Abstract
-
Purpose
This study examined the effects of cognitive function, health literacy, and social support influence the risk of self-care non-adherence among older adults with chronic kidney disease.
-
Methods
A cross-sectional survey was conducted using structured questionnaires. The study included 105 older adults (≥65 years) in the pre-dialysis stage, all of whom were receiving regular follow-up at a nephrology outpatient clinic in Jeonju, Jeollabuk-do, Korea. Data were collected through one-on-one interviews from April to May 2024 and analyzed using SPSS version 26.0. Predictive factors were assessed using hierarchical multiple regression analysis.
-
Results
The risk of self-care non-adherence was significantly correlated with cognitive impairment (r=.61, p<.001), lower social support from healthcare providers (r=–.36, p<.001), and reduced health literacy (r=–.42, p<.001). Multiple regression analysis indicated that decreased physical activity (β=.25, p=.002), greater cognitive impairment (β=.29, p<.001), and lower support from healthcare providers (β=–.26, p=.008) were significant predictors of increased risk of self-care non-adherence. The model explained 46% of the variance in the risk of self-care non-adherence. In contrast, family support and health literacy were not significant predictors.
-
Conclusion
To reduce the risk of self-care non-adherence in older adults with chronic kidney disease, routine cognitive screening and tailored education for those with cognitive impairment should be implemented in outpatient care. Promoting physical activity and strengthening support from healthcare providers are also key strategies to improve adherence in this population.
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Key Words: Chronic kidney disease; Cognitive function; Social support; Health literacy; Self-care
INTRODUCTION
In Korea, the prevalence of chronic kidney disease (CKD) among adults reached 7.6% in 2022, but increased sharply to 21.6% among those aged 70 years and older, reflecting a rapidly aging population [
1]. Over the past decade, both the number of CKD patients receiving medical care and the associated healthcare expenditures have grown substantially, establishing CKD as one of the most financially burdensome chronic conditions [
2].
CKD is defined by the presence of structural or functional abnormalities of the kidney regardless of glomerular filtration rate (GFR), or a GFR<60 mL/min/1.73 m² persisting for more than three months [
3]. However, CKD often lacks specific symptoms until advanced stages and is frequently perceived only as a complication of diabetes or hypertension, resulting in low awareness and inadequate preventive care [
4]. In Korea, the number of patients progressing to end-stage kidney disease requiring dialysis doubled from 9,335 in 2010 to 18,598 in 2022, with 59% of these patients aged 65 years or older. The proportion of older adults undergoing dialysis has exceeded 50% since 2018, reaching 59.8% in 2022 [
1]. Compared to younger populations, older adults face a disproportionate burden of kidney disease and related conditions, including multiple comorbidities, physical frailty, and geriatric syndromes [
5].
Patients with CKD require active management to preserve kidney function, slow disease progression, and lower the risk of cardiovascular complications [
6]. When kidney function declines to the point of requiring hemodialysis, patients experience increased rates of depression, anxiety, and reduced quality of life. Hemodialysis is also associated with a variety of physical symptoms, such as fatigue, anorexia, anemia, hypotension, and fluid overload [
7]. Therefore, it is critical to prioritize appropriate management and preventive strategies for CKD patients who have not yet begun dialysis.
Effective chronic disease management requires active patient engagement in maintaining, monitoring, and managing their condition [
8]. Self-care adherence is a key factor in reducing hospital readmissions and mortality rates [
9]. However, a study of CKD patients receiving hemodialysis found only moderate levels of self-care adherence (ranging from 3.30 to 3.61 on a 5-point scale) across various behaviors, including dietary control, vascular care, exercise and rest, medication intake, blood pressure and weight monitoring, and physical care [
10,
11]. Self-care behaviors are shaped by individual experiences, skills, motivation, cultural beliefs and values, confidence, and healthcare accessibility—all of which can promote or impede adherence [
12].
Previous studies have shown that cognitive impairment in CKD patients can affect their decision-making ability regarding self-care adherence [
13,
14]. As kidney function deteriorates, increasing toxin levels in the blood may impair brain function. Furthermore, CKD patients are at elevated risk for cerebrovascular diseases, which may contribute to cognitive decline [
15]. CKD is considered a disease of accelerated aging and is associated with decreased physical and cognitive function, increased risk of falls and fractures, reduced quality of life, appetite loss, and chronic inflammation [
16]. Accordingly, it is important to assess cognitive function in addition to kidney function and to develop tailored management strategies.
