Factors associated with eHealth literacy focusing on digital literacy components: A cross-sectional study of middle-aged adults in South Korea

Abstract

Background

Digital technology has dramatically changed the world in which we live, and the ability to access and understand information through these new technologies is becoming increasingly important. eHealth literacy is closely related to digital literacy, and some concepts may overlap to a certain extent. Identifying personal and digital-related factors related to eHealth literacy levels in middle-aged adults would be useful for planning tailored interventions and health promotion strategies.

Objective

We aimed to identify the differences in digital literacy and eHealth literacy levels according to demographic features, and to elucidate the factors associated with eHealth literacy in the middle-aged population.

Methods

We conducted a cross-sectional, observational study from 4th to 8th February 2021. A total of 320 South Korean participants aged 40–64 years were recruited and completed an online questionnaire, where demographic features, chronic disease status, frequency of Internet use, digital skills, digital competence, and eHealth literacy were measured. eHealth literacy was measured with the eHEALS. We used multiple regression analysis to elucidate the factors associated with eHealth literacy.

Results

Multiple regression analysis revealed that digital competence was the highest contributor to an individual's eHealth literacy (β = 0.330, P < 0.001), while digital skills was not significantly associated with eHealth literacy (β = 0.086, P = 0.267). In addition, eHealth literacy was positively associated with increasing age (β = 0.258, P < 0.001), female gender (β = −0.118, P = 0.022), and higher education levels (β = 0.114, P = 0.041), while marital state, chronic disease, and frequency of internet use were not significantly associated with eHealth literacy.

Conclusions

Our study provides valuable information on digital literacy and eHealth literacy in middle-aged adults and may be used to guide tailored interventions for improving eHealth literacy. Future studies should consider the differences in digital literacy levels across generations when assessing eHealth literacy or planning digital health interventions.

Keywords

eHealth literacy digital literacy digital skills digital competence middle-aged adults

Introduction

Digital technology has dramatically changed the world in which we live. Currently, 96% of people between 50 and 64 years use the Internet, and 83% have their own smartphones in the USA.¹ In South Korea, 91.9% of the total population use the Internet, 93.1% have their own smartphones, and 91.5% use the Internet on their mobile devices.² In this context, the ability to access and understand information through these new technologies is becoming increasingly important. For example, information and communications technology (ICT) has enabled patients to easily acquire disease-related knowledge or health-related information online, rather than from health personnel on outpatient visits.³ Using mobile devices to search for health-related information is becoming a major trend,⁴ and social media has become a potential channel for transmitting health-related information and knowledge to the masses.^5,6

Following this trend, the ability to properly understand and interpret health-related information on the Internet is becoming an important factor, which is called eHealth literacy.⁷ eHealth literacy is defined as “the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem.”⁷ eHealth literacy was found to be related to an individuals’ sociodemographic characteristics (age,^8–15 gender,^16,17 education level,^9–12 etc.), health-related factors (chronic diseases,^9,10 depression,⁸ perceived health status,¹¹ etc.), and digital-related factors (frequency of ICT use,^8,10,18 computer skills,¹² number of ICT devices,¹¹ etc.). Additionally, interventions to increase eHealth literacy may improve computer/web skills,^19–21 self-efficacy,^21–23 disease knowledge,²⁴ and health-related behavior/decision making.^19–22 As health-related information acquired from the Internet is related to health-related behavioral changes,^25,26 promoting eHealth literacy may help improve an individual's health status.

The term eHealth literacy was first introduced in 2006 which originally consisted of six components: traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy.⁷ However, the digital-related components, such as media literacy or computer literacy, have not been thoroughly evaluated in digital health interventions.²⁷ Additionally, the eHealth literacy scale (eHEALS),²⁸ which is the most frequently used instrument to assess eHealth literacy^27,29 and digital literacy,³⁰ has not been regarded as a tool that fully covers all aspects of digital health technologies.²⁹ Given that technology plays a dominant role in digital health interventions,^27,31 it is important to consider the factors associated with eHealth literacy from a technological standpoint.

