Factors Influencing eHealth Literacy in Maintenance Hemodialysis and Its Relationship with Emotional Status and Quality of Life

Abstract

Background

eHealth literacy (eHL) has emerged as an important determinant of health outcomes in chronic disease management. However, its role among patients undergoing maintenance hemodialysis (MHD) remains insufficiently explored. This study aimed to assess the level of eHL in MHD patients, identify associated sociodemographic and clinical factors, and examine the relationship between eHL, emotional status, and quality of life (QoL).

Methods

A total of 229 patients receiving MHD were recruited in a single-center cross-sectional study. Data were collected using validated questionnaires assessing eHL, emotional status (anxiety and depression), and quality of life (SF-36). Binary logistic regression and linear regression analyses were conducted to identify factors associated with eHL. Spearman correlation analysis was used to evaluate the associations between eHL, emotional status, and QoL.

Results

The mean age of participants was 57.36 ± 13.98 years, and 66.38% were male. The median dialysis duration was 4.00 years. Approximately half of the patients (50.66%) demonstrated high eHL. Patients with low eHL were significantly older, Patients with low eHL were significantly older, had lower educational attainment, and had a higher burden of multimorbidity. Patients with low eHL had significantly higher anxiety and depression scores and a higher prevalence of anxiety and depressive symptoms than those with high eHL (all P < 0.01). They also had poorer QoL across all eight domains of the SF-36 (all P < 0.05). eHEALS scores were positively correlated with overall QoL (r = 0.382, P < 0.0001) and negatively correlated with depression scores (r = −0.182, P = 0.0057), whereas the correlation with anxiety scores was not statistically significant (r = −0.124, P = 0.0613). In multivariable analyses, older age and lower educational attainment were independently associated with lower odds of high eHL, while higher comorbidity burden, widowed/divorced marital status, and longer dialysis duration were associated with continuous eHEALS scores.

Conclusion

eHL was significantly associated with emotional well-being and QoL in patients undergoing MHD. Lower eHL was more common among older and less educated patients, suggesting that these groups may benefit from targeted, patient-centered support for digital health engagement. However, whether improving eHL can reduce psychological distress or improve QoL remains uncertain and requires evaluation in longitudinal and intervention studies.

Introduction

Chronic kidney disease (CKD) is a major global public health concern, affecting approximately 10% of the adult population worldwide.¹ As the disease progresses irreversibly, many patients ultimately develop end-stage kidney disease (ESKD), necessitating renal replacement therapies such as maintenance hemodialysis (MHD) and peritoneal dialysis.² Among these, MHD is the most commonly employed treatment modality, capable of significantly prolonging life expectancy. However, long-term dialysis imposes a substantial physical and psychological burden, often leading to a decline in patients’ overall quality of life (QoL).^3–6

In parallel with the increasing digitization of healthcare, eHealth technologies have become essential tools for disease management. This transition is particularly relevant to MHD, where effective disease management extends beyond dialysis sessions and depends substantially on patients’ ongoing engagement in self-care. Digital health approaches, including telemedicine, remote monitoring, digital health education, and mobile health platforms, are increasingly being incorporated into or evaluated for dialysis care, creating new opportunities to extend clinical support, health information, and self-management resources beyond the dialysis setting.^7–10 However, the availability of digital health resources does not necessarily translate into patients’ ability to use them effectively. Meaningful engagement with these resources requires individuals to locate, understand, evaluate, and appropriately apply health information. eHealth literacy (eHL), defined as the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to address or solve a health problem, has emerged as a critical determinant of health behavior and outcomes.¹¹ This capacity may be particularly important for patients undergoing MHD, who must routinely access and interpret complex information related to dialysis treatment, medications, dietary and fluid restrictions, symptom management, and comorbidities. However, individuals with lower eHL may struggle to evaluate the credibility and relevance of online health information, potentially resulting in misinformation, poor treatment adherence, heightened emotional distress, and suboptimal self-care, especially among those with complex chronic conditions like ESKD.

Previous research has demonstrated that higher eHL is associated with improved health knowledge, proactive health behaviors, enhanced medication adherence, and better preventive care practices.^12–15 Beyond these behavioral outcomes, higher eHL has also been associated with better psychological well-being and health-related QoL in general and clinical populations, including lower levels of anxiety and depression.^16–19 These associations may be partly attributable to the capacity to access, evaluate, and apply health information, which may facilitate uncertainty reduction, perceived control, and adaptive coping in the context of chronic illness. Nonetheless, some findings indicate that higher eHL may also widen disparities in anxiety and depression.²⁰ Such inconsistencies suggest that the relationship between eHL and psychological outcomes may vary across populations and clinical contexts. However, evidence regarding the associations of eHL with anxiety, depression, and multidimensional QoL among patients undergoing MHD remains limited.

Despite increasing recognition of eHL as a determinant of health behavior and outcomes,^21–24 evidence in MHD populations remains limited. In our prior study, we identified distinct latent eHL profiles associated with health-promoting lifestyles, demonstrating substantial heterogeneity in digital health engagement among MHD patients.²⁵ However, this work was restricted to behavioral stratification and did not examine whether eHL was also associated with psychological well-being or health-related QoL. Building on this evidence, the present study extends the investigation of eHL to anxiety, depression, and multidimensional QoL, while also examining its sociodemographic and clinical correlates using multivariable regression models. This approach provides a broader assessment of eHL in MHD by considering both the factors associated with differences in eHL and its relationships with psychological and health-related outcomes. This study aimed to (1) assess the level of eHL among patients undergoing MHD; (2) identify its sociodemographic and clinical determinants; and (3) examine its associations with emotional status (anxiety and depression) and health-related QoL.