Health literacy also plays a crucial role in the management of CKD. According to previous studies, lower health literacy has been associated with poorer clinical outcomes, increased hospital admissions, and higher emergency department utilization [
15]. Among older adults, poor health literacy is linked with worse clinical outcomes due to difficulties accessing and understanding medical information, which ultimately hinders disease and self-management [
10]. Social support—particularly from family and healthcare providers—has been identified as a key factor influencing self-care adherence [
17]. Positive relationships with family and healthcare providers enhance patients’ role performance and self-care behaviors. Hemodialysis patients with strong family support demonstrate higher self-efficacy, quality of life, self-esteem, and role performance [
10,
17].
The term “adherence” refers to compliance with a prescribed treatment plan, whereas “non-adherence” denotes failure to follow it [
18]. Delaying CKD progression requires both pharmacological treatment and risk factor management [
6]. Thus, it is necessary to evaluate the risk of self-care non-adherence comprehensively, considering not only the extent of self-care behaviors but also factors such as cognitive function, health literacy, physical functioning, access to care, social support, motivation, and confidence [
18]. Ongoing attention and interventions are needed to prevent non-adherence and to improve self-care adherence.
As the population continues to age and chronic diseases become more prevalent, the number of patients with CKD is expected to rise. While many studies have investigated self-care in dialysis patients, few have examined factors influencing self-care non-adherence specifically among older adults with pre-dialysis CKD. In light of this gap, the present study aimed to examine how cognitive function, health literacy, and social support from family and healthcare providers affect the risk of self-care non-adherence in older adults with pre-dialysis CKD, by assessing the levels of these factors and analyzing their impact. These findings may serve as foundational data for developing educational programs and practical interventions in outpatient and inpatient settings to help delay disease progression in this population.
METHODS
1. Study Design
This descriptive survey study investigated the effects of cognitive function, health literacy, and social support on the risk of self-care non-adherence in older adults with CKD. The study is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (
http://www.strobe-statement.org).
The study included older adults (aged 65 years and older) diagnosed with CKD who had not yet progressed to the end stage requiring dialysis. Participants were recruited either during outpatient follow-up visits or while receiving inpatient treatment at the nephrology department of a tertiary university hospital in J Province, Korea. Inclusion criteria were a diagnosis of CKD, prescription of medication for symptom management, age 65 years or older, voluntary provision of informed consent, and the ability to communicate verbally and understand questionnaires. Exclusion criteria included a diagnosis of depression or dementia and receiving renal replacement therapy such as hemodialysis or peritoneal dialysis.
The minimum sample size was calculated using G*Power version 3.0, with an alpha level of .05, power of 0.95, and an effect size (f²) of 0.35 based on a previous study by Kwon [
17]. Thirteen explanatory variables were included, consisting of demographic and clinical characteristics shown to be significant in prior research—such as age, sex, education level, marital status, presence of a primary caregiver, duration of CKD, hypertension, diabetes, and exercise—along with study-specific variables including cognitive function, health literacy, and social support from family and healthcare providers. Based on this calculation, a minimum of 88 participants was required. To account for a potential 20% dropout rate, a total of 110 participants were recruited. Questionnaires were administered directly by the researcher and were either self-completed or read aloud when necessary. After excluding five incomplete questionnaires due to unanswered items or discontinuation because of health conditions, 105 fully completed questionnaires were included in the final analysis.
1) General and disease-related characteristics
General characteristics, such as sex, age, marital status, religion, education level, employment status, economic status, cohabitation with family members, presence of a caregiver, smoking and drinking habits, and exercise routine, as well as disease-related factors, including comorbidities, number of medications, hospitalization history, disease duration, and perceived health status, were collected from electronic medical records and a structured questionnaire.
