Quality of Life and Its Predictors among Patients with Selected Chronic Diseases

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Introduction
Health-related quality of life (HRQoL) is a major concern in patients with chronic diseases, which infuences the physical and psychological health of the patient as well as their treatment [1]. HRQoL is a term that has been used interchangeably with health and quality of life (QoL) but is considered to be confusing, and the existing tools have failed to measure the HRQoL [2]. Te current study focuses on the QoL and adopts the World Health Organization's defnition of quality of life (QoL): "an individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns" [3].
Chronic diseases can interrupt individual's normal activities and function, causing frustration and loss of hope in life [1]. In a cross-sectional study conducted in the US, the HRQoL was assessed among 21,133 participants [4]. Te participants were asked to indicate the presence or absence of 24 chronic diseases and to indicate any limitations in daily living. Te HRQoL was assessed in fve domains that included physical function, fatigue, pain, emotional distress, and social function. Out of the study sample, 19% reported none of the chronic diseases, 20% reported one chronic disease, and 61% reported two or more chronic diseases [4]. Te study found that the participants with chronic diseases reported poorer QoL across all domains compared to the participants who reported none of the chronic diseases. In addition, the presence of two or more chronic diseases was associated with worse QoL compared with the presence of one chronic disease [4]. Tis study included a large sample size. In addition, the QoL was assessed among the participants who reported the absence and presence of 24 chronic diseases.
Several studies have been conducted to assess the QoL among patients with chronic diseases. For example, a crosssectional study was conducted in Egypt to assess the QoL and its relationship with disease severity among patients with chronic obstructive pulmonary diseases (COPDs) [5]. Te assessment of the QoL was established by the St. George's Respiratory Questionnaire for COPD patients (SGRQ-C) in a total of 40 COPD patients. Te baseline data were established by full disease history, physical examination, chest X-ray, and pulmonary function tests. Ten, the participants completed the QoL assessment scale (SGRQ-C). Te main result of the study was the signifcant negative correlation between COPD severity and the QoL, as severity of the disease increased and the QoL decreased dramatically. Moreover, a high smoking index among COPD patients is a strong predictor of a poorer QoL [5]. Tis study emphasised the need for the QoL assessment and predictors of HRQoL among patients with COPD and other chronic diseases.
Another study was conducted to assess the QoL among patients with end-stage renal disease (ESRD) on haemodialysis. A total of 320 patients were enrolled in the study from one of the dialysis centres, and the QoL was assessed by using the Missoula VITAs Quality of Life Index (MVQOLI) [1]. Tis study found that the QoL decreases with age, possibly because of the decrease in physical and cognitive ability in ESRD patients. Moreover, the QoL was found to be better in ESRD patients with higher education, patients' awareness about the disease and treatment, better treatment adherence, good relationships with medical staf, and patients with family support [1].
Te QoL and its predictors are an important consideration in the care of patients with chronic diseases. A cross-sectional study was conducted in Ethiopia aimed to assess the HRQoL and its predictors among patients with all stages of chronic kidney disease including ESRD (stage 5) [6].Te QoL was assessed using the medical outcomes study short form 36-items (SF-36). A total of 256 participants were enrolled from the nephrology clinic of Tikur Anbessa Specialized Hospital. Te study found that the QoL was greatly afected across all domains, and the lowest mean scores across all domains were found among patients with ESRD (stage 5) except for the emotional role functioning in stage 4 [6]. Te mean score of the QoL in the mental domain among patients with ESRD was 42.8, and the mean score in the physical health domain was 33.4. Multiple linear regression was used to predict the QoL domains, and the analysis revealed that the higher income status and haemoglobin levels greater than 11 g/dL predicted a higher QoL among patients with chronic kidney disease in all domains of SF-36. In addition, a higher QoL in the physical domain was predicted by high family income, higher educational status, and haemoglobin levels greater than 11 g/dL. Moreover, in the absence of disease complications, high family income and haemoglobin levels greater than 11 g/dL were found to be predictors of a high QoL in the mental domain [6].
In the other hand, a cross-sectional study was conducted to assess the impact of chronic heart failure (CHF) on disability and the QoL [7]. A total of 257 adult patients with CHF were enrolled in the study. Te QoL was assessed using the Minnesota Living with Heart Failure Questionnaire, which consists of two sections, physical and emotional. Disability was assessed using the WHODAS 2 questionnaire, which has a global and six domain score that includes understanding and communication, getting around, selfcare, getting along with people, life activities, and participation in society. Te study found that the CHF efect on the QoL was mild. However, disability had a considerable efect. Moderate disability was found in 28% of the participants, and severe disabilities were observed in 16.7% of the patients. Te risk of a poor QoL was three to fve times higher in women without signifcant association with age. In addition, the QoL decreased as severity of disease increased [7]. Such a study found that female patients need to improve their QoL.
Moreover, it is well documented that not only chronic diseases impact the QoL. Besides the type of disease, both age and economic status of patient could be contributing factors for the level of the QoL of patients with chronic disease [8]. Similar results were reported by patients with diabetes mellitus in Saudi Arabia [9]. In addition, male gender, educational status, and presence of disease-related complications were all found to be associated factors with the QoL in persons with chronic disease [9]. Tus, socioeconomic factors need to be considered when examining any chronic disease and its relation to the QoL. Furthermore, since chronic diseases are known to have a limiting impact on the health and well-being of individuals, as well as their QoL, it is important to examine the QoL in patients with diferent conditions to be able to customize the care provided and meet the needs of these patients.
It is important to assess the QoL and its predictors for each population to identify areas that need improvement. Te QoL has become an important measure to understand the efect of diseases on patients' daily living, specifcally with long-term chronic diseases. Yet, the research on the QoL among patients with chronic diseases in Oman is scarce. Tere are a few published studies on the QoL among specifc populations, such as people with diabetes mellitus [10] and kidney disease [11]. However, no studies could be found on the QoL of persons with COPD, ESRD, or CHF in Oman during the last decade. Terefore, the purpose of this study was to assess the level and determine the predictors of the QoL among patients with the chronic diseases, namely, COPD, ESRD, and CHF in Oman.