Materials and Methods

Study Population

This study employed a single-center, cross-sectional design. Patients undergoing MHD at the hemodialysis unit of a tertiary hospital in Baoding City, China, were consecutively recruited between January and March 2025. The present study used data collected during a separate data collection period from that of our previous study, which was conducted between June and December 2024.²⁵ MHD was defined as long-term, scheduled hemodialysis provided as chronic renal replacement therapy for patients with ESKD. Inclusion criteria were as follows: (1) age ≥18 years; (2) a diagnosis of ESKD, with an estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m²; (3) received regular hemodialysis for at least 3 months; (4) being fully conscious and having sufficient cognitive and communication abilities to independently complete the questionnaire; and (5) voluntarily agreed to participate and provided written informed consent. Exclusion criteria included: (1) a diagnosed psychiatric disorder or cognitive impairment; (2) malignant tumors or other severe systemic diseases; (3) inability or unwillingness to complete the questionnaire. This study was approved by the Ethics Committee of Baoding No. 1 Central Hospital (approval number: Fast [2024] No. 199), and all procedures conformed to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection.

Sample Size

Sample size was estimated using an empirical rule commonly applied in studies of factors associated with binary outcomes, with approximately 5–10 participants recommended per candidate predictor variable.²⁶ Based on 22 prespecified candidate variables, the estimated sample size was 110–220 participants. Allowing for approximately 20% potential loss due to missing or invalid responses, the target sample size was increased accordingly. Ultimately, 229 eligible participants with valid data were included in the final analysis.

Data Collection

Data were collected using five instruments: a structured general information questionnaire, the eHealth Literacy Scale (eHEALS), the Self-Rating Anxiety Scale (SAS), the Self-Rating Depression Scale (SDS), and the 36-Item Short Form Health Survey (SF-36). All questionnaires were administered during routine hemodialysis sessions by trained research personnel, who provided standardized instructions and checked questionnaires for completeness after completion.

Sociodemographic, lifestyle, clinical, and dialysis-related data were collected using structured, self-designed questionnaires. Trained healthcare personnel conducted face-to-face interviews with participants during routine hemodialysis sessions to ensure the accuracy and completeness of the data. Sociodemographic variables included age, sex (male/female), marital status (unmarried, married, divorced, or widowed), educational attainment (junior high school or below, senior high school, college or above), residence type (living alone or with family), type of medical insurance (urban resident, urban employee, or self-paid), and monthly household income (<3000 RMB or ≥3000 RMB). Additionally, participants reported their residence location (urban or rural) and the distance from home to the dialysis center (<5 km or ≥5 km). Lifestyle and behavioral factors included smoking status (yes/no), alcohol consumption (yes/no), self-reported sleep quality over the past three months (good, average, or poor), and dietary status (good, average, or poor). Height (cm) and weight (kg) were measured to calculate body mass index (BMI), defined as weight in kilograms divided by the square of height in meters (kg/m²), and categorized into <18.5, 18.5–23.9, and ≥24.0. Clinical data included the presence of comorbid conditions such as stroke, coronary heart disease, diabetes, polycystic kidney disease, hyperlipidemia, and hypertension. Comorbidity burden was categorized as <2 or ≥2 types. Additional clinical variables included hospitalization within the past six months (yes/no), the number of currently used medications (0, 1–2, 3–4, or >4), and the use of assistive devices (eg, wheelchair, crutch, or others). Dialysis-related variables comprised age at initiation of MHD (<60 or ≥60 years), dialysis duration (<5 or ≥5 years), type of vascular access (arteriovenous fistula or central venous catheter), dialysis frequency (3 times/week, 2 times/week, or 5 times/2 weeks), and the presence of dialysis-related complications.

The eHEALS is a widely validated self-reported instrument that assesses individuals’ perceived ability to locate, understand, evaluate, and apply electronic health information to address health-related needs.^27,28 In this study, the culturally adapted and validated Chinese version of eHEALS was used to assess eHL among MHD patients.²⁹ The Cronbach’s alpha coefficient of the scale was 0.913. The scale consists of 8 items rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), with total scores ranging from 8 to 40. Higher scores reflect greater perceived eHL. Because no universally accepted or MHD-specific validated cutoff has been established for the eHEALS, a total score of 26 was prespecified as the operational threshold based on previously published studies, with scores ≥26 classified as high eHL and scores <26 as low eHL.^30–32 This threshold was not considered a diagnostic or clinically validated cutoff.

Emotional status was assessed using the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS), both of which are widely validated, self-administered instruments developed by Zung.^33,34 Each scale consists of 20 items that measure the severity of depressive and anxiety symptoms, respectively, over the past week. Respondents rate each item on a 4-point Likert scale ranging from 1 (none or a little of the time) to 4 (most or all of the time). The total raw score is multiplied by 1.25 to yield a standard score, with higher scores indicating more severe symptoms. SAS and SDS scores above 50 points indicate the presence of anxiety or depression. Although more recent instruments such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) are widely used internationally, SAS and SDS remain extensively validated and are routinely applied in Chinese clinical and research contexts, particularly among patients with chronic diseases.³⁵ Given their cultural suitability and strong psychometric performance in China, these scales were considered appropriate for the present study population.

