Abstract
-
Purpose
This study aimed to examine the influence of health literacy and autonomy support on health behavior adherence among patients with premature coronary artery disease (PCAD), defined as onset before age 55 years in males and 65 years in females.
-
Methods
A descriptive, cross-sectional design was employed. Data from 153 patients were collected at a hospital in Seoul, South Korea, between January and March 2023. Statistical analyses included the independent t-test, one-way analysis of variance, Pearson correlation coefficients, and hierarchical multiple regression, conducted using IBM SPSS WIN ver. 27.0.
-
Results
Health literacy (β=.36, p<.001) was the strongest determinant of health behavior adherence. A disease duration of more than one year (β=.17, p=.016) was positively associated with adherence, while male sex (β=–.16, p=.039) and the absence of comorbidities (β=–.17, p=.011) showed significant negative associations. Autonomy support from healthcare providers was not significantly associated with health behavior adherence.
-
Conclusion
Healthcare professionals should prioritize improving patient health literacy through tailored communication and educational strategies. Male patients and those newly diagnosed should be recognized as vulnerable groups for low adherence. Targeted interventions should be designed to meet their specific needs. Furthermore, patients with PCAD should be guided to increase their awareness and understanding of their condition.
-
Key Words: Coronary artery disease; Health behavior; Literacy; Personal autonomy
INTRODUCTION
Coronary artery disease (CAD) is the leading cause of death worldwide [
1]. In South Korea, heart disease ranks as the second leading cause of death after malignant neoplasms, with CAD accounting for 45% of all heart disease cases in 2024 [
2]. CAD is a chronic degenerative condition that predominantly affects older adults. However, interest in CAD among younger populations has grown since a U.S. autopsy study revealed that 20% of males and 8% of females in their 30s had progressive coronary lesions, based on 2,876 autopsies of individuals who died from non-cardiac causes such as accidents and suicide [
3].
In South Korea, chronic diseases that are major risk factors for CAD—such as hypertension, hyperlipidemia, and diabetes—have increased by 10.7%, 12.8%, and 63%, respectively, among people in their 30s over the past five years. During the same period, the incidence of myocardial infarction rose by 21% in those in their 20s, 6% in their 30s, and 9% in their 40s [
2]. These findings suggest that CAD is occurring at increasingly younger ages.
According to the guidelines of the American College of Cardiology (ACC) and the American Heart Association (AHA), CAD presenting as atherosclerotic stenosis or occlusion of the coronary arteries in males younger than 55 years and females younger than 65 years is defined as premature coronary artery disease (PCAD) [
4]. Globally, annual PCAD mortality increased by 25% between 1990 and 2019 [
5]. Despite the preventable nature of CAD and widespread awareness of its risk factors, PCAD incidence continues to rise among young and middle-aged adults. To counter this trend, it is necessary to identify factors and barriers contributing to inadequate disease management and prevention in these age groups.
Lifestyle modifications can substantially reduce cardiovascular risk. Individuals who improved their diet and exercise had a 50% lower risk compared to those who did not, while smokers who made no lifestyle changes had more than four times the cardiovascular risk of nonsmokers [
6]. Research on adherence to health behaviors that address CAD risk factors [
7], as well as government initiatives such as the “Primary Care Chronic Disease Management Pilot Program” [
8], has been ongoing. Nevertheless, CAD patients still demonstrate low engagement in aerobic activity, and 44% continue smoking even after coronary interventions [
9]. Early risk factor management is essential to prevent severe cardiovascular disease [
4]. Thus, patients diagnosed with PCAD at a younger age must recognize their risk factors and consistently adhere to health-promoting behaviors.
Among the factors influencing health behavior adherence in CAD patients, health literacy [
10] and autonomy support from healthcare providers [
7] are particularly important for those with PCAD. Health literacy refers to the ability to access, understand, and use health information to improve one’s health [
11]. Its importance is underscored by its inclusion in the National Health Promotion Plan [
12]. High health literacy in cardiovascular patients is associated with greater resilience, better quality of life [
13], and stronger adherence to health behaviors [
14]. In contrast, low health literacy is linked to poor disease management, including underutilization of preventive services, low medication adherence, and higher risks of chronic disease [
15]. For CAD patients, health literacy is especially critical, as lifelong management of risk factors directly affects survival [
14]. Although health literacy typically decreases with age, a recent national survey showed that 70% of adults had suboptimal levels, with a mean age of 44.4 years, indicating that many younger adults are affected [
15]. PCAD is generally diagnosed in people in their 40s and 50s, who may not prioritize health management. Younger adults often neglect their health due to overconfidence and may receive inadequate education about managing chronic conditions [
16]. Consequently, low health literacy can hinder PCAD patients from recognizing disease severity, interpreting complex medical information, and implementing lifestyle changes. Assessing health literacy in PCAD patients and evaluating its impact on health behavior adherence is therefore essential to promote behavioral change and prevent major cardiovascular events.
