Investigating the correlation of self-care and quality-of-life patients with heart failure

Abstract

Background

Heart Failure adversely affects the patients’ quality-of-life. Quality-of-life in patients with heart failure is related to their self-care and other factors. This study aimed to investigate the correlation of quality-of-life and self-care among patients with heart failure and to determine their associated factors.

Methods

This descriptive-correlational study was conducted on 217 patients with heart failure at the Farshchian Heart Hospital in Hamadan, Iran, from April 13, 2022, to March 29, 2023. Patients completed the self-care questionnaire for patients with heart failure and the Minnesota quality-of-life questionnaire. A quantile regression model was used to identify factors related to self-care and quality-of-life in patients with heart failure. Analysis was done using R.4.4.0 (P < 0.05).

Results

The mean(± SD) of age, quality-of-life and self-care were 62.16(± 7.86), 60.05(± 8.85), and 35.16(± 5.36), respectively, indicating a low level of quality-of-life and moderate level of self-care. There was no significant correlation between self-care and quality-of-life(r = 0.007; P = 0.916). The correlates of self-care which were significant in almost all Deciles included duration of disease(P < 0.05 for 4th and 5th deciles and P < 0.001 for other deciles), gender(P < 0.01 for the 1st,7th,8th,9th deciles and P = 0.017 for 2nd ), education (P < 0.001), income(P < 0.05 for 3rd -7th deciles and P < 0.001 for 8th and 9th deciles), substance abuse (P = 0 < 0.001 to P = 0.047 for various deciles), and history of hypertension (P < 0.05). Moreover, for the quality-of-life the associated variables included duration of disease (P < 0.001 for the 1st to 5th deciles and P = 0.028 for the 8th decile), sufficient income (P:0.001, 0.004, 0.018, 0.026,<0.001, and < 0.001 for the 2nd, 3rd, 5-6th, and 8-9th deciles).

Conclusion

The non-significant correlation between self-care and quality-of-life shows that lower self-care is linked to a diminished quality-of-life. Patients motivated to engage in self-care are likely to experience fewer hospital readmissions and an improved quality-of-life. Healthcare providers/policymakers should be aware of the importance of self-care in patients with heart failure and help promote their quality-of-life by enhancing their self-care behavior through personalized interventions as own efforts to prevent further worsening of HF. Specifically, such interventions should consider the multifactorial nature of these outcomes and the diverse demographic, socio-economic, and health-related characteristics of this population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-025-04986-0.

Introduction

Heart failure (HF) is considered among the most prevalent cardiovascular diseases worldwide [1]. HF is a progressive chronic disease that is characterized by increased hospitalizations and mortality rates, as well as a complex treatment plan [2, 3] and affects over 64 million people around the world [4]. In 2021, the global prevalence of HF was estimated at 56.5 million cases (with a 95% Uncertainty Interval (UI) ranging from 49.7 million to 63.7 million). Of these, 27.3 million cases (between 24.0 and 30.8 million) were in females, while males accounted for 29.2 million cases (ranging from 25.6 to 33.0 million). The age-standardized prevalence rose from 647.9 cases (with a UI of 556.3 to 739.9) per 100,000 in 1990 to 682.7 cases (ranging from 602.8 to 766.8) per 100,000 in 2021. In 2021, the North Africa and Middle East super region had the highest age-standardized prevalence at 780.5 per 100,000 (with a UI of 658.7 to 907.6), while South Asia recorded the lowest at 600.1 per 100,000 (ranging from 526.5 to 671.4) [5]. When compared to other chronic diseases, HF has a particularly grim prognosis and imposes a significant burden on patients, caregivers, and the healthcare system [2]. Studies have shown that a sizable proportion of hospitalized patients with HF face readmission rates of 25% within 30 days of leaving the hospital and 50% within a 6-month period post-discharge [6]. This puts extra costs on the patients and their family and healthcare systems [7]. The patients with HF also expose to many complications and a high risk of mortality after discharge [7, 8]. Various complications of HF, including shortness of breath, fatigue, depression and anxiety as well as edema, can significantly limit patients’ daily activities and reduce the patients’ Quality-of-Life (QoL) as an important aspect of their lives [9, 10].

QoL is a multifaceted concept encompassing sociological, economic, psychological, philosophical, and ethical dimensions [11, 12]. It is not solely focused on biological aspects but also includes subjective evaluations of well-being and satisfaction [12]. patients with HF often experience significantly reduced QoL due to physical and psychological limitations, leading to increased hospitalizations, and mortality [13]. This lower QoL is in turn associated with higher costs for patients and healthcare systems [14] and can lead to the abandonment of previous activities, social isolation, and the loss of social connections and support [15, 16]. Additionally, factors such as poverty, literacy level, access to preventive services, and insurance status can impact QoL in these patients [17]. Self-care behaviors also play a role in determining QoL [11].

Due to the progressive and incapacitating nature of HF, continuous management of the disease is essential in order to attain the intended therapeutic outcomes [18]. HF management and control encompass various approaches, and self-care represents a crucial aspect among them as a non-pharmacological strategy [19]. “Self-care is the practice of maintaining one’s health through preventative and health-promoting behaviors “ [20]. Studies have shown that self-care can improve health outcomes, prevent frequent re-hospitalization [19], reduce healthcare costs and mortality rate among patients with HF [20–23]. It has been estimated that about 40% of re-hospitalization can be prevented by effective self-care [24, 25]. Self-care involves a patient-centred decision-making process wherein individuals undertake appropriate measures, including clinical tests and interventions, to prevent disease progression [26]. In the context of HF, self-care encompasses essential elements such as adhering to prescribed diets and medication regimens, practicing sodium and fluid restriction, regular weight monitoring, engaging in regular exercise, and vigilantly monitoring signs and symptoms of disease exacerbation. Additionally, it involves actively seeking and making informed decisions regarding suitable treatment interventions [26]. Enhancing self-care behaviour empowers patients to exert greater control over their daily lives and effectively manage their social functioning [27]. By embracing self-care practices, individuals with HF can better navigate their condition, mitigate its impact, and optimize their overall well-being.

