Abstract
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Purpose
This study aimed to examine the influences of online health information-seeking behavior and e-health literacy on self-management among patients undergoing hemodialysis.
-
Methods
A correlational survey was conducted with 150 adult hemodialysis patients who had been receiving dialysis for at least three months. Data were collected from July to November 2023 using structured questionnaires. The variables measured included online health information-seeking behavior, e-health literacy, and self-management. Data were analyzed using descriptive statistics, the independent t-test, one-way analysis of variance, Pearson correlation coefficients, and hierarchical multiple regression with IBM SPSS/WIN 28.0.
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Results
Participants demonstrated moderate to high levels of online health information-seeking behavior, e-health literacy, and self-management. Self-management was positively correlated with online health information-seeking behavior (r=.34, p<.001) and e-health literacy (r=.45, p<.001). Hierarchical multiple regression analysis identified e-health literacy (β=.30, p<.001), regular exercise during the past year (β=.27, p<.001), and alcohol consumption during the past year (β=−.22, p=.002) as significant predictors of self-management, explaining 32% of the variance.
-
Conclusion
E-health literacy, regular exercise, and alcohol consumption significantly affect self-management among hemodialysis patients. Therefore, nursing interventions should focus on enhancing e-health literacy and promoting healthy lifestyle habits to strengthen self-management capabilities in this population.
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Key Words: Health literacy; Information seeking behavior; Internet use; Renal dialysis; Self-management
INTRODUCTION
According to the 2022 report of the Korean Renal Data System, the number of patients diagnosed with chronic kidney disease (CKD) exceeded 127,000 in 2021 and continues to rise [
1]. Among those receiving renal replacement therapy (RRT), hemodialysis remains the dominant modality, accounting for 83.6% in 2019 (n=15,587), 82.2% in 2020 (n=15,201), and 83.6% in 2021 (n=16,115) [
1]. This trend underscores the clinical importance of hemodialysis as a major treatment option for patients with CKD.
Despite advances in medical technology and improvements in dialysis techniques, patients undergoing hemodialysis continue to depend on dialysis machines throughout their lives and experience various physical and psychological challenges [
2]. Because hemodialysis cannot fully replace kidney function, patients must engage in extensive self-management activities such as adhering to dietary restrictions, controlling fluid intake, taking medications as prescribed, and preventing infections [
3]. Self-management among hemodialysis patients refers to proactive behaviors aimed at optimizing health outcomes, preventing complications, and minimizing disease burden through symptom control and appropriate use of healthcare resources [
4]. Specifically, it includes adherence to dietary and fluid restrictions, appropriate medication use, vascular access and infection management, regular exercise, and effective utilization of healthcare services [
4]. Effective self-management is essential for delaying the progression of complications and reducing healthcare costs [
4].
With advances in information and communication technology, patients now have easier access to a wide range of health information through the internet [
5]. At the same time, public interest in healthcare applications based on artificial intelligence and the internet of things has expanded rapidly [
6]. These developments have increased patient engagement in online health information-seeking behavior, creating a new model of health management that differs from traditional provider-centered approaches [
7]. Online health information seeking allows patients to access large amounts of information in real time without spatial or temporal limitations, which can be particularly valuable for individuals with restricted mobility or limited healthcare access, such as those undergoing hemodialysis [
8]. Hemodialysis patients, who visit hospitals multiple times per week and face financial and time constraints due to prolonged treatment [
9], may especially benefit from leveraging online health resources.
However, the reliability of online health information is inconsistent, and uncritical acceptance of inaccurate information can lead to adverse health outcomes [
10]. Therefore, the ability to effectively locate, evaluate, understand, and apply health information—known as e-health literacy—has emerged as a critical competency in modern healthcare [
11]. High e-health literacy improves health awareness and encourages the adoption of positive health behaviors [
12]. In contrast, low e-health literacy can hinder effective information use, making it difficult for patients to engage in appropriate health behaviors and potentially worsening health disparities [
13]. Given the complex and long-term nature of hemodialysis treatment, patients must play an active role in their own care. Thus, online health information-seeking behavior and e-health literacy are expected to exert significant influences on self-management.
Previous studies have shown that higher e-health literacy is associated with healthier lifestyle behaviors and better quality of life [
12,
13]. In Korea, the eHealth Literacy Scale has been validated for patient use [
14]. For hemodialysis patients, effective self-management—including dietary control, fluid restriction, and medication adherence—is vital for preventing complications and improving outcomes [
15,
16]. Although valid instruments for measuring self-management have been developed [
17,
18], few studies have explored how online health information-seeking behavior and e-health literacy affect self-management in this population. This study seeks to fill that gap by examining these relationships and providing evidence for the development of targeted nursing interventions and educational programs.