Autonomy support from healthcare providers also influences health behavior adherence. Grounded in self-determination theory, autonomy support refers to patients’ perception that providers encourage intrinsic motivation by offering opportunities for choice and self-decision [
17]. From this perspective, motivating PCAD patients to modify risk factors and adopt healthier behaviors requires fostering intrinsic motivation. Evidence shows that CAD patients demonstrate stronger adherence to health behaviors when supported by providers [
7], with similar findings in patients with other chronic illnesses [
18]. Increasing patient motivation to engage in health-promoting behaviors is a central role of nurses and other healthcare providers. Thus, it is necessary to assess autonomy support in PCAD patients and determine its influence on their adherence.
This study therefore aimed to investigate the associations between health literacy, autonomy support from healthcare providers, and adherence to health behaviors intended for risk factor modification in individuals with PCAD.
METHODS
1. Study Design
This study employed a descriptive, cross-sectional design.
2. Setting and Samples
Participants were male patients under 55 years of age and female patients under 65 years who were diagnosed with CAD (mild CAD, angina, or myocardial infarction) at a tertiary hospital in Seoul. The inclusion criteria were: (1) male patients aged 19 to 55 years and female patients aged 19 to 65 years according to the guidelines of the ACC and AHA [
4], and (2) a confirmed diagnosis of CAD, including stable angina, unstable angina, variant angina, myocardial infarction, or mild CAD. The exclusion criterion was (1) a diagnosis of psychiatric or cognitive disorders.
Sample size estimation was performed using G*Power 3.1.9.2 software. With 14 predictors (12 characteristics: sex, age, marital status, education, job, income, type of cardiovascular disease, treatment method, duration of illness, presence of comorbidities, family history of CAD, and subjective physical health status; two independent variables: health literacy and autonomy), a medium effect size (f²) of 0.15, a significance level of .05, and power of 0.80, the minimum required sample size was 139. To account for a 20% dropout rate, the target sample size was set at 174. A total of 174 questionnaires were distributed, and 153 were finalized for analysis after excluding 21 with incomplete or inaccurate responses.
3. Instruments
1) Demographic and clinical characteristics
Six demographic characteristics (sex, age, marital status, education, job, and income) and six clinical characteristics (type of cardiovascular disease, treatment method, duration of illness, presence of comorbidities, family history of CAD, and subjective physical health status) were collected through a self-report survey.
2) Health literacy
Health literacy, defined as the ability of cardiovascular patients to understand health information and make informed decisions, was measured using a tool developed by Sim [
19], with permission obtained via email. The instrument consists of 22 items across four subdomains: understanding and seeking health information (4 items), interaction with healthcare providers (4 items), utilization of health information resources (6 items), and active evaluation of health information (8 items). Each item is rated on a 4-point Likert scale ranging from 1 (never) to 4 (always). Four negatively worded items were reverse-coded. Higher scores indicate greater health literacy. The original tool demonstrated a Cronbach’s α of .89 [
19], while reliability in this study was quantified by a Cronbach’s α value of .73.
3) Autonomy support of health providers
Autonomy support of health providers was assessed using the Health Care Climate Questionnaire (HCCQ), developed by Williams et al. [
20] based on self-determination theory. The HCCQ was designed to measure patients’ perceptions of the autonomy-supportive behavior of healthcare professionals. The Korean version was translated and back-translated by Won and Kim [
7] to ensure content validity, and permission for academic use was obtained as stated on the provider’s website. The tool includes 15 items on a unidimensional scale, with responses rated on a 7-point Likert scale from 1 (not at all true) to 7 (very true). One negatively worded item was reverse-coded. Higher scores reflect greater perceived autonomy support from healthcare providers. Reliability was shown by Cronbach’s α values of .89 in the original study [
20] and .91 in this study.
