ABSTRACT
Aims
Based on the socio‐ecological model, the present study examined influencing factors of eHealth literacy among Chinese older adults at individual‐level (e.g., socio‐demographics, Internet use, and health status), interpersonal (e.g., informational support and instrumental support), and community‐level (e.g., available public facilities).
Design
A longitudinal study was conducted among 611 Chinese older adults aged 55 or over.
Methods
From February to December 2022, older people's eHealth literacy was collected at baseline, 3‐month and 6‐month follow‐up surveys and other variables were collected only at baseline, using online questionnaires.
Results
Among 611 participants, 464 (75.9%) completed both follow‐up surveys. At the individual level, participants who were older, female gender, rural residents, living alone, had lower education, occupational levels and income, and had chronic conditions and long‐term medication showed lower eHealth literacy. Older adults with longer Internet use history, online health information seeking, more frequent health‐related Internet use, higher self‐rated Internet skills and positive perceptions towards online health information exhibited higher eHealth literacy over time. In terms of interpersonal and community‐level factors, perceiving informational and instrumental support when using digital health, and having access to health facilities (e.g., health education) and technological training provided by the community could predict older individuals' higher eHealth literacy over time, after adjusting for covariates.
Conclusion
Older people's eHealth literacy can be influenced by factors at individual, interpersonal, and community levels.
Implications and Impact
This longitudinal study identified significant factors influencing older individuals' eHealth literacy at different levels. Understanding unmodifiable factors at the individual level can help identify the subgroups that may need targeted support and interventions for promoting eHealth literacy. Furthermore, findings can guide tailored interventions to improve eHealth literacy through modifiable factors at the technological, interpersonal and community levels.
Reporting Method
Adhered to the STROBE guidelines.
Patient or Public Contribution
No patient or public contribution.
Keywords: China, eHealth literacy, influencing factors, longitudinal study, older adults, socio‐ecological model
1. Introduction
The rapidly aging population has led to a rising healthcare demand due to the increasing prevalence of health conditions among older adults. Many health conditions require long‐term self‐care, making it imperative for older adults to be well‐informed in managing their health (Lawless et al. 2021). In this digital age, extensive health resources are available on the Internet for public access. Electronic health literacy, refers as the ability to locate, understand, and evaluate health information from electronic resources and apply them to address health problems (Norman and Skinner 2006), has been emphasised, as it enables individuals to effectively use online health resources for health management. Individuals with inadequate eHealth literacy can be less likely to benefit from digital health, exacerbating health disparities. Older adults, as one of the primary populations of healthcare use, remain to face significant challenges with digital health use (Arsenijevic, Tummers, and Bosma 2020). A recent systematic review has indicated that older people's eHealth literacy is closely linked to a range of behavioural, physical and mental health outcomes (Xie et al. 2022). Therefore, to develop effective interventions that enhance eHealth literacy in this population, a comprehensive understanding of factors influencing older people's eHealth literacy is of great importance.
2. Background
Increasing life expectancy and declining fertility rates have led to a rapid shift in the age structure and an increasingly aging population worldwide, presenting great challenges for healthcare services. Older adults have great demands for health information to manage and maintain health. The Internet has well integrated into our daily lives, becoming the most accessible channel for health resources. In Canada, 81% of older adults aged 65 to 74 years and 50% of older adults aged ≥ 75 years were reported to use the Internet for health information in 2016, respectively (Cherid et al. 2020). In China, as of the end of 2023, Internet users among older adults aged 60 or over have reached around 157 million, accounting for 57.4% of this population and 15.6% of total Internet users in China ((CNNIC), C.I.N.I.C 2024). This marks a significant increase from December 2010, when only 4.9% of those aged 60 and over (~8.69 million) were Internet users ((CNNIC), C.I.N.I.C 2011).
Compared to traditional approaches (e.g., medical books, TV channels, or face‐to‐face meeting with healthcare professionals) to obtaining health information, using the Internet for health information has several obvious advantages. First, online health information can be accessed anytime and from any location, which can greatly benefit older people who face physical constraints or have limited access to healthcare services (Perakslis and Ginsburg 2021). Second, the accessibility of extensive and up‐to‐date resources on the Internet expands opportunities for older adults to obtain health information that would be more tailored to their needs. Third, Internet‐based resources can help older individuals with chronic diseases to share their experiences and gather information, allowing them to actively participate in self‐care (Mcbride 2013). However, older adults remain one of the main populations who have the most difficulty in using eHealth technology. Insufficient digital skills, such as navigating through various platforms and conducting effective searches, are barriers to their health‐related Internet use (Chang, McAllister, and McCaslin 2015). More importantly, there is also a concern about false or distorted health information circulated on the Internet, especially on social media. Inappropriate and inaccurate health information can be especially confusing and misleading for older adults, leading to feelings of insecurity, increased anxiety, and even potential harm to their health (Choukou et al. 2022). Hence, despite the substantial benefits of online health information utilisation to older individuals, it should be noted that merely providing them with the access to Internet does not guarantee that they will fully reap its benefits. A huge gap exists between extensive health resources on the Internet and older individuals' skills to effectively utilise them. Therefore, adequate eHealth literacy would be particularly important for older adults.
To help older people bridge the digital divide in this digital age and improve eHealth literacy of this population, it is important to identify the factors which can predict their eHealth literacy. The socio‐ecological model (SEM) is a theoretical framework for understanding the multi‐faceted and interactive effects between individual, interpersonal, and community‐level factors that shape health outcomes. In this model, factors at the individual‐level focus on personal factors (e.g., knowledge, attitude and skills), which emphasises how individual characteristics influence a health outcome. The interpersonal level highlights the role of social relationships and networks on health; and the community level considers the broader social context, looking at how the community resources can impact health at the macro level (McLaren and Hawe 2005). The SEM has been adapted for various health promotion research, including in the digital context (Pan, Urban, and Schüz 2024; Hammond, Polizzi, and Bartholomew 2023).
