
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39029038
MD-D-24-05282
00040
10.1097/MD.0000000000038994
3
7400
Research Article
Observational Study
Health literacy as a predictor of cardiovascular disease risk factor knowledge level among women in Turkey: A community-based cross-sectional study
Yardimci Gürel Tuğba PhD yardimci.tugba@gmail.com
a
https://orcid.org/0000-0002-8302-9073
Güner Özlem PhD b*
a Department of Nursing, Sinop University Faculty of Health Sciences, Sinop, Turkey
b Department of Midwifery, Sinop University Faculty of Health Sciences, Sinop, Turkey.
* Correspondence: Özlem Güner, Department of Midwifery, Sinop University Faculty of Health Sciences, 57000, Sinop, Turkey (e-mail: ozcerezciozlem@gmail.com).
19 7 2024
19 7 2024
103 29 e3899413 5 2024
27 6 2024
28 6 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

While more common in men globally, heart diseases also rank as the leading cause of death among women. This study aimed to examine the relationship between Turkish women’s level of knowledge about cardiovascular disease (CVD) risk factors and their health literacy. Data for this descriptive and cross-sectional study were collected online by using Health Literacy Scale and CVD risk factor knowledge level scale from October 2022, to May 2023. The study sample consisted of 409 women. It was found that the total score average of the women on the CVD risk factor knowledge level was 20.65 ± 4.72 and the Health Literacy Scale was 107.06 ± 16.01. There was a moderate, significantly positive correlation between CVD knowledge levels and health literacy (r = .548, P = .000). It was found that women with high health literacy also had increased knowledge levels. Furthermore, all health literacy dimensions of access to information (P < .001), understanding information (P < .001), appraisal/evaluation (P < .001), and implementation (P < .001) were detected as the predictors of CVD risk factor knowledge levels. Factors such as educational level and economic status significantly influenced scores on both scales (P < .05). The study’s findings highlight the challenges encountered by women with low socioeconomic status in accessing accurate information. It may be recommended that nurses provide health education and consultancy services to these women on the prevention and management of cardiovascular diseases. Also, public education programs should consider socioeconomic and educational levels, focusing on women who encounter difficulties accessing information.

cardiovascular disease
health literacy
knowledge
risk factors
women
OPEN-ACCESSTRUE
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pmc1. Introduction

Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for an estimated 17.9 million deaths each year.[1] Similarly, in Turkey, CVDs are the most common cause of death in the adult population, accounting for 35.4% of all deaths.[2] Although more common in men globally, heart diseases are the leading cause of death among women as well. In 2018, approximately 400,000 deaths in women were attributable to heart diseases and stroke, accounting for 28% of all deaths. However, despite the significant impact of CVDs on women’s mortality rates, awareness and knowledge levels regarding CVDs are poor.[3] CVDs are associated with risk factors such as smoking, harmful alcohol use, salt intake, obesity, hypertension, diabetes, and physical inactivity. Modifying these risk factors (lifestyle habits) can lead to improved mortality rates, hospital readmission rates, and quality of life.[4] Although data indicate that CVD awareness levels among women, especially in developed countries, have increased in recent years owing to health education efforts, it is believed that this issue persists in developing countries, including Turkey.[5] Hence, it is crucial for women to possess a high level of knowledge about the risk factors of CVDs and be aware of the problem. One of the factors affecting people’s awareness levels is health literacy. Health literacy is defined as cognitive and social skills that determine an individual’s motivation and ability to access, understand, and use information in a way that develops and maintains knowledge.[6] It plays a pivotal and decisive role in the delivery and outcomes of health services. Low health literacy is an invisible barrier to health service delivery, and can incurs significant costs for both the public and healthcare sector. Additionally, health literacy and limited knowledge about health conditions and medicines have been associated with poorer overall health status, higher health costs, increased hospital readmission rates, and increased mortality rates.[7,8] The American Heart Association has recognized health literacy as fundamental to achieving its impact goals for improving the cardiovascular health of the population.[8] Previous studies have demonstrated that the rate of low health literacy is 39% in patients with heart failure and 30.5% in patients with coronary artery disease.[9,10] Thus, increasing the level of health literacy is crucial for management, prevention, and protection of cardiovascular health. Promotion and maintenance of good health by educating people about healthy lifestyle habits and increasing their knowledge is mainly done by nurses. By educating individuals regarding risk factors and promoting healthy habits, nurses can reduce the risk of workforce loss and death rates. To mitigate the negative effects of health issues in individuals, it is important to identify risk factors, raise awareness through education, and develop healthy habits. Thus, it is vital to provide health education and consultancy services to society, specifically to women who are a vulnerable group, regarding the risk factors, prevention, and management of cardiovascular diseases. To guide healthcare professionals, this study aims to examine the relationship between women’s level of knowledge about CVD risk factors and their health literacy.

2. Materials and methods

2.1. Study design and participants

Data for this descriptive, cross-sectional study were collected online using Google forms from October 15, 2022, to May 15, 2023. The study population comprised all women residing in Turkey. To participate, individuals had to meet the following criteria. The participant must be a woman, at least 18 years old, a Turkish resident of Turkish descent, literate, and willing to take part in the study. Men, women under 18 years of age, nonresidents of Turkey, individuals of different ethnicities, illiterate individuals, or those unwilling to participate were excluded from the study.

