Internet Use for Health-Related Purposes among Older People in Thailand: An Analysis of Nationwide Cross-Sectional Data

Abstract

As the global population ages, understanding the digital health behaviors of older adults becomes increasingly crucial. In Thailand, where the elderly population is rapidly growing, examining how older individuals use the internet for health-related purposes can provide valuable insights for enhancing healthcare accessibility and engagement. This study investigates the use of the internet for health-related purposes among older adults in Thailand, focusing on the socio-demographic factors influencing this behavior. Utilizing cross-sectional data from the “Thailand Internet User Behavior Survey 2022”, which includes responses from 4652 older adults, the study employs descriptive statistics, chi-square tests, and logistic regression analysis. The results reveal that approximately 10.83% of older adults use the internet for health purposes. The analysis shows that higher income (AOR = 1.298, p = 0.030), higher level of education (degree education: AOR = 1.814, p < 0.001), skilled occupations (AOR = 2.003, p < 0.001), residence in an urban area (AOR = 3.006, p < 0.001), and greater confidence in internet use (very confident: AOR = 3.153, p < 0.001) are significantly associated with a greater likelihood of using the internet for health purposes. Gender and age did not show significant differences in health-related internet use, indicating a relatively gender-neutral and age-consistent landscape. Significant regional differences were observed, with the northeastern region showing a markedly higher propensity (AOR = 2.249, p < 0.001) for health-related internet use compared to the northern region. Meanwhile, the eastern region (AOR = 0.489, p = 0.018) showed lower odds. These findings underscore the need for targeted healthcare policies to enhance digital health engagement among older adults in Thailand, emphasizing the importance of improving digital literacy, expanding infrastructure, and addressing region-specific health initiatives.

1. Introduction

The global internet-usage landscape is characterized by its extensive reach and diverse implications. Nevertheless, internet penetration rates show notable disparities between groups, with developed countries generally exhibiting greater access and use compared with their developing counterparts [1]. As of 2023, approximately 67% of the global population had internet access, marking a 4.7% increase from 2022. This growth was higher compared to the 3.5% increase observed between 2021 and 2022. In 2023, the number of people without internet access decreased to an estimated 2.6 billion, or 33% of the global population. Internet usage remains strongly correlated with the developmental status of a country. In 2020, 90% of people in high-income countries were internet users. By 2023, this figure increased to 93%, approaching near-universal access. Conversely, in low-income countries, internet usage is reported at 27%, up from 24% in 2022. This 66-percentage-point disparity underscores the significant digital divide between high- and low-income nations [2].

In Thailand, internet penetration has grown substantially, reaching 85.27% of the population in 2021 [3]. Although this rate places Thailand above the global average, it is still lower than the Organization for Economic Co-operation (OECD) average of 87.39% [4]. Moreover, significant disparities exist within the country, particularly between urban and rural areas. Urban areas in Thailand typically enjoy good broadband coverage, whereas rural regions lag. As of 2021, 91.7% of the urban population had internet access compared with only 84.9% in rural areas [5]. Mobile internet has played a crucial role in bridging this gap, with 4G coverage reaching 99% of the population by 2022 and 5G rollout beginning in major urban centers [6]. The Thai government has recognized the importance of widespread internet access and has implemented policies to address infrastructure gaps. Notable initiatives include the “Net Pracharat” project, which aims to provide broadband internet to all villages, and the “Smart Thailand” initiative, which focuses on comprehensive digital infrastructure development [7,8]. These efforts underscore Thailand’s commitment to bridging the digital divide and leveraging internet connectivity for various sectors, including healthcare information access.

Thailand is rapidly transitioning into an aged society. As of 2022, the average life expectancy in Thailand was 79.68 years (75.47 years for men and 83.93 years for women). This is higher than the global average of 72 years and the OECD average of 79.63 years [3]. People aged 60 and above accounted for approximately 20.08% of the population in Thailand in 2023, equating to about 13 million people [9]. These demographic trends underscore the growing importance of addressing the health-information needs of an aging population, particularly as Thailand, similar to many developing countries, grapples with the challenges of an expanding older cohort [10]. The extended life expectancy, especially among women, highlights the potential long-term effects of improving access to health information for older adults [11]. As many developing countries are experiencing similar demographic transitions and rapid digitalization, insights from Thailand can provide valuable lessons for policymakers and healthcare providers in comparable contexts. The intersection of population aging and digital health engagement presents both challenges and opportunities for healthcare systems worldwide.

Several international organizations have recognized the importance of digital technologies for supporting healthy aging and improving access to health information for older adults. The WHO Global Strategy on Digital Health 2020–2025 emphasizes the development of age-friendly digital health solutions and improving digital literacy among older populations while addressing the digital divide and ensuring data privacy [12]. The UN Decade of Healthy Ageing (2021–2030) initiative focuses on integrating care systems, promoting age-friendly digital environments, and combating ageism in technology use [13]. Furthermore, the OECD report Promoting Healthy Ageing underscores the potential of digital technologies in supporting healthy and active aging [14]. Additionally, the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) is working to establish standardized assessment frameworks for AI-based health solutions, which could significantly impact the access of older adults to reliable digital health information [15]. In alignment with these global initiatives, Thailand has developed policies to promote digital inclusion among older adults, such as the Second National Plan on the Elderly (2002–2021), which includes provisions for improving the access of older people to and their use of information and communication technologies [16].