4) Health behavior adherence
Health behavior adherence in patients with PCAD was measured using a tool modified for cardiac patients by Song et al. [
21], based on the Health Promoting Lifestyle Profile developed by Walker et al. [
22], with permission from the author. The tool consists of 21 items across five subdomains: health responsibility (5 items), exercise (4 items), dietary behavior (6 items), stress management (3 items), and smoking cessation (3 items). Each item is rated on a 4-point Likert scale ranging from 1 (never) to 4 (always). Higher scores indicate better adherence to health behaviors. The reliability was shown by Cronbach’s α values of .82 in the original study [
21] and .79 in this study.
Data collection was conducted from January 17 to March 10, 2023, in the cardiology outpatient clinic and wards of a tertiary hospital in the metropolitan area. Convenience sampling and self-administered questionnaires were used. Written informed consent was obtained from all participants who voluntarily agreed to participate. Completing the questionnaire required approximately 15 to 20 minutes. After data collection, the researcher held a question-and-answer session to address misconceptions about health information and provide guidance as needed.
5. Ethical Considerations
This study was approved by the Institutional Review Board of Hanyang University Hospital (IRB No. HYUH 2022-12-014-001). Participants were informed of the study’s purpose and procedures and assured that participation would not affect their usual care or provide additional benefits. Only consented information was used. Participants were informed of their right to voluntary participation and autonomy in decision-making. The researcher also assured anonymity and confidentiality of the collected data.
6. Data Analysis
Data were analyzed using IBM SPSS for Windows version 27.0 (IBM Corp., Armonk, NY, USA). Patients’ demographic and clinical characteristics were analyzed using frequencies and percentages. Differences in autonomy support of health providers, health literacy, and health behavior adherence according to participant characteristics were analyzed using the independent t-test and one-way analysis of variance, with post-hoc testing performed using the Scheffe method. Pearson’s correlation coefficients were calculated to examine the relationships among health literacy, autonomy support of health providers, and health behavior adherence. Hierarchical multiple regression analysis was used to identify predictors of health behavior adherence. In step one, control variables that showed significant differences in adherence were entered. In step two, health literacy and autonomy support were added as independent variables. Nominal control variables were converted into dummy variables. To assess multicollinearity among independent variables, tolerance and variance inflation factors (VIFs) were checked.
RESULTS
1. Patients’ Characteristics and Differences in Health Behavior Adherence
Among the 153 participants, 100 were male (65.4%) and 53 were female (34.6%). The mean age of male participants was 46.7±5.6 years, with the largest proportion in the 50–54 age group (50.0%). The mean age of female participants was 56.1±6.6 years, with the 50–59 age group being the most common (47.2%). Diagnoses included mild CAD (9.8%), angina (47.1%), and myocardial infarction (43.1%). The duration of illness exceeded one year for 87 participants (56.9%). Regarding lifestyle behaviors, 81 participants (52.9%) reported not consuming alcohol, and 105 participants (68.6%) were nonsmokers (
Table 1).
Health behavior adherence showed significant differences by sex (male) (t=–3.62,
p<.001), employment status (yes) (t=–2.29,
p=.023), duration of illness (less than 1 year) (t=–4.37,
p<.001), comorbidity status (no) (t=3.36,
p<.001), smoking (yes) (t=–4.78,
p<.001), and alcohol consumption (yes) (t=–2.58,
p=.011), with lower adherence in these groups compared to their counterparts. According to the post-hoc test, health behavior adherence was significantly lower in the 40–49 age group compared to those aged 50 years and above (F=6.89,
p=.001). Notably, male participants aged 30–39 demonstrated significantly lower adherence compared to those aged 50–54 (F=4.43,
p=.014) (
Table 1).
The overall mean score for health literacy was 3.18±0.31, the mean score for autonomy support of healthcare providers was 5.64±0.83, and the mean score for health behavior adherence was 2.66±0.41 (
Table 2).
Health behavior adherence was positively correlated with health literacy (r=.48,
p<.001) and autonomy support of healthcare providers (r=.26,
p<.001). In addition, health literacy and autonomy support of healthcare providers were positively correlated with each other (r=.34,
p<.001) (
Table 3).