A recent study identified multi‐level barriers of health literacy on disease prevention and care among patients with the application of SEM (Fenta et al. 2024). This model may also be applicable in guiding the selection of factors related to older people's eHealth literacy. Specifically, at the individual level, it has been shown that personal attributes were associated with higher eHealth literacy among older adults, including: (i) demographics, such as younger age (Liu et al. 2020; Arcury et al. 2020), higher education attainment (Lee, Kim, and Beum 2020), urban residence (LI, Hui‐lan, and Guang‐hui 2019) and higher social‐economic status (Choi and DiNitto 2013); (ii) technological factors, including higher accessibility to the Internet, longer Internet use experience, higher Internet use skills and more frequent Internet use (Arcury et al. 2020; Choi and DiNitto 2013); and (iii) psychosocial factors related to Internet use, including higher confidence in digital use (Lee, Kim, and Beum 2020), less computer anxiety (Arcury et al. 2020), perceived usefulness of online health information (Waterworth and Honey 2018), and trust in eHealth resources (Zulman et al. 2011). In addition to individual‐level factors, contextual influences, such as the interpersonal and community‐level factors based on the SEM, are also likely to impact older people's eHealth literacy. In the digital health era where interactions among Internet users are prevalent, the role of availability of social resources on eHealth literacy is increasingly emphasised (Levin‐Zamir and Bertschi 2018). Social relationships may affect older people's eHealth literacy by providing support, resources and reinforcement. For example, Wong et al. found a positive correlation between perceived social support and eHealth literacy among homebound older adults in Hong Kong (Wong, Bayuo, and Wong 2022). Aponte et al. interviewed 20 older patients with diabetes and found that they often relied on relatives' and friends' help when assessing health information from the Internet (Aponte and Nokes 2017). In addition, community resources, such as public libraries and health facilities offering access to Internet and health education to the public, can impact individuals' ability to engage with digital health (Lin et al. 2021). It should be noted that current research on the relationship between factors in social environment and eHealth literacy is limited, highlighting the need for further exploration of contextual influences on older people's eHealth literacy.
3. This Study
To date, most studies were cross‐sectional design, which might limit the interpretation of the longitudinal impacts of these influencing factors on older individuals' eHealth literacy. To support the development of effective interventions for eHealth literacy enhancement, this longitudinal study, based on the socio‐ecological model (SEM), aims to identify the factors influencing older people's eHealth literacy over time at individual‐level (e.g., socio‐demographics, Internet use‐related variables and health status), interpersonal (e.g., informational support and instrumental support), and community‐level (e.g., public facilities in community).
4. Materials and Methods
4.1. Study Design and Participants
A longitudinal study was conducted among older adults in Jiangxi Province, China, from February 2022 to December 2022. Chinese older adults who were aged 55 or over, had Internet use experience, and had the cognitive ability to complete a self‐report survey were included.
4.2. Study Site
Participants were recruited from Fuzhou City, which is in eastern Jiangxi Province. According to the 7th Chinese national census data, the total population of this city is about 3.6 million, among which older people aged 60 or over accounted for 16.4%, which was a bit lower than the national census (18.7%) (China, N.B.o.S.o 2020). As of 2023, Fuzhou City's GDP per capita lagged slightly behind both the provincial and national average levels (CEIC Data 2023). This study conveniently selected neighbourhoods in urban, town, and rural areas in Fuzhou city for participant recruitment. To conduct the study fieldwork, this study collaborated with local neighbourhood committees. In mainland China, these committees serve as bridges between the residents and local governance, which are responsible for managing community affairs, social services, public facilities, public health initiatives and so on. This collaboration enabled us to access a broader and more diverse pool of potential participants in communities.
4.3. Data Collection
For the eligible participants, the research staff provided them the details of study purposes and logistics. After obtaining their informed consent, a hyperlink was sent to them which was linked to the study questionnaire (shown in the Appendix S1) via social networking sites (e.g., WeChat). Their anonymity and right to withdraw from the study at any time were explained and ensured to participants. So Jump platform was used to administer the online questionnaires, which is a widely used platform for web‐based surveys in China. At both 3 and 6 months after the baseline survey, eHealth literacy was collected again using online questionnaires. The details of loss‐to‐follow were recorded, and participants who completed the follow‐up surveys received CNY20 (=USD2.90) to compensate for their time spent on this study.
4.4. Measures
Background information included age, gender, type of residence, education attainments, job (now or before retirement), monthly income, living arrangement and marital status. Their health status, including whether having any chronic disease and whether taking long‐term medication, was also collected.
eHealth literacy was assessed by the Chinese version of the digital health literacy instrument (C‐DHLI) (Van Der Vaart and Drossaert 2017), which was validated among Chinese older adults by our research team (Xie et al. 2023; Xie and Mo 2023). Example items include: “When you search the internet for health information, how easy or difficult is it for you to make a choice from all the information you find?”. Response options ranged from ‘1 = Very easy’ to ‘4 = Very difficult’ or from ‘1 = Never’ to ‘4 = Often’. Scores need to be reversed, and a higher score represents a higher level of eHealth literacy. Their Cronbach's α values were 0.95, 0.96 and 0.96 at baseline, 3‐month follow‐up and 6‐month follow‐up, respectively.
Factors regarding Internet use, including participants' Internet use history, self‐rated Internet use skills, online health information seeking, frequency of using the Internet for health information, perceived credibility of online health information, perceived importance of Internet use for health information, and perceived usefulness of the Internet in making health decisions, were collected. In addition, the reasons of Internet use for health information (e.g., having symptoms (yourself), having symptoms (your families or friends), disease treatment, disease prevention or self‐diagnosing, health maintenance and passive receipt of online health information) and problems causing difficulty of health‐related Internet use (e.g., lack of operational skills, lack of health knowledge, lack skills to search health information, unfamiliar with health information channels, hard to evaluate online information, limited reading ability) were also investigated.
Informational support when Internet use for health information was evaluated by three items adapted from the informational support subscale of the Krause et al. study (Krause and Markides 1990), such as “Told you what they did in a stressful situation that was similar to the one you were experiencing”, with 4‐Likert responses ranging from ‘1 = Never’ to ‘4 = Very Often’. Higher scores indicated higher informational support the participants received when using the Internet for health information. The Cronbach's α in this study was 0.89.