While self-care has been shown to have a positive impact on management of HF [28], existing research on the connection between self-care practices and QoL and their risk factors in patients with HF is currently limited in Iran. A recent systematic review indicated that the level of self-care activities among patients with HF is insufficient, with a notable portion of patients included in the analysis exhibiting a moderate QoL [28]. Moreover, some risk factors including educational level, place of residence, illness knowledge, presence of comorbidities, and functional classification of HF, age, and sex have been reported [28]. Nevertheless, in various contexts and cultures, the level of self-care and QoL, as well as their associations and risk factors among patients with HF, can vary significantly. Factors such as cultural beliefs, access to healthcare resources, socioeconomic status, and individual perceptions of illness can influence how patients manage their condition and perceive their QoL. In Iran, cardiovascular diseases are the leading cause of mortality, accounting for more than one-third of all deaths (around 39%) and posing the greatest challenge to the country’s healthcare system, imposing substantial financial burdens on healthcare systems [29]. According to studies, 33–57% of patients with HF are readmitted in hospitals in Iran [30]. Determining related factors of self-care and QoL based on understanding variations in the study context is essential for tailoring interventions and support strategies effectively to address the specific needs of patients with HF in different cultural and social settings. In this regard, few studies conducted in Iran to determine associated factors of self-care and QoL. A recent study have reported that there is no correlation between QoL (using SF-36 questionnaire) and self-care among a relatively small sample of Patients with HF [31]. Another Iranian study have shown a moderate correlation between self-care and QoL in Patients with HF [32]. Considering this inconsistent results and the significance of identifying variables related to self-care and patients’ QoL to enable necessary interventions tailored to individuals, in alignment with the recent advancement of personalized medicine, the objective of this study was to determine the level of QoL and self-care and to explore their related factors using quantile regression model among patients with HF.

Methods

Study design, participants and data collection

This descriptive-analytical study was conducted in the inpatient ward of the Farshchian Heart Hospital (referral hospital) in Hamadan city, west of Iran, from April 13, 2022, to March 29, 2023. The population for this study consisted of patients with HF who were admitted to the Farshchian Heart Training and Treatment Centre in Hamadan. The sample was 217 patients with HF, who consented to study, aged > 18 years, having past at least 6 months since the onset of the disease, had ejection fraction < 40%, had the literacy of reading and writing, and understood the Persian language, and having HF class 2 or 3 according to the American Heart Association (AHA) classifications. The samples were obtained using a convenience sampling method. Patients who met the inclusion criteria and consented to participate were provided with questionnaires. Therefore, the data collection method utilized was self-reportConsidering a 95% confidence level and a standard deviation of 7.2 for self-care (the variable with greated variance) from previous studies [31], and an estimation erreor (E) of 1 unit of self-care, the sample size was calculated as follows:

Finally by considering about 15% of non-response, the total sample size was considered as 217 patients with HF.

Data collection tools

Data collection tools included (1) Demographic and clinical information; (2) the European questionnaire of self-care behaviours of patients with HF; and (3) the Minnesota Living with Heart Failure Questionnaire (MLHFQ) for measuring QoL of patients with HF. The patients filled out the questionnaires.

Demographic and clinical information (independent variables)

Independent variables in this study were age (year), gender (fe/male), body mass index or BMI (kg/m²), marital status (un/married), ethnicity (Fars, Kurd, Turk), level of education (Only reading and writing, under diploma, diploma and higher), occupation (un/employed), income (sufficient, relatively sufficient, not sufficient), smoking, underlying diseases, having a familial history of HF, duration of illness (year), number of hospitalizations, having HF class 2 or 3 according to the American Heart Association (AHA) classification, cardiac ejection fraction and type of medicine.

European heart failure Self-care behavior scale (dependent variable)

This tool was developed by [33] and consists of 12 questions assessing self-care behaviours in patients with HF, including weight control, fluid restriction, medication management, physical activity, rest, seeking medical attention for weight gain, shortness of breath, leg swelling, feeling tired, annual influenza vaccination, etc. Each question is rated on a 5-option Likert scale ranging from “absolutely so” (score of 5) to “not at all” (score of 1). The total score ranges from 12 to 60, with higher scores indicating better self-care. The questionnaire categorizes total scores as poor self-care [12–28], average self-care [29–44], and good self-care [34, 45–60]. The validity and reliability of the Iranian version of this questionnaire was evaluated in the studies (cultural validity evaluation) using content validity, and external and internal consistency measures [34–36]. In the present study, the reliability of the tool was calculated using Cronbach’s alpha, resulting in a value of 0.78 using a sample of 30 patients with HF.

Minnesota living with heart failure questionnaire or MLHFQ (dependent variable)

QoL encompasses individuals’ subjective assessment of their health and their ability to engage in physical, mental, and social functioning [37]. Considering its significance as an indicator of health and living standards, the concept of QoL has been defined diversely to address various objectives and research areas [29]. In this study, we used the MLHFQ that was designed by Rector in 1984 [38] and has been psychometrically evaluated in Iran by some studies [32, 39, 40]. It comprises 21 items that assess the physical, psychological, and socioeconomic limitations caused by HF symptoms over the past month. The questionnaire captures patients’ perceptions of the impact of congestive HF on various aspects of their lives, including physical functioning, psychological well-being (e.g., depression, anxiety), social relationships, sexual activities, work, and emotions [38, 39]. The MLHFQ consists of three subscales including focus on physical performance, psychological and emotional aspects, and comprehensive assessment of socioeconomic conditions. Each item is scored on a 6-point Likert scale ranging from 0 to 5, where 0 represents no limitation and 5 indicates maximum limitation. The overall score ranges from 0 to 105, with higher scores indicating a poorer QoL [39, 41–43]. To determine the face and content validity of the demographic and clinical characteristics of the patients, it was given to 10 members of the faculty of the School of Nursing and Midwifery, Hamadan University of Medical Sciences, and it was used after applying the opinions of the faculties. The content validity was checked using content validity ratio (CVR) and index (CVI) and were resulted as 0.89 and 0.92, respectively. CVR was calculated using expert ratings to determine the essentiality of items, while CVI was calculated based on the consensus of experts regarding the clarity and relevance of items. The numerical ranges reported for CVR typically range from − 1 to + 1, with values above a 0.7 considered acceptable. For CVI, the numerical range is typically reported on a scale of 0 to 1, with values closer to 1 indicating a higher content validity.

Moreover, to evaluate the reliability of the MLHFQ in this study, it was given to 30 patients and the Cronbach’s α was calculated. The values of the Cronbach’s α for total score and the three subscales of the questionnaire were as follows: 0.893 for total score; 0.865 for physical performance; 0.772 for psychological and emotional aspects; and 0.922 for comprehensive assessment of socioeconomic conditions.