QoL was measured using the SF-36, a widely validated and internationally recognized instrument for assessing health-related quality of life across various populations and disease conditions.^36,37 The SF-36 consists of 36 items grouped into eight subscales: Physical Functioning (PF), Role Limitations due to Physical Problems (RP), Bodily Pain (BP), General Health Perceptions (GH), Vitality (VT), Social Functioning (SF), Role Limitations due to Emotional Problems (RE), and Mental Health (MH). Item 2, which assesses perceived changes in health over the preceding year, was not included in the calculation of the SF-36 scores. Each subscale score ranges from 0 to 100, with higher scores indicating better health status. For each of the eight subscales, item responses were first scored according to the SF-36 scoring rules and then transformed to a 0–100 scale using the standard linear transformation: Transformed score = [(Actual raw score – Lowest possible raw score)/Possible raw score range] × 100. An overall SF-36 score was subsequently calculated as the arithmetic mean of the eight transformed subscale scores, with higher scores indicating better overall health-related quality of life. In addition, an overall SF-36 score was calculated as the arithmetic mean of the eight transformed domain scores, consistent with approaches described in previous methodological and hemodialysis studies.^38,39 The Chinese version of the SF-36, which has demonstrated good reliability and validity in patients with chronic diseases, was used in this study.⁴⁰ Although dialysis-specific instruments such as the Kidney Disease Quality of Life Instrument (KDQOL) are widely applied to assess symptom burden and disease-specific concerns in patients receiving hemodialysis, the SF-36 was selected because of its robust psychometric properties, extensive validation in Chinese populations, and its advantage in facilitating comparisons with other chronic disease cohorts and international studies.

Statistical Analyses

Continuous variables were assessed for distributional characteristics using the Shapiro–Wilk test. Normally distributed variables are presented as mean ± standard deviation (SD), whereas non-normally distributed variables are presented as median (interquartile range [IQR]). Categorical variables are presented as frequencies and percentages. Differences between the high- and low-eHL groups were assessed using the independent-samples t test or Mann–Whitney U-test for continuous variables, as appropriate, and the chi-square test or Fisher’s exact test for categorical variables, as appropriate. Baseline demographic, socioeconomic, lifestyle, clinical, and dialysis-related characteristics, as well as anxiety, depression, and QoL measures, were compared between the two eHL groups.

Spearman rank correlation analysis was used to examine the associations among continuous eHEALS, anxiety, depression, and QoL scores. Correlations were evaluated between eHEALS scores and the overall SF-36 score and each of its eight domains, as well as between anxiety and depression scores and the overall SF-36 score and individual SF-36 domains. Correlation coefficients (r) and corresponding two-sided P values were reported.

Binary logistic regression analysis was performed to identify factors associated with eHL status, with high eHL as the outcome category and low eHL as the reference category. Candidate covariates were prespecified based on clinical relevance and previous literature, with additional consideration of associations observed in the present dataset. Univariable logistic regression analyses were first performed for each candidate covariate. Variables with P < 0.20 in univariable analyses or with established clinical or theoretical relevance were considered for inclusion in the multivariable model. Associations were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Categorical variables were entered according to the prespecified reference categories. Potential multicollinearity was assessed using variance inflation factors (VIFs), and clinically related covariates were examined for conceptual and statistical redundancy during model specification. A VIF >5 was considered indicative of potential multicollinearity.

Multivariable linear regression analysis was performed to identify factors associated with the continuous eHEALS score. The same covariate selection strategy was applied as in the logistic regression analysis. Univariable linear regression analyses were initially conducted for each candidate covariate. Associations were reported as regression coefficients (β) with 95% CIs and corresponding P values. Model assumptions were assessed by examining linearity, the distribution and homoscedasticity of residuals, and influential observations. Multicollinearity was assessed using VIFs, with a VIF >5 indicating potential multicollinearity. Model fit was summarized using the R² and adjusted R².

All statistical analyses were conducted using R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria). A two-sided p-value < 0.05 was considered statistically significant.

Results

Basic Characteristics

A total of 229 patients undergoing MHD were included in this study, with a mean age of 57.36 ± 13.98 years and a male predominance (66.38%) (Table 1). The median dialysis duration was 4.00 [1.50, 7.00] years. Based on total eHEALS scores, 113 patients (49.34%) were classified into the low eHL group, and 116 (50.66%) into the high eHL group. Compared with the high eHL group, patients in the low eHL group were significantly older (65.35 ± 10.53 vs 49.59 ± 12.48 years, P < 0.0001), more likely to be aged ≥60 years (70.80% vs 24.14%), had a lower educational level (college or above: 12.39% vs 31.90%, P < 0.0001), and a higher prevalence of multimorbidity (>2 comorbidities: 34.51% vs 10.34%, P < 0.0001). Additionally, the low eHL group had higher rates of prior hospitalization (34.51% vs 18.10%, P = 0.0075), greater use of walking aids (eg, wheelchair: 29.20% vs 8.62%, P = 0.0007), and had a less favorable food intake status (P = 0.0172). They also had a later dialysis initiation age (median 63.00 vs 45.50 years, P < 0.0001) and a shorter dialysis duration (median 3.00 vs 4.50 years, P = 0.0221). Furthermore, the number of medications differed significantly between the groups (P = 0.0064). In contrast, no significant differences were observed in sex, living arrangement, insurance type, income level, place of residence, distance to dialysis center, BMI, smoking or drinking status, sleep quality, vascular access type, or dialysis frequency (P > 0.05) (Table 1).

Table 1.

