How Technology,Health Information Seeking,and Socioeconomic Factors Are Associated With Coronavirus Disease 2019 Vaccination Readiness in Estonians Over 50 Years?

Abstract

Despite the proven effectiveness of COVID-19 vaccines in preventing severe illness, many individuals, including older adults who are most susceptible to the virus, have opted against vaccination. Various factors could shape vaccination decisions, including seeking health information (HI). The internet is the primary source of HI today; however, older adults are often referred to as those missing out on digital benefits. The study explores the correlations between information and communication technology (ICT) use, online HI seeking, socioeconomic factors, and COVID-19 vaccination readiness among individuals aged 50 and above in Estonia. The survey data were gathered from 501 people aged 50 and older after the first lockdown in 2020. The outcomes revealed that vaccination readiness positively correlated with factors such as higher educational attainment, greater income, male gender, access to ICT, a readiness to employ digital technologies for health-related purposes, a greater demand for HI, and a higher frequency of seeking it online. There was some discrepancy in the preference of HI sources; for example, vaccination consenters preferred online versions of professional press publications and specific health portals. Based on the findings, it is advisable to encourage older adults to utilize the internet and new technology for health-related purposes. This practice expands the range of information sources available to them, ultimately enabling better decision-making regarding their health behaviors.

Keywords

COVID-19 vaccination online health information seeking ICT use older adults

In March 2020, the World Health Organization (2020, 2023) declared COVID-19 a global pandemic, marking the onset of a significant health crisis that has led to over seven million deaths worldwide. Vaccinated individuals generally face a reduced risk of severe illness, death, and the need for hospitalization (Centers for Disease Control and Prevention, 2023b; Haque & Pant, 2022; Klompas, 2021). However, a significant portion of the population, including those aged 50 and older who are most susceptible to the virus, has remained unvaccinated (Centers for Disease Control and Prevention, 2023a; European Centre for Disease Prevention and Control, 2022; McSpadden, 2021; Siu et al., 2022). This has hindered a more rapid response to the COVID-19 virus (Zhao et al., 2023).

To increase vaccination rates and better prepare for future health crises, it is crucial to investigate the factors that foster interest in vaccination (Dambadarjaa et al., 2021; European Centre for Disease Prevention and Control, 2023; Roy et al., 2022). It has been established that variables supporting vaccine uptake include health beliefs and knowledge, trust in the health care system and authorities, cultural and social norms, religious beliefs, socioeconomic status, psychological factors, political ideologies, media influence, geographical location, safety, and previous vaccination history (Butter et al., 2022; Kim et al., 2023; Konstantinou et al., 2021; Qin et al., 2023; Roy et al., 2022; Troiano & Nardi, 2021).

The literature also underscores the significance of personal health information (HI) seeking and information source selection in shaping vaccination perceptions (Sakamoto et al., 2022; Zhuang & Cobb, 2022). Nevertheless, exploring how older adults engage with HI during the COVID-19 pandemic and its role in their vaccination choices remains underexplored (Principe & Weber, 2023; Zhang et al., 2024). Given the internet’s role as the primary source of health-related information today, including COVID-19 details, it is crucial to emphasize the importance of access to and use of information and communication technology (ICT), especially for older adults who are often referred to as those deprived of digital benefits (van Deursen, 2020; van Dijk, 2020).

Estonia has gained global recognition for its exceptional progress in digital transformation, positioning itself as one of the leading nations in this regard. With its well-established e-government and e-health systems, Estonia claims the top spot in the European Union for digital public services, showcasing its remarkable advancements in the realm of technology and administration (European Commission, 2022; Ojaperv & Virkus, 2023). Digital services are integrated into the health care system for all citizens, including older adults (Digital Healthcare, n.d.). However, Eurostat (2022) Statistics reports that nearly 60% of individuals use the internet to seek health-related information in Estonia. Although Estonians aged 55–74 have made strides in internet usage (Eurostat, 2023), a substantial number of older adults, more specifically 10% of the 55–64-year-olds and 27% of the 65–74-year-olds, are not using the internet (Statistics Estonia, 2024). Since internet usage begins to decline in Estonia starting from the fifth decade of life, this study also includes individuals in their 50s and older.