Statistical analysis

Descriptive statistics of the continuous and categorical variables are shown in mean (± standard deviation) and frequency (percentage). Pearson correlation coefficient was used to investigate the correlation between QoL and Self-care variables. To assess the impact of variables on self-care and QoL, quantile regression was used [44]. This model can be formulated as follows where is the quantile, s are the regression coefficients, s are the covariates/factors, i is the indicator if the patient, and n is the number of patients.

In quantile regression, the estimated beta coefficients represent the marginal effects of the independent variables on the corresponding quantiles of the dependent variable. These coefficients provide insights into how changes in the independent variables are associated with changes in specific quantiles of the distribution of the dependent variable. Interpreting the estimated beta coefficients in quantile regression is similar to interpreting coefficients in OLS regression, but with a focus on specific quantiles [44].

Software

All analysis was done using R v.4.4.0 software using “quantreg” package version 5.97 [45], “stats”.

Results

Descriptive statistics

Table 1 shows the characteristics of the participants. The mean age of the participants was 62.16 years (± 7.86). About 50% of the participants were male. The majority of the participants had Fars ethnicity (39.2%), were married (77.4%), had only reading and writing literacy (70.5%), were unemployed (57.1%), had relatively sufficient income (58.5%), did not smoke (77.4%), did not abuse substances (79.3%), and did not have a history of HF in their family members (72.4%). The patients mostly had a myocardial infarction (MI) history (55.8%).

Table 1.

Clinical and sociodemographic characteristics of the patients (N = 217)

Variable	N (%)/M(SD)
Gender
Female	110 (50.7)
Male	107 (49.3)
Etnicity
Fars	85 (39.2)
Kurd	54 (24.9)
Turk	78 (35.9)
Marital Status
Married	168 (77.4)
Single	49 (22.6)
Education level
Only reading	153 (70.5)
Under diploma	38 (17.5)
Diploma and higher	26 (12)
Job
Employed	93 (42.9)
Un-employed	124 (57.1)
Income
Sufficient	37 (17.1)
Relatively Sufficient	127 (58.5)
Not Sufficient	53 (24.4)
Smoking
Yes	49 (22.6)
No	168 (77.4)
Substance abuse
Yes	45 (20.7)
No	172 (79.3)
History of heart failure in family members
Yes	60 (27.6)
No	157 (72.4)
Having a history of any Diseases
MI	121 (55.8)
Diabetes	46 (21.2)
Hypertension	161 (74.2)
CVA	5 (2.3)
Renal	11 (5.1)
Pulmonary	10 (4.6)
Others	20 (9.2)
Disease Class
II	128 (59)
III	89 (41)
Age (Year)	62.16 (± 7.86)
BMI (Kg/m2)	25.91 (± 3.84)
Duration of the disease (Day)	11.71 (± 12.72)
Number of hospitalization	2.49 (± 2.1)
Ejection Fraction	32.56 (± 8.46)
Self-care(12–60)	35.16 (± 5.36)
Poor (12–28)	23 (10.6)
Moderate (29–44)	182 (83.9)
Good (45–60)	12 (5.5)
Quality-of-life score (0-105)
Physical subscale (0–40)	23.31 (± 5.65)
Emotional subscale (0–25)	14.91 (± 2.5)
Socio-economic subscale (0–40)	21.83 (± 4.99)
Total Scale	60.05 (± 8.85)
Good (< 51.5)	32 (14.7)
Moderate (51.5–68.9)	143 (65.9)
Poor (> 69)	42 (19.4)

The average score of QoL was 60.05(± 8.85). Based on cut-off points determined by average ± standard deviation, it was revealed that most of the patients had a moderate quality of life (65.9%). Also, the average score of self-care was 35.16 (± 5.36). According to the determined cut-off point, the majority of the patients (83.9%) had a moderate self-care score (see Table 1).

Correlation between QoL and self-care

The association between QoL and self-care was not statistically significant (r = 0.007; P = 0.916). The association between self-care and physical subscale (r = 0.006; P = 0.932), emotional subscale (r = 0.011; P = 0.867), and socio-economic subscale (r=−0.025; P = 0.713) was not statistically significant.

Factors associated with self-care

The regression coefficients for the quantile regression of Self-Care scores across 9 deciles are presented in Table 2. According to the results, several variables were significantly associated with self-care in various deciles. The correlates of self-care which were significant in almost all Deciles included duration of disease (P < 0.05 for 4th and 5th deciles and P < 0.001 for other deciles), gender (P < 0.01 for the 1 st,7th,8th,9th deciles and P = 0.017 for 2nd), education (P < 0.001), income (P < 0.05 for 3rd −7th deciles and P < 0.001 for 8th and 9th deciles), substance abuse (P = 0 < 0.001 to P = 0.047 for various deciles), and history of hypertension (P < 0.05). Nevertheless, many other variables were statistically significant in some of deciles and were not significant in other deciles.

Table 2.

Results of quantile regression for association of variables on 9 deciles of Self-Care score

