Abstract
Objective
To examine the association between digital health literacy and self-care behaviors and to test whether self-efficacy statistically mediates this association, after adjusting for relevant covariates, among older adults with cancer undergoing chemotherapy.
Methods
A descriptive cross-sectional study was conducted with 117 older adults with cancer undergoing chemotherapy at a tertiary hospital in the Republic of Korea. Participants completed validated measures of digital health literacy (eHEALS), self-efficacy (CSSES-K), and self-care behaviors. Data were analyzed using regression-based mediation analysis (Hayes' PROCESS macro Model 4) with 5000 bootstrap samples, adjusting for sex, age, educational level, cancer diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status.
Results
Digital health literacy was positively associated with self-efficacy (B = 0.55, 95% confidence interval (CI) [0.26, 0.84], P < 0.001). In the total effect model, digital health literacy was positively associated with self-care behaviors (B = 0.36, 95% CI [0.09, 0.62], P = 0.009). After self-efficacy was included, self-efficacy remained positively associated with self-care behaviors (B = 0.51, 95% CI [0.36, 0.66], P < 0.001), whereas the direct association between digital health literacy and self-care behaviors was not statistically significant (B = 0.08, 95% CI [−0.16, 0.31], P = 0.530). The indirect effect was significant (B = 0.28, 95% bootstrap CI [0.13, 0.51]).
Conclusions
The findings were compatible with an indirect-only mediation pattern, suggesting that digital health literacy may be linked to self-care behaviors primarily through self-efficacy among older adults undergoing chemotherapy. Interventions to promote self-care during chemotherapy may benefit from combining digital health literacy support with strategies that strengthen self-efficacy.
Keywords: Digital health literacy, Self-efficacy, Self-care, Neoplasms, Aged, Mediation analysis
Introduction
In the Republic of Korea, the proportion of individuals aged 65 years and older reached 18.2% in 2023 and is projected to rise to 20.3% by 2025, indicating a transition to a super-aged society.1 Similarly, cancer diagnoses among this age group increased from 47.7% in 2019 to 48.5% in 2021.2 Advances in early detection and treatment have notably improved the 5-year relative survival rate to 71.5% between 2016 and 2020, contributing to more than 2.2 million prevalent cases in 2020.2
Cancer treatment primarily involves surgery, chemotherapy, and radiotherapy, with chemotherapy serving as a key modality across various cancer stages.3 Chemotherapy is associated with symptoms and treatment-related adverse effects, including febrile neutropenia, nausea, vomiting, mucositis, decreased appetite, depression, and insomnia.4 These symptoms may impair quality of life among patients undergoing chemotherapy.4, 5 Treatment-related complications can also increase emergency department visits and hospitalizations.6, 7
With cancer care increasingly extending beyond the clinic, patients experience many effects of cancer and its treatment after clinic visits and are expected to recognize, report, and manage these effects as part of self-management.8 Consequently, patients bear greater responsibility for managing their own care, making self-care behaviors even more critical. Functional status is also an important consideration in the care of older adults with cancer.9 Older adults often experience functional decline, such as reduced mobility and sensory impairments,10 and have multiple comorbidities,11 which may increase the risk of drug interactions and treatment-related complications.12 Therefore, self-care behaviors related to medication adherence, nutrition management, and infection prevention are essential for improving treatment efficacy and quality of life in older adults with cancer.13
To support patients with cancer in managing chemotherapy side effects and preventing complications, healthcare institutions provide self-care education and counseling. Since the COVID-19 pandemic, the use of digital, non-face-to-face education methods has expanded.14 With the increasing adoption of digital tools for health information and self-care education, digital health literacy has become a crucial factor in cancer care.15
Digital health literacy refers to one's ability to access, evaluate, and effectively utilize reliable health information. It may facilitate self-care behaviors among older patients with cancer undergoing chemotherapy. Notably, limited digital health literacy can hinder patients' ability to comprehend and apply essential self-care information, such as strategies for managing chemotherapy side effects, thereby influencing their engagement in effective self-care.16 Consequently, improving digital health literacy is essential for empowering older patients with cancer in self-care practices.