The sample size was calculated by using OpenEpi software, considering a 95% confidence interval and a 5% margin of error. The sample size formula n = [DEFF × Np(1 − p)]/ [(d2/Z21−α/2 × (N − 1) + p × (1 − p)] was used to calculate the number of participants required for this study. The minimum sample size (n) was determined to be 385. We distributed the survey to 409 women. Due to the Google form features, incomplete and duplicate submissions of the survey questionnaire were not possible. As the total number of study participants exceeded the minimum required number of 385, the required sample size for the study was met.

2.2. Data collection procedure

The survey shared using Google forms aimed to reach women over 18 years of age. A secure link was shared with the participants to join the survey. The researchers shared the survey link with their friends across social media platforms, such as Facebook and Gmail. Participation was completely voluntary, with the participants having the liberty to retract their responses at any time. No personal information was collected, and all data was securely handled during data collection and analysis. Access to the survey data was restricted to team members only. Once all responses were gathered, the data was securely stored on a team member’s laptop, and the survey link was deactivated.

2.3. Data collection tools

Sociodemographic characteristics form: This form was developed to collect descriptive information about the women. The form comprised a total of 16 items and included questions regarding age, body mass index (BMI), marital status, place of residence, educational level, income level, occupation, chronic disease status, family history of heart disease, salt consumption, type of fat used in meals, cooking method, consumption of vegetables and fruits every day, smoking, and alcohol consumption.

Cardiovascular Diseases Risk Factors Knowledge Level (CARRF-KL) Scale: This scale was developed by Arikan et al (2009) to measure the level of knowledge regarding CVD risk factors.[11] It comprises 28 items overall. The first 4 items in the scale inquire about the characteristics of CVDs, 15 items (5, 6, 9–12, 14, 18–20, 23–25, 27, and 28) inquire about risk factors, and 9 items (7, 8, 13, 15, 16, 17, 21, 22, and 26) inquire about the outcome of change in risk behaviors. The scale items are in the form of statements and are expected to be answered as “Yes,” “No,” or “I don’t know,” based on whether they are true or false. Each “correct answer” is worth one point, whereas a “wrong answer” or “don’t know” is worth zero points. Items 11, 12, 16, 17, 24, and 26 are reverse scored. A minimum score of zero and a maximum score of 28 can be obtained from the scale. The scale has no cutoff point. A higher score indicates a higher knowledge level. The Cronbach alpha (α) value of the scale is 0.76. In the present study, the Cronbach alpha (α) value of the scale was found to be 0.84.

Health Literacy Scale (HLS): The HLS developed by Toçi et al (2013) and adapted into Turkish by Bayik Temel and Aras (2017) was used to measure the health literacy.[12,13] The scale consists of 25 items and 4 subscales. The scale is a 5-point Likert-type scale, and participants were asked to rate their level of skill in accessing, understanding, evaluating, and applying health information on a scale ranging from 1 (I cannot) to 5 (I can do it without difficulty). A minimum of 25 and a maximum of 125 points can be obtained from the scale. All items of the scale are positive and no reverse-scoring is done for the items. A higher score on the scale indicates a higher level of health literacy. The Cronbach alpha reliability coefficient value of the scale is 0.92. In the present study, the Cronbach alpha value of the scale was found to be 0.95.

2.4. Statistical analysis

The study data were analyzed using SPSS version 25.0 program. Parametric tests were used for statistical analysis as the data met the assumptions for such tests. Descriptive statistics were used to determine sociodemographic characteristics of the participants, and the relationship between variables was examined using the Pearson correlation coefficient. Stepwise regression analysis was performed to determine the variables predicting the level of knowledge of CVD risk factors in women. The statistical significance level was set at P value of < .05.

2.5. Ethical approval

This study was performed in accordance with the Declaration of Helsinki and its later amendments. Ethical approval was obtained by the ethics committee of Sinop University Human Research Ethics Committee (March 24, 2022; decision number 2022/29). Informed consent was obtained online by explaining the purpose and scope of the research in the introduction section of the online form. Participants read the information and agreed to participate, after which access to the forms was provided.

3. Results

3.1. Sociodemographic characteristics

This study included 409 women. Mean age of the participants was 33.14 ± 11.12 years, and the mean BMI was 23.80 ± 4.81. It was determined that 57% of the participants had a normal BMI, 53.3% were married, 68.5% lived in the province, 77% had undergraduate and graduate degrees, and 37.9% were civil servants. Additionally, 80.7% of the participants had no chronic disease, 78.5% did not exercise, and 54% consumed vegetables and fruits every day (Table 1).

Table 1 Comparison of Participants’ Sociodemographic Characteristics and CARRF-KL and HLS Scores (n = 409).

