Factors influencing fatigue among patients undergoing hemodialysis: a multi-center cross-sectional study

ABSTRACT

Fatigue has been reported to be the most common symptom experienced by patients receiving hemodialysis (HD) therapy. Fatigue can lead to a reduction in their ability to engage in both routine and self-care activities, which can negatively affect their self-confidence and quality of life. This study aimed to determine the level of fatigue and the factors that affecting its level among patients receiving uHD. Methods: A cross-sectional design was utilized to explore the level of fatigue among patients receiving maintenance HD using the Mul-tidimensional Assessment of Fatigue (MAF) scale. Data were collected from four dialysis centers in two Saudi Arabia cities, Hail and Al-Qassim, between January 2022 and October 2022. Results: The questionnaire was completed by 236 patients. Older patients, male patients, and retired pa-tients reported significantly higher levels of fatigue (p < 0.001). In contrast, marital status, educational level, and financial status did not significantly affect the level of fatigue among patients (p = 0.193, 0.285, and 0.126, respectively). Patients who had seven or more dependents reported more fatigue than those who had lower levels of fatigue or who did not have dependents (p = 0.004). In addition, patients who had a regular exercise regimen reported significantly lower fatigue than those who did not have an exercise regimen (p = 0.011). Multiple linear regression demonstrated that employment status (student), comorbidity condition (one chronic disease), dialysis duration, satisfaction with dialysis time, and dialysis time were found to affect the fatigue scores (R2 = 0.302, p ˂ 0.001). Conclusion: The findings of this study gives a broader understanding of factors influencing fatigue among patients with HD that will help to develop strategies of more focused interventions to reduce fatigue among patients with HD.

KEYWORDS:

• Fatigue
• hemodialysis
• factors
• influencing
• fatigue
• Saudi Arabia

1. Introduction

Chronic kidney disease (CKD) is a universal public health concern, specifically in developing countries [1]. This condition is characterized by a slow and progressive loss of kidney functionality, where the ability of the kidneys to operate efficiently diminishes steadily over time [2] Saudi Arabia has the highest estimated prevalence of CKD, at 24% [3], which largely attributed to the significant incidence of diabetes mellitus (DM) and hypertension in the country [4]. In Saudi Arabia, hypertension affects 22.7% of the population, and there was a significant rise in diabetes, from 0.9 cases in 1992 to 2.5 million in 2010 [5].

The majority of patients with CKD rely on dialysis to survive. Due to the unavailability of renal transplantation and lack of donors and medical facilities [6], 92% of patients undergo hemodialysis (HD) as a replacement therapy [7]. Patients who receive HD experience multiple symptoms that lead to limitations in everyday physical and mental activities as well as social restrictions, which can affect their psychological well-being [8] and their quality of life [9]. In fact, dialysis logistics can alone be a powerful source of exhaustion. Because of the physical strain involved in traveling to and from dialysis centers, energy is often depleted before treatment even begins. Difficult environmental conditions or remote locations are even more exhausting for patients. While dialysis is necessary to ensure appropriate filtering, each session can be psychologically and physically exhausting if the treatment times extend past patients ‘ability limits. Accordingly, if dialysis sessions are scheduled at inappropriate times so as to interfere with daily schedules or night sleep, this also affects circadian cycles and adds extravagant amounts of fatigue [10].

Fatigue has been reported to be the most common symptom experienced by patients who receive HD therapy. The prevalence of fatigue among patients who receive HD has been found to be 92.2% in Canada [11], 82% in the UK [12], and 85% in Saudi Arabia [13]. Fatigue is characterized by feelings of weakness and tiredness, declined cognitive function, lack of energy, and poor concentration [14–16]. Fatigue reduces the ability to engage in routine and self-care activities as well as fulfill familial and social roles; this can lead to unemployment, which can negatively affect patient’s self-confidence and quality of life [17]. Due to the disabilities associated with fatigue, patients are unable to perform their daily physical activities independently, which can also lead to limitations in their social interaction with others and cause them emotional distress.

