JMIR AGING

Kyaw et al

Original Paper

Sociodigital Determinants of eHealth Literacy and Related
Impact on Health Outcomes and eHealth Use in Korean Older
Adults: Community-Based Cross-Sectional Survey
Myat Yadana Kyaw1, MBBS; Myo Nyein Aung1,2,3, MBBS, MSc, PhD; Yuka Koyanagi1,4, MSc, PhD; Saiyud
Moolphate5, MPH, PhD; Thin Nyein Nyein Aung1,6, MBBS, MMedSc, PhD; Hok Ka Carol Ma7, PhD; Hocheol
Lee8, PhD; Hae-Kweun Nam9, MSc, PhD; Eun Woo Nam10, PhD; Motoyuki Yuasa1,2, MD, PhD
1

Department of Global Health Research, Graduate School of Medicine, Juntendo University, Tokyo, Japan
Faculty of International Liberal Arts, Juntendo University, Tokyo, Japan
3
Advanced Research Institute for Health Sciences, Juntendo University, Tokyo, Japan
4
Department of Judo Therapy, Faculty of Health Sciences, Tokyo Ariake University of Medical and Health Sciences, Tokyo, Japan
5
Department of Public Health, Faculty of Science and Technology, Chiang Mai Rajabhat University, Chiang Mai, Thailand
6
Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
7
S R Nathan School of Human Development, Singapore University of Social Sciences, Singapore, Singapore
8
Department of Health Administration, Yonsei University Graduate School, Wonju, Republic of Korea
9
Department of Preventive Medicine, Wonju College of Medicine Yonsei University, Wonju, Republic of Korea
10
Department of Health Administration, Software Digital Healthcare Convergence College, Yonsei University, Wonju, Republic of Korea
2

Corresponding Author:
Myo Nyein Aung, MBBS, MSc, PhD
Department of Global Health Research, Graduate School of Medicine
Juntendo University
Hongo-2-1-1 , Bunkyo Ku
Tokyo, 113-8421
Japan
Phone: 81 338133111 ext 2994
Fax: 81 338181168
Email: myo@juntendo.ac.jp

Abstract
Background: eHealth literacy is an essential skill for pursuing electronic health information, particularly for older people
whose health needs increase with age. South Korea is now at the intersection of a rapidly digitalizing society and an increasingly aged population. eHealth literacy enables older people to maximize the effective use of emerging digital technology for
their health and quality of life. Understanding the eHealth literacy of Korean older adults is critical to eliminating the graydigital divide and inequity in health information access.
Objective: This study aims to investigate factors influencing eHealth literacy in older Korean adults and its impact on healthoutcomes and eHealth use.
Methods: This was a cross-sectional survey. Community-dwelling older adults 65 years and older in 2 urban cities in South
Korea were included. eHealth literacy was measured by the eHealth Literacy Scale. Ordinal logistic regression was used
to analyze factors associated with eHealth literacy and multivariate ANOVA for the impact of eHealth literacy on healthoutcomes and eHealth use.
Results: In total, 434 participants were analyzed. A total of 22.3% (97/434) of participants had high eHealth literacy skills.
Increasing age, higher monthly income, and time spent on the internet were significantly associated with eHealth literacy(P<.001), and social media users were 3.97 times (adjusted odds ratio 3.97, 95% CI 1.02‐15.43; P=.04) more likely to havehigher skill. Higher eHealth literacy was associated with better self-perceived health and frequent use of digital technologies
for accessing health and care services (P<.001).
Conclusions: Disparity in socioeconomic status and engagement on the internet and social media can result in different
levels of eHealth literacy skills, which can have consequential impacts on health outcomes and eHealth use. Tailored eHealthhttps://aging.jmir.org/2024/1/e56061

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Kyaw et alinterventions, grounded on the social and digital determinants of eHealth literacy, could facilitate eHealth information accessamong older adults and foster a digitally inclusive healthy aging community.
JMIR Aging 2024;7:e56061; doi: 10.2196/56061
Keywords: eHealth literacy; eHEALS; electronic health information; eHealth information; health literacy; health education;eHealth education; health training; eHealth training; digital health; digital technology; digital intervention; digital interventions; gray digital divide; healthy aging; gerontology; geriatric; geriatrics; older adult; older adults; older person; older people;
aging; aging; eHealth Literacy Scale