Variables	n	%	CARRF-KL	HLS	
X ± SD	t/F	P	X ± SD	t/F	P	
Age		33.14 ± 11.12		
BMI		23.80 ± 4.81		
Under weight	29	7.1	20.86 ± 4.65	3.800	.010	104.10 ± 16.99	0.740	.529	
Normal weight	233	57.0	20.00 ± 5.30	107.15 ± 15.85	
Over weight	105	25.7	21.75 ± 3.35	108.39 ± 14.68	
Obezity	42	10.3	21.38 ± 3.70	105.30 ± 19.24	
Marital status	
Married	218	53.3	20.74 ± 4.40	0.433	.665	108.46 ± 15.24	1.894	.59	
Single	191	46.7	20.54 ± 5.08	105.47 ± 16.74	
Place of residence	
Province	280	68.5	20.57 ± 5.05	0.379	.685	107.77 ± 15.76	5.191	.006	
District	107	26.2	20.96 ± 3.97	107.38 ± 15.42	
Town–Village	22	5.4	20.18 ± 3.80	96.50+±8.89	
Educational status	
Literate	9	2.2	19.88 ± 3.40	3.984	.008	90.44 ± 19.33	8.263	.000	
Primary school	33	8.1	20.18 + 4.56	102.81 ± 15.89	
High school	52	12.7	18.69 ± 5.29	101.15 ± 18.68	
Undergraduate and graduate	315	77.0	21.04 + 4.61	108.96 ± 14.90
	
Occupation	
Housewife	70	17.1	19.35 ± 4.61	5.636	.000	102.21 ± 16.55	9.387	.000	
Employee	34	8.3	19.88 ± 4.05	103.41 ± 18.22	
Officer	155	37.9	22.11 ± 3.75	113.04 ± 12.14	
Small business	7	1.7	21.14 ± 2.79	116.0 ± 12.02	
Retired	12	2.9	21.5 ± 3.82	111.16 ± 13.81	
Not working	131	32.0	19.7 ± 5.63	102.68 ± 17.13	
Income status	
Income is less than expenses	103	25.2	21.49 ± 3.24	5.970	.003	107.47 ± 13.51	4.217	.015	
Income equals expenses	224	54.8	19.92 ± 5.49	105.34 ± 17.67	
Income is more than expenses	82	20.0	21.59 ± 3.62	111.26 ± 13.26

	
Tobacco use	
Smoker	106	25.9	19.34 ± 16.99	5.598	.004	103.33 ± 16.99	4.288	.014	
Non smoker	292	71.4	21.12 ± 15.44	108.52 ± 15.44	
Ex-smoker	11	2.7	20.72 ± 16.53	104.45 ± 16.53	
Alcohol use	
Yes	74	18.1	19.82 ± 4.72	-1.669	.096	102.75 ± 16.30	-2.577	.010	
No	335	81.9	20.83 ± 4.71	108.02 ± 15.81	
Chronic Disease Status	
Yes	79	19.3	21.70 ± 3.17	2.220	.027	108.94 ± 14.73	1.163	.246	
No	330	80.7	20.40 ± 5.00	106.61 ± 16.29	
Presence of Heart Disease in the Family	
Yes	197	48.2	20.61 ± 4.99	-0.138	.890	107.94 ± 15.94	1.072	.284	
No	212	51.8	20.68 ± 4.48	106.25 ± 16.07	
Exercise	
Yes	88	21.5	19.55 ± 5.35	‐2.469	.014	107.76 ± 16.68	0.458	.647	
No	321	78.5	20.95 ± 4.50	106.87 ± 15.84	
Salt intake	
Unsalted	12	2.9	22.50 ± 2.87	1.606	.202	107.16 ± 17.04	5.621	.004	
Low salt	277	67.7	20.77 ± 4.64	108.81 ± 15.45	
Salty	120	29.3	20.18 ± 5.02	103.01 ± 16.01	
Type of Oil Used	
Olive oil	151	36.9	21.43 ± 3.79	3.275	.039	111.21 ± 14.04	8.337	.000	
Vegetable oil	193	47.2	20.18 ± 5.16	104.79 ± 15.91	
Solid fat	63	15.4	20.23 ± 5.14	104.18 ± 18.66	
Cooking method	
Baking	66	16.1	21.15 ± 4.64	0.918	.454	108.06 ± 16.94	3.611	.007	
Boiled	21	5.1	21.28 ± 3.78	108.09 ± 12.97	
Deep frying	49	12.0	21.32 ± 3.24	99.04 ± 16.47	
Grill	9	2.2	19.22 ± 5.28	109.77 ± 16.12	
Pot meal	264	64.5	20.40 ± 5.01	108.13 ± 15.58	
Consuming fruits and vegetables every day	
Yes	221	54.0	19.78 ± 5.55	8.829	.000	107.53 ± 16.90	0.235	.791	
No	57	13.9	21.22 ± 3.58	106.07 ± 14.35	
Generally	131	32.0	21.87 ± 3.09	106.71 ± 15.22	
CARRF-KL = cardiovascular disease risk factor knowledge level, HLS: Health Literacy Scale, BMI = body mass index.

