obstructive pulmonary disease. Iran J Public Health 52(3):553-562. https://doi.org/10.18502/ijph.v52i3.12138\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Asthma, chronic obstructive pulmonary disease, anxiety, sleep quality, dyspnea","lastPublishedDoi":"10.21203/rs.3.rs-7597114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7597114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sleep problems lead to physical and psychological health problems in patients with chronic obstructive pulmonary disease and asthma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e This study aimed to determine the predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The sample of this cross-sectional study consisted of 205 patients. Patient Information Form, State-Trait Anxiety Scale (STAS), Chronic Obstructive Pulmonary Disease and Asthma Sleep Scale (COPDASS), Dyspnea-12 Scale (D-12S) and Chronic Obstructive Pulmonary Disease and Asthma Fatigue Scale (COPDFAS) have been used for data collection. Pearson correlation analysis and multiple linear regression analysis have been used to analyse the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e There is a weak correlation between sleep quality levels and state anxiety (r=0.175) and trait anxiety (r=0.155) levels. There is a moderate correlation between sleep quality and dyspnea (r=0.428) and fatigue (r=0.448) levels. State anxiety and trait anxiety explained 3% of the total variance, dyspnea and its sub-dimensions explained 18% and fatigue level explained 20% (p\u0026lt;0.05). The best model explaining the sleep quality of the patients was dyspnea and its sub-dimensions and fatigue levels, which explained 23% of the total variance. The most significant predictors were the physical sub-dimension of dyspnea (β=0.187, p=0.047) and the COPDFAS (β=0.291, p=0.000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The most important predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma are dyspnea and fatigue. Patients should be supported with psychological therapies and good nursing care in addition to drug treatment to reduce anxiety, dyspnea, and sleep problems.\u003c/p\u003e","manuscriptTitle":"Predictors of sleep quality in patients with chronic obstructive pulmonary disease and asthma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 08:21:17","doi":"10.21203/rs.3.rs-7597114/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":"fffa0c4a-4106-4e4b-9a33-3645696ba26a","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-09T16:12:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 08:21:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7597114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7597114","identity":"rs-7597114","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}