Design.
A cross-sectional correlational descriptive design was used. Nursing Forum

Sample.
Te sample was patients with one of the following chronic diseases: COPD, CHF, and ESRD. Patients who were 18 years or older, having one of the selected diseases for at least six months, able to speak and write in Arabic, and agreed to participate in the study were included. However, patients with cancer, those with highly infectious disease (i.e., , and patients who were not able to give written consent were excluded. Patients with cancer were excluded due to the belief that their symptoms may have diferent pathological pathways. Combining them with nonmalignant diseases could potentially contaminate the sample. All participants were conveniently recruited.

Settings
Te study took place in two hospitals and one dialysis centre in the Sultanate of Oman, Muscat Governate. Te two hospitals are large referral hospitals where most of the patients with chronic disease are treated. Patients who are visiting outpoints clinics within those hospitals were targeted. Te dialysis centre is one of the major dialysis centres in Muscat city. Te bed capacity of this centre is 52 and serves 350 patients. In addition, it is operated by the Ministry of Health of Oman and serves patients round the clock [12].

Te 36-Item Short Form Health Survey (SF-36).
Te 36-item short form health survey (SF-36) is a selfadministered survey that was developed by RAND healthcare as a measure of the QoL. Te SF-36 instrument consists of 36 items. Tis survey intends to measure the general health concept without specifcation on age, disease, or treatment group [13]. It evaluates eight health domains: frst, physical functioning (10 items) measures limitations in daily life caused by health problems. Second, the physical domain (four items) estimates role limitations caused by physical health problems. Tird, the bodily pain (two items) scale assesses pain frequency and pain interference within the usual roles. Fourth, the general health scale (fve items) assesses individual perceptions of general health. Fifth, the vitality scale (four items) measures energy levels and fatigue. Sixth, the social functioning scale (two items) measures the extent to which ill health interferes with social activities. Seventh, the role emotional scale (three items) assesses role limitations due to emotional problems, and eighth, the mental health scale (fve items) assesses psychological distress [13]. However, these eight domains are merged into two main domains: the physical health (physical functioning, physical domain, bodily pain, general health scale, and vitality scale) and the mental health domain (social functioning, role emotional scale, and mental health scale) based on their main content and in accordance with the previous studies using similar categorization [14][15][16]; Yusop et al. [17]. A higher total score of SF-36 indicates a better QoL, while the inverse indicates a poor QoL (Lins and Carvalho [18]. Te scoring methods of this scale as per RAND healthcare are performed in two steps. First, for every item, there is a numeric value for each response (ranging from 0 to 100). A high score defnes a more favourable health state. Te second step is to calculate the average of the items in each domain [19]. Te English and Arabic versions have been evaluated for reliability and equivalence by Coons et al. [20]. Te Cronbach's α for the Arabic version of the SF-36 was found to be more than 0.70 in multiple subgroups in every scale except one. Te English and Arabic versions of the SF-36 instrument are reliable and equivalent [20].

