Older adults’ eHealth literacy and the role libraries can play

Abstract

This study examined the eHealth literacy, health knowledge, health behavior of a population of older Chinese adults, and the impact of using library or community activities for health information seeking. A survey was conducted among 215 participants 45 years or older. Data were analyzed using chi-square test, one-way analysis of variance, bivariate correlation, and multiple regression. The results showed that participants who were urban residents, non-farm workers, and had 9 years of education or more were more likely to use the library or community activities for health information seeking. Health behavior had a significant relationship with eHealth literacy and health knowledge. Both eHealth literacy and health knowledge showed a significant positive relationship with using the library or community activities for health information. These results support the idea that libraries play an important role in providing high-quality eHealth literacy services to enhance healthy behavior and health outcomes in their communities.

Keywords

Library use community activities older adults health knowledge health behavior eHealth literacy

Introduction

Our aging society has become a global concern as populations shift and life spans increase (United Nations Department of Economic and Social Affairs, Population Division, 2017). Older adults tend to develop chronic disorders, such as cardiovascular disease, diabetes, or hypertension and are frequent users of health care (Ferris et al., 2018). Many such conditions can be prevented by behavioral changes that modify risk behaviors, such as harmful use of alcohol, physical inactivity, and tobacco use (World Health Organization, 2015). To change health-related behavior and improve quality of life, health information plays an indispensable role by empowering and engaging adults in decision making and care (Biesecker et al., 2013; Williamson, 2014).

With the advancement of information communication technologies (ICTs), the Internet becomes an important source for health information (Scantlebury et al., 2017). It provides a novel platform for people to get information about their health conditions, medication and treatments, and health providers, to connect with online communities for social support, to acquire mobile applications, and to facilitate self-care management and healthy lifestyles (Atkinson et al., 2009; Bujnowska-Fedak, 2015; Mo, 2012). However, older adults are less likely to benefit from accessing online health information resources compared to other age groups (Tennant et al., 2015). This creates a challenge for older adults in a technology-driven environment to acquire health knowledge and change their health behaviors, even though health knowledge is positively related to the use of preventive care strategies, timely diagnosis, understanding one’s medical condition, treatment adherence, and ultimately to patients’ health outcomes (He et al., 2016; Lavielle et al., 2018).

The reasons why older adults are often excluded from online resources are multifold. Previous studies have explored primary influential factors, including socio-demographic factors (e.g. gender, age, education, income, affordability) (Choi and DiNitto, 2013; Flynn et al., 2006; Hong and Cho, 2017; Levine et al., 2016); health factors (e.g. physical wellness, dementia diagnosis) (Levine et al., 2016); and individual factors (e.g. lack of easy access, limited skills of using digital devices, and lack self-efficacy of obtain health information) (Gibbons, 2011; Lyles et al., 2015; Sealy-Jefferson et al., 2015). In recent years, studies focused on this digital divide have shifted attention to factors associated with eHealth literacy. eHealth literacy refers to “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 and Skinner, 2006: e9). Norman and Skinner (2006) illustrate eHealth literacy with six core skills, including traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy. The application of eHealth literacy encompasses multiple cognitive functions from understanding medical instructions to decision making about a treatment (Serper et al., 2014). Increased eHealth literacy can empower older adults in both cognition and behavior to benefit from eHealth resources and to cope with age-related challenges.

Looking more broadly, older adults have been the focus of research in a subset of research on information seeking behavior. Not surprisingly, there is variation in the information behavior of older adults based on factors such as stage in the aging process (Wicks, 2004), level of technology acceptance (Hong et al., 2013), or type of information needed (Stanziano, 2016). Using Savolainen’s (1995) everyday life information seeking (ELIS) model, Choi (2019) explored the sources older adults use to seek health information and characteristics of their health information behavior in the context of their daily routines and their coping styles. Results from the research suggest older adults perform both passive monitoring of health information and purposeful seeking of health-specific information and in fact move back and forth between approaches, depending on the utility of the information they come across as well as the source. Several early, foundational studies of older adults’ information seeking should also be noted. Chatman (1991, 1992) examined the information world of retired women through the lens of social network theory exploring their most prominent information needs and the ways they searched for information. Her findings show that older women had information needs around health and financial information, and relied on mass media as well as personal social networks and the public library for information. Williamson (1998) reached similar conclusions from her largely qualitative study with 202 older adults in Australia. Health was the number one topic of information need, followed by income and financial information, then recreation information. The information sources most frequently consulted were family members, newspapers, friends, television, printed information, then radio. But within that, Williamson discovered differences in the sources consulted across the sub-groups within her study. The older participants relied more on family and medical professionals for information while those at the younger end of the age spectrum made more use of media to fulfill their information needs. These studies show the importance of health information in the life of older adults and the findings around their information behaviors suggests that older adult information literacy skills will almost necessarily look different from more traditional conceptualizations of information literacy (Williamson and Asla, 2009) which has implications for eHealth literacy skills.

