Prevalence of Computer Vision Syndrome and Associated Factors among Instructors in Ethiopian Universities: A Web-Based Cross-Sectional Study

BACKGROUND: In this globalized and high-tech era, the computer has become an integral part of daily life. A constant use of computer for 3 hours and more per day can cause computer vision syndrome (CVS), which is one of the leading occupational hazards of the 21(st) century. The visual difficulties are the most common health problems associated with excessive computer use. Therefore, this study aimed to assess the prevalence and associated factors of CVS among instructors working in Ethiopian universities. METHODS: A web-based cross-sectional study was conducted among 422 university instructors in Ethiopia from February 02 to March 24, 2021. A structured and self-administered questionnaire prepared by Google Forms was shared among instructors through their e-mail addresses, Facebook, and Telegram accounts. Data cleanup and cross-checking were done before analysis using SPSS version 23. A multivariable logistic regression was applied to identify factors associated with CVS using p value <0.05 and 95% confidence interval. RESULTS: Of the total 416 participants, about 293 (70.4%) were reported to have CVS (95% CI: 65.9–74.5%), of which 54.6% were aged 24–33 years. Blurred vision, pain in and around the eye, and eye redness were the main symptoms reported. Working in third-established universities (AOR = 8.44, 95% CI: 5.47–21.45), being female (AOR = 2.69, 95% CI: 1.28–5.64), being 44 years old and above (AOR = 2.73, 95% CI: 1.31–5.70), frequently working on the computer (AOR = 5.51, 95% CI: 2.05–14.81), and sitting in bent back position (AOR = 8.10, 95% CI: 2.42–23.45) were the factors associated with computer vision syndrome. CONCLUSIONS: In this study, nearly seven-tenths of instructors in Ethiopian universities reported having symptoms of computer vision syndrome. Working in third-generation universities, being female, age, frequently working on the computer, and sitting in bent back position were statistically significant predictors in computer vision syndrome. Therefore, optimizing exposure time, addressing ergonomic hazards associated with computer usage through on-the-job and off-the-job training, and making the safety guidelines accessible for all university instructors would be critical to address the problem.