Hierarchical multiple regression analysis was conducted to identify factors influencing health behavior adherence. Four control variables—sex, employment status, duration of illness, and presence of comorbidities—were included. Although age showed significant differences in adherence, these differences may have been influenced by sex-related age limits (males under 55 years, females under 65 years); therefore, age was not included as a control variable. Alcohol consumption and smoking, which also showed differences in adherence, were excluded due to overlap with components of the dependent variable and thus were not considered independent variables. Health literacy and autonomy support of healthcare providers, which were significantly correlated with adherence, were then added as independent variables. Dummy variables were coded as follows: female for sex, unemployed for job, less than one year for duration of illness, and presence of comorbidities. The tolerance values ranged from .71 to .98, exceeding the threshold of .10, and VIFs ranged from 1.01 to 1.39, well below the threshold of 10, indicating no multicollinearity. The Durbin–Watson statistic was 2.038, close to 2, suggesting no autocorrelation of residuals and confirming the assumption of normally distributed errors.
The regression model in Model 1 was statistically significant (F=10.69, p<.001), with the control variables explaining 20.3% of the variance in health behavior adherence. Among the control variables, duration of illness ≥1 year was a significant positive factor, while male sex and absence of comorbidities were significant negative factors. Model 2, which added health literacy and autonomy support, was also statistically significant (F=14.31, p<.001). The inclusion of these variables increased the explanatory power by 14.6%, yielding a total explained variance of 34.5%.
The final regression model identified health literacy (β=.36; 95% confidence interval [CI]=0.29–0.67), duration of illness ≥1 year (β=.17; 95% CI=0.03–0.25), absence of comorbidities (β=–.17; 95% CI=–0.27 to –0.04), and male sex (β=–.16; 95% CI=–0.27 to –0.01) as significant predictors of health behavior adherence. Among these, health literacy was the most influential factor. In summary, higher health literacy and longer disease duration were associated with better adherence, whereas the absence of comorbidities and male sex were associated with lower adherence. Autonomy support of healthcare providers did not have a significant effect (
Table 4).
DISCUSSION
This study aimed to assess the level of health behavior adherence among patients with PCAD and to analyze the impact of health literacy and autonomy support from healthcare providers on adherence. By providing foundational data, the study sought to identify the current status of health behavior adherence in this patient population.
In this study, health behavior adherence among patients with PCAD differed significantly by sex, employment status, disease duration, and presence of comorbidities. Among these, sex, disease duration, and comorbidities were identified as significant influencing factors. Male participants (mean age 46.7 years) had significantly lower adherence scores (mean 2.58) compared to female participants (mean age 56.1 years; mean 2.82). This result contrasts with previous research, which reported that adherence scores of female CAD patients (mean 2.95) were similar to those of male patients (mean 2.98) [
23]. The average age in that study [
23] was 65.1 years, and the overall adherence score was 2.97. Another study [
24] on older rural male CAD patients (≥65 years) reported higher adherence scores of 2.92 (raw data 72.99) compared with the current findings. However, a study of middle-aged males with CAD (mean age 57.3 years, range 40–64) reported an adherence score of 2.65 (raw data 72.99) [
25], which is comparable to the results for males in the present study. All of these studies employed the same instrument, which included five subdomains, developed by the same author. In particular, adherence among males in their 30s was significantly lower than that of males aged 50 years and above in this study. These findings suggest that adherence among male patients shows age-related differences. Mortality among male PCAD patients has also been reported to be higher than that of females [
5]. This study further identified male sex as a negative predictor of adherence. Prior research has shown that, among adult males, obesity, lack of physical activity, and alcohol consumption are major predictors of cardiovascular risk in the 30–44 age group [
26]. Thus, younger male patients with PCAD in their 30s should be considered a relatively vulnerable group for health behavior adherence. However, as the number of male participants in the 30–39 age group was small in this study, further research with a larger sample size is warranted.
Employed patients with PCAD had significantly lower adherence scores (mean 2.63) compared with unemployed patients (mean 2.85). This finding is consistent with previous research, which suggested that patients with CAD in their 40s and 50s often neglect adherence to health behaviors due to work responsibilities [
27]. In that study [
27], employed patients scored lower (mean 2.82; raw data 70.43) than unemployed patients (mean 2.93; raw data 73.29). Although job status was not identified as a direct predictor in the regression analysis, the lower adherence among employed patients highlights the need for tailored nursing interventions that consider occupational challenges. Examples include workplace-based health programs, flexible scheduling for cardiac rehabilitation, and mobile health tools for self-monitoring.