Accordingly, the present study aims to examine the effects of online health information-seeking behavior and e-health literacy on self-management among patients undergoing hemodialysis. The findings are expected to provide foundational data for developing tailored nursing interventions and educational programs that enhance access to and utilization of health information, ultimately improving quality of life and promoting health equity among this patient population.
METHODS
1. Study Design
This study employed a cross-sectional observational design to examine the influences of online health information-seeking behavior and e-health literacy on self-management among patients undergoing hemodialysis. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
2. Setting and Samples
Participants were recruited through convenience sampling among adult patients receiving hemodialysis. Inclusion criteria were as follows: (1) age ≥19 years; (2) undergoing hemodialysis for ≥3 months due to CKD; (3) receiving hemodialysis at least twice per week; and (4) ability to communicate verbally or in writing and to provide informed consent. Exclusion criteria included receiving other forms of RRT, such as peritoneal dialysis. The three-month threshold was used to exclude patients in the initial adaptation phase, which may involve heightened physical or psychological sensitivity that could confound study variables [
19].
The sample size was calculated using G*Power 3.1.9.7 for multiple regression analysis with a significance level of .05, power of .80, a medium effect size of 0.15 [
13], and 13 predictors (7 general characteristics, four health-related characteristics, and two independent variables). The required sample size was 131. To account for an anticipated 15% attrition rate, 154 participants were recruited. A total of 150 participants completed the survey and were included in the final analysis, comprising 35 recruited offline and 115 recruited online.
The survey consisted of 52 items, including general characteristics (7 items), health-related characteristics (4 items), online health information-seeking behavior (13 items), e-health literacy (8 items), and self-management (20 items). Permission to use each instrument was obtained via email from the original authors.
1) General and health-related characteristics
General characteristics included sex, age, education level, marital status, occupation, perceived socioeconomic status, and daily internet use time (7 items). Health-related characteristics included alcohol consumption during the past year, smoking status, regular exercise during the past year, and self-rated health status (4 items).
2) Online health information seeking behavior
This construct was measured using the instrument originally developed by Laflamme [
20] and later revised by Park and Lee [
21]. It consists of 13 items across three subdomains: production activities (7 items), use of health information communities (3 items), and search for health information (3 items). Each item is rated on a 5-point Likert scale (1=strongly disagree, 5=strongly agree), with higher scores indicating greater engagement in online health information-seeking behavior. The reliability of the instrument was confirmed by Cronbach’s α values of .89 in Park and Lee [
21] and .91 in this study.
3) E-health literacy
E-health literacy was assessed using the Korean version of the eHealth Literacy Scale (KeHEALS), adapted by Chang et al. [
14] from the original instrument developed by Norman and Skinner [
11]. This self-report scale includes eight items measuring the ability to locate, evaluate, and apply health information obtained from the internet. Each item is rated on a 5-point Likert scale (1=strongly disagree, 5=strongly agree), with higher scores indicating higher e-health literacy. The instrument demonstrated strong internal consistency, with Cronbach’s α values of .89 in both the original study [
14] and in this study.
4) Self-management
Self-management was assessed using the Hemodialysis Self-Management Instrument–Korea (HDSMI-K), developed by Cha and Kang [
17] based on the original HDSMI by Song and Lin [
18]. The instrument consists of 20 items across four subdomains: problem-solving and communication (7 items), hydration and weight control (3 items), diet and dialysis (5 items), and self-defense and emotional control (5 items). Each item is rated on a 4-point Likert scale (1=strongly disagree, 4=strongly agree), with higher scores indicating better self-management. The instrument demonstrated good reliability, with Cronbach’s α values of .87 in the original study [
17] and .91 in this study.
Data were collected from June 1 to November 30, 2023, after obtaining Institutional Review Board (IRB) approval. Both offline and online recruitment methods were used, but all surveys were ultimately completed online using Google Forms (Google LLC, Mountain View, CA, USA). For offline recruitment, study announcements were posted on bulletin boards within dialysis units following hospital approval. Eligible patients who expressed interest completed the survey during dialysis sessions or before/after treatment using tablet devices provided by the researcher. Research assistants were available to assist participants as needed. For online recruitment, notices were posted in a large kidney disease-related internet community with over 170,000 members and a medical open chat community on a social media platform with approximately 570 members. After obtaining permission from community administrators, recruitment notices were shared. Interested individuals contacted the researcher directly to receive the survey link. These communities used verification processes to ensure that members were actual hemodialysis patients, enhancing participant credibility. All participants—whether recruited offline or online—completed the same Google Forms survey. The first page of the survey provided detailed information about the study, including confidentiality, voluntary participation, and withdrawal rights. Informed consent was obtained electronically by selecting “I agree” before proceeding. No paper-based consent forms were used, and responses were restricted to one per participant.
