Atrial fibrillation (AF) is the most commonly sustained cardiac rhythm disorder in the world (Ball, Carrington, McMurray, & Stewart, 2013). AF prevalence is increasing parallel to the aging population, making AF the most prevalent arrhythmia in patients older than 65 (Karamichalakis et al., 2015). According to the National Health Insurance Service database in Korea, the prevalence of AF in Korea is approximately 0.5% in adults ages 40 to 59 and 4.2% in adults ages 60 to 79 (Lee, Choi, Han, Cha, & Oh, 2017).
AF is an important contributing factor to ischemic stroke and thromboembolism (Ball et al., 2013). Risk of stroke increased four- to five-fold in patients with AF, regardless of the type of AF (Ali & Abdelhafiz, 2016). AF–related stroke leads to greater mortality and disability than stroke caused by other factors (Ali & Abdelhafiz, 2016; Lip & Lane, 2015). The recommended management strategy for AF is oral anticoagulant treatment for appropriate thromboprophylaxis (January et al., 2014). However, oral anticoagulant treatment increases bleeding risk, particularly for older individuals. Previous hemorrhagic events, including stroke, often occurred in older adults with AF (Ali & Abdelhafiz, 2016; Ball et al., 2013). Oral anticoagulant treatment and fear of complications may discourage patients from associating with other individuals and reduce health-related quality of life (HRQoL; Altiok, Yilmaz, & Rencüsoğullari, 2015). Although AF alone is rarely life-threatening, it significantly impairs the QoL of many patients who develop complications from AF (Mas Dalmau et al., 2017; Perret-Guillaume, Briancon, Wahl, Guillemin, & Empereur, 2010). As the older population increases, focus should be on risks of complications in older adults with AF and their HRQoL.
Previous studies have confirmed that patients with AF have lower HRQoL than a normative sample (Arribas et al., 2010; Dorian et al., 2000; Perret-Guillaume et al., 2010). Serious complications and inconvenience of anticoagulation monitoring are thought to reduce individuals' HRQoL (Ali & Abdelhafiz, 2016; Altiok et al., 2015; Kotecha et al., 2016). However, these findings were from studies primarily conducted in Western populations. HRQoL in Korean older adults with AF is still a new issue compared with HRQoL in patients with other chronic diseases (Lee & Seo, 2013). Controversy remains regarding which sociodemographic and health-related factors play a role in HRQoL in patients with AF (Wasmer, Eckardt, & Breithardt, 2017; Zhang, Gallagher, & Neubeck, 2015). Thus, HRQoL in Korean older adults with AF needs to be examined in consideration of adults' basic sociodemographic and clinical characteristics.
With regard to the assessment of HRQoL, previous studies on patients with AF have applied generic QoL measurements, such as the 36-item Short Form Health Survey (SF-36; Zhang et al., 2015). Although generic instruments have the advantage of being validated in many languages, their main disadvantage is that they measure general health and functioning rather than health and functioning specific to disease. Scores from generic questionnaires may also be affected by patient demographics and comorbidities, which are prevalent in older patients with AF (Aliot, Botto, Crijns, & Kirchhof, 2014). However, disease-specific questionnaires allow patients to assess their health issues caused by specific diseases and subsequent HRQoL. The 18-item Atrial Fibrillation Quality of Life (AF-QoL-18) questionnaire is an AF–specific QoL instrument comprising three dimensions: psychological, physical, and sexual activity (Arribas et al., 2010). The AF-QoL-18 was developed to measure total and dimensional QoL and address major issues related to AF (Aliot et al., 2014). Using the questionnaire, the current study described the level of HRQoL and identified sociodemographic and clinical factors influencing total and dimensional HRQoL in Korean older adults with AF.
Study Design and Participants
A cross-sectional descriptive design was used to perform a secondary analysis of baseline data from a large longitudinal study to describe changes in HRQoL of patients after discharge from cardiovascular and medical intensive care units. Data were drawn from a cardiovascular outpatient clinic of a tertiary hospital between October and December 2014. The non-probabilistic sample chosen by the current researchers comprised 205 patients who presented to the cardiovascular outpatient clinic of the tertiary hospital for blood coagulation tests to verify their international normalized ratio (INR). The study included patients with all types of AF. It also included patients ages 65 and older who took warfarin for AF thromboprophylaxis for ≥2 months with therapeutic INR (2.0 to 3.0). Exclusion criteria were patients diagnosed with psychiatric or cognitive disorders or who had difficulty expressing themselves verbally.