Multiple factors make it challenging for many older adults to develop and maintain high levels of eHealth literacy. Aging slows down the cognitive functions to retain, understand, and interpret information and leads to lower perceived eHealth self-efficacy (Choi and DiNitto, 2013; Speros, 2009). Studies have shown that older adults’ experience using ICTs and digital health technologies is low compared to the overall population. Choi and DiNitto (2013) examined adults aged 60 years and older who received home-delivered meals in central Texas and found only 17% of them used the Internet. Although a wide option of health-related information products and services (e.g. patient portal, insurance systems, health monitoring technologies) were available, older adults were less likely to participate in those services than other age groups (Levine et al., 2016). Meanwhile, seniors who do use technology report greater eHealth literacy. In Tennant et al.’s (2015) study, participants who reported using one or more electronic devices to search for health information were more likely to use Web 2.0 for health information than respondents who use no electronic devices. In addition, those who reported use of Web 2.0 technologies also reported greater eHealth literacy (Tennant et al., 2015).

Trust of the digital information from the Internet also factors into older adults’ eHealth literacy. Research shows that trust in the Internet is a predictor of health information seeking, and that older adults are less likely, compared to the total population, to have trust in health information on the Internet (Zulman et al., 2011). Even though quality health information is accessible online, many online users find it difficult to evaluate the reliability and accuracy of such information (Morahan-Martin, 2004; Nguyen et al., 2017; Sbaffi and Rowley, 2017). While a distrust of information found on the Internet is not necessarily a bad instinct, factors that contribute to distrust, such as finding the Internet confusing, finding too much information, or not being able to discern the trustworthiness of a website can be addressed with greater eHealth literacy (Griffith and Ford, 2017; Paige et al., 2017).

Older adults have many diverse health information needs and the unique issues they face related to their aging and well-being present serious challenges for them to successfully find and use appropriate health information. Understanding eHealth literacy in older adults, and particularly those low-income and racial minority older adults who have had limited access to technology, is an important step in developing services to help facilitate eHealth literacy development (Levy et al., 2015; Richtering et al., 2017). However, research examining eHealth literacy among older adults is limited. This study bridges that gap by exploring the eHealth literacy, health knowledge, and health behavior of a population of older Chinese adults, and investigating the relationships among the use of library or community activities, eHealth literacy, health knowledge, and health behavior of the target group. Results from this study can help libraries understand their communities’ needs regarding health information and provide important insights about a vulnerable population that can inform the design and delivery of health information services.

Methods

Study participants

We conducted a cross-sectional study in 2017 in the Pukou district in Nanjing, in the Jiangsu province of China. Pukou is among the earliest districts to have established a health information system that covers all public health care services in its jurisdiction. With the assistance and collaboration of the local health bureau, we recruited participants from local communities in Pukou. Free physical examinations were provided as an incentive for the participants. The data collection took place in four local hospitals and clinics in Pukou District for convenience, but hospital and clinic staff were not involved in the data collection.

A total of 219 residents 45 years or older participated in the questionnaire survey, with 215 completed responses. The participants responded to the survey questions through in-person interviews. The interviewers were medical students who had received training on research ethics and interview skills. The interviewers read the questions to the participants and recorded their answers. Each interview took about 20 minutes. The relevant offices at Nanjing Medical University and Pukou District Health Bureau reviewed and approved the research protocol.