Online health information can serve as both a complement to and a potential substitute for in-person healthcare services, particularly in areas with limited access to healthcare facilities. As Mao et al. [17] highlighted in their study on the use of information communication technologies by older people, digital health resources can help reduce pressure on healthcare services and improve access to health information in underserved areas. This is particularly relevant in developing countries, such as Thailand, where rural areas may face challenges in accessing traditional healthcare services. Understanding the healthcare system in Thailand is crucial when examining the use of online health information among older adults, as it may influence their health-seeking behaviors and their reliance on digital health resources. Thailand implemented a universal healthcare coverage system in 2002, known as the Universal Coverage Scheme (UCS) or the “30 Baht Scheme.” This system provides healthcare coverage to all Thai citizens who are not covered by other government health insurance schemes. Under the UCS, patients pay a nominal fee of THB 30 (approximately USD 1) per visit for outpatient services, and inpatient services are free. However, this scheme primarily covers services at public health facilities and some private hospitals that have opted into the system [18]. Despite the low official fees, indirect costs, such as transportation costs, especially in rural areas, and potential loss of income due to time spent seeking care may be associated with accessing healthcare services. Moreover, some services or medications may not be fully covered, leading to out-of-pocket expenses. These factors could influence individuals, particularly older adults, to seek health information online as a complementary or sometimes alternative source of health guidance [19].

The use of the internet for health-related purposes constitutes a considerable and rapidly evolving trend, particularly among older adults. The digitalization of health was already gaining traction as a significant trend before COVID-19, but the pandemic has greatly accelerated this process [20,21]. The “Survey of Internet User Behavior 2022”, conducted by the Electronic Transactions Development Agency [22], revealed a significant integration of internet technology within the healthcare domain, fundamentally altering the way older adults interact with health services and information. Furthermore, 35.03% of the older population is involved in online activities related to exercise and health tracking and assessment, indicating an increased awareness of the importance of physical activity and routine health monitoring. Despite the advantages of using the internet to access health information and services from home, challenges such as the digital divide, misinformation, and a lack of digital literacy remain [23,24]. The abundance of online health information can lead to misinformation and self-diagnosis, potentially causing harm if individuals rely on unverified sources [25]. Additionally, the privacy and security of sensitive health data remain significant concerns, particularly with the increasing use of digital health records and telemedicine services [26]. The issue of digital health inequalities has gained increasing importance, highlighting the so-called digital health paradox. This paradox reflects unequal access and the ability of different social groups to benefit from digital transformation and opportunities [27]. Factors such as age, gender, socioeconomic status, and geographic location significantly influence the scope of healthcare evolution [28,29]. Research from various regions, including studies by Koo et al. [30] in Taiwan and AlGhamdi and Moussa [23] in Saudi Arabia, underscores the correlation between higher levels of education, economic status, and increased internet use for health information. Additionally, geographic location is critical, with urban dwellers generally having better access to health-related online information than their rural counterparts [28]. These issues are particularly pertinent for the aging population, in which a high proportion of individuals lives with chronic illness, disability, and isolation.

Although older adults could significantly benefit from digital health solutions, the global adoption of such technologies remains lower among this age group compared with younger individuals [31]. However, the role of the internet as a critical tool in health management for older adults has grown substantially, influencing their health behaviors and attitudes. Studies, such as those by Koo et al. [30], have demonstrated that access to online health information significantly impacts the health-related decisions of older adults. Ybarra and Suman [32] underscored the importance of reliable online disease information for alleviating user anxiety, increasing self-efficacy, and potentially reducing dependence on ambulatory care services. Nonetheless, significant disparities in internet use among older people persist [33,34], indicating a digital divide that affects health outcomes in older adults. Accordingly, this study aims to address the following research question:

RQ: 

which sociodemographic factors influence the use of the internet for health-related purposes among older adults in Thailand, and how do these factors affect digital health engagement?

This study is grounded in Andersen’s behavioral model of health-services use [35,36]. This model posits that health-services use is determined by three dynamics: predisposing factors (e.g., demographics, social structure, and health beliefs), enabling factors (personal/family and community resources), and need factors (perceived and evaluated health status). Although originally developed to explain traditional health-services use, this model has been successfully adapted to understand the use of health information technology and online health-information seeking [37]. In our study, we apply this model to understand internet use for health-related purposes among older adults in Thailand. Predisposing factors in our analysis include age, gender, and education level. Enabling factors encompass income, occupation, urban/rural residence, and confidence in using the internet. Although our dataset lacks direct measures of need factors (e.g., health status), we acknowledge their potential influence on health-related internet use. By applying Andersen’s model to the context of digital health engagement in Thailand, we aim to provide insights into how various individual and contextual factors shape older adult use of the internet for health purposes. This theoretical framework guides our analysis and interpretation of the sociodemographic factors influencing health-related internet use among older Thai adults. We hypothesize that predisposing factors (e.g., age, gender, and education) and enabling factors (e.g., income, occupation, urban/rural residence, and confidence in using the internet) will significantly influence the use of the internet for health-related purposes among older adults in Thailand. This study not only contributes to the understanding of digital health trends among older adults in Thailand but also provides insights into the broader implications of internet use in healthcare, underscoring the significance of this research in the evolving landscape of digital health. The findings from this study will have implications not only for Thailand but also for other countries, particularly developing nations facing similar challenges in digital health engagement among older adults.