The study performed a power analysis using G*power 3.1 (Faul, Erdfelder, Buchner, & Lang, 2009) to determine appropriate sample size. It was determined that 177 patients were needed for two-tailed analysis using multiple regression with a 0.05 alpha level, 90% power, and a moderate effect size (0.15); therefore, the current sample size was sufficient.
Sociodemographic and Clinical Characteristics. Sociodemographic characteristics, including age, gender, education level, martial status, family type, and employment status, were collected. Regarding clinical characteristics, duration of AF diagnosis, comorbidities, stroke risk, and bleeding risk were obtained from medical records. Duration of AF diagnosis was divided into four groups based on a previous study: <1 year, 1 to 5 years, 5 to 10 years, and >10 years (Reynolds, Lavelle, Essebag, Cohen, & Zimetbaum, 2006). The Charlson Comorbidity Index (CCI) was used to quantify comorbidity (Radovanovic et al., 2014). The CCI integrated 19 different medical categories: myocardial infarction, heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic lung disease, connective tissue disease, peptic ulcer disease, mild or moderate to severe liver disease, diabetes with or without target organ damage, hemiplegia, moderate to severe renal disease, malignant neoplasm, leukemia, lymphoma, metastatic solid tumor, and AIDS. Each category was weighted based on its potential influence on mortality. The CCI was originally divided into four score ranges, indicating burden of comorbidities: 0 points (none), 1 to 2 points (low), 3 to 4 points (moderate), and ≥5 points (high), as previously explained (Radovanovic et al., 2014). In the current study, the CCI was grouped into two score ranges based on the moderate score (3 to 4 points): ≤2 points and ≥3 points.
The CHA2DS2VASc score (congestive heart failure or left ventricular dysfunction, hypertension, age ≥75 [2 points], diabetes, stroke [2 points], age 65 to 74, sex category [female]) was used for stroke risk, with a total score range of 0 to 9 (Lip & Lane, 2015). The CHA2DS2VASc score was categorized by low (0), intermediate (1), or high (≥2) stroke risk and was best for identifying low-risk patients who did not need antithrombotic therapy (Lip & Lane, 2015). In the current study, as the CHA2DS2VASc score ranged from 1 to 6 points, it was categorized into three groups by quartile (lower 25%, middle 25% to 75%, and upper 25%) rather than the original categorization.
HAS-BLED score (hypertension, abnormal renal/liver function [1 point each], stroke, bleeding history or predisposition, labile INR [if on warfarin], elderly [age >65 or frail condition], and drugs [aspirin, non-steroidal anti-inflammatory drugs]/alcohol concomitantly [1 point each]) was used for bleeding risk, with a total score range of 0 to 9 (Lip & Lane, 2015). Patients with ≥3 points on the HAS-BLED are in the high bleeding risk group (Lip & Lane, 2015). In the current study, high (≥3 points) and low (<3 points) risk groups were divided according to Lip and Lane (2015).
Disease-Specific Quality of Life. The AF-QoL-18 questionnaire was used in the current study after written permission was obtained from the initial author (Arribas et al., 2010). The AF-QoL-18 was scored using a 5-point Likert scale (1 = totally disagree, 2 = sufficiently disagree, 3 = neither agree nor disagree, 4 = sufficiently agree, and 5 = totally agree). The scale comprises seven items for the psychological dimension of QoL, eight items for the physical dimension, and three items for the sexual activity dimension. After reversing the negative items, the current researchers calculated scale scores by summing scores of corresponding items and standardizing the result to a score ranging from 0 to 100. Thus, all dimensions have been standardized for a score between 0 (worst HRQoL) and 100 (best HRQoL) to facilitate interpretation and comprehension. For analysis of each item, researchers calculated and presented the means of original responses indicating the level of agreement to help understanding of negative items. A higher mean value of each item indicates a higher level of agreement. The original AF-QoL-18 was validated through comparison with the SF-36. Its reliability and responsiveness were assessed in clinically stable patients with AF (Arribas et al., 2010). To apply the questionnaire to Korean patients in the current study, the original English version of the AF-QoL-18 questionnaire was translated into Korean by two independent professional bilingual translators. Two physicians, three nursing professionals (Y.-J.S. and two others), and one native English-speaking translator examined this translation-back-translation process until agreement was reached that the original version and back translation were equivalent.
