Assessing the Effectiveness of a Nurse-Led Intervention on Health-Related Quality of Life in Hemodialysis Patients: A Pre-Post Study

Introduction

CKD affects 10-13% of the global population and is on the rise due to increasing rates of diabetes, hypertension, and aging.¹ As CKD progresses to ESKD, hemodialysis becomes vital for survival,² yet it may reduce health-related quality of life (HRQoL).³ Nurses, as frontline caregivers, play a critical role in supporting hemodialysis patients by improving self-care, adherence, and HRQoL.⁴ Nurse-led interventions (NLIs) involving education and support have shown positive outcomes in chronic disease management globally,⁵ yet localized research in India remains limited.⁶

India’s escalating CKD burden, alongside limited healthcare resources, particularly in government hospitals, results in overcrowding and restricted patient education.⁷ Many patients lack self-care knowledge and face psychological challenges.⁸ NLIs, adaptable within existing frameworks, can enhance patient knowledge, self-care, and HRQoL cost-effectively.^9,10 This study evaluates an NLI to improve hemodialysis patients’ outcomes in resource-constrained settings.¹¹

Materials and Methods

This quasi-experimental pretest-posttest study evaluated an NLI’s impact on HRQoL among hemodialysis patients with CKD. Conducted at ABVIMS & Dr. RML Hospital in Delhi, India, the study aimed to measure changes in HRQoL following the intervention.

A total of 50 hemodialysis patients were recruited using purposive sampling based on inclusion criteria: (i) age ≥18 years, (ii) on hemodialysis for at least a month, and (iii) with informed consent. Patients with severe comorbidities, recent surgeries, or significant cognitive impairments were excluded. The sample size was determined through a priori power analysis using G Power for a paired t-test, considering a moderate effect size (d = 0.6), α = 0.05, and 80% power. The study received Institutional Review Board (IRB) approval, and informed consent was obtained from all participants before data collection.

Data were collected using a Demographic and Clinical Data Sheet for socio-demographic and clinical information, the Kidney Disease Quality of Life Short Form (KDQOL-SF Tm) version 1.3 for assessing HRQoL across several health domains, and educational materials for the NLI. These materials included pamphlets, PowerPoint presentations, and exercise videos, covering key CKD management aspects like diet, exercise, and psychosocial support to foster patient engagement. The interview technique was employed for data collection to enhance accuracy and address literacy-related biases.

The intervention comprised individualized, structured educational sessions conducted by nurses one-on-one during hemodialysis, ensuring patient engagement. Each session lasted 40-45 minutes, and the educational content was validated by 14 experts in nephrology, medical-surgical nursing, and hemodialysis technicians. Baseline data were collected on Day 1, where HRQoL was assessed with the KDQOL-SF Tm tool, establishing a reference point for evaluating intervention efficacy. Structured educational sessions followed. On Day 3, a PowerPoint session addressed psychological stressors, covering self-care for arteriovenous fistulas (AVFs), weight and blood pressure management, dietary restrictions, and blood glucose monitoring. Day 6 included a session on physiological stressors, discussing symptoms like headaches, muscle cramps, and fatigue, alongside infection control practices and available support programs for dialysis. Day 9 featured a video demonstration of exercises to promote AVF function, emphasizing individualized physical activity progression to improve circulation and breathing techniques to enhance adherence and well-being.

On Day 11, a redemonstration session allowed patients to practice skills, with nursing staff providing feedback to reinforce understanding. The day 15 reinforcement session reviewed prior topics to solidify knowledge on dietary restrictions, exercise, and blood pressure control. Day 45 saw a post-intervention KDQOL SF Tm assessment evaluating changes in HRQoL, measuring the intervention’s impact on self-management skills, and overall quality of life (QoL).

Data analysis was conducted using SPSS (trial version), employing descriptive statistics to summarize participants’ demographic and clinical profiles. The Wilcoxon signed-rank test assessed pretest-posttest KDQOL score changes, and correlation analyses examined links between socio-demographic factors and HRQoL outcomes. This comprehensive NLI emphasized education, skill-building, and reinforcement, aimed at empowering hemodialysis patients to improve self-efficacy and HRQoL in a resource-limited setting.