The concept of digital literacy was introduced about two decades ago,³² and has generally evolved from a concept solely focused on technical use toward a more comprehensive idea, including cognitive, critical, and responsible perspectives.³³ Although the concept has been widely implicated, it has been described quite poorly in many scientific research papers.^33–35 The terms “digital literacy” and “digital competence” are used interchangeably,^33–35 and although “digital literacy” has been used more often and over a longer period of time, most publications actually do not refer to any previous documents.³³ Conversely, “digital competence” is mostly referred to the DigComp framework published by the European Union.^30,35 In this study, we define digital literacy with the definition proposed in the Digital Literacy Global Framework (DLGF) by UNESCO, because it integrates various governmental and non-governmental frameworks, including DigComp.³⁶ Here, digital literacy is defined as “the ability to access, manage, understand, integrate, communicate, evaluate, and create information safely and appropriately through digital technologies for employment, decent jobs, and entrepreneurship” that “includes competences that are variously referred to as computer literacy, ICT literacy, information literacy, and media literacy.”³⁶ When compared to DigComp, the DLGF additionally includes an “operation of digital devices” area. In this viewpoint, digital literacy can be roughly divided into basic technical skills and the engagement in practices with these digital tools.^34,35,37 The latter concept includes components such as information management, communication, collaboration, content creation, ethics, safety, problem solving, and so on, which is termed “digital competence.”^36,38,39 We follow this view and divide digital literacy into “digital skills” and “digital competence.”

By definition, digital literacy is closely associated with eHealth literacy. However, most studies that examine the relationship between digital literacy and eHealth literacy have either measured digital literacy from a limited scope, such as frequency of ICT use^8,10,18 or use of social media for health information,¹¹ or failed to evaluate the exact concept of eHealth literacy.³¹ Therefore, clarification and detailed examination of such similar concepts are required. Here, we hypothesized that individuals with higher levels of digital literacy components will show higher levels of eHealth literacy (H1), and posed the question: which component of digital literacy is more significantly associated with eHealth literacy? (RQ1)

Age is a critical factor associated with digital literacy^40,41 and eHealth literacy levels.^8–15 In this study, we focused on the middle-aged population. 40.3% of South Korea's current population are middle-aged⁴² and are now entering the older adult phase of life, which may greatly reshape the demographic composition in the future due to the spike in birth rates after the Korean war.⁴³ The middle-aged are unique in that most of them are currently developing health disorders themselves, have additional responsibilities of looking after their aging parents, and have to care for their children, which leads to a greater interest in searching for health-related information.^44,45 Additionally, they show higher education levels and have better economic status compared to older adults, which makes them more adaptive to new cultures and social changes,⁴⁶ including advances in technology.⁴⁷ Here, we introduce the following research question: which demographic and digital-related factors are significantly associated with eHealth literacy in middle-aged adults? (RQ2)

In this study, we aim to (1) identify the differences in digital skills, digital competence, and eHealth literacy levels according to demographic features, and (2) elucidate which demographic factors and digital literacy components are associated with eHealth literacy in the middle-aged population in South Korea.

Methods

Study population

We conducted a cross-sectional, observational study From February 4th to 8th 2021. Middle-aged adults from South Korea aged 40 to 64 years were recruited online by a professional agency (Macromill Embrain, Seoul, Korea), where 1.3 million online participants are available, which maintains a demographic distribution based on census data from the National Statistical Office. First, a weblink or notice was sent to the target audience via email or mobile application. On the starting page, all participants were informed about the purpose of the study via online documentation, which was available for download if needed. Participants voluntarily moved onto the survey by clicking the start button, which was considered equivalent to informed consent. Next, the participants were asked to respond to an online questionnaire. The online survey system did not allow the participants to move on to the next question if an answer was absent or inaccurate, which prevented the collection of incomplete or inaccurate data. The participants were free to drop out at any point of the survey. All personal identifying information was removed from the collected data. This study was approved by the Institutional Review Board of the Seoul National University (No. 2102/002-002).

The minimum sample size was calculated using the G*power 3 program. Considering an intermediate effect size of 0.15, significance level of 0.05, power of 0.95 and number of independent variables of 8, at least 160 samples were needed for proper analysis.⁴⁸

A total of 547 participants initially accessed the survey. Of those 547, two were not eligible based on age, 39 dropped out before completing the survey, and 144 were eliminated due to oversampling. Samples were collected using the proportionate quota sampling method for age and gender, where participants aged between 40 and 49, 50 and 59, and 60 and 64 were set at a 2:2:1 ratio, and men and women were set at a 1:1 ratio. Of the selected 362 people, 42 were excluded due to poor data quality, such as extremely short response time or the same responses for all questions. As a result, 320 cases were included in the final analysis. The response rate was 88.4%.

Measurements

Demographic features

The demographic features including age, gender, educational level, marital status, chronic diseases, and frequency of Internet use were assessed. Chronic disease status was assessed by asking respondents whether they had any chronic diseases or not. Frequency of Internet use was assessed by asking respondents how many days they used the Internet in the past month (range: 0–31 days). For categorization, we considered 30 or 31 days of use as “daily use,” and the remainder as “intermittent use.”

