Summary Statement Of Implications For Practice

What does this research add to existing knowledge in gerontology?

The study investigates the impact of socio-demographic and social support on health-related quality of life among  old Jordanian patients with coronary artery disease. The findings can inform healthcare workers, policymakers, and educators about the predictors that significantly affect health-related quality of life.

What are the implications of this new knowledge for nursing care with older people?

Social support must be assessed upon admission, and patient education programs should focus on all levels of social support. In-service continuing education programs should include specific lectures and workshops on these concepts.

How could the findings be used to influence policy or practice or research or education?

The study's findings can guide healthcare administrators in allocating resources effectively and prioritizing interventions that improve patient outcomes and satisfaction. The findings can also serve as a basis for future research to examine the predictors and their impact on health-related quality of life. Comparative studies across different regions or countries can help identify similarities and differences in predictors, and longitudinal studies can manifest HRQoL over time. The study findings are consistent with some studies conducted in other countries, but further research and clinical practices are needed to enhance knowledge in this area.

Background:

Coronary Artery Disease (CAD), also known as Ischemic Heart Disease (IHD) or Coronary Heart Disease (CHD), is a prevalent global disease characterized by reduced blood flow to the heart muscle. Atherosclerosis of the coronary artery is the most common cause. Psychological factors like stress, anger, hostility, anxiety, and depression can worsen the CAD prognosis and increase the risk of cardiovascular death. Approximately 382,820 people died due to CAD in 2020, with 2 in 10 deaths occurring in adults under 65 years old. More than 75% of CAD-related deaths occur in low- and middle-income countries.

Quality of life (QoL) is a significant indicator of treatment effectiveness; yet, because CAD patients have physical, social, and emotional limitations, it is unclear what factors affect QoL. The degree of the illness, the course of therapy, or the clinical state following discharge cannot be used to forecast it (Ahn, 2016). According to Etxeberria et al. (2019), psychological, biological, emotional, cognitive, personal, and environmental aspects are all included in health-related quality of life. The goals of current care should be to reduce morbidity and death while simultaneously enhancing HRQoL. Social support and stressful life events are important for CAD patients, especially females. The design of cardiac rehabilitation programs must take patients with high stress levels and low social support into account while providing targeted care. Achieving improved quality of life ought to be the aim of public health programs.

Social support is widely defined as the availability or presence of others on whom one may rely, individuals who communicate that they are valued, appreciated, and loved (Vaglio et al., 2004). The two categories of social support are structural and functional. Structural support can take many different forms, including the breadth, nature, density, and regularity of interactions with a person's social network (Vaglio et al., 2004). Variations are described by measures of social support density, interaction frequency, number of close versus distant acquaintances, marital status, group membership, and geographic closeness. The four types of functional support are instrumental (which helps with problem-solving, e.g., help with tangible tasks), financial (material aid, gifts), informational (help to provide crucial information, advice, and counseling), and emotional (help to provide the necessary information, advice, and counseling). Functional support is meeting particular social needs (expressions of sympathy, love, trust, and care) that might help a person build a social network. The word "tangible" is frequently used to describe assistance that can be seen or quantified clearly, such as financial or practical support (Pushkarev et al., 2019).

Social support can generally be measured in terms of how it is perceived, which refers to one's ideas about the availability of assistance during times of need, or how it is received, which refers to the precise sorts of support that one actually receives (Liu et al., 2017).

Social support has several positive effects on older individuals' general health and wellbeing. In particular, among older persons with prior life stressors like illness, social support from a range of sources (family, friends, and community) has been linked to a better outcome and better mental health (Wang et al., 2018). Additionally, research has indicated that older persons who have sufficient social support are less likely to experience the negative long-term impacts of life stressors (such as poor emotional health, a pessimistic attitude, hospitalization, and poor survival) (Bouchard et al., 2017). Importantly, a risk factor for older people that may be modified is a lack of social support. But a thorough examination of the social environment is necessary before intervention (Moser et al., 2012).

A wide range of health outcomes, including death rates, are improved by social support (Pushkarev et al., 2019). It enhances psychological and emotional functioning, healthy lifestyles, work functioning, and post-traumatic growth (Alaloul et al., 2021). Social support may assist patients and survivors in enhancing their psychological functioning by motivating them to adhere to their therapies more closely and changing their health alterations in their immune system and behavior. Negative social support is linked to greater levels of anxiety and depression. Social support could impact psychological health (Gonzalez-Saenz et al., 2017).