Cronbach's alpha of the questionnaire at the time of development was 0.92 for the total score and >0.80 for the three dimensions (Arribas et al., 2010). In the current study, Cronbach's alpha was 0.87 for the total score, 0.83 for the psychological dimension, 0.85 for the physical dimension, and 0.85 for the sexual activity dimension.
Data Collection and Ethical Considerations
All study procedures were conducted based on the Declaration of Helsinki. The original study was approved by the institutional review board before patient recruitment commenced. All participants provided written consent. Participants were informed in detail of the study aim, confidentiality and anonymity of their information, and voluntary participation. Participants could leave the study at any time under their free will without incurring penalty or limitations on later outpatient treatments.
Data were analyzed using SPSS version 23.0. Descriptive statistics such as means, standard deviations, and percentages were used to explain sociodemographic characteristics, clinical characteristics, and HRQoL. Researchers used Student t tests and one-way analysis of variance (ANOVA) with Scheffe's method to compare mean HRQoL scores according to sociodemographic and clinical characteristics.
To elucidate factors influencing HRQoL in patients with AF, multiple linear regressions using an enter method were performed. Researchers entered total and each dimensional HRQoL as dependent variables. Researchers also entered participants' characteristics, which had shown a statistically significant difference in total and dimensional HRQoL scores, as potential variables. Comorbidity (CCI), stroke risk (CHA2DS2VASc), and bleeding risk (HAS-BLED) scores were entered as potential variables, as they were important clinical factors to patients with AF (Lip & Lane, 2015). Multicollinearity was assessed using the variance inflation factor (VIF) and tolerance. There was no multicollinearity: VIF was <5 and tolerance was <0.1. Although the CHA2DS2VASc and HAS-BLED contain age as a criterion, there was no multicollinearity within age and each risk score. A previous study also analyzed age and risk scores as predictors (Başaran et al., 2017). All analyses were two-tailed, and the significance level was set at 0.05.
Sociodemographic and Clinical Characteristics of Participants
Participants' mean age was 74.24 (SD = 4.61 years) and 43.9% of participants were female. Of participants, 29 (14.1%) were illiterate. The mean duration of AF diagnosis was 5.8 (SD = 6.04) years and 31 (15.1%) participants had been diagnosed with AF within the past 1 year. With regard to the CCI comorbidity score, 45.4% of patients were in the moderate group (≥3 points). Reported comorbidities included hypertension (54.6%), diabetes (26.8%), heart failure (48.3%), and stroke (18%). The mean stroke risk (CHA2DS2VASc) and bleeding risk (HAS-BLED) scores were 3.57 (SD = 1.36) and 2.44 (SD = 0.62), respectively. Forty-nine (23.9%) patients were in the high-risk stroke group (upper 25%, 5 to 6 points). Seventy-seven (37.6%) patients had a high-risk (≥3 points) HAS-BLED score (Table 1).
Sociodemographic and Clinical Characteristics of Participants (N = 205)
Level of Health-Related Quality of Life in Older Adults With Atrial Fibrillation
AF-QoL-18 scores are shown in Table 2, with higher scores indicating better HRQoL. The total score in the current population was 49.55 (SD = 17.11) of a possible 100. The lowest dimension score was the physical dimension (mean = 43.41, SD = 21.39) followed by the psychological (mean = 48.67, SD = 21.93) and sexual activity (mean = 67.97, SD = 28.17) dimensions, all of a possible 100.