Introduction
Declining fertility rates and longer life expectancy primarily contribute to the demographic transition into an aging
population worldwide. This transition is more prominent
in Asia, where Japan, South Korea, China, and Singapore
entered the aged society within a period of 20 years. In 2021,
South Korea hosted 8.71 million people aged 65 years andolder (16.8% of the total population), and the proportion
increased by 5.1% compared to 2020 [1]. The rapid increasein the older population underscores the importance of anage-friendly environment in which older people can thinkor do what they value and age in a place to which they areattached.
Nowadays, with technological integration in every aspectof life, digital inclusiveness has become a basic componentof an age-friendly environment. Traditional health promotion and long-term care systems could be upgraded into
more productive and efficient systems with the diffusionof technology and digital devices. Gerontechnology, anemerging scientific field applying technological advances in
the aging sector, is now used to improve health and social
connections among older adults [2]. It is more evident in
the COVID-19 pandemic, where physical distancing wasinevitable. Digital health promotion activities and exercise
videos were used to maintain over 20-year-old community
exercise programs in Japan and preserve older people’sfunctional mobility [3]. The application of robotic technology for social connections of residents also showed better engagement and positive user perception in long-termcare facilities compared to conventional methods such as
telephone communication [4]. Additionally, older adults havea positive attitude toward using digital technology and arewilling to use it for health purposes and to maintain activities
of daily living [5,6].
Although older people have a positive attitude and arewilling to use digital health interventions and devices,
they face challenges and limitations when adopting digital technology. First, having access to digital devices andnetworks is the initial step in adopting digital technology
and its associated interventions [7]. Aged countries, such as
Japan, South Korea, and Singapore, prioritized digitalizationand promoted and implemented policies for digital inclusiveness [8]. Due to government efforts, the gap in accessto digital devices and the internet has narrowed. In 2022,
the digitization level reached 76.2% among South Korea’svulnerable populations (older people, low-income individuals, people with disabilities, fishermen, and farmers), andhttps://aging.jmir.org/2024/1/e56061

digital access was as high as 96% of the total population[9]. Regarding older people, 69.9% had access to digital
information in 2022, increasing from 69.1% in 2021 [9].
Ownership of digital devices and access to the internet do
not guarantee the actual application of digital technology for
health. Another barrier for older adults to fully embrace thebenefits of technology is not knowing how to acquire and use
information obtained in the digital context to make effective
health decisions [10].
According to Norman and Skinner [11], eHealth literacy is defined as “the ability to seek, discover, evaluate
and appraise eHealth information and apply the acquired
knowledge to solve health problems.” Older adults often see
eHealth literacy as a barrier to successfully using electronically delivered health information and interventions [10].
Due to technological advancements, health information is
one-click away, and older adults are faced with information
overload, in which misleading information could be exposed
physically and digitally. On the other hand, internet use forhealth-related searches by older American adults increased
from 24.8% in 2009 to 43.9% in 2018 [12]. Due to theprevalence of chronic diseases and age-related disorders inolder age, there are pros of searching health information on
the web. However, older adults could be harmed by fakeinformation on the web if they lack adequate skills to identify
the correct source and facts. Therefore, being eHealth literate
is essential, and exploring the factors associated with older
adults’ eHealth literacy is crucial.
Extensive research has shown that age, sex, level of
education, marital status, economic conditions, and social
support contribute to the eHealth literacy of older adults[13]. Arcury et al [14] stated that internet use is positively
associated with eHealth literacy. Little published data exist
on the relationship between social media and eHealth literacy,
especially in older populations. In addition, having a goodcommand of eHealth literacy skills enhances the physical,
mental, and social well-being of older people [15]. Higher
eHealth literacy skill is a protective factor against cognitive
decline and is positively associated with health-promoting
behaviors and better health outcomes [16,17]. There havebeen few attempts to investigate the impact of eHealthliteracy on perceived health status and the use of digital
devices and the internet for health, particularly in the older
population. The need for scientific literature in this field
is more urgent in South Korea, which is becoming a superaged society, and the proportion of older adults is expected to be as high as 44% in 2050 [18]. Additionally, theMinistry of Health and Welfare in South Korea started apilot project to use artificial intelligence and the Internet
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of Things to improve the health care of older adults [19].
Advanced technology is readily diffused into the daily livesof older Korean adults, and knowledge of embracing eHealthinformation becomes inevitable. Therefore, this study aims tofill the gap by exploring the factors associated with eHealthliteracy in community-dwelling older Korean adults and theimpact of eHealth literacy on health-related outcomes anddigital technology use for health purposes.