COVID-19 has significantly affected the older population in Estonia, as the average age of hospitalized patients was 67 for women and 63 for men (Health Board, 2023). Still, the COVID-19 statistics shows that older adults in Estonia reached the highest levels of vaccination (Health Board, 2023), possibly due to being the first group to receive the vaccine. Women under 60 have been more willing to vaccinate, while men over 70 have higher vaccination rates (Estonian Health Insurance Fund, 2022).

The aim of the study is to explore the correlations between ICT use, online health information seeking (OHIS), socioeconomic factors, and COVID-19 vaccination readiness among individuals aged 50 and above in Estonia. This approach enables the consideration of individual variances, offering valuable insights for designing targeted public health initiatives, policy development, and communication tactics to guarantee broad vaccination uptake. Notably, such an investigation has not been conducted in Estonia to date.

The first section of this article provides an overview of previous studies on vaccination intention and health information behavior (HIB). The definition of HIB in this study is adapted from Wilson’s (2000) definition of information behavior: the totality of human behavior in relation to health sources and channels of information, including both active and passive information seeking and information use (Ojaperv & Virkus, 2023). It thereafter introduces the methodology of the study, the main results, and concludes with a discussion of the findings.

Literature Review

The literature review provides an overview of factors influencing COVID-19 vaccination intentions, focusing on the role of HI and information-seeking behavior and the challenges older adults face in accessing online HI based on which to formulate the hypotheses for this study.

Factors Associated With COVID-19 Vaccination Intention

Research indicates that a range of indicators play a role in a person’s vaccination decision. Socioeconomic variables, such as higher levels of education and income, along with being male, married, and older age are associated with more favorable decisions toward vaccination (Jain et al., 2021; Lee & Huang, 2022; Limbu et al., 2022; Malik et al., 2020; Richter et al., 2022; Sharma et al., 2023; Yasmin et al., 2021; Zintel et al., 2023). Conversely, belonging to certain racial or ethnic groups, experiencing poverty, experiencing poor subjective health, having limited access to health care, and feeling unsafe in one’s neighborhood have been linked to higher rates of vaccine refusal (Moon et al., 2023; Mustafa et al., 2022; Richter et al., 2022). Discrepancies exist across various studies concerning the role of gender, age, and health status (Mustafa et al., 2022; Wong et al., 2021). Highlighting socioeconomic factors is considered crucial in health behavior (HB), as these factors contribute significantly to the persistence of disparities (van Deursen, 2020; Wilderink et al., 2022).

Psychological factors, including concerns about vaccine effectiveness and side effects, and mistrust in government, have been identified as reducing COVID-19 vaccine acceptance (McSpadden, 2021; Wolff, 2021). On the other hand, positive attitudes toward vaccines, the perception of risk associated with not getting vaccinated, and the absence of severe psychological distress contribute to an increased willingness to receive the vaccine (Al Naam et al., 2022; McSpadden, 2021; Roy et al., 2022; Sherman et al., 2021).

Access to relevant information is also critical in making informed health decisions, with limited access contributing to disparities (van Deursen, 2020). In today’s digital age, where information is predominantly sourced from the internet, obstacles to obtaining information could also hinder informed vaccination decisions, enhancing health disparities (van Deursen, 2020). Nevertheless, this has received less research focus than the factors mentioned earlier.

The Intersection of Online Health Information-Seeking Behavior (OHISB) and Health Decisions: A Focus on Vaccination

The link between seeking HI and engaging in behaviors that protect health is thoroughly established (Allington et al., 2021; Dadaczynski et al., 2021; Koh, 2023; Roberts et al., 2021; Skarpa & Garoufallou, 2021). The earlier research underlines the importance of having access to HI that is both accurate and relevant, empowering individuals to make choices that positively impact their health and well-being (Ghahramani & Wang, 2020; Jia et al., 2021; Lambert & Loiselle, 2007). The internet’s broad availability of HI can foster increased awareness, encourage engagement, and support the adoption of effective strategies for preventing, detecting, and treating health conditions (Zhang et al., 2024). Studies have linked frequent engagement with online HI to healthier behaviors (Zheng et al., 2022; Zhuang & Cobb, 2022). Nevertheless, the internet is also a source of considerable misinformation, which can negatively influence public opinion (Zhang et al., 2024).