Fatigue is caused by the side effects of HD, such as blood loss during HD sessions causing uremia, anemia [18], inadequacy of dialysis [19]. This means that the exhaustion so often experienced by hemodialysis patients is due to insufficient oxygenation combined with dysfunctional energy metabolism, owing both to anemic effects and dialysis efficiency. Yet at the same time inappropriate dialysis can also increase levels of uremic toxins and inflammatory mediators as cellular poisons. This additional metabolism, however, is what continually depletes the strength of these patients ‘muscles and adds to their loss of stamina. Also, the poor nutrition due to restriction on the intake of food and fluids, sleep disorders, and pain [14,20]. This metabolic deficiency manifests as diminished strength, a lowered threshold for exercise, and a pervasive sense of exhaustion, even when carrying out routine tasks. Sleep problems exacerbate the situation by interfering with the essential restorative processes that influence mood, memory, and cognitive function. Sleep fragmentation impairs the body’s ability to clear metabolic waste products and regulate inflammation, leading to a never-ending cycle of fatigue and weariness. Not to mention, chronic pain is an ongoing energy drain that further limits physical activity and diverts resources from other biological functions. A vicious cycle that significantly reduces quality of life is initiated when the psychological distress caused by pain exacerbates fatigue and disrupts sleep [21]. For this reason, it is important to recognize the interactions involved. Identifying targeted areas like nutritional support, insomnia treatment and pain management provide routes that can be helpful in remedying fatigue and enabling hemodialysis patients to regain their vitality.

It has been reported that fatigue is the key contributor to symptom burden among patients undergoing HD [13]. Fatigue can be influenced by numerous factors, such as age, sex, and marital status, level of education, employment condition, and income [22]. Traveling to and from dialysis facilities [23], duration of dialysis, and inappropriateness of dialysis time sessions allocated to patients might also contribute to increasing the levels of fatigue. In context, the energy needs of dialysis are exaggerated by the reduced physical reserve that stems from aging. It can be explained that the different levels of exhaustion may also be related to the differences in hormones and muscle mass between women and men. Marital status and other relationships affect the treatment of fatigue, as social support provides invaluable practical and emotional assistance. But those who are more knowledgeable perhaps can cope with their exhaustion better through possessing greater self-efficacy skills and being able to access resources faster. Further, employment status and income are strongly related to stress levels, feelings of financial insecurity, and the ability to have regular dialysis sessions.

Several studies have explored the association between fatigue and depression [24–26]. Debnath et al. Recently compared the level of fatigue in HD patients at different times on dialysis and non-dialysis it was found that fatigue was higher on a dialysis day than on a non-dialysis day [24]. No previous study has explored the impact of selecting an appropriate dialysis session for patients receiving HD. Horigan, commented that little is known with regard to the level of fatigue of HD patients and the factors associated with it [27].

The study’s focus on Saudi Arabia adds a crucial geographical dimension to the existing body of research on CKD and fatigue. Given the unique demographic, environmental, and healthcare factors in Saudi Arabia, this research provides valuable insights specific to this region, which may differ significantly from findings in other parts of the world. The study’s detailed examination of various factors influencing fatigue in CKD patients, including physiological, lifestyle, and healthcare-related aspects, offers a comprehensive understanding that goes beyond the scope of many existing studies. This thorough approach allows for a more nuanced understanding of how these factors interplay uniquely in the Saudi Arabian context.

This study is designed to explore the extent and determinants of fatigue in HD patients. It aims to understand the prevalence of fatigue in this group and how it affects their lives. By identifying the factors contributing to fatigue, the study can inform more effective and personalized treatment strategies. This research fills a crucial gap in nephrology, providing new insights that can guide future studies and enhance clinical practices. Additionally, it seeks to empower patients through education about fatigue management and to influence healthcare policies for improved dialysis services, ultimately aiming to elevate the standard of care and quality of life for these patients.

