Digital engagement and online health information seeking behaviour among older adults: findings from Türkiye

Abstract

Background

With advances in information technology and the widespread availability of online health resources, access to digital health information has become increasingly common. In this context, the ability of older adults to effectively access, evaluate, and use online health information has emerged as a critically important competence. Since the internet provides easy and often low-cost access to a wide range of information, older adults are increasingly using the internet for health-related purposes. This study aimed to examine the demographic, economic, and personal factors associated with online health information seeking behaviour among older adults in Türkiye.

Methods

This study used the microdata from the Household Information Technology (IT) Usage Survey conducted by the Turkish Statistical Institute (TurkStat) in 2023 and 2024. Binary logistic regression analysis was performed.

Results

Compared with older adults surveyed in 2023, those surveyed in 2024 had lower odds of online health information seeking behaviour (AOR = 0.760; 95% CI = 0.644–0.898), and men had lower odds of engaging in this behaviour than women (AOR = 0.620; 95% CI = 0.516–0.745). Higher educational attainment, particularly university education (AOR = 4.002; 95% CI = 2.405–6.660), searching for product and service information (AOR = 4.546; 95% CI = 3.783–5.462), social media use (AOR = 1.994; 95% CI = 1.646–2.415), and internet banking use (AOR = 1.649; 95% CI = 1.352–2.011) were associated with greater odds of online health information seeking behaviour.

Conclusion

The findings may help health information providers and healthcare professionals better understand the online health information seeking behaviour of older adults and inform the development of digital health strategies tailored to their needs.

Introduction

The world has transitioned into a digital era in the 21st century, characterised by the rapid evolution of digital technology through the internet [1]. As information technology advances, encompassing domains such as information sharing, telehealth, and virtual social interactions, electronic health management is becoming increasingly vital in enhancing the health of the global population [2]. The integration of digitalisation into healthcare services has transformed how medical resources are accessed through online platforms and has led to fundamental changes in individuals’ health information-seeking behaviour [3]. These developments have also changed how individuals access and seek health-related information, making online health information an increasingly important component of healthcare decision-making.

General information-seeking behaviour is the source of online health information seeking activity [4]. The widely accepted definition of information-seeking behaviour among scholars is: “Information-seeking behaviour pertains to the activities undertaken by users to fulfil specific informational needs” [5]. The rise of user-focused online social networks in recent years has created a knowledge base that has expanded opportunities for online health information seeking behaviour [6].

Individuals who are seeking health information on the internet are characterised as “internet users looking for online information regarding health subjects, whether they are acting as consumers, caregivers, or patients, with the intention of gaining greater control over their health” [7]. Online health information seeking serves various objectives, including understanding disease symptoms, evaluating health risks, exploring treatment options, managing chronic illnesses, and preparing for discussions with healthcare providers [8]. Research indicates that online health information seeking has become one of the most prevalent daily activities throughout a person’s lifetime [9].

There are numerous benefits associated with using the internet for online health information seeking. These include convenient access to health information, a wide range of health resources, opportunities to consult experts, anonymous communication with others facing similar health challenges, and improved health management and outcomes [7, 10, 11].

Although there are benefits to using online health information, the internet can also present considerable risks when consumers lack awareness of the quality of the health information they acquire [12]. Research has indicated that online health information may be scientifically incomplete or unreliable, which may adversely affect the doctor–patient relationship and make it more difficult for users to identify accurate and trustworthy information [13–15]. In addition, limited computer skills, low educational attainment, and geographical constraints may hinder access to online health information [7].

The world has entered an era of population ageing, resulting in unprecedented changes in the age structure of the global population [8]. The United Nations defines individuals aged 60 years and over as older adults, and consistent with previous studies, this definition was adopted in the present study [16–19]. In 2019, the population of individuals aged 60 years and above was 1 billion, and it is projected to increase to 2.1 billion by 2050 [20] As the older population continues to grow, older adults are becoming an increasingly significant demographic of internet and digital technology users [21].

Societies experiencing high rates of ageing face considerable challenges in their healthcare systems due to the rising prevalence of chronic illnesses among older adults and the increasing demand for healthcare resources [22]. As older adults are more susceptible to diseases and chronic conditions than younger individuals, they have greater needs for health information and are increasingly likely to seek health information to make informed health-related decisions [23, 24].

Information technology is expected to improve the lives of the ageing population, strengthen public health, and reduce pressure on healthcare systems; however, concerns remain about whether older adults are fully benefiting from these technological advances [25]. Although some studies report that older adults use information technology for health-related purposes at relatively low rates [26], others suggest that internet use among older adults is increasing [27]. These inconsistent findings suggest that online health information seeking behaviour among older adults and its associated demographic, economic, and personal factors remain insufficiently understood [8]. Against this background, understanding how older adults seek health information online has become increasingly important.

Older adults require health-related information on a wide range of topics, including illnesses, treatments, medications, health services, and health policies [28]. They primarily obtain this information from healthcare professionals, family and friends, and various media sources such as the internet, publications, and television [29, 30].