Level of Health-Related Quality of Life in Older Adults with Atrial Fibrillation (N = 205)
For analysis of each item, the current researchers presented the means of the original response, with higher mean values indicating higher levels of agreement. In the psychological dimension, the most disagreed-on item was “I have negative thoughts about my future” (mean = 2.45, SD = 1.21). However, many patients agreed with items about the fear of complications, such as “I am afraid of having sudden tachycardia,” “I fear pain or having a heart attack,” or “I am afraid that my condition will worsen.” In the physical dimension, the most disagreed-on item was “My illness has reduced my quality of life” (mean = 2.96, SD = 1.18). Many patients agreed with items related to physical tiredness in daily life (e.g., “When I walk fast I get tired”; “Not being able to do things affects me; I want to but my body cannot”; “When I walk for half an hour I get tired and I need to rest”); these items were the most agreed on among all 18 items. In the sexual activity dimension, patients generally disagreed with the three items.
Differences in Health-Related Quality of Life Based on Sociodemographic and Clinical Characteristics
Univariate analyses were performed to examine the characteristics associated with the AF-QoL-18 (Table A, available in the online version of this article). Physical and sexual activity dimensions of the AF-QoL-18 were significantly different depending on gender, education, and living with/without spouse. The physical dimension score was also significantly lower in older patients and those with a higher CHA2DS2VASc score, whereas the sexual activity dimension varied depending on family type and employment. However, no variables were associated with the total and psychological dimension of the AF-QoL-18.
Differences of Health-related Quality of Life depending on Participant Characteristics (N = 205)
Predictors of Health-Related Quality of Life in Older Adults With Atrial Fibrillation
Multiple regression analysis was performed to identify predictors of the AF-QoL-18 while controlling for education, living with/without spouse, family type, and employment status (Table 3). For the physical dimension, age (β = −0.16, p = 0.038) and gender (β = 0.23, p = 0.007) were identified as predictors, accounting for 14.5% of variance. For the sexual activity dimension, gender (β = −0.29, p < 0.001) and CCI scores (β = −0.18, p = 0.029) were identified as predictors, accounting for 23.7% of variance. However, no variables were identified as predictors for the total and psychological dimensions of the AF-QoL-18.
Factors Associated with Health-Related Quality of Life in Older Adults With Atrial Fibrillation (N = 205)
To the researchers' knowledge, the current study is the first to describe the level of disease-specific QoL and its predictors in Korean older adults with AF. The study used an AF–specific QoL questionnaire to assess HRQoL of patients with AF. According to a previous review on HRQoL in patients with AF, some studies suggested that insignificant results may be attributed to the low sensitivity of general HRQoL questionnaires (Arribas et al., 2010; Zhang et al., 2015). Therefore, it was meaningful that the current study examined the level of HRQoL using an AF–specific questionnaire and reported the significance of each item and dimension in the questionnaire.
The level of HRQoL in patients with AF, less than 50 of a possible 100, was low. HRQoL of the physical dimension was lowest. Results of previous studies on German or English patients with AF were similar to the total HRQoL of the current study (Clarkesmith, Pattison, Lip, & Lane, 2013; Goette et al., 2015). Studies on American, English, and European older adults with AF also showed that the physical dimension had the lowest score (Clarkesmith et al., 2013; Zhang et al., 2015); however, studies on German adult patients with AF showed that the psychological dimension had the lowest score (Goette et al., 2015). Difference in the dimension scores of HRQoL may be due to patients' age differences in previous studies. According to studies on adult patients taking oral anticoagulant agents, participants' emotional scores were lower than their physical scores (Almeida, Noblat, Passos, & do Nascimento, 2011). Lower physical scores of HRQoL in older adults with AF may be related to frailty caused by comorbid conditions and symptoms of AF, which are different than comorbid conditions and symptoms of AF in younger adults (Wasmer et al., 2017). Older adults had a higher prevalence of comorbidities and reported dyspnea and persistent or permanent AF more often than younger adults. Thus, health care providers should be concerned about comorbidities and symptom management for older adults with AF.