Variable	Estimate	Decile
		1	2	3	4	5	6	7	8	9
(Intercept)	β (S.E)	28.01(3.2)	37.89(6.25)	37.64(6.7)	51.66(6.89)	41.83(7.17)	40.58(6.93)	43.91(5.68)	49(3.89)	58.57(2.19)
	P-value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
Ejection fraction	β (S.E)	−0.1(0.03)	−0.14(0.05)	−0.12(0.06)	−0.08(0.06)	−0.02(0.06)	−0.02(0.06)	0.02(0.05)	−0.08(0.03)	−0.09(0.02)
	P-value	< 0.001	0.011	0.036	0.194	0.747	0.729	0.643	0.013	< 0.001
Age (year)	β (S.E)	−0.06(0.03)	0.02(0.05)	0.04(0.05)	−0.05(0.05)	−0.04(0.06)	−0.03(0.05)	0.04(0.04)	−0.05(0.03)	−0.08(0.02)
	P-value	0.023	0.655	0.505	0.365	0.512	0.591	0.362	0.086	< 0.001
Duration of the disease	β (S.E)	0.12(0.02)	0.09(0.03)	0.09(0.03)	0.07(0.03)	0.07(0.03)	0.05(0.03)	0.05(0.03)	0.06(0.02)	0.07(0.01)
	P-value	< 0.001	0.003	0.007	0.041	0.038	0.133	0.089	0.001	< 0.001
BMI	β (S.E)	0.27(0.05)	0.03(0.09)	0.01(0.1)	−0.06(0.1)	−0.05(0.11)	0.01(0.1)	−0.09(0.08)	0.03(0.06)	−0.04(0.03)
	P-value	< 0.001	0.748	0.890	0.582	0.655	0.952	0.274	0.643	0.180
Number of hospitalization	β (S.E)	−0.42(0.09)	−0.26(0.17)	−0.32(0.19)	0.01(0.19)	0(0.2)	−0.16(0.19)	−0.27(0.16)	−0.5(0.11)	−0.43(0.06)
	P-value	< 0.001	0.138	0.091	0.975	0.995	0.415	0.088	< 0.001	< 0.001
Gender (Female)	β (S.E)	1.88(0.57)	2.68(1.11)	2.26(1.19)	1.71(1.22)	1.89(1.27)	1.83(1.23)	2.99(1.01)	2.28(0.69)	1.37(0.39)
	P-value	0.001	0.017	0.059	0.163	0.138	0.137	0.003	0.001	< 0.001
Ethnicity
Fars	β (S.E)	0.41(0.42)	−0.39(0.82)	0.09(0.87)	0(0.9)	0.81(0.94)	0.69(0.9)	1.74(0.74)	1.42(0.51)	1.36(0.29)
	P-value	0.327	0.635	0.922	0.998	0.386	0.444	0.020	0.006	< 0.001
Kurd	β (S.E)	−1.96(0.47)	−1.1(0.91)	−0.2(0.97)	−1.72(1)	−1.54(1.04)	−0.91(1.01)	−0.67(0.83)	−0.27(0.57)	0.17(0.32)
	P-value	< 0.001	0.228	0.840	0.088	0.143	0.366	0.416	0.639	0.585
Turk	β (S.E)	0	0	0	0	0	0	0	0	0
Marital status (Married)	β (S.E)	1.32(0.45)	1(0.88)	0.59(0.94)	0.46(0.96)	0.41(1)	0.72(0.97)	−0.07(0.8)	0.4(0.54)	0.95(0.31)
	P-value	0.004	0.253	0.532	0.631	0.686	0.459	0.931	0.465	0.002
Education
Just reading and Writing	β (S.E)	−3.28(0.59)	−4.73(1.15)	−4.93(1.23)	−5.94(1.27)	−4.93(1.32)	−4.33(1.28)	−3.54(1.05)	−2.41(0.72)	−5.03(0.4)
	P-value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	0.001	0.001	0.001	< 0.001
Under Diploma	β (S.E)	−0.27(0.69)	−2.59(1.35)	−2.96(1.44)	−3.98(1.48)	−3.38(1.54)	−3.22(1.49)	−3.3(1.22)	−2.79(0.84)	−5.44(0.47)
	P-value	0.699	0.056	0.042	0.008	0.030	0.032	0.008	0.001	< 0.001
Diploma and higher	β (S.E)	0	0	0	0	0	0	0	0	0
Job (Employed)	β (S.E)	0.22(0.56)	1.66(1.09)	0.9(1.16)	1.17(1.2)	2.38(1.25)	1.58(1.2)	2.7(0.99)	0.87(0.68)	−0.44(0.38)
	P-value	0.698	0.128	0.442	0.328	0.058	0.192	0.007	0.198	0.253
Income
Sufficient	β (S.E)	0.57(0.6)	1.41(1.16)	2.93(1.25)	3.67(1.28)	3.4(1.33)	3.08(1.29)	3.01(1.06)	4.8(0.72)	5.19(0.41)
	P-value	0.339	0.227	0.020	0.005	0.011	0.018	0.005	< 0.001	< 0.001
Relatively Sufficient	β (S.E)	0.91(0.45)	0.46(0.87)	0.28(0.93)	1.21(0.96)	1.78(1)	1.74(0.97)	1.31(0.79)	1.71(0.54)	2.36(0.3)
	P-value	0.043	0.596	0.764	0.207	0.077	0.073	0.099	0.002	< 0.001
Not Sufficient	β (S.E)	0	0	0	0	0	0	0	0	0
Smoking (Yes)	β (S.E)	0.22(0.49)	0(0.95)	0.75(1.02)	−0.33(1.05)	0.29(1.09)	0.97(1.06)	2.18(0.87)	1.54(0.59)	0.49(0.33)
	P-value	0.657	1.000	0.462	0.751	0.792	0.360	0.013	0.010	0.146
Substance abuse (Yes)	β (S.E)	−2.35(0.48)	−1.88(0.94)	−2.01(1)	−2.61(1.03)	−2.47(1.08)	−2.32(1.04)	−2.45(0.85)	−1.9(0.58)	−0.86(0.33)
	P-value	< 0.001	0.047	0.047	0.012	0.023	0.027	0.005	0.001	0.009
History of Myocardial Infarction (No)	β (S.E)	1.2(0.47)	1.68(0.92)	2.13(0.98)	1.31(1.01)	1.31(1.05)	0.34(1.02)	0.52(0.83)	−0.38(0.57)	−0.4(0.32)
	P-value	0.012	0.069	0.032	0.197	0.216	0.738	0.534	0.507	0.209
History of Diabetes (No)	β (S.E)	0.29(0.5)	−0.42(0.97)	−2.73(1.04)	−3.33(1.07)	−3.09(1.11)	−2.23(1.08)	−2.17(0.88)	0.37(0.6)	0.09(0.34)
	P-value	0.558	0.666	0.009	0.002	0.006	0.040	0.015	0.538	0.790
History of Hypertension (No)	β (S.E)	−1.12(0.53)	−2.7(1.04)	−3.08(1.11)	−3.75(1.14)	−3.75(1.19)	−4.18(1.15)	−4.25(0.94)	−2.94(0.64)	−3.18(0.36)
	P-value	0.036	0.010	0.006	0.001	0.002	< 0.001	< 0.001	< 0.001	< 0.001
History of CVA (Yes)	β (S.E)	−3.83(1.22)	−1.47(2.38)	−0.35(2.55)	−1.32(2.62)	1.29(2.73)	1.76(2.63)	−2.7(2.16)	−3.1(1.48)	−3.53(0.83)
	P-value	0.002	0.537	0.892	0.615	0.637	0.505	0.213	0.037	< 0.001
History of Renal Disease (No)	β (S.E)	4.2(0.88)	−1.13(1.71)	−0.7(1.83)	−2.24(1.89)	−1.42(1.96)	0.17(1.9)	−2.2(1.56)	−1.84(1.07)	−4.15(0.6)
	P-value	< 0.001	0.509	0.705	0.236	0.470	0.930	0.159	0.085	< 0.001
Having other diseases (No)	β (S.E)	−0.22(0.65)	−0.65(1.27)	0.58(1.36)	−1.05(1.4)	−0.59(1.45)	−0.57(1.4)	−0.59(1.15)	−0.04(0.79)	0.52(0.44)
	P-value	0.740	0.608	0.671	0.451	0.687	0.687	0.606	0.961	0.246
History of Pulmonary Disease (No)	β (S.E)	1.23(0.92)	0.45(1.79)	0.84(1.92)	−0.66(1.97)	0.97(2.05)	−0.79(1.98)	−0.58(1.63)	−0.36(1.11)	−0.75(0.63)
	P-value	0.182	0.802	0.661	0.738	0.637	0.689	0.723	0.747	0.234
History of heart failure in family members (Yes)	β (S.E)	−2.57(0.42)	−1.19(0.83)	−0.95(0.89)	−0.35(0.91)	0.11(0.95)	1.02(0.92)	0.93(0.75)	0.02(0.51)	−0.18(0.29)
	P-value	< 0.001	0.153	0.285	0.704	0.910	0.269	0.215	0.964	0.539