Chu et al. (2021) underscored the value of accessing information from a variety of sources during crises, linking it to diverse perceptions and engagement in HB. Individuals who sought information from multiple and traditional sources exhibited more protective HB. Certain information sources, like social media and alternative media, can propagate conspiracy theories related to the pandemic and dissuade people from engaging in protective HBs (Al-Hasan et al., 2020; Bendau et al., 2021; Borah et al., 2022; Castellano-Tejedor et al., 2022; Cuello-Garcia et al., 2020; Figueiras et al., 2021). Utilizing trustworthy media channels has been linked with positive HB (Allington et al., 2021; Borah et al., 2022). Moon et al. (2023) discovered that excessive use of social media could undermine vaccine acceptance, with frequent internet use showing a negative association with vaccine hesitancy. van Dijk (2020) advocates disseminating trustworthy and helpful COVID-19 information via the internet and mobile phones as an effective measure to lessen digital inequality and combat the virus. Insufficient digital literacy can lead to misinformation about COVID-19 and hinder adherence to preventive measures (Carlos et al., 2022; Eronen et al., 2021; Jasuja et al., 2021).

Challenges and Opportunities Faced by Older Adults in Online Information Seeking

Regarding older adults, the key issue is digital exclusion, facing challenges like lack of access to technology, limited digital skills, and technophobia, increasing their digital exclusion risk (van Dijk, 2020). It has been established that older adults generally search less HI online, and those with higher socioeconomic status and internet skills benefit more from COVID-19 information (van Dijk, 2020). Digital inequality often exacerbates existing social disparities, making it crucial to examine and address these issues within this age group (van Deursen, 2020; van Dijk, 2020). However, the 50+ age group is far from homogeneous; many use the internet and digital devices as frequently and skilfully as younger individuals.

Zhao et al. (2023) conducted a comprehensive literature review revealing that older adults seek 10 distinct types of HI through six internet-based sources. Their findings categorize influencing factors into two main groups: individual-related and source-related, significantly affecting older adults’ OHIS. The study identifies three primary barriers older adults face in OHIS: individual, social, and technological. To address these challenges, several intervention programs, including educational training workshops, need to be developed to enhance and support the OHIS practices of older adults.

Research focusing on older adults’ OHIS and vaccination was scarce until recent years. Principe and Weber (2023) utilizing SHARE data investigated the effect of OHIS on COVID-19 vaccine hesitancy among Europeans aged 50 and older, revealing that OHIS significantly reduces vaccine hesitancy. Outcomes of Zheng et al. (2022) indicate that among other factors such as certain ethnicity, and unmarried status, older age correlates with an augmented probability of vaccination due to internet HI seeking. The authors suggest that older individuals, having grown up before the internet era, primarily depend on traditional media and institutions for HI. Their late internet adoption could make them more receptive to new information sources, significantly influencing their behavior.

Despite significant research on the associations of OHIS and HB, the relationship between OHIS and vaccination intentions, particularly among older adults who are most at risk from the virus and frequently face digital literacy challenges, warrants deeper examination (Zhang et al., 2024). As a digitally successful but socially less advanced small country compared to developed nations, Estonia presents a compelling case study in this context.

Based on previous studies, the following hypotheses were developed:

Hypothesis 1: Higher educational level, higher income, and being male support COVID-19 vaccination intention.

Hypothesis 2: ICT use is positively linked to vaccination intention.

Hypothesis 3: Seeking HI online is associated with willingness to vaccinate.

Method

A cross-sectional survey of 501 respondents aged 50+ in Estonia was conducted from July 20, 2020 to August 3, 2020, one month after the lockdown ended. Norstat, a market research company, selected the sample from a 20,000+ person research panel, enabling online, telephone, and face-to-face interviews. The panel participants were randomly selected from various surveys, ensuring diverse sociodemographic representation. A representative sample was carefully established with quotas, ensuring sufficient responses from each subgroup (e.g., specific age groups). Half of the participants completed an online questionnaire, and the other half were interviewed by phone, with identical questions for both methods. Interviewees were briefed on study goals, assured anonymity, participation was voluntary, and they could withdraw at any point.