Additionally, self-efficacy—the belief in one's ability to successfully perform specific behaviors—has been identified as an important factor related to health behaviors.17 Studies indicate that self-efficacy considerably influences health behaviors and quality of life in older adults.18 Specifically, older adults with high levels of self-efficacy are more likely to proactively utilize health information and make informed decisions regarding their care,19 which can enhance self-care adherence among patients with cancer. Recent research involving older adults and adolescents has shown that health literacy, digital health literacy, and self-efficacy are closely related in the context of health information use.19, 20 However, limited empirical research has examined the relationship between digital health literacy, self-efficacy, and self-care behaviors in older patients with cancer.
Thus, this study aimed to examine the association between digital health literacy and self-care behaviors among older patients with cancer undergoing chemotherapy and to assess the mediating role of self-efficacy in this relationship. We hypothesized that digital health literacy would be positively associated with both self-care behaviors and self-efficacy, and that self-efficacy would statistically mediate the association between digital health literacy and self-care behaviors. The findings provide foundational evidence for developing nursing interventions designed to enhance self-care behaviors in this vulnerable population.
Methods
Study design
This study employed a descriptive cross-sectional design to examine the associations among digital health literacy, self-efficacy, and self-care behaviors in older patients with cancer undergoing chemotherapy. Additionally, it aimed to identify factors influencing self-care behaviors and to assess the mediating role of self-efficacy in this relationship.
Participants
Participants were recruited from a tertiary hospital in the Republic of Korea with over 800 beds using convenience sampling. Eligible patients were individuals aged 65 years or older with cancer undergoing chemotherapy. The inclusion criteria were (1) confirmed cancer diagnosis with at least one completed cycle of chemotherapy and (2) ability to comprehend and verbally respond to the questionnaire. Patients were excluded if they (1) were receiving treatment in an intensive care unit, (2) had neurological impairments, or (3) had cognitive disorders. Cognitive-related exclusion was assessed at recruitment based on clinician confirmation and review of the electronic medical record to confirm the absence of documented diagnoses related to dementia or major cognitive disorders. The required sample size was determined using G∗Power 3.1 (version 3.1.9.4; Department of Psychology, University of Düsseldorf, Germany, 2019) for multiple regression analysis. The calculation was based on an effect size of 0.15, significance level (α) of 0.05, statistical power (1 − β) of 0.80, and eight predictor variables, yielding a minimum required sample size of 109 participants. To account for an expected dropout rate of 10%, 119 participants were recruited.
Procedure
This study was approved by the Institutional Review Board (IRB) of the designated institution (IRB No. 2024-07-012) and received authorization from the nursing department before data collection. Before the survey, the researcher collaborated with head nurses to provide participants with detailed information about the study's purpose, estimated duration, confidentiality of responses, data protection measures, and researcher contact details. The participants were explicitly informed that participation was voluntary, they could withdraw at any time without consequences, and they would not face any disadvantages for refusing to participate. Written informed consent was obtained before starting the survey only from those who voluntarily agreed to participate.
Data were collected between August 27 and October 31, 2024. The survey took approximately 30 minutes to complete. No personally identifiable information was collected, and all data were securely stored in a password-protected file with restricted access to ensure confidentiality and compliance with data protection protocols. A total of 119 participants were recruited, and after excluding two incomplete responses, 117 participants were included in the final analysis.
