Keywords
nurse-led dietary counseling - noncompliance behavior - hemodialysis patients - nutritional
requirements
Introduction
Nutritional health is one of the most critical considerations among patients with
chronic kidney disease, especially those undergoing hemodialysis. Nutritional education
and counseling for renal disease patients significantly preserve renal function and
overall well-being. In preparation for renal replacement therapy, a consultation with
the renal nutritionist or a nurse to establish a diet consistent with the current
diagnosis may increase the likelihood of reducing cardiovascular risk factors, preventing
malnutrition anemia, and slowing the progression of renal disease, all of which can
contribute to positive patient outcomes. Nutrition tips are another effective way
of providing a practical nutrition education message in a simple format.[1]
Nursing intervention has been progressively identified as increasingly important in
improving patients' compliance with dialysis. Such interventions, including education,
training, and behavioral introduction, help patients learn about dialysis and develop
healthy life habits, further improving their compliance with this treatment. The most
commonly reported indicators for assessing compliance include serum phosphorus level
and interdialytic weight gain (IDWG). In contrast, in some studies, compliance has
been directly evaluated, and the compliance rate has been reported. Nephrology nurses
can lead the way to implement numerous proactive interventions before, during, and
after hospitalization of patients on hemodialysis. Nurses often have more contact
with patients than other clinical personnel and are in an ideal position to optimize
assessment, management, and monitoring of clinical issues likely to affect patients
on dialysis, including malnutrition status. Nurses are encouraged to apply the principles
of successful nursing models to develop site-specific practices and processes to improve
the quality of care before, during, and after hospitalization of patients on dialysis.
Moreover, the role of nurses in the nutritional screening and counseling of these
patients has been shown to have positive impacts.
Materials and Methods
Study Design and Participants
The investigator adopted a quantitative research approach with a quasi-experimental
crossover design to determine the effect of dietary counseling on the noncompliance
behavior of patients undergoing hemodialysis at a private hospital specialized in
kidney diseases at Mangalore, Karnataka, India. Twenty-seven participants between
18 and 60 years who fulfilled the inclusion criteria participated in the study. Patients
with multiple organ failure, critically ill patients, unconscious patients, patients
not interested in the counseling program, patients with human immunodeficiency virus
or hepatitis, and patients preparing for kidney transplantation were excluded from
the study.
Data Collection Instruments
Data was collected using the sociodemographic pro forma. Self-reported checklist on
assessing noncompliance behavior among dialysis patients and self-reported checklist
on nutrition status (24-hour recall) was administered. The self-reported checklist
on the assessing compliance behavior scale was used to allow the samples into two
study groups: compliance and noncompliance. Based on biological parameters, the noncompliance
group is subdivided into control and experimental groups based on criteria prepared
by the clinician and reviews. The checklist consisted of four items related to drug,
diet, treatment, and fluid. It included biochemical parameters like hemoglobin, serum
potassium, serum phosphorus, and IDWG.
Data Collection Procedure
Before the data collection, formal written permission was obtained from the concerned
authorities. An informed written consent and participant information sheet were administered
to the participants.
The study was conducted in three phases.
-
First phase: The treatment regimen was explored for each patient using a self-rating rating scale
for compliance and noncompliance based on their noncompliance scores. Qualified participants
were randomly categorized into control groups and experimental groups according to
the criteria and biochemical parameters.
-
Second phase: Once the participants were allotted to the study group, their demographic pro forma
and noncompliance behavior were collected. Twenty-four-hour dietary recall was assessed
on the first selection day. After the preintervention assessment, the high-risk noncompliance
participants were subjected to nurse-led dietary counseling for 20 to 30 minutes (form
of video, demonstration, pamphlet on dietary guidelines, individualized dietary plan,
and PowerPoint presentation) thrice a week for 1 week. Reinforcement sessions were
provided if needed during their follow-up visits to the center.
-
Third phase: As it was a crossover design, the low-risk noncompliance participants were administered
the nurse-led dietary counseling. The high-risk noncompliance group became the control
group. The same intervention and assessments were performed for the low-risk noncompliance
group.
Results
Distribution of Sample Characteristics
The distribution of the subjects is based on their demographic variables. Most patients,
10 (35.71%), were in the age group of 51 to 60 years, and the mean age was 46.07 ± 9.67.