2) Cognitive function
Cognitive function was assessed using the Korean Dementia Screening Questionnaire-C (KDSQ-C), developed by Yang et al. [
19] for the early detection of dementia in older adults and designed to be unaffected by age, sex, or education. Permission was obtained from the original authors. The KDSQ-C assesses community-dwelling older adults with 15 items: five on memory, five on language ability, and five on the performance of complex tasks. Each item is rated on a 3-point scale: “not at all” (0 points), “sometimes” (1 point), and “often” (2 points), yielding a total score between 0 and 30. A score less than six was considered normal, while a score of six or higher indicated possible cognitive impairment. The original instrument’s reliability was shown by a Cronbach’s α=.81; in this study, it was .85.
3) Health literacy
Health literacy was evaluated using the Short Form of the Korean Functional Health Literacy Test (S-KFHLT), developed by Kim [
20] for older adults. The S-KFHLT includes eight items: four assessing reading comprehension and four assessing numeracy. Each correct answer receives 1 point, and each incorrect answer receives 0, resulting in a total score from 0 to 8, with higher scores indicating greater health literacy. The original KR-20 measure for internal consistency was .84; in this study, it was .74.
4) Social support
Social support was measured in two domains: family support and healthcare provider support. The original tool was developed by Kim [
21] and revised and expanded by Sim [
22] to assess social support from both family and healthcare providers. Family support was measured with 12 items on a 5-point Likert scale from “strongly disagree” (1) to “strongly agree” (5), with higher scores reflecting greater family support. Cronbach’s α was .94 in the original and .93 in this study. Healthcare provider support was assessed using nine items on the same Likert scale, with higher scores indicating greater support from healthcare professionals. Cronbach’s α was .91 in the original and .86 in this study.
5) Risk of self-care non-adherence
Risk of self-care non-adherence was measured using a tool developed by Jo and Oh [
18] for individuals with two or more chronic conditions, used here with the original authors’ permission. The tool includes 18 items based on six factors: knowledge and skills, physical functioning, access to care, social support, motivation, and confidence. Each item was rated on a 5-point Likert scale: “not at all” (1), “mostly no” (2), “neutral” (3), “mostly yes” (4), and “very much so” (5). The total score ranges from 18 to 90, with higher scores indicating a greater risk of self-care non-adherence. Twelve of the items are reverse-scored and were recoded accordingly. Cronbach’s α for the original subdomains ranged from .65 to .81; in this study, overall reliability was shown by a Cronbach’s α value of .89.
Data were collected from April 5 to May 30, 2024. After formal approval from both the nursing and nephrology departments of the participating tertiary hospital, the researcher (a geriatric nurse specialist) met with participants individually in outpatient consultation rooms or nephrology ward counseling areas to explain the study’s purpose and procedures. Participants were recruited by convenience sampling, with the researcher screening outpatient clinic patients according to predefined criteria and inviting eligible individuals to participate. Those diagnosed with dementia were excluded, but individuals with mild or undiagnosed cognitive decline were included if judged capable of understanding and consenting. If participants had difficulty responding, the researcher read the questionnaire aloud and completed it on their behalf, repeating questions as needed. All participants completed the questionnaire either independently or with assistance. The researcher collected the completed questionnaires, with an average response time of approximately 25 minutes.
5. Ethical Considerations
This study was approved by the Institutional Review Board (IRB) for Biomedical Research, Jeonbuk National University Hospital (IRB No. 2024-02-020-005). Participants were given sufficient time to decide about participation, and written informed consent was obtained from those who agreed. The researcher explained the study’s purpose and procedures, potential risks or discomforts, anticipated benefits, voluntary participation, the right to withdraw at any time, and data confidentiality. Personally identifiable information was kept confidential, and unique identification codes were assigned to each participant. All data were securely stored in a locked location and will be retained for three years before being destroyed. Given that participants were older adults who might have diminished cognitive or decision-making capacity, the researcher assessed each participant’s ability to provide informed consent; if necessary, written consent from a legal representative was obtained in addition to the participant’s own assent.
6. Data Analysis
Data analysis was performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA), with statistical significance set at p<.05. Descriptive statistics (mean, standard deviation, frequencies, and percentages) were used to summarize general characteristics and main study variables. Differences in cognitive function, health literacy, and social support according to general characteristics were analyzed using the independent t-test and analysis of variance, with post hoc comparisons conducted using the Games–Howell method. Pearson’s correlation coefficients were calculated to assess relationships among general characteristics, cognitive function, health literacy, and social support. Hierarchical multiple regression analysis was used to identify factors influencing the risk of self-care non-adherence.