5. Ethical Considerations
The study protocol and instruments were reviewed and approved by the IRB of Gangneung-Wonju National University (IRB No.: GWNUIRB-2022-14, GWNUIRB-2022-14-5). Permission to use the instruments was obtained from their original developers. Participants provided informed consent after reading a detailed explanation of the study’s purpose, procedures, and ethical safeguards. They were informed that participation was voluntary and could be withdrawn at any time without penalty. All data were anonymized and used solely for research purposes. After data analysis, all digital records were stored securely on the researcher’s password-protected computer and will be retained for three years in accordance with institutional guidelines, after which they will be permanently deleted. Participants who consented to provide contact information received a convenience store gift voucher, after which their contact data were permanently deleted.
6. Data Analysis
Data were analyzed using IBM SPSS ver. 28.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (frequency, percentage, mean, and standard deviation [SD]) were used to summarize participants’ general and health-related characteristics, as well as levels of online health information-seeking behavior, e-health literacy, and self-management. Data normality was examined using skewness, kurtosis, and normality test statistics. Skewness ranged from −0.48 to 0.72, and kurtosis ranged from −0.91 to 0.65, indicating that the data were approximately normally distributed. The Kolmogorov–Smirnov and Shapiro–Wilk tests were nonsignificant (K–S p=.200; S–W p=.083), confirming that the normality assumption was met. Accordingly, parametric tests were used. Group differences were examined using the independent t-test and one-way analysis of variance, followed by the Scheffe post hoc test. Pearson correlation coefficients were used to assess relationships among the main variables. Hierarchical multiple regression analysis was conducted to identify factors influencing self-management. In the first step, general demographic and health-related characteristics were entered to control for their effects. In the second step, online health information-seeking behavior and e-health literacy were added to determine their additional explanatory power for self-management. This approach enabled assessment of the independent contributions of the main variables beyond general characteristics.
RESULTS
1. General and Health-Related Characteristics of Participants
Table 1 presents the general and health-related characteristics of the participants. Among the 150 participants, 62.7% were male (n=94) and 37.3% were female (n=56), with a mean age of 49.5 years (SD=11.43; range=25–80 years). The largest age group was 40–49 years (27.3%, n=41). Regarding education level, 80.7% (n=121) held a university degree or higher. Most participants were married (74.7%, n=112). In terms of occupation, office workers comprised the largest group (51.4%, n=77). Regarding perceived socioeconomic status, 71.3% (n=107) reported a middle or higher level. For daily internet use, 44.0% (n=66) used the internet for 2 to <4 hours, followed by 23.3% (n=35) for 4 to <6 hours, 20.0% (n=30) for <2 hours, and 12.7% (n=19) for ≥6 hours. Among health-related characteristics, 44.7% (n=67) reported alcohol consumption during the past year. For smoking status, 42.7% (n=64) were non-smokers, 38.0% (n=57) were former smokers, and 19.3% (n=29) were current smokers. The proportion reporting regular exercise in the past year was similar between those exercising 1–2 times per week (41.3%, n=62) and those exercising three or more times per week (43.4%, n=65). Regarding self-perceived health status, 44.7% (n=67) rated their health as poor, 44.0% (n=66) as fair, and 11.3% (n=17) as good.
2. Levels of Online Health Information Seeking Behavior, E-health Literacy, and Self-Management
The mean score for online health information-seeking behavior was 3.43 (SD=0.79) on a 5-point scale. Subdomain scores were as follows: production activities, 3.28 (SD=0.90); use of health information communities, 3.48 (SD=0.97); and search for health information, 3.71 (SD=0.81).
The mean e-health literacy score was 3.67 (SD=0.63) on a 5-point scale. The highest-rated item was “I know how to use the internet to answer my questions about health” (3.88, SD=0.82), while the lowest-rated item was “I have the skills I need to evaluate the health resources I find on the internet” (3.49, SD=0.90).
The mean self-management score was 3.24 (SD=0.42) on a 4-point scale. Subdomain scores were: hydration and weight control, 3.33 (SD=0.60); diet and dialysis, 3.29 (SD=0.44); problem-solving and communication, 3.23 (SD=0.46); and self-defense and emotional control, 3.14 (SD=0.45) (
Table 2).
Significant differences in online health information-seeking behavior were observed by education level (t=−3.13, p=.002), occupation (F=5.86, p<.001), and daily internet use time (F=7.75, p<.001). Post hoc analyses revealed higher scores among professionals and technicians compared with unemployed or service workers, and among those who used the internet for ≥4 hours per day compared with those using it for <2 hours. Significant differences by health-related characteristics were also found for regular exercise in the past year (F=3.72, p=.026), with higher scores among participants exercising three or more times per week. No significant differences were observed for sex, age, marital status, perceived socioeconomic status, alcohol consumption, smoking status, or self-perceived health status.