Measures
General characteristics
The general characteristics of the participants included age, sex, educational level, cancer diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status. Age was categorized into three groups: 65–69 years, 70–74 years, and 75 years or older. Educational level was categorized as middle school or less versus high school or higher. ECOG performance status (0–2) was obtained from the electronic medical record based on documentation closest to the time of survey completion. ECOG performance status scores were categorized as follows: 0 indicated no symptoms and the ability to perform routine daily activities, 1 indicated mild symptoms with independent living, and 2 indicated the ability to perform self-care but requiring some assistance.21
Digital health literacy
Digital health literacy was measured using the eHealth Literacy Scale (eHEALS), developed by Norman and Skinner22 and translated into Korean by Lee et al.23 The eHEALS consists of eight items rated on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater digital health literacy. The reliability of the scale was validated in previous studies, with a Cronbach α of 0.88 reported in the original study by Norman and Skinner and 0.88 in the Korean adaptation by Lee et al. In this study, the reliability of the scale was 0.98.
Self-efficacy
Self-efficacy was assessed using the Korean version of the Cancer Survivors' Self-Efficacy Scale (CSSES-K), adapted by Kim et al.24 from the original scale developed by Foster et al.25 The CSSES-K comprises 10 items categorized into two domains: five assessing health problem management and five assessing help-seeking and social support. Each item is rated on a ten-point Likert scale, ranging from 0 (not at all confident) to 10 (very confident), with higher scores indicating greater self-efficacy. The reliability of the original scale was reported as Cronbach α = 0.92, while the Korean adaptation demonstrated Cronbach α = 0.92 reported by Kim et al. In this study, the Cronbach α was 0.86.
Self-care behaviors
Self-care behaviors were assessed using a measurement tool originally developed by Oh et al.26 for leukemia patients and later modified by Kim et al.27 for broader applicability. The original tool consisted of 20 items; however, five were excluded on the basis of expert feedback and a content validity index score of < 0.80, as they were deemed redundant, inappropriate for older cancer patients, or unsuitable for the clinical environment in South Korean hospitals. The final version included 15 items, with details of the excluded items provided in Supplementary Table S1.
The content validity of the revised tool was evaluated by an expert panel comprising two medical oncologists, two nursing professors specializing in adult nursing, and two oncology nurse specialists. The final version included five items on infection prevention, four on bleeding prevention, one on nausea and vomiting management, three on nutrition management, one on exercise, and one on stress reduction. Each item was rated on a 7-point Likert scale, ranging from 1 (not at all) to 7 (very well), with higher scores indicating better self-care performance. The original tool, developed for leukemia patients, demonstrated a reliability of Cronbach α = 0.80, while the revised version by Kim et al. reported a Cronbach α of 0.89. In this study, the tool demonstrated a Cronbach α of 0.88.
Statistical analysis
Data were analyzed using IBM SPSS Statistics 29.0, with statistical significance set at P < 0.05. Descriptive statistics were used to summarize participants' general characteristics and levels of digital health literacy, self-efficacy, and self-care behaviors. Independent t-tests and one-way analysis of variance were conducted to examine differences in digital health literacy, self-efficacy, and self-care behaviors according to participants' general characteristics. Pearson's correlation coefficients were used to assess associations among digital health literacy, self-efficacy, and self-care behaviors. Mediation was tested using Andrew F. Hayes' PROCESS macro Model 4 with 5000 bias-corrected bootstrap resamples.28 Digital health literacy was specified as the independent variable, self-efficacy as the mediator, and self-care behaviors as the outcome. Sex, age, educational level (middle school or less vs. high school or higher), cancer diagnosis, and ECOG performance status were included as covariates. The indirect effect was considered significant when the 95% bootstrap confidence interval (CI) did not include zero.
Results
General characteristics of participants
The general characteristics of the participants are presented in Table 1. The mean age was 69.92 ± 4.89 years, with 58.1% aged 65–69 years, 21.4% aged 70–74 years, and 20.5% aged 75 years or older. Men accounted for 59.8% of the sample. Regarding educational level, 43.6% had completed middle school or less, and 56.4% had completed high school or higher. The most common cancer diagnoses were hepato-pancreato-biliary cancer (29.1%), lower gastrointestinal cancer (26.5%), and female cancer (12.0%). ECOG performance status was 0 in 37.6% of participants, 1 in 35.0%, and 2 in 27.4%.
Table 1.