Results

Of 50 patients, 47 completed the study, and three were lost to follow-up. Demographic analysis revealed that the majority (40.4%) were aged 31-50 years, and 66% were male. Maximum were Hindu (80.9%), married (59.6%), and from nuclear families (59.6%), while 70.2% were from the upper-middle class. Notably, 97.9% were non-smokers, 95.7% abstained from alcohol, and a slight majority (53.2%) were vegetarians. Significantly, 87.2% lacked health insurance, highlighting a substantial barrier to adequate care access [Table 1].

Using the Wilcoxon signed-rank test, pretest-posttest KDQOL scores indicated notable improvement after 45 days. The overall KDQOL score improved significantly, with the mean rank increasing from 16.42 to 26.60 (Z = 3.884, p = 0.001). Additionally, symptom management improved with mean ranks rising from 19.63 to 22.92 (Z = 2.874, p = 0.004), and perceptions of the disease’s impact were substantially enhanced, evidenced by an increase in mean rank from 0.00 to 24.00 (Z = 5.970, p = 0.001). Physical functioning and emotional well-being also demonstrated significant improvements, with mean ranks increasing from 0.00 to 3.50 (Z = 2.264, p = 0.024) and 2.00 to 6.40 (Z = 2.773, p = 0.006), respectively. The SF-12 mental composite score showed a significant rise from 10.33 to 11.11 (Z = 2.938, p = 0.003), reflecting enhanced mental health. However, domains such as kidney disease burden (Z = 0.485, p = 0.628), work status (Z = 1.00, p = 0.317), and cognitive function (Z = 0.603, p = 0.547) displayed no significant changes [Figure 1].

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Figure 1:

Comparison of pretest and posttest (Day 45) overall and domain wise QOL score of HD patients. QoL: Quality of life, HD: Hemodialysis, KDQOL: Kidney disease quality of life.

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Demographic analysis showed no significant associations between KDQOL risk levels and age (p = 0.502), gender (p = 0.471), religion (p = 0.649), marital status (p = 0.511), or family type (p = 0.859). However, socioeconomic status (SES) and health insurance emerged as significant determinants, with individuals from lower middle-class backgrounds more frequently classified as high-risk (p = .036), and uninsured patients demonstrating higher KDQOL risk levels (p = 0.007) [Table 2]. Clinical parameters such as BMI (p = 0.721), diabetes (p = 0.276), hypertension (p = 0.447), and dialysis frequency or missed sessions (p = 0.192 and p = 0.524, respectively) were not significantly associated with KDQOL outcomes. The NLI effectively improved symptom management, emotional well-being, and disease perception among hemodialysis patients. The findings highlight the critical role of SES and insurance coverage in patient outcomes, underscoring the need for equitable access to health resources to enhance QoL in this population.

Discussion

This study examined an NLI’s effectiveness on the QOL of hemodialysis patients with CKD, showing significant improvements in emotional well-being, physical functioning, and the perceived impact of kidney disease. The intervention resulted in statistically significant improvements in emotional well-being, physical functioning, and overall kidney disease impact. These results align with Yonata et al., who found that economic status and comorbidities significantly influence QOL, with more comorbidities linked to poorer QOL (p = 0.014). Both studies highlight the critical role of socioeconomic factors, such as financial barriers and comorbidities, in shaping QOL among hemodialysis patients.¹²

The current findings also resonate with Mary and Priya, whose research identified significant associations between demographic variables and QOL. Their study noted better QOL among male patients and those from nuclear families, which is consistent with the male predominance (66%) and SES’s significance (p = 0.036) in QOL outcomes.¹³ These findings highlight demographic and social determinants in patient care.

Despite these positive outcomes, certain QOL domains, such as cognitive function and kidney disease burden, showed no significant improvement. This could be attributed to the multifaceted nature of cognitive function, which may require longer-term interventions beyond education and self-management strategies. Cognitive impairment in CKD is often linked to vascular damage, uremic toxins, and metabolic imbalances, factors that may not be directly addressed through an educational intervention alone. Similarly, the kidney disease burden reflects long-term psychosocial and physiological distress because patients continue to experience CKD’s chronicity, frequent hospital visits, and ongoing dialysis-related stressors, which were beyond the scope of the intervention to modify. Similarly, the burden of kidney disease is a complex construct encompassing physical, financial, and emotional stressors that may persist despite improved self-care behaviors, necessitating extended psychosocial support, broader systemic and policy-level changes, rather than short-term nurse-led education alone.