To evaluate the instrumental support the participants received when using the Internet for health information, three items were adapted from Yeung et al. study (Yeung and Fung 2007): Others (a) were willing to help, (b) gave support in time of difficulty and (c) gave helpful suggestions, when you used the Internet for health information. The 5‐Likert response options ranged from ‘1 = Strongly disagree’ to ‘5 = Strongly agree’, with a higher score indicating higher instrumental support. The Cronbach's α in the present study was 0.92.
Public health services provided by the community in mainland China context were investigated, including health archives management, health education, vaccinations, senior health management, health management for chronic patients and health management for traditional Chinese medicine. In addition, the available digital devices and any technology training related to Internet use or health‐related Internet use provided by the community were also investigated.
4.5. Data Analysis
First, descriptive statistics were reported, using mean and standard deviation (SD), median and interquartile range (IQR), or frequencies and percentages. Second, attrition analysis was performed to compare the differences in demographics between participants who remained in the follow‐up and those who were lost to follow‐up. Third, a series of linear mixed models (LMMs) were employed with maximum likelihood estimation to examine the impact of multi‐level influencing factors on older individuals' eHealth literacy over time, including factors at individual level (e.g., demographics, Internet use, and health status), interpersonal (e.g., informational support and instrumental support), and community‐level (e.g., available public facilities). LMMs allow for the estimation of both fixed effects (e.g., predictors of interest and time) and random effects (individual variations) (Verbeke, Molenberghs, and Verbeke 1997). In this study, LMMs provided more precise estimates of the relationships between predictors and outcome variable over time. Univariate analysis was first to examine the impact of each influencing factor on eHealth literacy. Then, a series of multivariable LMMs were conducted to examine the impacts of health status, technological, interpersonal, and community‐level factors on older individuals' eHealth literacy after adjusting for covariates. Socio‐demographic variables with p‐values less than 0.10 in univariate analyses were included as covariates in multivariable analyses. Adjusted β and its 95% CI of each influencing factor were reported. All analyses were performed using R version 4.1.1 with ‘stats’ and ‘lmerTest’ packages.
4.6. Ethics Considerations
Ethical approval was obtained from the Survey and Behavioural Research Ethics Committee of the Chinese University of Hong Kong (No. SBRE‐21‐0395B). Informed consent was obtained from all individual participants included in the study.
5. Results
5.1. Characteristics of the Participants
A total of 611 participants completed the baseline assessment (T0). Among the 611 participants, 520 (85.1%) and 464 (75.9%) completed the 3‐month follow‐up (T1) and 6‐month follow‐up (T2) surveys. Table 1 shows participants' socio‐demographic characteristics and health status. Among the 611 participants, their median age was 61 years old (IQR: 57–66). More than half of them were female (55.3%), married (86.1%), and lived in a town (63.5%). Nearly half of them (49.9%) had a monthly income between 2500 to 4999 RMB, and more than one‐third (36.3%) worked as government workers or professionals (e.g., doctors, teachers and engineers) before retirement. In addition, 20.3% of them had the primary school level of education or below. For their health status, nearly half (49.8%) reported at least one kind of chronic conditions and 41.9% of them took long‐term medication.
TABLE 1.
Socio‐demographic characteristics and health status of participants (N = 611).
| Variables | Total sample (n, %) |
|---|---|
| Gender | |
| Male | 273 (44.7) |
| Female | 338 (55.3) |
| Age (median, IQR) | 61.00 [57.00, 66.00] |
| Type of residence | |
| City | 132 (21.6) |
| Town | 388 (63.5) |
| Village | 91 (14.9) |
| Living alone | |
| No | 565 (92.5) |
| Yes | 46 (7.5) |
| Education attainment | |
| Primary school or below | 124 (20.3) |
| Middle school | 184 (30.1) |
| High school | 219 (35.8) |
| College or above | 84 (13.7) |
| Occupation (now or before retirement) | |
| Government workers/professionals | 222 (36.3) |
| Trader/labour/farmer/others | 340 (55.6) |
| Unemployed | 49 (8.0) |
| Marital status | |
| Married | 526 (86.1) |
| Widowed | 45 (7.4) |
| Divorced/unmarried/others | 40 (6.5) |
| Monthly income (RMB) | |
| < 2500 | 160 (26.2) |
| 2500–4999 | 305 (49.9) |
| 5000–9999 | 103 (16.9) |
| ≥ 10,000 | 43 (7.0) |
| Having chronic diseases | |
| No | 307 (50.2) |
| Yes | 304 (49.8) |
| Long‐term medication | |
| No | 355 (58.1) |
| Yes | 256 (41.9) |
Table 2 presents the Internet‐use characteristics of participants. The median year of participants' Internet use history was 6 years (IQR: 3–10). Over half of them rated their Internet skills as “Average” (51.9%). Over two‐third had sought health information on the Internet in the last 3 months (68.7%), but the frequency was less than 1 h a week (66.8%). The most common reasons for their health‐related Internet use were having symptoms (themselves) (60.9%), followed by their families or friends having symptoms (42.1%). The difficulties for their health‐related Internet use included hard‐to‐evaluate online information quality (53.2%), lack of skills to search for health information (51.1%), and lack of operational skills (44.4%). Over half of the participants perceived the credibility of online health information, the importance of accessing online health information, and the usefulness of the Internet in making health decisions as “Uncertain”, with the proportions as 74.3%, 63.3% and 52.4%, respectively.
TABLE 2.
Internet use patterns of participants (N = 611).
