The channels through which people get information about health care are diverse, and include health care professionals, peers, and internet sources (Fox, 2011). Most people still consult with physicians or other health care providers about their health issues as a first choice; however, growing numbers of internet users search for health information online (Fox, 2011). The internet has become integrated into daily life, and people can quickly send, receive, and produce information from a variety of devices, such as smartphones, desktop computers, and laptop computers (Korea Internet & Security Agency, 2018). Thus, the internet is a convenient channel for getting health information in the digital era (Medlock et al., 2015). Consequently, the field of electronic health (eHealth), an “emerging field of medical informatics, referring to the organization and delivery of health services and information using the internet and related technologies,” is becoming widespread (Eysenbach, 2001, p. 1).
Although internet service and information are relatively easy to access and reasonably prevalent, the accuracy and quality of data are not guaranteed (Fast et al., 2013). Moreover, people who search the internet for health information must possess the knowledge, skills, interest, and self-efficacy to use technology; they also must have an understanding of their own health conditions (Zulman et al., 2011). eHealth literacy is “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” (Norman & Skinner, 2006b, p. 2), and it is necessary when electronic resources are used for health-related purposes (Norman & Skinner, 2006b). There have been numerous studies that provide a general perspective of the skills, comfort levels, and knowledge required to use health information (Park & Lee, 2015; Stellefson et al., 2019). eHealth resources are useful for a variety of populations and in many contexts, so assessing not only direct skills of eHealth but also perceived eHealth literacy could help promote internet health information users' confidence and competencies (Norman, 2011).
Previous studies have shown that eHealth literacy fosters healthy behaviors such as exercise, diet, sleep (Hsu et al., 2014; Mitsutake et al., 2016), and cancer-screening practice (Mitsutake et al., 2012). Moreover, eHealth literacy can affect one's treatment decisions (Kim & Son, 2017). Consequently, eHealth intervention studies have increased over the years (Boogerd et al., 2015). In addition, some factors that affect eHealth literacy have been identified, including sociodemographic factors such as age, sex, and education (Aponte & Nokes, 2017; Gazibara et al., 2016; Milne et al., 2015), as well as computer-related factors, such as the number of electronic devices used and the user's internet experience (Richtering et al., 2017; Tennant et al., 2015). However, there has been limited examination of the associated factors of eHealth literacy (Richtering et al., 2017), and few studies have addressed the differences in these factors between young adults and older adults. Young adults are active users of the internet, so they use internet health information for a variety of reasons and actively try to solve health problems based on online information (Ybarra & Suman, 2008). Although older adults often lack the knowledge and skills to use internet health information, they could use these inexpensive and convenient health resources to manage their chronic conditions (Zulman et al., 2011). Young adults are considered a highly connected group, and older adults are considered a vulnerable population of digital inequality (McDonough, 2016). Therefore, the predictors of eHealth literacy could differ between these two age groups (Reiners et al., 2019). Hence, the characteristics and predictors of the target populations' eHealth literacy must be considered during the design and development of appropriate eHealth programs (Nelson et al., 2016).
In the current study, we compared the factors associated with eHealth literacy of young and older adults, including sociodemographic, health, and internet-usage related factors, to obtain more in-depth research through the lens of age stratification.
A secondary data analysis was conducted using existing data to gain secondary perspectives on the data. The original data were collected from November 2017 to February 2018 for a research project that focused on the development of the Korean version of the eHealth Literacy Scale (eHEALS) (Chang et al., 2018).
According to previous studies, eHealth literacy is related to age and internet experience (Neter & Brainin, 2012; van der Vaart & Drossaert, 2017). In South Korea, approximately 100% of teenagers and those in their 20s and 30s use the internet; however, adults age ≥60 are less likely to use the internet (Korea Internet & Security Agency, 2018). For these reasons, participants were recruited through convenience sampling based on age stratification. Inclusion criteria were: (a) ≥65 years of age (older adults) or 18 to 34 years of age (young adults), and (b) individuals who had used the internet at least once in the past 1 month. Participants were excluded if they had speech disorders (excluded from face-to-face surveys only), diagnosed psychiatric disorders (e.g., major depressive disorder, schizophrenia, alcoholism), or evidence of cognitive impairment with self-reported history, as these individuals may be vulnerable or lack the capacity to decide whether to participate in the research (Humphreys et al., 2015). To screen for cognitive impairment, authors (H.R., H.J.K.) conducted a Mini-Mental State Examination; individuals with a score <24 were excluded (Yun et al., 2013).