Furthermore, in contrast to Yonata et al. and Manavalan et al., who found a strong relationship between comorbidities and QOL, we did not establish significant associations between clinical factors such as diabetes, hypertension, or BMI and QOL outcomes.^12,14 This lack of correlation may be attributed to several factors. First, the relatively short follow-up period (45 days) might not have been sufficient to capture the long-term impact of clinical variables on QoL. Additionally, the structured NLI may have influenced patients’ perceptions of their well-being, leading to improvements in HRQoL independent of their clinical condition. Furthermore, individual variations in disease adaptation and coping mechanisms may have played a role, as some patients with comorbidities may have developed resilience over time, minimizing the perceived burden of their illness. The individualized educational approach used in this study may have also empowered participants to manage symptoms more effectively, thereby reducing the influence of clinical factors on QOL scores. Moreover, the sample characteristics, including the predominance of younger patients and those from nuclear families, may have contributed to these findings, as younger individuals may have better coping mechanisms and social support systems that buffer the impact of clinical factors on QOL. Finally, the small sample size could have limited the statistical power to detect associations, highlighting the need for larger, longer-term studies to explore these relationships further.

Manavalan et al. further support the role of socioeconomic determinants in QOL outcomes. Their study reported that illiteracy and unemployment were key predictors of lower QOL among CKD patients in a rural, underprivileged population.¹⁴ Similarly, SES (p = 0.036) and lack of health insurance (p = 0.007) significantly influenced QOL outcomes, reinforcing the need for targeted interventions addressing financial and educational disparities in our patients.

Adding to this, Togay and Akyüz found that demographic features and disease-related factors significantly impacted QOL in 97 hemodialysis patients. Their research revealed that older age (≥51 years), family history of dialysis, and vascular access type influenced QOL, with most patients demonstrating poor QOL.¹⁵ These findings further underscore the multifactorial nature of QOL in hemodialysis patients and the necessity of addressing both demographic and clinical factors in interventions aimed at improving patient outcomes.

While this study provides valuable insights, certain limitations should be acknowledged. The sample was from a single hospital, which may affect the generalizability of the findings. The follow-up period was 45 days, which may not be sufficient to observe long-term behavioral or physiological changes in QOL, particularly in domains such as cognitive function and kidney disease burden. Future research should consider longer follow-up durations and larger, more diverse samples to strengthen the applicability of findings across broader populations.

Beyond individual patient benefits, NLIs in dialysis units could significantly enhance the broader healthcare system in India by improving continuity of care, reducing hospitalization rates, and increasing patient adherence to treatment regimens. Given the growing CKD burden and the limited availability of nephrologists, nurse-led care can bridge critical gaps by providing continuous patient education, regular monitoring, and early complication management, thereby reducing hospital admissions and improving long-term outcomes. Additionally, integrating NLIs within dialysis centers can enhance self-care adherence, support mental well-being, and promote a more patient-centered approach, ultimately alleviating the strain on tertiary healthcare facilities. This model also contributes to cost-effective healthcare delivery by optimizing workforce utilization, allowing specialized medical professionals to focus on complex cases while nurses take on preventive and supportive roles. Expanding dialysis nurses’ role can help India’s healthcare system transition toward a more sustainable, community-based model that improves accessibility, enhances quality of care, and reduces the economic burden on both patients and healthcare institutions.

In conclusion, this study underscores the pivotal role of NLIs in improving QOL among hemodialysis patients by addressing key psychosocial stressors and promoting self-management. However, the lack of improvement in cognitive function and kidney disease burden, along with the absence of significant associations between clinical factors and QOL, suggests that additional interventions, such as cognitive rehabilitation programs and broader systemic healthcare changes, may be required to enhance outcomes. Future studies should investigate the long-term impact of such interventions and consider integrating multidisciplinary approaches to optimize QOL for this population.