2. Methods

2.1. Research design

A cross-sectional design was utilized in this study.

2.2. Setting

The study was conducted in Hail and Al-Qassim cities in Saudi Arabia between January and October 2022. In Hail city, the following two dialysis units were included (1) King Salman hospital and (2) King Khalid Hospital. In Al-Qassim city, the following two dialysis units were included: (1) DaVita Dialysis Center and (2) Buraidah Central Hospital.

2.3. Sampling technique and sample size

A total of 236 patients who are on HD therapy were selected by employing a convenience sampling technique.

Inclusion criteria: Patients aged 18 years or over, diagnosed with renal failure, and on HD for more than three months were included. Moreover, patients who were cognitively able to cooperate with the assessment, able to read and write, and were willing to take part in the study were included. Patients with CKD experiencing acute conditions, those undergoing narcotic therapy, and individuals noncompliant with HD therapy were excluded from the study.

During the recruitment phase, nursing staff at the HD centers, acting as gatekeepers, facilitated access to the study’s settings and participants. They pre-screened potential participants for interest and eligibility. Eligible patients were then introduced to the researcher, who provided them with an invitation letter, information sheet, and consent form. Patients had 48 hours to express interest in participating. Those who agreed were given the questionnaire to complete. Upon receiving the questionnaire, patients were expected to complete it while undergoing their dialysis session and return it before leaving the center.

2.4. Data collection tools

Part I – Socio-demographic data: This data includes social and demographic characteristics of participants, such as age, sex, marital status, level of education, occupational status, number of dependents, income status, duration in hospital, duration of dialysis, exercise status, and number of co-morbidity conditions.

Part 2—Participants’ data related to dialysis: Dialysis session allocation refers to the allotment of day and timing to the patients in the units of dialysis for the selected hospitals. Patients were requested to report their satisfaction with their allocated session times and whether they were the ones who chose the day and time for their sessions and if they would prefer to change them. The patients were also asked if they wish to change the times of their dialysis session to a flexible dialysis schedule.

Part 3—Multidimensional Assessment of Fatigue (MAF): Patients receiving maintenance dialysis had their levels of fatigue assessed using the MAF scale in Arabic language. MAF scale was chosen for its established reliability and validity in measuring fatigue in chronic diseases [28]. It comprehensively assesses fatigue, including its intensity, severity, associated distress, timing over the past week, fluctuations, and impact on daily activities [28]. Additionally, its concise format makes it suitable for dialysis patients, who often experience poor concentration associated with mental and physical fatigue. The 16-item tool assesses four aspects of fatigue: severity, discomfort, timing, and the effect of exhaustion on everyday activities. The first 14 items contain numerical rating scales ranging from 1= ‘Not at all’ to 10 = ‘A great deal,’ while the last two items were rated on a four-point Likert scale. The total possible scores for MAF range from 1 (no fatigue) to 50 (severe fatigue) with high scores reveal high level of fatigue, where lower scores reveal less or absence level of fatigue. The MAF was described to be simple in use, valid, reliable, and effective across a wide range of languages, different diseases and settings around the world [29]. The user agreement required patients to use the Arabic version of the MAF, which was obtained from the Mapi Research Trust.

2.5. Ethical considerations

Ethical approval was taken from the research committee of ethics at Hail University (Number [H-2021-206]). In addition, ethical approval was taken from the Institutional Review Board committee at Hail health clusters (number [H-08-L-074]). In Al-Qassim city, approval was also taken from the General Directorate of Health Affairs (number [607-44-2091]). The gatekeeper, a nurse who works in the dialysis unit, assisted with the recruiting phase by giving a list of potential participants who met the eligibility requirements and expressed interest in taking part in the study. After introducing themselves to the patients, the researchers went on to describe the study’s goals and methodology. Before beginning the study procedures, each participant gave their written, informed consent. Participants were made aware that their participation in the study was entirely voluntary and that they might leave at any time. Completing the questionnaire took approximately 10 minutes or less.