Bold means P < .05

3.2. Mean CARRF-KL and HLS scores of the participants according to their sociodemographic characteristics

Examination of the mean CARRF-KL and HLS scores of the participants according to their sociodemographic characteristics revealed a significant difference between BMI (P = .010), educational level (P = .008), occupation (P = .000), income status (P = .003), smoking (P = .004), chronic disease status (P = .027), exercise (P = .014), consuming vegetables and fruits every day (P = .000), and the mean CARRF-KL scores of the participants. Participants who were overweight according to their BMI, had undergraduate and graduate degrees, were civil servants, had a surplus of income over expenses, were nonsmokers, had chronic diseases, did not exercise, and generally consumed vegetables and fruits every day were found to have higher mean CARRF-KL scores. A significant difference was observed between the place of residence (P = .006), educational level (P = .000), occupation (P = .000), income status (P = .015), smoking (P = .014), alcohol consumption (P = .010), salt consumption (P = .004), type of oil used (P = .000), and cooking method (P = .007) of the participants and the mean HLS score. It was found that the mean HLS score was higher in those who lived in the province, had undergraduate and graduate degrees, had a surplus of income over expenses, did not smoke or consume alcohol, consumed a low-salt diet, used olive oil for cooking, and cooked their meals using the grilling method (Table 1).

3.3. Mean scores of the scales

Mean scores of the scales used in the study are presented in Table 2. The mean total CARRF-KL score was 20.65 ± 4.72. When the mean subscale scores were examined, the mean score was 2.21 ± 1.00 in the characteristics of CVD subscale, 12.35 ± 2.90 in the risk factors of CVD subscale, and 6.08 ± 1.53 in the protection from CVD subscale. The mean total HLS score was 107.06 ± 16.01. When examining the mean subscale scores, the mean score was 21.64 ± 3.71 in the access to information subscale, 30.32 ± 4.79 in the understanding information subscale, 34.52 ± 5.56 in the appraisal/evaluation subscale, and 20.57 ± 3.83 in the implementation subscale (Table 2).

Table 2 Distribution of participants’ CARRF-KL and HLS mean scores (n = 409).

Scales/subdimensions	Min–Max	X ± SD	
CARRF-KL			
CVDs manifestations	0–4	2.21 ± 1.00	
CVDs risk factors	2–15	12.35 ± 2.90	
Prevention from CVD	1–9	6.08 ± 1.53	
Total CARRF-KL scale score	5–27	20.65 ± 4.72	
Health Literacy Scale			
Access to Information	11–25	21.64 ± 3.71	
Understanding Information	16–35	30.32 ± 4.79	
Appraisal/Evaluation	19–40	34.52 ± 5.56	
Implementation	10–25	20.57 ± 3.83	
HLS Total Score	62–125	107.06 ± 16.01	
CARRF-KL = cardiovascular disease risk factor knowledge level, HLS: Health Literacy Scale, BMI = body mass index.

3.4. Relationship between CARRF-KL and HLS

Relationship between CARRF-KL and HLS is presented in Table 3. Correlation analysis revealed a moderate, significantly positive correlation between the total score and subscale scores of HLS and total score and subscale scores of CARRF-KL (r = .548, P = .000). The higher the participants’ scores on the HLS is, the higher is their level of knowledge regarding CVD risk factors (Table 3).

Table 3 The relationship between CARRF-KL and HLS.

	CARRF-KL	
CVDs manifestations	CVDs risk factors	Prevention from CVD	CARRF-KL total score	
Health Literacy Scale	Access to information	r = .373**	r = .499**	r = .313**	r = .488**	
P = .000	P = .000	P = .000	P = .000	
Understanding information	r = .393**	r = .557**	r = .320**	r = .530**	
P = .000	P = .000	P = .000	P = .000	
Appraisal/evaluation	r = .381**	r = .525**	r = .342**	r = .515**	
P = .000	P = .000	P = .000	P = .000	
Implementation	r = .325**	r = .413**	r = .258**	r = .406**	
P = .000	P = .000	P = .000	P = .000	
HLS total score	r = .415**	r = .564**	r = .349**	r = .548**	
P = .000	P = .000	P = .000	P = .000	
CARRF-KL = cardiovascular disease risk factor knowledge level, HLS: Health Literacy Scale, BMI = body mass index.

** P < .01, r: Pearson correlation analysis.

3.5. Predicting factors of the CARRF-KL

Stepwise regression analysis was performed to determine the predictive power of the HLS subscales among the variables predicting knowledge levels of CVD risk factors (Table 4). As depicted in Table 4, in the first step of the stepwise regression analysis, the access to information subscale accounted for 23.8% of the participants’ knowledge level of CVD risk factors. In the second step, the information comprehension subscale was added to the access to information subscale. The access to information and information comprehension subscales collectively accounted for 29.4% of the knowledge level of CVD risk factors. In the third step, the appraisal/evaluation subscale was added to the model. Moreover, access to information, information comprehension, and appraisal/evaluation subscales collectively accounted for 30.9% of the knowledge level of CVD risk factors. Finally, in the fourth step, the implementation subscale was added to the model. The access to information, information comprehension, appraisal/evaluation, and implementation subscales collectively accounted for 30.9% of the knowledge levels of CVD risk factors (P < .001).