RESULTS
1. General Characteristics of Participants
The mean age of participants was 75.7±7.1 years, and 63 participants (60.0%) were male. A majority of participants (n=83, 79.0%) was married. Of the participants, 65 (61.9%) had a middle school education or less, and 92 (87.6%) were unemployed. Most participants (n=80, 76.2%) rated their economic status as “moderate,” and 76 participants (72.4%) lived with a spouse. Fourteen participants (13.3%) were current smokers, with an average smoking duration of 38.6±14.1 years. Nineteen participants (18.1%) reported alcohol consumption, with an average drinking duration of 31.6±14.6 years. Nearly half of the participants (n=52, 49.5%) reported engaging in regular exercise (
Table 1).
The mean duration since CKD diagnosis was 59.4±68.64 months. Most participants (98.1%) had comorbidities, with hypertension (80.0%) and diabetes (65.7%) being the most prevalent. Regarding CKD stage, 40 participants (38.1%) were in stage 2 and 45 (42.9%) were in stage 3, together comprising 80.9% of the sample. A total of 44.8% reported a history of hospitalization, with 42.9% citing kidney failure as the reason for admission (
Table 2). Suspected cognitive impairment, defined as a score ≥6 on the KDSQ-C, was observed in 43.8% of participants. The average family support score was 51.03±11.27 out of a maximum possible score of 60, and the average healthcare provider support score was 39.96±6.16 out of 45. Health literacy, measured by the S-KFHLT, had a mean score of 4.15±2.09 out of 8. The mean score for risk of self-care non-adherence was 39.76±15.45 out of a possible 90 (
Table 2).
Significant differences in self-care non-adherence risk scores were found according to sex, marital status, education level, employment status, perceived economic status, living with family members, alcohol consumption, regular exercise, and cognitive function (
Table 3). Female participants had significantly higher risk scores than males (t=–2.29,
p=.024). Those who were single, divorced, or widowed had higher risk scores than married participants (t=–2.33,
p=.022). Education level showed significant differences (F=15.17,
p<.001), with post hoc Games–Howell tests indicating that participants with only middle or high school education had significantly higher risk scores than those with college education or higher. Unemployed participants had significantly higher risk scores than those who were employed (t=–2.80,
p=.011). Participants with lower perceived economic status had significantly higher risk scores compared to those reporting moderate status (t=–2.53,
p=.013). Those living with a spouse had significantly lower risk scores than those living with children, caregivers, or living alone (F=4.46,
p=.025), with post hoc analysis confirming these differences. Participants who did not consume alcohol had significantly higher risk scores than those who did (t=–3.13,
p=.003). Those who did not exercise regularly had significantly higher risk scores than those who did (t=–3.87,
p<.001). Participants with suspected cognitive impairment (≥6 points on the KDSQ-C) had significantly higher risk scores than those with normal cognitive function (<6 points) (t=–6.65,
p<.001) (
Table 3).
The risk of self-care non-adherence was positively correlated with cognitive impairment (r=.61,
p<.001), and negatively correlated with family support (r=–.32,
p=.001), healthcare provider support (r=–.36,
p<.001), and health literacy (r=–.42,
p<.001) (
Table 4).
To identify factors influencing the risk of self-care non-adherence, hierarchical multiple regression analysis was performed. Variables were entered in two steps, based on theoretical and empirical rationale. In Step 1, general characteristics significantly associated with the dependent variable in bivariate analysis (sex, marital status, education level, employment status, perceived economic status, living with family members, alcohol consumption, and exercise) were included as control variables, in line with previous studies on self-care behaviors. In Step 2, key psychosocial variables—cognitive function, social support, and health literacy—were added, as these were central to the study’s conceptual framework and showed significant correlations.
Assumption testing confirmed model adequacy (Durbin-Watson=1.759; all variance inflation factors<5; residuals were normally distributed and homoscedastic). Model 1 was significant (F=6.85,
p<.001), with an adjusted R² of .31. A higher risk of self-care non-adherence was observed among those with lower education, unemployment, low economic status, no alcohol consumption, and lack of regular exercise. In Model 2, the addition of the main study variables increased explanatory power to 46% (F=7.83,
p<.001). Significant predictors in the final model included lack of regular exercise (β=.25,
p=.002), impaired cognitive function (β=.29,
p<.001), and lower support from healthcare providers (β=–.26,
p=.008) (
Table 5).