| Variables | Total samples (n, %) |
|---|---|
| Internet use history (year, Median, IQR) | 6.00 [3.00, 10.00] |
| Self‐rated internet skills | |
| Poor or fail | 239 (39.1) |
| Average | 317 (51.9) |
| Good or excellent | 55 (9.0) |
| Having sought online health information in last 3 months | |
| Yes | 420 (68.7) |
| No | 191 (31.3) |
| Frequency of health‐related internet use (weekly) | |
| < 1 h/w | 408 (66.8) |
| 1–2 h/w | 161 (26.4) |
| 2–4 h/w | 26 (4.3) |
| > 4 h/w | 16 (2.6) |
| Reasons of online health information seeking a | |
| Having symptoms (yourself) | 372 (60.9) |
| Having symptoms (your families or friends) | 257 (42.1) |
| Disease treatment | 110 (18.0) |
| Disease prevention or self‐diagnosing | 167 (27.3) |
| Health maintenance | 199 (32.6) |
| Passive receipt of online health information | 178 (29.1) |
| Never seek information online | 87 (14.2) |
| Difficulties of using the Internet for health a | |
| Lack of operational skills | 271 (44.4) |
| Lack of health knowledge | 261 (42.7) |
| Lack skills to search health information | 312 (51.1) |
| Unfamiliar with health information channels | 181 (29.6) |
| Hard to evaluate online information | 325 (53.2) |
| Limited reading ability | 100 (16.4) |
| Perceived credibility | |
| Very unreliable or unreliable | 79 (12.9) |
| Uncertain | 454 (74.3) |
| Reliable or very reliable | 78 (12.7) |
| Perceived importance | |
| Not important at all or not important | 84 (13.8) |
| Uncertain | 387 (63.3) |
| Important or very important | 140 (22.9) |
| Perceived usefulness | |
| Not useful at all or not useful | 70 (11.4) |
| Uncertain | 320 (52.4) |
| Useful or very useful | 221 (36.2) |
Multiple choice question.
For the public facilities supported by participants' community of residence, as shown in Table 3, only 29.3% reported there were available digital devices public to residents in their community; 84.8% never received any technological training regarding Internet use or health‐related Internet use from their community. 43.7% of participants did not have access to any basic health services in their community. Most reported the available health services as prevention inoculation (44.0%), followed by senior health management and health education (both were 17.2%).
TABLE 3.
Community‐supported health and technology facilities for participants (N = 611).
| Variables | Total sample |
|---|---|
| Available digital devices in your community | |
| Yes | 179 (29.3) |
| No | 432 (70.7) |
| Basic public health services in your community | |
| Health archives management | 99 (16.2) |
| Health education (e.g., healthy lifestyles, medical policy) | 105 (17.2) |
| Vaccinations | 269 (44.0) |
| Senior health management | 105 (17.2) |
| Health management for chronic conditions | 100 (16.4) |
| Health management for traditional Chinese medicine | 24 (2.9) |
| No health services | 267 (43.7) |
| Trainings provided by your community | |
| Internet/technology‐use related training | 41 (6.7) |
| Health‐related Internet use training | 52 (8.5) |
| Neither of them | 518 (84.8) |
5.2. Attrition Analysis
The results of the attrition analysis for participants' socio‐demographics are presented in Table 4. It showed that participants who were lost to follow‐up after 3 months were more likely to live in cities, compared to those who completed the survey at T1 (p = 0.031); such difference was not significant between participants who competed and lost‐to‐follow at T2. Other socio‐demographic variables showed no significant difference between completed and loss‐to‐follow groups at both T1 and T2 (ps > 0.05).
TABLE 4.
Attrition analysis for loss to follow‐up during the study period.
| Variables | 3‐month follow‐up (n, %) | 6‐month follow‐up (n, %) | ||||
|---|---|---|---|---|---|---|
| Completers at T1 (N = 520) | Loss to follow at T1 (N = 91) | p | Completers at T2 (N = 464) | Loss to follow at T2 (N = 147) | p | |
| Gender | ||||||
| Male | 231 (44.4) | 42 (46.2) | 0.848 | 204 (44.0) | 69 (46.9) | 0.591 |
| Female | 289 (55.6) | 49 (53.8) | 260 (56.0) | 78 (53.1) | ||
| Age (median, IQR) | 61.00 [57.00, 66.00] | 60.00 [57.00, 64.00] | 0.405 | 61.00 [57.00, 66.00] | 60.00 [57.00, 65.00] | 0.664 |
| Type of residence | ||||||
| City | 105 (20.2) | 27 (29.7) | 0.031 | 97 (20.9) | 35 (23.8) | 0.384 |
| Town | 331 (63.7) | 57 (62.6) | 293 (63.1) | 95 (64.6) | ||
| Village | 84 (16.2) | 7 (7.7) | 74 (15.9) | 17 (11.6) | ||
| Living alone | ||||||
| No | 482 (92.7) | 83 (91.2) | 0.780 | 433 (93.3) | 132 (89.8) | 0.218 |
| Yes | 38 (7.3) | 8 (8.8) | 31 (6.7) | 15 (10.2) | ||
| Education attainment | ||||||
| Primary school or below | 112 (21.5) | 12 (13.2) | 0.238 | 101 (21.8) | 23 (15.6) | 0.226 |
| Middle school | 157 (30.2) | 27 (29.7) | 138 (29.7) | 46 (31.3) | ||
| High school | 183 (35.2) | 36 (39.6) | 167 (36.0) | 52 (35.4) | ||
| College or above | 68 (13.1) | 16 (17.6) | 58 (12.5) | 26 (17.7) | ||
| Occupation (now or before retirement) | ||||||
| Government workers/Professionals | 189 (36.4) | 33 (36.3) | 0.687 | 166 (35.8) | 56 (38.1) | 0.079 |
| Trader/Farmer/Labour/Others | 290 (55.7) | 50 (55.0) | 262 (56.5) | 78 (53.1) | ||
| Unemployed | 41 (7.9) | 8 (8.8) | 36 (7.8) | 13 (8.8) | ||
| Marital status | ||||||
| Married | 446 (85.8) | 80 (87.9) | 0.195 | 401 (86.4) | 125 (85.0) | 0.866 |
| Widowed | 42 (8.1) | 3 (3.3) | 34 (7.3) | 11 (7.5) | ||
| Divorced/unmarried/others | 32 (6.2) | 8 (8.8) | 29 (6.2) | 11 (7.5) | ||
| Monthly income (RMB) | ||||||
| < 2500 | 143 (27.5) | 17 (18.7) | 0.148 | 123 (26.5) | 37 (25.2) | 0.106 |
| 2500–4999 | 255 (49.0) | 50 (54.9) | 224 (48.3) | 81 (55.1) | ||
| 5000–9999 | 89 (17.1) | 14 (15.4) | 87 (18.8) | 16 (10.9) | ||
| ≥ 10,000 | 33 (6.3) | 10 (11.0) | 30 (6.5) | 13 (8.8) | ||
5.3. Multi‐Level Influencing Factors Associated With eHealth Literacy
5.3.1. Factors at Individual Level
5.3.1.1. Socio‐demographic factors
A total of 464 participants, who completed the three‐round surveys during the study period, were included in the LMM analyses. As shown in Table 5, the results of univariate analyses showed that among the socio‐demographic variables, age was negatively associated with older adults' eHealth literacy (p < 0.001). Participants who were living in a town or village, living alone, widowed, had primary, middle school, or high school education, and/or worked as farmers/labours/traders or unemployed before retirement showed lower eHealth literacy, compared to those living in a city, living with others, married/divorced/unmarried, having a college or above education attainment, and/or working as government workers or professionals before retirement (ps < 0.001). In addition, older adults who were male and had higher monthly income showed higher eHealth literacy than those females and/or with no more than 2500 RMB income each month (ps < 0.001).