Digital literacy (digital skills and digital competence)

Digital-related factors were measured with questions extracted from a national survey on the digital divide in South Korea (see Supplemental Information).⁴⁹ Each item was rated on a 4-point Likert scale, with scores ranging from ([1 = strongly disagree] to [4 = strongly agree]). According to the 2019 report,⁴⁹ the measures were highly reliable for both digital skills (Cronbach's alpha = 0.956) and digital competence (Cronbach's alpha = 0.911).

Digital skills were evaluated with seven items designed to seek information pertaining to basic skills for using digital devices, especially mobile phones. Some examples are as follows: “I am comfortable with adjusting the settings of display/volume/security/alarm on mobile devices”; “I am comfortable with sending files/photos from my mobile devices to others.” Total scores ranged from 7 to 28, where higher scores represent higher digital skill levels. Internal consistency of the measure was high in this study (Cronbach's alpha = 0.928).

Digital competence was measured with four items on the ability or attitude towards use of digital devices for different purposes such as communication, collaboration, safety, and ethics. Each item was measured with the following questions: “I am comfortable with communicating and collaborating with others to solve problems or accomplish tasks on the Internet”; “I am comfortable with actively sharing my opinions on political/social issues or participating in various activities such as discussions, donations, or volunteering for public issues on the Internet”; “I am comfortable with protecting myself and others from the various risks of using the Internet, such as personal information leakage”; “I am able to use the Internet responsibly by avoiding illegal media use or infringing on the rights of others, and also understand and accept different opinions from others.” Total scores ranged from 4 to 16, where higher scores represent higher digital competence levels. Internal consistency of the measure was high in this study (Cronbach's alpha = 0.877).

eHealth literacy

eHealth literacy was measured with eHEALS, which was developed by Norman and Skinner²⁸ and adapted in Korean by Chang et al.⁵⁰ The scale consists of eight items measuring an individual's perceived skills and comfortability with eHealth. Some examples of items are: “I know what health resources are available on the Internet”; and “I know where to find helpful health resources on the Internet.” The responses were rated on a 5-point Likert scale with scores ranging from ([1 = strongly disagree] to [5 = strongly agree]). Total scores ranged from 8 to 40, where higher scores represent higher eHealth literacy levels. Internal consistency of the measure was high in this study (Cronbach's alpha = 0.926).

Statistical analysis

Data were analyzed using the SPSS (version 22.0; IBM Corp.) software. Differences between groups were analyzed using either Student's t-test or one-way ANOVA, and P values <0.05 were considered statistically significant. Post-hoc analyses were performed using Scheffe's method. Bivariate analyses were conducted using Pearson's correlation coefficients to observe correlations between the measured variables. We performed multiple regression analysis to find the extent to which demographic features and digital-related factors contributed to eHealth literacy. Multicollinearity effects were measured using VIF values, where values lower than 10 were considered insignificant.

Results

Demographic features

Table 1 shows the demographic features of the study participants. The mean age was 51.33 years, and participants were similarly distributed across age and gender. All participants received high school education, while 76.9% received advanced education. The majority of participants had experience of being married, and 35% reported to have had at least one chronic disease. Most of the participants used the Internet on a daily basis (mean of number of days per month = 27.78 days).

Table 1.

Demographic features of the study sample (N = 320).

Variables	Categories	N	%	Mean ± SD
Age (years)	40–49	124	38.8	51.33 ± 7.24
	50–59	129	40.3
	60–64	67	20.9
Gender	Male	157	49.1
	Female	163	50.9
Educational level	High school	74	23.1
	College	211	66.0
	Graduate school	35	10.9
Marital state	Single	40	12.5
	Married	256	80.0
	Divorced	21	6.6
	Widowed	2	0.6
	Other	1	0.3
Chronic diseases	Yes	112	35
	No	208	65
Frequency of internet use	Intermittent use	53	16.6	27.78 ± 6.49
	Daily use	267	83.4

Comparison of measured variables according to demographic features

Descriptive statistics of the measured variables according to demographic features are illustrated in Table 2. People in their 40s showed higher levels of digital skills and digital competence, but lower levels of eHealth literacy as compared to those in their 60s. Digital competence was higher in men than in women, while no significant difference was found between digital skills of men and women. Participants with basic education showed lower level digital skills, digital competence, and eHealth literacy as compared to those who received advanced education. Those who were single showed lower eHealth literacy than those who had a spouse. People with chronic diseases had higher levels of eHealth literacy as compared to healthy individuals. Finally, people who used the Internet daily showed advanced digital skills than intermittent users.

Table 2.

Comparison of measured variables according to demographic features.

