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This is a Cross-Sectional Study study.

December 2, 2025Open Access

Bridging the digital divide: A snapshot on e-health literacy among medical students in Tamil Nadu, South India

Key Points

E-health literacy scores average 29.01 in medical students, linked to factors like family income and internet activity.
The cross-sectional study assessed 481 medical students using structured questionnaires and e-health literacy scales.
Multivariable regression indicates internet usage over 40 hours per week boosts e-health literacy scores markedly.
Findings highlight the need for medical schools to enhance e-health literacy to prepare well-informed healthcare providers.

Abstract

BACKGROUND: E-health is the integration of information and communication technology into healthcare, and it has become essential in this digital age, particularly for medical professionals. However, its effective use hinges on e-health literacy, that is, the ability to access, understand, and apply digital health information. This study aimed to determine the independent predictors of e-health literacy among medical students of a medical college in Tamil Nadu, South India. MATERIALS AND METHODS: A cross-sectional study was conducted from April 2024 to July 2024 among medical students attending a medical college in Tamil Nadu, South India. A pre-tested, structured questionnaire was used to collect the data. E-health literacy scale (e-HEALS) was used to assess e-health literacy among the students. The responses were scored, and the total score ranged from 8 to 40. The data were entered into Microsoft Excel, numerically coded, and analyzed using IBM SPSS v26. Descriptive analysis was conducted to characterize the study participants and was expressed in frequencies, percentages, and mean (±standard deviation). Independent samples t -test and one-way ANOVA were used to determine the significant difference between the mean e-HEALS score and the independent variables. A multivariable linear regression model was used to determine the independent predictors of the e-HEALS score. A P value 40 hours of internet usage per week ( B = 1.07; 95% CI: 0.36–2.38). The independent predictor that had a negative impact on e-HEALS scores was only one online health resource utilized ( B = −1.30; 95% CI: −2.43 to 0.17). CONCLUSION: Medical schools must prioritize cultivating e-health literacy to equip future healthcare professionals with evidence-based knowledge. Factors such as family income, internet access, and online health resource use seem to impact e-health literacy. A better understanding of these factors can improve the national medical education system.

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