2. Materials and Methods

2.1. Sample

The sample for this study was derived from the “Thailand Internet User Behavior Survey 2022”, conducted by the Electronic Transactions Development Agency (ETDA). This survey produced a comprehensive dataset representing a broad and diverse spectrum of internet users across Thailand. The survey targeted various segments of the Thai population, ensuring balanced coverage across different age groups, regions, and provinces. The sampling process was systematically controlled and weighted to reflect the disproportional distribution of internet users across the country, which was shown by the “Household Information and Communication Technology Use Survey 2021” conducted by the National Statistical Office. The data collection for the survey was primarily conducted through online methods. The questionnaire was distributed via email using the ETDA’s database and was further promoted through banners on websites and social media platforms, such as Facebook, LINE@, and Instagram. Additionally, the survey received sponsorship from various government and private agencies, enhancing access and participation rates. In certain areas, targeted efforts were made to ensure the questionnaire reached a broad spectrum of internet users. This strategy ensured a comprehensive dataset with a wide variety of responses. The survey was conducted from April to July 2022, inviting voluntary participation and successfully garnering responses from 46,348 individuals.

The focus of the current study was narrowed to a subgroup dataset pertaining to older persons. Data related to individuals aged 60 years and older were meticulously extracted from the main dataset, resulting in a significant sample size of 4652 older people. We conducted a thorough examination of the missing data in our variables of interest. While most variables had complete data, we found some missing responses in income (0.214% missing). We used multiple imputation techniques to handle these missing data, ensuring that our analysis utilized the full sample of 4652 older adults. This dataset provided rich and targeted data for the analysis of health-related internet behavior among older persons in Thailand. This targeted approach allows for an in-depth exploration of digital health engagement within this demographic, offering valuable insights into the unique patterns, preferences, and challenges in the context of internet use for health-related activities. It is important to note that the nature of this online survey may not be fully representative of the entire older population in Thailand. While older adults (60+) make up about 20% of Thailand’s population, they represent only 10% of our sample. This underrepresentation is likely due to the online nature of the survey, which may have excluded older adults with limited internet access or digital literacy.

2.2. Dependent Variable

The primary dependent variable in this study is internet use for health purposes (health activities). This binary variable indicates whether respondents have utilized the internet primarily for accessing health-related information and services over the past year. It includes activities such as seeking online public health services, looking up health information, following health news, engaging in online health assessments, and participating in telemedicine consultations. The variable is coded as a 1 for respondents who reported using the internet for any of these health-related purposes and a 0 for those who did not. This variable provides a clear measure of general health engagement online among the older population in Thailand.

2.3. Independent Variables

The study examines several independent variables to understand their influence on the dependent variable. These variables are crucial for identifying patterns and determinants of internet use for health purposes among older people in Thailand (See

Table 1. Operational definitions of independent variables.

Independent Variables	Categories/Operational Definitions
Gender	0: Female 1: Male
2. Age	1: Young old = 60–64 years old 2: Middle old = 65–74 years old 3: Very old = 75 years old and older
3. Region	1: Northern 2: Northeastern 3: Central 4: Southern 5: Western 6: Eastern
4. Income	0: Less than 15,000 THB 1: 15,000 THB and above
5. Education	0: Non-degree education (below primary school to high school/vocational certificate) 1: Degree education (associate degree and above)
6. Occupation	0: Manual occupation (farmers, general laborers or drivers, butlers or housekeepers) 1: Skilled occupation (public officials, private employees, business owners, freelancers, pensioners or retirees)
7. Residence (Urban/Rural)	0: Rural area 1: Urban area
8. Degree of Confidence in Using the Internet	0: Not confident at all 1: Slightly confident 2: Very confident 3: Most confident

). Gender is coded as a 0 for females and a 1 for males, allowing the analysis to distinguish between the two genders and examine potential differences in internet use for health purposes based on gender. The age variable categorizes participants into three groups: “young old” (60–64 years), “middle old” (65–74 years), and “very old” (75 years and older). This classification, inspired by Kramanon and Gray [38], is tailored to the Thai context, recognizing 60 as the onset of older age and reflecting Thailand’s average life expectancy of around 79.68 years [3].

The region variable categorizes participants into six groups based on their geographic location within Thailand: northern, northeastern, central, southern, western, and eastern regions. This classification helps analyze regional differences in internet use for health purposes. Income has been simplified into two categories: less than THB 15,000 and THB 15,000 and above. This categorization allows the study to investigate how financial status influences internet usage for health purposes.

Education levels have been collapsed into two main categories: non-degree education (from below primary school to high school/vocational certificate) and degree education (associate degree and above). This variable enables an analysis of how educational attainment impacts older adults’ engagement with online health resources. The study includes two main occupational categories: manual occupation (farmers, general laborers or drivers, butlers, or housekeepers) and skilled occupation (public officials, private employees, business owners, freelancers, pensioners, or retirees). This variable examines whether occupational background influences internet use for health purposes.

The residence variable distinguishes between urban and rural settings, considering geographic and infrastructural disparities that might exist in internet access and usage. Urban and rural residents may face different challenges and opportunities when utilizing online health services. The degree of confidence in using the internet, rated on a scale from 0 (not confident at all) to 3 (most confident), assesses how comfortable and skilled the elderly feel in navigating the internet for health-related purposes. It highlights whether confidence, or the lack thereof, is a major factor influencing the adoption and usage of online health resources among older people.