Measures

Demographic characteristics

Participants were asked to report information including gender, age, education level, marital status, place of residence (urban vs rural), years of schooling, occupation (current or before retirement), and monthly household income in Chinese Yuan (CNY). There were seven categories of education level in the survey (no schooling, primary school, middle school, high school/vocational school, technical secondary school, junior college, university and above). We combined the education background categories into two categories (“less than 9 years” and “9 years and more”) as the Chinese government implements a 9-year compulsory education system.

Health conditions

The health conditions of the participants were assessed using self-reported data. The participants were asked to report their perceived state of health status and diagnosed chronic disorder condition. We dichotomized their chronic disorder condition into “having no chronic disorder,” “having one type of chronic disorder,” and “having two or more chronic disorders.”

Library utilization

In China, a large number of county-level rural libraries are embedded within what is known as the township comprehensive cultural station. The principal responsibilities of the township comprehensive cultural station include establishing libraries, supporting reading and learning activities among community residences, disseminating knowledge, and providing information resources. These responsibilities are achieved through a series of community activities, such as hosting lectures, developing shows and collections, maintaining book collections and circulation services, organizing community activities, and providing digital information services. Although operating under a different theoretical framework and professional aim, the major functions of the township comprehensive cultural stations and libraries are highly overlapped (Yu and Quan, 2016). Given that libraries and community information sharing activities are so closely connected under this model of township comprehensive cultural stations, we combined these two sources of information into a single question on the survey. Participants were asked if they had sought health information from a library or a community activity in the past 12 months. The answer “yes” was coded into 1, and “no” as 0.

eHealth literacy

eHealth literacy was measured by having participants rate their agreement with statements reflecting their capacities for locating, retrieving, evaluating, and applying health information to health problems from online environment. We used a modified version of the eHEALS scale (Norman and Skinner, 2006). As pointed out by previous scholars that people may pick a neutral option to avoid the cognitive effort to choose between their positive and negative feelings (Nowlis et al., 2002) in order to help the participants overcome the ambivalence, we used a 4-point Likert-type scale to collect the scores. The response options included “1 = strongly disagree,” “2 = disagree,” “3 = agree,” and “4 = strongly agree” which were recoded into 0 (responses to 1 and 2) and 1 (responses to 3 and 4). The six items were combined into a single index, with an alpha = 0.82 and a mean score of 0.90 ± 1.54. The range of the score was 0 to 6. A higher composite score indicated higher eHealth literacy. A score of 6 indicated that the participants was with high eHealth literacy (see Supplemental Appendix A).

Health knowledge

Health knowledge was measured by a 10-item test, with correct and incorrect responses. The scale was developed based on existing literature (Kung and Lee, 2006; Yu et al., 2015; Yuan et al., 2015), covering the risk factors, prevention knowledge, and understanding of health. The participants answered “agree” or “disagree” to each item. The correct responses were coded as 1, and the incorrect responses were coded as 0. A composite score was calculated from the sums of the 10 items. The range of the composite score was 1 to 10. A higher score indicated greater health knowledge. Cronbach’s alpha for the 10 items was 0.74. The percentages of correct responses to each item ranged from 9.3% to 94%, with 0.5% of the participants correctly answering all 10 items (see Supplemental Appendix B).

Health behavior

Health behavior was assessed with eight items where participants reported their daily health-related activities, such as diet, physical exercise, hygiene, disease prevention activities, and social activities. The practice of the activities that were positively related to better health outcomes were coded as “1,” and those negatively related to better health outcomes were coded as “0.” The eight items were combined into a single index, representing good health behaviors. The composite score ranged from 1 to 8. The higher score the participants reported, the more good health behavior they kept in their daily life. The average score for health behavior among the participants was 5.51 ± 1.57, with 11.7% of the participants scoring 8 (see Supplemental Appendix C).