Information regarding sample size calculation has been detailed in another study (Chang et al., 2018). Because the current study is a secondary data analysis, we conducted a post hoc power analysis to verify the power. The powers were 0.99 (young adults) and 0.98 (older adults) considering an 0.17 effect size (Tennant et al., 2015), an alpha of 0.05, and nine predictor variables.
Sociodemographic Characteristics. Participants' sociodemographic factors, such as age, education level, financial status, the presence of a spouse, and current disease, were investigated. Internet usage characteristics were also examined, such as duration of use and search history concerning internet health information (Jung et al., 2011).
eHealth Literacy. The eHEALS is a self-report instrument designed to assess perceived skills, comfort, and knowledge concerning seeking, evaluating, and using health-related information resources based on social cognitive theory and self-efficacy theory, and not necessarily actual skills (Norman & Skinner, 2006a). This scale comprises eight items measured with a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Total scores range from 8 to 40, with higher scores reflecting higher eHealth literacy. The eHEALS has two additional items about the usefulness of internet information for health-related decision making and the importance of accessibility of internet health resources. These two items involve arousing participants' interest and are not a formal part of the tool; therefore, they are excluded from summed scores according to the tool's guideline. Cronbach's alpha coefficients, concerning internal reliability, were 0.88 in the eHEALS development study (Norman & Skinner, 2006a) and 0.89 in the current study. The principal component analysis supported one factor, which accounted for 56% of variance (Norman & Skinner, 2006a).
Attitudes Toward Internet Health Information. The tool used to measure attitudes toward internet health information comprises five items using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) (Jung et al., 2011). The items include: Internet health information is (1) reliable, (2) helpful, (3) concrete, (4) not accurate, and (5) up-to-date. Total scores range from 5 to 25, and higher scores indicate more positive attitudes toward internet health information. Cronbach's alphas were 0.80 in a previous study (Chang & Im, 2014) and 0.67 in the current study.
Individuals' Perceived Health Status. The questionnaire used to measure individuals' perceived health status comprised 5 items measured with a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree): (1) I think that I am healthy; (2) I am healthier than others of the same age; (3) I seem to get sick more easily than others; (4) I usually take good care of my health; and (5) I take care of my health better than others of the same age. Higher scores indicated better perceived health status. Cronbach's alphas were 0.79 in a previous study (Park et al., 2013) and 0.80 in the current study.
An online survey was conducted with young adults (n = 210), and a face-to-face survey was conducted with older adults (n = 192). Although the web-based survey is suitable for young adults who are avid internet users, it is not suitable for the older adult group (Eysenbach & Wyatt, 2002). Therefore, we collected data using two methods, such as online surveys and face-to-face surveys considering participant preferences and characteristics. The online survey used a Google survey system and young adults were recruited from an internet portal site. Young adults who wanted to voluntarily participate in the study after answering the screening questions about cognitive dysfunction and psychiatric disease history were considered qualified. Potential participants received an email with survey links and an information sheet. Face-to-face surveys for eligible older adults were administered at two older adult welfare centers that are highly accessible by community members. The older adult welfare centers are for various welfare needs, such as leisure activities, hobbies, continuing education, and health management.
The parent study and current secondary data analysis were reviewed and approved by the researchers' Institutional Review Board. Eligible young adults who passed the screening questions received online survey links and a consent form. The research information addressed research purposes and processes, which included that participants could withdraw at any time. All young adults provided informed written consent.
Concerning older adults, we received approval from the directors of the two welfare centers to post recruitment notices on bulletin boards. Written informed consent was obtained from each participant after explaining research purposes and processes. We assured participants that they could withdraw at any time. All personal identification information was replaced with a serial number after completed questionnaires were submitted.