An examination of the pertinent literature indicates that earlier research has demonstrated that the probability of younger individuals seeking health information online increases with age due to personal factors [31, 32]. Conversely, the likelihood of older adults seeking health information online decreases as they age [33]. Previous studies have attributed this to the higher likelihood of older adults experiencing vision and hearing loss [34, 35], declining cognitive abilities [34], and deteriorating health status [22, 31].

Previous studies have shown that socioeconomic factors, including social status, educational attainment, income, marital status, and social support, are associated with online health information seeking behaviour among older adults [36–38]. Educational attainment may be associated with older adults’ ability to understand, evaluate, and use online health information, whereas higher income may be associated with greater access to digital devices and online health resources [39, 40].

Previous studies also suggest that marital status and social support are associated with online health information seeking behaviour among older adults, as family and social networks may facilitate the use of digital health resources [32]. Conversely, older adults living alone or in social isolation may experience greater difficulties in understanding and using e-health resources because of limited social support [22, 28].

Based on earlier research, factors associated with online health information seeking behaviour among older adults can be broadly categorised into personal characteristics and health information characteristics [28, 41–43]. Personal characteristics include age, gender, health literacy, and cognitive abilities, whereas health information characteristics include comprehensibility, presentation, and reliability [41–43]. Cognitive abilities, health literacy, and internet search skills may be associated with older adults’ ability to seek and use online health information [44, 45]. Health literacy interventions have been reported to improve older adults’ digital skills and health-related decision-making [46, 47].

Previous studies have examined the content, determinants, barriers, and other aspects of online health information seeking behaviour among older adults [48]. They suggest that older adults often have limited online search experience, rely on previous experiences, and may have difficulty evaluating online health information [49]. Although several reviews have examined online health information seeking behaviour or general internet use among older adults [8, 27, 34, 50], comprehensive nationally representative evidence on the demographic, economic, and personal factors associated with this behaviour remains limited.

Recent studies have further expanded the evidence on online health information seeking and digital health literacy among older adults. Recent reviews indicate that online health information seeking plays an increasingly important role in healthy ageing and may support health management, informed decision-making, and access to health-related resources. However, these studies also emphasise that substantial inequalities in digital skills, digital health literacy, and access to online health information persist among older adults [1, 3, 51]. In addition, recent evidence suggests that factors such as self-efficacy, previous digital experience, and broader digital engagement may be associated with online health information seeking, while the digital divide continues to limit equitable access to digital health resources [52]. In Türkiye, recent research has also demonstrated that patterns of online health information seeking differ across population groups and have changed over time, highlighting the importance of examining these behaviours within the national context [53, 54]. These findings underscore the need for a conceptual framework to better understand the factors associated with online health information seeking behaviour among older adults.

The selection of variables in the present study was conceptually informed by the Comprehensive Model of Information Seeking (CMIS), which proposes that health information-seeking behaviour is shaped by antecedent characteristics of individuals and contextual factors influencing information acquisition [55]. Previous studies have successfully applied and adapted the CMIS to explain online health information seeking across different populations and settings, including India, the United States, China, and Hong Kong, supporting its use as a conceptual framework rather than as a rigid predictive model [56–59].

Despite the growing body of evidence, important gaps remain in the existing literature. Most previous studies have focused on selected determinants of online health information seeking or have been conducted in specific populations and settings. Nationally representative evidence simultaneously examining demographic, economic, and broader digital engagement factors associated with online health information seeking behaviour among older adults remains limited, particularly in Türkiye. Given that patterns of online health information seeking may differ across population groups and have changed over time in Türkiye, nationally representative evidence is needed to better understand these patterns among older adults. Addressing this gap may contribute to a better understanding of digital inequalities among older adults and provide evidence to support the development of targeted digital health policies and interventions.

Therefore, the aim of this study was to examine the demographic, economic, and personal factors associated with online health information seeking behaviour among older adults in Türkiye using nationally representative survey data. Identifying these factors may provide evidence to support the development of interventions and policies aimed at improving digital health literacy and promoting equitable access to online health information among older adults.

Method

Data

The Household Information Technology Usage Survey has been conducted annually by the Turkish Statistical Institute (TurkStat) since 2004, except in 2006, to collect information on households’ and individuals’ access to and use of information and communication technologies. The survey covers the non-institutional population living in private households throughout Türkiye and provides comprehensive information on digital technology use, internet access, and online activities. The Nomenclature of Territorial Units for Statistics (NUTS) Level 1 classification is used as the stratification criterion, and the sample size is determined to produce estimates at both the national and NUTS Level 1 levels. Because of the multistage sample design, sampling weights based on selection probabilities are calculated. TurkStat derives the final weights by incorporating initial design weights, adjustments for out-of-scope units and non-response, and calibration and trimming procedures.