2.4. Statistical Analysis

The statistical analysis employed in this study involves several steps to comprehensively examine the factors influencing internet use for health-related purposes among older adults in Thailand. Descriptive statistics are used to outline the characteristics of the study population. Frequencies and percentages describe the distribution of participants across various categories. To examine the differences in the use of the internet for health purposes among different groups based on the independent variables, the chi-square test of independence is utilized. This statistical test assesses whether there is a significant association between categorical variables. It is enabled for the identification of significant differences in internet use for health purposes across different demographic and socioeconomic factors. The main statistical analysis involves logistic regression, used to explore the factors influencing the use of the internet for health-related purposes among older adults. This analysis provides adjusted odds ratios (AOR) with the corresponding robust standard errors (SE), 95% confidence intervals (CI), and p-values for each independent variable under consideration, such as gender, age, region, income, education level, occupation, residence, and degree of confidence in using the internet. Logistic regression is particularly suited to this analysis, as it deals with a binary dependent variable (use vs. non-use of the internet for health purposes) and allows for the estimation of the likelihood of internet use for health purposes as a function of various predictors. The results are reported with specific attention to statistical significance, indicated by p-values. The conventional thresholds for significance (p < 0.05, p < 0.01, p < 0.001) are adhered to, with significant findings highlighted accordingly to demonstrate the strength and reliability of the associations found between the independent variables and the use of the internet for health-related purposes.

3. Results

3.1. Characteristics of Older Adults Based on Internet Use for Health Purposes

Table 2. Characteristics of older adults in Thailand based on internet use for health purposes.

Characteristics	All N = 4652 (100%)	Used Internet for Health Purposes N = 504 (10.83%)	Did Not Use Internet for Health Purposes N = 4148 (89.17%)	p-Value 1
Gender				0.757
Male	2596 (55.80%)	278 (5.98%)	2318 (49.83%)
Female	2056 (44.20%)	226 (4.86%)	1830 (39.34%)
Age				0.328
60–64 (young old)	2255 (48.47%)	230 (4.94%)	2025 (43.53%)
65–74 (middle old)	2007 (43.14%)	233 (5.01%)	1774 (38.13%)
75+ (very old)	390 (8.38%)	41 (0.88%)	349 (7.50%)
Region				<0.001
Northern	476 (10.23%)	45 (8.93%)	431 (10.39%)
Northeastern	1592 (34.22%)	245 (48.61%)	1347 (32.47%)
Central	1344 (28.89%)	130 (25.79%)	1214 (29.27%)
Southern	437 (9.39%)	30 (5.95%)	407 (9.81%)
Western	415 (8.92%)	36 (7.14%)	379 (9.14%)
Eastern	388 (8.34%)	18 (3.57%)	370 (8.92%)
Income				0.035
Less than THB 15,000	3439 (73.93%)	353 (7.59%)	3086 (66.34%)
THB 15,000 and above	1213 (26.07%)	151 (3.25%)	1062 (22.83%)
Education				<0.001
Non-degree education	3891 (83.64%)	366 (7.87%)	3525 (75.77%)
Degree education	761 (16.36%)	138 (2.97%)	623 (13.39%)
Occupation				<0.001
Manual occupation	3369 (72.42%)	284 (6.10%)	3085 (66.32%)
Skilled occupation	1283 (27.58%)	220 (4.73%)	1063 (22.85%)
Residence				<0.001
Rural	2794 (68.63%)	177 (4.35%)	2617 (64.28%)
Urban	1858 (39.94%)	327 (7.03%)	1531 (32.91%)
Degree of Confidence in Using the Internet				<0.001
Not confident at all	319 (6.86%)	14 (0.30%)	305 (6.56%)
Slightly confident	1258 (27.04%)	83 (1.78%)	1175 (25.26%)
Very confident	1555 (33.43%)	212 (4.56%)	1343 (28.87%)
Most confident	1520 (32.67%)	195 (4.19%)	1325 (28.48%)

Note: ¹ Pearson’s chi-squared test.

shows the characteristics of 4652 older adults in relation to their use of the internet for health purposes. Among this group, 504 individuals (10.83%) used the internet for health purposes, while 4148 (89.17%) did not. Analyzing gender, the study found no significant differences in the use of the internet for health purposes (p-value: 0.757). Of the total participants, 2596 (55.80%) were male, and 2056 (44.20%) were female. Within the subgroup of internet users for health purposes, 278 (5.98%) were male, and 226 (4.86%) were female, indicating a relatively balanced gender distribution among both users and non-users. In terms of age, participants were categorized into three groups: 60–64 years (48.47%), 65–74 years (43.14%), and 75 years and older (8.38%). Internet use for health purposes was slightly more prevalent in the 65–74 age group, with 233 individuals (5.01%) using it, followed by 230 (4.94%) in the 60–64 age group and 41 (0.88%) in the 75+ age group. However, no significant differences were found across these age groups (p-value: 0.328).

When examining the region, significant differences were observed (p-value: <0.001). The northeastern region had the highest proportion of internet users for health purposes (48.61%), followed by the central region (25.79%), northern region (8.93%), western region (7.14%), southern region (5.95%), and eastern region (3.57%). Income level showed a significant difference (p-value: 0.035). The majority of users, 353 (7.59%), fell into the “Less than 15,000 THB” income bracket, while 151 (3.25%) were in the “15,000 THB and above” category.