Methods
Participants, Setting, and Data Collection
It is a community-based survey conducted in 2 urban cities of
the Republic of Korea, Wonju-si and Yeoju-si, in 2022. Thiscross-sectional study is part of the digitally inclusive healthy
aging communities study, a cross-cultural study in 4 rapid
aging countries: Japan, the Republic of Korea, Singapore,
and Thailand [20]. After obtaining ethics approval, participation in the study was announced in the study area. It
was accomplished through the cooperation of local seniorwelfare centers and senior citizen centers. Community-dwelling older adults aged 65 years and older, both male andfemale adults with ongoing health promotion activities inresiding communities, were included in the study.
The sample size was calculated by 1 sample estimation of
proportion in Stata SE (version 16.0; Stata Corp) based on
the proportion of internet use among older adults in South
Korea. The calculated sample size was inflated by 20% for
nonresponses. A sample of 444 participants responded tothe questionnaire, and 434 were analyzed after excluding 10
participants who were younger than 65 years.

Ethical Considerations
The Juntendo University Ethical Committee (approvalE22-0057-M01) and Yonsei University Institutional Review
Board (approval 1041849‐202304SB-073-02) approved theethics of the digitally inclusive healthy aging communities
study. Participation in the study was completely voluntary. The study’s purposes and procedures were thoroughly
explained, and written informed consent was obtained.
Data were collected using a structured questionnaire in
the participants’ native language (Korean). The data wereanonymized and identifiable features were not included. The
participants were compensated with a financial incentive
equivalent to US $10 for answering the questionnaire.

Measures
Demographic Characteristics
Demographic characteristics of age, sex, education, living
arrangements, and financial status were investigated. Age
was described as a continuous variable for distribution anddivided into 4 categories (65-70, 71-75, 76-80, and >80 years)
for eHealth distribution and regression analysis. Education
was asked for the highest level of completed education, with
4 groups (did not go to school, primary school completed,junior high school completed, and high school and above).
For living arrangements, the participants were asked whether
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Kyaw et al
they lived alone or with someone and categorized into living
alone, living only with a spouse, and living only with children
or grandchildren. Financial status included average income
per month based on the 4 income quartiles in South Korea,
which is divided into 2 groups of low (less than or equal to 1
million Won; a currency exchange rate of ₩ 1=US $0.0008
is applicable) and high (more than 1 million Won) and adichotomous question on receiving a pension of any type.

Internet Use and Social Media Use
We determined internet use by a dichotomous question about
the internet use derived from the internet environment anddigital devices used to access the internet. The frequency isthen determined by number of hours per day and number ofdays per week spent on the internet. Negative responses inthe dichotomous question, 0 hours and 0 days of using theinternet, were defined as internet nonusers. Positive responses
in the dichotomous question, more than 0 hours and 0 days ofusing the internet, were defined as internet users. Engagementin 1 or more social media platforms prevalent in South Korea
determined social media use.

Health-Related Outcomes and eHealth Use
Self-perceived health status was measured using a single-item
4-point Likert scale (1=very healthy to 4=not healthy at all).
A single-item 4-point scale has been used to reflect the
subjective health status of community-dwelling older adults
in longitudinal and cross-sectional studies and to predictmortality, health outcomes, and digital use among older adults[21-24]. Annual medical checkup was measured using a
5-point Likert scale ranging from 1=never to 5=always, andthe participants were asked how frequently they participatedin regular medical checkups. The technology used in healthwas measured by how often participants used the internet
and digital technology to improve eating habits and accesshealth care and long-term care services. The scale was a
5-point Likert scale ranging from 1=never to 5=always. For
analysis, annual medical checkups, digital technology, andinternet use for health purposes were regrouped according totheir participation and frequency of use as never or nonuser,
low participation or low user, and frequent participation or
frequent user.

eHealth Literacy
eHealth literacy was measured by an 8-item 5-pointed
eHealth Literacy Scale (eHEALS) developed by Norman
and Skinner [25]. The scale was developed using the Lilymodel of eHealth literacy, which consisted of 6 aspects(healthy, traditional, information, scientific, media, andcomputer literacy). The scale tends to measure perceived
skill and comfort with eHealth rather than the actual skill
itself [25]. Although eHEALS was primarily developed forusing computers for health purposes, it has been validated with the use of mobile devices and social media andshows good reliability [26,27]. In this study, the scale has a
Cronbach α reliability coefficient of 0.99, which indicateshigh internal consistency and reliability. For distribution,eHealth literacy is categorized into 3 groups: lack of eHealthliteracy (eHEALS 8‐15.9), low to moderate eHealth literacy
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Kyaw et al(eHEALS 16‐31.9), and high eHealth literacy (eHEALS
32‐40) [28].

Results

Statistical Analysis

Overview

The statistical analysis was done by using Stata SE (version
16.0; StataCorp). Sociodemographic characteristics, the useof the internet and social media, eHealth literacy, self-perceived health, annual medical checkups and eHealth use for
improving eating habits, and access to health care services
and long-term care services were described by descriptive
statistics. Frequency and percentage were used to describe
categorical data and mean and SD for continuous data.
The normality of the data was checked by using the Shapiro-Wilk test. The distribution of eHealth literacy acrosssociodemographic factors, internet, and social media use were
described by descriptive statistics. A Kruskal Wallis H test
and Mann-Whitney U tests analyzed the difference in meanscores.