The Sample

Individuals aged ≥50 years were eligible to participate in the study. The sample included 204 men (40.7%) and 297 women (59.3%). Participants ranged in age from 50 to 94, with a median age of 65. The representative age group 50–54 comprised 88 (17.6%) and 55–64 age group comprised 162 (32.3%) people, whereas 134 (26.7%) made up the 65–74 age group and 117 (23.4%) belonged to the 75+ age group. A total of 359 (71.7%) were Estonians, and 142 (28.3%) belonged to other nationalities. Over half (291; 58.1%) had basic or secondary education. The sample was representative of gender, age, and nationality.

The Questionnaire

The questionnaire had 15 questions, with 10 relevant to this article (Supplemental Appendix Table A1). Nominal and multiple-choice scales were used to answer questions such as access to a computer or preferred information sources. The range of online sources was comprehensive, from international organization websites to social and alternative media. Socioeconomic indicators included gender, age, nationality, educational level, employment status, and monthly income.

Data Analysis

The data were analyzed using SPSS Statistics version 27.0. Frequency tables and cross-tabulations were used to compare the collected data, while Spearman’s correlation was calculated to examine relationships between data measured on the ranking scale. Analysis of variance was applied to compare group means. The Chi-square test was employed to analyze proportion variations. Different significance levels were used, such as .05, .01, and .001. To aggregate the data, frequencies of responses were added together to arrive at aggregate characteristics, such as the number of different channels or types of information sought.

Results

Vaccination Intention, Socioeconomic Indicators, and Health Status

About half (53.5%, n = 268) of the respondents agreed to vaccinate, 30.5% (153) hesitated, and only 16% (80) were unwilling (see Table 1). Education level exhibited a strong positive correlation with vaccination intention, as detailed in Table 1. A higher proportion of individuals with higher education (64.3%) were inclined to vaccinate, in contrast to just 30.8% among those with only basic education. This notable disparity in vaccine willingness based on educational attainment was statistically significant (χ²(6) = 39.585, p < .001).

Table 1.

Correlation of Vaccination Willingness With Gender, Age, Education Level, Income, and Health Status in Estonians Over 50 Years.

Willingness to vaccinate		Of course	I doubt it	No	Test statistics
Total		268 (53.5)	153 (30.5)	80 (16.0)
Gender	M	124 (60.0)	51 (25.0)	29 (14.2)	χ² (2) = 7.539p = .023
Gender	F	144 (48.5)	102 (34.3)	51 (17.2)	χ² (2) = 7.539p = .023
Age (years)	50–54	53 (60.2)	22 (25.0)	13 (14.8)	χ²(6) = 9.365p = .154
	55–64	79 (48.8)	57 (35.2)	26 (16.0)
	65–74	72 (53.7)	46 (34.3)	16 (11.9)
	75+	64 (54.7)	28 (23.9)	25 (21.4)
Education level	Basic	8 (30.8)	7 (26.9)	11 (42.3)	χ²(6) = 39.585p < .001
	Secondary	46 (46.0)	29 (29.0)	25 (25.0)
	Secondary/vocational	79 (47.9)	55 (33.3)	31 (18.8)
	Higher	135 (64.3)	62 (29.5)	13 (5.2)
Income	<€350	11 (30.6)	11 (30.6)	14 (38.9)	χ²(10) = 44.123p < .001
	€351–€550	75 (48.7)	44 (28.6)	35 (22.7)
	€551–€750	74 (62.2)	39 (32.8)	6 (5.0)
	€751–€1,000	39 (63.9)	19 (31.1)	3 (4.9)
	€1,001–€1,250	24 (51.1)	18 (38.3)	5 (10.6)
	>€1,251	25 (67.6)	6 (16.2)	6 (16.2)
Health status	Very good	10 (43.5)	5 (21.7)	8 (34.8)	χ²(6) = 6.968p = .324
	Good	164 (55.2)	91 (30.6)	42 (14.1)
	Fair	80 (54.1)	44 (29.7)	24 (16.2)
	Poor	6 (50.0)	4 (33.3)	2 (16.7)

Similarly, income levels played a role in influencing vaccine acceptance. The data show a difference in vaccination willingness across income brackets: only 30.6% of individuals earning below €350 were willing to get vaccinated, whereas the following higher income groups demonstrated increased willingness (see Table 1). Among those earning more than €1,251 a month, 67.6% were willing to get vaccinated. These differences related to income were also statistically significant (χ²(10) = 44.123, p < .001) underscoring the influence on socioeconomic status on vaccination decisions.