2.6. Data analysis

Data were analyzed using the IBM SPSS Statistics software, Version 27 (IBM Corp., Armonk, NY, USA). Participants’ characteristics were described using descriptive statistics such as frequencies, percentages, means, and standard deviations. The normality of distribution was assessed using the Kolmogorov – Smirnov test in which the results were normally distributed and consequently, parametric statistics were used. Independent sample t-tests and one-way ANOVA were used to determine the factors affecting nurses’ fatigue. Factors significantly associated with nurses’ fatigue were further analyzed using multiple linear regression. Statistical significance was set at p ˂ 0.05.

3. Results

Table 1 illustrates the distribution of participants according to their socio-demographic characteristics. Over half (56.8%) of the participants were male and less than half (44.1%) were aged 50 years or older, with the mean age of 50.05 ± 16.10. Most of them (82.2%) were married and more than one-third (39%) were unemployed, followed by employed and retired (29.2 and 25.8, respectively). With regard to the level of education, most of them (44.9%) had secondary school education followed by illiterate, higher education, and primary education (24.2%, 18.6%, and 12.3, respectively). Approximately one-third of them had either 1–3 children or 4–6 dependents and 23.3% had seven dependents or more while 12.7% had no dependents. Most of the participants had an acceptable financial status and did not adhere to a strict exercise regimen (73.3% and 87.7%, respectively). Most of the participants (33.9%) had three or more chronic diseases, followed by two chronic diseases (28.8%), while 25% did not have any chronic diseases.

Table 1. Socio-demographic characteristics of participants (N = 236).

Characteristics		no	(%)
Age (years)
	˂35	49	(20.8)
	35–50	83	(35.2)
	˃50	104	44.1
	Mean± SD: 50.05 ± 16.10
Sex
	Male	134	(56.8)
	Female	102	(43.2)
Marital status
	Single	42	(17.8)
	Married	194	(82.2)
Occupational status
	Employed	69	(29.2)
	Unemployed	92	(39.0)
	Student	14	(5.9)
	Retired	61	(25.8)
Education level
	illiterate	57	(24.2)
	primary	29	(12.3)
	secondary	106	(44.9)
	higher education	44	(18.6)
Number of dependent
	No dependent	30	(12.7)
	1–3	71	(30.1)
	4–6	80	(33.9)
	≥7	55	(23.3)
Financial status
	excellent	43	(18.2)
	acceptable	173	(73.3)
	bad	20	(8.5)
Exercise
	Yes	29	(12.3)
	No	207	(87.7)
Number of Comorbidities
	I don’t have comorbid	59	25.0
	one chronic disease	29	12.3
	two chronic diseases	68	28.8
	three chronic diseases and more	80	33.9

Table 2 indicates that there is a significant relationship between the age and the total scores of the MAF (p < 0.001) in which older patients over 50 years of age scored higher than younger patients. Additionally, male patients scored higher on fatigue than females. The occupational status was also significantly associated with MAF scores, were patients who retired experienced more fatigue than other groups (p < 0.001). On the other hand, marital status, educational level, financial status and hemoglobin level did not significantly affect the level of fatigue among patients (p = 0.193, 0.285, and 0.126, respectively). Further, the number of dependents was also found to significantly affect the level of MAF scores, with patients who had seven or more dependents reporting higher fatigue than those who had fewer dependents or no dependents (p = 0.004). Finally, patients who did have an exercise regimen significantly expressed less fatigue than those who did not have a regular exercise regimen (p = 0.011).

Table 2. Relationship between participants’ socio-demographic characteristics and MAF scores.