This study also found that patients with a disease duration of more than one year demonstrated significantly higher adherence compared to those diagnosed within the past year. Disease duration of one year or more was a positive predictor of adherence. These findings are consistent with Ha and Seo [
28], who reported that CAD patients who had undergone coronary interventions more than a year earlier scored higher in adherence. Longer disease duration likely improves self-management capabilities through continuous education, counseling, and increased disease awareness [
28]. Previous studies have also demonstrated that adherence to lifestyle recommendations regarding smoking cessation, diet, and exercise within 30 days of an acute coronary syndrome significantly reduces mortality [
6]. Early identification and modification of risk factors are therefore crucial for CAD management [
4]. Assessing health behavior patterns immediately after diagnosis and providing targeted education to improve awareness are essential. In particular, regular follow-up during outpatient visits within the first year of diagnosis may help patients adopt and maintain healthier behaviors.
In addition, this study found that patients with PCAD who had comorbidities exhibited significantly higher adherence scores, while the absence of comorbidities emerged as a negative predictor. This result aligns with the findings of Han and Kim [
29], who reported that myocardial infarction patients with comorbid conditions such as hypertension and diabetes demonstrated higher adherence. In this study, 32% of participants reported no comorbidities. Although the absence of additional diseases may seem beneficial, it may lead patients to underestimate the seriousness of their CAD and overestimate their health status, which could reduce adherence to health behaviors [
16]. Therefore, educating PCAD patients without comorbidities about the importance of adherence is critical to slowing disease progression and preventing adverse cardiac events. Targeted education that emphasizes both the risks of neglecting health behaviors and the benefits of consistent adherence may help these patients manage their condition more effectively.
Health literacy emerged as the most significant factor influencing health behavior adherence in this study. Previous research supports this finding, demonstrating that health literacy affects adherence in elderly CAD patients [
14] and also impacts health behaviors in middle-aged CAD patients [
30]. This study reinforces the importance of health literacy not only for older adults, who may struggle to assimilate new health information, but also for younger patients with PCAD. To improve the health literacy of PCAD patients, efforts are required from both healthcare providers and patients. First, healthcare professionals must recognize the importance of clear, effective communication in enhancing patient health literacy [
31]. Nurses, in particular, should understand the positive influence of health literacy on adherence and, when necessary, undergo training to strengthen their communication skills. Second, patient education must be tailored to individual literacy levels. Effective approaches include preemptively identifying patients with low health literacy, applying guidelines that emphasize plain and simple language in healthcare settings, and incorporating these into routine clinical practice. Such strategies have been shown to improve both health behaviors and health outcomes [
15]. For PCAD patients, structured education systems should highlight the benefits of adherence and the risks of neglect, using accessible language and beginning early in the disease trajectory. This approach can better equip patients to manage their condition effectively.
However, autonomy support from healthcare providers did not significantly influence health behavior adherence in this study. This finding contrasts with Park [
32], who identified autonomy support as a determinant of adherence in CAD patients. Several explanations are possible. First, the mean level of autonomy support in this study was higher and less variable compared to previous studies [
32], while adherence scores were notably lower than those reported in earlier research [
23,
24]. Although autonomy support and adherence were positively correlated, the relationship was weak, which may explain why autonomy support did not emerge as a significant predictor in regression analysis. Second, the relatively low adherence among PCAD patients may reflect a diminished perception of disease severity or intentional noncompliance with health behaviors [
33]. To strengthen adherence among PCAD patients, healthcare professionals should prioritize raising disease awareness. For example, connecting patients with specialized professionals, such as those in smoking cessation programs, may help address specific risk factors. Moreover, as prior studies suggest that autonomy support influences health behavior adherence [
7] and represents a key psychological component of self-determination theory [
17], further research—particularly longitudinal studies or intervention trials—is needed to clarify the long-term effects of autonomy support in this population.
This study has several limitations. First, it was conducted as a descriptive survey using a convenience sample from a single tertiary hospital, which limits the generalizability of the findings to all patients with PCAD. Additional research across diverse clinical settings is warranted. Second, the reliance on self-reported questionnaires may have introduced response bias, as participants could have exaggerated or minimized their answers, particularly for sensitive questions. Third, the cross-sectional design prevents causal inferences between predictors (e.g., health literacy) and health behavior adherence. Although the results suggest potential influencing factors, longitudinal or experimental studies are necessary to establish causal relationships.