The mean age was 76.8 (SD 6.6) years, and participants 80
years and older occupied 30% (131/432) of the total sample.
Of the total sample (N=434), 315 (72.6%) were female, and
192 (44%) participants of the sample had primary school
education or lower. A total of 136 (31.5%) older people lived
alone, 341 (78.6%) had monthly income lower than or equal
to 1 million Won, and 385 (88.7%) received a pension (Table1). Regarding digital technology use (N=434), 208 (47.9%)
participants used the internet, and 184 (42.4%) engaged insocial media. The mean days of internet use in a week were
2.4 (SD 2.9), and the mean hours of internet use in a day
were 0.8 (SD 1.4). Over one-third of the sample population
spent more than 3 days a week (142/404, 35.2%) and 1‐2hours a day (155/404, 38.4%) on the web. The mean ofthe eHEALS score was 15.4 (SD 10.8). Over half of theparticipants (289/434, 66.6%) lacked eHealth literacy, and 97(22.4%) had high eHealth literacy (Table 2).

Ordinal logistic regression was used to identify factors influencing eHealth literacy. Univariate ordinal logistic
regression treated eHealth literacy as the dependent variable
and age, sex, education, monthly income, pension receiving
status, living arrangement, internet use, and social media
use as independent variables. The statistically significant
variables (P<.2) and conceptually relevant variables were
included in the multivariate analysis. Association wasreported as an adjusted odds ratio (aOR) and 95% CI.
Statistical significance is defined as a P value ≤.05 with a
95% CI. Age, sex, income, and education were included ascovariates in the multivariate analysis.
Furthermore, multivariate analysis of variance was applied
to assess the difference in health-related outcomes such as
perceived health status, annual medical checkup, and eHealthuse to improve eating habits, access to health care, andlong-term care services among the different orders of eHealthliteracy. The covariates for the impact of eHealth literacy onhealth outcomes and eHealth use were age and sex.

Regarding health-related outcomes, almost every
participant had an annual medical checkup (n=423, 97.9%).
Over half of the participants (n=242, 55.8%) reported not
being in good health. Regarding eHealth use, 353 (81.3%)
participants did not have experience using digital technology
to improve eating habits. A total of 350 (80.6%) and 370(85.3%) users did not use digital technology to access healthcare services or long-term care services, respectively (Table
2).
Table 3 shows the distribution of eHealth literacy acrossdifferent groups of sociodemographic factors and digitaltechnology use. The results showed significant differences inmean eHEALS scores across all variables except for receiving
pension. The level of eHealth literacy descends with older age
and ascends with daily time spent on the web (Figure 1).

Table 1. Sociodemographic characteristics and digital technology use in older Korean adults (N=434).
Age (years)
65‐70
71‐75
76‐80
Sex

>80

Male

FemaleEducation

Values, n (%)
94 (22)108 (25)
99 (23)131 (30.3)
119 (27.4)
315 (72.6)

Did not go to school70 (16)

Secondary school

93 (21)Primary school

High school and above

Living arrangements

https://aging.jmir.org/2024/1/e56061

Mean (SD)76.8 (6.6)—a—

124 (28.6)
147 (33.8)—

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Values, n (%)

Living alone

136 (31.5)

Living with a child or grandchild55 (13)

Living with spouseMonthly incomebLess than or equal to 1 million Won
More than 1 million Won

Pension receiving status
Not receiving

Receiving pension

Internet use

Nonuser
User

Days of internet use per week

241 (55.8)

341 (78.6)
93 (21)49 (11)385 (88.7)
226 (52.1)

208 (47.9)

0 days223 (55.2)

More than 3 days

142 (35.2)

1 to 3 days

Hours of internet use per day

219 (54.2)

2 hours

63 (15)

3 hours and above

Social media use
Nonuser
User

aNot applicable.
bA currency exchange rate of ₩ 1=US $0.0008 is applicable.