Table 4 Stepwise Regression Analysis Results for the Prediction of CARRF-KL.

Variables	B	S.H	β	t	P	VIF	
Stage 1							
Constant	7.196	1.211		5.942	.000		
Access to information	0.622	0.055	0.488	11.273	.000	1.000	
R = 0.488    R2 = 0.238    F =  127.090    P<.001	
Stage 2							
Constant	4.031	1.292		3.120	.002		
Access to information	0.240	0.086	0.188	2.801	.005	2.593	
Understanding information	0.377	0.066	0.382	5.698	.000	2.593	
R = 0.543    R2 = 0.294    F =  84.690    P < .001	
Stage 3							
Constant	3.154	1.316		2.397	.017		
Access to Information	0.162	0.089	0.127	1.825	.069	2.849	
Understanding information	0.257	0.078	0.260	3.309	.001	3.626	
Appraisal/evaluation	0.180	0.062	0.211	2.908	.004	3.096	
R = 0.556    R2 = 0.309    F =  60.315    P < .001	
Stage 4							
Constant	3.130	1.327		2.360	.019		
Access to information	0.162	0.089	0.127	1.821	.069	2.849	
Understanding information	0.256	0.078	0.260	3.294	.001	3.636	
Appraisal/evaluation	0.174	0.073	0.205	2.387	.017	4.300	
Implementation	0.012	0.079	0.009	0.147	.883	2.422	
R = 0.556    R2 = 0.309    F =  45.133    P < .001	
CARRF-KL = cardiovascular disease risk factor knowledge level.

4. Discussion

This study examined the relationship between women’s knowledge of CVD risk factors and their health literacy. It was observed that both the women’s knowledge level of CVD risk factors and their health literacy scores were adequate. In our study, the mean CARRF-KL score of women was 20.65 ± 4.72. Considering the scale’s maximum score of 28 points, the participants’ mean score can be considered high. In a similar study examining women’s level of knowledge about CVD risk factors, mean CARRF-KL score was noted to be 17.67 ± 4.85, whereas in another study, it was observed to be lower, with a mean of 13.05 ± 6.93.[5,14] In a UK study measuring the knowledge level of women aged 35 to 55 years on CVD risk factors, the participants’ knowledge level was found to be high.[15] In our study, it was observed that women’s level of knowledge about CVD risk factors was higher than that observed in other studies conducted with Turkish women. This result may be attributed to the educational level of the study group. Owing to the use of online data collection methods and prevalence of internet use among young and middle-aged individuals, the study sample primarily comprised women with undergraduate and postgraduate educational levels (77%). Similar results have been reported in the literature.[3,5,14–25] In addition to educational level, occupation and income level were also found to affect women’s level of CVD knowledge. Women who were civil servants and had higher income had higher level of CVD knowledge. Similar results have been reported in previous studies.[5,15,21,26–28] Based on the present study findings, it is considered that training programs directed toward prevention of CVDs should primarily focus on individuals with low educational and socioeconomic levels. In this study, participants’ modifiable risk factors for heart diseases and their knowledge scores were compared. Accordingly, we observed that those who were overweight according to their BMI, who did not smoke, who had chronic diseases, who did not exercise regularly, and who generally consumed vegetables and fruits every day had higher levels of knowledge about CVD risk factors. CVD risk factor knowledge level did not significantly differ according to other characteristics. In our study, over half of the participants (57%) had a BMI within normal limits. Considering that the mean age of the study group was 33.14 ± 11.12 years, it is encouraging to find that weight control among the younger generation is satisfactory. In the present study, only BMI values of the participants were analyzed, and waist circumference measurements were not performed. Overweight and high BMI are globally recognized as significant risk factors. For the improved management of CVD risk factors, it is important to raise awareness regarding BMI and undertake measures to prevent abdominal adiposity. Smoking is another modifiable risk factor. In this study, the level of knowledge about CVD risk factors of nonsmokers was found to be significantly higher than that of smokers. In literature, modifiable risk factors and CARRF-KL scores of individuals have been compared, and some parameters were found to have a significant effect on the results, similar to the findings of our study.[19,29,30] Chukwuemeka et al (2023) evaluated the knowledge and awareness levels as well as CVD risks of academic and administrative staff of the School of Health Sciences and found that smokers had high levels of knowledge and low levels of awareness of CVD risk factors. Similarly, in a study conducted with nursing students, nonsmoking students were observed to have a higher level of knowledge about CVD risk factors than students that smoked, and no significant relationship was noted with BMI.[30] Similarly, Topuz and Bozdemir (2022) observed that the scores of nonsmokers were significantly higher than those of smokers; however, no significant difference was found between BMI and CARRF-KL scores. In a study conducted by Tan et al (2013), when the mean CARRF-KL scores were examined according to women’s smoking status, mean knowledge level scores of women who smoked were found to be significantly higher than those of women who did not smoke. Conversely, in some studies, no significant difference was found between smoking status and CVD knowledge level.[15,22,27,31] In our study, CVD knowledge levels were found to be significantly higher among individuals with chronic diseases who did not exercise and who consumed vegetables and fruits every day. It is believed that the presence of any chronic disease increases the awareness levels of individuals and motivates them to research further, consequently enhancing their knowledge level. In literature, significant results were obtained when comparing the risk factors for heart diseases and the knowledge levels of individuals regarding CVD in various areas.[3,5,15,17,19,27,31] In our study, we found that the majority of the participants (78.5%) did not exercise regularly. In its 2016 guidelines, the European Society of Cardiology recommended at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity physical activity per week for preventing CVDs.[32] However, the consistently low levels of physical activity observed in our study and other related studies suggest that physical activity habits are generally inadequate in the population.