CONCLUSION
The level of health behavior adherence among PCAD patients in this study was lower than that of general CAD patients. Specifically, male patients, those diagnosed within the past year, and patients without comorbidities demonstrated lower adherence, indicating that they should be classified as vulnerable groups requiring targeted interventions. Health literacy was the most significant factor influencing adherence. Therefore, healthcare professionals, particularly nurses, should provide CAD-related education tailored to the literacy levels of PCAD patients, beginning during the initial hospitalization and continuing through outpatient follow-up. Although autonomy support from healthcare providers was not significantly associated with adherence in this study, strategies to enhance patients’ disease awareness and emphasize the importance of managing modifiable risk factors are essential. By focusing on improving health literacy and addressing the needs of vulnerable subgroups, healthcare interventions can better promote adherence to health-promoting behaviors in patients with PCAD.
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CONFLICTS OF INTEREST
The authors declared no conflict of interest.
-
AUTHORSHIP
Study conception and design - SRC and YY; data collection - SRC; analysis and interpretation of the data - SRC and YY; drafting and critical revision of the manuscript - SRC and YY.
-
FUNDING
None.
-
ACKNOWLEDGEMENT
This article is a revision of the Seong Rae Cho's master's thesis from Hanyang University.
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DATA AVAILABILITY STATEMENT
The data can be obtained from the first author.
Table 1.Differences in Health Behavior Adherence According to Participants’ Characteristics (N=153)
|
Variables |
Categories |
n (%) or M±SD |
Health behavior adherence |
|
M±SD |
t or F |
p (Scheffe) |
|
Sex |
Male |
100 (65.4) |
2.58±0.38 |
–3.62 |
<.001 |
|
Female |
53 (34.6) |
2.82±.042 |
|
|
|
Age |
30–39a
|
16 (10.5) |
2.60±0.50 |
6.89 |
.001 |
|
40–49b
|
53 (34.6) |
2.52±0.38 |
|
(b<c) |
|
≥50c
|
84 (54.9) |
2.77±0.38 |
|
|
|
Age of males (n=100) |
30–39a
|
14 (14.0) |
2.43±0.47 |
4.43 |
.014 |
|
40–49b
|
46 (46.0) |
2.51±0.34 |
|
(a<c) |
|
50–54c
|
50 (50.0) |
2.71±0.35 |
|
|
|
46.7±5.6 |
|
|
|
|
Age of females (n=53) |
30-49 |
9 (17.0) |
2.82±0.55 |
0.00 |
.999 |
|
50-59 |
25 (47.2) |
2.82±0.40 |
|
|
|
60-64 |
19 (35.8) |
2.83±0.39 |
|
|
|
56.1±6.6 |
|
|
|
|
Marital status |
Unmarried |
31 (20.3) |
2.60±0.41 |
–1.02 |
.308 |
|
Married |
117 (76.5) |
2.68±0.40 |
|
|
|
Other |
5 (3.3) |
- |
|
|
|
Education |
Under middle school |
14 (9.2) |
2.72±0.46 |
0.35 |
.702 |
|
High school |
60 (39.2) |
2.63±0.41 |
|
|
|
Above college |
79 (51.6) |
2.68±0.39 |
|
|
|
Job |
Yes |
131 (85.6) |
2.63±0.40 |
–2.29 |
.023 |
|
No |
22 (14.4) |
2.85±0.37 |
|
|
|
Income (10,000 won) |
Less than 100 |
24 (15.7) |
2.84±0.38 |
1.75 |
.140 |
|
100–200 |
18 (11.8) |
2.72±0.31 |
|
|
|
201–300 |
25 (16.3) |
2.63±0.41 |
|
|
|
301–400 |
31 (20.3) |
2.56±0.42 |
|
|
|