To elucidate the influence of sociodemographic and clinical factors on HRQoL, multiple linear regressions were performed. In univariate analysis, the scores of the physical or sexual activity dimensions varied depending on education, living with/without spouse, family type, and employment. However, these variables did not affect the total HRQoL score. Thus, the current researchers considered these factors as covariates. When controlling for covariates, age and gender were significant predictors of the physical dimension of HRQoL. This finding was consistent with those of previous studies (Ball et al., 2013; Lee et al., 2017; Lip et al., 2015). Aging of the population was a key factor for the AF epidemic (Dilaveris & Kennedy, 2017). However, age itself was not an issue, considering that the physical HRQoL in patients with AF was significantly lower than that in an age-matched control group without AF (Arribas et al., 2010; Perret-Guillaume et al., 2010). Different types and symptoms of AF and comorbid conditions might affect decline in physical function (Dilaveris & Kennedy, 2017). As clinical characteristics of AF and comorbid conditions varied by ages, aging appeared to influence physical HRQoL. Clinical presentation of AF was different between genders (Gillis, 2017). AF diagnosis rate was different by gender because of symptom intensity and/or medical attention (Wasmer et al., 2017). Lip et al. (2015) reported that women are an independent risk factor for stroke caused by AF. These studies could support the gender differences in HRQoL of patients with AF.
For the sexual activity dimension, gender and comorbidity were significant factors. Men or individuals with more comorbidities had low scores in the sexual activity dimension. Little discussion occurred regarding the sexual activity of patients with AF, although lack of energy for daily activities was a common complaint. Sexual activity of older adults with chronic diseases, including AF, was of secondary importance. However, increased burden of chronic diseases in older adults adversely affected sexual activity (Merghati-Khoei, Pirak, Yazdkhasti, & Rezasoltani, 2016; Wylie & Kenney, 2010). The adverse effect of medications used for comorbid conditions could lead to decreased libido and sexual function (Merghati-Khoei et al., 2016). In a male-dominated Korean society, sexual concerns of men may be a source of anxiety and low HRQoL.
In summary, significant factors were not found in total and psychological HRQoL. However, comorbid conditions had a negative impact on HRQoL in the physical and sexual activity dimensions. Women had low HRQoL in the physical dimension, whereas men had low HRQoL in the sexual activity dimension. Aging also led to low HRQoL in the physical dimension. These results are regarded as being due to comorbid conditions and/or risk of complications, which is supported by results of detailed items on the HRQoL. Many patients with AF in the current study did not think that AF itself decreased QoL. Patients also did not imagine a negative future. On the other hand, they feared complications and difficulty performing daily activities. Previous studies reported that HRQoL in patients with AF could be affected by concern of complications, such as stroke and bleeding risks, and limitation on social activities (Altiok et al, 2015; Mas Dalmau et al., 2017). Dorian et al. (2000) also reported that HRQoL in patients with AF was lower than that of patients with significant structural heart disease. Therefore, health care providers should be concerned with suitable care to manage complications and limitations on daily activities in older adults with AF to improve HRQoL. In addition, patients need to overcome fears of lethal complications and manage comorbidities. For example, intervention for an individual activity plan can minimize the restraints patients experience every day. Intervention for rigorous management of risk factors can reduce potential complications, subsequently improving patients' overall HRQoL (Pathak et al., 2014).
Risk scores for stroke and bleeding did not affect the total and dimensional HRQoL in older adults with AF. The practical purpose of these scores was to minimize unnecessary antithrombotic and anticoagulant therapy for patients and identify patients potentially at high risk of bleeding (Lip & Lane, 2015; Senoo, Lane, & Lip, 2014). Therefore, these scores might not be directly related to level of and/or change in HRQoL in patients with AF on anticoagulant treatment, as most patients on anticoagulant treatment already had high scores for these risks. A previous study (Dorian et al., 2000) discovered a similar result: objective disease index and subjective QoL had a poor correlation. Another study (Senoo et al., 2014) suggested that ethnic groups should be considered, finding that Asian individuals have higher stroke and bleeding risks than Western individuals. Consequently, individuals' stroke and bleeding risks should be monitored for possible complications. Their impact on patients' conditions and daily lives should also be studied for various ethnoracial groups (Lip & Lane, 2015). Health care providers need to apply comprehensive and continued intervention for all patients with AF undergoing antithrombotic and anticoagulant therapy considering the meaning of the risk score.
