Optimizing Prevention Strategies for Computer Vision Syndrome in Malaysia’s Digital Learning Environment

This study investigates prevention strategies for Computer Vision Syndrome (CVS) among university students in Malaysia during the COVID-19 pandemic, a period of heightened digital device usage. A survey was conducted with 387 students from UiTM Kedah, UiTM Johor, and Universiti Selangor, selected via convenience sampling. Data was gathered using online questionnaires to assess CVS symptoms and the preventive measures employed by students. The findings revealed that 57.9% of students reported CVS symptoms after 5-6 hours of computer use, while 30% experienced symptoms in less than 5 hours. A significant 85% indicated that CVS negatively impacted their lifestyle and eye health. Adjusting screen brightness emerged as the most effective preventive measure (mean score 3.94), while using eye drops was the least effective (mean score 2.12). Taking breaks (mean score 3.46) and maintaining proper posture (mean score 3.35) were moderately effective and commonly practiced. This study contributes to the understanding of CVS prevention by highlighting the relative effectiveness of various strategies and underscoring the importance of institutional support in fostering a healthier digital learning environment. The findings emphasize the need for comprehensive interventions, including ergonomic adjustments and educational programs on proper technology use, to mitigate CVS symptoms. Implementing proactive interventions, such as enhancing ergonomic support and providing education on optimal technology usage, may significantly alleviate the symptoms of CVS. This study advances body of literature by demonstrating the relative efficacy of various preventive strategies and emphasizing the pivotal role of institutional support in safeguarding student well-being within an increasingly digitalized educational landscape.

Computer vision syndrome; preventive measures; online learning; digital learning; COVID-19

1. Basar, Z. M., Mansor, A. N., Jamaludin, K. A., & Alias, B. S. (2021). The effectiveness and challenges of online learning for secondary school students–A case study. Asian Journal of University Education, 17(3), 119-129. https://doi.org/10.24191/ajue.v17i3.14514 [Google Scholar] [Crossref]

2. Hadi, K. N., Rehman, M. H., Toru, H. K., Orakzai, A. A., Khalid, S., & Iftikhar, B. (2021). Assessment of Computer Vision Syndrome in University Students in Peshawar; A Descriptive Cross-Sectional Study. THE STETHO, 2(6), 6-14. https://doi.org/10.5281/zenodo.4936508 [Google Scholar] [Crossref]

3. Jamaludin, M. F., Saharan, M. S., Adzahar, K. A., Ya'acob, F. F., & Faimi, M. F. A. (2023). Online Learning during Pandemic: A Study on Computer Vision Syndrome in Malaysian University Students. Journal of Management & Marketing Review (JMMR), 8(1). https://doi.org/10.35609/jmmr.2023.8.1(4) [Google Scholar] [Crossref]

4. Peiris, V., Pavitra S Mahendran, Rajasekharan, R., Ameresekera, K., & Atheeb Faiz, M. (2020). Prevalence and Associated Factors of Computer Vision Syndrome Among Undergraduate Students of Malaysia During the COVID 19 Pandemic. International Journal of Biomedical and Clinical Sciences, 5(4), 399–407. http://www.publicscienceframework.org/journal/paperInfo/ijbcs?paperId=5045. [Google Scholar] [Crossref]

5. Reddy, S. C., Low, C. K., Lim, Y. P., Low, L. L., Mardina, F., & Nursaleha, M. P. (2013). Computer vision syndrome: a study of knowledge and practices in university students. Nepalese Journal of Ophthalmology, 5(2), 161–168. https://doi.org/10.3126/nepjoph.v5i2.8707 [Google Scholar] [Crossref]

6. Seresirikachorn, K., Thiamthat, W., Sriyuttagrai, W., Soonthornworasiri, N., Singhanetr, P., Yudtanahiran, N., & Theeramunkong, T. (2022). Effects of digital devices and online learning on computer vision syndrome in students during the COVID-19 era: an online questionnaire study. BMJ Paediatrics Open, 6(1). https://doi.org/10.1136/bmjpo-2022-001429 [Google Scholar] [Crossref]

7. Tika, R. M., & Fitria, L. (2023). Risk factors of computer vision syndrome in students during online learning period. International Journal of Public Health Science (IJPHS), 12(2), 875. https://doi.org/10.11591/ijphs.v12i2.22739 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study