Low to moderate eHealth literacy (16‐31.9)
High eHealth literacy (32-40)

Perceived health status

30 (7)

250 (57.6)
184 (42.4)

Values, n (%)
289 (66.6)
97 (22.3)

65 (15)

Not very healthy

150 (34.6)

Unhealthy

Annual medical checkup

92 (21.2)
9 (2.1)Frequent participation

389 (89.6)

Digital technology and the internet use to improve eating habits

353 (81.3)

Frequent user

28 (6.5)

Digital technology and the internet use to access health care
Nonuser

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0.8 (1.4)—

Mean (SD)
15.4 (10.8)—a—36 (8.3)

NonuserLow user

2.4 (2.9)

127 (29.2)

Never

Low participation—

48 (11.1)

Very healthy

Moderately healthy—

92 (23)

Table 2. eHealth literacy, health-related outcomes, and eHealth use distribution in older Korean adults (N=434).
eHealth Literacy Scale
Lack of eHealth literacy (8‐15.9)—

39 (10)0 hours
1 hourMean (SD)—53 (12.2)

350 (80.6)—

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Values, n (%)

Low user

Mean (SD)55 (12.7)

Frequent user

29 (6.7)

Digital technology and the internet use to access long-term care services
Nonuser—

370 (85.3)

Low user
Frequent user

53 (12.2)
11 (2.5)

aNot applicable.

Table 3. eHealth literacy distribution across sociodemographic factors, internet use and social media use in older Korean adults (N=434).
Age (years)

eHealth Literacy Scale score, mean (SD)65‐70

26.9 (9.7)

76‐80

11.8 (8.3)

71‐75

Sex

Education

>80

Male

FemaleLiving arrangement

Pension receiving status

Internet use

18.1 (11.7)
14.5 (10.4)

Secondary school

13.6 (9.8)

High school and above

23.0 (11.5)
12.8 (9.1)

Living only with child or grandchild14.6 (10.3)
14.3 (10.2)

Yes

15.5 (10.9)

No

NonuserDays of internet use per week

19.8 (12.1)

15.2 (10.2)
8.3 (2.7)

23.2 (11.1)

0 days8.3 (2.4)

More than 3 days

24.7 (10.8)

1 to 3 days
Hours of internet use per day

8.1 (1.1)

2 hours

26.1 (9.9)

3 hours and above
Nonuser
UseraA currency exchange rate of ₩ 1=US $0.0008 is applicable.
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.002

<.001

.97

<.001

<.001

23.2 (9.6)

0 hours
1 hour<.001

17.0 (11.6)

Less than or equal to 1 million Won
More than 1 million Won.002

10.4 (6.7)

Living alone

User

Social media use

9.2(4.5)

11.2 (7.7)

Living only with spouseMonthly incomea

16.6 (11.3)

Did not go to school
Primary school

P value
<.001

<.001

22.6 (10.7)
25.4 (11.2)
8.6 (3.2)
24.9 (10.6)<.001

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Figure 1. Level of eHealth literacy of different age groups by different categories of daily time spent on the web among older Korean adults.
eHEALS: eHealth Literacy Scale.

Social and Digital Determinants ofeHealth Literacy
Ordinal logistic regression predicted eHealth literacy fromsociodemographic factors and the use of digital technology.
eHealth literacy was associated with age, income, internet use, and social media use, while other variables were
controlled. Compared to the 65‐ to 70-year age group, theodds of having high eHealth literacy were decreased by 76%(aOR 0.24, 95% CI 0.1‐0.6) for the 76‐ to 80-year age groupand 88% (aOR 0.12, 95% CI 0.03‐0.42) for the >80-year age
group. A monthly income of more than 1 million Won permonth increased the odds of having a higher eHealth literacy

2.8 times (aOR 2.8, 95% CI 1.35‐5.8) than a monthly incomeof less than or equal to 1 million Won. Univariate analysis
showed that internet users had significantly higher eHealthliteracy (odds ratio 108.57, 95% CI 42.63‐276.54; P<.001).
In multivariable analysis, hours spent on the web daily weresignificantly associated with eHealth literacy. Users who
spent an hour per day had 19.2 times (aOR 19.2, 95% CI
2.33‐158.81; P=.006), 2 hours per day had 25.7 times (aOR
25.72, 95% CI 2.83‐233.47; P=.004), and 3 hours and more
had 35.3 times (aOR 35.39, 95% CI 3.70‐338.63; P=.002)
higher eHealth literacy than nonusers of the internet. Social
media users had 3.9 times (aOR 3.97, 95% CI 1.02‐15.43;
P=.04) higher eHealth literacy than nonusers (Table 4).