Among the subjects, 16 (57.14%) were males, the majority of the samples 23 (82.14%)
were married, 12 (42.86%) of them had high school education (10th standard), 5 (17.86%)
were unemployed, and 11 (39.29%) had a monthly income of 5,001 to 10,000. Nearly half
of the subjects, 11 (39.29%), had undergone dialysis for 1 to 5 years, and most of
them 12 (42.86%) were diabetic.
Assessment of Noncompliance Behavior among Patients Undergoing Hemodialysis
The data were analyzed using frequency and percentage. The findings reveal that 27
(96.3%) exhibited noncompliant behavior, whereas only a minimal rate, 1 (3.7%), adhered
to the compliance criteria. Based on biochemical parameters and according to predefined,
clinician-reviewed criteria, the noncompliant group was further categorized into control
(low-risk noncompliance) and experimental groups (high-risk noncompliance). Among
dialysis patients, 20 (74.07%) were classified in the control group, while 7 (25.92%)
were in the experimental group ([Table 1]).
Table 1
Frequency and percentage distribution of noncompliance behavior among patients undergoing
hemodialysis (n = 28)
|
Compliance (0–1)
|
Noncompliance (> 1)
|
Control group (0–2)
(low-risk level)
|
Experiment group (3–4)
(high-risk level)
|
|
f
|
%
|
f
|
%
|
f
|
%
|
f
|
%
|
|
1
|
3.7
|
27
|
96.3
|
20
|
74.07
|
7
|
25.92
|
The result shows that most participants are in good compliance (n = 22, 81.5%) with the drugs and treatment (n = 23, 85.2%). On the other hand, all the participants (n = 27, 100%) showed noncompliance to fluid intake and 96.6% noncompliance to diet
([Table 2]).
Table 2
Domain-wise noncompliance report of hemodialysis patients, n = 27 (20 + 7)
|
Domains
|
Noncompliance (n = 27)
|
Control group (low-risk level)
|
Experiment group (high-risk level)
|
|
f
|
%
|
f
|
%
|
f
|
%
|
|
Drugs
|
5
|
18.5
|
3
|
60
|
2
|
40
|
|
Fluid
|
27
|
100
|
20
|
74.0
|
7
|
25.9
|
|
Diet
|
24
|
96.6
|
15
|
57.6
|
9
|
34.6
|
|
Treatment
|
4
|
14.8
|
2
|
50
|
2
|
50
|
Among the participants, most demonstrated low-risk noncompliance (control group) for
almost all biological parameters, including IDWG at 80%, serum phosphorous at 93%,
and serum potassium at 100%, except hemoglobin levels. Specifically, 63% of the participants
exhibited high-risk noncompliance (experiment group) in maintaining hemoglobin levels
([Table 3]).
Table 3
Level of noncompliance among hemodialysis patients based on biological parameters
(n = 27)
|
Biological parameters
|
Reference value
|
Mean
|
SD
|
Control group (low-risk level)
|
Experiment group (high-risk level)
|
|
f
|
%
|
f
|
%
|
|
IDWG
2–6 kg
|
HL > 6 kg
LR < 2 kg
|
3.66
|
0.90
|
7
|
20
|
20
|
80
|
|
Hemoglobin
M: 14–18 g/dL
F: 12–15 g/dL
|
HR < 10 mg/dl
LR > 10 mg/dl
|
9.65
|
1.97
|
17
|
63
|
10
|
37.0
|
|
Serum phosphorus
2.5–4.5 mg/dL
|
HR < 3.5 and > 5.5 mg/dL
LR 3.5–5.5 mg/dL
|
6.26
|
2.12
|
2
|
7
|
25
|
93
|
|
Serum potassium
3.5–5.5 mg/dL
|
HR < 3 and > 7 mg/dL
LR 3–3.5 mg/dL
5.5–7 mg/dL
|
5.45
|
0.86
|
–
|
–
|
27
|
100
|
Abbreviations: F, female; HR, high risk; IDWG, interdialytic weight gain; LR, low
risk; M, male; SD, standard deviation.