The evidence from the literature suggests that psychological risk factors for CAD, such as social deprivation, have a major impact on the development and progression of CAD. Studies have indicated that those who are socially isolated are more likely to die from CAD prematurely; patients with CAD who lack social support have worse survival rates and worse prognoses. According to a meta-analysis by Mookadam and Arthur (2004), people who experience social isolation after a myocardial infarction have a roughly two-thirds higher risk of dying. Low social support is linked to mental stress, difficulties changing behavioral risk factors, a more pronounced progression of CAD symptoms, and a worse prognosis in patients with pre-existing CAD in the field of cardiology (Mookadam and Arthur, 2004).

Method:
Design:
A descriptive correlational design was used for the purpose of this study.

Settings:
Cardiac center in Jordan.

Population and Sample:
The target population of this study was all adult Jordanian patients with coronary artery disease. The accessible population was the adult Jordanian patients who had been admitted to the center. A consecutive sample of all eligible adult patients who have CAD and were admitted to the center during the period of data collection (from the middle of November 2022 to the end of March 2023) was recruited. Patients who met the inclusion criteria were contacted on admission after stabilization; a consent form was obtained; and then they were asked to answer questions about their sociodemographic characteristics and social support surveys on the day of discharge. After one month of being discharged, patients were contacted by phone to answer the questionnaire to assess social support and HRQoL levels.

The proposed sample size was calculated using G*Power 3.1.9.2 for Windows. The power was chosen at 0.90, with an alpha of 0.05, a medium effect size of 0.15, and a number of predictors of 11. The estimation was based on “Linear multiple regression: fixed model, R2 deviation from zero." The minimum sample size for this study was 152. The researcher increased the sample by at least 20% to compensate for the attrition of 182 participants. For this study, there were 180 participants.

Inclusion and Exclusion Criteria:
Jordanian Patients hospitalized with CAD for medical interventions, aged 18 years or older, able to read and understand the Arabic language, and able to provide informed consent, were included in the study. Any patients with cognitive impairment who were discharged to another health care facility and who had physical limitations that would make it impossible to fill out forms (e.g., blindness, deafness) were excluded.

Ethical Considerations:

Approval was obtained from the Institutional Review Board (IRB) at the University,  and the hospital ethical committee before data collection. After that, a brief summary was presented for the hospital manager and head nurse. Then, the researcher contacted the participants who were admitted at the time of data collection and who met the eligibility criteria to explain the goal of the study, how to obtain data, and how to sign a consent form. Patients also had the opportunity to ask questions regarding this study. Participation was voluntary, and participants had the right to withdraw from the study at any time. Patient confidentiality was guaranteed during the study. The patient’s data was protected and saved on the researcher’s personal computer; soft copy materials were stored on a password-protected computer; and the study's hard copy materials were stored in a locked cabinet in a locked office. Only the researcher had access to the participants’ data.

Measurement and Instruments:
There were three main instruments used in the study: socio-demographic, medical outcome social support survey, and RAND SF-36 item health survey.

Data Collection Procedure:
Patients with CAD were recruited and screened for eligibility. The study objectives were explained to eligible participants who completed a socio-demographic survey and social support questionnaires. The researcher contacted participants after one month of discharge and asked questions about social support and health-related quality of life. If participants declined, another patient was chosen until the target sample size was reached. Participants were sincerely thanked and informed of the study findings.

Data Management:
The entered data were checked for accuracy by performing ascending and descending sorting for each variable. Screening and cleaning the data were completed by running the frequencies for every variable and examining those frequencies carefully for invalid, unusual values, missing data, and adequate variability. There was no missing data. Then, to examine the quality of the data,. The study scale variables in terms of normality, skewness, and kurtosis were evaluated using frequency tables, histograms, and scatterplots. The results showed that all variables had no outliers. Actively checking for skewness, the results showed that all scale variables had no skewness and were normally distributed, ranging between +2 and -2 (George and Mallery, 2010).