Data analysis

Statistical analyses were conducted using SPSS. First, chi-square tests for categorical variables and independent t tests were performed to examine differences between participants who reported using the library or community activities and those who did not in key demographic variables, health status, eHealth literacy, health knowledge level, and health behavior. Second, t tests for categorical variables and one-way ANOVAs for continuous variables were conducted to analyze the demographic correlates of eHealth literacy, health knowledge level, and health behavior. Third, bivariate correlation analysis was performed among eHealth literacy, health knowledge, health behavior, and library or community activities utilization. Thereafter, three multiple linear regression models were executed. The first model examined the main effects of eHealth literacy and health knowledge on health behavior. The second model examined the relationship between eHealth literacy and health knowledge and health behavior while controlling for the library or community activities use factor. The third model tested the hypothesized interaction of eHealth literacy and health knowledge with library utilization on health behavior adoption while controlling significant demographic characteristics (p < 0.05). Standardized regression coefficient (B) for multiple linear regression with a 95% confidence interval (CI) were employed to depict the relationship between the dependent and independent variables.

Results

Sample characteristics

The sample characteristics are shown in Table 1. The average age was 71.27 years old (SD = 10.82). Of all the participants, 43.3% were male, 68.1% were married, 73.5% lived in rural areas, and 60.5% worked as farm workers before retirement or as a current job. Most of the participants (63.8%) received fewer than 9 years of education, which means they did not complete middle school. More than half of the participants reported that their monthly household income was less than 1000 CNY; 38.1% of the participants reported their health status as “excellent/good,” and only 14.9% lived with no diagnosed chronic disorders. The sample population reported an average score of eHealth literacy of 0.90 (SD = 1.54), health knowledge of 6.70 (SD = 2.28), and health behavior of 5.51 (SD = 1.57). The skewness values for health knowledge, eHealth literacy, and health behavior were –0.73, 1.93, –0.32, respectively. The kurtosis for health knowledge, eHealth literacy, and health behavior were –0.67, 2.99, –0.22, respectively. Therefore, the data were not highly skewed.

Table 1.

Sample characteristics by library utilization status.

	Overall	Seek health information via library utilization
	Overall	Yes	No
	n = 215^a	n = 27 (12.6)	n = 188 (87.4)
	n (%)/Mean ± SD	n (%)/Mean ± SD	n (%)/Mean ± SD
Demographic
Gender (male)	93 (43.3)	13 (48.1)	80 (42.6)
Age (years)	71.27 ± 10.82	72 ± 13.93	71.16 ± 10.33
Marital status (married)	145 (68.1)	17 (63)	128 (68.8)
Residence
Urban	57 (26.5)	12 (44.4)	45 (23.9)*
Rural	158 (73.5)	15 (55.6)	143 (76.1)
Education (year)
<9	136 (63.8)	10 (37)	126 (67.7)**
⩾9	77 (36.2)	17 (63)	60 (32.3)
Career (current or before retirement)
Farm worker	130 (60.5)	11 (40.7)	119 (63.3)*
Non-farm worker	85 (39.5)	16 (59.3)	69 (36.7)
Monthly household income (CNY)
<1000	110 (52.9)	6 (23.1)	104 (57.1)**
1000–1999	27 (13)	5 (19.2)	22 (12.1)
2000–2999	14 (6.7)	1 (3.8)	13 (7.1)
⩾3000	57 (27.4)	14 (53.8)	43 (23.6)
Health
Perceived state of health
Excellent/good	82 (38.1)	13 (48.1)	69 (36.7)
Moderate	79 (36.7)	12 (44.4)	67 (35.6)
Bad/very bad	54 (25.1)	2 (7.4)	52 (27.7)
Chronic disorders
0	32 (14.9)	5 (18.5)	27 (14.4)
1	65 (30.2)	8 (29.6)	57 (30.3)
⩾2	118 (54.9)	14 (51.9)	104 (55.3)
eHealth Literacy	0.90 ± 1.54 [Range: 0–6]	1.63 ± 1.90 [Range: 0–6]	0.79 ± 1.46* [Range: 0–6]
Health Knowledge	6.70 ± 2.28 [Range: 1–10]	7.85 ± 1.79 [Range: 2–10]	6.53 ± 2.30*** [Range: 1–9]
Health Behavior	5.51 ± 1.57 [Range: 1–8]	5.74 ± 1.70 [Range: 2–8]	5.47 ± 1.55 [Range: 1–8]

SD: standard deviation; CNY: Chinese Yuan.

The number of various items did not add to the total due to missing data.

p < 0.05; **p < 0.01; ***p < 0.001.