The current study had several limitations. First, researchers were cautious in generalizing findings because participants were included using a convenience sampling from a single hospital in Korea. Second, patients of the study treated at the tertiary care facility could have different arrhythmia severity and treatment characteristics from those seen in general cardiology practices. Third, researchers could not include more comprehensive social factors. Older patients' social circumstances may be complicated. Therefore, further studies should extensively consider social factors, including perceived social support and social networks with family, friends, neighbors, and community members, as well as environmental factors, such as geographic location and access to health care services.
Findings showed that older adults with AF were a vulnerable group who needed considerable improvements in their HRQoL. Although risk scores were not a critical factor in participants' HRQoL, concern about lethal complications was an essential element that impaired overall HRQoL. Tiredness after physical activities reduced total and physical HRQoL. Moreover, age, gender, and comorbid conditions affected the physical and sexual activity dimensions of HRQoL. Therefore, health care providers should help improve the HRQoL of older adults with AF by reducing restraints, comorbidities, and concerns about complications by gender. Future studies are needed to compare the level of HRQoL depending on diverse conditions (i.e., younger and older adults, women and men, Western and Eastern patients with AF) considering comprehensive risk factors.
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Sociodemographic and Clinical Characteristics of Participants (N = 205)
|Age (years) (mean [SD])||74.24 (4.61)|
| 65 to 74||104 (50.7)|
| 75 to 85||101 (49.3)|
| Male||115 (56.1)|
| Female||90 (43.9)|
| Illiterate||29 (14.1)|
| Completed elementary school||89 (43.4)|
| Completed middle school||34 (16.6)|
| Completed high school or higher||53 (25.9)|
| Married||154 (75.1)|
| Single||51 (24.9)|
| Living alone||35 (17.1)|
| Living with spouse||119 (58.0)|
| Living with spouse and/or children||51 (24.9)|
| Unemployed||173 (84.4)|
| Employed||32 (15.6)|
|Duration of AF diagnosis (years) (mean [SD])||5.80 (6.04)|
| ≤1||31 (15.1)|
| 2 to 5||88 (42.9)|
| 6 to 9||48 (23.4)|
| ≥10||38 (18.5)|
|CCI scorea (mean [SD])||2.37 (1.12)|
| 0 to 2||112 (54.6)|
| ≥3||93 (45.4)|
| No||93 (45.4)|
| Yes||112 (54.6)|
| No||150 (73.2)|
| Yes||55 (26.8)|
| No||106 (51.7)|
| Yes||99 (48.3)|
| No||168 (82.0)|
| Yes||37 (18.0)|
|CHA2DS2 VASc scoreb (mean [SD])||3.57 (1.36)|
| 1 to 2||49 (23.9)|
| 3 to 4||107 (52.2)|
| 5 to 6||49 (23.9)|
|HAS-BLED scorec (mean [SD])||2.44 (0.62)|
| 0 to 2||128 (62.4)|
| ≥3||77 (37.6)|
Level of Health-Related Quality of Life in Older Adults with Atrial Fibrillation (N = 205)
|AF-QoL-18 Dimension||Scorea (Mean [SD] [Range])|
|Total||49.55 (17.11) (1 to 100)|
|Psychological||48.67 (21.93) (1 to 100)|
| I have negative thoughts about my future||2.45 (1.21)|