Effectiveness of Nurse-Led Dietary Counseling on Nutritional Status among Hemodialysis
Patients
H1: There will be a significant difference between the nutritional status among hemodialysis
patients before and after the dietary counseling program.
A paired t-test showed that there is a significant difference in nutritional status among the
participants before and after the dietary counseling program as t (26) = –6.30, p ≤ 0.001 at 5% level of significance. There was a notable (14.91%) improvement in
the average caloric intake score (mean difference 5.22) following the nurse-led counseling.
Hence, the research hypothesis (H1) is accepted at 5% level of significance ([Table 4]).
Table 4
Effectiveness of nurse-led dietary counseling on nutritional status among hemodialysis
patients before and after the intervention (n = 27)
|
Nutritional status (caloric intake)
|
Mean
|
SD
|
MD
|
Standard error
|
t-Value
|
p-Value
|
|
Pretest
|
17.48
|
2.26
|
5.22
|
0.86
|
6.309
|
< 0.001*
|
|
Posttest
|
22.70
|
3.07
|
Abbreviations: MD, mean difference; SD, standard deviation.
* refers to level of significance.
Effectiveness of Nurse-Led Dietary Counseling on Noncompliance among Hemodialysis
Patients
H2: There will be a significant difference between noncompliance among hemodialysis
patients before and after the dietary counseling program.
A paired t-test showed a significant difference between noncompliance among hemodialysis patients
before and after the intervention as p ≤ 0.001 at 5% level of significance. Hence, the research hypothesis (H2) is accepted at a 5% significance level ([Table 5]).
Table 5
Effectiveness of nurse-led dietary counseling on noncompliance among hemodialysis
patients (n = 27)
|
Noncompliance (
n
= 26)
|
Variable
|
Mean
|
SD
|
MD
|
t
-Value
|
p
-Value
|
95% CI, the difference lower to upper
|
|
Pre-compliance
|
5.04
|
0.20
|
0.75
|
–5.41
|
< 0.001*
|
–1.02 to –0.47
|
|
post-compliance
|
5.79
|
0.88
|
Abbreviations: CI, confidence interval; MD, mean difference; SD, standard deviation.
Association of Noncompliance, Selected Demographic Variables
H3: Noncompliance will have a significant association with selected demographic variables.
The table depicts that Fisher's exact test was used to compute the association, and
all the p-values of Fisher's exact for the demographic variables were > 0.05 except for the
cause for kidney failure (p = 0.003) and duration of the disease (p = 0.046). It indicates a significant association between causes of disease and duration
of illness at 5% significance level. Hence, the research hypothesis (H3) is accepted for those two variables at 5% significance level ([Table 6]).
Table 6
Association of noncompliance with selected demographic variables (n = 28)
|
Demographic characteristics
|
Compliance
|
Noncompliance
|
Fisher's exact test
|
p-Value
|
|
Low risk
|
High risk
|
|
f
|
%
|
f
|
%
|
f
|
%
|
|
Age (y)
|
< 40
|
1
|
3.57
|
4
|
14.29
|
3
|
10.71
|
3.68
|
0.40
|
|
41–50
|
–
|
–
|
7
|
25.00
|
2
|
7.14
|
|
51–60
|
|
–
|
9
|
32.14
|
2
|
7.14
|
|
Gender
|
Male
|
|
–
|
14
|
50
|
4
|
14.29
|
2.19
|
0.47
|
|
Female
|
1
|
3.57
|
6
|
21.43
|
3
|
10.71
|
|
Occupation
|
Government Employee
|
|
–
|
2
|
7.14
|
2
|
7.14
|
6.46
|
0.7
|
|
Private employee
|
1
|
3.57
|
3
|
10.71
|
1
|
3.57
|
|
Daily wager
|
|
–
|
6
|
21.43
|
1
|
3.57
|
|
Unemployed
|
|
–
|
4
|
14.29
|
2
|
7.14
|
|
Food
|
Vegetarian
|
|
–
|
5
|
17.86
|
2
|
7.14
|
0.65
|
1
|
|
Nonvegetarian
|
1
|
3.57
|
15
|
53.57
|
5
|
17.86
|
|
Duration of disease
|
> 1
|
1
|
3.57
|
7
|
25.00
|
1
|
3.57
|
10.55
|
0.046*
|
|
1–5 y
|
|
–
|
7
|
25.00
|
4
|
14.29
|
|
6–10 y
|
|
–
|
6
|
21.43
|
0
|
–
|
|
> 10 y
|
|
–
|
0
|
–
|
2
|
7.14
|
|
Comorbidity illness
|
Diabetes mellitus
|
|
–
|
2
|
7.14
|
2
|
7.14
|
18.66
|
0.003*
|
|
Hypertension
|
|
–
|
8
|
28.57
|
0
|
–
|
|
Glomerulonephritis
|
|
–
|
4
|
14.29
|
1
|
3.57
|
|
Polycystic kidney
|
1
|
–
|
1
|
3.57
|
4
|
7.14
|
|
DM and HTN
|
|
–
|
5
|
17.86
|
0
|
–
|
Abbreviations: DM, diabetes mellitus; HTN, hypertension.