Table 4. Univariate and multivariable analysis of factors associated with eHealth literacy in older Korean adults (n=400)a.
Univariate analysis, ORb (95% CI)

Multivariable analysis, aORc (95% CI)

0.18d (0.11‐0.32)

0.53 (0.24‐1.17)

0.02d (0.009‐0.04)

0.12 (0.03‐0.42)

Female0.52d (0.34‐0.78)

0.61 (0.32‐1.18)

Primary school

0.78 (0.34‐1.79)

0.39 (0.09‐1.60)

High school and above

11.12d (5.44‐22.73)

1.46 (0.39‐5.47)

Independent variables
Age (65‐70 as reference)
71‐75
76‐80
>80

Sex (male as reference)
Education (did not go to school as reference)
Secondary

0.06d (0.03‐0.12)

2.01 (0.91‐4.41)

Income (less than or equal to 1 million Won as reference)e

0.24f (0.09‐0.59)
f

0.99 (0.26‐3.76)

fMore than 1 million Won

2.66d (1.70‐4.16)

2.8 (1.35‐5.81)

No

1.01 (0.56‐1.82)—g

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Univariate analysis, ORb (95% CI)

Multivariable analysis, aORc (95% CI)

Living with child and grandchild

2.28d (1.44‐3.61)

1.49 (0.77‐2.93)

1.22 (0.61‐2.44)

User

108.57d (42.63‐276.54)

7.3 (0.59‐89.67)

1 to 3 days

108.4d (37.99‐309.31)

1.52 (0.26‐8.74)

Independent variables
Living status (living alone as reference)
Living with spouse

Internet use (nonuser as reference)

1.02 (0.35‐2.91)

Days of internet use per week (0 days as reference)
More than 3 days

157.93d (60.25‐413.95)

1.45 (0.26‐7.94)

1 hour251.88d (59.01‐1075.19)

19.23f (2.33‐158.81)

3 hours and above

589.94d (120.99‐2876.56)

Hours of internet use per day (0 hours as reference)
2 hours

Social media use (nonuser as reference)
Userf

530.87d (119.98‐2348.9)

25.72f (2.83‐233.47)

87.34d (42.56‐179.26)

3.97h (1.02‐15.43)

35.39 (3.7‐338.63)

aModel parameter: likelihood ratio (LR) χ2 = 390.6 (P<.001), pseudo r2=0.35, LR test of proportionality of odds=199.07 (P=.78).
17bOR: odds ratio.
caOR: adjusted odds ratio.
dP<2.
eA currency exchange rate of ₩ 1=US $0.0008 is applicable.
fP<.01.
gNot applicable.
hP<.05.

Impacts of eHealth Literacy on HealthRelated Outcomes and eHealth Use
Self-perceived health was significantly better in participants
with higher mean eHealth literacy scores (very healthygroups: mean 22.2, SD 12.0 and unhealthy groups: mean
11.6, SD 8.1; P<.001). Participants with higher mean eHealthliteracy scores attended annual medical checkups frequently.
Regarding the use of digital technology for health, frequent
users had a statistically significant higher mean in all 3
measures: the use of the internet and digital technology for
improving eating habits (mean 25.4, SD 11.8; P<.001), accessto health care services (mean 28.4, SD 10.4; P<.001), andlong-term care services (mean 23.3, SD 14.1; P=.004; Table
5).
The influence of eHealth literacy on health-related
outcomes was analyzed using multivariate ANOVA (Table
6). The result showed eHealth literacy had an effect at
the multivariate level on self-perceived health status, digitaltechnology, and internet use to improve eating habits toaccess health care and long-term care (P<.001). In multivariate analysis, the eHealth literacy level did not significantly
influence participation in annual medical checkups.

Table 5. eHealth Literacy Scale (eHEALS) and different levels of health outcomes and digital technology use for health in older Korean adults(N=434).
Health outcomes and eHealth use
Perceived health status
Very healthy

Moderately healthy
Not very healthy

Annual medical checkup

Unhealthy

22.2 (12.0)
12.1 (8.8)
11.6 (8.1)
9.9 (4.4)

Frequent participation

16.1 (11.1)

Use of digital technology and the internet to improve eating habits.004

10.0 (6.0)

Nonuser

14.3 (10.4)

Frequent user

25.4 (11.8)

Low user

P value<.001

18.8 (11.6)

Never

Low participation

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eHEALS score, mean (SD)<.001

17.9 (10.6)

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Health outcomes and eHealth use

eHEALS score, mean (SD)Use of digital technology and the internet to access health care
Nonuser

14.3 (10.4)

Frequent user

28.4 (10.4)

Low user<.001

16.4 (9.6)

Use of digital technology and the internet to access long-term care
Nonuser.004

14.8 (10.6)

Low user
Frequent user

18.4 (10.9)
23.1 (14.1)

Table 6. The influence of eHealth literacy on health outcomes and eHealth use in older Korean adults (n=430).

Self-perceived healthstatuseHealthliteracy (β)
95% CIaP<.001.