Regarding education level, there was a significant difference (p-value: <0.001). The majority of users, 366 (7.87%), had non-degree education, while 138 (2.97%) had degree education. Occupation also showed a significant difference (p-value: <0.001). Among users, 284 (6.10%) had manual occupations, while 220 (4.73%) had skilled occupations. There was a significant difference in terms of residence (p-value: <0.001). A higher proportion of users resided in urban areas (327 individuals, 7.03%) compared to rural areas (177 individuals, 4.35%).

A significant difference was noted in the degree of confidence in using the internet (p-value: <0.001). Among non-users, a larger proportion reported low confidence levels in using the internet, with 319 individuals (6.86%) reporting not feeling confident at all and 1258 individuals (27.04%) feeling slightly confident. In contrast, users of the internet for health purposes showed higher levels of confidence, with 212 individuals (4.56%) feeling very confident and 195 individuals (4.19%) reporting themselves to be the most confident. These findings highlight the various sociodemographic factors that influence internet use for health purposes among older adults in Thailand, providing a foundation for further analysis of the determinants of digital health engagement in this population.

3.2. Comparative Analysis of Internet Use for Health Purposes: Older Adults vs. General Population

Before analyzing the factors associated with older adults’ use of the internet for health-related purposes, we conducted a comparative analysis to contextualize this usage within the broader survey population. Among the entire survey population of 45,670 individuals (aged 15 and above), the overall rate of internet use for health purposes was 13.70%. In contrast, only 10.83% of individuals aged 60 and above reported using the internet for health purposes. To determine if this difference was statistically significant, we performed a chi-square test. The results indicated a significant difference (χ² = 35.88, p < 0.001), suggesting that older adults are less likely to use the internet for health purposes compared to the younger population in our sample.

This significant difference underscores the digital divide that exists along age lines. While 13.70% of the overall survey population used the internet for health purposes, only 10.83% of individuals aged 60 and above did so. This finding aligns with previous research that indicates lower rates of digital health engagement among older populations [31,33]. These results highlight the need for targeted interventions to increase digital health literacy and access among older adults in Thailand. However, it is important to note that our sample of older adults, being drawn from an online survey, may already represent a more digitally engaged subset of the older population. This suggests that the actual disparity in digital health engagement between older adults and the general population in Thailand might be even more pronounced than our results indicate.

3.3. Comparison of Health-Related Internet Use with Other Online Activities among Older Adults

The comparison of internet use for health purposes with other online activities provides an important context for understanding the digital behaviors of older adults in Thailand. The high engagement in social media and online communication (60.62%) suggests that a significant portion of older internet users are comfortable with basic online interactions. This could be leveraged as a potential platform for disseminating health information or promoting digital health services. The higher use of e-government services (21.79%) compared to health-related internet use indicates that older adults are willing to engage with official online platforms for practical purposes. This could inform strategies for integrating health services with other government online services to increase engagement. While online banking (12.66%) is used slightly more than the internet for health purposes, both rates are relatively low. This suggests that activities requiring more complex interactions or involving sensitive personal information are less commonly adopted by older adults. It highlights the need for user-friendly interfaces and robust security measures on digital health platforms to encourage greater adoption. The relatively lower use of the internet for health purposes (10.83%) compared to these other activities underscores the potential for growth in this area. It suggests that, while older adults are engaging in various online activities, there may be specific barriers or a lack of awareness regarding online health resources.

3.4. Analysis of Factors Influencing Health-Related Internet Use among Older People in Thailand

Table 3. Factors influencing health-related internet use among older adults in Thailand; results of logistic regression analysis.

Variables	Adjusted Odds Ratio (Robust SE)	95% CI	p-Value	Sig.
Gender
Female (ref.)	1.00	1.00
Male	1.064 (0.107)	0.87–1.29	0.537
Age
60–64 (young old) (ref.)	1.00	1.00
65–74 (middle old)	1.115 (0.117)	0.91–1.37	0.297
75+ (very old)	0.898 (0.166)	0.63–1.29	0.559
Region
Northern (ref.)	1.00	1.00
Northeastern	2.249 (0.426)	1.55–3.26	<0.001	***
Central	0.739 (0.144)	0.51–1.08	0.120
Southern	0.674 (0.182)	0.40–1.14	0.144
Western	1.169 (0.292)	0.72–1.91	0.533
Eastern	0.489 (0.147)	0.27–0.88	0.018	*
Income
Less than THB 15,000 (ref.)	1.00	1.00
THB 15,000 and above	1.298 (0.156)	1.03–1.64	0.030	*
Education
Non-degree education (ref.)	1.00	1.00
Degree education	1.814 (0.223)	1.43–2.30	<0.001	***
Occupation
Manual occupation (ref.)	1.00	1.00
Skilled occupation	2.003 (0.222)	1.61–2.49	<0.001	***
Residence
Rural (ref.)	1.00	1.00
Urban	3.006 (0.334)	2.42–3.74	<0.001	***
Degree of Confidence in Using the Internet
Not confident at all (ref.)	1.00	1.00
Slightly confident	2.608 (0.767)	1.46–4.64	0.001	**
Very confident	3.153 (0.917)	1.78–5.57	<0.001	***
Most confident	2.438 (0.706)	1.38–4.30	0.002	**
Pseudo R2	0.104

Note: Robust standard errors in parentheses, *** p < 0.001, ** p < 0.01, * p < 0.05.

presents the analysis results for the various factors associated with older adults’ use of the internet for health-related purposes, identifying the key determinants that significantly affect their inclination to utilize digital health resources. Using logistic regression analysis, the study provides a detailed perspective on how different factors, such as sociodemographic attributes, economic status, educational attainment, occupational backgrounds, residential settings, and degree of confidence in using the internet, influence the older population’s engagement with online health activities.

