Unravelling the influence of sociodemographic factors on eHealth literacy and online health seeking behaviour: a rural-urban comparative study

INTRODUCTION

eHealth literacy is defined as a capacity to navigate, locate, comprehend, and evaluate health-related information obtained from digital platforms, and to effectively utilize this acquired knowledge in addressing or resolving health issues. Central to eHealth literacy are six fundamental competencies, often referred to as literacies: traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy. Traditional literacy, encompassing reading and coherent language use, lays the groundwork for comprehending text-heavy eHealth materials. Information literacy is crucial for effectively navigating the vast online landscape, including developing search strategies and evaluating sources. Media literacy is essential for critically analysing health information presented in various media formats, while health literacy ensures understanding of health terminology and informed decision-making. Computer literacy is necessary for accessing and utilizing digital health resources, while scientific literacy enables comprehension of scientific methodologies and research findings in health contexts.¹

Access to online health information can be beneficial, but it hinges on individuals' eHealth literacy-their ability to locate, comprehend, evaluate, and utilize such information effectively.² Poor regulation leads to varying quality, posing challenges for those lacking critical eHealth literacy. Misleading or promotional content can harm those unable to discern quality.^2-4 The expanding presence of digital technology and services has created a divide between those who are digitally included and those who are excluded. Those lacking digital literacy often find it challenging to navigate different aspects of their lives, increasing the risk of struggling to adapt and thrive in an increasingly digitalized world.⁵ The eHEALS scale effectively measures eHealth literacy over multiple assessments, offering a valuable tool for evaluating consumer proficiency in health-related information technology.⁶

India has initiated development programs to foster financial growth and generate employment opportunities, with a focus on digitizing services for widespread accessibility and transparency. Increased investment in technology aims to combat corruption and promote financial integrity in public affairs. The Digital India program, a cornerstone initiative of the Government of India, aims to reshape the nation into a digitally empowered society and knowledge economy.⁷ The Government, through the Ministry of Electronics & Information Technology, launched schemes like the National Digital Literacy Mission (NDLM), Digital Saksharta Abhiyan (DISHA), and Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA) to combat digital illiteracy, aiming to train millions in digital skills.

Reliance Jio's affordable Internet access and Chinese smartphone companies' offerings democratized technology access in India, fuelling a surge in popular culture consumption and social networking. Web 2.0 facilitated user participation in media production, while real-time media analytics transformed service industries, leading to personalized content delivery. Despite socioeconomic challenges, India's digital population has grown rapidly, yet concerns remain regarding equitable digital benefits distribution amidst pervasive poverty and inequality. India has entered the era of the digital society, yet socioeconomic inequality remains unaddressed, giving rise to a substantial digital divide. This disparity is prominent between rural and urban areas, as well as within cities themselves. Rural and under-resourced urban areas suffer from inadequate digital infrastructure and limited access to digital resources due to poor socioeconomic conditions. Conversely, affluent urban areas boast advanced technology and widespread digital access. Bridging this digital gap is crucial for inclusive societal development, as membership in the digital society becomes increasingly unavoidable.^7,8

In India, the digital divide operates across social, individual, and institutional levels. Higher castes typically have more digital access and proficiency than lower castes, reflecting broader socioeconomic disparities. Individual factors like access, literacy, and usage habits also contribute. Institutions, including education and governance, show disparities in digital access and integration, highlighting the multifaceted nature of the divide.⁸ Thus this study was conducted with aim to examine the influence of sociodemographic factors on eHealth literacy and online health-seeking behaviour, with a specific focus on rural-urban disparities. The objectives of study are, to assess the impact of sociodemographic factors on eHealth literacy and online health seeking behaviour of people and to study rural-urban disparities in eHealth literacy and online health-seeking behaviour of people.