Data were analyzed using SPSS version 23.0. Descriptive analysis using frequencies, percentages, means, and standard deviations was conducted to determine the sociodemographic factors and characteristics of internet usage. Chi-square tests were used to compare the categorical variables between age groups. Independent t tests were used to compare eHEALS survey results, attitudes toward internet health information, and individuals' perceived health status between age groups. Lastly, hierarchical multiple regression analyses were conducted: we entered sociodemographic factors in the first block, health-related factors in the second block, and internet usage-related factors in the third block. We considered personal (e.g., sociodemographic status), situational (e.g., health concerns), and environmental (e.g., access to the internet) contexts to make blocks based on the complexity of eHealth literacy (Levin-Zamir & Bertschi, 2018).
Participants' Sociodemographic Characteristics
Participants' general characteristics and other variables are presented in Table 1. All 210 young adults who wished to participate completed the survey (valid response rate = 100%). Young adults' mean age was 25.5 years (SD = 4.71, range = 18 to 34). One-hundred eighty-eight (89.5%) young adults had at least a college education. Most were financially stable (88.1%), did not have a spouse (81.4%), currently did not have a disease (89%), and had used the internet for at least 6 years (86.7%). Most (90.5%) used smartphones to search the internet. Frequently searched content concerning health information included diseases (84.8%), treatment (69%), and medicine (57.6%).
General Characteristics and Health-Related Variables of Participants (N = 397)
In total, 195 older adults were approached for face-to-face surveys at the two welfare centers; however, 187 participants completed the survey (valid response rate = 95.9%). Older adults' mean age was 73.2 years (SD = 4.78, range = 65 to 86). Two-thirds (63.1%) of older adults had at least a college education. Most were financially stable (71.7%), had a spouse (75.9%), had a disease (57.8%), and had used the internet for at least 6 years (59.4%). One fourth of older adults searched for internet health information using a mobile device. The most frequently searched content concerning health information included diseases (58.8%), health behaviors (36.4%), and treatment (30.5%).
Attitude Toward Internet Health Information and Perceived Health Status
Young and older adults' scores concerning attitudes toward internet health information were 16.49 (SD = 2.54, range = 8 to 22) and 17.04 (SD = 2.41, range = 7 to 25), respectively, which was significantly different (p = 0.029). Older adults held more positive attitudes concerning internet health information. The highest scoring item was Internet health information is helpful at 3.72 (SD = 0.63, range = 1 to 5) and 3.75 (SD = 0.71, range = 1 to 5), respectively. The lowest scoring item was Internet health information is accurate at 2.93 (SD = 0.73, range = 1 to 5) and 2.90 (SD = 0.81, range = 1 to 5), respectively.
A significant difference was found between groups concerning individuals' perceived health status. Older adults perceived themselves as healthier than their younger counterparts (mean = 21.68 [SD = 3.81, range = 8 to 29] and 19.19 [SD = 4.07, range = 7 to 29], respectively; p < 0.001). The highest scoring items were I think that I am healthy among young adults (mean = 4.50 [SD = 0.96, range = 1 to 6]) and I usually take good care of my health among older adults (mean = 4.75 [SD = 0.93, range = 2 to 6]).
Predictors of eHealth Literacy
The average eHealth literacy scores in each group were 30.50 (SD = 4.62, range = 16 to 40) for young adults and 30.95 (SD = 4.17, range = 15 to 40) for older adults, which was not significantly different (t = 1.027, p = 0.305).
Before conducting hierarchical multiple regression analyses, data were examined for multicollinearity of independent variables using the variance inflation factor (VIF) and tolerance limit. VIF (≤1.93 in young adults and ≤1.49 in older adults) and tolerance limit (0.526 to 0.964 in young adults and 0.673 to 0.955 in older adults) met recommended cutoff values (Field, 2013); therefore, multicollinearity was not of concern. Durbin-Watson statistics were calculated to determine autocorrelation. The results were 1.748 in young adults and 2.146 in older adults, indicating no severe autocorrelation.