More than 400 |
55 (35.9) |
2.64±0.42 |
|
|
|
Type of cardiovascular disease |
Mild coronary artery disease |
15 (9.8) |
2.67±0.44 |
0.29 |
.747 |
|
Angina pectoris |
72 (47.1) |
2.68±0.37 |
|
|
|
Myocardial infarction |
66 (43.1) |
2.59±0.37 |
|
|
|
Treatment method |
Only medication |
67 (43.8) |
2.63±0.41 |
–0.80 |
.423 |
|
Intervention and medication |
86 (56.2) |
2.69±0.40 |
|
|
|
Duration of illness |
<1 year |
66 (43.1) |
2.51±0.37 |
–4.37 |
<.001 |
|
≥1 year |
87 (56.9) |
2.78±0.39 |
|
|
|
Comorbidities |
Yes†
|
104 (68.0) |
2.74±0.39 |
3.36 |
<.001 |
|
Diabetes mellitus |
41 (39.4) |
|
|
|
|
Hypertension |
73 (70.1) |
|
|
|
|
Dyslipidemia |
65 (62.5) |
|
|
|
|
Cerebrovascular disease |
7 (6.7) |
|
|
|
|
Kidney disease |
7 (6.7) |
|
|
|
|
Peripheral vascular disease |
3 (2.8) |
|
|
|
|
Congestive heart failure |
3 (2.8) |
|
|
|
|
No |
49 (32.0) |
|
|
|
|
2.51±0.40 |
|
|
|
|
Family history of cardiovascular disease |
Yes |
58 (37.9) |
2.61±0.36 |
–1.32 |
.186 |
|
No |
95 (62.1) |
2.70±0.43 |
|
Body mass index (kg/m2) |
<25 |
71 (46.4) |
2.71±0.42 |
1.36 |
.176 |
|
≥25 |
82 (53.6) |
2.62±0.39 |
|
Alcohol consumption |
Yes |
72 (47.1) |
2.58±0.40 |
–2.58 |
.011 |
|
No |
81 (52.9) |
2.74±0.39 |
|
Smoking |
Yes |
48 (31.4) |
2.44±0.39 |
–4.78 |
<.001 |
|
No |
105 (68.6) |
2.76±0.37 |
|
Subjective physical health status |
Good or better |
24 (15.7) |
2.76±0.39 |
1.18 |
.309 |
|
Moderate |
96 (62.7) |
2.67±0.40 |
|
Bad or worse |
33 (21.6) |
2.59±0.43 |
Table 2.Level of Health Literacy, Autonomy Support of Health Providers, and Health Behavior Adherence (N=153)
|
Variables |
Possible range |
M±SD |
|
Health literacy |
1–4 |
3.18±0.31 |
|
Autonomy support of health providers |
1–7 |
5.64±0.83 |
|
Health behavior adherence |
1–4 |
2.66±0.41 |
Table 3.Correlations among the Study Variables (N=153)
|
Variables |
Health behavior adherence |
Health literacy |
|
r (p) |
|
Health literacy |
.48 (<.001) |
- |
|
Autonomy support of health providers |
.26 (<.001) |
.34 (<.001) |
Table 4.Factors Influencing Health Behavior Adherence among Patients with Premature Coronary Artery Disease (N=153)
|
Variables |
Model 1 |
Model 2 |
|
B |
β |
t |
p
|
95% CI |
B |
β |
t |
p
|
95% CI |
|
(Constant) |
2.73 |
|
32.06 |
<.001 |
|
0.97 |
|
3.14 |
.002 |
|
|
Sex (male) |
–0.17 |
–.19 |
–2.34 |
.021 |
–0.32 to 0.02 |
–0.14 |
–.16 |
–2.08 |
.039 |
–0.27 to –0.01 |
|
Job (yes) |
–0.05 |
–.04 |
–0.50 |
.614 |
–0.24 to 0.14 |
–0.07 |
–.06 |
–0.87 |
.383 |
–0.23 to 0.09 |
|
Duration of illness (≥1 year) |
0.26 |
.31 |
4.32 |
<.001 |
0.14 to 0.38 |
0.14 |
.17 |
2.42 |
.016 |
0.03 to 0.25 |
|
Comorbidities (no) |
–0.17 |
–.19 |
–2.60 |
.010 |
–0.30 to –0.04 |
–0.15 |
–.17 |
–2.57 |
.011 |
–0.27 to –0.04 |
|
Health literacy |
|
|
|
|
|
0.48 |
.36 |
4.93 |
<.001 |
0.29 to 0.67 |
|
Autonomy support of health provider |
|
|
|
|
|
0.04 |
.09 |
1.38 |
.168 |
–0.02 to 0.10 |
|
R2
|
.224 |
.370 |
|
Adjusted R2
|
.203 |
.345 |
|
⊿ R2
|
|
.146 |
|
F (p) |
10.69 (<.001) |
14.31 (<.001) |
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