Baseline Characteristics

	Overall (N=229)	Low eHL (n=113)	High eHL (n=116)	P
Age (mean (SD))	57.36 (13.98)	65.35 (10.53)	49.59 (12.48)	<0.0001
Age group (n, %)				<0.0001
<45 years	42 (18.34)	3 (2.65)	39 (33.62)
45–60 years	79 (34.50)	30 (26.55)	49 (42.24)
≥ 60 years	108 (47.16)	80 (70.80)	28 (24.14)
Sex (n, %)				0.1236
Male	152 (66.38)	69 (61.06)	83 (71.55)
Female	77 (33.62)	44 (38.94)	33 (28.45)
Marital status (n, %)				0.0037
Unmarried	9 (3.93)	0 (0.00)	9 (7.76)
Married	204 (89.08)	102 (90.27)	102 (87.93)
Widowed/Divorced	16 (6.99)	11 (9.73)	5 (4.31)
Education (n, %)				<0.0001
Junior high school or below	110 (48.03)	76 (67.26)	34 (29.31)
High school or vocational	68 (29.69)	23 (20.35)	45 (38.79)
College or above	51 (22.27)	14 (12.39)	37 (31.90)
Living style (n, %)				0.0769
Living alone	21 (9.17)	6 (5.31)	15 (12.93)
Living with family	208 (90.83)	107 (94.69)	101 (87.07)
Medical insurance (n, %)				0.3817
Urban medical care	110 (48.03)	59 (52.21)	51 (43.97)
Employee medical care	113 (49.34)	52 (46.02)	61 (52.59)
Out of pocket	6 (2.62)	2 (1.77)	4 (3.45)
Income (n, %)				0.3796
<3000 RMB	124 (54.15)	65 (57.52)	59 (50.86)
≥3000 RMB	105 (45.85)	48 (42.48)	57 (49.14)
Place of residence (n, %)				0.3496
Urban	185 (80.79)	88 (77.88)	97 (83.62)
Rural	44 (19.21)	25 (22.12)	19 (16.38)
Distance to dialysis center (n, %)				0.7391
< 5 km	113 (49.34)	54 (47.79)	59 (50.86)
≥ 5km	116 (50.66)	59 (52.21)	57 (49.14)
BMI group (n, %)				0.5417
<18.5 kg/m2	23 (10.04)	13 (11.50)	10 (8.62)
18.5–23.9 kg/m2	103 (44.98)	47 (41.59)	56 (48.28)
≥24.0 kg/m2	103 (44.98)	53 (46.90)	50 (43.10)
Smoke (n, %)				1.0000
Yes	33 (14.41)	16 (14.16)	17 (14.66)
No	196 (85.59)	97 (85.84)	99 (85.34)
Drinking (n, %)				0.9552
Yes	17 (7.42)	9 (7.96)	8 (6.90)
No	212 (92.58)	104 (92.04)	108 (93.10)
Sleep status (n, %)				0.0599
Good	67 (29.26)	26 (23.01)	41 (35.34)
Normal	139 (60.70)	72 (63.72)	67 (57.76)
Poor	23 (10.04)	15 (13.27)	8 (6.90)
Food intake status (n, %)				0.0172
Good	105 (45.85)	47 (41.59)	58 (50.00)
Normal	117 (51.09)	59 (52.21)	58 (50.00)
Poor	7 (3.06)	7 (6.19)	0 (0.00)
Comorbidity status (n, %)				<0.0001
<= 2 types	178 (77.73)	74 (65.49)	104 (89.66)
>2 types	51 (22.27)	39 (34.51)	12 (10.34)
Hospitalization history (n, %)				0.0075
Yes	60 (26.20)	39 (34.51)	21 (18.10)
No	169 (73.80)	74 (65.49)	95 (81.90)
Number of medication (n, %)				0.0064
0	4 (1.75)	1 (0.88)	3 (2.59)
1–2	44 (19.21)	16 (14.16)	28 (24.14)
3–4	87 (37.99)	37 (32.74)	50 (43.10)
>4	94 (41.05)	59 (52.21)	35 (30.17)
Walking aid (n, %)				0.0007
Wheelchair	43 (18.78)	33 (29.20)	10 (8.62)
Crutch	4 (1.75)	2 (1.77)	2 (1.72)
Others	174 (75.98)	76 (67.26)	98 (84.48)
None	8 (3.49)	2 (1.77)	6 (5.17)
Age at dialysis (median [IQR])	54.00 [43.00, 65.00]	63.00 [55.00, 69.00]	45.50 [36.00, 54.00]	<0.0001
Age at dialysis group (n, %)				<0.0001
<60 years	143 (62.45)	46 (40.71)	97 (83.62)
≥ 60 years	86 (37.55)	67 (59.29)	19 (16.38)
Dialysis duration (median [IQR])	4.00 [1.50, 7.00]	3.00 [1.00, 6.00]	4.50 [2.00, 8.00]	0.0221
Dialysis duration group (n, %)				0.1209
<5 years	127 (55.46)	69 (61.06)	58 (50.00)
≥ 5 years	102 (44.54)	44 (38.94)	58 (50.00)
Conduit system (n, %)				0.0712
Internal fistula	198 (86.46)	92 (81.42)	106 (91.38)
Catheter	30 (13.10)	20 (17.70)	10 (8.62)
Both	1 (0.44)	1 (0.88)	0 (0.00)
Dialysis frequency (n, %)				0.3800
3 times/week	111 (48.47)	60 (53.10)	51 (43.97)
2 times/week	32 (13.97)	14 (12.39)	18 (15.52)
5 times/2 weeks	86 (37.55)	39 (34.51)	47 (40.52)
eHEALS (median [IQR])	26.00 [10.00, 32.00]	10.00 [8.00, 19.00]	32.00 [31.00, 39.00]	<0.0001

Note: P <0.05 indicates statistical significance.

Abbreviations: eHL, eHealth literacy; SD, Standard deviation; IQR, Interquartile range; BMI, Body mass index; MHD, Maintenance hemodialysis; RMB, Renminbi (Chinese Yuan); eHEALS, eHealth Literacy Scale.

Comparison of Emotional Status Between MHD Patients with Low and High eHL

As shown in Table 2, participants with low eHL exhibited significantly higher levels of anxiety and depressive symptoms compared to those with high eHL. Specifically, the median [IQR] SAS score in the low eHL group was 43.00 [37.00, 50.00], which was significantly higher than 40.00 [31.50, 45.00] in the high eHL group (P = 0.0006). A similar pattern was observed for the SDS, where the median SDS score in the low eHL group was 55.00 [45.00, 62.00], compared with 47.00 [35.75, 56.25] in the high eHL group (P = 0.0001).