Variables	Categories	Digital literacy				e-Health literacy
		Digital skills		Digital competence		e-Health literacy
		Mean ± SD	P	Mean ± SD	P	Mean ± SD	P
Age (years)	40–49	23.79 ± 3.70		12.61 ± 2.18		26.31 ± 5.36
	50–59	22.43 ± 3.64	<0.001	11.83 ± 2.40	0.003	27.35 ± 4.99	0.023
	60–64	21.55 ± 3.94		11.55 ± 2.30		28.45 ± 5.26
Gender	Male	23.17 ± 3.73		12.42 ± 2.18		27.00 ± 5.39
	Female	22.39 ± 3.87	0.070	11.74 ± 2.42	0.009	27.34 ± 5.11	0.559
Education level	High school	21.32 ± 3.90		10.58 ± 2.26		25.64 ± 5.77
	Advanced^a	23.21 ± 3.69	<0.001	12.52 ± 2.16	<0.001	27.64 ± 4.99	0.004
Marital state	Single	22.38 ± 3.32		11.63 ± 1.76		25.20 ± 4.79
	Other^b	22.83 ± 3.88	0.483	12.14 ± 2.40	0.106	27.46 ± 5.25	0.011
Chronic diseases	Yes	22.32 ± 3.43		11.89 ± 2.31		28.14 ± 4.71
	No	23.01 ± 4.00	0.105	12.17 ± 2.33	0.306	26.65 ± 5.45	0.015
Frequency of internet use	Intermittent	21.23 ± 4.00		11.50 ± 2.29		27.44 ± 5.61	.
	Daily	23.04 ± 3.72	0.002	12.18 ± 2.33	0.064	27.13 ± 5.18	0.707

College education or above.

Includes married, divorced, widowed, and separated.

Bivariate relationships between measured variables

Bivariate relationships between subcomponents of digital literacy and eHealth literacy are depicted in Table 3. The results indicated strong positive correlations between digital skills and digital competence, digital skills and eHealth literacy, and digital competence and eHealth literacy.

Table 3.

Bivariate relationships between digital skills, digital competence, and eHealth literacy.

	Mean ± SD	Min	Max	Digital skills	Digital competence	eHealth literacy
Digital skills	22.77 ± 3.82	9	28	1
Digital competence	12.08 ± 2.33	5	16	0.751^a	1
eHealth literacy	27.18 ± 5.24	8	40	0.275^a	0.366^a	1

P < 0.001.

Factors associated with eHealth literacy among middle-aged adults

The results of the multiple regression analysis are presented in Table 4. Age, frequency of internet use, digital skills, and digital competence were included as continuous variables, while gender, educational level, marital state, and chronic disease state were converted into dummy variables. eHealth literacy was positively associated with increasing age, female gender, higher education levels, and higher digital competence. Digital skills were not significantly associated with eHealth literacy. According to VIF values, multicollinearity effects were considered insignificant.

Table 4.

Factors associated with eHealth literacy among middle-aged adults.

Variable	β	95% CI^a	t	P	VIF^b
(constant)		(−0.565, 12.092)	1.792	0.074
Age	0.258	(0.105, 0.269)	4.468	<0.001	1.355
Gender (Male = 1)	−0.118	(−2.297, −0.180)	−2.303	0.022	1.071
Education level (Advanced = 1)	0.114	(0.059, 2.773)	2.054	0.041	1.253
Marital state (Single = 1)	−0.049	(−2.387, 0.827)	−0.955	0.340	1.081
Chronic disease (Yes = 1)	0.074	(−0.365, 1.979)	1.355	0.176	1.196
Frequency of internet use	−0.022	(−0.100, 0.064)	−0.428	0.669	1.075
Digital literacy—Digital skills	0.086	(−0.091, 0.328)	1.112	0.267	2.435
Digital literacy—Digital competence	0.330	(0.390, 1.092)	4.153	<0.001	2.559
	F = 11.845 (P < 0.001), R² = 0.234, Adjusted R² = 0.214, Durbin-Watson = 1.938

CI, confidence interval.

VIF, variance inflation factor.

Regarding our hypothesis and research questions, H1 was partially supported, as digital competence, but not digital skills, was positively associated with eHealth literacy, where RQ1 is also answered. RQ2 is answered, as age, gender, and educational level were positively associated with eHealth literacy in middle-aged adults. All questions are further discussed in the following section.

Discussion

Principal findings

In this study, we observed demographic and digital-related factors associated with eHealth literacy in the middle-aged population. Results of multiple regression analysis revealed that individuals with higher eHealth literacy were older, female, possessed higher educational qualifications, and had higher digital competence levels as compared to those with lower eHealth literacy. Marital state, chronic disease status, frequency of Internet use, and digital skills were not significantly associated with eHealth literacy levels.