TABLE 5.
Univariate LMMs for longitudinal effects of influencing factors on eHealth literacy at different levels (N = 464).
| Variables | Crude β | 95% CI | p |
|---|---|---|---|
| Socio‐demographic variables | |||
| Age | −0.02 | (−0.02 ~ −0.01) | < 0.001 |
| Gender | |||
| Female | ref | ||
| Male | 0.14 | (0.05 ~ 0.24) | 0.004 |
| Type of residence | |||
| City | ref | ||
| Town | −0.18 | (−0.29 ~ −0.07) | 0.001 |
| Village | −0.77 | (−0.91 ~ −0.62) | < 0.001 |
| Living status | |||
| Living with others | ref | ||
| Living alone | −0.27 | (−0.47 ~ −0.08) | 0.007 |
| Education | |||
| College or above | ref | ||
| Senior high school | −0.25 | (−0.38 ~ −0.12) | < 0.001 |
| Junior middle school | −0.54 | (−0.67 ~ −0.41) | < 0.001 |
| Primary school and below | −0.96 | (−1.1 ~ −0.82) | < 0.001 |
| Occupation (or before retirement) | |||
| Government workers or professionals | ref | ||
| Farmer/labour/trader/others | −0.37 | (−0.47 ~ −0.27) | < 0.001 |
| Unemployed | −0.46 | (−0.64 ~ −0.27) | < 0.001 |
| Marital status | |||
| Married | 0.02 | (−0.18 ~ 0.22) | 0.831 |
| Widowed | −0.32 | (−0.58 ~ −0.06) | 0.017 |
| Separated/divorced/unmarried/others | ref | ||
| Monthly income (RMB) | |||
| Below 2500 | ref | ||
| 2500–5000 | 0.25 | (0.14 ~ 0.37) | < 0.001 |
| 5000–10,000 | 0.45 | (0.31 ~ 0.59) | < 0.001 |
| Over 10,000 | 0.45 | (0.25 ~ 0.66) | < 0.001 |
| Health status | |||
| Chronic disease | |||
| No | ref | ||
| Yes | −0.13 | (−0.23 ~ −0.03) | 0.009 |
| Long term medication | |||
| No | ref | ||
| Yes | −0.12 | (−0.22 ~ −0.02) | 0.015 |
| Technological factors | |||
| Internet use history | 0.05 | (0.04 ~ 0.06) | < 0.001 |
| Self‐rated internet skills | 0.34 | (0.3 ~ 0.39) | < 0.001 |
| Online health information seeking behaviour | |||
| No | ref | ||
| Yes | 0.38 | (0.32 ~ 0.44) | < 0.001 |
| Frequency of health‐related internet use | |||
| < 1 h/w | ref | ||
| 1‐2 h/w | 0.20 | (0.15 ~ 0.25) | < 0.001 |
| 2‐4 h/w | 0.26 | (0.17 ~ 0.35) | < 0.001 |
| > 4 h/w | 0.19 | (0.04 ~ 0.34) | 0.012 |
| Perceived credibility of online health information | 0.29 | (0.21 ~ 0.37) | < 0.001 |
| Perceived importance of internet use for health information | 0.29 | (0.22 ~ 0.35) | < 0.001 |
| Perceived usefulness of the internet in making health decisions | 0.24 | (0.18 ~ 0.3) | < 0.001 |
| Interpersonal factors | |||
| Informational support | 0.28 | (0.2 ~ 0.36) | < 0.001 |
| Instrumental support | 0.23 | (0.16 ~ 0.3) | < 0.001 |
| Community‐level factors | |||
| Available digital devices in community | |||
| No | ref | ||
| Yes | 0.10 | (−0.01 ~ 0.21) | 0.062 |
| Basic public health services in community | |||
| Health archives management | 0.11 | (−0.03 ~ 0.24) | 0.113 |
| Health education (e.g., healthy lifestyles, medical policy) | 0.15 | (0.02 ~ 0.28) | 0.023 |
| Prevention inoculation | 0.07 | (−0.02 ~ 0.17) | 0.14 |
| Senior health management | 0.24 | (0.11 ~ 0.36) | < 0.001 |
| Health management for chronic conditions | 0.09 | (−0.04 ~ 0.22) | 0.183 |
| Health management for traditional Chinese medicine | 0.37 | (0.13 ~ 0.62) | 0.003 |
| No health services | −0.10 | (−0.20 ~ 0) | 0.04 |
| Trainings provided by community | |||
| Internet/technology‐use related training | 0.32 | (0.12 ~ 0.51) | 0.002 |
| Health‐related internet use training | 0.40 | (0.23 ~ 0.57) | < 0.001 |
| Neither of them | ref | ||
Note: All analyses have included IV and time points as fixed effects and adjusted for a participant‐level random effect.