METHODS

After obtaining permission from relevant authorities, a community-based cross-sectional study was conducted. Individuals aged over 18 years and residing in the area for at least a year, and willing to participate, were included, while those not meeting these criteria were excluded. The sample size was determined using the Cochran formula 4 pq/d², with a prevalence (p) of 38%, q of 62%, and a relative error (d) of 5% of the prevalence, resulting in an estimated sample size of 376.⁹ Subsequently, 190 participants each were recruited via simple random sampling from rural and urban areas, resulting in a total sample size of 380 individuals. Data collection involved face-to-face administration of a pre-designed and pre-tested structured questionnaire to gather sociodemographic characteristics and eHealth literacy using the eHealth Literacy Scale (eHEALS) in the month of April 2024.

Statistical analysis entailed descriptive statistics to summarize data and multivariate regression to compare variables and identify associated factors using SPSS Statistics 29.0 with statistical level of significance fixed at p<0.05.

RESULTS

Out of the total enrolment of 380 participants, 190 were from rural areas, and 190 were from urban areas. Mean age of participants was 41.24±15.73 years and median age was 37 years. Table 1 shows urban areas exhibited a higher proportion of female participants (65.26%) compared to rural areas (58.95%). Urban regions also had a larger share of younger adults aged 18-39 (62.11%) compared to rural areas (41.58%), while the >60 age group was more represented in rural settings (20.53%) than urban (9.47%). Hindu participants dominated in both rural (90.00%) and urban (95.79%) areas. Marital status distribution showed a slightly higher prevalence of married individuals in rural areas (81.58%) compared to urban areas (77.89%). Educational attainment varied, with higher proportions of illiterate participants in rural areas (25.79%) compared to urban (25.26%), while urban areas had a higher percentage of graduates (18.95%) than rural areas (7.89%). Unemployment rates were slightly higher in rural areas (61.05%) compared to urban areas (55.79%). Financial independence was slightly more prevalent in urban areas (44.21%) compared to rural areas (38.95%). Socio-economic class distribution varied, with a higher proportion of lower middle-class participants in urban areas (54.74%) compared to rural areas (41.58%). Family type distribution showed similar percentages for nuclear, joint, and three-generation families between rural and urban areas.

The health characteristics of participants revealed a predominantly positive perception of health status, with 93.68% reporting good health and 6.32% indicating otherwise. Regarding comorbidities, 37.37% reported their presence, while the majority, 62.63%, reported no comorbid conditions as shown in Table 2. As seen in Figure 1, hypertension was major comorbidity in both rural and urban areas.

Table 3 revealed that the participants' digital literacy characteristics varied between rural and urban areas. In rural areas, 46.32% reported daily internet usage compared to 70.53% in urban areas. Smartphone ownership was also higher in urban settings, with 64.74% compared to 51.05% in rural areas. While 35.23% of rural participants spent 0-2 hours daily online, 47.01% of urban participants spent 2-4 hours. In rural areas, the highest internet usage peaks during the evening (35.23%), while in urban areas, its highest during the afternoon (40.30%). Regarding preferred modes of information, a majority in both rural (61.36%) and urban (83.58%) areas preferred a mix of text, picture, and video content. In both rural and urban settings, the preferred language for internet information is the native language. Ownership of laptops or computers was significantly higher in urban areas (27.37%) than in rural areas (10.00%). Demand for digital health technology was higher in urban areas (61.58%) than in rural areas (43.16%). However, rural participants generally exhibited lower eHEALS scores, with 67.89% having inadequate digital health literacy, compared to 52.11% in urban areas.

Figure 2 shows urban areas exhibited higher utilization across all categories compared to rural counterparts, particularly evident in entertainment and education, with a minor discrepancy noted in social media usage. In both rural and urban areas, WhatsApp and Instagram were the most used platforms, with similar usage patterns across other platforms seen in Figure 3.

In urban areas, there was notably higher demand for telemedicine, online payment, online appointment scheduling, e-pharmacy services, and insurance e-cards compared to rural regions as seen in Figure 4. Rural areas exhibit lower costs but higher connectivity challenges and device unavailability compared to urban areas, while urban settings face more family restrictions and time constraints on technology access (Figure 5).

As per Table 4, urban residents had higher eHealth literacy (59.87%) than rural residents (40.13%) (p=0.002). Males showed higher eHealth literacy (56.58%) than females (43.42%) (p<0.001). Younger age groups (18-39 years) exhibited higher eHealth literacy (63.82%) compared to older age groups (p=0.001). Graduates had higher eHealth literacy (28.95%) than illiterate individuals (5.26%) (p<0.001). Skilled workers showed higher eHealth literacy (57.24%) than unemployed individuals (33.55%) (p<0.001). Ownership of digital devices correlated positively with eHealth literacy (p<0.001). A strong association existed between demand for digital health technology and eHealth literacy (p<0.001). There is significant association between presence of comorbidities and high eHealth literacy (p<0.001).

The multivariate logistic regression analysis reveals significant predictors of eHealth literacy in Table 5. Factors such as education levels (p<0.005), comorbidity (p<0.001), occupation (p<0.005), demand for digital health technology (p<0.001), internet usage time (p<0.005), owning a smartphone (p=0.015), and using a laptop or computer (p=0.007) significantly influence eHealth literacy. While other parameters such as residence, age, gender, and marital status showed no significant influence in this analysis (p>0.05). The sensitivity of the model is 90.4%, and the specificity is 80.3%.

*Indicates significance at 0.05LOS

Omnibus χ2 = 288.597, p< .001, R2 = .532 (Cox & Snell), .719 (Nagelkerke), Hosmer and Lemeshow Test χ2 = 11.679, p=0.166

DISCUSSION

The findings of this study shed light on the intricate dynamics of digital health literacy and technology adoption among participants from rural and urban areas. One of the notable observations was the higher prevalence of digital literacy and technology usage in urban regions compared to rural counterparts similar to report by Dattopant Thengadi.⁹ This observation emphasizes the persistent digital divide between these regions.

The disparities in digital literacy between rural and urban areas can be attributed to various socio-economic factors, including access to education, employment opportunities, and infrastructure development. Urban areas typically have better access to educational resources and employment opportunities, which may contribute to higher levels of digital literacy among residents. Additionally, the availability of digital infrastructure such as high-speed internet and smartphone penetration is generally higher in urban settings, facilitating greater engagement with digital technologies and higher demand for digital health services.

Furthermore, the study identified several factors influencing eHealth literacy, including education levels, comorbidity status, occupation, demand for digital health technology, internet usage time, and ownership of digital devices. While both studies found occupation and digital literacy to be significant predictors of eHealth literacy, gender emerged as a significant factor in Rasekaba et al study but not in ours.¹⁰ These findings underscore the importance of addressing socio-economic disparities and improving access to digital resources and education to enhance eHealth literacy among diverse populations.

Echoing our study's conclusions, the link between limited usage of digital technology and poor digital literacy was also identified in the studies conducted by Rasekaba et al study and Neter et al study.^10,11 Unlike our study, Masilamani et al found text and picture to be the preferable method, but similar to our findings, native language was preferred.¹²

The multivariate logistic regression analysis highlighted the significant predictors of eHealth literacy, emphasizing the role of education, digital infrastructure, and health needs in shaping individuals' ability to navigate and utilize digital health services effectively. The absence of significant influence from demographic factors such as age, gender, and marital status suggests that interventions aimed at improving eHealth literacy should focus on addressing socio-economic determinants and promoting digital inclusion across all demographic groups.

Relying on self-reported data for assessing e-health literacy levels may introduce response bias, was limitations of study.

CONCLUSION

The study highlights significant disparities in digital literacy and eHealth literacy between rural and urban populations, with urban areas demonstrating higher levels of both. Factors such as education, occupation, presence of comorbidities, and demand for digital health technology play crucial roles in determining eHealth literacy. Efforts focusing on improving digital literacy, particularly among rural and less educated populations, are essential for ensuring equitable access to digital health resources and improving overall health outcomes.

ACKNOWLEDGEMENTS

We would like to thank the medical officers at urban health training centre and rural health training centre for their invaluable assistance in conducting this research.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: The study was approved by the Institutional Ethics Committee

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