Te Memorial Symptoms Assessment Scale (MSAS).
Te MSAS was developed by Portenoy et al. [21]. It was developed to assess the common physical and psychological symptoms experienced by cancer patients; however, many recent studies have used the scale for symptom assessment in other chronic disease, such as COPD [22,23] and heart failure [24,25]. Tis scale is designed to assess symptom prevalence, and it assesses symptom severity, frequency, and distress. A Likert scale is used to evaluate each dimension. [21]. Each symptom score is an average of its dimensions, and a higher score refects higher severity, frequency, and distress. Te total score in the MSAS is the average of the symptom scores for all 32 symptoms [26].

Karnofsky Performance Status Scale. Te Karnofsky
Performance Status Scale (KPSS) was developed in 1948 to guide the assessment of the functional status of hospitalised patients [27]. Its applicability for other medical conditions, such as ESRD [28], chronic pulmonary diseases [29,30], and other chronic conditions have been documented [27].
Te KPSS evaluates the functional status with scores of 11 elements ranging from 0 to 100. Te maximum score is 100 for an individual with full functional capabilities to carry out normal daily activities. Te minimum score is zero, which implies death. Signifcant scores on the scale include the score of 70, which indicates care of the self but unable to carry normal activities or do active work. A score of 50 indicates individuals who require considerable assistance and frequent medical care [31].
Furthermore, participants' demographic data including age, gender, marital status, educational level, monthly income, work status, and living place were collected. Moreover, data regarding participants' health status including diagnosis, comorbidities, number of hospital emergency visits, number of admissions, length of hospital admissions, and the time since diagnosis were collected from the patients' medical records.

Data Collection and Ethical
Considerations. Data collected started after obtaining ethical approval and administrative permission from designated authorities within the selected settings. Ten, the patients in the waiting areas with outpatients' clinics were approach. Ten, the study purposes and requirements were explained, and interested patients who agreed to take part in the study were asked to sign a consent form. Ten, clinical data were extracted from the electronic medical record. One of the research team was Nursing Forum 3 present to provide help if needed. Questionnaire was either handed to one of the members of the research team or put in the designated box within the nursing station. All participants were assured that their participation was voluntary, and they had the right to withdraw from the study at any time and not to answer any research questions without afecting their medical and nursing care. Furthermore, they were informed that no identifable data were needed, and only aggregated data would be presented or published.
3.3. Data Analysis. Data were analyzed statistically using IBM SPSS software version 25. Descriptive and inferential statistics were applied. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to describe the study sample characteristics. In addition, an ANOVA test was used to identify the variance in the mean scores of the QoL domains among disease groups (i.e., COPD, ESRD, and CHF). Furthermore, multiple linear regression analysis was used to conclude the predictors for the QoL domain scores (i.e., mental and physical). Te study used Hosmer and Lemeshow's approach to build a multiple linear regression model [32]. First, a bivariate analysis was conducted on all independent variables to determine their signifcance. Simple linear regression and independent ttests were used for continuous and dichotomous categorical variables, respectively. Dummy coding was applied to variables with more than two categories. Variables with a signifcance level of p ≤ 0.25 were included in the regression model, while nonsignifcant ones were removed. Te regression analysis was repeated until a fnal stable model was reached to predict the mental health domain of the QoL, with a level of signifcance of p ≤ 0.05.