Factors associated with QoL

The regression coefficients of the quantile regression for 9 deciles of QoL score was shown in Table 3. According to the Table 3, several variables were significantly associated with QoL in various deciles. For example, QoL was associated with duration of disease (P < 0.001 for the 1 st to 5th deciles and P = 0.028 for the 8th decile), sufficient income (P: 0.001, 0.004, 0.018, 0.026, < 0.001, and < 0.001 for the 2nd, 3rd, 5-6th, and 8-9th deciles).

Table 3.

Results of quantile regression for association of variables on 9 deciles of of Minnesota heart failure Quality-of-life questionnaire score

Variable	Estimate	Decile
		1	2	3	4	5	6	7	8	9
(Intercept)	β (S.E)	37.41(8.75)	69.18(8.64)	59.68(11.6)	65.86(12.85)	59.85(14.6)	70.89(14.42)	73.46(15.55)	99.55(8.8)	92.59(4.92)
	P-value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
Ejection fraction	β (S.E)	−0.04(0.08)	−0.04(0.07)	0.09(0.1)	0.12(0.11)	0.14(0.13)	0.07(0.12)	0.18(0.13)	0.15(0.08)	0.05(0.04)
	P-value	0.584	0.555	0.363	0.279	0.254	0.602	0.181	0.054	0.231
Age (year)	β (S.E)	0.09(0.07)	−0.03(0.07)	−0.01(0.09)	0.03(0.1)	0.08(0.11)	0.07(0.11)	−0.02(0.12)	−0.02(0.07)	0.13(0.04)
	P-value	0.202	0.644	0.935	0.736	0.508	0.536	0.855	0.762	0.001
Duration of the disease	β (S.E)	0.2(0.04)	0.14(0.04)	0.2(0.06)	0.22(0.06)	0.18(0.07)	0.1(0.07)	0.11(0.08)	0.09(0.04)	0.03(0.02)
	P-value	< 0.001	0.001	< 0.001	< 0.001	0.010	0.144	0.135	0.028	0.193
BMI	β (S.E)	−0.06(0.13)	−0.14(0.13)	−0.31(0.17)	−0.2(0.19)	−0.14(0.21)	−0.24(0.21)	−0.17(0.23)	−0.44(0.13)	−0.26(0.07)
	P-value	0.632	0.258	0.070	0.292	0.515	0.251	0.458	0.001	< 0.001
Number of hospitalizations	β (S.E)	−0.03(0.24)	0.45(0.24)	0.33(0.32)	0.59(0.36)	0.11(0.41)	0.19(0.4)	−0.13(0.43)	0.41(0.25)	0.48(0.14)
	P-value	0.896	0.066	0.306	0.103	0.782	0.646	0.771	0.093	0.001
Gender (Female)	β (S.E)	2.95(1.55)	−3.35(1.53)	−2.34(2.06)	−3.08(2.28)	−2.79(2.59)	−3.63(2.55)	−3.76(2.75)	−1.84(1.56)	−0.95(0.87)
	P-value	0.059	0.030	0.256	0.177	0.283	0.157	0.174	0.239	0.277
Ethnic
Fars	β (S.E)	1.34(1.14)	0.66(1.13)	0.42(1.52)	−0.03(1.68)	−0.15(1.91)	0.09(1.88)	−0.88(2.03)	−5.74(1.15)	−6.4(0.64)
	P-value	0.241	0.558	0.781	0.987	0.939	0.963	0.666	< 0.001	< 0.001
Kurd	β (S.E)	−3.84(1.27)	−1.36(1.26)	−0.72(1.69)	−1.55(1.87)	−0.26(2.12)	1.49(2.1)	−0.8(2.26)	−5.65(1.28)	−2.54(0.72)
	P-value	0.003	0.281	0.669	0.408	0.902	0.477	0.724	< 0.001	< 0.001
Turk	β (S.E)	0	0	0	0	0	0	0	0	0
Marital status (Married)	β (S.E)	−0.79(1.22)	−3.58(1.21)	−2.76(1.62)	−2.15(1.8)	−1.76(2.04)	−4.68(2.02)	−4.14(2.18)	−6.04(1.23)	−5.94(0.69)
	P-value	0.518	0.003	0.091	0.233	0.390	0.022	0.058	< 0.001	< 0.001
Education
Just reading and Writing	β (S.E)	4.63(1.61)	−1.28(1.59)	0.83(2.14)	−3.11(2.37)	0.06(2.69)	1.17(2.66)	−0.49(2.87)	0.92(1.62)	−2.44(0.91)
	P-value	0.005	0.422	0.698	0.191	0.983	0.660	0.864	0.573	0.008
Under Diploma	β (S.E)	4.82(1.89)	−1.37(1.86)	1.41(2.5)	−0.11(2.77)	0.52(3.14)	3.13(3.11)	1.13(3.35)	1.39(1.89)	−1.38(1.06)
	P-value	0.011	0.461	0.574	0.968	0.868	0.315	0.736	0.465	0.194
Diploma and higher (Reference)	β (S.E)	0	0	0	0	0	0	0	0	0
Job (Employed)	β (S.E)	−0.3(1.52)	−5(1.5)	−2.51(2.02)	−4.51(2.23)	−2.81(2.54)	−3.95(2.51)	−4.84(2.7)	−3.63(1.53)	−4.71(0.86)
	P-value	0.845	0.001	0.215	0.045	0.270	0.117	0.075	0.018	< 0.001
Income
Sufficient	β (S.E)	1.45(1.63)	5.54(1.61)	6.31(2.16)	4.44(2.39)	6.48(2.72)	6.02(2.68)	4.16(2.89)	5.98(1.64)	6.42(0.92)
	P-value	0.374	0.001	0.004	0.065	0.018	0.026	0.152	< 0.001	< 0.001
Relatively Sufficient	β (S.E)	−0.82(1.22)	1.14(1.2)	0.57(1.62)	−0.24(1.79)	0.47(2.03)	1.29(2.01)	1.98(2.17)	3.35(1.23)	2.77(0.69)
	P-value	0.504	0.345	0.723	0.893	0.818	0.523	0.363	0.007	< 0.001
Not Sufficient	β (S.E)	0	0	0	0	0	0	0	0	0