E-health literacy differed significantly by education level (t=−3.43, p=.002), occupation (F=5.31, p=.002), perceived socioeconomic status (t=−2.83, p=.005), and daily internet use time (F=11.60, p<.001). Post hoc results were similar to those for online health information seeking. Regular exercise in the past year also showed significant differences (F=10.73, p<.001), with higher e-health literacy among participants who exercised at least once per week. No significant differences were found for sex, age, marital status, alcohol consumption, smoking status, or self-perceived health status.
Self-management scores differed significantly by sex (t=−2.22,
p=.028) and perceived socioeconomic status (t=−2.08,
p=.041). Significant differences were also found for alcohol consumption during the past year (t=2.56,
p=.011) and regular exercise during the past year (F=14.49,
p<.001), with higher self-management scores among those exercising weekly or more. However, no significant differences were observed for age, marital status, occupation, daily internet use time, smoking status, or self-perceived health status (
Table 3).
Self-management was positively correlated with both online health information-seeking behavior (r=.34,
p<.001) and e-health literacy (r=.45,
p<.001). Online health information-seeking behavior was also positively correlated with e-health literacy (r=.58,
p<.001) (
Table 4).
Hierarchical multiple regression analysis was conducted to identify predictors of self-management. Model 1 included sex, perceived socioeconomic status, alcohol consumption during the past year, and regular exercise during the past year as control variables. Based on post hoc results, the exercise variable was simplified into a binary category (exercise vs. no exercise).
Multicollinearity was assessed, with tolerance values ranging from .91 to .96 in Model 1 and .58 to .92 in Model 2. Variance inflation factor values were within acceptable limits (Model 1, 1.04–1.10; Model 2, 1.09–1.71). Durbin–Watson statistics were 1.98 and 1.96, respectively, indicating no autocorrelation. Cook’s distance values were below .10 for both models, confirming the absence of influential outliers.
Model 1 was significant (F=10.44,
p<.001) and explained 20.0% of the variance in self-management. Significant predictors included regular exercise during the past year (β=.39,
p<.001) and alcohol consumption during the past year (β=−.19,
p=.013). In Model 2, online health information-seeking behavior and e-health literacy were added. The model explained 32.0% of the variance, representing a significant 12% increase in explanatory power (Δadjusted R²=.12, F=12.73,
p<.001) compared with Model 1. Significant predictors in Model 2 were e-health literacy (β=.30,
p<.001), regular exercise during the past year (β=.27,
p<.001), and alcohol consumption during the past year (β=−.22,
p=.002) (
Table 5).
DISCUSSION
This study examined the levels of online health information-seeking behavior, e-health literacy, and self-management among patients undergoing hemodialysis, and identified factors influencing self-management. Hierarchical regression analysis revealed that e-health literacy, regular exercise, and alcohol consumption were significant predictors, with e-health literacy emerging as the strongest explanatory variable. This finding suggests that the ability to understand, evaluate, and apply information—rather than the mere frequency of searching—plays a decisive role in determining self-management levels.
The mean self-management score in this study was 3.24, consistent with the findings of Kang et al. [
15]. Among the subdomains, “hydration and weight control” had the highest score, while “self-defense and emotional control” had the lowest. High scores in hydration and weight management may be attributed to patients’ awareness of their direct relationship with blood pressure control and the prevention of cardiovascular complications [
3]. Conversely, the low score in self-defense and emotional regulation indicates that patients may experience difficulty asserting their rights or coping with emotional challenges during treatment [
22], underscoring the need for psychosocial support and interventions. In this study, general characteristics such as sex and perceived socioeconomic status were significantly associated with self-management. Specifically, higher perceived socioeconomic status was associated with higher self-management scores. This finding aligns with previous research [
16], which reported that patients with higher socioeconomic status, greater health literacy, and better access to self-management resources exhibited higher levels of self-management. Therefore, targeted educational interventions are necessary to support socioeconomically disadvantaged groups who may lack such resources.
E-health literacy was identified as the most influential explanatory variable for self-management among hemodialysis patients, consistent with findings from a previous study of cardiovascular patients [
23]. In that study, the mean e-health literacy score was 1.82, and higher literacy levels were significantly associated with healthier behaviors and better health-related quality of life. Similarly, a study of patients with hypertension [
24] reported a mean score of 3.00, showing significant correlations with health beliefs. In the present study, the mean score was 3.67, which is relatively higher than in these prior populations. This difference may be partially explained by age. The mean age of participants in this study was 49.5 years (range 25–80), substantially younger than the average ages in the hypertension (64.7 years) [
24] and cardiovascular (71.3 years) [
23] studies. Younger patients are generally more familiar with digital devices and online information seeking, and many participants in this study had prior experience with online health communities, likely contributing to higher e-health literacy levels.
Beyond mean differences, this study revealed notable variations across subdomains. Specifically, the item “I know how to use the internet to answer my questions about health” received the highest rating, suggesting that participants were relatively skilled in accessing and searching for health information online. In contrast, the item “I have the skills I need to evaluate the health resources I find on the internet” received the lowest rating, indicating difficulty in assessing the credibility of online information. This suggests that although hemodialysis patients can easily locate health information, they may lack the critical judgment needed to translate it into reliable self-management strategies.