It is emphasized that health literacy plays an important role in CVD prevention, and low health literacy levels are associated with adverse outcomes in heart diseases.[8] Women constitute two-thirds of the world’s population without basic literacy skills.[33] Hence, understanding women’s health literacy levels is crucial for preventing heart diseases and promoting awareness of risk factors. In our study, the mean health literacy score of women was 107.06 ± 16.01. Based on these data, women in our study group had adequate health literacy levels. Moreover, we observed that as the level of education and income increased, the level of health literacy increased as well. In literature, it is stated that educational level affects health literacy and individuals evaluate their health better as the educational level increases.[34] Similarly, there are studies indicating that women’s health literacy increases with increasing educational and income levels.[17,35–41] Additionally, in our study, it was observed that the scores of employed women were generally higher than those of unemployed women. Unlike our study, Amoah and Philips (2020) examined the relationship between health literacy and sociodemographic characteristics in Ghana and found that women’s employment status exhibited no relationship with health literacy.[42] In another study, the health literacy of employed women was found to be higher than that of unemployed women.[43] Similarly, in a study conducted in Italy, the health literacy level of employed individuals was found to be higher than that of unemployed individuals; this could be because employed women have better socioeconomic status and can access appropriate information and health services more easily.[44] In our study, based on the findings regarding CVD risk factors among women, it was found that those who were overweight, nonsmokers, had chronic diseases, consumed a low-salt diet, consumed mostly olive oil, and cooked meals using the grilling method had higher levels of health literacy.

After evaluation of study results, it was observed that the scores of women with healthy lifestyle habits were generally high. Health literacy provides individuals with the health-related knowledge and self-efficacy necessary for the development of healthy behaviors such as physical activity and general health maintenance. The studies in the literature suggest a relationship between health literacy and obesity, dietary choices, and exercise.[8] In our study, the relationship between the participants’ health literacy and CARRF-KL scores was examined, which revealed a significant positive correlation between health literacy and the level of knowledge about CVD risk factors. Thus, as the health literacy level of the women in our study group increased, their CARRF-KL scores also increased. Additionally, in the regression analysis examining the predictive power of the CVD risk factor knowledge level, it was observed that the access to information subscale of the HLS accounted for 23.8% of the participants’ CVD risk factor knowledge level. It is expected that individuals who have access to accurate information will also have a high level of knowledge about CVD risk factors. In our study, women with higher levels of education and income had higher mean scores on both the CARRF-KL and HLS compared to other women. This result may be attributed to the ability and opportunity to access qualified and accurate information. Therefore, access to information subscale of the HLS can be considered the most important predictor of increased CVD risk factor knowledge level. Furthermore, when the access to information subscale and the information comprehension subscale (another important predictor) were added to the regression model, they collectively accounted for 29.4% of the CVD risk factor knowledge level. Based on these findings, it is evident that individuals’ access to and understanding of basic health information and services are essential for guiding their decisions and behaviors related to personal and community health.

There are certain limitations to the present study. This study involved women over 18 years of age in Turkey and was conducted through an online survey. Consequently, given the limitations inherent in quantitative research, the findings are limited to individuals who use social networks and agree to participate in the study, thus limiting the generalizability of the results. Moreover, conducting the study within a specific timeframe is a common limitation, especially in such studies. Nevertheless, the study investigated the factors that affect Turkish women’s awareness of CVD risk factors, considering their health literacy and other relevant factors. The findings of this research could pave the way for implementing suitable interventions to enhance women’s knowledge of CVD risk factors through improved healthcare services.

5. Conclusions

This study found that women aged 18 years and older in Turkey have adequate knowledge of CVD risk factors and health literacy. Additionally, a positive and significant correlation was observed between CVD knowledge levels and health literacy. As health literacy increased, CVD risk factors knowledge levels also increased. Male sex ranks among the most critical nonmodifiable risk factor for CVD, often resulting in lower awareness levels among women owing to the perception of lower risk. However, this study revealed that women with high health literacy scores also had increased knowledge levels. This finding is essential for shaping strategies to raise women’s awareness of CVD. Moreover, factors such as educational level, economic status and employment status significantly influenced scores on both scales. These findings highlight the challenges encountered by women with low socioeconomic status in accessing accurate information. It is crucial to educate women about the various risk factors associated with cardiovascular diseases and methods to prevent and manage these conditions. Nurses play a crucial role in providing health education and consultancy services to society, with a particular focus on women who are one of the vulnerable groups. It is also important to plan and execute initiatives that motivate people to adopt healthy habits that can help them live healthier lives. Therefore, public education programs should consider socioeconomic and educational levels, with a special focus on women who encounter difficulties in accessing information. It is recommended to encourage individuals to adopt positive healthy lifestyle habits that can help reduce the risk of developing cardiovascular diseases. Overall, our study is expected to contribute to the development of national and international policies aimed at enhancing women’s awareness of CVD risk factors.