Results of the hierarchical multiple regression analyses for factors associated with eHealth literacy are summarized in Table 2. Concerning young adults, in the first block, sociodemographic variables explained 3.1% of eHealth literacy (R2adj = 0.031, F[2,203] = 2.639, p = 0.035). Age was negatively associated with eHealth literacy, whereas education was positively associated. The second block, regarding health-related factors, did not significantly increase R2adj (ΔR2adj = 0.016, Fchange = 1.704, p = 0.181). Finally, internet usage-related factors explained an additional 22.8% of eHealth literacy, indicating that positive attitude toward internet health information was associated with better eHealth literacy (ΔR2adj = 0.228, Fchange = 20.966, p < 0.001).
Hierarchical Regression Analysis for Variables Predicting eHealth Literacy
Concerning older adults, in the first block, sociodemographic variables explained 4.8% of eHealth literacy (R2adj = 0.048, F[2,180] = 3.288, p = 0.013), indicating that financial status was negatively associated with eHealth literacy. The second block, regarding health-related factors, did not significantly increase R2adj (ΔR2adj = 0.014, Fchange = 1.343, p = 0.260). Finally, internet usage-related factors explained an additional 20.3% of eHealth literacy, and attitude toward internet health information emerged as the strongest predictor (ΔR2adj = 0.203, Fchange = 16.295, p < 0.001). In sum, attitude toward internet health information and age were predictors of eHealth literacy among young adults, and attitude toward internet health information was the most significant predictor among older adults.
In the current study, we examined the characteristics of young and older adults regarding their health and internet health information use, especially focusing on group differences in eHealth literacy and the associated factors. The most reliable predictor in both groups was individuals' attitude toward internet health information. Age was only a predictor among young adults.
Some factors may have created selection bias in this study. Among the older adults, approximately 63% were highly educated, 71% were financially stable, and 60% were regular technology users. In previous studies, a correlation between education level and digital literacy has been found (van der Vaart & Drossaert, 2017). Moreover, socioeconomic status could also be correlated with digital health literacy, as people with a low socioeconomic status have a deficit of financial resources to obtain devices that provide internet access (Choi & Di-Nitto, 2013). Regarding the eHealth literacy level, older adults' level was comparable to that of young adults in this study. This result was inconsistent with another study that compared the differences between college students and older adults concerning eHealth literacy (Hsu, 2019). In addition, the level of eHealth literacy among older adults in the current study was higher than what was found in a prior study (Milne et al., 2015), but similar to older adult users of social media (Tennant et al., 2015). In fact, older adults in the current study reported eHealth literacy levels even higher than college students and young adults previously reported in some recent studies (Shiferaw et al., 2020). One possible reason for this difference is that we included older adult participants who had used the internet at least once within the past 1 month, so these older participants might have similar characteristics to young adults and regular internet users. In addition, our sample may not be generalizable to all older adults for the reasons mentioned above; however, considering the rapidly growing number of older Korean internet users and that approximately 90% of people have access to the internet (Korea National Information Society Agency, 2018), this result may prove helpful for understanding this group.
Concerning the predictors of eHealth literacy, individuals' attitudes toward internet health information were robust predictors in both age groups. The older adults' proportion of positive attitude was higher than the young adult group in the current study and higher than previously reported in older adults aged >55 years (Jung et al., 2011). Many theorists suggest that attitude is one of the major components affecting behavioral intention, which leads to certain behaviors (Ajzen, 1991; Davis, 1989). In addition, a positive attitude toward technology and internet information foster involvement in technology and the use of internet information (Wong et al., 2014). In this context, a positive attitude leads to frequent searching for internet health information, which can lead to higher eHealth literacy. Therefore, people who hold positive attitudes toward internet health information may be more likely to be health-oriented and have healthy activities than non-users (Park et al., 2013). Because the concerns and preferences of eHealth are usually triggered by an individual's experiences in the health care system (Ware et al., 2017), educators and health care providers, especially gerontological nurses, need to consider this common factor when developing efficient strategies to improve eHealth literacy.