DISCUSSION
This study aimed to examine levels of cognitive function, health literacy, and social support among older adults with CKD who had not yet initiated dialysis, and to analyze how these factors influenced the risk of self-care non-adherence. The discussion is organized around the major findings of the study. In terms of disease-related characteristics, 80.9% of participants were in CKD stages 2 or 3, and 98.1% had at least one comorbidity. Notably, hypertension (80.0%) and diabetes (65.7%) were highly prevalent, supporting the idea that diabetic nephropathy and hypertension-related renal damage are major causes of CKD in older adults [
23]. Previous international studies have also demonstrated that advanced stages of CKD are associated with higher readmission rates, primarily due to cardiovascular complications and acute kidney injury [
24]. As such, the high prevalence of comorbidities, including cardiovascular risk factors, underscores the need for systematic management to prevent CKD progression.
Hierarchical regression analysis was conducted to identify factors affecting the risk of self-care non-adherence. In the first step of the model, lower educational attainment, unemployment, lower economic status, absence of alcohol consumption, and lack of regular exercise were associated with higher risk of self-care non-adherence. Specifically, individuals with only a high school education or less exhibited a greater risk of non-adherence, consistent with prior research indicating that higher education is associated with better self-care capacity in chronic conditions [
25]. Higher education is typically linked to better disease awareness and health knowledge, which can positively influence self-care behaviors. Therefore, when implementing nursing interventions to reduce self-care non-adherence, it is essential to provide tailored educational programs that account for patients’ educational backgrounds. Additionally, 87.6% of participants were unemployed, most likely reflecting their age and retirement status. The loss of social roles following retirement can lead to reduced social engagement and increased isolation, which in turn may lower motivation for self-care [
26]. This highlights the importance of assessing social networks in older CKD patients and implementing strategies to strengthen emotional support. Interestingly, individuals who did not consume alcohol showed a higher risk of self-care non-adherence, which may reflect a subgroup who were advised to abstain for health reasons. Furthermore, self-reported questionnaires introduce the possibility that participants underreported actual alcohol consumption. Future research using larger samples, objective measures, or longitudinal designs should address these limitations.
In the second step of the hierarchical regression, when the main study variables—cognitive function, health literacy, and social support—were included, the explanatory power of demographic variables diminished. This suggests that core variables such as cognitive function play a more substantial role in predicting self-care non-adherence. Thus, intervention strategies should focus primarily on modifiable factors, such as promoting exercise, compensating for cognitive impairment, and enhancing social support from healthcare professionals, rather than on relatively immutable demographic characteristics.
Cognitive function emerged as the most influential factor increasing the risk of self-care non-adherence in the final regression model. Assessment results showed that 43.8% of participants were possibly cognitively impaired. This finding is consistent with previous research demonstrating a significant relationship between cognitive function and self-care among older adults with hypertension [
27], and it reflects the higher prevalence of cognitive impairment in older adults with kidney disease. CKD can contribute to cognitive decline through the accumulation of toxins in the blood resulting from reduced kidney function, thereby hindering self-care [
27]. Therefore, it is important to assess cognitive function and provide personalized education and interventions based on patients’ cognitive status to reduce the risk of self-care non-adherence in older adults with CKD. This study suggests that nursing interventions such as tailored education using visual aids and repeated sessions, provision of medication management checklists, and shared decision-making involving healthcare professionals and family members may be effective strategies to enhance self-care adherence in this population. Although individuals formally diagnosed with dementia were excluded and interviews were conducted one-on-one, a substantial proportion of older participants (mean age, 76 years) exhibited cognitive decline, which may have influenced the findings. Thus, further research is needed to validate these results.
Regular exercise was also identified as a key factor in reducing the risk of self-care non-adherence. Lack of exercise was associated with increased risk, which aligns with prior research showing that physical activity promotes self-care behaviors in older adults with hypertension and diabetes [
28,
29]. Moreover, exercise interventions in pre-dialysis CKD patients have demonstrated significant benefits in improving GFR and preventing deterioration of renal function [
30]. Thus, assessing exercise habits should be a central part of self-care risk evaluation, and strategies to promote physical activity are necessary.
