Abstract
-
Objectives
This study examined the associations between digital literacy and self-rated health (SRH), depression, and life satisfaction among older adults in the Republic of Korea.
-
Methods
Data were obtained from the 2023 National Survey of Older Koreans (n=9,951). Digital literacy was evaluated based on participants’ ability to use 8 smartphone functions and their perceived difficulty adapting to a digital society. Dependent variables included SRH, depression (measured using the short form of Geriatric Depression Scale), and life satisfaction (assessed through a composite score across 7 domains). Multiple logistic regression was applied for SRH and depression, while linear regression was conducted for life satisfaction, adjusting for sociodemographic and health-related covariates.
-
Results
Higher digital literacy was significantly associated with better SRH and greater life satisfaction. Compared to participants with no digital skills, those with moderate or high digital literacy had higher odds of reporting good SRH and significantly higher life satisfaction scores. Older adults who reported difficulty adapting to a digital society demonstrated significantly lower SRH and life satisfaction. However, after adjusting for covariates, the association between digital literacy and depression was not statistically significant.
-
Conclusion
Higher digital literacy is linked to better SRH and greater life satisfaction. Moreover, digital literacy reduced the explanatory power of socioeconomic variables, suggesting that it serves as an important behavioral determinant. These findings underscore the importance of promoting digital literacy as a means of improving health equity and well-being in aging populations.
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Keywords: Depression; Digital literacy; Life satisfaction; Older adults; Self-rated health
Introduction
The rapid digital transformation of modern society is occurring alongside accelerated population aging, raising increasing interest in how digital literacy relates to quality of life among older adults. Smartphones have become a primary tool for information access and social interaction across all age groups, including older adults. According to the National Information Society Agency [
1], the digital access rate among Korean adults aged 65 years or older was 95.3%, while the digital utilization rate was 75.0%. Although 94.0% of this population owned a smartphone, many used it for activities beyond basic communication, such as searching for information, watching videos, and performing financial transactions.
These technological changes affect older adults’ daily lives directly and indirectly and are increasingly linked to psychological and health-related outcomes such as self-rated health, depression, and life satisfaction. Previous studies have suggested that digital literacy can mitigate social isolation and strengthen self-efficacy in older adults, thereby reducing depression and improving life satisfaction [
2,
3]. Digital literacy refers not only to the ability to operate digital devices but also to the skills required to access, evaluate, and apply digital information effectively [
4]. As the digital divide expands from access to competency, concerns have emerged that disparities in digital literacy may exacerbate health inequalities [
5]. Limited access to digital information can lead to exclusion from health-related services and resources, impeding individuals’ ability to manage their health and negatively affecting both perceived and actual health status.
Smartphones have been reported to support social connections, facilitate access to information, and encourage personal development in older adults, thereby improving overall quality of life [
6]. Digital competencies such as information searching, video calling, and social media use can strengthen older adults’ social networks, reduce feelings of isolation, and enhance life satisfaction [
7]. Kim et al. [
8] noted that, given older adults’ strong interest in health, many actively seek health-related information while learning to use smartphones. Furthermore, higher digital literacy levels have been linked to greater self-efficacy, autonomy, and sense of control [
9]. Such digital engagement has been shown to promote social participation and increase subjective well-being in older adults [
3]. Korean studies similarly report that older adults who regularly use smartphones experience higher life satisfaction and more positive health perceptions compared with non-users [
8].
Numerous studies have found that higher digital literacy is associated with fewer depressive symptoms and better self-rated health in older adults [
2]. Conversely, limited use of social media has been associated with increased feelings of emotional loneliness [
10]. Video calls and social media engagement may therefore provide important emotional support and contribute to psychological well-being.
Life satisfaction is a key indicator of subjective well-being in later life and is shaped by multiple factors, including health, emotional stability, social connections, and financial security [
11]. Recent studies identify digital literacy as a significant predictor of life satisfaction, suggesting that digital engagement enhances quality of life by fostering self-expression, social connectedness, and access to information [
12]. In contrast, digital exclusion may result in emotional withdrawal, psychological vulnerability, and reduced life satisfaction [
6,
13]. Older adults with stronger digital skills typically access a wider range of social and cultural resources, which contributes to higher life satisfaction [
14,
15]. For example, an earlier study using data from the 2020 National Survey of Older Koreans found that life satisfaction was higher among women, individuals with higher education or household income, better SRH, and more frequent and longer smartphone or tablet use, as well as among those who reported fewer difficulties using digital devices [
16].
Therefore, this study aimed to assess the impact of digital literacy on self-rated health, depression, and life satisfaction among older adults, drawing on data from the 2023 National Survey of Older Koreans. By adjusting for relevant sociodemographic and health-related covariates, this study provides evidence to guide digital inclusion strategies and information and communication technology education initiatives for older adults.
Materials and Methods
Research Design
This study employed a cross-sectional observational design to examine associations between digital literacy and 3 key health-related outcomes—self-rated health, depression, and life satisfaction—among older adults in the Republic of Korea. The analysis was based on secondary data from the 2023 National Survey of Older Koreans, which provides nationally representative information on the health status and social circumstances of the country’s aging population. Given the cross-sectional design, the purpose was to identify associations and patterns rather than infer causality. Digital literacy was conceptualized as a potential behavioral determinant of well-being in later life, and multiple regression models were applied to estimate adjusted associations while controlling for sociodemographic and health-related covariates.
Data Source and Study Population
Data were obtained from the 2023 National Survey of Older Koreans, conducted by the Korea Institute for Health and Social Affairs. This survey targets community-dwelling adults aged 65 years and older and is administered every 3 years to track their health status, socioeconomic conditions, and overall quality of life. A stratified multistage probability sampling method was employed to ensure national representativeness, taking into account factors such as geographic region, urban–rural distribution, and housing type. Trained interviewers collected data through structured, face-to-face interviews.
The total sample included 10,078 respondents, of whom approximately 1.2% provided proxy responses through family members due to physical or cognitive limitations. For the analytic dataset, cases with missing values on key variables were excluded (
Figure 1). Accordingly, all proxy responses were also removed. The final analytic sample consisted of 9,951 older adults.
Variables
The study aimed to examine associations between digital literacy and health outcomes among older adults. The dependent variables were self-rated health, depression, and life satisfaction. Self-rated health was dichotomized as “good health” if respondents reported “very good” or “good.” Depression was measured with the short form of Geriatric Depression Scale [
17], with scores ≥8 indicating the presence of depressive symptoms. Life satisfaction was evaluated across 7 domains: (1) health status, (2) financial situation, (3) relationship with spouse, (4) relationship with children, (5) participation in social activities, (6) social relationships, and (7) overall life satisfaction. Each item was rated on a 5-point Likert scale (1=very dissatisfied to 5=very satisfied). A mean score was calculated using the items for which the respondent provided valid responses, and this score was used as a continuous variable in the analysis. In line with the survey design, respondents without a living spouse or children were not asked items (3) and/or (4); these were excluded from the mean calculation for those individuals. Specifically, 40.9% of respondents had no living spouse, and 5.5% had no living children. The remaining 5 items were answered by all participants.
The key independent variable was digital literacy, measured through respondents’ ability to use smartphone functions and their perceived difficulty adapting to a digital society. Digital literacy was assessed with the question: “Without assistance, can you use a PC, smartphone, tablet PC, or kiosk to perform the following activities?” Respondents answered “yes” or “no” for each of 8 functions: (1) sending messages, (2) video calling, (3) searching for information, (4) taking photos or videos, (5) using social networking services (SNS), (6) engaging in e-commerce, (7) conducting online banking transactions, and (8) searching for and installing applications. Each “yes” response was coded as 1 and each “no” as 0. The composite digital literacy score ranged from 0 to 8 and was categorized as 0, 1–2, 3–4, or 5 or more. Additionally, respondents were asked whether they experienced difficulties using digital devices in their daily lives.
Covariates included sociodemographic characteristics such as sex, age, educational attainment, income level, employment status, household composition, and residential area, as well as health-related variables including the number of chronic diseases, history of falls, disability status, physical and cognitive impairment, and frailty. These variables were selected based on prior research demonstrating significant associations with self-rated health, depression, and life satisfaction among older adults [
3,
18–
20].
Physical impairment was defined as requiring assistance with activities of daily living or instrumental activities of daily living, both of which have been associated with functional decline and lower life satisfaction in older populations [
21]. Cognitive function was assessed using the standardized Korean Mini-Mental State Examination, 2nd Edition (K-MMSE-2) [
22]. The K-MMSE-2 provides raw scores ranging from 0 to 30, and cognitive impairment was determined using T-scores adjusted for age and educational level. Respondents with scores below the established threshold were classified as having suspected cognitive impairment.
Frailty was assessed using the Korean version of the fatigue, resistance, ambulation, illnesses, & loss of weight (FRAIL) scale, adapted from the original tool developed by Morley et al. [
23]. Its clinical reliability and validity in older Korean adults have been confirmed by Jung et al. [
24]. The scale comprises 5 items: (1) Have you felt fatigued in the past month? (2) Do you have difficulty climbing 10 stairs without assistance or rest? (3) Do you have difficulty walking 300 meters without assistance? (4) Have you been diagnosed by a physician with 5 or more of the following 11 chronic conditions: hypertension, diabetes, cancer, chronic lung disease, myocardial infarction, heart failure, angina, asthma, arthritis, stroke, or kidney disease? (5) Have you lost more than 5% of your body weight in the past year? Respondents scoring 3 or more were classified as frail, those scoring 1–2 as pre-frail, and those scoring 0 as non-frail.
Detailed definitions of all variables, including the questionnaire items and response categories used in their construction, are provided in
Table S1 for transparency and reproducibility [
17,
22–
24].
Statistical Analysis
Descriptive statistics were used to summarize the sociodemographic characteristics, health status, and digital literacy of the study population. Univariate analyses were conducted to assess group differences in self-rated health, depression, and life satisfaction according to digital literacy levels. Specifically, the chi-square test was used for categorical dependent variables (self-rated health and depression), while the independent-sample t-test and 1-way analysis of variance (ANOVA) were employed to compare mean scores of the continuous variable (life satisfaction). The independent t-test was used for comparisons based on binary indicators of digital literacy, and ANOVA was applied for comparisons across the categorized digital literacy scores.
To investigate associations between digital literacy and health outcomes, multiple logistic regression analyses were performed for binary outcomes (self-rated health and depression), and multiple linear regression analysis was conducted for the continuous outcome (life satisfaction), adjusting for all covariates. Three separate regression models were constructed to evaluate the independent associations of digital literacy with self-rated health, depression, and life satisfaction. Covariates were consistent across models; however, self-rated health and depression were also included as covariates in the life satisfaction model. This decision was supported by extensive empirical evidence indicating that both self-rated health and depression are strong predictors of life satisfaction in older adults. Including them in the life satisfaction model allowed us to better isolate the specific contribution of digital literacy to subjective well-being. In contrast, life satisfaction was not included as a covariate in the models for self-rated health and depression to avoid overadjustment, which could obscure the direct effects of digital literacy on these outcomes. Supplementary analyses, not shown in the main manuscript, additionally included all 3 psychosocial variables as covariates in each model and produced results largely consistent with the primary findings, suggesting that the observed associations were robust to model specification. All analyses were performed using SAS version 9.4 (SAS Institute Inc.).
Ethics Statement
This study was approved by the Institutional Review Board (IRB) of Hanyang Cyber University (IRB No: HYCU-IRB-2025-004) and conducted in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived because the study used de-identified, publicly available secondary data.
Results
General Characteristics of the Study Population
Among the 9,951 respondents, 3,824 (38.4%) were men and 6,127 (61.6%) were women. The largest age group was 65 to 74 years (57.4%), followed by those aged 75 to 84 years (34.6%) and those aged 85 years or older (8.0%). In terms of educational attainment, 35.0% had completed high school or higher, 29.3% were elementary school graduates, 21.2% were middle school graduates, and 14.4% had no formal education. A total of 39.6% were currently employed, and 70.1% resided in urban areas. With respect to household composition, the most common arrangement was living with a spouse aged 65 years or older (54.5%), followed by living alone (34.4%) and living with children or other cohabitants (11.1%). Additionally, 50.3% reported participating in social activities, such as hobby groups, social gatherings, religious services, or political events, at least once per month (
Table 1).
Digital Literacy of the Study Population
Among older adults aged 65 and over, an assessment of digital competency across 8 smartphone functions revealed substantial variation in ability. The highest proportion of respondents (70.6%) reported being able to send text messages, whereas fewer were able to make video calls (41.8%), search for information (46.5%), or take photos or videos (49.2%). Utilization rates were lower for SNS (8.0%), e-commerce (10.8%), online banking (17.9%), and searching for or installing applications (11.9%).
Based on the number of functions respondents could perform, digital literacy scores were categorized. Notably, 26.5% of older adults were unable to use any of the 8 functions (score=0). Meanwhile, 26.4% could use 1–2 functions, 27.8% could use 3–4 functions, and 19.3% could use 5 or more functions. Furthermore, 93.0% of respondents reported experiencing difficulties in coping with the demands of an increasingly digital society (
Table 2).
Health Status and Life Satisfaction according to Digital Literacy Levels
Univariate analyses revealed significant differences in self-rated health, depression, and life satisfaction according to the level of digital literacy among older adults.
Respondents who reported being able to perform functions such as sending messages, making video calls, searching for information, taking photos or videos, using SNS, conducting e-commerce or online banking, and installing applications were more likely to report better self-rated health, fewer depressive symptoms, and greater life satisfaction (p<0.001). Higher composite digital literacy scores were likewise significantly associated with better self-rated health, fewer depressive symptoms, and higher life satisfaction (p<0.001).
In contrast, respondents who reported difficulties adapting to a digital society exhibited significantly lower self-rated health and life satisfaction than those without such difficulties (
p<0.001) (
Table 3).
Effects of Digital Literacy on Older Adults’ Health Status and Life Satisfaction
After adjusting for sociodemographic characteristics and health-related variables, multiple regression analyses were conducted to assess the effects of digital literacy on self-rated health, depression, and life satisfaction. While sociodemographic and health-related factors showed strong and consistent associations with both self-rated health and life satisfaction, digital literacy remained a significant predictor even after adjustment for covariates.
Older adults with digital literacy scores corresponding to the ability to use 3–4 functions had significantly higher odds of reporting good self-rated health (odds ratio [OR], 1.530; 95% confidence interval [CI], 1.309–1.788), as did those with scores reflecting the ability to use 5 or more functions (OR, 2.316; 95% CI, 1.922–2.792), compared to those with a score of 0. By contrast, respondents who reported difficulties coping with the digital society were significantly less likely to report good self-rated health (OR, 0.499; 95% CI, 0.411–0.606).
Regarding life satisfaction, respondents with digital literacy scores of 3–4 and 5 or more functions reported significantly higher mean scores by 0.106 points (p<0.001) and 0.163 points (p<0.001), respectively, compared to those with a score of 0. Conversely, those reporting digital difficulties had significantly lower mean life satisfaction scores by 0.117 points (p<0.001).
Although differences in depression across digital literacy levels were statistically significant in univariate analyses, the association became non-significant after adjusting for sociodemographic and health-related covariates (
Table 4).
Discussion
We are living in an era where the rapid transition to a digital society coincides with the demographic shift toward super-aged populations. In this context, the quality of life of older adults has become a major determinant of overall population well-being, and the use of digital technology is expected to be a critical factor in everyday life. This study sought to empirically examine the impact of digital literacy on key mental and emotional health indicators, including self-rated health, depression, and life satisfaction. The 2023 National Survey of Older Koreans, which included responses from 10,078 participants, provided a nationally representative dataset with broad coverage of health indicators, living environments, social relationships, and life satisfaction, making it a highly appropriate source for this research.
Comparison with Previous Surveys and Age Cohorts
Compared with the 2020 wave of the same survey, the 2023 data reveal further aging of the older population, accompanied by notable improvements in education and income levels [
25]. Smartphone ownership reached 94%, a dramatic increase compared with the early 2000s, when access to such technology was limited [
1]. However, actual utilization remains modest, with only 48.3% reporting experience using health-related apps, highlighting a persistent gap between device ownership and effective use. The main functions used by older adults in 2023 were sending messages (70.6%), taking photos or videos (49.2%), searching for information (46.5%), and video calling (41.8%). In contrast, utilization of SNS (8.0%), e-commerce (10.8%), application installation (11.9%), and online banking (17.9%) remained notably lower. These patterns suggest that older adults still face barriers to meaningful social participation and information use. More importantly, such disparities extend beyond device access and are closely tied to quality of life. Compared with younger cohorts, older adults demonstrate the lowest digital engagement, especially in domains such as SNS use and financial transactions, where generational gaps are particularly pronounced. When compared internationally, older adults in the Republic of Korea report higher accessibility to e-government services but lower SNS usage than their counterparts in the United States, Japan, and Europe [
9,
15]. Such low utilization is not a mere technical issue; it is tied to reduced autonomy and unequal access to health resources. A holistic approach—incorporating user-friendly digital environments, tailored education, and social support—is therefore essential.
Adjusted Associations between Digital Literacy and Depression
Digital literacy encompasses more than technical skills; it involves the ability to access, evaluate, and apply digital information [
4]. In this study, higher digital literacy, measured by the number of smartphone functions used, was initially associated with better self-rated health, higher life satisfaction, and fewer depressive symptoms. However, after adjusting for demographic and health-related covariates, the association with depression was no longer statistically significant.
This result indicates that while digital literacy may be related to psychological well-being descriptively, its independent effect on depressive symptoms is likely mediated or confounded by other factors such as physical health, social relationships, or economic resources. Depression in older adults is shaped by a complex interplay of vulnerabilities, including chronic disease, functional limitations, and social isolation [
26,
27]. Consequently, digital literacy alone may not be sufficient to buffer against depression unless it occurs within socially meaningful contexts.
Furthermore, the benefits of digital engagement for mental health arise less from the number of functions used and more from the purpose and context of use. For example, video calling may alleviate loneliness if it facilitates regular communication with family or friends, but offers little benefit if used sporadically or without emotional reciprocity. Park [
28] found that the context and intent of digital use were more influential for emotional outcomes than frequency or technical proficiency alone.
These results collectively imply that digital literacy could serve as a potential enabler, but not a direct determinant, of mental health in older adults. Interventions should therefore go beyond technical training to emphasize socially and emotionally meaningful digital engagement.
Substitutive Effect of Digital Literacy on Socioeconomic Variables
Hierarchical regression analyses demonstrated that digital literacy attenuated or eliminated the explanatory power of several socioeconomic variables across all 3 health outcomes.
For self-rated health, older age (75–84 years: OR, 0.657; 95% CI, 0.539–0.728; ≥85 years: OR, 0.596; 95% CI, 0.489–0.727) and rural residence (urban: OR, 1.108; 95% CI, 1.000–1.227) were significantly associated with poorer subjective health in models without digital literacy. However, when digital literacy was included, these associations became statistically non-significant, suggesting a mediating or substitutive role.
For depression, higher educational attainment was significantly associated with fewer depressive symptoms (elementary school: OR, 0.678; 95% CI, 0.566–0.812; middle school: OR, 0.589; 95% CI, 0.472–0.736) in models without digital literacy. These effects disappeared once digital literacy was incorporated, again suggesting a substitutive effect.
For life satisfaction, higher education (middle school: b=0.142, p<0.001; high school: b=0.260, p<0.001) and current employment (b=0.098, p<0.001) were significantly associated with higher scores in models without digital literacy, but these associations were no longer significant after its inclusion.
These results highlight the potential of digital literacy as a behavioral determinant that can reduce disparities traditionally explained by education or income. Rather than functioning simply as an additional skill, digital literacy enables older adults to access resources, practice self-care, and maintain autonomy, thereby influencing well-being in ways comparable to, or substitutive of, structural socioeconomic advantages. This perspective aligns with emerging arguments that frame digital inclusion as a matter of equity, particularly in aging societies where participation and access to information are increasingly mediated by digital technologies [
14,
26,
27].
Accordingly, digital literacy interventions should prioritize older adults most at risk of digital exclusion, including those with lower education, lower income, or those living alone. Training should emphasize core functions—such as information searching, video calling, and social networking—that are most strongly linked to health and life satisfaction. Incorporating peer mentoring and community resources can simultaneously enhance social interaction and amplify the positive effects on life satisfaction identified in this study.
Recent Research on Digital Literacy
Recent studies further emphasize the importance of digital literacy. Lee et al. [
15] reported that older adults with higher digital competence scored, on average, 2.3 points higher in life satisfaction than those with lower competence. Jung et al. [
29] found that digital engagement improved life quality through the mediating role of social capital. Xin et al. [
30] reported that 74.5% of digitally active seniors underwent regular health checkups, compared with 52.7% of non-active seniors—a difference of more than 20%. Their analysis also revealed lower chronic disease prevalence and higher levels of both physical and social activity among digitally literate seniors. These findings underscore that digital literacy extends beyond technical proficiency, significantly influencing preventive health behaviors, emotional stability, and overall quality of life.
Policy Implications and Recommendations
This study highlights the persistently low digital literacy levels among older adults in the Republic of Korea. Despite high rates of device ownership, effective use remains limited, and many seniors report feelings of exclusion from the digital information society. Improving digital literacy is not merely a matter of teaching technology; it is fundamental to promoting autonomy in health management and ensuring the realization of health rights. In today’s health environment, where telemedicine, mobile health apps, and online medication management are increasingly prevalent, digital usage disparities can rapidly translate into health disparities. Accordingly, comprehensive strategies are needed that extend beyond device distribution. These include designing age-friendly digital interfaces, developing sustainable community-based digital education programs, and fostering intergenerational mentoring systems. Furthermore, older adults with low digital literacy often have weaker social support networks, underscoring the need for integrated interventions targeting multi-vulnerable groups. In conclusion, digital literacy is a decisive factor in promoting both equity and quality of life for older adults. Sustainable aging policies should integrate policy design with community-level implementation. Bridging the digital divide must therefore be reframed as a social responsibility—encompassing not only technical training but also the rights to health, information, and dignified living.
Conclusion
Digital literacy is a critical determinant of health equity and quality of life among older adults. Promoting digital competence and encouraging active community engagement are essential for building a sustainable health care system in an aging society. In this context, digital accessibility and literacy should no longer be considered optional, but fundamental components of realizing the health rights of older populations. Stratified interventions are especially needed for vulnerable subgroups, such as those with limited education or weaker social support. Policies must address not only technological barriers but also psychological resistance and social exclusion.
This study has several limitations. Its cross-sectional design restricts causal inference, and the measurement of digital engagement emphasized functional use rather than qualitative or emotional dimensions. In addition, the reliance on self-reported data may have introduced response bias. While this study focused on overall digital literacy levels and perceived difficulty in adapting to a digital society, future research should investigate patterns and clusters of digital function use among older adults. Identifying which functions tend to co-occur could reveal distinct usage typologies and inform targeted digital inclusion strategies. Although this study was not designed to capture such behavioral patterns, future work could employ advanced analytical methods to examine the co-occurrence of digital behaviors and their contextual significance. Longitudinal research designs are also needed to track changes in digital engagement and health outcomes over time. Furthermore, qualitative approaches—such as in-depth interviews and participant observations—should be incorporated to capture motivations, lived experiences, and barriers to digital engagement in greater depth.
HIGHLIGHTS
• Digital literacy is positively associated with self-rated health and life satisfaction in older adults.
• Difficulty adapting to digital society is associated with poorer health outcomes.
• Promoting digital literacy may reduce health disparities in aging populations.
Supplementary Material
Article information
Ethics Approval
This study was approved by the Institutional Review Board of Hanyang Cyber University (IRB No: HYCU-IRB-2025-004) and performed in accordance with the principles of the Declaration of Helsinki. Informed consent was waived because this study used secondary data from a nationally representative survey.
Conflicts of Interest
The authors have no conflicts of interest to declare.
Authors’ Contributions
Conceptualization: all authors; Data curation: NHY; Formal analysis: NHY; Investigation: MGK, JH; Methodology: MGK, NHY; Project administration: JH; Supervision: MGK, JH; Validation: all authors; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.
Figure 1.Flow chart of study population selection.
Table 1.General characteristics of the study population (n=9,951)
Table 1.
| Variable |
Value |
| Sociodemographic factors |
|
| Sex |
|
| Male |
3,824 (38.4) |
| Female |
6,127 (61.6) |
| Age (y) |
|
| 65–74 |
5,710 (57.4) |
| 75–84 |
3,442 (34.6) |
| ≥85 |
799 (8.0) |
| Education |
|
| None |
1,435 (14.4) |
| Elementary school |
2,920 (29.3) |
| Middle school |
2,114 (21.2) |
| ≥High school |
3,482 (35.0) |
| Income quartile |
|
| 1st |
2,484 (25.0) |
| 2nd |
2,481 (24.9) |
| 3rd |
2,474 (24.9) |
| 4th |
2,512 (25.2) |
| Employment status |
|
| Not working |
6,009 (60.4) |
| Working |
3,942 (39.6) |
| Household |
|
| Living alone |
3,423 (34.4) |
| Living with spouse |
5,419 (54.5) |
| Others |
1,109 (11.1) |
| Residence area |
|
| Urban |
6,977 (70.1) |
| Rural |
2,974 (29.9) |
| Social activities |
|
| <Once a month |
4,946 (49.7) |
| ≥Once a month |
5,005 (50.3) |
| Health status |
|
| Comorbidities |
|
| 0 |
1,363 (13.7) |
| 1–2 |
4,907 (49.3) |
| ≥3 |
3,681 (37.0) |
| Fall history |
|
| No |
9,339 (93.8) |
| Yes |
612 (6.2) |
| Disability |
|
| Non-disabled |
9,547 (95.9) |
| Disabled |
404 (4.1) |
| Cognitive impairments |
|
| No |
7,503 (75.4) |
| Yes |
2,448 (24.6) |
| Functional impairments |
|
| No |
8,303 (83.4) |
| Yes |
1,648 (16.6) |
| Frailty |
|
| Non-frail |
6,251 (62.8) |
| Pre-frail |
3,214 (32.3) |
| Frail |
486 (4.9) |
| Total |
9,951 (100.0) |
Table 2.Digital literacy of the study population (n=9,951)
Table 2.
| Variable |
Value |
| Sending messages |
|
| No |
2,930 (29.4) |
| Yes |
7,021 (70.6) |
| Video calling |
|
| No |
5,793 (58.2) |
| Yes |
4,158 (41.8) |
| Information searching |
|
| No |
5,325 (53.5) |
| Yes |
4,626 (46.5) |
| Taking photos or videos |
|
| No |
5,056 (50.8) |
| Yes |
4,895 (49.2) |
| Using social networking services |
|
| No |
9,150 (92.0) |
| Yes |
801 (8.0) |
| Engaging in e-commerce |
|
| No |
8,877 (89.2) |
| Yes |
1,074 (10.8) |
| Conducting online banking transactions |
|
| No |
8,169 (82.1) |
| Yes |
1,782 (17.9) |
| Searching for and installing applications |
|
| No |
8,769 (88.1) |
| Yes |
1,182 (11.9) |
| Digital literacy levels (0–8) |
|
| 0 |
2,633 (26.5) |
| 1–2 |
2,623 (26.4) |
| 3–4 |
2,770 (27.8) |
| ≥5 |
1,925 (19.3) |
| Perceived difficulties in coping with a digitalized society |
|
| No |
701 (7.0) |
| Yes |
9,250 (93.0) |
Table 3.Health status and life satisfaction according to digital literacy levels
Table 3.
| Variable |
Self-rated health |
Depression |
Life satisfaction (1–5) |
|
n (%) |
χ2 (p) |
n (%) |
χ2 (p) |
Mean±SD |
t/F (p) |
| Sending messages |
|
673.48 (<0.001) |
|
211.54 (<0.001) |
|
–27.09 (<0.001) |
| No |
630 (21.5) |
|
532 (18.2) |
|
3.12±0.54 |
|
| Yes |
3,483 (49.6) |
|
570 (8.1) |
|
3.43±0.53 |
|
| Video calling |
|
659.96 (<0.001) |
|
168.59 (<0.001) |
|
–31.20 (<0.001) |
| No |
1,772 (30.6) |
|
842 (14.5) |
|
3.20±0.55 |
|
| Yes |
2,341 (56.3) |
|
260 (6.3) |
|
3.53±0.50 |
|
| Information searching |
|
1,055.44 (<0.001) |
|
188.38 (<0.001) |
|
–33.89 (<0.001) |
| No |
1,405 (26.4) |
|
804 (15.1) |
|
3.17±0.54 |
|
| Yes |
2,708 (58.5) |
|
298 (6.4) |
|
3.53±0.51 |
|
| Taking photos or videos |
|
844.77 (<0.001) |
|
175.09 (<0.001) |
|
–31.93 (<0.001) |
| No |
1,376 (27.2) |
|
767 (15.2) |
|
3.17±0.55 |
|
| Yes |
2,737 (55.9) |
|
335 (6.8) |
|
3.51±0.50 |
|
| Using social networking services |
|
349.74 (<0.001) |
|
36.86 (<0.001) |
|
–18.50 (<0.001) |
| No |
3,532 (38.6) |
|
1,065 (11.6) |
|
3.31±0.55 |
|
| Yes |
581 (72.5) |
|
37 (4.6) |
|
3.64±0.47 |
|
| Engaging in e-commerce |
|
432.78 (<0.001) |
|
85.73 (<0.001) |
|
–23.73 (<0.001) |
| No |
3,352 (37.8) |
|
1,073 (12.1) |
|
3.30±0.55 |
|
| Yes |
761 (70.9) |
|
29 (2.7) |
|
3.66±0.45 |
|
| Conducting online banking transactions |
|
689.21 (<0.001) |
|
129.04 (<0.001) |
|
–29.24 (<0.001) |
| No |
2,882 (35.3) |
|
1,041 (12.7) |
|
3.27±0.55 |
|
| Yes |
1,231 (69.1) |
|
61 (3.4) |
|
3.64±0.46 |
|
| Searching for and installing applications |
|
461.60 (<0.001) |
|
73.62 (<0.001) |
|
–23.76 (<0.001) |
| No |
3,283 (37.4) |
|
1,058 (12.1) |
|
3.30±0.55 |
|
| Yes |
830 (70.2) |
|
44 (3.7) |
|
3.65±0.47 |
|
| Digital literacy level (0–8) |
|
1,284.53 (<0.001) |
|
291.07 (<0.001) |
|
508.59 (<0.001) |
| 0 |
555 (21.1) |
|
485 (18.4) |
|
3.10±0.55 |
|
| 1–2 |
830 (31.6) |
|
338 (12.9) |
|
3.23±0.53 |
|
| 3–4 |
1,379 (49.8) |
|
202 (7.3) |
|
3.46±0.50 |
|
| ≥5 |
1,349 (70.1) |
|
77 (4.0) |
|
3.65±0.46 |
|
| Perceived difficulties in coping with a digitalized society |
|
321.13 (<0.001) |
|
27.01 (<0.001) |
|
19.84 (<0.001) |
| No |
515 (73.5) |
|
36 (5.1) |
|
3.70±0.49 |
|
| Yes |
3,598 (38.9) |
|
1,066 (11.5) |
|
3.31±0.55 |
|
| Total |
4,113 (41.3) |
|
1,102 (11.1) |
|
3.34±0.55 |
|
Table 4.Effects of digital literacy on older adults’ health status and life satisfaction
Table 4.
| Variable |
Category |
Self-rated health |
Depression |
Life satisfaction |
| OR (95% CI) |
OR (95% CI) |
Coefficients |
SE |
p |
| Digital literacy |
|
|
|
|
|
|
| Digital literacy levels (ref. none) |
1–2 |
1.029 (0.888–1.191) |
1.163 (0.964–1.402) |
0.007 |
0.014 |
0.588 |
| 3–4 |
1.530 (1.309–1.788) |
1.016 (0.806–1.281) |
0.106 |
0.015 |
<0.001 |
| ≥5 |
2.316 (1.922–2.792) |
0.833 (0.598–1.161) |
0.163 |
0.019 |
<0.001 |
| Perceived difficulties in coping with a digitalized society (ref. no) |
Yes |
0.499 (0.411–0.606) |
0.935 (0.645–1.355) |
–0.117 |
0.018 |
<0.001 |
| Sociodemographic factors |
|
|
|
|
|
|
| Sex (ref. male) |
Female |
0.930 (0.839–1.030) |
0.724 (0.614–0.852) |
0.028 |
0.010 |
0.005 |
| Age (ref. 65–74 y) |
75–84 |
0.931 (0.829–1.045) |
0.720 (0.606–0.856) |
0.080 |
0.011 |
<0.001 |
| ≥85 |
1.006 (0.806–1.256) |
0.763 (0.591–0.985) |
0.083 |
0.020 |
<0.001 |
| Education (ref. none) |
Elementary school |
1.174 (0.982–1.403) |
0.866 (0.709–1.058) |
0.048 |
0.015 |
0.002 |
| Middle school |
1.380 (1.132–1.681) |
0.838 (0.654–1.073) |
0.005 |
0.018 |
0.764 |
| ≥High school |
1.623 (1.327–1.984) |
0.735 (0.563–0.959) |
0.038 |
0.019 |
0.041 |
| Income quartile (ref. 1st quartile) |
2nd |
1.186 (1.029–1.366) |
0.929 (0.774–1.115) |
0.058 |
0.013 |
<0.001 |
| 3rd |
1.484 (1.281–1.720) |
0.779 (0.626–0.969) |
0.123 |
0.014 |
<0.001 |
| 4th |
1.514 (1.289–1.778) |
0.634 (0.490–0.819) |
0.152 |
0.016 |
<0.001 |
| Employment status (ref. not working) |
Working |
1.304 (1.176–1.447) |
0.544 (0.453–0.652) |
0.014 |
0.010 |
0.175 |
| Household (ref. others) |
Living alone |
0.931 (0.781–1.110) |
1.014 (0.787–1.306) |
0.020 |
0.017 |
0.237 |
| Living with spouse |
0.960 (0.819–1.125) |
0.693 (0.542–0.884) |
0.091 |
0.015 |
<0.001 |
| Residence area (ref. rural) |
Urban |
1.048 (0.939–1.171) |
1.443 (1.221–1.704) |
–0.062 |
0.011 |
<0.001 |
| Social activities (ref. <once a month) |
≥Once a month |
1.236 (1.120–1.363) |
0.635 (0.544–0.742) |
0.109 |
0.010 |
<0.001 |
| Health status |
|
|
|
|
|
|
| Comorbidities (ref. none) |
1–2 |
0.354 (0.306–0.409) |
1.410 (1.015–1.960) |
–0.037 |
0.014 |
0.009 |
| ≥3 |
0.164 (0.140–0.192) |
2.746 (1.978–3.811) |
–0.110 |
0.016 |
<0.001 |
| Fall history (ref. none) |
Yes |
0.683 (0.542–0.862) |
1.884 (1.514–2.345) |
0.001 |
0.019 |
0.972 |
| Disability (ref. none) |
Yes |
0.471 (0.345–0.641) |
1.834 (1.420–2.367) |
–0.156 |
0.023 |
<0.001 |
| Cognitive impairments (ref. none) |
Yes |
0.707 (0.631–0.793) |
1.382 (1.189–1.606) |
–0.089 |
0.011 |
<0.001 |
| Function impairments (ref. none) |
Yes |
0.695 (0.594–0.813) |
1.857 (1.572–2.194) |
–0.096 |
0.014 |
<0.001 |
| Frailty (ref. non-frail) |
Pre-frail |
0.670 (0.601–0.747) |
2.377 (2.011–2.810) |
–0.080 |
0.011 |
<0.001 |
| frail |
0.252 (0.170–0.371) |
6.867 (5.339–8.833) |
–0.305 |
0.023 |
<0.001 |
| Self-rated health (ref. poor) |
Good |
|
|
0.310 |
0.011 |
<0.001 |
| Depression (ref. not depressed) |
Depressed |
|
|
–0.161 |
0.019 |
<0.001 |
| Model fit |
|
Nagelkerke R²=0.333 |
Nagelkerke R²=0.274 |
Adjusted R²=0.344 |
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