To conclude, [Table 6] depicted that compliance behavior was significantly associated with selected demographic
variables. Hence, the research hypothesis (H3) is accepted for compliance behavior with demographic variables. The null hypothesis
is accepted for the level of knowledge with demographic variables at a 5% level of
significance.
Discussion
The patient's adherence to the treatment regimen directly impacts the clinical outcomes.
A patient's noncompliance with fluid and diet can increase IDWG, cardiovascular morbidity,
and death. Consumption of excessive sodium in the diet increases thirst and volume
intake, leading to an increase in total body water and, consequently, an increase
in IDWG.[1] We need to emphasize the importance of dialysis therapy since it requires patients
to adhere to the treatment regimen criteria and change their lifestyle, optimize their
diet, and so on. It is, therefore, essential to improve compliance by utilizing nursing
interventions that are usually readily available and affordable.[2] Research studies have shown that a person's level of education affects how well
they adhere to treatment regimens for chronic diseases such as kidney disease. Due
to a poor correlation between knowledge of disease and treatment, low education has
been linked to lower adherence.[3] In the current study, most participants (42.86%) had a high school education.
The current study revealed that around 96.3% had noncompliance behavior, and only
a trivial percentage, 3.7%, fulfilled the compliance criteria. Based on biochemical
parameters, 74.07% were classified under low-level noncompliance behavior and 25.92%
under high-level noncompliance behavior. In addition, most participants were in good
compliance (81.5%) with the drugs and treatment (85.2%). On the other hand, all the
participants (100%) showed noncompliance to fluid intake and 96.6% noncompliance to
diet. The previous studies reported compliance rate ranging from 96.6 to 26%, 98.8
to 17.6%, 98.7 to 19%, and 100 to 67.7%, respectively, in terms of fluid and dietary
restrictions, medicines, and dialysis regular sessions.[4]
[5] On the other hand, the study by Beerappa and Chandrababu, reveals the adherence
rate was 46.6% (good adherence) and 51.6% (fair adherence) for fluid restrictions,
and 60 to 68.3% (good adherence) and 20 to 30% (fair adherence) for dietary restrictions.[1]
Patients with end-stage renal disease benefit from nursing interventions that include
educational, cognitive, behavioral, and dietary techniques.[2] The present study showed that there is a significant difference in nutritional status
among the participants before and after the dietary counseling program as t (26) = –6.30, p ≤ 0.001 at 5% level of significance. There was a notable improvement in the average
caloric intake score (mean difference 313.04) following the nurse-led counseling.
The current study showed that p-values of multivariate analysis of variance for selected demographic variables like
gender, religion, and level of education with physical and biochemical parameters
of nutritional status were < 0.05. It indicated a significant association between
selected demographic variables and nutritional status, namely, gender, religion, and
level of education. Hossain and Sitara showed a significant relationship between fluid
restriction behavior with age (p = 0.018) and nutritional education (p = 0.01). Another considerable relationship exists between diet restriction behavior
and nutritional education (p = 0.006).[6] In addition to the dialysis regimen, dietary restrictions, and the need for multiple
medications with potential side effects, hemodialysis patients must also manage multiple
comorbid conditions. To educate dialysis patients on adhering to a prescribed regimen,
it is necessary to setup patient education centers in hospitals equipped with appropriate
materials, media, and audiovisual aids.