Table 2.

Comparison of Anxiety and Depression Status Between MHD Patients with Low and High eHL

	Overall (N=229)	Low eHL (n=113)	High eHL (n=116)	P
SAS score (median [IQR])	42.00 [35.00, 46.00]	43.00 [37.00, 50.00]	40.00 [31.50, 45.00]	0.0006
Anxiety status (n, %)				0.0007
No	189 (82.53)	83 (73.45)	106 (91.38)
Yes	40 (17.47)	30 (26.55)	10 (8.62)
SDS score (median [IQR])	51.00 [37.00, 60.00]	55.00 [45.00, 62.00]	47.00 [35.75, 56.25]	0.0001
Depressive status (n, %)				0.0016
No	100 (43.67)	37 (32.74)	63 (54.31)
Yes	129 (56.33)	76 (67.26)	53 (45.69)

Note: P <0.05 indicates statistical significance.

Abbreviations: MHD, maintenance hemodialysis; eHL, eHealth literacy; IQR, Interquartile range; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale.

In terms of categorical classification, a significantly greater proportion of participants in the low eHL group exhibited anxiety (26.55%) compared to the high eHL group (8.62%), with P = 0.0007. Similarly, the prevalence of depressive symptoms was substantially higher in the low eHL group (67.26%) than in the high eHL group (45.69%) (P = 0.0016).

Comparison of QoL Scores (SF-36) Between MHD Patients with Low and High eHL

Patients with high eHL exhibited significantly better QoL compared to those with low eHL, as reflected in both the total SF-36 score (73.16 [58.61, 82.12] vs 54.94 [43.38, 68.96], P < 0.0001) and all eight domain scores. Specifically, significant differences were observed in PF (90.00 [75.00, 95.00] vs 65.00 [30.00, 85.00], P < 0.0001), RP (75.00 [0.00, 100.00] vs 0.00 [0.00, 100.00], P < 0.0001), BP (74.00 [74.00, 82.00] vs 74.00 [64.00, 74.00], P = 0.0014), GH (55.00 [50.00, 60.00] vs 50.00 [45.00, 55.00], P = 0.0001), VT (70.00 [60.00, 80.00] vs 60.00 [50.00, 70.00], P < 0.0001), SF (75.00 [75.00, 87.50] vs 62.50 [50.00, 75.00], P < 0.0001), RE (100.00 [33.33, 100.00] vs 33.33 [0.00, 100.00], P = 0.0008), and MH (68.00 [60.00, 80.00] vs 60.00 [52.00, 80.00], P = 0.0275) (Table 3). These findings indicate that participants with high eHL had higher QoL scores across both physical and mental health domains.

Table 3.

Comparison of QoL Scores (SF-36) Between MHD Patients with Low and High eHL

	Overall (N=229)	Low eHL (n=113)	High eHL (n=116)	P
PF (median [IQR])	80.00 [55.00, 90.00]	65.00 [30.00, 85.00]	90.00 [75.00, 95.00]	<0.0001
RP (median [IQR])	50.00 [0.00, 100.00]	0.00 [0.00, 100.00]	75.00 [0.00, 100.00]	<0.0001
BP (median [IQR])	74.00 [74.00, 80.00]	74.00 [64.00, 74.00]	74.00 [74.00, 82.00]	0.0014
GH (median [IQR])	55.00 [45.00, 60.00]	50.00 [45.00, 55.00]	55.00 [50.00, 60.00]	0.0001
VT (median [IQR])	65.00 [55.00, 75.00]	60.00 [50.00, 70.00]	70.00 [60.00, 80.00]	<0.0001
SF (median [IQR])	75.00 [62.50, 87.50]	62.50 [50.00, 75.00]	75.00 [75.00, 87.50]	<0.0001
RE (median [IQR])	66.67 [0.00, 100.00]	33.33 [0.00, 100.00]	100.00 [33.33, 100.00]	0.0008
MH (median [IQR])	68.00 [56.00, 80.00]	60.00 [52.00, 80.00]	68.00 [60.00, 80.00]	0.0275
SF36 (median [IQR])	62.75 [49.62, 78.02]	54.94 [43.38, 68.96]	73.16 [58.61, 82.12]	<0.0001

Note: P <0.05 indicates statistical significance.

Abbreviations: QoL, Quality of Life; MHD, maintenance hemodialysis; PF, Physical Functioning; RP, Role-Physical; BP, Bodily Pain; GH, General Health; VT, Vitality; SF, Social Functioning; RE, Role-Emotional; MH, Mental Health; SF-36, Short Form 36 Health Survey.

Correlation Analysis Between eHL in MHD Patients and Emotional Status, and QoL

Spearman correlation analysis showed a weak negative association between continuous eHEALS scores and SAS scores, but this correlation did not reach statistical significance (r = −0.124, P = 0.0613). In contrast, eHEALS scores were significantly negatively correlated with SDS scores (r = −0.182, P = 0.0057). Continuous eHEALS scores were positively associated with the overall SF-36 score (r = 0.382, P < 0.0001), and significantly correlated with most of its domains, including PF (r = 0.452, P < 0.0001), RP (r = 0.293, P < 0.0001), BP (r = 0.166, P = 0.0116), GH (r = 0.277, P < 0.0001), VT (r = 0.282, P < 0.0001), SF (r = 0.293, P < 0.0001), and RE (r = 0.232, P < 0.0001), while the association with MH was not significant (r = 0.050, P = 0.4504). Anxiety (SAS) was negatively correlated with SF-36 total score (r = –0.318, P < 0.0001) and was significantly associated with PF (r = –0.220, P < 0.0001), BP (r = –0.318, P < 0.0001), GH (r = –0.198, P = 0.0026), VT (r = –0.459, P < 0.0001), SF (r = –0.351, P < 0.0001), and MH (r = –0.482, P < 0.0001), but not with RP (r = –0.096, P = 0.1472) or RE (r = –0.126, P = 0.0576). Similarly, depression (SDS) showed significant negative correlations with the total SF-36 score (r = –0.267, P < 0.0001), and with BP (r = –0.252, P < 0.0001), GH (r = –0.249, P < 0.0001), VT (r = –0.451, P < 0.0001), SF (r = –0.235, P < 0.0001), RE (r = –0.140, P = 0.0346) and MH (r = –0.400, P < 0.0001), whereas the correlations with PF (r = –0.129, P = 0.0509) and RP (r = –0.067, P = 0.3121) were not statistically significant (Table 4).