Surprisingly, the results revealed a gender disparity in vaccine intention. Specifically, 60.0% of male respondents showed a likelihood of getting vaccinated, in contrast to 48.5% of female respondents. The statistically significant difference was validated by the Chi-square test result (χ²(2) = 7.539, p = .023, see Table 1).

The data span various age groups, showing a mixed response across different age brackets. However, the Chi-square test (χ²(6) = 9.365, p = .154) suggests that these differences were not statistically significant, indicating that age may not be a primary factor in vaccine hesitancy.

The survey data do not indicate a significant correlation between health status and vaccine hesitancy (χ²(6) = 6.968, p = .324). However, a slightly higher percentage of respondents with “good” or “fair” health were willing to vaccinate compared to those reporting “very good” or “poor” health.

Intersection of Technology Adoption and Vaccine Intentions

Most respondents (79%, N = 369) reported access to a computer or a smart device, which can be used for online searches. Younger individuals (50–64 years) reported better access than older ones (65+ years): only 8% of those aged 54 and under lacked access, while 29% of those aged 55–64 did not have digital devices. More men (86.3%) than women (74.1%) reported access. Just 23.1% of participants with basic education had access to a computer or smart device, compared to 74% among those with secondary/vocational education and 92.4% with higher education. The relationship between access to a computer and willingness to vaccinate was statistically significant (χ²(2) = 22.216, p < .001, see Table 2)

Table 2.

Associations Between Willingness to Vaccinate and ICT Adoption.

Willingness to vaccinate		Of course	I doubt it	No	Test statistics
Access to a computer/digital device	Yes, I do	224 (56.6)	124 (31.3)	48 (12.1)	χ²(2) = 22.216p < .001
Access to a computer/digital device	No, I don’t	44 (41.9)	29 (27.6)	32 (30.5)	χ²(2) = 22.216p < .001
Willingness to use digital health applications	Definitely yes	31 (79.5)	6 (15.4)	2 (5.1)	χ²(6) = 45.808p < .001
	Likely yes	78 (70.3)	27 (24.3)	6 (5.4)
	Likely no	70 (46.1)	60 (39.5)	22 (14.5)
	Definitely no	71 (47.3)	38 (25.3)	41 (27.3)
Interest in remote communication with Medical Doctors	Important	111 (57.5)	64 (33.2)	18 (9.3)	χ²(4) = 8.209p = .084
	Not very important	39 (58.2)	18 (26.9)	10 (14.9)	χ²(4) = 8.209p = .084
	Rather unimportant	77 (50.7)	45 (29.6)	30 (19.7)

Note. ICT = information and communication technology.

Thirty percent of participants expressed interest in digital health devices like sleep trackers and robot communication. The study revealed a positive relationship between the willingness to vaccinate and the need for a health-related digital application. The greater the need for a health-related digital application, the higher the willingness to vaccinate and vice versa. The difference was statistically significant (χ²(6) = 45.808, p < .001). However, no correlation was established between engaging with a doctor remotely and being in favor/against the vaccine (χ²(4) = 8.209 p = .084).

The Relationship Between HIB and Vaccination Intention

Among those who had no need for HI (or claimed to need it only a few times a year), there were fewer vaccine supporters (47.4%) and more refusers (22.8%). The more recently the respondent searched for information about health on the internet, the more he or she was willing to vaccinate and vice versa (see Table 3). The difference was statistically significant (χ²(6) = 21.163, p = .002).

Table 3.

Associations Between Willingness to Vaccinate and Health Information Behavior.