Age was a significant predictor among young adults, which coincides with several earlier studies (Duplaga, 2015; Hargittai et al., 2019; Hofstede et al., 2014); however, it was not a significant predictor among older adults. One possible reason is that although young adults are a highly connected group, the homogeneity of eHealth literacy is not guaranteed. There is heterogeneous digital fluency between people born in the digital era and people who learned to use computers and the internet in adult life (Wang et al., 2013). Owing to the dramatic rise in high-speed internet access in South Korea since the 2000s, people in their 30s may likely differ compared to those in their early 20s concerning digital fluency and eHealth literacy (An, 2014). When a health care provider meets young adult patients who need internet health information or eHealth services, age should be considered in addition to other factors regarding eHealth literacy.
Implications for Clinical Practice
Our findings are inconsistent with the common concerns that older adults hold negative attitudes toward eHealth and display lower levels of eHealth literacy than younger people (Saied et al., 2014). In addition, the predictors of eHealth literacy were similar between young and older adults. Therefore, our study has some practical implications for both older adults and the field of gerontological nursing.
In South Korea, internet usage of people age ≥65 years has increased three-fold compared to 5 years ago (Korea Internet & Security Agency, 2018). In addition, age-related barriers have been weakened through high internet penetration. Older adults have shown increased awareness of the benefits of technology use; hence, like young adults, they also feel positive about the acquired information (Broady et al., 2010). Our study sheds light on older adults' positive attitudes toward internet health information and their high level of perceived knowledge, skills, and self-efficacy of eHealth. Therefore, gerontological nurses could use this information for health education.
In terms of health promotion efforts transferred from offline to online environments, knowledge, skills, and comfort levels of accessing health information using technology are essential to narrow health disparities (Werts & Hutton-Rogers, 2013). Although older adults' internet accessibility is growing quickly in Korea, their competency and practical use are still below that of the general population (Korea National Information Society Agency, 2018). Our results show that older adults have a positive attitude toward internet health information, and they, like young adults, may favor eHealth as their eHealth literacy predictors were similar to those of their young counterparts. However, eHEALS measures perceived skills, not actual skills, so further studies are needed about actual skills in eHealth literacy and health behaviors to prepare strategies for promoting health-related internet use among older adults. Through such research, gerontological nurses may find a way to enhance not only older adults' perceived eHealth skills but also their actual eHealth literacy skills.
The current study had several limitations. First, as we mentioned above, eHEALS, which is a self-report scale, has been criticized for representing self-efficacy, not actual eHealth literacy competencies (Park et al., 2013). A more comprehensive survey tool that measures actual ability to use eHealth according to the various type of applications could help verify adults' actual skills. Nonetheless, eHEALS is one of the most useful tools to determine appropriateness and to provide guidance to people using eHealth (Collins et al., 2012). Furthermore, as eHEALS has been translated into many languages and has been used in different populations and in a variety of contexts (Karnoe & Kayser, 2015), it has the advantage of comparing people in various contexts. Second, the tool for attitudes toward internet health information had a Cronbach's alpha of 0.67 in the current study. Alphas <0.70 are deemed to have questionable internal consistency (Cortina, 1993). Third, people with psychiatric disorders were excluded due to ethical and practical challenges, potentially compromising generalizability of data.
Although previous studies suggest that older adults are a vulnerable population in this modern digital society, our study indicates that older adults hold positive attitudes toward internet health information. Moreover, older adults' eHealth literacy and its predictors are comparable to those of young adults. This study thus bridges earlier research gaps about eHealth. Health care providers should assist older adults in their acquisition of internet health information and service as they do with young adults.