Table 4.

Correlations of eHEALS, Anxiety, and Depression with QoL (SF-36 and Its Subscales)

Variable	eHEALS		SAS		SDS
	r	P	r	P	r	P
SAS	−0.124	0.0613	–	–	–	–
SDS	−0.182	0.0057	–	–	–	–
SF-36	0.382	<0.0001	−0.318	<0.0001	−0.267	<0.0001
PF	0.452	<0.0001	−0.220	<0.0001	−0.129	0.0509
RP	0.293	<0.0001	−0.096	0.1472	−0.067	0.3121
BP	0.166	0.0116	−0.318	<0.0001	−0.252	<0.0001
GH	0.277	<0.0001	−0.198	0.0026	−0.249	<0.0001
VT	0.282	<0.0001	−0.459	<0.0001	−0.451	<0.0001
SF	0.293	<0.0001	−0.351	<0.0001	−0.235	<0.0001
RE	0.232	<0.0001	−0.126	0.0576	−0.140	0.0346
MH	0.050	0.4504	−0.482	<0.0001	−0.400	<0.0001

Note: P <0.05 indicates statistical significance.

Abbreviations: QoL, Quality of Life; MHD, maintenance hemodialysis; eHEALS, eHealth Literacy Scale; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; PF, Physical Functioning; RP, Role-Physical; BP, Bodily Pain; GH, General Health; VT, Vitality; SF, Social Functioning; RE, Role-Emotional; MH, Mental Health; SF-36, Short Form 36 Health Survey.

Binary Logistic Regression Analysis of Factors Influencing the eHL in MHD Patients

A binary logistic regression analysis was performed with high eHL as the outcome category and low eHL as the reference category. The multivariable model included sex, age group, educational attainment, living arrangement, sleep status, comorbidity status, hospitalization history, medication use, walking-aid use, and dialysis duration. The results showed that age and educational attainment were independently associated with eHL status (Table 5). Compared with patients aged <45 years, those aged 45–60 years had significantly lower odds of high eHL (OR = 0.12, 95% CI: 0.03–0.50, P = 0.0039), as did those aged ≥60 years (OR = 0.03, 95% CI: 0.01–0.14, P < 0.0001). Compared with patients with junior high school or below, those with a high school or vocational education (OR = 5.70, 95% CI: 2.50–12.99, P < 0.0001) and those with college or above (OR = 7.74, 95% CI: 2.75–21.76, P = 0.0001) had significantly higher odds of high eHL. The remaining covariates were not independently associated with eHL (all P > 0.05).

Table 5.

Multivariable Logistic Regression Analysis of Factors Associated with eHL Among Patients Undergoing MHD

Variable	β	SE	OR	95% CI	P
Sex: female vs male	−0.54	0.41	0.59	0.26, 1.32	0.1952
Age 45–60 vs <45 years	−2.14	0.74	0.12	0.03, 0.50	0.0039
Age ≥60 vs <45 years	−3.42	0.75	0.03	0.01, 0.14	<0.0001
Education: High school vs junior high or below	1.74	0.42	5.70	2.50, 12.99	<0.0001
Education: College or above vs junior high or below	2.05	0.53	7.74	2.75, 21.76	0.0001
Living arrangement: Living with family vs living alone	−0.50	0.67	0.61	0.16, 2.24	0.4547
Sleep status: Fair vs very good	−0.10	0.41	0.91	0.40, 2.04	0.8094
Sleep status: Poor vs very good	−0.42	0.69	0.66	0.17, 2.56	0.5477
Comorbidities >2 vs ≤2	−0.82	0.49	0.44	0.17, 1.15	0.0943
Hospitalization history: No vs yes	0.16	0.43	1.17	0.50, 2.74	0.7165
Medications 1–2 vs 0	1.71	1.51	5.52	0.29, 106.83	0.2584
Medications 3–4 vs 0	1.45	1.48	4.27	0.23, 77.71	0.3271
Medications >4 vs 0	0.41	1.46	1.51	0.09, 26.15	0.7789
Walking aid: Crutch vs wheelchair	1.62	1.27	5.06	0.42, 60.92	0.2017
Walking aid: Other vs wheelchair	0.73	0.53	2.08	0.73, 5.90	0.1674
Walking aid: None vs wheelchair	0.80	1.01	2.22	0.30, 16.16	0.4315
Dialysis duration ≥5 vs <5 years	0.59	0.38	1.80	0.86, 3.77	0.1202

Note: P <0.05 indicates statistical significance.

Abbreviations: MHD, maintenance hemodialysis; eHL, eHealth Literacy; β, regression coefficient; SE, standard error; OR, odds ratio; CI, confidence interval.