Willingness to vaccinate		Of course	I doubt it	No	Test statistics
Frequency of perceived HI need	Once a week or more often	31 (58.5)	14 (26.4)	8 (15.1)	χ²(6) = 19.109p = .004
	A few times a month	56 (53.8)	35 (33.7)	13 (12.5)
	A few times per 3 months	73 (62.9)	36 (31.0)	7 (6.0)
	A few times a year or less often	108 (47.4)	68 (29.8)	52 (22.8)
The latest online HI search	In the past 7 days	53 (60.9)	25 (28.7)	9 (10.3)	χ²(6) = 21.163p = .002
	Past 30 days	78 (61.9)	38 (30.2)	10 (7.9)
	Past 6 months/less often	74 (54.8)	47 (34.8)	14 (10.4)
	I don’t look HI online	19 (39.6)	14 (29.2)	15 (31.3)

Note. HI = health information.

The main sources of HI were doctors and hospitals (73.5%), followed by online (57.3%), print media (32.1%), acquaintances (27.7%), and TV/radio (26.5%). Women and Estonians used varied sources more than men and other nationalities. Older participants used fewer sources. Those who sought HI more used diverse sources. Among those who agreed with vaccination, 63.8% mentioned the internet as an essential source of HI. For those who did not plan to get vaccinated, the corresponding figure was only 36.3%. The difference was statistically significant (χ²(2) = 19.135, p < .001).

For online information sources, a statistically significant relationship emerged between vaccination readiness and the use of online versions of professional press publications (χ²(2) = 18.372, p < .001). In addition, 44.6% of individuals who expressed readiness to get vaccinated used specific e-health portals and websites, in contrast to only 20.8% of those against vaccination. The difference was statistically significant (χ²(2) = 9.45, p = .009). The 22.9% of vaccine refusers claimed to use social media platforms as important HI sources; for those agreeing to vaccination, the corresponding indicator was only 10.3% (χ²(2) = 6.689, p = .035). A quarter (25.4%) of those who agreed to vaccination said they get health-related information from Wikipedia. In the case of doubters and deniers, the figure was lower. The difference was statistically significant (χ²(2) = 11.098, p = .004). For other sources of information, no significant distinctions were found between individuals who favored vaccination and those who rejected it. However, individuals who consented to vaccination were more likely to select the “other sources” category compared to those who were either opposed or uncertain, indicating a statistically significant difference (χ²(2) = 15.216, p < .001).

There were notable differences in the types of COVID-19 information that various groups felt were lacking. Those enthusiastic about vaccination more frequently reported a lack of information on where to obtain proper masks and other protective equipment (χ²(2) = 11.789, p = .003), and where to seek help in case of suspected COVID-19 infection (χ²(2) = 7.932, p = .019), compared to those hesitant or opposed to vaccination. This group also expressed greater concern regarding which disinfection tools are appropriate (χ²(2) = 4.704, p = .095) and where to find medication and food if they were to fall ill (χ²(2) = 8.977, p = .011).

Thus, these findings suggest that seeking HI and specific information needs can relate to the intention to get vaccinated. Actively searching HI is linked to a higher likelihood of willingness to vaccinate. Preferences for information sources also differed somewhat between the vaccinated individuals and those who declined or were hesitant, as well as in terms of the information they felt was lacking.

Discussion

The study explored the correlations between ICT use, OHISB, socioeconomic factors, and COVID-19 vaccination readiness among individuals aged 50 and above in Estonia. Over half of the participants were ready to vaccinate in this study. The findings confirmed the first hypothesis, which posited that higher levels of education, greater income, and being male are linked to a willingness to vaccinate. The strong positive correlation between higher education and vaccination intention underscores the importance of education in influencing health-related decisions. The statistically significant differences in vaccination willingness by income indicate that socioeconomic status plays a pivotal role, similar to previous studies (Jain et al., 2021; Lee & Huang, 2022; Richter et al., 2022; Sharma et al., 2023). Higher income individuals are more willing to get vaccinated, emphasizing the need for targeted interventions to address vaccine hesitancy in lower income groups. The gender disparity in vaccine intention, with a higher percentage of males expressing willingness, raises intriguing questions. Further exploration is warranted to understand the underlying reasons and develop gender-specific vaccination communication strategies.