Smoking (Yes)	β (S.E)	3.71(1.33)	1.49(1.32)	0.3(1.77)	−0.22(1.96)	2.31(2.22)	3(2.2)	3.04(2.37)	5.05(1.34)	3.98(0.75)
	P-value	0.006	0.259	0.864	0.909	0.299	0.174	0.201	< 0.001	< 0.001
Substance abuse (Yes)	β (S.E)	0.2(1.31)	−0.22(1.3)	0.5(1.74)	0.52(1.93)	0.34(2.19)	0.02(2.16)	−1.11(2.33)	−2.69(1.32)	−0.31(0.74)
	P-value	0.877	0.865	0.776	0.788	0.876	0.992	0.634	0.043	0.671
History of Myocardial Infarction (No)	β (S.E)	−1.95(1.28)	−1.55(1.27)	−1.21(1.7)	−2.3(1.89)	−4.31(2.14)	−3.54(2.12)	−1.66(2.28)	−1.84(1.29)	0.53(0.72)
	P-value	0.131	0.223	0.477	0.224	0.046	0.096	0.468	0.157	0.461
History of Diabetes (No)	β (S.E)	2.3(1.36)	0.41(1.34)	1.5(1.8)	1.72(2)	1.9(2.27)	2.21(2.24)	1.69(2.42)	−0.84(1.37)	−1.61(0.76)
	P-value	0.092	0.762	0.407	0.389	0.403	0.325	0.486	0.541	0.036
History of Hypertension (No)	β (S.E)	4.47(1.45)	2.14(1.43)	1.48(1.92)	0.94(2.13)	0.44(2.42)	1.03(2.39)	0.63(2.58)	−1.13(1.46)	0.09(0.82)
	P-value	0.002	0.137	0.442	0.659	0.856	0.668	0.808	0.441	0.915
History of CVA (Yes)	β (S.E)	1.06(3.33)	−2.66(3.28)	0.45(4.41)	−7.14(4.89)	−4.15(5.55)	−2.7(5.49)	−1.57(5.91)	−9.31(3.35)	−8.71(1.87)
	P-value	0.750	0.418	0.919	0.146	0.456	0.623	0.791	0.006	< 0.001
History of Renal Disease (No)	β (S.E)	−2.61(2.4)	−1.8(2.37)	−1.93(3.18)	−2.09(3.52)	−2.08(4)	−7.37(3.95)	−5.87(4.26)	−3.89(2.41)	−4.89(1.35)
	P-value	0.278	0.448	0.545	0.553	0.604	0.064	0.170	0.108	< 0.001
Having other diseases (No)	β (S.E)	1.68(1.77)	−3.4(1.75)	−0.78(2.35)	1.52(2.6)	−0.54(2.96)	−0.91(2.92)	−0.94(3.15)	−3.49(1.78)	−4.6(1)
	P-value	0.346	0.054	0.741	0.559	0.855	0.755	0.766	0.052	< 0.001
History of Pulmonary Disease (No)	β (S.E)	1.53(2.51)	1.07(2.47)	1.89(3.32)	1.66(3.68)	1.31(4.18)	3.5(4.13)	3.39(4.45)	−1.46(2.52)	0.55(1.41)
	P-value	0.544	0.665	0.570	0.652	0.754	0.398	0.448	0.563	0.698
History of heart failure in family members (Yes)	β (S.E)	−1.89(1.16)	−0.57(1.14)	−2.56(1.53)	−3.27(1.7)	−2.29(1.93)	−2.24(1.91)	−2.41(2.06)	−2.33(1.16)	−1.54(0.65)
	P-value	0.103	0.618	0.097	0.056	0.236	0.241	0.243	0.047	0.019

For a complete explanation of results see supplementary file 1.

Discussion

This study aimed to investigate the level of QoL and self-care in patients with HF disease and their related factors in west of Iran, Hamadan city.

Moderate levels of self-care and quality of life

The results of our study indicate that the patients with HF had moderate levels of both self-care and QoL. Our results were consistent with those of other studies study [41, 46, 47]. This suggests that while patients were engaging in some self-care behaviors, there is still room for improvement to optimize self-care practices. Moderate QoL scores also point to the need to enhance various aspects of patients’ physical, emotional, social and cognitive functioning to achieve a better overall quality of life. The finding of moderate self-care levels is consistent with previous research showing that adherence to self-care recommendations is often suboptimal among patients with HF [31, 35]. Factors such as lack of knowledge, presence of comorbidities, and inadequate social support can hinder patients’ ability to consistently engage in self-care [48]. Targeted patient education, close monitoring by healthcare providers, and involvement of caregivers may help improve self-care practices. The moderate QoL scores underscore the significant burden that HF places on patients’ well-being. HF symptoms, treatment side effects, and the need for lifestyle modifications can negatively impact physical functioning, emotional state, social relationships and independence. Holistic management strategies addressing medical, psychosocial and functional aspects are crucial to enhance QoL in this population. These findings show the unused capacity for enhancement of self-care and QoL among these patients.