Social support, especially from healthcare professionals, was found to be a significant factor in reducing the risk of self-care non-adherence. Lack of professional support increased the risk of non-adherence, consistent with previous findings that information and guidance from healthcare providers positively influence patients’ role performance and engagement in care [
31]. The active involvement and regular counseling provided by healthcare professionals play a critical role in promoting adherence to self-care behaviors among older adults with CKD.
However, in this study, health literacy did not significantly affect the risk of self-care non-adherence. This finding differs from previous systematic reviews and survey studies on patients with CKD, which have identified low health literacy as a negative factor influencing self-care adherence [
15]. One possible explanation is that participants in this study were older adults, and health literacy showed a moderate negative correlation with cognitive function (r=.45). Although no multicollinearity was detected, as both variables were included in the regression model, their overlapping influence may have affected the results. Future studies should include replication with older populations with CKD and comparative studies focusing on patients with normal cognitive function to better understand the impact of health literacy on self-care non-adherence.
In this study, only social support from healthcare providers was significantly associated with the risk of self-care non-adherence, whereas family support was not. This finding contrasts with previous research indicating that patients who receive higher levels of family support demonstrate better adherence to self-care [
31]. One possible explanation for this discrepancy may be related to the characteristics of the study population, who were generally in relatively stable health, able to live independently, and capable of attending outpatient visits. These factors may have reduced their reliance on family support. Additionally, emotional or physical distance from family members other than a spouse could have contributed to the diminished influence of family support. This interpretation is supported by a qualitative study suggesting that, among patients with relatively stable health, the relationship with healthcare providers plays a more critical role in self-care adherence [
32]. Nevertheless, the present study also emphasizes the importance of involving family members in the decision-making process for older adults with CKD.
Based on these findings, there is a clear need for policy-level strategies to support early detection and effective management of older adults with CKD. Specifically, integrating community-based resources, expanding health insurance coverage for education and counseling services, and strengthening collaboration with primary care providers should be considered as part of a comprehensive policy approach.
This study has several limitations. First, as the research was conducted at a single institution, the generalizability of the findings is limited. Second, the use of a cross-sectional design and self-reported questionnaires introduces potential recall bias and limits the ability to establish causal relationships among the variables. Third, although participants with a formal diagnosis of dementia were excluded, formal cognitive screening tools were not used during participant selection. Notably, 43.6% of participants scored low enough to raise concerns about potential cognitive impairment, suggesting that the accuracy of some self-reported data may have been compromised. Despite these limitations, this study is meaningful in that it identifies key factors associated with self-care non-adherence and highlights the importance of early screening to support self-care behaviors in older adults with CKD.
CONCLUSION
This study identified greater cognitive impairment, lack of regular exercise, and insufficient support from healthcare providers as key predictors of an increased risk of self-care non-adherence among older pre-dialysis adults with CKD. Therefore, before the disease progresses to the stage requiring dialysis, it is crucial to identify individuals at high risk of self-care non-adherence by assessing cognitive function early, providing personalized education, developing programs to encourage physical activity, and strengthening emotional and informational support from healthcare providers. Future studies should employ longitudinal designs to objectively assess the risk of self-care non-adherence and to evaluate the effectiveness of tailored interventions aimed at mitigating this risk and improving patient outcomes.
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CONFLICTS OF INTEREST
The authors declared no conflict of interest.
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AUTHORSHIP
Study conception and design - JYS and SYH; supervision - SYH; data collection and processing - JYS; analysis and interpretation - JYS and SYH; drafting and critical revision of the manuscript - JYS and SYH.
-
FUNDING
None.
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ACKNOWLEDGEMENT
This article is partial excerpt from the Ji-Yeon Seo's master thesis from Hanyang university graduate school.
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DATA AVAILABILITY STATEMENT
The data can be obtained from the corresponding authors.