The most interesting finding in this study was that digital competence was the highest contributor to an individual's eHealth literacy, while digital skills were not significantly associated with eHealth literacy. This is in line with a previous study reporting that information literacy was positively associated with eHealth literacy.¹² However, computer literacy¹² or frequency of ICT use^8,10,18 was also positively associated with eHealth literacy,¹² which partially disagrees with our results. The contradictory results may be due to differences in the study population, as all of the reports mentioned above evaluate the general adult population. National surveys indicate that rates of Internet use and owning digital devices sharply decrease in older adults,^2,49 and previous studies showed that older adults are less likely to use digital technologies to access health information compared to middle-aged adults.⁵¹ Accordingly, the inclusion of older adults in the study population may affect the relationship between digital literacy and eHealth literacy. In addition, middle-aged adults have an increased need for health information and services, and they rarely face technical problems when searching for and acquiring such information online.⁴⁵ Therefore, in order to develop eHealth literacy in the middle-aged population, merely improving basic skills on how to handle digital devices or increasing the frequency of Internet use may not be the right approach. Instead, working on what to do with these skills, such as communicating with others or using the media more safely and appropriately would actually be more effective. In short, interventions should focus on the “what,” rather than the “how.”

Several studies conducted earlier have supported this notion. A study on cancer survivors reported that patients increased their eHealth literacy by exchanging information through online health communities.¹⁶ Such positive associations with eHealth literacy is considered to be a result of their online interactions with others as a form of social support. Additionally, digital privacy protection skills were found to be key elements in critically evaluating the credibility and security of online health information.⁵² Currently, most interventional studies aiming to increase eHealth literacy levels have targeted older adults and applied skill-based interventions, such as providing training for searching health information online.^{19,21,22,53,54} In addition, only a few studies have included content related to privacy or security in the intervention design.^19,21,22 The results of our study suggest that different approaches should be adopted for middle-aged adults, with a focus on digital competence measures, such as teaching them how to enhance collaboration through the web or discussing privacy and ethical issues pertaining to the use of the Internet. Another example could be teaching how to navigate through misinformation, given that incorrect health information has shown a profound impact on health-related decision making throughout the COVID-19 pandemic.⁵⁵

Our results showed that age was positively correlated to eHealth literacy. Previous studies have reported that younger people have higher eHealth literacy levels in various age groups, such as adults aged over 50,¹¹ over 18 years of age^10,13 or between 20 and 65 years of age.⁹ These conflicting results may be explained by the different characteristics between age groups, as all reports mentioned above include two or more different generations. Since only a few studies focus on a single generation,^21,56 further research is needed to explore the relationship between age and eHealth literacy in specific age groups. Additionally, as most studies seeking to enhance eHealth literacy have focused on older adults, where digital literacy levels are low, future studies should consider the differences in digital literacy levels across generations when assessing eHealth literacy or planning digital health interventions.

The positive correlation between age and eHealth literacy in middle-aged adults may be partially explained by the difference in digital skills. In this study, digital skill levels were lower with increasing age, while previous studies have reported that people with higher digital skills show less trust in online information,⁵⁷ and the level of trust is strongly correlated to online health information seeking behavior,⁵⁸ which in turn is positively associated with health literacy levels.⁵⁹

The results of this study also revealed that women have higher eHealth literacy levels than men and the finding is in conformity with the finding of a previous study on cancer survivors.¹⁶ Although many studies have reported no significant relationships between gender and eHealth literacy,^10–12 women search the Internet for health-related information more frequently than men.^11,44,60 In addition, women seek health information not only for their own health but also for the health of their family members.⁶¹ Considering that the frequency of searching for health-related information is positively associated with health literacy,⁵⁹ women may show higher eHealth literacy levels than men.

In our study, people with higher education levels showed higher eHealth literacy levels, which is consistent with previous studies.^9–12 People with higher education are more likely to use the Internet for productive purposes, such as searching health-related information, purchasing products, and online banking,^62–64 which may result in increased ability and confidence in seeking and understanding health-related information through use of the Internet.

The t-test results from our study indicated that people suffering from at least one chronic disease showed higher eHealth literacy levels as compared to healthy individuals, although regression analysis showed that chronic disease status did not predict eHealth literacy. Disease-related factors have been examined and analyzed in various ways in related studies, which may have led to contradictory results. For example, Neter et al.'s comparative study found a higher frequency of chronic disease occurrences in people with lower eHealth literacy levels,¹⁰ and Guo et al.'s study reported negative correlations between duration of diabetes and eHealth literacy by bivariate analyses.⁹ On the other hand, Tennant et al.'s study reported that perceived health status had a positive effect on eHealth literacy from the results of multiple regression analysis, which contradicts the above stated finding.¹¹ Therefore, it is not clear whether chronic disease status is associated with eHealth literacy, and the effects of perceived health status and disease duration should also be considered in order to investigate an individual's health more comprehensively.