General characteristics of the participants (N = 117).
| Variables | n (%) | Mean | SD |
|---|---|---|---|
| Sex | |||
| Male | 70 (59.8) | ||
| Female | 47 (40.2) | ||
| Age (years) | 69.92 | 4.89 | |
| 65–69 | 68 (58.1) | ||
| 70–74 | 25 (21.4) | ||
| ≥ 75 | 24 (20.5) | ||
| Education level | |||
| ≤ Middle school | 51 (43.6) | ||
| ≥ High school | 66 (56.4) | ||
| Diagnosis | |||
| Hepato-pancreato-biliary cancer | 34 (29.1) | ||
| Female cancera | 14 (12.0) | ||
| Upper gastrointestinal cancer | 9 (7.7) | ||
| Lower gastrointestinal cancer | 31 (26.5) | ||
| Lung cancer | 10 (8.5) | ||
| Others | 19 (16.2) | ||
| ECOG PS | |||
| 0 | 44 (37.6) | ||
| 1 | 41 (35.0) | ||
| 2 | 32 (27.4) | ||
ECOG PS, Eastern Cooperative Oncology Group performance status; SD, standard deviation.
Female cancer includes breast cancer, cervical cancer, endometrial cancer, and ovarian cancer.
Digital health literacy, self-efficacy, and self-care behaviors
The participants' digital health literacy, self-efficacy, and self-care behaviors are presented in Table 2. The mean digital health literacy score was 2.69 ± 1.11 on a 5-point scale. Self-efficacy had a mean score of 6.78 ± 1.55 on a 10-point scale. By subdomains, the health problem management domain had a mean score of 7.07 ± 1.93, while the help-seeking and social support domain had a mean score of 6.49 ± 1.56.
Table 2.
Levels of digital health literacy, self-efficacy, and self-care behaviors (N = 117).
| Variables | Mean | SD |
|---|---|---|
| Digital health literacy (range: 1–5) | 2.69 | 1.11 |
| Self-efficacy (range: 1–10) | ||
| Health problem management | 7.07 | 1.93 |
| Help-seeking and social support | 6.49 | 1.56 |
| Total | 6.78 | 1.55 |
| Self-care behaviors (range: 1–7) | ||
| Infection prevention | 5.60 | 1.08 |
| Bleeding prevention | 5.72 | 0.93 |
| Nausea and vomiting management | 5.51 | 1.27 |
| Nutrition management | 5.30 | 1.25 |
| Exercise | 5.10 | 1.91 |
| Stress reduction | 3.63 | 2.01 |
| Total | 5.40 | 0.91 |
SD, standard deviation.
The mean self-care behaviors score was 5.40 ± 0.91 on a 7-point scale. By subdomains, infection prevention, bleeding prevention, and nutrition management had mean scores of 5.60 ± 1.08, 5.72 ± 0.93, and 5.30 ± 1.25, respectively. Additionally, nausea and vomiting management, exercise management, and stress reduction had mean scores of 5.51 ± 1.27, 5.10 ± 1.91, and 3.63 ± 2.01, respectively. Among the self-care domains, bleeding prevention had the highest score, followed by infection prevention and nausea and vomiting management.
Digital health literacy, self-efficacy, and self-care behaviors by general characteristics
Table 3 presents the results of the analysis of digital health literacy, self-efficacy, and self-care behaviors by general characteristics. Digital health literacy significantly differed by age (F = 5.65, P = 0.005) and educational level (t = −2.52, P = 0.013). Participants aged 65–69 years (2.95 ± 1.11) had higher scores than those aged 75 years or older (2.12 ± 1.01), and participants with a high school education or higher (2.91 ± 1.17) had higher scores than those with a middle school education or less (2.40 ± 0.97).
Table 3.