Table 1.General Characteristics of Study Participants (N=105)
|
Variables |
Categories |
n (%) or M±SD |
|
Age (year) |
|
75.7±7.1 |
|
65–75 |
52 (49.5) |
|
75–94 |
53 (50.5) |
|
Sex |
Male |
63 (60.0) |
|
Female |
42 (40.0) |
|
Marital status |
Married |
83 (79.0) |
|
Single/divorced/bereaved |
22 (21.0) |
|
Education level |
≤Middle school |
65 (61.9) |
|
High school |
26 (24.8) |
|
≥College |
14 (13.3) |
|
Employed |
Yes |
13 (12.4) |
|
No |
92 (87.6) |
|
Economic status |
Middle |
80 (76.2) |
|
Low |
25 (23.8) |
|
Living with |
Spouse |
76 (72.4) |
|
Alone |
18 (17.1) |
|
Children |
10 (9.5) |
|
Caregiver |
1 (1.0) |
|
Current smoking |
Yes |
14 (13.3) |
|
No |
91 (86.7) |
|
Alcohol drinking |
Yes |
19 (18.1) |
|
No |
86 (81.9) |
|
Regular exercise |
Yes |
52 (49.5) |
|
No |
53 (50.5) |
Table 2.Clinical Characteristics of Study Participants and Research Variables (N=105)
|
Variables |
Categories |
n (%) or M±SD |
Actual range |
Possible range |
|
Comorbidities |
Yes†
|
103 (98.1) |
|
|
|
Hypertension |
84 (80.0) |
|
Diabetes |
69 (65.7) |
|
Hyperlipidemia |
39 (37.1) |
|
Angina pectoris |
11 (10.5) |
|
Myocardial infarction |
11 (10.5) |
|
Heart failure |
12 (11.4) |
|
Cardiovascular |
28 (26.7) |
|
Cerebrovascular |
17 (16.2) |
|
Others |
14 (13.3) |
|
No |
2 (1.9) |
|
Period after CKD diagnosis (month) |
59.4±68.64 |
|
|
|
CKD stage |
Ⅱ |
40 (38.1) |
|
|
Ⅲ |
45 (42.9) |
|
Ⅳ |
10 (9.5) |
|
Ⅴ |
10 (9.5) |
|
Hospitalization in the past year |
Yes |
47 (44.8) |
|
|
|
Kidney problem |
20 (42.6) |
|
Others |
27 (57.4) |
|
Perceived health status |
Poor |
43 (41.0) |
|
|
|
Fair |
51 (48.6) |
|
Good |
11 (10.5) |
|
Cognitive function |
Normal <6 |
59 (56.2) |
0–23 |
0–30 |
|
Impaired ≥6 |
46 (43.8) |
|
|
|
Social support |
Family support |
51.03±11.27 |
12–60 |
12–60 |
|
Healthcare provider support |
39.96±6.16 |
21–45 |
9–45 |
|
Health literacy |
|
4.15±2.09 |
0–8 |
0–8 |
|
Risk of non-adherence to self-care |
39.76±15.42 |
18–82 |
18–90 |
Table 3.Differences in the Risk of Non-Adherence to Self-Care by Study Participants' Characteristics and Cognitive Function (N=105)
|
Variables |
Categories |
M±SD |
t or F |
p
|
|
Age (year) |
65–75 |
36.96±13.95 |
–1.86 |
.065 |
|
75–94 |
42.51±46.45 |
|
|
Sex |
Male |
37.00±14.85 |
–2.29 |
.024 |
|
Female |
43.90±15.56 |
|
|
Marital status |
Married |
37.99±14.59 |
–2.33 |
.022 |
|
Single/divorced/bereaved |
46.45±17.05 |
|
|
Education level |
≤Middle schoola
|
41.80±15.29 |
15.17 |
<.001†
|
|
High schoolb
|
41.58±15.98 |
|
a, b>c |
|
≥Collegec
|
26.93±7.93 |
|
|
|
Occupation |
Yes |
32.00±9.77 |
–2.80 |
.011 |
|
No |
40.86±15.82 |
|
|
Economic status |
Middle |
37.69±14.33 |
–2.53 |
.013 |
|
Low |
46.40±17.24 |
|
|
Living with |
Spouse |
37.03±13.60 |
4.46 |
.025†