Limitations

This study has several limitations. First, digital literacy is a very broad concept that includes more than the two areas investigated in our study, which may not have been fully investigated. For instance, the DigComp framework divides digital competence into five areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem solving,^36,39 which examine the concept more comprehensively. Therefore, a more detailed questionnaire evaluating the detailed aspects of digital literacy and using them to elucidate the specific factors associated to eHealth literacy may be beneficial in further studies.

Second, since the survey questionnaire was administered online, there is a possibility that the questions might have been misunderstood by the respondents, leading to unintended responses. Additionally, bias toward higher digital literacy levels may have been present, because individuals who are comfortable with using mobile applications would have had better access to the questionnaire. Therefore, use of multiple data collection methods, such as telephone-based interviews or in-person meetings, would provide a better picture of the subject.

Third, the study population showed relatively high educational levels, as all participants had at least a high school diploma. Although the education rates of South Koreans are relatively high due to cultural reasons and educational policies, where 88.7% of adults aged 25 to 64 have finished high school,⁶⁵ our sample may have shown higher levels of digital skills, digital competence, and eHealth literacy compared to the general population. Therefore, further studies should consider a study sample with more diversity, including digital and educational minorities, to yield results which can be generalized.

Finally, we assessed the frequency of Internet usage by the number of days per month, although most respondents used the Internet on a daily basis. Therefore, determining the frequency of use with hours per day, or both, might be more appropriate in future studies, as the use of digital devices is continuously increasing.

Conclusions

This study evaluated the factors associated with eHealth literacy in the middle-aged population, focusing on the specific subcomponents of digital literacy. Middle-aged adults who were older, were female, had received an advanced education, and had higher digital competence levels showed higher eHealth literacy levels. Middle-aged individuals not only have considerable interest in health-related information but are also open to the use of digital devices and media, for which investigating the link between digital-related factors and eHealth literacy, like our study, holds substantial value. Our study provides unique and valuable information on digital literacy and eHealth literacy in middle-aged adults, and may be used to guide tailored interventions for improving eHealth literacy.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076221102765 - Supplemental material for Factors associated with eHealth literacy focusing on digital literacy components: A cross-sectional study of middle-aged adults in South Korea

Supplemental material, sj-docx-1-dhj-10.1177_20552076221102765 for Factors associated with eHealth literacy focusing on digital literacy components: A cross-sectional study of middle-aged adults in South Korea by Jaegyeong Lee and Sunghee H Tak in Digital Health

Footnotes

Contributorship

J. Lee conceptualized the study, collected and analyzed the data, and wrote the manuscript. S.H. Tak supervised the study and provided insights into the study design.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This study was approved by the institutional review board of Seoul National University (No. 2102/002-002).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Guarantor

S.H. Tak.

ORCID iDs

Jaegyeong Lee

Sunghee H Tak

Supplemental Material

Supplemental material for this article is available online.

References

Internet/Broadband Fact Sheet, https://www.pewresearch.org/internet/fact-sheet/internet-broadband/ (accessed 7 April 2021).

2020 Survey on the internet usage. Report no. NIA VIII-RSE-C-20064, 2020. Ministry of Science and ICT; National Information Society Agency.

Shang

Zuo

. Investigating older adults’ intention to learn health knowledge on social media. Educ Gerontol 2020; 46: 350–363.

Choi

Kim

. Predictors of smartphone uses for health information seeking in the Korean elderly. Soc Work Public Health 2018; 33: 43–54.

Liu

. Social Media use, eHealth literacy, disease knowledge, and preventive behaviors in the COVID-19 pandemic: cross-sectional study on Chinese netizens. J Med Internet Res 2020; 22: e19684.

Thai

Gaysynsky

Falisi

, et al. Trust in health information sources and channels, then and now: evidence from the health information national trends survey (2005–2013). In: Hale

Chou

W-YS

Cotten

, et al. (eds) Ehealth: current evidence, promises, perils and future directions. Bingley, UK: Emerald Publishing Limited, 2018, pp. 43–67.

Norman

Skinner

. Ehealth literacy: essential skills for consumer health in a networked world. J Med Internet Res 2006; 8: e9.

Choi

DiNitto

. The digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/internet use. J Med Internet Res 2013; 15: e93.

Guo

Hsing

Lin

, et al. Relationships between Mobile eHealth literacy, diabetes self-care, and glycemic outcomes in Taiwanese patients with type 2 diabetes: cross-sectional study. JMIR Mhealth Uhealth 2021; 9: e18404.