Linear Regression Analysis of Factors Influencing the eHL in MHD Patients

To further assess factors associated with continuous eHEALS scores, multivariable linear regression analysis was performed with the eHEALS score as the dependent variable. The multivariable model included sex, age group, marital status, educational attainment, BMI group, sleep status, food intake status, comorbidity status, hospitalization history, medication use, walking-aid use, dialysis duration, and dialysis frequency. The overall model was statistically significant (F = 8.136, P < 0.001) and explained 48.9% of the variance in eHEALS scores (R² = 0.489; adjusted R² = 0.429). Variance inflation factors were <5 for all covariates, indicating no evidence of substantial multicollinearity. Compared with patients aged <45 years, those aged 45–60 years had lower eHEALS scores (β = −6.25, 95% CI: −10.10 to −2.40, P = 0.0016), and those aged ≥60 years had substantially lower scores (β = −10.96, 95% CI: −14.89 to −7.04, P < 0.0001). Compared with patients with junior high school education or below, those with high school or vocational education (β = 6.16, 95% CI: 3.36 to 8.96, P < 0.0001) and those with college education or above (β = 6.08, 95% CI: 2.97 to 9.18, P = 0.0002) had higher eHEALS scores. Patients with >2 comorbidities had lower eHEALS scores than those with ≤2 comorbidities (β = −4.46, 95% CI: −7.64 to −1.28, P = 0.0061). Compared with unmarried patients, those who were widowed or divorced had lower eHEALS scores (β = −9.22, 95% CI: −17.01 to −1.43, P = 0.0205). Dialysis duration ≥5 years was positively associated with eHEALS scores compared with <5 years (β = 2.72, 95% CI: 0.29 to 5.16, P = 0.0285). No other covariates showed statistically significant associations with eHEALS scores (all P > 0.05) (Table S1).

Discussion

This study provides a comprehensive examination of eHL among patients undergoing MHD, exploring its associations with emotional well-being and QoL, and identifying key sociodemographic and clinical factors associated with eHL. Our findings reveal a relatively low overall level of eHL in this population, with nearly half of the participants classified into the low eHL group. Patients with low eHL experienced significantly higher levels of anxiety and depression, and reported markedly poorer health-related QoL across all eight SF-36 domains. Furthermore, in the logistic regression model, advanced age and lower educational attainment were independently associated with lower odds of high eHL, and multivariable linear regression analysis identified advanced age, lower educational attainment, higher comorbidity burden, widowed/divorced marital status, and longer dialysis duration as factors associated with eHEALS scores. These findings demonstrate substantial heterogeneity in eHL among MHD patients and highlight the relevance of considering both patient characteristics and health-related factors when understanding digital health literacy in this population.

Association Between eHL, Emotional Status, and Health-Related Quality of Life

Notably, patients with lower eHL exhibited more severe emotional distress and significantly poorer QoL. The low eHL group had significantly higher median scores for both anxiety (SAS) and depression (SDS), and a greater proportion of patients in this group had anxiety or depressive symptoms. These findings suggest that limited eHL may represent an important dimension of vulnerability among MHD patients, who often face complex treatment regimens and substantial self-management demands. Patients with lower eHL may experience greater difficulty in accessing, interpreting, and applying health-related information, which could plausibly be related to greater uncertainty, lower confidence in disease management, and increased psychological burden.^41,42 Furthermore, the low eHL group had significantly lower scores across all eight domains of the SF-36, indicating poorer QoL across multiple physical and mental health domains. This pattern may indicate that patients with lower eHL have greater challenges in engaging with health-related information and self-management activities, although the direction of this relationship cannot be determined from the present data.^43,44 The observed disparities underscore the need for tailored interventions that address both informational and emotional support needs, particularly among older patients and those with lower educational attainment who may face greater challenges in navigating digital health resources. Rather than simply providing digital tools, future interventions should consider patients’ baseline eHL, educational background, psychological status, and ability to independently navigate digital resources to facilitate meaningful engagement with digital healthcare.

Interrelationship Between eHL, Emotional Status, and QoL

Correlation analyses further supported the interrelationships among eHL, emotional status, and QoL. eHL was significantly positively correlated with overall SF-36 scores and seven of the eight SF-36 domains, but was not significantly correlated with MH, while its correlation with anxiety was negative but did not reach statistical significance. In contrast, eHEALS scores were significantly negatively correlated with depression. Interestingly, anxiety and depression were also significantly negatively associated with multiple QoL domains, particularly VT, MH, and SF. The consistent associations between eHL, psychological symptoms, and QoL suggest that eHL may represent one component of the broader set of resources involved in chronic disease self-management. In MHD patients, effective disease management requires attention not only to complex treatment regimens but also to continuous access to health information, communication with healthcare providers, and engagement in health-related decision-making. Accordingly, meaningful engagement with digital health resources among MHD patients may depend not only on access to technology but also on informational confidence, health literacy, and psychological readiness. These findings are consistent with previous studies in other chronic disease populations, suggesting that higher eHL is associated with greater confidence in self-management, greater ability to access health information, and more favorable psychosocial profiles. One possible explanation is that individuals with higher eHL may be better positioned to identify reliable health information, seek appropriate support, and participate actively in healthcare communication. These capabilities may enhance perceived control over disease management and reduce uncertainty associated with chronic illness, although the underlying pathways require further longitudinal investigation.³² Conversely, low eHL may serve as an indicator of broader vulnerability, reflecting the combined influence of demographic characteristics, educational background, disease burden, and psychosocial factors. Therefore, eHL assessment may provide additional information for identifying patients who require enhanced support when digital health resources are introduced into routine kidney care.⁴⁵

Factors Associated with eHL Among Patients Undergoing MHD

Our multivariable analyses identified age and educational attainment as the most consistent factors associated with eHL, with older age associated with lower eHL and higher educational attainment associated with higher eHL. These findings are consistent with previous research showing that older adults may encounter greater barriers to digital health engagement, potentially related to differences in digital experience, confidence in technology use, and the ability to adapt to rapidly evolving digital health environments.⁴⁶ In contrast, higher educational attainment may facilitate eHL by supporting information processing, critical evaluation of health information, and confidence in navigating digital resources. The consistent associations of age and education across the logistic and linear models suggest that eHL disparities among patients undergoing MHD may be rooted partly in differences in both digital readiness and health-information processing capacity.