Variables		N	Mean± SD	test value	p-value
Age
	˂35	49	21.27 ± 8.77	F(10.406)	<0.001
	35–50	83	22.95 ± 6.96
	˃50	104	27.47 ± 8.77
Sex
	Male	134	26.40 ± 8.32	t (3.926)	˂0.001
	Female	102	22.19 ± 8.31
Marital status
	Single	42	22.99 ± 9.99	t (1.306)	0.193
	Married	194	24.91 ± 8.28
Occupational status
	Employed	69	24.44 ± 6.69	F (13.526)	<0.001
	Unemployed	92	23.15 ± 8.49
	Student	14	15.23 ± 7.30
	Retired	61	28.99 ± 8.23
Education level
	illiterate	57	25.23 ± 8.51	F (1.270)	0.285
	primary	29	23.04 ± 9.26
	secondary	106	25.33 ± 8.00
	higher education	44	22.86 ± 9.44
Number of dependent
	No	30	20.71 ± 10.88	F (4.640)	0.004
	1–3	71	24.05 ± 8.29
	4–6	80	24.36 ± 7.55
	≥7	55	27.64 ± 8.30
Financial status
	excellent	43	26.80 ± 9.01	F (2.093)	0.126
	acceptable	173	23.90 ± 8.69
	bad	20	25.49 ± 6.34
Exercise
	Yes	29	20.77 ± 8.35	t (−2.561)	0.011
	No	207	25.10 ± 8.54
Hemoglonin level
	Low	213	5.34 ± 2.43	t (1.747)	0.082
	Normal	23	4.45 ± 1.83

Table 3 depicts the relationship between patients’ dialysis data and their total scores on the MAF. As evident from the table, there is a significant relationship between the total scores of MAF and the items ‘duration in hospital,’ number of comorbidities,” ‘duration of dialysis in number of years,’ ‘patients’ satisfaction with time,” and ‘person who selects the time of dialysis’ (p = 0.035, 0.001, 0.028, 0.001, and 0.001, respectively). Contrarily, there is no significant relationship between the item ‘preference of changing time’ and the total MAF scores (p = 0.054).

Table 3. Relationship between participants’ dialysis data and MAF scores.

Variables		N	Mean± SD	test value	p-value
Duration to hospital
	˂10	49	25.44 ± 8.66	F (2.918)	0.035
	10–15	103	23.52 ± 9.11
	16–20	52	23.56 ± 7.39
	˃20	32	28.24 ± 7.99
Number of Comorbidities
	I don’t have comorbid	59	17.98 ± 6.12	F (19.367)	0.001
	one chronic disease	29	27.08 ± 7.83
	two chronic diseases	68	25.87 ± 7.85
	three chronic diseases and more	80	27.40 ± 8.67
Duration of dialysis in years
	≤1	59	23.94 ± 9.18	F (3.614)	0.028
	2–3	67	22.63 ± 9.06
	≥4	110	26.08 ± 7.80
Are you satisfied with time
	Yes	226	24.19 ± 8.45	t (−3.243)	0.001
	No	10	33.05 ± 8.37
Preference to change time
	Yes	16	24.46 ± 8.10	F (2.960)	0.054
	No	201	24.14 ± 8.39
	Sometimes	19	29.14 ± 10.45
Who choose the time
	Patient	110	21.82 ± 8.78	t (−4.781)	0.001
	Not the patient	126	26.96 ± 7.75

Table 4 presents the multiple linear regression that demonstrates the factors of employment status (student), comorbidity condition (one chronic disease), dialysis duration, satisfaction with time, and person who selects time of dialysis as factors that influence the level of MAF scores (R2 = 0.302, p ˂ 0.001).

Table 4. Multiple linear regression for factors affecting MAF scores.