10.

Neter

Brainin

. Ehealth literacy: extending the digital divide to the realm of health information. J Med Internet Res 2012; 14: e19.

11.

Tennant

Stellefson

Dodd

, et al. Ehealth literacy and web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res 2015; 17: e70.

12.

Xesfingi

Vozikis

. Ehealth literacy: in the quest of the contributing factors. Interact J Med Res 2016; 5: 81–93.

13.

Hoogland

Mansfield

Lafranchise

, et al. Ehealth literacy in older adults with cancer. J Geriatr Oncol 2020; 11: 1020–1022.

14.

Mitsutake

Shibata

Ishii

, et al. Associations of eHealth literacy with health behavior among adult internet users. J Med Internet Res 2016; 18: e192.

15.

Hsu

. The effect of age on electronic health literacy: mixed-method study. JMIR Hum Factors 2019; 6: e11480.

16.

Zhou

Wang

. Improving cancer survivors’ e-health literacy via online health communities (OHCs): a social support perspective. J Cancer Surviv 2020; 14: 244–252. 2019 Dec 9.

17.

Huang

Yang

S-C

Chiang

C-H

. The associations between individual factors, eHealth literacy, and health behaviors among college students. Int J Environ Res Public Health 2020; 17: 2108. 2020 Mar 22.

18.

Knitza

Simon

Lambrecht

, et al. Mobile health usage, preferences, barriers, and eHealth literacy in rheumatology: patient survey study. JMIR Mhealth Uhealth 2020; 8: e19661.

19.

Xie

. Effects of an eHealth literacy intervention for older adults. J Med Internet Res 2011; 13: e90.

20.

Xie

. Older adults, e-health literacy, and collaborative learning: an experimental study. J Am Soc Inf Sci Tec 2011; 62: 933–946.

21.

Xie

. Improving older adults’ e-health literacy through computer training using NIH online resources. Libr Inf Sci Res 2012; 34: 63–71.

22.

Nahm

Zhu

Bellantoni

, et al. The effects of a theory-based patient portal e-learning program for older adults with chronic illnesses. Telemed J E Health 2019; 25: 940–951. 2018 Nov 16.

23.

Chu

Huber

Mastel-Smith

, et al. Partnering with seniors for better health": computer use and internet health information retrieval among older adults in a low socioeconomic community. J Med Libr Assoc 2009; 97: 12–20.

24.

Moussa

Sherrod

Choi

. An e-health intervention for increasing diabetes knowledge in African Americans. Int J Nurs Pract 2013; 19: 36–43.

25.

Xiao

Sharman

Rao

, et al. Factors influencing online health information search: an empirical analysis of a national cancer-related survey. Decis Support Syst 2014; 57: 417–427.

26.

Hunsaker

Hargittai

Micheli

. Relationship between internet use and change in health status: panel study of young adults. J Med Internet Res 2021; 23: e22051.

27.

Benny M

Kabakian-Khasholian

El-Jardali

, et al. Application of the eHealth literacy model in digital health interventions: scoping review. J Med Internet Res 2021; 23: e23473.

28.

Norman

Skinner

. eHEALS: the eHealth literacy scale. J Med Internet Res 2006; 8: e27.

29.

Karnoe

Kayser

. How is eHealth literacy measured and what do the measurements tell us? A systematic review. Knowl Manage E-Learning 2015; 7: 576–600.

30.

Kim

, et al. Measurement of digital literacy among older adults: systematic review. J Med Internet Res 2021; 23: e26145.

31.

Neter

Brainin

Baron-Epel

, et al. The third digital divide in the health domain: is internet use for health purposes associated with health benefits? In: Hale

Chou

W-YS

Cotten

(eds) Ehealth: current evidence, promises, perils and future directions. Bingley, UK: Emerald Publishing Limited, 2018, pp.153–175.

32.

Gilster

. Digital literacy. New York: Wiley Computer Pub., 1997.

33.

Spante

Hashemi

Lundin

, et al. Digital competence and digital literacy in higher education research: systematic review of concept use. Cogent Educ 2018; 5: 1519143. DOI: 10.1080/2331186x.2018.1519143

34.

Oberlander

Beinicke

Bipp

. Digital competencies: a review of the literature and applications in the workplace. Comput Educ 2020; 146: 103752. DOI: 10.1016/j.compedu.2019.103752

35.

van Laar

van Deursen

AJAM

van Dijk

JAGM

, et al. The relation between 21st-century skills and digital skills: a systematic literature review. Comput Hum Behav 2017; 72: 577–588.

36.

A global framework of reference on digital literacy skills for indicator 4.4.2. Report no. UIS/2018/ICT/IP51, 2018. UNESCO Institute for Statistics.