These results highlight differences between this study and previous research. For instance, Kim and Kim [
25] reported that e-health literacy did not significantly influence health-promoting behaviors among cancer patients, possibly because the complexity of cancer treatment limits patient autonomy in decision-making. In contrast, hemodialysis patients must engage in daily self-management activities such as dietary regulation, fluid and weight control, and medication adherence [
4]. Thus, the ability to critically evaluate and apply health information becomes a key determinant of effective self-management in this population. Taken together, the findings have important clinical and educational implications. Patient education and nursing interventions for hemodialysis patients should not only focus on the provision of health information but also emphasize strengthening vulnerable subdomains—particularly “information evaluation skills.” Moreover, because e-health literacy was associated with occupation and socioeconomic status, digital health education should be tailored for older adults and socioeconomically disadvantaged groups who may have lower digital competence [
25]. In the context of rapid digital transformation in healthcare, such targeted approaches are essential to enhance patients’ autonomy and self-management capacity.
The second factor influencing self-management among patients undergoing hemodialysis was regular exercise, consistent with previous research. A systematic review [
26] reported that aerobic or combined exercise programs performed three times per week for 8 weeks to 12 months improved aerobic capacity, walking ability, and overall health-related quality of life. Similarly, another systematic review [
27] found that regular, long-term exercise significantly enhanced physical function and quality of life in hemodialysis patients. Regular exercise increases self-efficacy and serves as a motivational factor, thereby promoting self-management [
27]. However, while previous studies primarily evaluated the effects of structured exercise interventions under experimental conditions [
26,
27], the present study identified a relationship between patients’ actual exercise levels in daily life and their self-management performance using observational data. Some studies have reported that patients on hemodialysis experience fatigue and musculoskeletal pain, which limit their ability to engage in regular exercise [
28]. Such barriers have been recognized as major obstacles to sustaining and maximizing the benefits of exercise. Nevertheless, because this study recruited many participants from online communities, the sample may have included individuals with greater interest in health management and stronger self-motivation, potentially contributing to higher reported levels of regular exercise compared with previous research. Therefore, the findings suggest that regular exercise among patients undergoing hemodialysis should not be understood solely in terms of intervention outcomes but also within the context of environmental and psychological strategies that facilitate exercise maintenance in daily life [
26,
27]. For example, nurses could enhance self-management by helping patients set individualized exercise goals during dialysis sessions or by connecting them to online communities for motivational and peer support.
The third factor influencing self-management was alcohol consumption. In this study, alcohol use remained an independent negative predictor even after controlling for e-health literacy, regular exercise, and other variables, underscoring its clinical significance. Alcohol use represents not only a lifestyle behavior but also a behavior that heightens physical and clinical risk. A recent case report [
29] described a 70-year-old woman who experienced severe symptoms—including loss of consciousness, respiratory depression, and hypotension—after ingesting disinfectant alcohol. Notably, these complications occurred at relatively low blood alcohol levels (82 mg/dL) and resolved only after hemodialysis. This case illustrates how alcohol consumption can precipitate acute deterioration in renal function among dialysis patients [
29]. Moreover, prior studies have shown that individuals with CKD experience elevated stress and depression, with some resorting to alcohol as a coping mechanism [
9]. Other research [
30] has also reported that patients with a history of drinking exhibit lower self-management scores, suggesting that alcohol use is a persistent and structural barrier to effective self-management. Consequently, patient education for individuals on hemodialysis should extend beyond simple advice to limit alcohol intake and instead emphasize that alcohol use can trigger severe, potentially life-threatening complications [
29]. In clinical practice, a comprehensive approach integrating alcohol prevention education with alternative stress management strategies, such as regular exercise or social support programs, is warranted.
Meanwhile, although online health information-seeking behavior was positively correlated with self-management, it did not emerge as a significant predictor in the regression analysis. This suggests that online health information seeking may not directly influence self-management but could exert indirect effects through other variables such as e-health literacy. The mean score for online health information-seeking behavior in this study was 3.43, comparable to that reported by Son and Kang [
31] (3.40) but notably higher than the score reported by Son and Lee [
32] (1.68), despite the use of the same measurement instrument. These discrepancies may be attributable to differences in study populations. In Son and Kang [
31], participants were middle-aged women, whereas this study included patients undergoing hemodialysis—a group with a greater need for health information due to the demands of chronic disease management. By contrast, in Son and Lee [
32], most participants were middle-aged men in their 40s, who may have perceived a lower need for health information seeking. Additionally, the absence of significant age-related differences in online health information-seeking behavior suggests that older patients are also actively utilizing digital health resources. This reflects improved access to digital technologies among older adults and indicates that educational programs for hemodialysis patients should not assume limited digital accessibility based solely on age [
33]. Nevertheless, when older adults engage with online health information, evaluating its reliability and applying it appropriately may remain particularly challenging [
8]. In this regard, Lim [
34] found that while e-health literacy was associated with online health information-seeking behavior among adults aged 20 years and older, it was not a significant predictor of self-management—a finding consistent with the present study. This underscores that the frequency of information searching does not directly translate into effective self-management. Rather, the qualitative aspects of information use, including critical evaluation and practical application, as well as educational interventions that facilitate behavioral translation, are crucial. Therefore, improving self-management among hemodialysis patients requires tailored digital health education programs that strengthen information appraisal and selection skills, with a specific focus on enhancing e-health literacy.