Contrary to common assumptions that gender may play a pivotal role in digital health utilization, the findings reveal an intriguing parity. The adjusted odds ratio for male participants is 1.064 (SE = 0.107, p-value = 0.537), indicating no substantial difference from females, which was set as the reference group. This suggests a relatively gender-neutral landscape in digital health engagement among the Thai elderly, challenging stereotypes of gendered internet use. Age, a factor often thought to influence technological adoption, similarly did not exhibit a stark differential impact across the age groups studied. The analysis indicates no significant difference in health-related internet use when comparing the middle old (65–74 years) and very old (75+ years) groups to the young old (60–64 years) reference group. Specifically, the adjusted odds ratio is 1.115 (SE = 0.117, p-value = 0.297) for the middle old group and 0.898 (SE = 0.166, p-value = 0.559) for the very old group, suggesting that advancing age does not significantly deter or enhance the likelihood of utilizing the internet for health-related activities.

Significant regional differences are observed. The northeastern region had a markedly higher adjusted odds ratio of 2.249 (SE = 0.426, p-value < 0.001) compared to the Northern region, indicating a greater propensity for health-related internet use. Conversely, the eastern region (AOR = 0.489, SE = 0.147, p-value = 0.018) showed lower odds compared to the northern region, suggesting regional disparities in digital health engagement. Income emerged as a significant determinant. Compared to the reference group, earning less than THB 15,000, individuals with higher incomes (THB 15,000 and above) are more inclined to use the internet for health purposes (AOR = 1.298, SE = 0.156, p-value = 0.030). This finding underscores the influence of higher economic status on accessing digital health resources.

The study further delineates the role of education in facilitating health-related internet use. Individuals with degree education exhibit an increased propensity (AOR = 1.814, SE = 0.223, p-value < 0.001) towards digital health engagement compared to those with non-degree education, suggesting that higher educational levels equip individuals with the requisite skills and confidence to navigate online health resources effectively. Occupational background also significantly influences digital health utilization patterns. Those in skilled occupations show a higher likelihood of utilizing health-related internet services compared to those in manual occupations (AOR = 2.003, SE = 0.222, p-value < 0.001). Residential setting plays a pivotal role, with urban dwellers displaying a markedly higher propensity (AOR = 3.006, SE = 0.334, p-value < 0.001) to engage in online health activities compared to their rural counterparts. This urban–rural divide accentuates the disparities in access to digital infrastructure and health resources.

Lastly, the degree of confidence in using the internet is a strong predictor of health-related internet use. Compared to those not confident at all, individuals who are slightly confident (AOR = 2.608, SE = 0.767, p-value = 0.001), very confident (AOR = 3.153, SE = 0.917, p-value < 0.001), and most confident (AOR = 2.438, SE = 0.706, p-value = 0.002) in using the internet show notably higher odds of engaging in online health activities. This finding highlights the importance of digital literacy and self-efficacy in navigating health information online. Additionally, the model’s pseudo-R2 value of 0.104 indicates that the included variables explain about 10.4% of the variance in health-related internet use among older adults in Thailand.

4. Discussion and Conclusions

4.1. Summary of the Findings

This study examines the relationship between socio-demographic factors and health-related internet use among older people in Thailand. Our findings reveal that, while gender and age do not significantly influence the use of the internet for health purposes, factors such as region of residence, income, education, occupation, urban–rural residence, and confidence in using the internet are crucial determinants.

The lack of gender difference in digital health utilization among older Thai adults contrasts with global trends. Previous studies have consistently shown that women, especially elderly women, are more engaged in seeking health information online [23,24,32,34,39,40,41,42]. The findings from this study present an interesting contrast, as no significant gender differences in the utilization of the internet for health purposes were found among older adults in Thailand. This discrepancy may suggest cultural or societal differences that influence the engagement patterns of older Thai adults with digital health resources. Alternatively, it may reflect a broader acceptance and utilization of digital health platforms among older Thai adults, irrespective of gender. Therefore, while the global literature highlights a clear gender disparity that favors women in digital health engagement, the specific context of Thailand suggests that a more nuanced understanding is required. This highlights the need for targeted digital health interventions that consider the unique sociocultural dynamics influencing health-information-seeking behaviors among older adults in Thailand.

The findings also reveal significant regional differences in health-related internet use among older adults in Thailand. The northeastern region exhibited markedly higher internet use compared to the northern region, indicating a greater propensity for digital health engagement in this area. These findings underscore the necessity for location-specific strategies to improve digital health engagement among older adults. The higher engagement in the northeastern region may be attributed to regional initiatives, cultural factors, or community support systems that facilitate internet use for health purposes. In contrast, the lower engagement in the eastern region highlights the need for targeted interventions to address barriers such as digital literacy, internet accessibility, and socioeconomic factors. Our findings underscore the necessity for location-specific strategies, as recommended by the World Health Organization’s Global Strategy on Digital Health 2020–2025 [12].