Digital health literacy, self-efficacy, and self-care behaviors according to general characteristics (N = 117).
| Characteristics | Digital health literacy |
Self-efficacy |
Self-care behaviors |
|||
|---|---|---|---|---|---|---|
| Mean ± SD | t/F (P) | Mean ± SD | t/F (P) | Mean ± SD | t/F (P) | |
| Sex | ||||||
| Male | 2.72 ± 1.15 | 0.37 (0.710) | 6.94 ± 1.54 | 1.38 (0.17) | 5.24 ± 0.97 | 2.39 (0.018) |
| Female | 2.64 ± 1.07 | 6.54 ± 1.55 | 5.64 ± 0.78 | |||
| Age (years) | ||||||
| 65–69 | 2.95 ± 1.11 | 5.65 (0.005) | 7.03 ± 1.33 | 2.18 (0.118) | 5.31 ± 0.82 | 0.86 (0.426) |
| 70–74 | 2.54 ± 1.01 | 6.52 ± 1.83 | 5.46 ± 1.21 | |||
| ≥ 75 | 2.12 ± 1.01 | 6.35 ± 1.76 | 5.59 ± 0.81 | |||
| Education level | ||||||
| ≤ Middle school | 2.40 ± 0.97 | −2.52 (0.013) | 6.43 ± 1.68 | −2.08 (0.040) | 5.24 ± 0.97 | −1.67 (0.098) |
| ≥ High school | 2.91 ± 1.17 | 7.05 ± 1.40 | 5.53 ± 0.86 | |||
| Cancer diagnosis | ||||||
| Hepato-pancreato-biliary | 2.56 ± 1.07 | 1.56 (0.176) | 6.86 ± 1.44 | 0.73 (0.603) | 5.45 ± 0.83 | 3.32 (0.008) |
| Female cancera | 3.29 ± 0.95 | 6.46 ± 1.30 | 5.94 ± 0.59 | |||
| Upper gastrointestinal | 2.58 ± 1.60 | 6.50 ± 2.11 | 5.21 ± 0.85 | |||
| Lower gastrointestinal | 2.49 ± 1.13 | 6.54 ± 1.76 | 5.04 ± 0.98 | |||
| Lung cancer | 2.44 ± 0.78 | 7.28 ± 1.32 | 6.01 ± 0.88 | |||
| Others | 2.99 ± 1.06 | 7.13 ± 1.43 | 5.26 ± 0.92 | |||
| ECOG PS | ||||||
| 0 | 2.69 ± 1.12 | 0.59 (0.555) | 7.60 ± 1.25 | 16.14 (< 0.001) | 5.69 ± 0.98 | 5.58 (0.005) |
| 1 | 2.81 ± 1.06 | 6.69 ± 1.46 | 5.40 ± 0.84 | |||
| 2 | 2.53 ± 1.18 | 5.78 ± 1.46 | 5.01 ± 0.78 | |||
SD, standard deviation; ECOG PS, Eastern Cooperative Oncology Group performance status.
Post hoc analyses: Participants aged 65–69 years scored significantly higher than those aged ≥ 75 years in digital health literacy (P < 0.05). Participants with ECOG PS 0 scored significantly higher than those with ECOG PS 2 in both self-efficacy and self-care behaviors (P < 0.05).
Female cancer includes breast cancer, cervical cancer, endometrial cancer, and ovarian cancer.
Self-efficacy significantly varied by ECOG performance status (F = 16.14, P < 0.001), with the highest scores observed in the group with ECOG performance status 0 (7.60 ± 1.25), followed by the group with ECOG performance status 1 (6.69 ± 1.46) and ECOG performance status 2 (5.78 ± 1.46). Self-care behaviors exhibited significant differences by sex (t = 2.39, P = 0.018), cancer diagnosis (F = 3.32, P = 0.008), and ECOG performance status (F = 5.58, P = 0.005). Women (5.64 ± 0.78) had higher self-care scores than men (5.24 ± 0.97). Participants with ECOG performance status 0 (5.69 ± 0.98) scored higher than those with ECOG performance status 2 (5.01 ± 0.78). By cancer diagnosis, self-care scores appeared highest among participants in the lung cancer and female cancer groups.