Ultimately, improving self-management in patients undergoing hemodialysis requires not merely an increase in the quantity of online health information available but rather a qualitative enhancement of education and intervention strategies based on strengthened e-health literacy. This study demonstrated that e-health literacy is the most influential factor for self-management among hemodialysis patients, while also identifying regular exercise promotion and alcohol use reduction as essential components of comprehensive self-management strategies.
This study has several limitations. First, because some participants were recruited through online communities, there is a potential overrepresentation of individuals with relatively high e-health literacy. Therefore, the generalizability of the findings may be limited due to the use of convenience sampling. Second, as data collection relied on self-reported questionnaires, response bias may have occurred. Third, the self-management instrument used in this study did not comprehensively capture lifestyle behaviors such as smoking, alcohol consumption, and regular exercise, which may limit the thorough assessment of associations between self-management and lifestyle factors. Nonetheless, methodological consistency was maintained by ensuring that both online and offline participants completed the same online survey platform.
Despite these limitations, this study provides several important implications. First, it empirically validated that e-health literacy is the primary determinant of self-management among hemodialysis patients, addressing a research gap that has been insufficiently explored in previous studies. Second, the findings offer practical guidance for nursing practice by underscoring that patient education and intervention programs should move beyond simple information delivery and instead focus on systematically strengthening patients’ abilities to understand, critically evaluate, and effectively utilize health information. Such strategies can enhance patients’ self-management capabilities and serve as foundational evidence for developing targeted nursing interventions and educational programs.
CONCLUSION
This study identified e-health literacy, regular exercise, and alcohol consumption as key determinants of self-management among patients undergoing hemodialysis, with e-health literacy emerging as the most influential factor. Although patients demonstrated proficiency in searching for health information online, their limited ability to critically evaluate information highlights the need for targeted educational strategies. Regular exercise functioned as a facilitator of self-management, whereas alcohol consumption acted as a persistent barrier, underscoring the importance of comprehensive lifestyle modification. Future research should utilize more representative samples and develop validated assessment tools that include lifestyle behaviors. Moreover, tailored interventions for patients with low e-health literacy are particularly warranted to strengthen self-management and enhance the effectiveness of nursing education and clinical practice.
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CONFLICTS OF INTEREST
Jaehee Jeon has been editorial board member of the Korean Journal of Adult Nursing since 2018. She was not involved in the review process of this manuscript. Otherwise, there was no conflict of interest.
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AUTHORSHIP
Study conception and/or design acquisition - MYK and JJ; analysis - MYK and JJ; interpretation of the data - MYK and JJ; and drafting or critical revision of the manuscript for important intellectual content - MYK and JJ.
-
FUNDING
None.
-
ACKNOWLEDGEMENT
This article is a revision of the Myeong-yi Kim’s master’s thesis from Gangneung-Wonju National University. Year of 2023.
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DATA AVAILABILITY STATEMENT
The data can be obtained from the corresponding authors.