5.3.1.2. Technological factors
For the Internet use‐related factors pertinent to eHealth literacy, as shown in Table 5, the results of univariate analyses suggested that longer Internet use history, higher self‐rated Internet skills, having online health information seeking, more frequent use of health‐related Internet, and/or perceived online health information to be more reliable, important, and useful were positively associated with older individuals' eHealth literacy over time (ps < 0.001). In addition, after adjusting for significant background variables, older individuals' longer Internet use history (adjusted β = 0.03, 95% CI: 0.02 ~ 0.03), higher self‐rated Internet skills (adjusted β = 0.24, 95% CI: 0.20 ~ 0.29), perceived online health information more reliable (adjusted β = 0.19, 95% CI: 0.12 ~ 0.26), perceived Internet use for health information more important (adjusted β = 0.22, 95% CI: 0.16 ~ 0.27), and perceived the Internet in making health decisions more useful (adjusted β = 0.18, 95% CI: 0.13 ~ 0.23) were still positively associated with their eHealth literacy (ps < 0.001). In addition, older adults who sought online health information in the past 3 months (adjusted β = 0.33, 95% CI: 0.27 ~ 0.39, p < 0.001) and/or had health‐related Internet more frequently (1–2 h/w: adjusted β = 0.19, 95% CI: 0.14 ~ 0.24; 2–4 h/d: adjusted β = 0.25, 95% CI: 0.16 ~ 0.34; > 4 h/d: adjusted β = 0.16, 95% CI: 0.02 ~ 0.31; ps < 0.001) showed higher eHealth literacy than those who did not seek health information online and/or use the Internet for health information less than 1 hour weekly (see Table 6).
TABLE 6.
Multivariate LMMs for longitudinal effects of influencing factors on eHealth literacy at different levels (N = 464).
| Variables | Adjusted β | 95% CI | p |
|---|---|---|---|
| Health status | |||
| Chronic disease at baseline | |||
| No | |||
| Yes | −0.11 | (−0.19 ~ −0.03) | 0.008 |
| Long term medication at baseline | |||
| No | |||
| Yes | −0.1 | (−0.18 ~ −0.02) | 0.013 |
| Technological factors | |||
| Internet use history | 0.03 | (0.02 ~ 0.03) | < 0.001 |
| Self‐rated Internet skills | 0.24 | (0.2 ~ 0.29) | < 0.001 |
| Online health information seeking behaviour | |||
| No | |||
| Yes | 0.33 | (0.27 ~ 0.39) | < 0.001 |
| Frequency of health‐related internet use | |||
| < 1 h/w | |||
| 1–2 h/w | 0.19 | (0.14 ~ 0.24) | < 0.001 |
| 2–4 h/w | 0.25 | (0.16 ~ 0.34) | < 0.001 |
| > 4 h/w | 0.16 | (0.02 ~ 0.31) | 0.027 |
| Perceived credibility of online health information | 0.19 | (0.12 ~ 0.26) | < 0.001 |
| Perceived importance of internet use for health information | 0.22 | (0.16 ~ 0.27) | < 0.001 |
| Perceived usefulness of the internet in making health decisions | 0.18 | (0.13 ~ 0.23) | < 0.001 |
| Interpersonal factors | |||
| Informational support | 0.17 | (0.1 ~ 0.24) | < 0.001 |
| Instrumental support | 0.17 | (0.12 ~ 0.23) | < 0.001 |
| Community‐level factors | |||
| Available digital devices in your community | |||
| No | ref | ||
| Yes | 0.02 | (−0.07 ~ 0.12) | 0.638 |
| Basic public health services in your community | |||
| Health archives management | 0.14 | (0.03 ~ 0.24) | 0.014 |
| Health education (e.g., healthy lifestyles, medical policy) | 0.07 | (−0.04 ~ 0.18) | 0.188 |
| Prevention inoculation | 0.09 | (0.01 ~ 0.17) | 0.033 |
| Senior health management | 0.15 | (0.05 ~ 0.26) | 0.005 |
| Health management for chronic conditions | 0.13 | (0.02 ~ 0.23) | 0.019 |
| Health management for traditional Chinese medicine | 0.22 | (0.01 ~ 0.42) | 0.037 |
| No health services | −0.09 | (−0.17 ~ −0.01) | 0.034 |
| Trainings provided by your community | |||
| Internet/technology‐use related training | 0.15 | (−0.01 ~ 0.32) | 0.074 |
| Health‐related internet use training | 0.25 | (0.11 ~ 0.40) | < 0.001 |
| Neither of them | ref | ||
Note: Each adjusted LMM included IV, time points, and significant background variables, such as age, gender, residence, living alone, education, occupation, marital status, and income, as fixed effects and adjusted for a participant‐level random effect.
5.3.1.3. Health status
In terms of the impact of health status, older individuals with poor health (e.g., having chronic diseases and/or taking long‐term medication) were found to display lower eHealth literacy (see Table 5). After adjusting for covariates, it was still found that older adults who had at least one chronic disease (adjusted β = −0.11, 95% CI: −0.19 ~ −0.03, p = 0.008) and/or took long‐term medication (adjusted β = −0.10, 95% CI: −0.18 ~ −0.02, p = 0.013) reported lower eHealth literacy compared to those without chronic disease and medication use (shown in Table 6).
5.3.2. Factors at Interpersonal Level
The results supported the important roles of receiving support in improving eHealth literacy among Chinese older adults. It was shown that both information support and instrumental support were significantly associated with older individuals' eHealth literacy, after considering repeated measures. In addition, further results suggested that after adjusting for significant background variables, informational support (adjusted β = 0.17, 95% CI: 0.10 ~ 0.24) and instrumental support (adjusted β = 0.17, 95% CI: 0.12 ~ 0.23) could still significantly improve older individuals' eHealth literacy (ps < 0.001).
5.3.3. Factors at Community Level
For the factors related to community facilities, in the univariate analyses, the results showed that older individuals who received technological training (e.g., Internet use or health‐related Internet use) and had access to public health services (e.g., health education, senior health management, or health management for traditional Chinese medicine) in their community showed to have higher eHealth literacy, compared to those who had no such training and public health services in their community. No basic health service in community was negatively associated with older individuals' eHealth literacy. Other factors, including having available digital devices and health services (e.g., health archives management, prevention inoculation, health management for chronic conditions), were not significantly associated with older individuals' eHealth literacy (ps > 0.05). After adjusting for significant background variables, having available digital devices was still not significantly associated with older individuals' eHealth literacy; having health education and receiving Internet‐use training also showed no significant impact older people's eHealth literacy over time. However, having health services, including health archives management, prevention inoculation, senior health management, health management for chronic conditions, and health management for traditional Chinese medicine, and received training about health‐related Internet use could significantly improve older individuals' eHealth literacy over time, after adjusting for covariates.
6. Discussion
6.1. Main Findings
As older adults are one of the fastest growing groups for Internet use and online health information seeking, eHealth literacy is essential for them to effectively utilise online health resources. This longitudinal study is the first to provide a comprehensive understanding of factors influencing older individuals' eHealth literacy over time at individual, interpersonal and community levels, based on the socio‐ecological model.