Group differences

The sample population was coded into two groups based on their reported use of the library or community activities to seek health information. As shown in Table 1, gender, age, marital status, perceived state of health, chronic disorders, and health behavior were not significantly different between the two groups. The group who did use the library or community activities to seek health information had a higher proportion of urban residence (44.4% vs 23.9%, p < 0.05), non-farm workers (59.3% vs 36.7%, p < 0.05), and received education for 9 years or more (63% vs 32.3%, p < 0.01) as compared to the group who didn’t. The participants who used library or community activities also reported a lower probability of earning below 1000 CNY per month (23.1% vs 57.1%, p < 0.01). The composite eHealth literacy score ranged from 0 to 6 for both groups. The composite score of health knowledge ranged from 2 to 10 for the group who used library or community activities, and 1 to 9 for the group who did not use library or community activities. The composite score of health behavior ranged from 2 to 8 for the group who used community activities, and 1 to 8 for their counterparts. Results showed that the participants who used library or community activities reported higher eHealth literacy (1.63 ± 1.90 vs 0.79 ± 1.46, p < 0.05), and greater levels of health knowledge (7.85 ± 1.79 vs 6.53 ± 2.30, p < 0.001). No significant difference was found between the average composite scores of health behaviors of these two groups.

Association between sample characteristics and key variables

Table 2 summarizes the associations among key sample characteristics and eHealth literacy, health knowledge, and health behavior. Participants who were non-farm workers were more likely to score higher on eHealth literacy than their counterparts. Participants who lived in urban areas and were non-farm workers tended to have better health knowledge. Participants who reported “excellent/good” and “moderate” health statuses were found to have better health knowledge than those who reported “bad/very bad” health status. The results showed that female, unmarried, urban, non-farm worker participants were more likely to adopt more health behaviors. Education and monthly household income were significantly associated with all three key variables. Those who received education for 9 years or longer reported higher scores in eHealth literacy, health knowledge, and health behavior than their counterparts (all ps < 0.001). Participants who earned a monthly household income of 2000 to 2999 and ⩾3000 demonstrated better eHealth literacy and health knowledge, and adopted more health behaviors than two lower income groups (all ps < 0.001). We also found that age is positively related to health behavior (p < 0.001), but negatively associated with eHealth literacy, though not statistically significant.

Table 2.

Sample characteristics by eHealth literacy, health knowledge, and health behavior.

	eHealth literacy	Health knowledge	Health behavior
	Mean ± SD/r	Mean ± SD/r	Mean ± SD/r
	0.90 ± 1.54	6.70 ± 2.28	5.51 ± 1.57
Demographic
Male	0.89 ± 1.56	6.63 ± 2.29	4.88 ± 1.60***
Female	0.90 ± 1.54	6.75 ± 2.28	5.98 ± 1.38
Age (years)	−0.12	0.04	0.25***
Married	0.98 ± 1.61	6.70 ± 2.27	5.32 ± 1.62*
Unmarried	0.74 ± 1.41	6.63 ± 2.32	5.87 ± 1.37
Urban	1.12 ± 1.65	7.95 ± 1.69***	6.54 ± 1.19***
Rural	0.82 ± 1.50	6.25 ± 2.30	5.14 ± 1.53
Education (year)
<9	0.60 ± 1.19***	6.03 ± 2.38***	5.24 ± 1.46***
⩾9	1.45 ± 1.92	7.84 ± 1.54	5.96 ± 1.67
Career (current or before retirement)
Farm worker	0.68 ± 1.37*	6.15 ± 2.31***	5.16 ± 1.52***
Non-farm worker	1.22 ± 1.73	7.54 ± 1.96	6.04 ± 1.49
Monthly household income (CNY)
<1000	0.52 ± 1.06***	6.07 ± 2.30***	5.06 ± 1.49***
1000–1999	1.00 ± 1.47	6.07 ± 2.46	5.44 ± 1.65
2000–2999	2.36 ± 2.56	7.71 ± 1.64	6.07 ± 1.33
⩾3000	1.18 ± 1.73	7.88 ± 1.64	6.23 ± 1.41
Health
Perceived state of health
Excellent/good	1.09 ± 1.55	6.77 ± 2.20*	5.66 ± 1.68
Moderate	0.73 ± 1.56	7.06 ± 2.16	5.61 ± 1.43
Bad/very bad	0.85 ± 1.51	6.06 ± 2.47	5.13 ± 1.55
Chronic disorders
0	0.91 ± 1.42	6.59 ± 2.08	5.06 ± 1.73
1	1.05 ± 1.67	7.05 ± 2.29	5.74 ± 1.47
⩾2	0.81 ± 1.51	6.53 ± 2.32	5.50 ± 1.56

SD: standard deviation; CNY: Chinese Yuan.

p < 0.05; ***p < 0.001.