Sample Characteristics.
Te sample comprised 340 participants with chronic disease, 120 with ESRD, 120 with CHF, and 100 with COPD. Te mean age of the participants was 60.6 years (SD 14.4) and most of them were males (63.5%).  Table 3 presents the disease-specifc QoL physical and mental domains and pooled together.
To examine the diference in the mean score of the two QoL domains by the disease group, an ANOVA test was conducted and Bonferroni correction for the post hoc analysis was implemented. Tere was a statistically signifcant diference in the mean score of the QoL between diseases for the mental health domain (F (2, 337) � (8.58), p < 0.001) and for the physical health domain (F (2, 337) � (7.31), p < 0.001). Te post hoc analysis (Bonferroni corrected) for multiple comparisons in the QoL domains between disease groups showed the following results. For the mental health domain, there was a statistically signifcant diference in the mean scores between CHF (mean � 76.8, SD � 13.7) and COPD (mean � 68.4, SD � 17.9) (p < 0.001, 95% C.I. � (3.13, 13.63)). In addition, there was a statistically signifcant diference in the mean scores of the mental health domain between ESRD (mean � 75.8, SD � 16.7) and COPD (mean � 68.4, SD � 17.9) (p � 0.002, 95% C.I � (2.15, 12.65)).

Predictors of the Quality of Life.
A multiple linear regression was conducted to examine the predictors of the QoL for the two main domains (mental and physical). Te Hosmer and Lemeshow's approach for modelling in multiple linear regression was followed. First, the bivariate analysis was conducted using a simple linear regression model for continuous variables (i.e., age, diagnosis duration, emergency room visits, admissions, LOS, total symptom number, KPSS score, frequency level, severity level, and distress level) and an independent t-test was conducted for the dichotomous variables (gender, MS, educational level, working status, monthly income, family caregiver, medical diagnosis, and having a chronic diseases). Dummy coding was applied to all variables with more than two categories (medical diagnosis and having chronic diseases). Second, all variables with signifcant results from the frst step at the conservative level of signifcance (p ≤ 0.25) were entered to the regression model. Ten, those variables from the second step were entered separately in the multiple linear regression 4 Nursing Forum  Regression modules were assessed for signifcance and variance explained. All variables with nonsignifcant results were removed and the regression analysis was conducted again, and the change in R 2 was observed. Tis process was repeated to observe the changes in the model parameters for each contributing variable until the fnal stable models shown in Tables 4 and 5 were reached. Te results shown in Table 4 indicate that having a high score on the KPSS and being married predicted a higher score in the mental domain of the QoL, while having a higher total symptom number and COPD predicted a lower score in the same domain. Te model explained 37.5% of the variance in the mean score of the mental domain of the QoL. For the physical domain of the QoL (Table 5), the variables having a high score on the KPSS and being married predicted a higher mean score. Older age, higher total symptom number, and a higher distress level predicted a lower score on the physical domain of the QoL. Te model explained 66.8% of the variance in the mean scores of the physical domain of the QoL.

Discussion
Te fndings revealed that patients with selected chronic diseases (COPD, ESRD, and CHF) reported a relatively low QoL in the physical health domain with a mean score of 51.1 (SD � 24.5). In addition, they demonstrated a high QoL in the mental health domain with a mean score of 74 (SD � 16.5). Te highest mean score of the QoL in the physical health domain was reported by participants with ESRD. Moreover, the highest QoL score in the mental health domain was reported by participants with CHF. Furthermore, the lowest reported QoL in both physical and mental domains was reported by participants with COPD who presented a low and poor QoL in the physical health domain and a relatively high QoL in the mental health domain.
When comparing these results with the previous studies, no studies were found that compared the QoL scores of patients with the selected disease together. In addition, diferent measures to assess the QoL were implemented; so, the ability to compare and discuss the QoL among those with disease was limited. However, the results were in line with the most previous fndings of studies that included at least one of the selected chronic diseases [1,4,5,7,33]. For example, a study found that the participants with any chronic disease demonstrated a poorer QoL compared with individuals who did not have a chronic disease diagnosis [4]. Tey assessed the QoL among patients sufering from any of the 24 chronic diseases using the PROMIS that assesses the QoL in fve domains. In another study, it was found that the efect of CHF on the QoL was mild [7]. Tey assessed the QoL using the Minnesota Living with Heart Failure questionnaire [7]. In addition, in another study, the authors found that the QoL was poor in patients with severe COPD [5]. Moreover, Cardin et al. [1], found that the average QoL among patients with ESRD was 17.4 with a total score ranging from 0 to 30. Furthermore, patients with chronic kidney disease on dialysis reported a low QoL, especially in the physical domain [33].
Te QoL is a complex concept and is afected by many dimensions of human life, such as physical health, psychological health, and social status [34]. Its complexity makes it a concept difcult to be objectively measured. Te diference between the current study fndings and the previous studies can be explained by several factors. First, the current study used the SF-36, while all the other studies used diferent assessment tools, such as the health-related quality of life (HRQoL), which was used by Rothrock et al. [4] and the kidney disease quality of life short version 36, which was used by Almutary [33]. Tese tools difer in their components, domains, and scoring methods and some are disease-specifc, such as the kidney disease quality of life short version 36. Second, the QoL is highly afected by the stage and severity of the disease. Te severe and advanced stages of the disease are usually associated with a poor QoL [5]. In the current study, the stage of chronic disease was not  6 Nursing Forum considered. Tird, the QoL is greatly afected by the age of the individual; younger age is usually associated with a better QoL [35]. Finally, future studies should consider the assessment of the QoL among healthy individuals in Oman to compare it with the participants with chronic diseases.