The second hypothesis on the relationship between ICT use and vaccination readiness proved true. Access to computers and smart devices, more prevalent among younger, educated individuals, particularly men, along with a willingness to utilize digital applications, was positively correlated with vaccination readiness, highlighting the significant role of technology in HB. Further investigation is necessary to determine why interest in digital health tools is associated with a greater interest in vaccination. A propensity for utilizing ICT likely signals a heightened inclination to keep abreast of the latest information, a willingness to explore novel concepts, and a keen interest in expanding knowledge on a variety of subjects, health topics included. The outcome also signals a pressing need to enhance digital literacy among older adults lacking ICT skills, particularly those eager to engage with technology but who are held back by a lack of proficiency.

The third hypothesis was also confirmed, highlighting a positive correlation between online HI and vaccination decisions. This outcome is consistent with a recent European study on older adults’ OHISB and vaccination (Principe and Weber, 2023). The significant association between frequent searches for HI in the internet environment and the intention to vaccinate confirms that individuals engaging in active HI searches are more inclined toward better health choices aligning with outcomes of several previous studies (e.g., Jia et al., 2021; Zhang et al., 2024). Zhang et al. (2024) were interested in the reasons behind the higher COVID-19 vaccination rates among individuals who seek HI online, finding that these individuals are more inclined to recognize the positive influence of online information on HB.

The study outcomes also emphasize the importance of addressing information needs through accessible and reliable channels. In line with some earlier works (Allington et al., 2021; Castellano-Tejedor et al., 2022), this study established a connection, albeit a very tenuous one, between using less reliable sources (e.g., social media) and vaccine refusal. The diverse sources of HI, including online platforms, doctors, and hospitals, underscore the need for multifaceted communication strategies. Tailoring information to different sources can enhance vaccine acceptance, similar to Chu et al. (2021). They note that access to diverse information sources is critical for informed decision-making during a crisis. The internet, with its wide range of sources of HI, is an indispensable aid in times of crisis.

Thus, the intersection of socioeconomic indicators, technology adoption, and HIB underscores the need for nuanced and targeted interventions to address vaccine hesitancy.

Conclusions

This study elucidates the nuanced interplay between ICT use, OHSIBs, socioeconomic indicators, and vaccination intentions among Estonians aged 50 and above during the COVID-19 pandemic.

The findings reveal a positive correlation between digital engagement and OHIS regarding vaccination readiness, underscoring the potential of digital platforms to enhance health communication and promote vaccination and indicating the importance of improving individuals’ technological proficiency. Future studies ought to explore more thoroughly the obstacles and enablers of vaccination among older adults, employing qualitative methods to refine and enhance communication strategies.

Looking ahead, the study advocates for more in-depth qualitative research to explore the underlying reasons behind vaccination hesitancy or refusal among older adults. Such insights are invaluable for refining health communication tactics and developing more nuanced and effective interventions tailored to this demographic’s unique needs and preferences.

In summary, the study’s findings contribute to the knowledge of the intersection of HIB, technology use, and health decision-making. It offers insights for policymakers, health communicators, and researchers aiming to improve public health outcomes through targeted interventions.

Supplemental Material

sj-docx-1-heb-10.1177_10901981241249972 – Supplemental material for How Technology, Health Information Seeking, and Socioeconomic Factors Are Associated With Coronavirus Disease 2019 Vaccination Readiness in Estonians Over 50 Years?

Supplemental material, sj-docx-1-heb-10.1177_10901981241249972 for How Technology, Health Information Seeking, and Socioeconomic Factors Are Associated With Coronavirus Disease 2019 Vaccination Readiness in Estonians Over 50 Years? by Marianne Paimre, Sirje Virkus and Kairi Osula in Health Education & Behavior

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 research received funding from the ‘Ülikoolide arengufondid’ (‘University Development Funds’) through grant TF3320, ‘Surveying the Health Information Behavior of Older Estonian Adults (50+) in the Online Environment’.

ORCID iDs

Marianne Paimre

Sirje Virkus

Supplemental Material

Supplemental material for this article is available online at .

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