Bivariate correlation between key variables

The bivariate associations (r) of library and community activity use with the key health-related variables were summarized in Table 3. Health behavior had a significant relationship with eHealth literacy (r = 0.17, p < 0.05) and health knowledge (r = 0.24, p < 0.001). Both eHealth literacy (r = 0.18, p < 0.01) and health knowledge (r = 0.19, p < 0.01) evidenced significant positive relationship with library use.

Table 3.

Correlation matrix of main variables.

Name (range)	eHealth literacy	Health knowledge	Health behavior	Library utilization
eHealth literacy (0–6)	1.00
Health knowledge (1–10)	0.15*	1.00
Health behavior (1–8)	0.17*	0.24***	1.00
Library utilization (0–1)	0.18**	0.19**	0.06	1.00

p < 0.05; **p < 0.01; ***p < 0.001.

Multiple regression models predicting health behavior

The results of the multiple regression models are shown in Table 4. In the first step, both eHealth literacy (B = 0.14, 95% CI = [0.01, 0.28]) and health knowledge (B = 0.22, 95% CI = [0.06, 0.24]) accounted for significant positive predictors of health behavior (R² = 6.9%). The use of library or community activity as health information resource was added to the prediction equation at step 2. Both eHealth literacy and health knowledge were positively related to health behavior while library/community activity utilization reflected a non-significant predicting effect. At step 3, the demographic variables were added to the model. They increased the amount of predicted variance in health behavior (R² = 27.7%). By controlling these demographic variables, the analysis indicates that better adoption of health behavior is positively predicted by eHealth literacy (B = 0.13, 95% CI = [0.01, 0.26]), age (B = 0.18, 95% CI = [0.002, 0.05]), and having a monthly income between 2000 and 2999 (B = 0.16, 95% CI = [0.19, 1.85]), but negatively related to being male (B = –0.33, 95% CI = [–1.43, –0.64]) and living in rural area (B = –0.24, 95% CI = [–1.67, –0.05]). There was no significant interaction between seeking health information from the library and health behavior in this model.

Table 4.

Results of linear regression models predicting health behavior.

Predictor	Health behavior
	Model 1	Model 2	Model 3
	B (95% CI)	B (95% CI)	B (95% CI)
Main effects
eHealth Literacy	0.14 [0.01, 0.28]*	0.15 [0.01, 0.28]*	0.13 [0.01, 0.26]*
Health Knowledge	0.22 [0.06, 0.24]***	0.22 [0.06, 0.25]***	0.12 [–0.01, 0.17]
Health information resource
Library Utilization (no = 1)		−0.01 [–0.69, 0.58]	−0.01 [–0.64, 0.51]
Confounder variables
Male (female = 1)			−0.33 [–1.43, –0.64]***
Age			0.18 [0.002, 0.05]*
Married (unmarried = 1)			0.01 [–0.43, 0.48]
Rural (urban = 1)			−0.24 [–1.67, –0.05]*
Education ⩾ 9 (“< 9 years” = 1)			−0.08 [–0.80, 0.27]
Farm worker (Non-farm worker = 1)			−0.01 [–0.64, 0.59]
Monthly household income 1000–1999(“<1000” = 1)			0.09 [–0.19, 1.02]
Monthly household income 2000–2999(“<1000” = 1)			0.16 [0.19, 1.85]*
Monthly household income ⩾ 3000 (“<1000” = 1)			0.07 [–0.47, 0.96]
R²	6.9%	6.5%	27.7%

CI: confidence interval.

p < 0.05; ***p < 0.001.