Mediation analysis
A mediation analysis was conducted to examine whether self-efficacy statistically mediated the association between digital health literacy and self-care behaviors. Digital health literacy was positively associated with self-efficacy (B = 0.55, 95% CI [0.26, 0.84], P < 0.001). In the total effect model, digital health literacy was positively associated with self-care behaviors (B = 0.36, 95% CI [0.09, 0.62], P = 0.009). When self-efficacy was included in the model, self-efficacy remained positively associated with self-care behaviors (B = 0.51, 95% CI [0.36, 0.66], P < 0.001), whereas the direct association between digital health literacy and self-care behaviors was not statistically significant (B = 0.08, 95% CI [−0.16, 0.31], P = 0.530) (Table 4). Bootstrapping supported the indirect effect of digital health literacy on self-care behaviors through self-efficacy (B = 0.28, 95% bias-corrected bootstrap CI [0.13, 0.51]) (Table 5). Fig. 1 illustrates the mediation model.
Table 4.
Three-step regression analysis among digital health literacy, self-efficacy, and self-care behaviors (N = 117).
| Step | Dependent variable | Independent variable | B | SE | 95% CI | t (P) | R2 | F (P) |
|---|---|---|---|---|---|---|---|---|
| 1 | Self-efficacy | Digital health literacy | 0.55 | 0.15 | [0.26, 0.84] | 3.75 (< 0.001) | 0.42 | 7.67 (< 0.001) |
| 2 | Self-care behaviors | Digital health literacy | 0.36 | 0.14 | [0.09, 0.62] | 2.65 (0.009) | 0.38 | 6.41 (< 0.001) |
| 3 | Self-care behaviors | Digital health literacy | 0.08 | 0.12 | [−0.16, 0.31] | 0.63 (0.530) | 0.57 | 12.62 (< 0.001) |
| Self-efficacy | 0.51 | 0.07 | [0.36, 0.66] | 6.85 (< 0.001) |
B, unstandardized regression coefficient; SE, standard error; CI, confidence interval. All models were adjusted for sex, age, educational level, cancer diagnosis, and Eastern Cooperative Oncology Group performance status.
Table 5.
Mediation effect of self-efficacy in the relationship between digital health literacy and self-care behaviors (N = 117).
| Effect type | Coefficient | SE | 95% CI [lower, upper] |
|---|---|---|---|
| Total effect | 0.36 | 0.14 | [0.09, 0.62] |
| Direct effect | 0.08 | 0.12 | [−0.16, 0.31] |
| Indirect effect | 0.28 | 0.05 | [0.13, 0.51] |
SE, standard error; CI, confidence interval. Indirect effects were estimated using 5000 bias-corrected bootstrap resamples.
Fig. 1.
Mediation model of self-efficacy in the relationship between digital health literacy and self-care behaviors. This figure illustrates the mediation model in which self-efficacy mediates the association between digital health literacy and self-care behaviors among older patients with cancer undergoing chemotherapy, after adjusting for covariates (sex, age, educational level, cancer diagnosis, and ECOG performance status). The direct association between digital health literacy and self-care behaviors was not statistically significant after including self-efficacy (B = 0.08, 95% CI [−0.16, 0.31], P = 0.530), which is compatible with an indirect-only mediation pattern. Dotted lines indicate non-significant paths. All path coefficients are unstandardized. The indirect effect was significant (B = 0.28, 95% bootstrap CI [0.13, 0.51]). ECOG PS, Eastern Cooperative Oncology Group performance status; CI, confidence interval.
Discussion
Main findings
In this study, we examined whether self-efficacy statistically mediated the association between digital health literacy and self-care behaviors among older patients with cancer undergoing chemotherapy. Overall, the findings were compatible with an indirect-only mediation pattern, in which digital health literacy was associated with self-care behaviors through self-efficacy. Given the cross-sectional design, these findings should be interpreted as statistical mediation rather than evidence of causality.