| I get depressed when I notice that I get tired||2.80 (1.24)|
| I am afraid of having sudden tachycardia||3.26 (1.32)|
| I get depressed when I think about the fact that my illness is for the rest of my life||3.03 (1.24)|
| I fear pain or having a heart attack||3.36 (1.29)|
| What affects me the most is the feeling of helplessness during a crisis||3.17 (1.18)|
| I am afraid that my condition will worsen||3.31 (1.23)|
|Physical||43.41 (21.39) (1 to 100)|
| When I do physical exercise I feel more tired than usual||3.26 (1.24)|
| I have stopped doing physical exercise||3.02 (1.34)|
| When I walk for half an hour I get tired and I need to rest||3.43 (1.31)|
| When I walk fast I get tired||3.64 (1.11)|
| It is difficult to leave the house to do any activity||2.99 (1.25)|
| Not being able to do things affects me, I want to but my body cannot||3.53 (1.26)|
| My illness has reduced my quality of life||2.96 (1.18)|
| I felt more vitality before I was diagnosed with the disease||3.28 (1.04)|
|Sexual activity||67.97 (28.17) (1 to 100)|
| There have been changes in my sexual activity due to the medication||2.10 (1.19)|
| My sexual activities are less frequent than before the diagnosis of my illness||2.44 (1.33)|
| I am afraid that my heart will explode when having sexual activity||2.30 (1.34)|
Factors Associated with Health-Related Quality of Life in Older Adults With Atrial Fibrillation (N = 205)
|Factor||β||tTest||95% CI||p Value||Adjusted R2||F (p Value)|
| Age||−0.04||−0.44||[−0.74, 0.47]||0.661|
| Gender (male)||0.12||1.29||[−2.11, 10.04]||0.200|
| CCI||−0.05||−0.52||[−3.56, 2.07]||0.604|
| CHA2DS2VASc||−0.02||−0.16||[−2.94, 2.51]||0.875|
| HAS-BLED||0.02||0.24||[−3.73, 4.76]||0.810|
| Age||0.06||0.76||[−0.47, 1.07]||0.447|
| Gender (male)||0.14||1.53||[−1.75, 13.72]||0.128|
| CCI||0.07||0.72||[−2.27, 4.90]||0.471|
| CHA2DS2VASc||−0.01||−0.10||[−3.64, 3.29]||0.921|
| HAS-BLED||0.03||0.39||[−4.34, 6.46]||0.700|
| Age||−0.16||−2.09||[−1.46, −0.04]||0.038|
| Gender (male)||0.23||2.73||[2.71, 16.90]||0.007|
| CCI||−0.06||−0.66||[−4.39, 2.19]||0.509|
| CHA2DS2VASc||−0.03||−0.33||[−3.71, 2.65]||0.743|
| HAS-BLED||−0.01||−0.06||[−5.11, 4.80]||0.952|
|Sexual activitya||0.237||6.03 (<0.001)|
| Age||0.08||1.11||[−0.39, 1.37]||0.270|
| Gender (male)||−0.29||−3.65||[−25.17, −7.52]||<0.001|
| CCI||−0.18||−2.20||[−8.66, −0.48]||0.029|
| CHA2DS2VASc||0.03||0.26||[−3.44, 4.47]||0.798|
| HAS-BLED||0.02||0.33||[−5.12, 7.21]||0.739|
Differences of Health-related Quality of Life depending on Participant Characteristics (N = 205)
|Mean (SD)||t/F (p)||Mean (SD)||t/F (p)||Mean (SD)||t/F (p)||Mean (SD)||t/F (p)|
|Age||65∼74 years||50.57 (18.00)||0.87 (0.387)||47.42 (22.80)||−0.83 (0.408)||47.72 (21.95)||2.98 (0.003)||65.54 (28.16)||−1.25 (0.212)|
|75∼85 years||48.50 (16.15)||49.96 (21.03)||38.99 (19.96)||70.46 (28.10)|
|Gender||Male||51.28 (17.03)||1.64 (0.102)||50.84 (21.22)||1.60 (0.111)||48.94 (20.23)||4.36 (<0.001)||58.55 (27.38)||−5.83 (<0.001)|
|Female||47.35 (17.04)||45.91 (22.62)||36.35 (20.85)||80.00 (24.44)|