First, on the individual level, the findings supported that socio‐demographics, including older age, rural residence, living alone, lower education attainment, lower occupational levels, and lower income, were significant predictors of lower eHealth literacy among Chinese older adults over time. Previous cross‐sectional studies also demonstrated that these background variables were closely associated with older people's eHealth literacy (Liu et al. 2020; Arcury et al. 2020; Lee, Kim, and Beum 2020; LI, Hui‐lan, and Guang‐hui 2019; Choi and DiNitto 2013). The demographics were likely to influence older individuals' exposure and familiarity with digital devices, health knowledge, and necessary skills to navigate through digital platforms, thereby impacting their eHealth literacy levels (Arcury et al. 2020; LI, Hui‐lan, and Guang‐hui 2019; Tennant et al. 2015). Some inconsistent findings were shown on the association between gender and eHealth literacy among older adults in previous literature. Some studies found no significant gender differences (Lee, Kim, and Beum 2020; Tennant et al. 2015), while others suggested that older males tend to have higher eHealth literacy levels than females (Liu et al. 2020). This longitudinal study was consistent with the latter, indicating that eHealth literacy levels were higher among Chinese older males than their female counterparts. Such disparities are likely to be attributed to traditional gender roles in China, especially among the older generations, which expected women to do domestic responsibilities, while men had more opportunities for education and formal employment (Yang and Guo 1999). As a result, older females might have less exposure to knowledge and technology, potentially contributing to their limited eHealth literacy compared to older females. The above non‐modifiable demographics provide important information to identify older individuals who are disadvantaged and potentially at risk of inadequate eHealth literacy, in which healthcare professionals and researchers particularly need to target and implement health education or training programs among the populations.
In addition to socio‐demographics, technological factors at the individual level have also been commonly studied for their relationships with eHealth literacy. This study expands evidence in the longitudinal design and suggested that older individuals who had longer Internet use history, sought online health information, used the Internet and/or for health information more frequently, had higher self‐rated Internet skills, and/or had positive perceptions (e.g., perceived more reliable, important, and useful) towards health information on the Internet showed to have higher eHealth literacy over time. The results were consistent with previous cross‐sectional studies, which suggested the positive associations between frequency of Internet use, prior experience with Internet use, and computer skills and older people's eHealth literacy (Liu et al. 2020). Older individuals are not the generation that grew up with digital technology, and the rapid evolution of technology may be challenging for them to keep up with. Thus, the familiarity and skills for Internet use would impact their effective usage of Internet for health purposes. Also, their perceptions of health‐related Internet use might impact their motivation to engage with health resources on the Internet. If they perceived it was difficult to receive benefits of using the Internet for health information, they may be less likely to develop eHealth literacy skills.
Individuals' health status may also impact their eHealth literacy levels, as their demands for health information would be different. As we all know, chronic conditions are closely related to aging. However, limited studies have examined the association between health conditions and eHealth literacy among older adults. In previous literature, Choi et al. found that the number of chronic medical conditions was positively correlated with Internet use among US older adults but not significantly related to their eHealth literacy (Choi and DiNitto 2013). Wong et al. also found that having a medical condition was not significantly associated with eHealth literacy among primary care patients in Hong Kong, but poor self‐rated health was a significant negative predictor of their eHealth literacy (Wong and Cheung 2019). In this study, the findings suggested that having chronic conditions and taking long‐term medication could significantly predict lower levels of eHealth literacy among Chinese older adults. However, it should be noted that previous studies demonstrated that individuals with poor health status tended to use the Internet for health information more often, potentially to meet their health demands (Choi and DiNitto 2013; Wong and Cheung 2019). Therefore, if older patients exhibit lower levels of eHealth literacy, this group can be more vulnerable to unreliable or distorted health information on the Internet, compared to healthy older adults, thereby further exacerbating health risks within this population. In addition, the recent findings from our research team indicated that older people's higher eHealth literacy could predict their subsequent improvement in health behaviours, and the temporal relationship only significant in those with chronic conditions (Xie and Mo 2024a). Hence, the importance of improving eHealth literacy among older adults, especially those with health conditions, should be highlighted.
Second, on the interpersonal level, this longitudinal study is one of the first to support that older individuals' social resources, including receiving informational and instrumental support when using the Internet for health, could play important roles in improving their eHealth literacy. The novel findings were similar to Wong et al.'s study, which suggested a positive correlation between perceived social support and eHealth literacy among homebound older adults in Hong Kong (Wong, Bayuo, and Wong 2022). Taking eHealth literacy on a social dimension, support and resources available in social environments may be considered as important factors for enhancing eHealth literacy and alleviating health disparities (Levin‐Zamir and Bertschi 2018). Social relations, such as receiving informational and instrumental support from others, may help older people understand and proficiently utilise the technology for health purposes, as well as reduce computer anxiety or stress and develop a sense of control (Choi 2020). In previous research, Bo Xie (2011) implemented an eHealth literacy intervention among 174 US older adults by developing collaborative learning tactics based on the social interdependence theory, which implied that collaborative learning could be helpful in improving eHealth literacy among older adults. Collaborative learning centers on active social interaction and support with each other. Therefore, the findings in this study are conceivable, since informational and instrumental support could assist older individuals in health‐related Internet use in practice, especially those having difficulties in using the Internet for health information, navigating through the digital tools and accessing reliable eHealth resources.
Third, on the community level, this study also supported some public health facilities (e.g., health education, programs of senior health management) and technology training provided by the community of residence could enhance older people's eHealth literacy to some extent as well. In Chinese resident communities, these health infrastructure and training programs often serve as platforms for health resources dissemination, community engagement, and technology support. Interactions with these settings may provide older individuals more opportunities for peer learning and experience sharing to improve their health knowledge and technological skills, thereby contributing to the enhancement of eHealth literacy among older adults.
6.2. Implications for Policy, Profession, and Practice
In the digital age, online health information seeking is becoming increasingly prevalent among older adults; eHealth literacy plays an important role in their effective use of online health resources for improving or maintaining health and quality of life (Xie and Mo 2024b). Findings of this study provide significant implications for the development of tailored interventions to enhance older people's eHealth literacy.