Correlation between self-care and quality of life

We anticipated that the patients who showed a higher self-care score report a better QoL score as well, however there was no statistically significant relationship between these two variables. Another study in Iran [31], reported similar findings, however a study revealed that there was a direct and statistically significant relationship between QoL and self-care [49]. Moreover, another study in Ethiopia found a negative association between QoL and self-care, indicating a better self-care leads to a better QoL among patients with HF. It seems that the difference in the results of our study with other studies findings is related to the use of different measurement tools and different research communities with specific cultures of each region [31, 50]. Moreover, the present study used a sample form a referral hospital, where patients with a worse condition refer to the hospital. It is suggested in the future studies, the sample includes patients from healthcare centers as well.

The quantile regressions showed that there were important factors shaping patients with HF’ self-care and QoL which have been discussed in the following sections.

Correlates of self-care

The quantile regression analysis conducted in this study shed light on the diverse factors influencing self-care behaviours among patients with HF across different deciles. Notably, several key variables demonstrated significant associations with self-care scores in various deciles, providing valuable insights into the nuanced nature of self-care practices in this population. Based on the results, the factors associated with self-care in each quantile differed except for substance abuse, reading and writing literacy, and history of hypertension, which were influential factors in all quantiles.

The findings of the present study revealed that ejection fraction had an inverse and significant association with self-care scores in several deciles of the distribution. Specifically, the regression coefficients showed that lower ejection fraction was associated with higher self-care scores in the 1 st, 2nd, 3rd, 8th, and 9th deciles. This finding is in agreement with the results of [51] and is inconsistent with the results of [52]. Our study had a sample size more than twice as large as the later. A lower ejection fraction is indicative of reduced cardiac pumping ability, which is a hallmark of HF. The inverse relationship between ejection fraction and self-care suggests that as the heart’s function deteriorates, patients tend to engage in higher levels of self-care behaviours, potentially as a response to their worsening clinical condition.

The findings of the present study showed that with increasing in age, the self-care score decreases in the 1th and 9th deciles. Other studies [34, 53] showed that there is an inverse statistical relationship between age and self-care. Carlson et al. believe that the changes caused by aging, including the reduction of vision, hearing and cognition of the patient, lead to the inability in performing self-care behaviors, and as a result, they become dependent on others to perform self-care behaviours [54]. Also, Mansouri et al. [53] showed that there is an inverse statistical relationship between age and self-care. However, in another study [55] age was found to be unrelated to self-care.

Regarding the patients’ health status, the results of the present study showed that in most deciles, except for the 6th and 7th deciles, the self-care score increases with the increase in the duration of the disease, but in the study conducted by [34] it was shown that with the increase in the duration of HF, the self-care behaviour become weaker. Rakul believes that the progression of HF causes shortness of breath, extreme fatigue, and lack of energy, and patients become unable to perform self-care behaviours [56]. However, other studies have found that by increasing in the disease duration, patients showed an enhanced level of self-care which is in contrast to our findings [23].

The findings of our study showed that with the increase in the number of re-hospitalizations in the 1th, 8th, and 9th deciles, the self-care score decreased. In a study conducted by [34], self-care behaviours were weaker in patients who were hospitalized more often. Rockwell believes that the progression of HF causes shortness of breath, extreme fatigue, and lack of energy, and patients become unable to perform self-care behaviours [56]. Another study also revealed that patients which had hospitalization history in the previous year had lower self-care score compared to other [57].

In the present study, for most deciles (except 3th to 6th), women had higher overall self-care scores than men. A study also showed that women have higher self-care scores than men, which indicates that females are more confident about their abilities to perform self-care activities, including understanding and following a low-sodium diet, which is one of the main HF treatments, which are needed to prevent further exacerbation of HF, are more reliable [58]. According to the results of [59], women performed better than men in understanding, adhering to, and maintaining low-sodium diet guidelines. Other studies indicated that gender was not related to self-care [49, 60]. These conflicting findings suggest that the relationship between gender and self-care in patients with HF is complex and may be influenced by various cultural, social, and individual factors.

The findings of the present study showed that patients which their ethnicity were Fars in the 7th, 8th, and 9th deciles have higher self-care compared to Turk people, Kurd people in the 1th decile have less self-care than Turk people. This may be due to the fact that in this study, the proportion of patients with sufficient income was significantly greater among Fars ethnicity. Moreover they were more educated than other ethnicities. No study was found to investigate the association between ethnicity and self-care in Iran. Nevertheless, in a study conducted by [55], the effect of ethnicity on self-care was evaluated and it was shown that being of Newar ethnicity was associated with higher self-care management compared to Brahmin, this may be because the Newar participants were younger, more educated, and predominantly lived in urban areas with greater access to healthcare resources.

The findings of our study showed that in the 1th and 9th deciles, married people had higher self-care scores than unmarried people. The results of [53] and other studies [35, 61] showed that there is a significant and direct connection between marital status and self-care. Maybe having social support explains the finding of our study. Having a source of social support from husband/wife in married patients could enhance their self-care behaviours as a study in Netherland revealed patients with higher level of social support reported more self-care behaviours [62].

The findings of our study showed that, in all deciles, persons who were only literate in reading and writing, and also, in most deciles, except for the 1th and 2th deciles, persons under diploma have a lower self-care score than persons with over diploma. Also [53], and [35], showed that the self-care status of persons with a diploma and higher education was more favourable than those with under diploma and reading and writing literacy. It seems that higher education can be effective in expanding critical thinking skills and people’s ability to participate in decisions and treatment and care programs [56]. In the study by e [60] education was not related to self-car.

Socio-economic position and self-care have been investigated by many researches. In the present study income as an economic factor showed a statistically significant relationship with patients’ self-care. In most deciles, except for the 1th and 2th deciles, people with sufficient income, and also in the 1th, 8th, and 9th deciles, people with relatively sufficient income had higher self-care than people with insufficient income. The results of other studies of [35, 63],and [53] aligned to our finding and monthly self-care was directly and statistically related to patients’ income. It seems that the level of income can affect the individual’s ability, self-confidence, better job opportunities and independence, and as a result, the supply of resources needed for self-care [53].

Being employed was another socio-economic factor affecting people’s ability to take care of themselves in the 7th decile. The results of the studies of [35] and [64] showed that people’s jobs have a direct relationship with the level of self-care ability. It seems having a better job status implies high levels of education and high income [65] and provide patients’ resource in taking-care of him/herself. However, a study in Brazil showed being employed and having a sedentary job was related to a worse status of self-care [57].