The negative association between higher comorbidity burden and continuous eHEALS scores warrants particular attention. Patients with multiple comorbidities may face more complex treatment regimens and greater volumes of health-related information, potentially increasing the demands placed on their ability to identify, interpret, and apply relevant information. Alternatively, greater disease burden may reflect a broader vulnerability that is accompanied by functional, social, or healthcare-access barriers not fully captured in the present analysis. Because these potential pathways were not directly assessed, the observed association should be interpreted cautiously rather than regarded as evidence of a causal effect.

The positive association between longer dialysis duration and eHEALS scores may provide another perspective. Patients receiving dialysis for a longer period may have accumulated greater experience with disease-related information, healthcare communication, and routine self-management, which could potentially strengthen their confidence in seeking and using health information. This interpretation is speculative, however, and should not be taken to imply that longer dialysis exposure directly improves eHL. Notably, marital status was also associated with continuous eHEALS scores, with widowed or divorced patients showing lower scores than unmarried patients. This finding may suggest that social circumstances could influence patients’ ability or confidence to engage with digital health resources, although the underlying mechanisms remain uncertain. Overall, these findings indicate that eHL among patients undergoing MHD is associated with a combination of demographic, educational, disease-related, and social characteristics, highlighting the need for more individualized approaches to digital health support.

Implications for Digital Health Equity and Future Kidney Care

The increasing integration of digital health approaches into kidney care highlights the need to ensure that patients can meaningfully engage with these resources. Our findings suggest that eHL should be considered when implementing digital health strategies in MHD care, as patients may differ substantially in their readiness and ability to navigate digital health information. Equitable digital healthcare delivery therefore requires more than technological availability; it should also incorporate patient-centered support, including accessible health education, individualized guidance, and assisted digital navigation for patients who have difficulty using digital resources independently. Future implementation strategies should therefore consider patients’ baseline eHL and individual characteristics, particularly age and educational attainment, when designing digital education and communication pathways. Such approaches may be particularly relevant for patients with greater comorbidity burden, who may face additional challenges in managing complex health information. Future intervention studies should determine whether tailored eHL-oriented strategies can improve digital engagement and patient-centered outcomes, while multicenter longitudinal studies are needed to examine changes in eHL over time and clarify its temporal associations with psychological, behavioral, and clinical outcomes among MHD patients.

Strengths and Limitations

The strengths of this study include its relatively large sample size, comprehensive assessment of emotional symptoms and health-related QoL, and the application of complementary logistic and linear regression models to explore factors associated with eHL. Moreover, this study addresses an understudied area by focusing specifically on patients undergoing MHD, a population characterized by complex treatment demands and increasing exposure to digital healthcare approaches. However, several limitations should be acknowledged. First, the cross-sectional design prevents determination of temporal relationships and causal directions. Although significant associations were observed between eHL, emotional status, and QoL, these findings should be interpreted as associations rather than causal relationships. Reverse causation cannot be excluded, as poorer psychological well-being or impaired functional status may be associated with reduced engagement with digital health resources and lower perceived eHL. Second, participants were recruited from a limited number of clinical centers, which may restrict generalizability to MHD populations with different healthcare systems, socioeconomic characteristics, or levels of digital access. In addition, because questionnaire completion required sufficient cognitive, visual, and functional ability to respond independently, patients with more severe functional impairment or limited ability to engage with digital health information may have been underrepresented, potentially introducing selection bias. Third, eHL was assessed using the eHEALS scale, which captures perceived rather than objectively measured digital health skills. Therefore, responses may be influenced by self-perception and reporting bias. Future studies incorporating performance-based assessments of digital health skills may provide a more comprehensive evaluation of eHL. Fourth, although multivariable analyses adjusted for several demographic and clinical variables, residual confounding from unmeasured factors cannot be completely excluded. Variables such as socioeconomic status, previous digital experience, internet accessibility, and social support may be associated with eHL but were not fully captured in the present analysis. Future research should incorporate objective measures of digital health skills, relevant socioeconomic and digital-access variables, and longitudinal designs to better characterize determinants of eHL and clarify its temporal relationships with patient-centered outcomes. Intervention studies are also warranted to determine whether targeted eHL-oriented strategies can improve digital engagement and patient-centered outcomes among MHD patients.

Conclusion

In conclusion, this study demonstrates that eHL is significantly associated with emotional well-being and health-related quality of life among patients undergoing MHD. Patients with lower eHL had higher levels of anxiety and depressive symptoms and poorer quality of life across all eight SF-36 domains. Older age and lower educational attainment were independently associated with lower odds of high eHL in the logistic regression analysis, while higher comorbidity burden, widowed/divorced marital status, and longer dialysis duration were also associated with continuous eHEALS scores in the linear regression analysis. These findings suggest that eHL may serve as an important marker of vulnerability in MHD patients, particularly among those with older age and lower educational attainment. Targeted, patient-centered strategies to support digital health engagement may be warranted; however, whether improving eHL can reduce psychological distress or improve quality of life remains uncertain. Longitudinal and multicenter intervention studies are needed to establish temporal relationships and evaluate the effectiveness of eHL-oriented interventions in this population.

Funding Statement

This work was supported by the Baoding Science and Technology Plan Project (2441ZF236).

Data Sharing Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Author Contributions

The author made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The author reports no conflicts of interest in this work.