Discussion

The current study examined using the library as a health information resource in relation to the health behavior of users among older adults in Jiangsu, China. There are three important findings to highlight from this study. First, the results reveal differences by demographic groups in terms of participants’ use of the library and community activities as a source of health information. Significant relationships were found for those older adults in urban settings, those with fewer than 9 years of education, farm workers, and those with lower incomes not using the library as a source of health information. Naturally, this suggests some opportunities for libraries to direct more outreach efforts to users from these demographic groups around eHealth literacy. It also points to the need for more research to better understand the complex landscape of users’ health information needs and seeking behaviors. As the health information landscape becomes increasingly more reliant on information and communication technologies, the risk of older adults in many vulnerable demographic groups being left behind only increases. Libraries are well situated to help respond to this need and more research in this vein can help clarify even further the service opportunities that exist.

Second, the data show that participants who did not report using the library or community activity for health information also had significantly lower eHealth literacy and health knowledge scores than those who did. While this result is not causal, it is reasonable to consider that information seeking activities, such as using a library or community activity to learn more about health information, would contribute to greater eHealth literacy and health knowledge. Research shows that older adults seek health information from a variety of sources; however, the quality and trustworthiness of those sources may range considerably (Turner et al., 2018). Librarians are experts in information seeking and libraries are consistently viewed as valuable community resources for finding reliable information. These strengths should be brought to bear to address the findings we report here.

Finally, the data revealed that eHealth literacy and health knowledge were significant predictors of healthy behaviors. This is perhaps the most important outcome from the research study. Our study data indicate that with more knowledge and greater ability to understand and access health information from electronic sources, older adults report more health behaviors, an outcome that brings many benefits to their lives. This is an empowering finding; health behaviors can be modified by enhancing the skills and knowledge around health knowledge and eHealth literacy.

Even though we did not find a direct relationship between use of the library or community activity and health behavior, the significant relationships between eHealth literacy and health knowledge and library or community activity, along with the significant relationships between eHealth literacy and health knowledge and health behavior still suggest that libraries have a role to play in older adult health.

Historically, though, libraries have drawn a line at helping users with health-related information needs. Several reasons for this hesitance exist including a fear of being asked for medical advice; a fear of mis-informing a patron, and a lack of knowledge of health information resources (Luo & Lili, 2015). Yet the number of health-related questions is on the rise and remains in the top 5 to 10 categories of questions asked at a public library (Wood et al., 2000). Past research has explored how libraries can support successful delivery of health literacy initiatives, providing strong evidence that public libraries can fill the gap for health information by enhancing the eHealth literacy levels in their communities (Xie, 2011; Xie & Bugg, 2009). With a growing population of older adults who face declining health due to aging, the need for high-quality eHealth literacy skills is tremendous and librarians are uniquely situated to provide that service in their communities. The fact that patrons already bring health information needs to their public libraries is evidence of an established level of trust in librarians and demonstrates an openness—perhaps even an expectation—of receiving help with those needs (Philbin et al., 2019).

What exactly that role should be—what products and services should libraries offer older adults—merits strategic and creative thinking. Recent research offers helpful conceptualizations of an array of possible services as well as specific examples of successful programs and collaborations. For example, Luo’s (2018) analysis of the health-related programming at the San Jose, CA Public Library (SJPL) provides a useful categorization of types of health-related programming libraries might wish to pursue. Luo identified programs on (1) health knowledge and resources, (2) healthy physical activity, (3) basic health needs, (4) health care, and (5) supporting health causes. While these categories reflect the types of programs one library system is offering, they also suggest a framework other libraries might wish to consider when building out programs or services for their own communities. In addition to examples from SJPL, Philbin et al. (2019) analyzed public libraries’ contributions to 10 health domains, identified through a systematic review of research literature. The 10 domains include a number of indirect social determinants of health such as early life well-being, social exclusion, work status, and social support as well as more direct areas of emphasis including health care access, addiction, stress, and food. While none of the domains focused exclusive on services for older adults, many of the programs identified within the domains would be applicable to seniors such as nutrition programs, social support programs, and access to health care and health information.