Having other comorbidities was another investigated factor in our study. Our findings showed that people with a history of diabetes in the 3th, 4th, 5th, 6th, and 7th deciles, a history of hypertension and substance abuse in all deciles, and a history of renal disease in the 1th and 9th deciles of self-care score are superior in self-care. In a study, it was shown that more comorbidity were associated with higher self-care. It seems that with the increase of co-morbidities, the need and motivation for self-care is felt more and as a result, they adhere to self-care behaviours [55]. Another study on patients with HF revealed that in univariate analysis having comorbidities was negatively related to self-care, but after adjusting for other predictors the regression revealed that there was no statistically significant relationship [18].

Not having a history of myocardial infarction in the 1th and 3th deciles, not having a history of CVA in the 1th, 8th and 9th deciles, smoking in the 7th and 8th deciles, not having a history of HF in family members in the 1th decile and an increase in BMI in the 1th decile, from other factors affecting better self-care of patients with HF in the present study.

Correlates of QoL

Regarding factors related to QoL, the quantile regression analysis showed that this variable varies across various quantiles.

Regarding gender disparity in QoL, for the 2th decile, women had higher overall QoL than men. In other studies, the QoL of women was lower than that of men [41, 66, 67]. In a study conducted by [60] gender was shown not to be related to QoL. Experiencing a higher burden of disease in women compared to men could explain this finding [68, 69].

Similar to self-care score, patients’ marital status was related to QoL. The findings of our study showed that in the 2th, 6th, 8th, and 9th deciles, married people had a better QoL than single people. In the study of [34] married people had a better QoL compared to single, divorced, and widowed individuals. Another study also showed marital status had a negative statistically significant relationship with patients’ QoL [70]. But in the study conducted by [31] there was no relationship between marital status and QoL. The role of marital status in providing social support have been documented in other studies [71].

Patients’ age was another Qol predictor. This study showed that with increasing in age, the QoL decreases in the 9th decile. Another study revealed that there was a significant negative correlation between the QoL and age [31]. Furthermore, a study showed that increasing age is associated with a decrease in the QoL in patients with HF [72]. However, surprisingly a systematic review revealed that among two studies which included patients’ age as a predictor, one of them have reported that despite having less comorbidities, young patients had lower QoL level which is in contrast to our finding [73].

Patients’ duration time facing heart failure and re-hospitalization history were other predictors of QoL. Our results showed that in most deciles, except for the 6th, 7th, and 9th deciles, the QoL decreased with the increase in the duration of the disease; also, with the increase in the number of re-hospitalizations in the 9th decile, the QoL decreased. In a study, it was shown that the duration of the disease had an effect on QoL [74]. As another study has shown by increasing in the disease duration QoL would decrease, especially the phycological aspects of QoL [75]. Disease duration as a measure of disease symptoms’ severity would affect various aspects of patients by extending the disease duration they would become more dependent for doing their daily activities [76] which could effect on their Qol.

Being employed was one of the other factors affecting improving the QoL in the 2th, 4th, 8th, and 9th, deciles. The results of other studies [74, 77] showed that unemployment was correlated with a low QoL. Another study in Finland showed being employed was related to good QoL which is in contrast to our finding. The underlying reason could be related to the difference in surveyed population as the Finland study conducted on foreign origin population [78].

Comorbidities showed a significant relationship with patients’ QoL. Our findings showed that in the 9th decile people with a history of renal disease, having other diseases, and a history of diabetes have a worse QoL. In the study by [79] it was shown that chronic kidney disease was significantly associated with worse QoL. Moreover, studies have shown that type 2 diabetes may operate as a negative factor that results in a low QoL for patients with CHD [80].

Limitations, weaknesses and strengths

There were some weaknesses and strengths to this study. Although this study was conducted in Farshchian hospital, the referral hospital for cardiovascular disease in Hamadan province, the patients admitted to other hospitals of other Iran provinces could have different status in terms of self-care and QoL levels and determinants of these outcomes. Therefore our sample might not be representative of the population characteristic. So, we should generalize our findings with cautious. Another limitation in our study which is worth to mention is that the patients with HF in outpatient wards were not included in this study; however they could have different characteristics. The future studies could investigate the level of self-car and QoL and their dominants in these patients and compare them with inpatient patients with HF to reach a better picture for the future interventions. Moreover, the cross-sectional design of our study limits understanding of the predictive relationships. Another limitation of this study is the lack of information regarding health literacy, which future research should consider as an important risk factor. Nonetheless, we employed a relatively large sample size and utilized advanced statistical modeling techniques to assess the associations and identify risk factors. These methods effectively handle nonlinear associations between variables by accounting for various regression coefficients across different quantiles.

Conclusions

Our patients exhibited moderate levels of self-care, highlighting the need for personalized education to assist them in achieving optimal self-care practices, which may help reduce future hospitalizations. Additionally, our study revealed moderate levels of QoL. Although we did not find a significant correlation between self-care and quality of life, we can infer that lower self-care is linked to a diminished QoL. Patients who are motivated to engage in self-care are likely to experience fewer hospital readmissions and an improved QoL. Our findings highlighted that several factors influences self-care and QoL. Based on our conclusions, we recommend that health policymakers prioritize the development of targeted interventions aimed at improving self-care and QoL for patients with HF. Specifically, such interventions should consider the multifactorial nature of these outcomes as well as the diverse demographic, socio-economic, and health-related characteristics of this population. Tailored approaches that address individual patient needs—taking into account factors such as gender and age—are essential for enhancing health outcomes. Moreover, investing in educational initiatives to improve health literacy, addressing substance abuse issues, and providing comprehensive support for managing comorbidities may significantly enhance self-care practices among HF patients.

Abbreviations

MLHFQ

Minnesota Heart Failure Quality-of-life Questionnaire

OLS

Ordinary least squares

CHD

Congenital heart defects Declarations

Authors’ contributions

LT, OH, PA, and AA conceived the research topic and explored that idea, LT, OH, and PA performed the statistical analysis, and LT, OH, PA, VR, SP, and AA collected and prepared the initial draft. All authors revised the manuscript and gave critical comments.

Funding

This study was supported and approved by Hamadan University of Medical Sciences (Grant No: 140303082166).

Data availability

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

Declarations

Ethics approval and consent to participate

This study was submitted to and approved by the Ethical Committee of Hamadan University of Medical Science (IR.UMSHA.REC.1403.168).

Written informed consent was obtained from the participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.