Factor		B	SE	β	t	P-value
Age		.013	.055	.024	.228	.820
Gender
	Male	Ref
	Female	.108	1.524	.006	.071	.944
Employment status
	Employed	Ref
	Unemployed	−.879	1.587	−.050	−.554	.580
	Student	−6.070	2.785	−.167	−2.179	.030
	Retired	2.476	1.672	.126	1.481	.140
Number of dependent		−.162	.227	−.058	−.711	.478
Do you perform exercise	Yes	Ref
	No	2.124	1.548	.081	1.372	.171
Distance to hospital		.058	.059	.058	.985	.326
Comorbidity condition
	None	Ref
	1	−5.113	1.619	−.257	−3.158	.002
	2	.357	1.694	.014	.211	.833
	3 ≤	−.292	1.467	−.016	−.199	.842
Dialysis Duration		.022	.010	.155	2.331	.021
Satisfaction with time
	Yes	Ref
	No	6.704	2.641	.157	2.539	.012
Who choose dialysis time
	Patient	Ref
	Not the patient	3.278	1.058	.190	3.098	.002

R2 = 0.302, Adjusted R2 = 0.258, p ˂ 0.001.

4. Discussion

This study aimed to assess the level of fatigue among patients who undergo HD and explored the factors that influence the levels of fatigue. The results revealed that there was a significant relationship between age and the total scores of MAF in which older patients (>50 years old) scored higher than younger patients. Older individuals often have more comorbid conditions (e.g. cardiovascular diseases, diabetes) that are not only common in CKD patients but also known to increase fatigue. Another explanation is that the process of dialysis can be particularly taxing on older patients due to their reduced physiological reserve. This implies that a patient’s frailty increases with age. Studies have revealed that those who have CKD are more likely to experience fatigue [30]. According to Debnath et al. [24], patients undergoing dialysis frequently experience weariness, which can have a major effect on their quality of life. The authors specified that muscle atrophy, weakness, and reduced oxidative capacity may make elderly dialysis patients more prone to weariness and HD patients who adhered to a stringent dialysis treatment routine experience greater weariness than those who did not [24]. A few dialysis patients may even rank reduced weariness above continued life as the most essential outcome of treatment [24]. Thus, muscle atrophy, weakness, and reduced oxidative capacity are all potential causes of weariness in elderly dialysis patients. An aspect that contributes to muscle weariness and lethargy is the substantial exercise limits placed on dialysis patients [31]. CKD patients often face significant exercise limitations due to a combination of factors such as muscle weakness, anemia, and the accumulation of toxins that the kidneys fail to filter. These limitations can lead to reduced physical fitness and muscle atrophy, contributing to increased fatigue compared to individuals without CKD. Metabolic disturbances like electrolyte imbalances and altered energy metabolism. These changes can impair muscle function and endurance, leading to quicker onset of muscle fatigue during physical activity, unlike in individuals with normal kidney function [32]. The process of HD itself can be physically taxing. The fluctuation in fluid and electrolyte balance during dialysis sessions can lead to post-dialysis fatigue. The chronic nature of CKD and the lifestyle limitations it imposes can also lead to psychological stress and depression, which are closely linked to fatigue [33].

To enhance their quality of life and health outcomes, the capabilities, current health conditions, and family history of elderly dialysis patients are taken into account while designing a specific fitness program for them; moreover, it is essential that their fatigue symptoms are closely managed and monitored. The occupational status of HD patients was also significantly associated with MAF scores, where patients who retired reported more fatigue than other groups, which suggests that the risk that a patient would be fatigued increases in proportion to the patient’s advanced age, as mentioned in the previous paragraph. One study indicated that the fatigue levels of dialysis patients increased with both age and treatment duration [34]. In a trial conducted by Bossola et al., the authors discovered that HD patients who scored highly on a scale measuring their functional status and independence (Instrumental Activities of Daily Living) experienced less fatigue [35]. Fatigue is a common complaint among people on HD, and its intensity may be affected by a number of factors. Rest times during dialysis treatments, encouraging physical activity when appropriate, and offering emotional support are all ways in which nurses can assist HD patients in coping with fatigue. As part of the health care team, nurses can also provide care for conditions like anemia and depression that may cause fatigue. Further, whether retirement contributes to greater fatigue in HD patients remains an aspect for future research.