In different ways, each of these studies can serve as a blueprint for libraries who seek to enhance their services for older adult eHealth literacy. What both articles offer, however, is a much more holistic and extensive understanding of what libraries can do in terms of meeting health needs of the older adults in their communities. From programs on health topics, physical activity classes, providing for basic needs of shelter and food, or guidance on health care systems (Luo, 2018) to providing flu vaccines, nutrition workshops, disaster relief resources, and stress reduction classes (Philbin et al., 2019), the range of ideas is vast and significantly more extensive than the traditional approach of answering health-related reference questions behind a desk. Clearly libraries can and should continue to offer help with health information literacy, but there is so much more they can and should do. But while the range of services is potentially quite broad, the resources to support health-related services are finite. Therefore, libraries must consider the specific needs of the older adults in the communities they serve before embarking on new services. As the data from our study show, farm workers and those with lower incomes had significantly lower eHealth literacy, health knowledge, and health behavior, making the case for targeted services to those populations. Similar demographic data coupled with needs assessment data will be important to have as libraries plan and invest in strategic health-related services.

It is important to interpret the findings in the context of several limitations. First, the participants were from rural areas with limited education in China. Therefore, the extent to which these findings can be generalized to other countries is unknown. Second, the utilization of library and community activity, eHealth literacy, and health behavior were assessed using self-report data, which may be subject to social desirability bias. Third, the library services and community activity are highly overlapped due to the specific socio-cultural context, therefore, use of library and the use of community activity were treated as one variable in this study. The research group will separate these two phenomena in future studies.

Conclusion

Given older adults’ tremendous need for health information and libraries’ expertise in information literacy and service to their communities, the opportunity is ripe for librarians to take the lead in providing products and services to support their older adult community members. By assisting older adults with their health information needs, the public library makes a larger societal contribution by reducing some of the economic costs of health care for older adults, relieving the constraint of limited medical resources for some communities, and empowering people to constructively cope with future aging issues. These promising contributions will help the public library live out its mission of supporting lifelong learning while also enhancing its position in the community as the primary information and knowledge center for health information needs in a technological and data-driven environment.

Supplemental Material

Appendix – Supplemental material for Older adults’ eHealth literacy and the role libraries can play

Supplemental material, Appendix for Older adults’ eHealth literacy and the role libraries can play by Zhenping Lin, Yao Zhang, Miriam Matteson, Xiaoming Li, Xiaoming Tu, Yeqin Zhou and Jing Wang in Journal of Librarianship and Information Science

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Social Science Fund of China (grant number 15BSH121), the “Thirteenth Five-Year Plan” Medical Science and Technology Innovation Platform Project of Nanjing Municipal Health and Family Planning Commission in Jiangsu Province (grant number ZDX16018), and the Fundamental Research Funds for the Central Universities (grant number 63202034).

ORCID iD

Yao Zhang

Supplemental material

Supplemental material for this article is available online.

Author biographies

Zhenping Lin is an associate professor in the School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology. He also works as an associate professor in the School of Health Policy and Management. His research focus on public health policy and healthcare for the aging population.

Yao Zhang is an assistant professor in the Department of Information Resources Management, Business School, Nankai University. She worked as a Post-doctoral Fellow in the Arnold School of Public Health at the University of South Carolina. Her research interests include ICT application in healthcare for chronical disease, technology adoption among older adults, and eHealth literacy.

Miriam Matteson is an associate professor in the School of Information, Kent State University. Her research focus on information literacy instruction and library services. She has studied emotional labor, dispositional affect, and interpersonal and emotional soft skills in a variety of library settings.

Xiaoming Li is a professor and chair of the South Carolina SmartState Center for Healthcare Quality (CHQ), Arnold School of Public Health, University of South Carolina. He is an expert in Health Behavior, HIV prevention, and Big data application in healthcare arena. He is also an awardee of multiple NIH grants.

Xiaoming Tu is an associate professor in the School of Basic Medical Sciences, Nanjing Medical University. He is an expert in Biostatistics. His research focus on the healthcare and disease prevention among rural population.

Yeqin Zhou is a professor in the School of Humanities and Social Sciences, Nanjing Medical University. His research focus on community-based healthcare service and chronic disease management.

Jing Wang is an associate professor in the School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology. Her research focus on the chronic disease management among rural population.

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