Income and education emerge as significant determinants of health-related internet use, aligning with global observations that higher income and education levels correlate with increased engagement in online health activities [23,28,39,40,41,43,44]. This suggests that socioeconomic status significantly impacts access to and utilization of digital health resources. Not only do higher levels of education and income provide better access to digital tools, but they also equip individuals with the skills necessary to navigate and utilize these resources effectively for health purposes. This finding supports the United Nations’ Sustainable Development Goal 3 (Good Health and Well-being), highlighting the need to address digital health disparities. The OECD’s “Bridging the Digital Gender Divide” report (2018) emphasizes the importance of targeted interventions to enhance digital skills among disadvantaged groups, which our findings reinforce.

Occupational background and residential setting significantly influence digital health utilization. Business owners and freelancers are more likely to use the internet for health purposes, suggesting that certain occupational experiences may facilitate digital engagement. Moreover, urban residents show a higher propensity towards online health activities than do their rural counterparts, underscoring the urban–rural divide in digital access and infrastructure. Addressing these disparities requires targeted efforts to improve digital literacy and infrastructure in rural areas. This finding is consistent with the literature, which indicates that the level of urbanization and residence in remote areas significantly affect access to and use of digital health services [23,28,45,46]. These findings underscore the need for initiatives like the UN’s Decade of Healthy Ageing (2021–2030), which emphasizes equitable access to digital health resources. Addressing these disparities requires targeted interventions to improve digital infrastructure and access in rural areas, ensuring equitable access to digital health services across different geographic locations. In other words, our findings highlight the potential for online health information to serve as a valuable complement to traditional healthcare services, particularly in rural or underserved areas. As Thailand continues to develop its digital infrastructure, policymakers should consider how online health resources can be leveraged to improve healthcare access and reduce pressure on existing healthcare services, especially for older adults in rural areas.

Our findings align with and extend Andersen’s behavioral model of health-services use in the context of digital health engagement among older Thai adults. The significant effects of education and income on health-related internet use support the model’s emphasis on predisposing and enabling factors. Education, as a predisposing factor, likely equips individuals with the skills and knowledge necessary to navigate online health resources. Income, an enabling factor, facilitates access to technology and the internet. Notably, confidence in using the internet emerged as a crucial enabling factor, highlighting the importance of digital self-efficacy in promoting health-related internet use. This aligns with Andersen’s model but extends it by emphasizing the role of technology-specific confidence in the digital health context. The influence of occupation and urban/rural residence further underscores the role of enabling factors in shaping digital health behaviors. Interestingly, while age and gender are typically considered predisposing factors in Andersen’s model, they did not significantly impact health-related internet use in our study. This suggests that, in the context of digital health engagement among older adults in Thailand, other factors may play a more prominent role. The regional differences observed in our study highlight the importance of considering broader contextual factors when applying Andersen’s model to digital health behaviors, particularly in diverse settings like Thailand. These findings contribute to the evolving application of Andersen’s model in the digital health landscape and underscore the need for nuanced, context-specific approaches for understanding and promoting health-related internet use among older adults.

In conclusion, the findings from this study provide valuable insights into the socio-demographic factors influencing health-related internet use among older adults in Thailand. While gender and age do not significantly impact digital health engagement, income, education, occupation, region, residence, and confidence in using the internet are crucial determinants. These results highlight the importance of targeted interventions and policies that consider the unique needs and circumstances of different demographic groups to enhance digital health engagement and promote equitable access to health information and services among the elderly population in Thailand.

4.2. Policy Implications

Based on the findings of this study, the following policy implications can be suggested to enhance health-related internet use among older adults in Thailand and other developing countries facing similar challenges:

