Prolonged Use of Computing Devices and Computer Vision Syndrome Among University Lecturers During the Covid-19 Pandemic in Malaysia: A Cross-Sectional Study

Objective To determine the prevalence of computer vision syndrome (CVS) and its associated factors among university lecturers in Malaysia during the COVID-19 pandemic. Methods A cross-sectional study was conducted among 174 lecturers from Universiti Putra Malaysia (UPM) and Universiti Teknologi MARA (UITM) between March and May 2021. A self-administered questionnaire, validated and pretested among 40 respondents, was used to collect data on sociodemographic characteristics, working characteristics, working conditions, and computer vision syndrome (CVS) symptoms. Chi-square tests and multivariable logistic regression were used to examine associations between the study variables and CVS. Exploratory machine-learning models were also constructed to examine the combined contribution of these factors to CVS. Results The prevalence of CVS was 77.6% (135/174). The most frequently reported symptoms were eye fatigue (52.3%), headache (44.3%), and eye strain (43.1%). University, working hours, seating distance, computer glasses, eye examination, and eye lubrication were associated with CVS in the chi-square analysis. In the multivariable logistic regression, only university affiliation remained an independent predictor of CVS (aOR = 2.79, 95% CI: 1.27–6.11, p = 0.010), with UITM lecturers having approximately 2.8 times higher odds of CVS compared with UPM lecturers. In the exploratory machine-learning analysis, the random forest model achieved the best overall performance (AUC = 0.70), with university, working hours during COVID-19, and seating distance identified as the leading predictors. Conclusion CVS was highly prevalent among university lecturers during the COVID-19 pandemic. University affiliation was the only independent predictor of CVS after adjustment for confounders. Work-related and workstation-related factors were associated with CVS and should be considered in occupational health interventions targeting lecturers who work long hours with computing devices.