Acknowledgments

The authors would like to thank the participants of this study for their valuable contributions and their time.

Author contributions

Conceptualization: Tuğba Yardimci Gürel, Ozlem Güner.

Data curation: Tuğba Yardimci Gürel, Ozlem Güner.

Investigation: Tuğba Yardimci Gürel, Ozlem Güner.

Methodology: Tuğba Yardimci Gürel, Ozlem Güner.

Writing – original draft: Tuğba Yardimci Gürel.

Writing – review & editing: Ozlem Güner.

Abbreviations:

BMI body mass index

CARRF-KL cardiovascular disease risk factor knowledge level

CVD cardiovascular disease

HLS Health Literacy Scale

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Yardimci Gürel T, Güner Ö. Health literacy as a predictor of cardiovascular disease risk factor knowledge level among women in Turkey: A community-based cross-sectional study. Medicine 2024;103:29(e38994).
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References

[1] Turkish Statistical Institute, Death and Cause of Death Statistics, 2022. Available from: https://data.tuik.gov.tr/Bulten/Index?p=Olum-ve-Olum-Nedeni-Istatistikleri-2022-49679. Accessed: August 8, 2023.
[2] World Health Organization (WHO), Cardiovascular Disease. 2022. Available from: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1. Accessed: August 8, 2023.
[3] Cushman M Shay CM Howard VJ . Ten-year differences in women’s awareness related to coronary heart disease: Results of the 2019 American Heart Association national survey: a special report from the American Heart Association. Circulation. 2022;143 :e239–48.
[4] Kanejima Y Shimogai T Kitamura M Ishihara K Izawa KP . Impact of health literacy in patients with cardiovascular diseases: a systematic review and meta-analysis. Patient Educ Couns. 2022;105 :1793–800.34862114
[5] Hebcan Örs S Tümer A . The correlation between adult women’s knowledge level of risk factors related to cardiovascular diseases and healthy lifestyle behaviors. Univ Health Sci J Nurs. 2020;2 :81–8.
[6] Nutbeam D . The evolving concept of health literacy. Soc Sci Med. 2008;67 :2072–8.18952344
[7] Jayasinghe UW Harris MF Parker SM . The impact of health literacy and life style risk factors on health-related quality of life of Australian patients. Health Qual Life Outcomes. 2016;14 :68.27142865
[8] Magnani JW Mujahid MS Aronow HD . Health literacy and cardiovascular disease: Fundamental relevance to primary and secondary prevention: a scientific statement from the American Heart Association. Circulation. 2018;138 :e48–74.29866648
[9] Cajita MI Cajita TR Han HR . Health literacy and heart failure: a systematic review. J Cardiovasc Nurs. 2016;31 :121–30.25569150
[10] Ghisi GLM Chaves GSDS Britto RR Oh P . Health literacy and coronary artery disease: a systematic review. Patient Educ Couns. 2018;101 :177–84.28899710
[11] Arikan I Metintaş S Kalyoncu C Yildiz Z . The cardiovascular disease risk factors knowledge level (CARRF-KL) scale: a validity and reliability study. Turk Kardiyol Dern Ars. 2009;37 :35–40.19225251
[12] Toçi E Burazeri G Sorensen K . Health literacy and socioeconomic characteristics among older people in transitional kosovo. Br J Med Med Res. 2013;3 :1646–58.
[13] Bayik Temel A Aras Z . Evaluation of validity and reliability of the Turkish version of health literacy scale. Florence Nightingale J Nurs. 2017;25 :85–94.
[14] Tan M Dayapoğlu N Şahin AZ Cürcani M Polat H . Determining cardiovascular disease risk factors knowledge level of women living in rural area. Gümüşhane Univ J Health Sci. 2013;2 :331–41.
[15] Konicki AJ . Knowledge of cardiovascular risk factors, self-nurturance, and heart-healthy behaviors in Women. J Cardiovasc Nurs. 2012;27 :51–60.21372730
[16] Al Hamarneh YN Crealey GE McElnay JC . Coronary heart disease: Health knowledge and behaviour. Int J Clin Pharm. 2011;33 :111–23.21365403
[17] Efe Arslan D Kiliç Akça N . Caridyovacular risk awareness of academic staff. Kocaeli Med J. 2020;9 :31–8.
[18] Awad A Al-Nafisi H . Public knowledge of cardiovascular disease and its risk factors in Kuwait: a crosssectional survey. BMC Public Health. 2014;14 :1131.25367768
[19] Chukwuemeka UM Okoro FC Okonkwo UP . Knowledge, awareness, and presence of cardiovascular risk factors among college staff of a Nigerian University. Bull Fac Phys Ther. 2023;28 :1–11.
[20] Dalusung-Angosta A . CHD Knowledge and risk factors among Filipino-Americans connected to primary care services. J Am Assoc Nurse Pract. 2013;25 :503–12.24170655