In the present study, the cross-sectional microdata from the 2023 and 2024 survey waves were combined. In accordance with the study objective, only individuals aged 60 years and older were included in the analysis. The final analytical sample consisted of 4,599 older adults. No missing data were identified for the variables included in the analysis; therefore, no imputation or other missing-data procedures were required. Because the dependent variable was binary, binary logistic regression analysis was employed [60].

Variables

The study’s dependent variable was online health information seeking behaviour (including injuries, illnesses, nutrition, and healthy living) during the past three months, specifically regarding activities performed on the internet for particular purposes. Participants aged 60 years and above who engaged in online health information seeking behaviour during the three months preceding the survey were assigned a code of “1”. In contrast, those who did not engage in such behaviour were assigned a code of “0”. The independent variables were grouped into demographic, economic, and personal factors and are presented in Table 1. Demographic factors included year, gender, age, and level of education. Economic factors included occupation and income level. Personal factors included frequency of internet usage, social media usage, product and service information, sale of goods and services, use of internet banking, and e-commerce usage.

Table 1.

Frequency and percentages of online health information seeking behaviour among older adults

Variables		Online Health Information Search Behaviour		χ2	P
		No, n (%)	Yes, n (%)
Year	2023	1,223(58.8)	856(41.2)	4.636	0.031
	2024	1,561(61.9)	959(38.1)
Gender	Women	1,253(62.4)	756(37.6)	5.025	0.025
	Men	1,531(59.1)	1,059(40.9)
Age	60–64	1,308(57.4)	971(42.6)	18.66	< 0.001
	65 and over	1,476(63.6)	844(36.4)
Level of education	Did not graduate from school	301(90.7)	31(9.3)	620.462	< 0.001
	Elementary school	1,609(72.5)	609(27.5)
	General secondary school/ Vocational or technical secondary school/ Primary education	259(55)	212(45)
	General high school/Vocational or technical high school	399(46.8)	454(53.2)
	University	216(29.8)	509(70.2)
Occupation	Managers	39(46.4)	45(53.6)	71.187	< 0.001
	Professionals	28(29.2)	68(70.8)
	Technicians. Technicians and Assistant Professionals	14(51.9)	13(48.1)
	Office Staff	8(40.0)	12(60.0)
	Service and Sales Staff	58(62.4)	35(37.6)
	Qualified Agriculture, Forestry, and Aquaculture Workers	86(74.8)	29(25.2)
	Artists and Related Workers	26(54.2)	22(45.8)
	Plant and Machine Operators and Assemblers	30(60.0)	20(40.0)
	Workers in Non-Skilled Jobs	63(76.8)	19(23.2)
	Not working	2,432(61.0)	1,552(39.0)
Frequency of internet usage	Several times a day	2,199(56.0)	1,730(44.0)	253.759	< 0.001
	At least once a week	450(86.7)	69(13.3)
	Less than once a week	135(89.4)	16(10.6)
Social media usage	No	1,299(80.6)	312(19.4)	419.253	< 0.001
	Yes	1,485(49.7)	1,503(50.3)
Product and service information	No	2,255(78.9)	602(21.1)	1068.221	< 0.001
	Yes	529(30.4)	1,213(69.6)
Sale of goods and services	No	2,757(61.6)	1,721(38.4)	75.988	< 0.001
	Yes	27(22.3)	94(77.7)
Use of internet banking	No	1,912(75.4)	623(24.6)	524.147	< 0.001
	Yes	872(42.2)	1,192(57.8)
E-commerce usage	No	2,553(67.5)	1,228(32.5)	434.38	< 0.001
	Yes	231(28.2)	587(71.8)
Income level	1. income level (lowest)	1,063(75.6)	344(24.4)	257.841	< 0.001
	2. income level	468(60.8)	302(39.2)
	3. income level	537(59.2)	370(40.8)
	4. income level	409(51.5)	385(48.5)
	5. income level (highest))	307(42.6)	414(57.4)

Total analytical sample size = 4,599

Income levels represent the five predefined income categories provided in the TurkStat microdata

The age variable was classified into two categories (60–64 and 65 and over), consistent with previous research on older adults in Türkiye [61] This categorisation was adopted to ensure consistency with the existing literature and to provide sufficient observations within each age group for robust statistical analysis while avoiding sparse data in more finely stratified age categories. The income variable was provided in the TurkStat microdata as a predefined categorical variable consisting of five income levels, ranging from the first (lowest) to the fifth (highest) income category. These categories were constructed by TurkStat prior to the release of the microdata and were used in the present study in their original form; the authors did not derive or recategorise the income groups. The microdata available to the authors did not provide the underlying continuous income values or the monetary thresholds used to define the five categories. Therefore, specific income ranges in Turkish lira could not be reported [62].

Statistical analysis

SPSS version 25 and Stata version 17 were used for statistical analyses. Descriptive statistics were presented as frequencies, percentages, proportions, and 95% confidence intervals. Binary logistic regression analysis was performed to identify the demographic, economic, and personal factors associated with online health information seeking behaviour among older adults. The survey weights provided by the Turkish Statistical Institute were incorporated into the regression analyses. Reference categories were selected to facilitate the interpretation of the logistic regression results based on the characteristics of each variable and the meaningfulness of the comparison. Accordingly, for the frequency of internet usage variable, “At least once a week” was retained as the reference category because it represents a regular level of internet use among older adults and provides a meaningful baseline for comparing more frequent and less frequent internet users. The results were reported as adjusted odds ratios (AORs), 95% confidence intervals, regression coefficients, and marginal effects. Multicollinearity was assessed using variance inflation factor (VIF) statistics. Model performance was evaluated using the likelihood ratio test, pseudo-R² statistics, goodness-of-fit, model discrimination (area under the receiver operating characteristic curve), predictive performance (sensitivity, specificity, positive predictive value, negative predictive value, and overall classification accuracy), and model calibration using the GiViTI Calibration Belt. Statistical significance was defined as p < 0.05.

Findings

Descriptive Statistics

According to the 2024 survey, 38.1% of participants reported having sought health-related information online within the previous three months. As presented in Table 1, during the same period, 40.9% of male participants and 37.6% of female participants reported engaging in online health information seeking behaviour. The estimated proportions and their corresponding 95% confidence intervals for all study variables are presented in Table 2.

Table 2.

Proportions and 95% confidence intervals

Variables				Confidence Interval (95% CI)
		Proportion	S.E	Lower	Upper
Year	2023	0.459	0.008	0.442	0.475
	2024	0.541	0.008	0.525	0.558
Gender	Women	0.448	0.008	0.432	0.464
	Men	0.552	0.008	0.535	0.568
Age	60–64	0.509	0.008	0.493	0.526
	65 and over	0.491	0.008	0.474	0.507
Level of education	Did not graduate from school	0.069	0.004	0.061	0.077
	Elementary school	0.466	0.008	0.449	0.482
	General secondary school/ Vocational or technical secondary school/ Primary education	0.106	0.005	0.096	0.117
	General high school/Vocational or technical high school	0.199	0.007	0.186	0.213
	University	0.160	0.006	0.149	0.173
Occupation	Managers	0.019	0.002	0.015	0.024
	Professionals	0.022	0.002	0.017	0.027
	Technicians. Technicians and Assistant Professionals	0.007	0.002	0.004	0.010
	Office Staff	0.004	0.001	0.003	0.007
	Service and Sales Staff	0.023	0.003	0.018	0.029
	Qualified Agriculture, Forestry, and Aquaculture Workers	0.019	0.002	0.016	0.024
	Artists and Related Workers	0.012	0.002	0.008	0.016
	Plant and Machine Operators and Assemblers	0.013	0.002	0.009	0.018
	Workers in Non-Skilled Jobs	0.020	0.003	0.016	0.026
	Not working	0.861	0.006	0.849	0.873
Frequency of internet usage	Several times a day	0.870	0.005	0.859	0.881
	At least once a week	0.099	0.005	0.090	0.109
	Less than once a week	0.031	0.003	0.026	0.037
Social media usage	No	0.333	0.008	0.318	0.349
	Yes	0.667	0.008	0.651	0.682
Product and service information	No	0.591	0.008	0.574	0.607
	Yes	0.409	0.008	0.393	0.426
Sale of goods and services	No	0.969	0.003	0.963	0.975
	Yes	0.031	0.003	0.025	0.037
Use of internet banking	No	0.536	0.008	0.520	0.553
	Yes	0.464	0.008	0.447	0.480
E-commerce usage	No	0.806	0.007	0.792	0.819
	Yes	0.194	0.007	0.181	0.208
Income level	1. income level (lowest)	0.260	0.007	0.246	0.274
	2. income level	0.152	0.006	0.141	0.164
	3. income level	0.204	0.007	0.191	0.217
	4. income level	0.186	0.007	0.173	0.199
	5. income level (highest))	0.199	0.007	0.185	0.214

Model estimation

The overall model demonstrated good performance. The likelihood ratio test indicated that the model was statistically significant (LR χ² = 1598.98, p < 0.001). The model explained a moderate proportion of the variance (McFadden pseudo-R² = 0.259; Nagelkerke R² = 0.398). The goodness-of-fit test indicated no evidence of lack of fit (p = 0.944). Model discrimination was good, with an area under the receiver operating characteristic curve (AUC) of 0.827. The model correctly classified 76.6% of the observations, with a sensitivity of 65.3% and a specificity of 83.9%. Model calibration was satisfactory according to the GiViTI Calibration Belt (test statistic = 0.17, p = 0.679), indicating no evidence of significant miscalibration.

The research examined the presence of multicollinearity among the independent variables intended for inclusion in the binary logistic regression model. A variance inflation factor (VIF) value of 5 or higher is regarded as indicative of moderate multicollinearity, whereas a value of 10 or more is considered indicative of high multicollinearity [63, 64]. The VIF values are presented in Table 4. In this analysis, no evidence of multicollinearity was observed among the independent variables.

Table 4.

Model coefficients and marginal effect results

Variables	β	S.E	M.E	S.E	VIF
Year (reference: 2023)
2024	-0.274a	0.085	-0.158a	0.049	1.02
Gender (reference: women)
Men	-0.478a	0.094	-0.274a	0.054	1.22
Age (reference: 60–64)
65 and over	-0.146c	0.085	-0.085	0.05	1.06
Level of education (reference: did not graduate from school)
Elementary school	0.536b	0.230	0.359b	0.163	4.25
General secondary school/ Vocational or technical secondary school/ Primary education	0.926a	0.256	0.588a	0.174	2.40
General high school/Vocational or technical high school	0.970a	0.248	0.613a	0.171	3.45
University	1.387a	0.260	0.824a	0.173	3.58
Occupation (reference: not working)
Managers	-0.201	0.346	-0.118	0.209	1.05
Professionals	-0.210	0.292	-0.123	0.176	1.14
Technicians. Technicians and Assistant Professionals	-0.795	0.545	-0.504	0.379	1.02
Office Staff	-0.343	0.754	-0.205	0.472	1.02
Service and Sales Staff	-0.541	0.341	-0.332	0.224	1.02
Qualified Agriculture, Forestry, and Aquaculture Workers	-0.535	0.289	-0.329	0.189	1.03
Artists and Related Workers	0.057	0.301	0.033	0.169	1.02
Plant and Machine Operators and Assemblers	-0.329	0.413	-0.197	0.257	1.03
Workers in Non-Skilled Jobs	-0.755b	0.351	-0.477b	0.242	1.02
Frequency of internet usage (reference: at least once a week)
Several times a day	0.645b	0.318	0.402	0.214	4.20
Less than once a week	-0.006	0.345	-0.004	0.233	3.98
Product and service information (reference: no)
Yes	1.514a	0.345	0.847a	0.233	1.52
Sale of goods and services (reference: no)
Yes	0.740b	0.312	0.384a	0.142	1.06
Use of internet banking (reference: no)
Yes	0.500a	0.101	0.289a	0.059	1.61
Social media usage (reference: no)
Yes	0.691a	0.098	0.417a	0.062	1.24
E-commerce usage (reference: no)
Yes	0.271b	0.120	0.153b	0.066	1.42
Income level (reference: 1. income level (lowest))
2. income level	0.359a	0.131	0.209a	0.076	1.33
3. income level	0.162	0.124	0.097	0.074	1.41
4. income level	0.207	0.135	0.123	0.08	1.46
5. income level (highest))	0.354b	0.138	0.207b	0.08	1.59

^ap<0.01; ^bp<0.05

The corresponding adjusted odds ratios (AORs) and their 95% confidence intervals are presented in Table 3.

Table 3.

Adjusted Odds Ratios (AORs) and 95% confidence intervals

Variables	AOR	S.E	P	Confidence Interval (95% CI)
				Lower limit	Upper limit
Year (reference: 2023)
2024	0.760	0.064	0.001	0.644	0.898
Gender (reference: women)
Men	0.620	0.058	< 0.001	0.516	0.745
Age (reference: 60–64)
65 and over	0.864	0.074	0.087	0.731	1.021
Level of education (reference: did not graduate from school)
Elementary school	1.709	0.393	0.020	1.089	2.682
General secondary school/ Vocational or technical secondary school/ Primary education	2.524	0.646	< 0.001	1.528	4.170
General high school/Vocational or technical high school	2.639	0.654	< 0.001	1.623	4.291
University	4.002	1.040	< 0.001	2.405	6.660
Occupation (reference: not working)
Managers	0.818	0.283	0.562	0.415	1.614
Professionals	0.811	0.237	0.472	0.458	1.436
Technicians. Technicians and Assistant Professionals	0.452	0.246	0.145	0.155	1.315
Office Staff	0.710	0.535	0.650	0.162	3.112
Service and Sales Staff	0.583	0.199	0.113	0.299	1.137
Qualified Agriculture, Forestry, and Aquaculture Workers	0.585	0.169	0.064	0.332	1.033
Artists and Related Workers	1.059	0.319	0.849	0.587	1.910
Plant and Machine Operators and Assemblers	0.719	0.297	0.426	0.320	1.617
Workers in Non-Skilled Jobs	0.470	0.165	0.032	0.236	0.935
Frequency of internet usage (reference: at least once a week)
Several times a day	1.906	0.606	0.043	1.022	3.556
Less than once a week	0.994	0.343	0.986	0.505	1.955
Product and service information (reference: no)
Yes	4.546	0.426	< 0.001	3.783	5.462
Sale of goods and services (reference: no)
Yes	2.097	0.653	0.018	1.138	3.862
Use of internet banking (reference: no)
Yes	1.649	0.167	< 0.001	1.352	2.011
Social media usage (reference: no)
Yes	1.994	0.195	< 0.001	1.646	2.415
E-commerce usage (reference: no)
Yes	1.311	0.157	0.023	1.037	1.657
Income level (reference: 1. income level (lowest))
2. income level	1.432	0.188	0.006	1.107	1.852
3. income level	1.175	0.146	0.192	0.922	1.499
4. income level	1.230	0.166	0.126	0.944	1.603
5. income level (highest))	1.425	0.197	0.011	1.086	1.870

An adjusted odds ratio (AOR) greater than 1 indicates higher odds of the outcome relative to the reference category, whereas an AOR less than 1 indicates lower odds of the outcome. The magnitude of the AOR reflects the strength of the observed association between the predictor and the outcome after adjustment for the other variables included in the model [54].

According to Table 3, compared with older adults surveyed in 2023, those surveyed in 2024 had lower odds of online health information seeking behaviour (AOR = 0.760; 95% CI = 0.644–0.898). Compared with women, men had lower odds of online health information seeking behaviour (AOR = 0.620; 95% CI = 0.516–0.745). Compared with older adults aged 60–64 years, those aged 65 and over had lower odds of online health information seeking behaviour, although this association was not statistically significant (AOR = 0.864; 95% CI = 0.731–1.021). Compared with older adults who did not graduate from school, those with elementary school (AOR = 1.709; 95% CI = 1.089–2.682), general secondary school/vocational or technical secondary school/primary education (AOR = 2.524; 95% CI = 1.528–4.170), general high school/vocational or technical high school (AOR = 2.639; 95% CI = 1.623–4.291), and university education (AOR = 4.002; 95% CI = 2.405–6.660) had higher odds of online health information seeking behaviour. Compared with older adults who were not working, workers in non-skilled jobs had lower odds of online health information seeking behaviour (AOR = 0.470; 95% CI = 0.236–0.935). Compared with older adults who used the internet at least once a week, those who used it several times a day had higher odds of online health information seeking behaviour (AOR = 1.906; 95% CI = 1.022–3.556), whereas those who used the internet less than once a week did not differ significantly from the reference group (AOR = 0.994; 95% CI = 0.505–1.955). Older adults who searched for product and service information (AOR = 4.546; 95% CI = 3.783–5.462), sale of goods and services (AOR = 2.097; 95% CI = 1.138–3.862), use of internet banking (AOR = 1.649; 95% CI = 1.352–2.011), social media usage (AOR = 1.994; 95% CI = 1.646–2.415), and e-commerce usage (AOR = 1.311; 95% CI = 1.037–1.657) had higher odds of online health information seeking behaviour than those who did not. Compared with older adults in the lowest income level, those in the second income level (AOR = 1.432; 95% CI = 1.107–1.852) and the highest income level (AOR = 1.425; 95% CI = 1.086–1.870) had higher odds of online health information seeking behaviour, whereas those in the third income level (AOR = 1.175; 95% CI = 0.922–1.499) and the fourth income level (AOR = 1.230; 95% CI = 0.944–1.603) did not differ significantly from the reference group.

The estimated regression coefficients, marginal effects, and VIF values are presented in Table 4.

Compared with individuals who did not graduate from school, individuals with General high school/Vocational or technical high school education had a 61.3%-point higher predicted probability of engaging in online health information seeking behaviour. Similarly, individuals with University education had an 82.4%-point higher predicted probability of engaging in online health information seeking behaviour than those who did not graduate from school.

Individuals who used social media had a 41.7%-point higher predicted probability of engaging in online health information seeking behaviour than those without social media usage. Individuals who searched for product and service information had an 84.7%-point higher predicted probability of engaging in online health information seeking behaviour than those who did not search for product and service information. Individuals who sold goods and services online had a 38.4%-point higher predicted probability of engaging in online health information seeking behaviour than those who did not sell goods and services online.

Individuals who used internet banking had a 28.9%-point higher predicted probability of engaging in online health information seeking behaviour than those who did not use internet banking. Likewise, individuals who used e-commerce had a 15.3%-point higher predicted probability of engaging in online health information seeking behaviour than those without e-commerce usage.

Compared with individuals in the (1) income level (lowest) category, those in the (2) income level category had a 20.9%-point higher predicted probability of engaging in online health information seeking behaviour. Similarly, individuals in the 5. income level (highest) category had a 20.7%-point higher predicted probability of engaging in online health information seeking behaviour than those in the 1. income level (lowest) category.

Discussion

The results show that men had a lower predicted probability of engaging in online health information seeking behaviour than women. Prior research has shown that, when provided with equal internet access, women are more likely than men to utilise online resources to obtain health information [65, 66]. This scenario is grounded in earlier research suggesting that women tend to utilise interpersonal communication and social media to sustain their relationships with others [67, 68]. Conversely, some scholars have concentrated on emotional, social, and perceptual factors to elucidate the gender disparity. Evidence suggests that women’s awareness of nutrition, caregiving roles, and preferences for anonymous information seeking may contribute to greater use of health-related websites for information, although these factors were not measured in the present study [69, 70].

In contrast to the results of this study, a prior investigation found that older men were more likely to engage in online health information seeking behaviour than older women [71]. According to some previous studies, this situation may be related to older men spending more time online and their social media habits [72, 73]. Similarly, an earlier study found that older men in rural areas were more likely to engage in online health information seeking behaviour than older women in rural areas [74].

Individuals who participated in the study in 2024 had a lower predicted probability of engaging in online health information seeking behaviour than those who participated in 2023. Although the survey data do not allow the underlying reasons for this temporal difference to be determined, this finding may reflect changes in older adults’ digital behaviours or preferences for obtaining health information between the two survey years, which may have been associated with differences in their engagement in online health information seeking behaviour. Although age was not significantly associated with online health information seeking behaviour in the adjusted analysis, previous studies have reported inconsistent findings regarding the relationship between age and online health activities. The literature still lacks a consensus on the association between age and online health activities [68]. While some researchers propose that there is an inverse relationship between age and online health information seeking behaviour [75, 76], others indicate that older adults, despite being more inclined to utilise the internet for health-related information, continue to depend on medical professionals for decision-making [65, 77]. A prior study revealed that one of the reasons older adults may engage in online health information seeking behaviour less frequently may be their lack of trust in online platforms [78]. Previous research has shown that advanced age may be associated with lower levels of digital health literacy [74, 79].

The study found that individuals with higher levels of education had a higher predicted probability of engaging in online health information seeking behaviour. A previous study conducted on a sample in Türkiye indicated that education level was significantly associated with individuals’ searches for health information on the internet [53]. In particular, older adults with higher levels of education are more likely to search for health information online [80].

One possible explanation is that individuals with higher educational attainment may have better skills in identifying and evaluating online information and a greater ability to understand health-related information [81]. In addition, previous research suggests that more highly educated individuals tend to have greater trust in online health information, which may be associated with greater engagement in online health information seeking behaviour [82]. Lower education levels, particularly those at or below the elementary school level, may be associated with lower digital health literacy among older adults, as suggested by previous research [83].

Individuals working in unskilled occupations had a lower predicted probability of engaging in online health information seeking behaviour than individuals who are not employed. A review of the relevant literature reveals that findings related to employment status are relatively more complex [68]. While some research indicates that employment has no discernible association with online health information seeking behaviour [84, 85], other studies show that employment is positively associated with e-health involvement [86].

Although the frequency of internet use was not significantly associated with online health information seeking behaviour in the marginal effects analysis, previous studies have consistently reported that participation in e-health activities and online health information seeking behaviour are closely related to the frequency of internet use. A review of the existing literature indicates a positive relationship between e-health activities, online health information seeking behaviour, and the frequency of internet use [87, 88]. Previous research suggests that a greater frequency of online health information seeking behaviour may reflect a higher level of digital health literacy among older adults [71]. For older adults residing in rural regions, increased internet usage frequency may be associated with a higher level of digital health literacy [74].

Furthermore, the amount of time spent online may be positively associated with digital health literacy. The duration of internet usage and the daily time allocated to online activities may be associated with older adults’ propensity to engage in online health information seeking behaviour [1]. Individuals who use with social media had a higher predicted probability of engaging in online health information seeking behaviour than those who do not utilise social media. This finding is consistent with a previous study suggesting that social media and mobile application use are positively associated with technological adaptation in individuals [89].

Those who search for product and service information online had a higher predicted probability of engaging in online health information seeking behaviour than those who do not search for product and service information online. Attitudes toward online health information may also be associated with the likelihood of older adults searching for health information online [90] Information self-sufficiency, that is, confidence in searching for, comparing, and evaluating information, may be associated with digital health literacy [71]. A prior study indicates that the perceived reliability and validity of online health information may be positively associated with individuals’ subjective search for electronic sources and their utilisation of the acquired information to address and manage health issues [91]. A diminished perception of risk regarding online health literacy may be associated with greater willingness among older adults to engage with digital health services and with higher levels of digital health literacy [1].

Those who sell goods and services online had a higher predicted probability of engaging in online health information seeking behaviour than others. The emergence of various online marketplaces and auction sites may be associated with greater engagement in online information seeking and purchasing activities [92, 93].

Individuals who used online banking had a higher predicted probability of engaging in online health information seeking behaviour compared with those who did not use online banking. Previous research suggests that greater internet accessibility and the expansion of online banking are associated with higher levels of online purchasing and online health information seeking behaviour [94]. This finding may suggest that individuals who actively use digital services also have a greater tendency to engage in online health information seeking behaviour, reflecting broader digital engagement and greater familiarity with online environments [54].