The participants had a mean digital health literacy score of 2.69 ± 1.11 (on a 5-point scale), a value lower than those reported in studies involving adult Internet users, the general population, and younger adults.29, 30, 31 Digital health literacy declined with increasing age, a trend consistent with the findings of previous research.31 In addition, participants with a high school education or higher exhibited higher digital health literacy than those with a middle school education or less, reinforcing prior findings that educational attainment is an important determinant in navigating and interpreting health information through digital platforms.32 These results highlight a potential risk of digital health disparities among older patients with cancer, particularly those with lower educational attainment.
The mean self-efficacy score in this study was 6.78 ± 1.55 (on a 10-point scale), indicating a moderate level. This value is comparable to those reported in previous studies involving cancer survivors within 12 months of surgery, radiation, or chemotherapy.33 However, active cancer treatment is associated with increased physical fatigue, psychological distress, and treatment-related side effects, all of which can negatively affect self-efficacy. Prior studies have reported that persistent symptoms and uncertainty regarding treatment outcomes can reduce patients' confidence in their ability to manage their health.34 Consequently, patients receiving active treatment often exhibit lower self-efficacy than long-term cancer survivors, who may experience improvements in physical recovery, reduced treatment burden, and greater adaptation to self-management strategies over time.25 These findings underscore the dynamic nature of self-efficacy throughout the cancer care continuum and highlight the need for targeted interventions that support patients during active treatment.
Furthermore, in our study, participants with higher ECOG performance status scores exhibited lower self-efficacy and self-care behaviors. This finding aligns with prior evidence suggesting that poorer functional status, such as a higher ECOG performance status, is associated with lower self-efficacy and reduced engagement in self-care behaviors among adults receiving cancer treatment.35 As functional impairment increases, patients may perceive greater difficulties in adhering to self-care routines, which can be negatively associated with overall health outcomes.
A key finding of this study is that the results were compatible with an indirect-only mediation pattern, in which self-efficacy statistically mediated the relationship between digital health literacy and self-care behaviors. Prior evidence in cancer populations suggests that health literacy may be linked to self-care through self-efficacy, supporting our hypothesized sequence.36 However, because the data are cross-sectional, alternative directional relationships (e.g., self-care behaviors → self-efficacy → digital health literacy) cannot be ruled out. These findings align with those of prior research indicating that self-efficacy is a significant determinant of self-care behaviors in patients with cancer. Notably, Papadakos et al.37 reported that health literacy plays a vital role in promoting self-care behaviors among patients with cancer. Similarly, while this study found a positive association between digital health literacy and self-efficacy, the direct association between digital health literacy and self-care behaviors was not statistically significant after accounting for self-efficacy. This point reinforces the concept that self-efficacy may function as a psychological mechanism linking digital health literacy to effective self-management.
Implications for nursing practice and research
Given the increasing reliance on digital platforms for health information, individuals with limited digital literacy may face challenges in accessing and utilizing essential health resources, potentially exacerbating health inequities. Beyond technical accessibility, the ability to interpret and apply digital health information effectively is crucial for informed decision-making and self-management.38 Furthermore, patients with lower digital health literacy may encounter difficulties in understanding treatment options, communicating with healthcare providers, and adhering to self-care regimens. Therefore, tailored interventions addressing both technological skills and comprehension strategies are warranted. Healthcare providers should proactively offer clear, structured guidance and ensure that digital health education is adapted to the cognitive and literacy levels of older patients.
In addition, our findings suggest that interventions aimed at improving self-care behaviors among older patients with cancer should simultaneously incorporate strategies to enhance digital health literacy and self-efficacy. Healthcare professionals should not only focus on providing digital literacy training but also implement self-efficacy-enhancing interventions, such as goal setting, self-monitoring, and cognitive-behavioral approaches. By addressing both cognitive and psychological aspects of self-management, healthcare providers can better support older patients with cancer in navigating digital health resources and engaging in effective self-care practices. Moreover, the findings highlight the need for personalized interventions that consider patients' functional limitations while fostering self-efficacy to promote better self-care behaviors. Future longitudinal and interventional studies are needed to examine how these relationships evolve over time across different stages of cancer treatment and survivorship.