Annual medical
checkup

P value

Digital technology use toimprove eating habits

Digital technology use toaccess health care services

Digitaltechnology use
to access Longterm careservices−0.018a0.001

0.016a0.019a0.008a−0.027 to −0.008−0.003 to 0.004

0.01 to 0.02

0.013 to 0.025

0.004 to 0.013

Discussion
Principal Findings
Competent in eHealth literacy skills is a characteristic ofolder people with healthy behaviors and those with better
health knowledge and attitudes [15]. This study fills thegaps in eHealth literacy evidence by identifying social anddigital determinants of eHealth literacy in community-dwelling Korean older adults and its consequential impact on
subjective health status and digital technology application forlifestyle improvement and health needs.
In this study, older age, monthly income of lower or equal
to 1 million Won, internet nonusers, and those not engaged
in social media have a higher risk of having poor eHealthliteracy (Table 4). Furthermore, the findings indicated thateHealth literacy has a significant effect on self-perceived
health and eHealth use. It was also associated with lifestyle
behaviors using digital resources for promoting health (Tables
5 and 6), such as improving healthy eating habits. Using
smartwatches in physical activity promotion is an ideal recent
example globally [29]. Additionally, eHealth literacy enabledolder persons to seek health care services digitally and accesslong-term care services digitally (Tables 5 and 6). Integrating
all these findings, we can highlight the significant influenceof eHealth literacy upon general well-being, self-care, andopportunity to use health and social care services and healthpromotion through multiple evidence. The findings in otherrecent publications agreed with our findings [30,31].
The eHealth literacy of community-dwelling older Korean
adults is relatively low, and 66.6% (n=289) need support
and facilitation to improve their skills (Table 2). Since
eHealth literacy is a predictor of eHealth information seeking,the disparity could result in inequity in health information
https://aging.jmir.org/2024/1/e56061

access. Incompetency in eHealth literacy skills in older adults
reflects lower self-efficacy in the effective use of electronically delivered information and services and the risk of
exclusion from these services [10]. Therefore, regarding
the country’s high-paced demographic and digital transition,urgent interventions in eHealth literacy promotion targetinghigh-risk groups should be prioritized to ensure older adults’
healthy and inclusive aging.
The effect of age on eHealth literacy varies among
different age groups (Table 4). The skill increases with age
in young and middle-aged adults and declines with age inolder adults [32,33]. Younger older adults are more likely tohave better eHealth literacy than those who are older. Thismay be due to an age-related decline in physiological andcognitive ability to access eHealth information. A qualitative
study stated that younger older adults adopt digital technology
considering literacy, benefits, and support from relatives andfamilies, while those who are older are more likely to adopt
with support from friends and relatives [34]. Therefore, older
adults are not homogenous, and improving eHealth literacy
in older adults should consider age-specific interventions
tailored to the needs of different older adults.
Previous studies have stated that economic status has
a significant association with eHealth literacy, which is
concurrent with this study [35]. Older adults with higher
incomes are more likely to adopt digital devices and havemore autonomy in internet use [36]. Lower-income older
adults may face poor access to digital technology, leading
to skill disparity. As South Korea ranked highest in old
age poverty among Organization for Economic Co-operation
and Development countries [37], the relationship between
financial security, social support for older adults, and eHealthliteracy needs in-depth investigations to prevent the potential
loss of the silver economy in a digitalized society.
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JMIR AGING
Regarding other socioeconomic factors, there is inconsistency in the relationship between sex and eHealth literacy,
and this study did not find any significant differences among
different sex (Table 4). Level of education is a significant
predictor of eHealth literacy in several studies [33,38].
However, this study did not find favorable relationships
between education level and eHealth literacy, except thatthose with a high school education or above were more likely
to have higher eHealth literacy in univariate analysis (Table
4).
Internet and social media use had a strong association
with eHealth literacy. The frequency and time spent on theinternet lead to different levels of eHealth literacy amongolder people, and those spending 3 hours or more have 35
times higher eHealth literacy than nonusers (Table 4). Theinternet penetration of older people is around 90% of thegeneral population [39] in South Korea, which means that
Korean older adults have favorable environmental access tothe internet, and they are more likely to adopt digital devices[40]. In addition, having confidence in using the internet
promotes the use of the internet for seeking health information and thus improves eHealth usability [41]. Therefore,
comprehensive internet coverage serving as a foundation,
boosting older adults’ self-efficacy with digital technology
and comfortability with eHealth skills, is a promising way tonarrow the usability gap.
The eHEALS was developed before the widespread useof social media, and there were few studies regarding thevalidity of eHEALS on social media use. This study showedthat social media users had 3.97 times higher eHealth literacy
than nonusers (Table 4), which is concurrent with Tennant
et al’s [42] findings. The diffusion of social media into
people’s daily lives has become a channel to distributehealth information and promote health, especially during the
COVID-19 pandemic. South Korea and China have effectively used social media for disease notification, updating
health information, and promoting preventive behaviors[43,44]. People who use social media frequently are more
likely to keep abreast with updated health information, havebetter eHealth literacy, and be able to adopt healthy behaviors.
This study also found that eHealth literacy significantly
impacts the self-perception of health (Table 6). Better levels
of eHealth literacy are related to better-perceived healththrough access to quality health information and prompt andadequate health-related decisions [45,46]. Healthy behavior,