|
|
Children |
49.10±13.74 |
|
|
Caregiver or single |
45.79±19.87 |
|
|
Smoking |
Yes |
42.07±14.43 |
0.60 |
.550 |
|
No |
39.41±15.19 |
|
|
Alcohol drinking |
Yes |
32.21±10.44 |
–3.13 |
.003 |
|
No |
41.43±15.91 |
|
|
Regular exercise |
Yes |
34.23±13.74 |
–3.87 |
<.001 |
|
No |
45.19±15.21 |
|
|
Cognitive function |
Normal <6 |
34.32±10.43 |
–6.65 |
<.001 |
|
Impaired ≥6 |
49.30±15.67 |
|
Table 4.Correlations among Research Variables (N=105)
|
Variables |
Cognitive function |
Family support |
Healthcare provider support |
Health literacy |
Risk of self-care non-adherence |
|
r (p) |
|
Cognitive function |
1 |
|
|
|
|
|
Family support |
–.16 (.096) |
1 |
|
|
|
|
Healthcare provider support |
–.09 (.338) |
.59 (<.001) |
1 |
|
|
|
Health literacy |
–.45 (<.001) |
.04 (.724) |
.11 (.263) |
1 |
|
|
Risk of self-care non-adherence |
.61 (<.001) |
–.32 (.001) |
–.36 (<.001) |
–.42 (<.001) |
1 |
Table 5.Factors Influencing Risk of Self-Care Non-Adherence (N=105)
|
Variables |
|
Model 1 |
Model 2 |
|
B |
SE |
95% CI |
β |
t |
p
|
B |
SE |
95% CI |
β |
t |
p
|
|
(Constant) |
10.32 |
5.43 |
–0.42 to 21.11 |
|
1.92 |
<.001 |
53.07 |
10.88 |
8.57 to 34.56 |
|
4.70 |
<.001 |
|
Female (ref: male) |
1.87 |
3.01 |
–3.56 to –8.20 |
.06 |
0.62 |
.536 |
0.33 |
2.69 |
–3.20 to 7.33 |
.01 |
0.12 |
.903 |
|
Marital status (ref: married) |
2.42 |
5.88 |
–0.08 to 13.45 |
.06 |
0.41 |
.682 |
1.48 |
5.26 |
–2.74 to 9.63 |
.04 |
0.28 |
.779 |
|
≤ High school (ref: ≥college) |
8.47 |
3.95 |
0.67 to 16.37 |
.19 |
2.14 |
.035 |
2.33 |
3.75 |
–4.49 to 10.48 |
.05 |
0.62 |
.536 |
|
Not employed (ref: yes) |
7.89 |
3.88 |
0.21 to 15.60 |
.17 |
2.03 |
.045 |
5.15 |
3.60 |
–3.60 to 10.63 |
.11 |
1.43 |
.156 |
|
Low economic status (ref: middle) |
7.32 |
3.18 |
2.06 to 14.15 |
.20 |
2.30 |
.023 |
3.99 |
3.05 |
–2.07 to 10.05 |
.11 |
1.31 |
.195 |
|
Living with (ref: spouse) |
4.80 |
5.38 |
3.02 to 8.20 |
.14 |
0.89 |
.375 |
2.10 |
4.91 |
–7.64 to 11.85 |
.06 |
0.43 |
.669 |
|
No drinking (ref: yes) |
7.39 |
3.44 |
0.46 to 14.01 |
.19 |
2.15 |
.034 |
4.49 |
3.08 |
–1.52 to 10.82 |
.11 |
1.46 |
.149 |
|
No exercise (ref: yes) |
9.54 |
2.59 |
4.51 to 14.77 |
.31 |
3.68 |
<.001 |
7.76 |
2.42 |
2.95 to 12.56 |
.25 |
3.20 |
.002 |
|
Cognitive function ≥6 (ref: normal <6) |
|
|
|
|
|
|
9.01 |
2.75 |
0.60 to 1.67 |
.29 |
3.28 |
<.001 |
|
Family support |
|
|
|
|
|
|
–0.03 |
0.13 |
–0.29 to 0.23 |
–.02 |
–0.24 |
.810 |
|
Healthcare provider support |
|
|
|
|
|
|
–0.64 |
0.24 |
–1.11 to –0.16 |
–.26 |
–2.69 |
.008 |
|
Health literacy |
|
|
|
|
|
|
–1.00 |
0.64 |
–2.25 to 0.29 |
–.14 |
–1.56 |
.123 |
|
Adjusted R2
|
.31 |
.46 |
|
Δ R2
|
|
.15 |
|
F value (p) |
6.85 (<.001) |
7.83 (<.001) |
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