Limitations
This study has several limitations that should be considered when interpreting the findings. First, it was conducted in a single regional university hospital among patients with cancer aged 65 years and older undergoing chemotherapy. Therefore, the generalizability of the findings may be limited. Future research should explore these relationships in more diverse populations, including patients with different cancer diagnoses and those with chronic conditions, to enhance external validity. In addition, although the final analytic sample met and exceeded the a priori minimum required sample size, the relatively modest sample may have limited the precision of the estimates and the ability to detect smaller effects. Treatment-related factors such as prior surgery, radiotherapy, and chemotherapy line were not collected and therefore could not be adjusted for in the analyses, which may also limit interpretation of sample heterogeneity and generalizability.
Second, digital health literacy measurement may vary across healthcare systems and technology environments, including differences in the availability of patient portals and digital education resources. In addition, the lack of universally standardized definitions and instruments may limit comparability across studies. Future studies should refine and validate digital health literacy assessment tools to improve their applicability across different populations and healthcare settings. Third, we did not administer a standardized cognitive screening instrument such as the Mini-Mental State Examination or the Montreal Cognitive Assessment; therefore, subtle cognitive impairment without a formal diagnosis may not have been detected. Lastly, the cross-sectional design of this study limits its ability to establish causal relationships between digital health literacy, self-efficacy, and self-care behaviors. Accordingly, the observed statistical mediation pattern should not be over-interpreted as definitive evidence of causal mechanisms or directionality. To better understand how these relationships evolve over time, longitudinal studies are needed to examine changes in digital health literacy, self-efficacy, and self-care behaviors across different stages of cancer treatment and survivorship.
Conclusions
This study examined the mediating role of self-efficacy in the relationship between digital health literacy and self-care behaviors among older patients with cancer undergoing chemotherapy. The findings were compatible with an indirect-only mediation pattern, suggesting that digital health literacy may be linked to self-care behaviors primarily through self-efficacy. Moreover, digital health literacy was significantly associated with age and educational level, highlighting potential disparities in the access to and use of digital health information among older adults. Additionally, patients with poorer functional status (higher ECOG performance status scores) demonstrated lower self-efficacy and self-care behaviors, emphasizing the need for tailored interventions that address functional limitations while fostering self-efficacy.
Given these findings, healthcare providers may consider strategies that simultaneously support digital health literacy and self-efficacy to improve self-care behaviors among older patients with cancer. These strategies may include tailored digital health education, self-monitoring techniques, and cognitive-behavioral approaches to strengthen patients' confidence in managing their health. Despite its limitations, this study provides valuable insights into the mechanisms linking digital health literacy, self-efficacy, and self-care behaviors. Longitudinal research is needed to further investigate these relationships over time and to develop interventions that effectively support older patients with cancer in navigating digital health resources and engaging in self-care practices.
CRediT authorship contribution statement
Ji Hee Seo: Investigation, Data curation, Formal analysis, Writing – Original draft. Su Jung Lee: Conceptualization, Methodology, Supervision, Writing – Review & editing. All authors have read and approved the final manuscript.
Ethics statement
This study was approved by the Institutional Review Board of the designated institution (IRB No. 2024-07-012) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. All participants provided written informed consent before participation.
Data availability statement
The data that support the findings of this study are available from the corresponding author, SJL, upon reasonable request. The data are not publicly available due to restrictions related to participant confidentiality.
Declaration of generative AI and AI-assisted technologies in the writing process
No AI tools or services were used during the preparation of this work.
Funding
This study received no external funding.
Declaration of competing interest
The authors declare no conflict of interest.
Acknowledgments
The authors thank all participants who took part in this study. This article is based on a revised and supplemented version of the first author's master's thesis.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.apjon.2026.100971.
Appendix A. Supplementary materials
The following is the Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, SJL, upon reasonable request. The data are not publicly available due to restrictions related to participant confidentiality.