[21] Karatay G Yeşiltepe A Aktaş H . Cardiovascular diseases risk factors knowledge levels of individuals over 40 years old and their relationship with some variables. Acta Medica Nicomedia. 2021;4 :49–55.
[22] Kirağ N Çalişkan G . Determination of factors related to cardiovascular disease knowledge and depression level of patients applied to the family health center. Med Sci. 2019;15 :1–11.
[23] Mullie P Clarys P . Association between cardiovascular disease risk factor knowledge and lifestyle. Food Nutr Sci. 2011;02 :1048–53.
[24] Uçar A Arslan S . The cardiovascular disease risk factors knowledge level of the adults living in a family health center region. J Cardiovasc Nurs. 2017;8 :121–30.
[25] Yilmaz M Boylu M . Determining the levels of knowledge about cardiovascular risk factors and behaviours of desk-based staffs. Hemşirelikte Eğitim ve Araştirma Dergisi. 2016;13 :259–67.
[26] Aminde LN Takah N Ngwasiri C . Population awareness of cardiovascular disease and its risk factors in Buea, Cameroon. BMC Public Health. 2017;17 :545.28583117
[27] Çürük GN Korkut Bayindir S Oğuzhan A . The relationship of the healthy lifestyle behaviors and cardiovascular disease risk factors knowledge level of patients with cardiovascular disease and their relatives. Saglik Bilim Derg. 2018;27 :4047.
[28] Zeb J Zeeshan M Zeb S . Knowledge about risk factors and warning symptoms in patient suffering from cardiovascular disease. Pak Heart J. 2016;49 :50–5.
[29] Topuz AN Bozdemir N . Evaluation of cardiovascular disease risk factors knowledge level, Framingham score, and cardiac markers in a healthy population. Cukurova Med J. 2022;47 :1086–94.
[30] Gürel TY . Determination of nursing students’ cardiovascular diseases risk factors knowledge levels. J Samsun Health Sci. 2023;8 :103–12.
[31] Abdo NM Mortada EM El Seifi OS . Effect of knowledge about cardiovascular diseases on healthy lifestyle behavior among freshmen of Zagazig University: An intervention study. Open Public Health J. 2019;12 :300–8.
[32] Piepoli MF Hoes AW Agewall S . 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Atherosclerosis. 2016;252 :207–74.27664503
[33] United Nations Educational, Scientific and Cultural Organization (UNESCO), Adult and youth literacy: National, Regional and Global Trends, 1985–2015. https://unesdoc.unesco.org/ark:/48223/pf0000217409. Accessed: September 20, 2023.
[34] Souto TS Ramires A Leite A Santos V Santo RE . Health perception: validation of a scale for the Portuguese population. Temas Em Psicologia. 2018;26 :2185–201.
[35] Aktan GV Özdemir F . Health literacy levels of women in climacteric period. Çukurova Med J,. 2020;45 :352–61.
[36] Karakayali Ay C Benli TE Özşahin Z . Health literacy and associated factors in postpartum women. J Inonu Univ Health Serv Vocational School. 2023;11 :1068–80.
[37] Aydin D Aba YA . The relationship between mothers’ health literacy levels and their perceptions about breastfeeding self-efficacy. E J Dokuz Eylul Univ Nurs Faculty. 2019;12 :31–9.
[38] Charoghchian Khorasani E Peyman N Esmaily H . Measuring maternal health literacy in pregnant women referred to the healthcare Centers of Mashhad, Iran, in 2015. J Midwifery Reprod Health. 2018;6 :1157–62.
[39] Dadipoor S Ramezankhani A Alavi A Aghamolaei T Safari-Moradabadi A . Pregnant women’s health literacy in the South of Iran. J Family Reprod Health. 2017;11 :211–8.30288168
[40] Goto E Ishikawa H Okuhara T Kiuchi T . Relationship between health literacy and adherence to recommendations to undergo cancer screening and health-related behaviors among insured women in Japan. Asian Pac J Cancer Prev. 2018;19 :3409–13.30583347
[41] Yeşilçinar I Şahin E Mercan D . Investigation of the relationship between health literacy and the traditional practices of women who were in the postpartum period. Turkish J Family Med Primary Care. 2021;15 :594–601.
[42] Amoah PA Phillips DR . Socio-demographic and behavioral correlates of health literacy: a gender perspective in Ghana. Women Health. 2020;60 :123–39.31092133
[43] Maricic M Curuvija RA Stepovic M . Health literacy in female – association with socioeconomic factors and effects on reproductive health. Serbian J Exp Clin Res. 2020;21 :127–32.
[44] Palumbo R Annarumma C Adinolfi P Musella M Piscopo G . The Italian Health Literacy Project: Insights from the asspss sessment of health literacy skills in Italy. Health Policy. 2016;120 :1087–94.27593949