37.

Meyers

Erickson

Small

. Digital literacy and informal learning environments: an introduction. Learning. Media Technol 2013; 38: 355–367.

38.

Ferrari

. Digital competence in practice: an analysis of frameworks. Report no. EUR 25351 EN, 2012. Joint Research Centre, Institute for Prospective Technological Studies.

39.

Ferrari

. DIGCOMP: a framework for developing and understanding digital competence in Europe. Report no. EUR 26035 EN, 2013. Joint Research Centre, Institute for Prospective Technological Studies.

40.

Hargittai

Hinnant

. Digital inequality - differences in young adults’ use of the internet. Commun Res 2008; 35: 602–621.

41.

van Deursen

AJAM

Helsper

. A nuanced understanding of internet use and non-use among the elderly. Eur J Commun 2015; 30: 171–187.

42.

Population by Age, https://jumin.mois.go.kr/ageStatMonth.do (2020).

43.

Korean Social trends 2016. 2016. Daejeon: Statistics Research Institute.

44.

Ybarra

Suman

. Help seeking behavior and the internet: a national survey. Int J Med Inform 2006; 75: 29–41. 2005 Sep 1.

45.

LeRouge

Van Slyke

Seale

, et al. Baby boomers’ adoption of consumer health technologies: survey on readiness and barriers. J Med Internet Res 2014; 16: e200.

46.

Kim

Lee

Seo

. An analysis of the middle-aged adults’ mobile information behavior focused on their demographic characteristics. J Korean Soc Library Inf Sci 2015; 49: 335–353.

47.

Liang

Guo

. Social interaction, internet access and stock market participation—an empirical study in China. J Comp Econ 2015; 43: 883–901.

48.

Cohen

. Statistical Power Analysis for the Behavioral Sciences. 2013.

49.

The report on the digital divide. Report no. NIA VIII-RSE-C-19055, 2019. Ministry of Science and ICT; National Information Society Agency.

50.

Chang

Yang

Ryu

, et al. Cross-cultural adaptation and validation of the eHealth literacy scale in Korea. Korean J Adult Nurs 2018; 30: 504–515.

51.

Gordon

Crouch

. Digital information technology use and patient preferences for internet-based health education modalities: cross-sectional survey study of middle-aged and older adults with chronic health conditions. JMIR Aging 2019; 2: e12243.

52.

. Understanding eHealth literacy from a privacy perspective: eHealth literacy and digital privacy skills in American disadvantaged communities. Am Behav Sci 2018; 62: 1431–1449.

53.

Chiu

Lin

, et al. The attitudes, impact, and learning needs of older adults using apps on touchscreen mobile devices: results from a pilot study. Comput Hum Behav 2016; 63: 189–197.

54.

Kim

Xie

. Health literacy in the eHealth era: a systematic review of the literature. Patient Educ Couns 2017; 100: 1073–1082. 2017 Feb 9.

55.

Chong

Cheng

Chan

HYL

, et al. COVID-19 pandemic, infodemic and the role of eHealth literacy. Int J Nurs Stud 2020; 108: 103644. 2020 May 25.

56.

Kim

S-H

Son

Y-J

. Relationships between eHealth literacy and health behaviors in Korean adults. Comput Inform Nurs 2017; 35: 84–90.

57.

Sbaffi

Rowley

. Trust and credibility in web-based health information: a review and agenda for future research. J Med Internet Res 2017; 19: e218.

58.

Wang

Shi

Kong

. Online health information seeking: a review and meta-analysis. Health Commun 2021; 36: 1163–1175. 2020 Apr 16.

59.

Seo

Lee

, et al. Effects of smartphone-based memory training for older adults with subjective memory complaints: a randomized controlled trial. Aging Ment Health 2018; 22: 526–534. 2017 Jan 11.

60.

Alvarez-Galvez

Salinas-Perez

Montagni

, et al. The persistence of digital divides in the use of health information: a comparative study in 28 European countries. Int J Public Health 2020; 65: 325–333. 2020 Apr 10.

61.

Baur

. An analysis of factors underlying e-health disparities. Camb Q Healthc Ethics 2008; 17: 417–428.

62.

Baker

Wagner

Singer

, et al. Use of the internet and e-mail for health care information: results from a national survey. JAMA 2003; 289: 2400–2406.

63.

Hassani

. Locating digital divides at home, work, and everywhere else. Poetics 2006; 34: 250–272.

64.

Howard

PEN

Rainie

Jones

. Days and nights on the internet: the impact of a diffusing technology. Am Behav Sci 2001; 45: 383–404.

65.

OECD. Data warehouse. OECD iLibrary, 2021.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB