Burden and determinants of computer vision syndrome in university students: A cross-sectional study

BACKGROUND: Computer vision syndrome (CVS) may significantly impact the academic performance and well-being of students. We aimed to determine the prevalence of CVS and its associated factors among university students. MATERIALS AND METHODS: A cross-sectional study was conducted among university students in Saudi Arabia. Data were collected through an electronic tool, including the validated Computer Vision Syndrome Questionnaire (CVS-Q). Descriptive and inferential analyses were conducted. Multiple logistic regression was employed to identify predictors of CVS. RESULTS: Of the 458 participants, 234 (51.1%) met the criteria for CVS. Female gender (adjusted odds ratio (aOR) = 2.13 (95% confidence interval (CI): 1.32–3.45), P = 0.002), refractive errors (aOR = 1.84, 95% CI: 1.20–2.83, P = 0.005), and daily screen time exceeding 6 h (aOR = 2.28, 95% CI: 1.47–3.54, P < 0.001) were significant predictors of CVS. No significant associations were found for protective measures, such as taking eye rests or adjusting screen contrast. The use of artificial tears was associated with a higher prevalence of CVS in univariate analysis (crude odds ratio = 1.80, 95% CI: 1.24–2.61, P = 0.002). Around 40% of students reported being aware of what CVS is, with social media being the most common source of information (52.3%). CONCLUSION: Excessive screen time, female gender, and refractive errors are significant predictors of CVS among university students. Interventions should prioritize reducing screen exposure and promoting regular eye checkups to mitigate CVS and its adverse effects.