|Educational level||No education a||48.32 (16.51)||0.98 (0.402)||50.86 (22.82)||0.79 (0.502)||34.27 (21.99)||4.40 (0.005) c,d>a||79.89 (25.15)||3.25 (0.023) a>d|
|Elementary school b||47.78 (17.36)||46.27 (22.22)||40.91 (20096)||69.66 (28.04)|
|Middle school c||53.19 (16.65)||52.42 (18.65)||49.63 (22.01)||64.46 (27.09)|
|Above high school d||50.86 (17.29)||49.12 (22.96)||48.64 (19.32)||60.85 (28.85)|
|Living with/without spouse||With spouse||49.71 (17.37)||0.23 (0.818)||48.91 (21.96)||0.27 (0.791)||45.62 (21.10)||2.60 (0.010)||62.50 (27.27)||−5.12 (<0.001)|
|Without spouse||49.07 (16.45)||47.97 (22.03)||36.76 (21.09)||84.48 (24.33)|
|Family type||Live alone a||49.01 (15.76)||0.82 (0.440)||47.45 (21.46)||0.79 (0.454)||35.80 (21.59)||2.88 (0.058)||87.86 (21.80)||16.72 (<0.001) a>b,c|
|Live with spouse b||48.58 (18.06)||47.60 (22.32)||44.38 (21.41)||62.04 (27.44)|
|Live with family c||52.21 (15.68)||52.03 (21.38)||46.38 (20.41)||68.14 (27.87)|
|Employment||Yes||50.74 (19.25)||0.43 (0.671)||47.10 (23.24)||−0.44 (0.659)||51.17 (24.35)||2.01 (0.052)||58.07 (27.72)||−2.18 (0.030)|
|No||49.33 (16.73)||48.97 (21.73)||41.98 (20.56)||69.80 (27.95)|
|Duration of AF diagnosis||≤ 1 year||50.63 (18.92)||0.99 (0.401)||50.46 (22.88)||2.43 (0.067)||41.63 (22.77)||0.63 (0.600)||75.00 (25.64)||2.34 (0.075)|
|2 ∼ 5 years||48.30 (16.48)||46.39 (21.78)||41.69 (19.84)||70.36 (28.95)|
|6 ∼ 9 years||48.06 (16.04)||45.46 (20.01)||46.03 (21.53)||59.55 (28.35)|
|≥ 10 years||53.47 (18.29)||56.58 (22.57)||45.56 (23.76)||67.32 (26.52)|
|Charlson comorbidity index||0∼2||49.39 (16.57)||−0.15 (0.883)||46.33 (20.52)||−1.69 (0.093)||44.17 (21.59)||0.55 (0.581)||70.46 (27.37)||1.39 (0.165)|
|≥ 3||49.75 (17.83)||51.50 (23.31)||42.51 (21.24)||64.96 (28.96)|
|Hypertension||No||48.76 (17.99)||0.60 (0.547)||48.89 (21.74)||−0.13 (0.901)||42.31 (21.74)||0.68 (0.500)||65.68 (28.23)||1.06 (0.291)|
|Yes||50.21 (16.40)||48.50 (22.18)||44.34 (21.15)||69.87 (28.10)|
|Diabetes mellitus||No||49.69 (17.67)||−0.18 (0.855)||48.24 (22.83)||0.47 (0.638)||43.83 (22.17)||−0.46 (0.645)||68.67 (28.36)||−0.59 (0.559)|
|Yes||49.19 (15.63)||49.87 (19.40)||42.27 (19.25)||66.06 (27.82)|
|Heart failure||No||51.09 (15.78)||−1.33 (0.185)||49.66 (20.56)||−0.67(0.506)||44.69 (21.22)||−0.89 (0.377)||71.46 (25.64)||−1.838 (0.068)|
|Yes||47.91 (18.37)||47.62 (23.36)||42.05 (21.60)||64.23 (30.33)|
|Stroke||No||49.89 (17.29)||−0.61 (0.546)||48.49 (22.16)||0.26 (0.797)||44.22 (22.01)||−1.14 (0.255)||68.30 (27.95)||−0.36 (0.717)|
|Yes||48.01 (16.39)||49.52 (21.11)||39.78 (18.16)||66.44 (29.49)|
|CHA2DS2VASc score||1.0∼2.0 a||51.02 (16.83)||0.31 (0.737)||48.62 (22.13)||0.17 (0.843)||49.55 (19.83)||4.97 (0.009) a>c||60.54 (26.01)||2.59 (0.077)|
|3.0∼4.0 b||49.44 (17.72)||48.00 (21.28)||43.34 (22.81)||69.08 (28.98)|
|5.0∼6.0 c||48.33 (16.23)||50.22 (23.45)||37.44 (18.12)||72.96 (27.51)|
|HAS-BLED score||0∼2||48.77 (16.82)||−0.84 (0.402)||46.37 (21.03)||−1.95 (0.052)||43.46 (21.67)||0.04 (0.971)||68.55 (28.05)||0.38 (0.701)|
|≥ 3||50.85 (17.61)||52.50 (22.97)||43.34 (21.06)||66.99 (28.53)|