First, the un‐modifiable factors (e.g., older age, female, lower education attainment, rural residence, living alone, lower occupational levels and income, having chronic conditions) identified to be associated with lower eHealth literacy among Chinese older adults, which provide information to locate the subgroups. Those with lower eHealth literacy cannot take full advantage of digital health and are more likely to be susceptible to misleading health information online (Lwin et al. 2020). Given the vulnerabilities of this population, healthcare providers can play critical roles in supporting and promoting their eHealth literacy. Particularly, it was reported that the main barriers to health literacy for patients included a lack of knowledge and awareness regarding disease prevention and care (Fenta et al. 2024). Serving as one of their primary sources of health information, healthcare providers are well‐positioned to offer guidance and support in their Internet use for health purposes. For example, they can recommend trustworthy sources of health information on the Internet to older individuals with inadequate eHealth literacy, supply educational materials, and provide simplified and clear instructions on digital health use (Nokes and Reyes 2019). Their involvements are essential in empowering this population necessary skills to digital use and access to quality online health information.
Second, taking technological factors into consideration is important in developing eHealth literacy interventions. Some technology training programs can be carried out to help older people to improve the Internet use skills and encourage them to seek health information on the Internet. Meanwhile, health technologies should be designed to be as user‐friendly as possible, to facilitate use even among those with low literacy levels or who are visually impaired (Choi and DiNitto 2013). In addition, the quality of online information varies, leading to negative perceptions of the credibility, importance, and usefulness of eHealth resources among older adults (Lu et al. 2018). To improve their positive perceptions of online health resources, healthcare professionals, website developers, and policymakers can work together to create and promote online health information that is evidence‐based and health technologies that are as user‐friendly as possible. For example, technology designers hold the responsibility of designing user‐friendly eHealth platforms, websites, and applications that cater the needs and preferences of older users. This can involve incorporating features such as clear navigation, larger fonts, voice commands and simplified instructions. Robust technical support is also important in mitigating technical problems and enhancing older people's overall online experience. In addition, the quality of online health information varies, which may lead to older people's negative perceptions regarding the credibility and usefulness of eHealth resources (Lu et al. 2018). Governmental agencies may help to establish quality standards and develop a monitoring system for online health information producers. Healthcare professionals can also engage in the process to ensure the health resources for older adults are accurate, easy‐to‐understand, regularly updated and tailored to their healthcare needs.
Third, this study also yielded significant implications regarding the important roles of social environments, including social support and community‐level factors, in enhancing older people's eHealth literacy. To enrich their social resources for enhancing older people's eHealth literacy, a joint effort of stakeholders (e.g., family members, peers and communities) is needed to create supportive environments for them to engage in digital health. A systematic review has revealed the vital roles played by family members in supporting digital health technologies and enhancing eHealth literacy among Chinese older adults. Children's ‘digital feedback’ (i.e., teaching older people how to use the Internet for health purposes) is important to their parents (Shi et al. 2021). Therefore, family members can encourage older individuals' active engagement with eHealth platforms, provide hands‐on training sessions for effective use of health‐related Internet, offer continuous technological support and educate them on critical evaluation of online health resources. In addition, strengthening the infrastructure of public health facilities, especially those regarding health education and health management, may serve as a practical strategy to promote older people's eHealth literacy within communities. The creation of age‐friendly communities is also needed to enhance social interactions among older adults. The local communities and senior centres are encouraged to organise educational sessions and technology trainings tailored to older adults for developing their necessary skills and knowledge for health‐related Internet use. Moreover, peer support programs within the community are encouraged to facilitate knowledge and skill sharing among older individuals. Experienced eHealth users can mentor and support their older peers, sharing experiences, tips and best practices for utilising eHealth resources.
6.3. Limitations
Some limitations should be noted in this study. First, participants were selected from one city in Jiangxi Province, China, and were those who voluntarily joined the survey. Selection bias may exist. Second, the web‐based survey might restrict the participation of older individuals who had visual impairments or other difficulties in responding to online questionnaires. However, it should be noted that given the circumstances of the COVID‐19 pandemic, web‐based surveys presented a more feasible and accessible approach for data collection. Third, reporting bias (e.g., social desirability and recall bias) may exist as the questionnaires were self‐reported and self‐administered by the participants. Fourth, the six‐month follow‐up interval may not fully capture long‐term changes in eHealth literacy; future research could explore extended intervals (e.g., 1 year or longer) to further understand the development of eHealth literacy. Fifth, most influencing factors were measured once at baseline, except potential time‐varying nature of some variables, such as informational and instrumental support. Future studies should consider repeated measurements of these variables to examine their effects on eHealth literacy over time and their temporal relationships with eHealth literacy among older adults.
7. Conclusions
This longitudinal study advances the understanding of factors influencing Chinese older individuals' eHealth literacy over time at individual, interpersonal and community levels. The un‐modifiable factors, such as demographics, help to identify the subgroups with inadequate eHealth literacy, who need special attention and interventions for eHealth literacy enhancement. The modifiable factors, such as technological, interpersonal, and community‐level factors, identified to be associated with older people's eHealth literacy can guide tailored interventions to promote their eHealth literacy from different aspects.
Author Contributions
Conceptualization: Luyao Xie and Phoenix K.H. Mo. Data curation: Luyao Xie. Formal analysis: Luyao Xie. Investigation: Luyao Xie; Methodology: Luyao Xie and Phoenix K.H. Mo. Project administration: Luyao Xie and Phoenix K.H. Mo. Supervision: Phoenix K.H. Mo. Roles/Writing – original draft: Luyao Xie. Writing – review and editing: Phoenix K.H. Mo.
Ethics Statement
Ethical approval was obtained from the Survey and Behavioural Research Ethics Committee of the Chinese University of Hong Kong (No. SBRE‐21‐0395B). Informed consent was obtained from all individual participants included in the study.
Conflicts of Interest
The authors declare no conflicts of interest.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer‐review/10.1111/jan.16702.
Supporting information
Appendix S1.
Acknowledgements
We express our gratitude to the participants who have taken part in this study and thank the local communities in Jiangxi province, China, for their support of data collection.
Funding: The authors received no specific funding for this work.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.