In contrast, marital status, educational level, and financial status did not significantly affect the level of fatigue among the participants in our study, which challenges the results obtained from other studies. According to a study conducted in Athens, HD patients with lower levels of education reported higher levels of fatigue [36]. Furthermore, financial hardship can affect various aspects of a patient’s existence, including their well-being and fatigue levels [37]. It is plausible to presume that financial difficulties may indirectly affect the level of fatigue among HD patients. Financial difficulties can result in increased tension, restricted access to necessary resources, and inadequate health care, all of which may contribute to or exacerbate fatigue.

Unemployed individuals and those with minimal levels of education reported greater levels of fatigue [36]. This suggests that socioeconomic factors, such as education level, may contribute to HD patients’ fatigue. Thus, it is essential for health care providers, including nurses, to conduct thorough evaluations of HD patients in order to determine the factors that contribute to their fatigue levels. This implies considering socioeconomic factors, social support, lifestyle factors, and psychological well-being [38]. By evaluating the unique circumstances and stressors of each patient, nurses can tailor interventions and provide the necessary support to meet their specific requirements. Moreover, the number of dependents also significantly affects the level of MAF scores, with patients who had seven or more dependents reporting higher fatigue than those who had lower levels of fatigue or who did not have dependents. This implies that the responsibilities associated with having seven or more dependents may contribute to elevated levels of fatigue.

Having a greater number of dependents may increase tension and decrease the availability of support, thereby resulting in increased fatigue. Further, the majority of severely fatigued patients reported poor levels of social support from their loved ones [39]. However, it is essential to note that fatigue in HD patients is multifactorial, with other factors such as treatment regimen, symptom burden, and overall health status playing significant roles [40]. Nurses can educate and assist HD patients who have multiple dependents by emphasizing energy conservation, time management, and task prioritization. Additionally, nurses can collaborate with social workers and other relevant professionals to help patients access additional support systems and resources. This may include investigating financial assistance programs, respite care options, and caregiver support groups.

Finally, patients who had an exercise regimen reported significantly lower fatigue than those who did not have a regular exercise regimen, which implies that patients on HD can benefit from regular exercise to reduce their fatigue [41]. In these patients, exercise has been proven to increase exercise capacity, enhance quality of life, and reduce fatigue [17,42]. Our results revealed that the exercise capacity and health-related quality of life of these patients were lower than those of healthy individuals and that their perception of the benefits of exercise influenced their exercise behavior [42]. In addition, a randomized controlled clinical trial investigated the efficacy of mini-bike exercise on fatigue in HD patients [17]. The study found that exercising on mini bikes reduced fatigue in HD patients. Another study discussed the need for specific exercise prescriptions for HD patients and emphasized the role of physical therapists in designing individualized exercise regimens [43]. Mostly, the evidence suggests that HD patients who engage in a regular exercise program may experience substantially less fatigue than those who do not. Health care providers, such as physical therapists, should develop individualized exercise plans for HD patients to maximize the health benefits of exercise and minimize weariness [43].

Further, the current study found that there was a significant relationship between the total scores of MAF and the items of ‘duration to hospital,’ number of comorbidities,” ‘duration of dialysis in years,’ ‘patients’ satisfaction with time,” and ‘person who choose the time of dialysis.’ These findings suggest that extended hospital stays may result in an increase in physical and mental stress, which may exacerbate fatigue in HD patients; increased fatigue may result from the presence of multiple comorbidities; the number of years of dialysis may indirectly contribute to fatigue by influencing patients’ overall health; psychological and social elements may have an indirect effect on patients’ reports of fatigue by altering their perceptions of the passage of time; and the timing of dialysis treatments can influence a variety of factors, including sleep patterns, daily routines, and lifestyle. These factors may affect an HD patient’s fatigue levels.