Results:

Sociodemographic Characteristics of the Sample:

A total of 180 questionnaires were completed by the patients with coronary artery disease, with a desirable response rate of 96%. Results Table 1 indicated that the participants had a mean age of 54.32 (SD = 9.13) years. The majority of participants were males 86.1% (n = 155).  married 94.4% (n = 170). Had at least one chronic disease 68.3% (n = 123). Regarding educational level, 49.4% (n = 89) of participants held a secondary school degree, 21.7% (n = 39) held a bachelor’s degree, 17.8% (n = 32) held a primary school degree, and 11.1% (n = 20) held a diploma degree. About working status, 55% (n = 99) of participants were not working, while 45% (n = 81) were working. The participants’ monthly income ranged from 34.4% (n = 62) for the 601–800 JD category to 15% (n = 27) for the 200–400 JD category.

Table 1: Characteristics of the Study Participants (N= 180).

M= mean, SD= standard deviation, n= number, % = percentage.

Social Support Level:

The total social support score mean is 82.12 (SD = 17.71). The 19 items are divided into five subscales as in Table 2. Social support has five subscales; the higher the score, the higher the level of support. The mindset is the highest subscale, with a mean of 87.22 (SD = 12.78). On the other hand, the lowest subscale is affectionate support, with a mean of 79.54 (SD = 19.58).

Table 2: Social Support Scale and Subscales(n=180).
M= mean, SD= standard deviation

Health Related Quality of Life Level:
In the current study, the researcher measures health related quality of life. The Short Form-36 (SF-36) health status survey RAND version 1 was used, which includes eight subscales: physical functioning, social functioning, role limitations due to physical health, role limitations due to emotional problems, emotional well-being, energy/fatigue, pain, and general health perception. Furthermore, the HRQoL subscales are divided into two health dimensions: physical health and mental health. Each item is scored on 0 to 100 scale; the higher score, the higher level of health related Quality of Life among participants. In this study, the physical health total score mean was 46.16 ± 18.86 higher than the mental health 43.82 ± 15.72 table 3.

Table 3: Health Related Quality of Life Subscales and Dimensions(n=180).
M= mean, SD= standard deviation there is a positive relationship between social support level and physical health (r =.272, P < .001) and mental health (r =.220, P =.003) quality of life. This implies that patients with a higher social support level are positively correlated with a higher level of physical and mental health related quality of life table 4.

Table 4: Relationship Between HL, SS and Health Related Quality of Life)n=180).

Health Related Quality of Life Predictors:

Multiple linear regression was used to predict the value of dependent variable (mental and physical health related quality of life) based on the provided values of independent variables (predictors).

Table 4: Predictors of Mental Health Related Quality of Life.

Analysis revealed that the final four factors model table4 (regression equation) was significant F _(4,175) = (6.303, P <.001). The overall model fits ΔR² = 0.106, which indicate that this model has explained about 10.6% of the variance of mental health related quality of life among patients with coronary artery disease. Mental health related quality of life among patients with coronary artery disease decreased by 8.663 point for the bachelor degree compared to the secondary degree (B = - 8.663, P =.002). Also, mental health related quality of life was significantly predicted by participant gender (B = - 7.803, P =.020). Female patients have lower mental health score by 7.803 than male (the code: male = 1, female = 2). Moreover, quality of life among patients with coronary artery disease was significantly predicted by their social support level (B = 5.495, P =.020). when the social support increased by one point, the mental health increased by 5.495 point as well.

To predict the physical health related quality of life for patients with acute coronary artery disease. Backward stepwise regression was used Table 5 by entering the significant independent variables (social support, chronic disease and education) to determine the most predictable variables.

Table 5: Predictors of Physical Health Related Quality of Life.

Analysis revealed that the final three factors model (regression equation) was significant F _(4,175) = (9.016, P <.001). The overall model fits ΔR² is 0.152, which indicate that this model has explained about 15.2 % of the variance of physical health related quality of life among patients with coronary artery disease. Physical health related quality of life was significantly predicted by educational level (B = 14.860, P < .001), which means patients with diploma degree have increased in their physical health by 14.860 units than patients with secondary degree. Social support level is positive predictor (B = .258, P <.001), when social support increased by one unit, the physical health increased by .258 units. physical health related quality of life was significantly predicted by presence of chronic disease (B = - 8.560, P = .003). Presence of chronic disease has decreased physical health by 8.560 unit.