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General Characteristics and Health-Related Variables of Participants (N = 397)
|Variable||n (%)||p Value|
|Young Adults (n = 210)||Older Adults (n = 187)|
| College and above||188 (89.5)||118 (63.1)|
| High school or less||22 (10.5)||69 (36.9)|
| Stable||185 (88.1)||134 (71.7)|
| Not stable||25 (11.9)||53 (28.3)|
|Presence of a spouse||<0.001|
| No||171 (81.4)||45 (24.1)|
| Yes||39 (18.6)||142 (75.9)|
| No||187 (89)||78 (41.7)|
| Yes||23 (11)||108 (57.8)|
| Missing||1 (0.5)|
|Duration of internet usage||<0.001|
| ≥6 years||182 (86.7)||111 (59.4)|
| <6 years||28 (13.3)||76 (40.6)|
|Place to search for internet health information|
| Phone||190 (90.5)||47 (25.1)||<0.001|
| Home||141 (67.1)||156 (83.4)||<0.001|
| Workplace||39 (18.6)||4 (2.1)||<0.001|
| Internet café||6 (2.9)||1 (0.5)||0.126|
| Public office||1 (0.5)||39 (20.8)||<0.001|
|Health information searched|
| Disease||178 (84.8)||110 (58.8)||<0.001|
| Treatment||145 (69)||57 (30.5)||<0.001|
| Medication||121 (57.6)||42 (22.5)||<0.001|
| Diagnostic methods||91 (43.3)||35 (18.7)||<0.001|
| Health behaviors||81 (38.6)||68 (36.4)||0.523|
| CAM||15 (7.1)||40 (21.4)||<0.001|
| Other||17 (8.1)||24 (12.8)||0.167|
|Age (years)||25.5 (4.71) (18 to 34)||73.2 (4.78) (65 to 86)||<0.001|
|KeHEALS score||30.50 (4.62) (16 to 40)||30.95 (4.17) (15 to 40)||0.305|
|Attitude toward internet health information||16.49 (2.54) (8 to 22)||17.04 (2.41) (7 to 25)||0.029|
|Individual's perceived health status||19.19 (4.07) (7 to 29)||21.68 (3.81) (8 to 29)||<0.001|
Hierarchical Regression Analysis for Variables Predicting eHealth Literacy
|Variable||Model 1||Model 2||Model 3|
|Young adults (n = 210)|
| Education level (1 = college and above)||2.465||0.164||2.209||0.028||2.392||0.159||2.154||0.032||1.253||0.083||1.279||0.202|
| Financial status (1 = financially unstable)||0.237||0.017||0.242||0.809||0.468||0.033||0.480||0.632||1.148||0.081||1.340||0.182|
| Presence of a spouse (1 = yes)||0.910||0.077||0.918||0.360||1.212||0.102||1.222||0.223||0.915||0.077||1.049||0.296|
| Current disease (1 = yes)||1.208||0.082||1.155||0.250||1.282||0.087||1.398||0.164|
| Individual's perceived health status||0.184||0.162||2.252||0.025||0.056||0.049||0.759||0.449|
|Internet usage–related factors|
| Place of internet use (1 = mobile)||−0.967||−0.062||−1.016||0.311|
| Duration of internet usage (1 = ≥6 years)||1.408||0.102||1.687||0.093|
| Attitude toward internet health information||0.888||0.485||7.618||<0.001|
|R2adj (ΔR2adj)||0.031||0.047 (0.016)||0.275 (0.228)***|
|Older adults (n = 187)|
| Education level (1 = college and above)||1.389||0.162||1.930||0.055||1.471||0.172||2.055||0.041||0.964||0.113||1.446||0.150|
| Financial status (1 = financially unstable)||−1.520||−0.165||−2.027||0.044||−1.248||−0.136||−1.651||0.101||−0.829||−0.090||−1.217||0.225|
| Presence of a spouse (1 = yes)||−0.640||−0.067||−0.841||0.402||−0.653||−0.068||−0.851||0.396||−0.321||−0.034||−0.470||0.639|
| Current disease (1 = yes)||0.484||0.058||0.737||0.462||0.487||0.058||0.839||0.403|
| Individual's perceived health status||0.177||0.163||2.109||0.036||0.058||0.053||0.755||0.451|
|Internet usage–related factors|
| Place of internet use (1 = mobile)||0.520||0.055||0.843||0.400|
| Duration of internet usage (1 = ≥6 years)||0.861||0.102||1.404||0.162|
| Attitude toward internet health information||0.790||0.461||6.959||<0.001|
|R2adj (ΔR2adj)||0.048||0.062 (0.014)||0.265 (0.203)***|