Kyaw et alsuch as participation in annual medical checkups, is not
associated with eHealth literacy in this study population. The
study population’s participation in annual medical checkups
is 90% (Table 2), higher than the national level of 80.3%
in the group older than 40 years [47]. Due to the well-established health screening programs and high participation, thisstudy could not find a significant impact of eHealth literacy
on participation in health screening programs.
In addition, this study found that eHealth literacy
influences digital technology use for health and care purposes(Table 6). People with better eHealth literacy can navigate
the required information correctly, whereas people with poor
eHealth literacy skills lack eHealth self-efficacy and posea barrier to the adoption of eHealth services. Moreover, ahigher eHealth literacy level increases the positive impact
on perceived usefulness and ease of use in technology andfacilitates the adoption of digital health technology [45].
Recently, a municipality in South Korea has introduced an
artificial intelligence–featured call to prevent social isolationin the older population [46]. Such development affirmed that
digital technology would narrow the unmet needs of healthcare and long-term care services in the future, provided thatthe users have the proper knowledge and self-efficacy ineHealth. Therefore, promoting eHealth literacy for the older
population is fundamental in expanding eHealth services andeliminating the gray digital divide.

Limitations
Due to the demographics and aging rate of the sample
population and the location of the study site, generalizing thefindings may underestimate internet use in the older Korean
population. In addition, eHEALS measures perceived eHealthskills rather than the actual performance of using digitaltechnology. The possible gap between perceived skill andactual application of eHealth literacy could not be excluded.

Conclusions
eHealth literacy is an essential skill in the rapidly digitalizing
world. It is important to learn about the factors associatedwith eHealth literacy in community-dwelling Korean older
adults and the impact of eHealth literacy on health-related
outcomes and digital technology use for health purposes.
With the application of the results from this study, interventions to improve the eHealth literacy skills of older adults canbe tailored to high-risk populations and narrow the gap in theusability of eHealth services by older adults.

Acknowledgments
This research was funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology, Grant-in-Aid
for Scientific Research, 21H00795 and 23K20657, entitled “Digitally inclusive, healthy ageing communities (DIHAC): A
cross-cultural study in Japan, Republic of Korea, Singapore, and Thailand” [48].
Authors’ Contributions
MNA, MYK, MY, and YK conceptualized the research. MYK and MNA performed the formal analysis and visualization
and were involved in writing the original draft. MNA contributed toward using the software. MNA, MY, SM, and TNNA
supervised the project. MNA and MY were responsible for funding acquisition. MNA, YK, EWN, MY, HKCM, and TNNA
administered the project, and MYK, MNA, YK, SM, TNNA, EWN, HL, and HKN were responsible for validation. All authors
were responsible for investigation, methodology, writing, reviewing, and editing of the manuscript.
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Kyaw et al

Conflicts of Interest
None declared.
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Abbreviations
aOR: adjusted odds ratio
eHEALS: eHealth Literacy Scale

Edited by Megan O'Connell; peer-reviewed by Ji-Ho Lee, Susanne Javorszky; submitted 06.01.2024; final revised version
received 06.05.2024; accepted 23.05.2024; published 13.08.2024
Please cite as:
Kyaw MY, Aung MN, Koyanagi Y, Moolphate S, Aung TNN, Ma HKC, Lee H, Nam HK, Nam EW, Yuasa M
Sociodigital Determinants of eHealth Literacy and Related Impact on Health Outcomes and eHealth Use in Korean Older
Adults: Community-Based Cross-Sectional Survey
JMIR Aging 2024;7:e56061
URL: https://aging.jmir.org/2024/1/e56061
doi: 10.2196/56061©Myat Yadana Kyaw, Myo Nyein Aung, Yuka Koyanagi, Saiyud Moolphate, Thin Nyein Nyein Aung, Hok Ka Carol Ma,
Hocheol Lee, Hae-Kweun Nam, Eun Woo Nam, Motoyuki Yuasa. Originally published in JMIR Aging (https://aging.jmir.org),
13.08.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to theoriginal publication on https://aging.jmir.org, as well as this copyright and license information must be included.

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