Discussion:
The current study result revealed that the predictors for health-related quality of life among patients in Jordan with coronary artery disease after one month of being discharged are higher educational level and gender for mental health. while a higher level of education, social support level, health literacy, and the presence of chronic disease affect physical health.

The current study result is congruent with the Santoso et al. (2017) study result, where researchers found that patients who have difficulties in daily tasks due to the presence of chronic diseases have a lower quality of life in comparison to those who did not previously have health problems. Furthermore, patients who had a high level of social and tangible support had a higher quality of life (Santoso et al., 2017). Moreover, patients with significant functional impairments and those receiving family care need intervention measures aimed at enhancing quality of life (Yujeong, 2022). Moreover, the study recruited 4278 patients who underwent cardiac catheterization, and the RAND SF-36 HRQoL was used to assess the quality of life. Researchers found that a lower level of HRQoL was associated with low social support, female patients, and the presence of disease. while higher HRQoL is associated with education (Bosworth et al., 2000). Moreover, low social support levels significantly and independently increased the risk of death in CAD patients after PCI and were correlated with age and gender (Pushkarev et al., 2019). In contrast, this study's results were not consistent with Yujeong. In the 2022 result, the researcher found that age, marital status, and primary caregiver were the significant general variables that were proven to have an impact on HRQoL in patients who had undergone PCI. Furthermore, in Wang et al. (2016) study results, they found that the predictors of health-related quality of life among patients with myocardial infarction included low monthly income, whereas predictors of mental HRQoL included ex-smokers, alcohol use, hypertension, anxiety, and depression. Moreover, a cross-sectional correlational study aimed to determine health-related quality-of-life predictors among patients with myocardial infarction. 128 patients were enrolled. The result showed that predictors of physical HRQoL included low monthly household income, whereas predictors of mental HRQoL included ex-smokers, alcohol use, hypertension, anxiety, and depression (Wang et al., 2016).

This study result might be explained by the fact that educational attainment is often associated with various psychosocial factors that can influence mental health and well-being. Higher levels of education are generally linked to better cognitive abilities, problem-solving skills, and access to resources, including health information. Individuals with higher education levels may have greater awareness of physical and mental health issues, better coping strategies, and the ability to seek appropriate support, leading to improved physical and mental health-related quality of life.

Gender is a sociocultural factor that can influence mental health experiences and outcomes. Men and women may have different social roles, expectations, and societal pressures that can impact their mental health. For instance, women may face higher rates of mood disorders. Additionally, gender-based discrimination can affect help-seeking behaviors and access to health services. Considering gender as a predictor helps identify potential disparities and design interventions that address the unique mental health needs of different gender groups, thereby influencing the mental domain of health-related quality of life. Moreover, social support plays a crucial role in maintaining physical health and well-being. Having strong social connections and a support network can provide practical assistance, encouragement, and emotional support, which can positively impact patients' physical health. Social support can help patients adhere to treatment plans, engage in healthy behaviors, and manage stress, all of which contribute to improved physical health-related quality of life.

Chronic diseases, such as diabetes, heart disease, arthritis, etc., have a significant impact on physical health-related quality of life. Having a chronic disease can affect patients daily functioning, mobility, and overall well-being. The presence of a chronic disease can also influence one's access to healthcare, social support, and self-management abilities. Understanding and considering chronic disease as a predictor helps identify patients who may have specific needs and require targeted interventions to enhance their physical well-being and quality of life.

Conclusion:
The study looks into how social support and sociodemographic factors affect the health-related quality of life for Jordanian individuals suffering from coronary artery disease. The results provide valuable insights for educators, politicians, and healthcare professionals regarding the determinants that substantially impact health-related quality of life. All levels of social support should be covered in patient education programs, and social support has to be evaluated at the time of admission. Lectures and workshops on these topics should be included in in-service continuing education programs for professionals. The results of the study can help hospital executives prioritize actions that lead to better patient outcomes and satisfaction and allocate resources more wisely. The results can also be used as a foundation for further studies looking at the predictors and how they affect quality of life related to health. Comparative research between several nations or regions might be beneficial.