Table 1.General and Health-Related Characteristics of the Participants (N=150)
|
Characteristics |
Categories |
n (%) |
M±SD |
|
General |
|
|
|
|
Sex |
Male |
94 (62.7) |
|
|
Female |
56 (37.3) |
|
|
Age (year) |
19 to <40 |
38 (25.3) |
49.5±11.43 |
|
40 to <50 |
41 (27.3) |
|
|
50 to <60 |
34 (22.7) |
|
|
≥60 |
37 (24.7) |
|
|
Education level |
≤High school graduate |
29 (19.3) |
|
|
≥University degree |
121 (80.7) |
|
|
Marital status |
Yes |
112 (74.7) |
|
|
No |
38 (25.3) |
|
|
Occupation |
Office workers |
77 (51.4) |
|
|
Service workers |
29 (19.3) |
|
|
Professionals and technicians |
15 (10.0) |
|
|
Unemployed |
29 (19.3) |
|
|
Perceived socioeconomic status |
Lower class |
43 (28.7) |
|
|
≥Middle class |
107 (71.3) |
|
|
Internet use time (hour/day) |
<2 |
30 (20.0) |
3.3±2.13 |
|
2 to <4 |
66 (44.0) |
|
|
4 to <6 |
35 (23.3) |
|
|
≥6 |
19 (12.7) |
|
|
Health-related |
|
|
|
|
Alcohol consumption during the past year |
Yes |
67 (44.7) |
|
|
No |
83 (55.3) |
|
|
Smoking status |
Non-smokers |
64 (42.7) |
|
|
Former smokers |
57 (38.0) |
|
|
Current smokers |
29 (19.3) |
|
|
Regular exercise in the past year (frequency) |
None |
23 (15.3) |
|
|
1–2/week |
62 (41.3) |
|
|
≥3/week |
65 (43.4) |
|
|
Self-recognized health status |
Poor |
67 (44.7) |
|
|
Fair |
66 (44.0) |
|
|
Good |
17 (11.3) |
|
Table 2.Descriptive Statistics for Online Health Information Seeking Behavior, E-health Literacy, and Self-Management (N=150)
|
Variables |
Categories |
Items |
M±SD |
|
Online health information seeking behavior (1–5) |
Total |
13 |
3.43±0.79 |
|
Production activities |
7 |
3.28±0.90 |
|
Use of health information communities |
3 |
3.48±0.97 |
|
Search for health information |
3 |
3.71±0.81 |
|
E-health literacy (1–5) |
Total |
8 |
3.67±0.63 |
|
1. I know what health resources are available on the internet |
|
3.68±0.81 |
|
2. I know where to find helpful health resources on the internet |
|
3.69±0.86 |
|
3. I know how to find helpful health resources on the internet |
|
3.74±0.82 |
|
4. I know how to use the internet to answer my questions about health |
|
3.88±0.82 |
|
5. I know how to use the health information I find on the internet to help me |
|
3.69±0.81 |
|
6. I have the skills I need to evaluate the health resources I find on the internet |
|
3.49±0.90 |
|
7. I can tell high quality health resources from low quality health resources on the internet |
|
3.55±0.93 |
|
8. I feel confident in using information from the internet to make health decisions |
|
3.62±0.83 |
|
Self-management (1–4) |
Total |
20 |
3.24±0.42 |
|
Problem-solving and communication |
7 |
3.23±0.46 |
|
Hydration and weight control |
3 |
3.33±0.60 |
|
Diet and dialysis |
5 |
3.29±0.44 |
|
Self-defense and emotional control |
5 |
3.14±0.45 |
Table 3.Differences in Online Health Information-Seeking Behavior, E-health Literacy, and Self-Management According to Participants’ General and Health-Related Characteristics (N=150)
|
Characteristics |
Categories |
Online health information seeking behavior |
E-health literacy |
Self-management |
|
M±SD |
t or F (p) Scheffé |
M±SD |
t or F (p) Scheffé |
M±SD |
t or F (p) Scheffé |
|
General |
|
|
|
|
|
|
|
|
Sex |
Male |
3.38±0.83 |
–0.84 (.403) |
3.66±0.69 |
–0.18 (.859) |
3.18±0.43 |
–2.22 (.028) |
|
Female |
3.50±0.72 |
3.68±0.53 |
3.33±0.37 |
|
Age (years) |
19 to <40a
|
3.68±0.70 |
2.14 (.097) |
3.87±0.53 |
1.83 (.144) |
3.28±0.39 |
0.90 (.441) |
|
40 to <50b
|
3.43±0.66 |
3.57±0.59 |
3.26±0.44 |
|
50 to <60c
|
3.34±0.79 |
3.59±0.52 |
3.14±0.37 |