People who shop online had a higher predicted probability of engaging in online health information seeking behaviour than people who do not shop online. Previous studies have shown that consumers who shop online are more likely to use e-commerce [92, 95, 96]. One possible explanation is that online shopping may reflect a greater tendency to search for and compare information across digital platforms, which may also be associated with greater engagement in online health information seeking behaviour. Previous research suggests that online shopping is associated with quicker and easier access to product information from various sellers [97].

The study indicates that individuals in higher income categories had a higher predicted probability of engaging in online health information seeking behaviour than those in the lowest income category. A greater income level may be associated with an enhanced level of digital health literacy among older adults [83]. Previous research has shown that personal income [73], monthly income, or annual income may be positively associated with engagement in online health information seeking behaviour [71].

Limitations

This study has several limitations that should be considered when interpreting the findings. First, the study was based on secondary data from the Turkish Statistical Institute, limiting the analysis to the variables available in the dataset. Consequently, several potentially important determinants of online health information seeking behaviour, such as chronic diseases, disability, health literacy, digital literacy, cognitive status, marital status, living arrangements, urban or rural residence, self-rated health, and trust in online health information, could not be included in the analysis. Therefore, residual confounding cannot be ruled out, and some of the observed associations should be interpreted with caution. Second, because the study used a cross-sectional design, causal relationships between the independent variables and online health information seeking behaviour cannot be established. Consequently, reverse causation cannot be ruled out. For example, although internet banking, e-commerce, and other online activities were associated with online health information seeking behaviour, it is equally plausible that older adults with greater overall digital competence are more likely to engage in a wide range of online activities, including seeking health information, rather than these activities directly increasing online health information seeking behaviour. Third, the data were based on self-reported responses, which may be subject to recall bias or social desirability bias. Despite these limitations, the study is strengthened by the use of a large, nationally representative sample of older adults in Türkiye, providing findings that are broadly generalisable to the target population. In addition, the income variable was available in the TurkStat microdata only as five predefined income categories. The underlying continuous income values and the monetary thresholds used to define these categories were not available to the authors. Consequently, the specific income ranges corresponding to each category could not be reported, and more detailed analyses of income-related differences were not possible.

Conclusion

As information technology advances and online health information becomes increasingly accessible, the ability of older adults to access and utilise this information has gained significant importance. Older adults are increasingly turning to the internet for health-related issues, as it offers quick and often inexpensive access to a wide range of information.

The purpose of this study was to examine the demographic, economic, and personal factors associated with online health information seeking behaviour among older adults in Türkiye.

This study, which utilised nationally representative survey data from older adults aged 60 years and above in Türkiye, revealed notable variations in online health information seeking behaviour across different population subgroups.

The results of the current study indicated that some subgroups were more likely to use internet resources to look up health information than others, despite the internet offering greater access to information and the potential to increase the population’s level of health literacy. The findings may help identify subgroups of older adults who are less likely to engage in online health information seeking behaviour and may provide useful evidence to inform future digital health strategies tailored to different socioeconomic and demographic groups. However, because this study was based on cross-sectional observational data and did not evaluate interventions or health outcomes, these findings should be interpreted with caution and should not be considered evidence of the effectiveness of specific interventions or policies. Further longitudinal and intervention studies are needed to evaluate whether such strategies improve online health information seeking behaviour or related health outcomes.

To better understand the difficulties older adults encounter when dealing with health information and to assist them in making informed decisions while searching for medical and health information online, further research is required. Healthcare providers must collaborate to support their patients with trustworthy sources and help them evaluate the quality of online information accurately, thereby encouraging internet users to use online health information in a safe, reliable, and responsible manner.

To enhance the reliability of intervention results, future research may prioritise the use of rigorous study designs. Additionally, it may be beneficial to examine the online health information seeking behaviour among older adults using thorough and concise assessment instruments. Such approaches may inform future research and digital health initiatives aimed at supporting healthy ageing, but their effectiveness should be established through intervention studies.

Authors’ contributions

ÖA conceived and led the design and development of the study proposal. ÖA and ŞÜ supervised data collection, led the data analysis and drafting the manuscript. ÖA and ŞÜ made substantial contributions to the conceptualization and design of the study, data interpretations and writing the manuscript. All authors read and approved the final version of the manuscript.

Funding

This study was supported by the Atatürk University Scientific Research Projects Coordination Unit under Grant No. SBA-2025-16279.

Data availability

The data underlying this study is subject to third-party restrictions by the Turkish Statistical Institute. Data are available from the Turkish Statistical Institute (bilgi@tuik.gov.tr) for researchers who meet the criteria for access to confidential data. The authors of the study did not receive any special privileges in accessing the data.

Declarations

Ethics approval and consent to participate

This study was based on anonymized secondary data obtained from the Turkish Statistical Institute’s (TurkStat) Household Information Technology Usage Survey. Official permission to access and use the microdata was obtained from TurkStat in accordance with its data access procedures, including the completion of a Letter of Undertaking. Because the study involved the secondary analysis of anonymized survey data and no direct contact with participants, additional ethical approval and informed consent were not required in accordance with national regulations and institutional requirements.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.