• Enhancing digital literacy programs: Building on Thailand’s existing initiative of schools for the elderly, the government should consider expanding and refining these programs to specifically target health-related internet use. Given our findings on the significant influence of internet confidence, these programs should focus on building digital skills among older adults, especially those with lower educational backgrounds and income levels. Enhancements could include (1) incorporating health-specific modules on navigating online health resources and telemedicine platforms; (2) implementing peer-to-peer learning components where digitally confident older adults mentor their peers; (3) introducing intergenerational elements, similar to South Korea’s model, where younger individuals teach older adults about technology [47,48]; (4) deploying mobile learning units to reach rural areas; and (5) conducting regular assessments to adapt the curriculum based on feedback and emerging digital health trends. These enhancements can further empower older adults, reduce social isolation, and improve their engagement with digital health services;
• Bridging the urban–rural digital divide: Our study highlights a significant urban–rural divide in digital health engagement. To address this, policies should focus on enhancing digital infrastructure in rural areas by expanding broadband internet access, providing affordable services, and ensuring reliable connectivity. Establishing community digital hubs in rural areas can promote equitable access to digital health resources, offering internet access and assistance to older adults. However, these hubs must also ensure privacy and security for individuals accessing health-related information. Measures such as private cubicles and secure internet connections should be implemented to protect confidentiality. In addition, it is crucial to address the issue of hardware accessibility. Policies should include provisions for subsidizing or providing affordable smartphones and other digital devices to older adults, particularly those in low-income and rural areas. This approach will ensure that all individuals have the necessary tools to access online health services effectively. Thailand could draw inspiration and experiences from initiatives like India’s ‘Digital Village’ program [49], adapting it to the Thai context. Partnerships with telecommunications companies could be explored to incentivize rural infrastructure development through tax breaks or subsidies. Additionally, deploying mobile health clinics equipped with digital resources to remote areas could provide temporary access and training. This multi-faceted approach, combining infrastructure development, community hubs, and mobile solutions, can create a more inclusive digital health ecosystem for Thailand’s rural older population, reducing social inequalities in access to digital health resources;
• Implementing region-specific digital health strategies: Our findings reveal significant regional disparities in digital health engagement, with the northeastern region showing higher adoption rates. This indicates the potential success of regional initiatives such as The Khon Kaen Smart Health Project, implemented in the Northeastern province of Khon Kaen, which is a collaborative initiative involving local hospitals, businesses, and academia, with support from government agencies. This project aimed to develop a health-sensor platform for monitoring the health and behavior of household residents, particularly elderly individuals living alone [50]. This suggests the need for location-specific strategies that cater to the unique cultural, economic, and social contexts of each region. Policymakers should develop and implement tailored digital health programs in collaboration with local community leaders and organizations. These region-specific strategies should consider local social dynamics and support systems, ensuring that interventions are culturally appropriate and socially inclusive. The success factors in the northeastern region, such as the integration of home health monitoring with medical records and the use of smart technologies, could be studied and potentially adapted for other areas. Regional digital health ambassadors could be appointed to champion initiatives and provide localized support. Additionally, partnering with local healthcare providers and community centers can help integrate digital health resources into existing trusted networks;
• Addressing socioeconomic barriers to digital health access: Our study identifies income and education as critical determinants of health-related internet use among older adults. To ensure inclusive access to digital health services, policymakers should implement targeted measures to address these socioeconomic disparities. This could include subsidizing internet costs for low-income elderly households and providing affordable or subsidized digital devices tailored for older users. Developing simplified, user-friendly health platforms with intuitive interfaces can increase engagement among those with limited digital literacy. Additionally, online health information should be made available in multiple languages and formats, catering to diverse educational backgrounds and literacy levels. Collaborations with NGOs and tech companies could be established to create and distribute easy-to-understand digital health content. By implementing these measures, Thailand can work towards ensuring that all older adults, regardless of their financial or educational status, have equitable opportunities to benefit from digital health resources, thereby promoting better health outcomes across all socioeconomic groups;
• Leveraging occupational contexts for digital health promotion: Our findings reveal that individuals in skilled occupations are more likely to use the internet for health purposes compared to those in manual occupations. To extend digital health engagement across all occupational groups, policies should focus on integrating digital health resources into workplace wellness programs. Employers, particularly those with a higher proportion of manual workers, should be incentivized to provide digital health training and access to online health services as part of their employee wellness initiatives. Partnerships with industry associations, labor unions, and professional bodies can help disseminate digital health information more effectively. For retired individuals, collaborations with pensioners’ associations could be established to provide ongoing digital health education. These workplace and post-retirement interventions can support the health and well-being of older adults across various occupational backgrounds, promoting a culture of lifelong learning and health awareness. Such targeted approaches can help bridge the occupational divide in digital health engagement.

4.3. Limitations and Future Research

While this study presents insightful findings on the use of the internet for health-related purposes among older adults in Thailand, it has several limitations. First, the cross-sectional design limits our ability to infer causality between socio-demographic factors and internet use. Longitudinal studies could provide a more nuanced understanding of how digital health engagement evolves over time. Second, our study is limited by the lack of health-related variables in our dataset. We did not have information on respondents’ health status (such as self-rated health, mental health, or the number of chronic conditions) or their healthcare-utilization patterns. These factors, as highlighted by studies like Mao et al. [17] and Andersen [35,36], are likely associated with the likelihood of seeking health information online. Third, the online nature of the survey introduces potential bias. Older adults who participated are likely to be more digitally literate and have better internet access than the general older population in Thailand, potentially leading to an overestimation of internet use for health purposes. Fourth, while we have a large sample size, the reduction from the full survey population to our subsample of older adults may limit the generalizability of our findings. Despite our efforts to address missing data through imputation techniques, there remains a possibility that missing data could have influenced our results. Additionally, the reliance on self-reported data may introduce bias if respondents overestimate their internet usage or misreport their engagement with online health activities. Future research would benefit from objective measures of internet use. The focus on older adults in Thailand may limit the generalizability of the findings to other populations. Comparative studies across different cultural and economic contexts would enrich the understanding of digital health engagement among the elderly.

The study suggests several avenues for future research. First, longitudinal studies are needed to explore the dynamic nature of internet use among the elderly by tracking changes in digital health engagement over time and identifying causal relationships between socio-demographic factors and health-related internet use. Second, expanding the current study to include a more diverse array of socio-demographic backgrounds and geographic locations within Thailand could provide a more comprehensive understanding of the digital divide. Investigating disparities between urban and rural areas and the impact of regional infrastructural differences on internet use for health purposes would be valuable. Third, while our study focused on individual effects of sociodemographic factors, future research could explore the complex interrelationships between region, income, education, and occupation, potentially uncovering nuanced insights to further inform targeted policy interventions and program development. Additionally, future studies could explore the qualitative aspects of older adults’ internet use for health purposes. In-depth interviews or focus groups could uncover the motivations behind seeking health information online, the types of information sought, and the perceived reliability and quality of online health resources.