According to Yonata et al., comorbidities are medical conditions that affect numerous body systems [44]. Thus, HD patients with chronic ailments and disorders frequently experience greater fatigue and decreased well-being as a result of the additional physical and psychological responsibilities imposed on them. In addition, according to Horigan, psychosocial variables like depression, anxiety, and social support also contribute to fatigue among HD patients [27]. Research on HD patients revealed a negative correlation between the number of comorbidities and the quality-of-life score [44].

Therefore, recognizing the effect of prolonged hospital stays on physical and mental stress, addressing the effects of comorbidities on fatigue, considering the influence of dialysis duration on overall health and fatigue, attending to psychological and social elements that may affect fatigue perceptions, and working collaboratively with patients to optimize dialysis schedules based on individual needs are all ways in which nurses can contribute significantly to the care of HD patients. By addressing these aspects, health care practitioners can help improve the health and quality of life of HD.

The multiple linear regression test demonstrated that employment status (student), comorbidity condition (one chronic disease), dialysis duration, satisfaction with time, and person who selects the time of dialysis were factors that affected the level of MAF scores. These results suggest that the fatigue of HD patients may be influenced by both their demographic characteristics and the course of their dialysis treatment. Several extant studies support this finding [20,37]. For example, an individual’s employment status prior to their illness can affect the employment status of patients with kidney failure.

4.1. Limitations

Although our study was conducted in multiple centers of HD centers in Saudi Arabia that considered strength attribute but there are some limitations. Another limitation of our study is the exclusion of data on blood pressure fluctuations and intradialytic weight gain, both of which are significant contributors to fatigue in hemodialysis patients. This oversight may affect the comprehensive assessment of fatigue in this context. Our study was a cross-sectional study, however using longitudinal in addition to qualitative study may provide more illustration about the factors that affect fatigue among HD patients. In addition, the time of assessment of fatigue was at the day of dialysis. Additional study for measurement of fatigue at different time are recommended to measure the fatigue and additional factors such as level of hemoglobin and other laboratory tests are recommended to measure.

5. Conclusions

The findings of the present study indicated high levels of fatigue in old, male patients, retired, with a large number of dependents, and who have a regular exercise regimen. Moreover, there was a significant relationship between the total scores of MAF and the items of ‘duration in hospital,’ number of comorbidities,” ‘duration of dialysis in years,’ ‘patients’ satisfaction with time,” and ‘person who selects the time of dialysis.’ Understanding the burdens of fatigue and the factors associated with it can help health professionals to prioritize improvement of fatigue symptoms by developing strategies to reduce it among patients undergoing HD. Furthermore, the findings of this study create a substantial foundation for future research, which should focus on identifying appropriate interventions to reduce fatigue among patients undergoing HD.

Abbreviations

CKD, Chronic Kidney Disease; HD, Hemodialysis; MAF, Multidimensional Assessment of Fatigue; QOL, Quality of Life

Author contributions

‘Conceptualization, B.A.; methodology, B.A. and S.A; formal analysis, B.A., S.A., J.A., and E.P. writing – original draft preparation B.A., S.A., J.A., and E.P.; writing – review and editing, B.A., S.A., A.A., N.M., A.K.A, M.A., S.O.A, A.A.A, F.A., J.A., and E.P.; supervision, B.A.; project administration, B.A.; funding acquisition, B.A. All authors have read and agreed to the published version of the manuscript.’

Institutional Review Board Statement

Ethical approvals were obtained from the research ethics committees at the University of Hail number, Institutional Review Board committee at Hail health clusters, and General directorate of health affairs in Al-Qassim city (Approval Nos. (H-2021-206), (H-08-L-074), and (607-44-2091), respectively).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.

Data availability statement

The data presented in this study are available on request from the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Scientific Research Deanship at University of Ha’il - Saudi Arabia through project number RG-21 170.