Predictors of the Quality of Life among Patients with
Chronic Diseases. Te current study fndings reveal that the better functional status (a higher KPSS score) and being married predict a higher score in the mental and physical health domains. In addition, a lower QoL in the mental domain was predicted by a higher total symptom number and diagnosis with COPD. Furthermore, a lower physical health domain was predicted by a higher total symptom number and a higher distress level. Although there is no study that has assessed the predictors of the QoL among patients with selected chronic diseases, all together, the results are comparable with studies that included at least one of the selected chronic diseases. Te fndings are inconsistent with the previous studies as they report diferent predictors to this study [6,33,36]. For example, the higher income status and greater than 11 g/dL haemoglobin level were predictors of the high QoL among patients with CKD in all domains of SF-36 [6]. In addition, high family income, higher educational status, and greater than 11 g/dL haemoglobin level are predictors of a higher QoL in the physical domain, while the absence of disease complications, high family income, and greater than 11 g/dL haemoglobin level are predictors of a high QoL in the mental domain [6]. Being in an advanced disease stage, receiving fve or more medications, having three or more comorbidities, and haemoglobin levels of less than 11 g/dL are predictors of a lower QoL in the physical and mental domains [6]. In another study, it was found that selfreported health and the habit of daily regular activity were predictors for the high QoL among patients with cardiovascular chronic diseases [36]. Moreover, it was reported that older age, male gender, and lower education level were predictors of a lower score of QoL among patients with ESRD [33]. Te diference in the predictors of the QoL in the current study and the previous studies can be explained by several factors. First, no study has explored the predictors of the QoL among the selected chronic diseases all together, which limits the discussion and comparisons. Second, the QoL assessment tools have diferent domains and diferent scoring systems, which may result in diferent predictors. Te current study used the SF-36 and reported the predictors of the two main domains; physical and mental health. Other studies have used diferent QoL assessment tools, such as the EQ-5D-3L (Euro QOL) [36]. Tird, determining the predictors of the QoL depends on the used model of multiple linear regression and the entered variables, which may result in diferent predictors of the QoL from a study to another.

Limitations.
Te results of this study need to be interpreted putting in mind the following limitations. First, the symptoms experienced are highly altered by the stage of the disease that was not considered in this study, and this may limit the generalizability of the study. Second, this study was conducted during the breakthrough of COVID-19, which might have impacted the reporting of symptoms and patient access to healthcare settings. Finally, the convenience samples carry the limitation of low representation of the targeted population.

Conclusion
Understanding the QoL and its predictors among patients with chronic diseases is essential when planning and implementing management plans. Our results call for special attention to the physical health domain of the QoL that might beneft from managing patients' total symptom number and their distress level. It is also critical for health care providers and policymakers to take the abovementioned predictors into consideration when implementing interventions to improve the QoL of this patient category. An example of an intervention that aims to promote patients' QoL is the adoption and implementation of palliative care services for patients with chronic disease. Moreover, selfmanagement programmes and training for the patients may enhance their perception of the own QoL.

Data Availability
Te datasets used and/or analyzed during the current study are available from the frst author upon reasonable request.

Additional Points
Te Following Are known about the Topic. (i) Quality of life is a major concern in patients with chronic diseases which has an impact on the physical and psychological health of the patients.

Conflicts of Interest
Te authors declare that they have no conficts of interest.