|
≥60d
|
3.24±0.96 |
3.64±0.82 |
3.26±0.45 |
|
Education level |
≤High school graduate |
3.02±0.89 |
–3.13 (.002) |
3.24±0.78 |
–3.43 (.002) |
3.09±0.51 |
–1.86 (.071) |
|
≥University degree |
3.52±0.74 |
3.77±0.55 |
3.27±0.38 |
|
Marital status |
Yes |
3.45±0.82 |
–0.68 (.498) |
3.70±0.67 |
–1.03 (.305) |
3.27±0.39 |
–1.86 (.065) |
|
No |
3.35±0.72 |
3.58±0.50 |
3.13±0.48 |
|
Occupation |
Office workersa
|
3.49±0.71 |
5.86 (<.001) b, d<c |
3.78±0.56 |
5.31 (.002) b, d<c |
3.27±0.38 |
2.42 (.069) |
|
Service workersb
|
3.24±0.87 |
3.49±0.61 |
3.24±0.36 |
|
Professionals and techniciansc
|
4.06±0.48 |
4.00±0.63 |
3.39±0.29 |
|
Unemployedd
|
3.12±0.86 |
3.38±0.70 |
3.07±0.57 |
|
Perceived socioeconomic status |
Lower class |
3.30±0.89 |
–1.19 (.234) |
3.44±0.73 |
–2.83 (.005) |
3.12±0.48 |
–2.08 (.041) |
|
≥Middle class |
3.47±0.75 |
3.76±0.56 |
3.29±0.38 |
|
Internet use time (hours/day) |
<2a
|
2.92±0.82 |
7.75 (<.001) a<c, d |
3.23±0.72 |
11.60 (<.001) a, b<d a<c |
3.15±0.46 |
1.99 (.119) |
|
2 to <4b
|
3.42±0.60 |
3.61±0.51 |
3.19±0.37 |
|
4 to <6c
|
3.63±0.91 |
3.90±0.60 |
3.33±0.40 |
|
≥6d
|
3.87±0.72 |
4.11±0.47 |
3.37±0.49 |
|
Health-related |
|
|
|
|
|
|
|
|
Alcohol consumption during the past year |
Yes |
3.71±0.65 |
–0.67 (.503) |
3.49±0.76 |
–0.92 (.354) |
3.14±0.42 |
2.56 (.011) |
|
No |
3.64±0.62 |
3.37±0.82 |
3.31±0.40 |
|
Smoking status |
Non-smokersa
|
3.44±0.79 |
0.55 (.579) |
3.65±0.08 |
0.48 (.622) |
3.33±0.38 |
2.72 (.070) |
|
Former smokersb
|
3.35±0.85 |
3.63±0.60 |
3.17±0.43 |
|
Current smokersc
|
3.54±0.67 |
3.77±0.69 |
3.17±0.45 |
|
Regular exercise in the past year (frequency) |
Nonea
|
3.06±0.86 |
3.72 (.026) a<c |
3.14±0.72 |
10.73 (<.001) a<b, c |
2.85±0.49 |
14.49 (<.001) a<b, c |
|
1–2/weekb
|
3.40±0.78 |
3.74±0.57 |
3.27±0.36 |
|
≥3/weekc
|
3.58±0.74 |
3.78±0.57 |
3.34±0.36 |
|
Self-recognized health status |
Poor |
3.28±0.89 |
2.12 (.124) |
3.55±0.73 |
2.12 (.124) |
3.20±0.46 |
0.45 (.638) |
|
Fair |
3.55±0.67 |
3.44±0.51 |
3.27±0.35 |
|
Good |
3.52±0.75 |
3.70±0.63 |
3.24±0.47 |
Table 4.Correlations between Online Health Information-Seeking Behavior, E-health Literacy, and Self-Management (N=150)
|
Variables |
OHISB |
eHL |
SM |
|
r (p) |
|
OHISB |
1 |
|
|
|
eHL |
.58 (<.001) |
1 |
|
|
SM |
.34 (<.001) |
.45 (<.001) |
1 |
Table 5.Factors Influencing the Self-Management (N=150)
|
Variables |
Model 1 |
|
Model 2 |
Collinearity statistics |
|
B |
SE |
β |
t |
p
|
|
B |
SE |
β |
t |
p
|
Tolerance |
VIF |
|
(Constant) |
2.86 |
.09 |
|
30.90 |
<.001 |
|
2.01 |
.17 |
|
12.21 |
<.001 |
|
|
|
Female sex†
|
0.09 |
.07 |
.10 |
1.31 |
.194 |
|
0.09 |
.06 |
.10 |
1.43 |
.156 |
.91 |
1.10 |
|
Perceived socioeconomic status (≥Middle class)‡
|
0.06 |
.07 |
.06 |
0.80 |
.426 |
|
–0.00 |
.07 |
–.00 |
–0.04 |
.970 |
.87 |
1.15 |
|
Alcohol consumption (during the past year) (yes)§
|
–0.16 |
.06 |
–.19 |
–2.52 |
.013 |
|
–0.19 |
.06 |
–.22 |
–3.16 |
.002 |
.92 |
1.09 |
|
Regular exercise in the past year (yes)‖
|
0.45 |
.09 |
.39 |
5.22 |
<.001 |
|
0.31 |
.08 |
.27 |
3.74 |
<.001 |
.86 |
1.16 |
|
Online health information-seeking behavior |
|
|
|
|
|
|
0.06 |
.04 |
.12 |
1.44 |
.153 |
.66 |
1.52 |
|
E-health literacy |
|
|
|
|
|
|
0.20 |
.06 |
.30 |
3.39 |
<.001 |
.58 |
1.71 |
|
R² (ΔR²) |
|
|
|
.22 |
|
|
|
|
.35 (.13) |
|
|
|
Adjusted R² (ΔAdjusted R²) |
|
|
|
.20 |
|
|
|
|
.32 (.12) |
|
|
|
F (p) |
|
|
|
10.44 (<.001) |
|
|
|
|
12.73 (<.001) |
|
|
|
Durbin–Watson |
|
|
|
1.98 |
|
|
|
|
1.96 |
|
|
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