PREVALENCE AND RISK FACTORS OF COMPUTER VISION SYNDROME AMONG UNIVERSITY STUDENTS DURING THE COVID-19 PANDEMIC: A CROSS-SECTIONAL STUDY
DOI:
https://doi.org/10.17501/24246735.2024.9111Keywords:
computer vision syndrome, university students, COVID-19, screen time, digital eye strain, health interventionsAbstract
Online schooling and prolonged screen time can cause eye strain, dry eyes, watery eyes, itching, and headaches in college students. This study investigates how spending more time on screens during online classes affects the eye health of university students in South Africa. The study involved 349 university students attending online classes during the COVID lockdown. Data were collected via questionnaires, and univariate and multiple logistic regression analyses were used to evaluate association strengths. The prevalence of Computer Vision Syndrome among students was 68%, a higher prevalence of CVS was found among female students (63%) compared to male students (37%). Results from the adjusted model showed that female students were more likely to report CVS than male students (OR = 1.70, 95% CI: 1.07-2.67, p=0.023). Students within the age group 26-35 were less likely to report CVS as compared to students within the age group 18-25 (OR = 0.40, 95% CI: 0.22-0.72, p =0.002). Postgraduate students were less likely to report CVS than undergraduate students (OR = 0.41, 95% CI: 0.25-0.67, P <0.001). Students with more than one gadget were more likely to report having CVS, 2 gadgets (OR = 2.73, 95% CI: 1.32-5.62, p=0.007) and >2 gadgets (OR = 2.47, 95% CI: 0.99-6.14, P= 0.005). Students with family history of eye-defect were more likely to report eye defect (OR = 2.59, 95% CI: 1.53-4.38. P<0.001). CVS has a high prevalence amongst university students. Frequent pauses during screen use using customized apps or the 20-20-20 rule (to focus on 20 feet every 20 minutes for 20 seconds) should be performed by students while using their gadgets.
Downloads
References
Abuallut, I., Qumayi, E. A., Mohana, A. J., Almalki, N. M., Ghilan, M. E., Dallak, F. H., Mahzari, S. M., Makrami, A., Tawhari, A., & Ajeebi, R. E. (2022). Prevalence of Asthenopia and Its Relationship with Electronic Screen Usage During the COVID-19 Pandemic in Jazan, Saudi Arabia: A Cross-Sectional Study. Clinical Ophthalmology, 3165-3174. https://doi.org/10.2147/OPTH.S377541
Agarwal, R., Tripathi, A., Khan, I. A., & Agarwal, M. (2022). Effect of increased screen time on eyes during COVID-19 pandemic. Journal of Family Medicine and Primary Care, 11(7), 3462-3467. https://doi.org/10.4103/jfmpc.jfmpc_2219_21
Akinbinu, T. R., & Mashalla, Y. (2013). Knowledge of computer vision syndrome among computer users in the workplace in Abuja, Nigeria. http://hdl.handle.net/10311/1814
Al Rashidi, S. H., & Alhumaidan, H. (2017). Computer vision syndrome prevalence, knowledge and associated factors among Saudi Arabia University Students: Is it a serious problem? International journal of health sciences, 11(5), 17. PMC5669505.
Alemayehu, M., Nega, A., Tegegne, E., & Mule, Y. (2014). Prevalence of self-reported computer vision syndrome and associated factors among secretaries and data processors who are working in University of Gondar, Ethiopia. Journal of Biology, Agriculture and Healthcare, 4(15). 2224-3208.
Alexander, L. M., & Currie, C. (2004). Young people's computer use: implications for health education. Health Education. 0965-4283.
Alrahhal, M., Moez, A., Saleh, M., & Khanafer, A. (2021). "A review of the effects of prolonged screen exposure on digital eye strain and its health implications during the COVID-19 era." Ophthalmology Research & Clinical Trials. 2021. DOI: 10.9734/OR/2021/335896
Cartes, C., Segovia, C., Salinas-Toro, D., Goya, C., Alonso, M. J., Lopez-Solis, R., Zapata, C., Cabezas, M., Yañez, P., & Flores-Rodriguez, P. (2022). Dry eye and visual display terminal-related symptoms among university students during the coronavirus disease pandemic. Ophthalmic epidemiology, 29(3), 245-251. https://doi.org/10.1080/09286586.2021.1943457
Cherinet, F. M., Tekalign, S. Y., Anbesse, D. H., & Bizuneh, Z. Y. (2018). Prevalence and associated factors of low vision and blindness among patients attending St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia. BMC ophthalmology, 18, 1-6. https://doi.org/10.1186/s12886-018-0899-7
Chiemeke, S.C., Akhahowa, A.E. & Ajayi, O.B. (2007). Evaluation of Vision-Related Problems amongst Computer Users: A Case Study of University of Benin, Nigeria. In World Congress on Engineering, 1(2), pp. 217-221.
Chipeta, E., Surujlal, J., & Koloba, H. (2016). Influence of gender and age on social entrepreneurship intentions among university students in Gauteng province, South Africa. Gender and Behaviour, 14(1), 6885-6899. https://doi.org/hdl.handle.net/10520/EJC192341
Hassan, H. M. J., Ehsan, S., & Arshad, H. S. (2016). Frequency of computer vision syndrome & ergonomic practices among computer engineering students. International Journal of Scientific Research, 5(5), 121-125.
Kawashima, M., Uchino, M., Yokoi, N., Uchino, Y., Dogru, M., Komuro, A., Sonomura, Y., Kato, H., Kinoshita, S. & Tsubota, K. (2016). The association of sleep quality with dry eye disease: the Osaka study. Clinical ophthalmology (Auckland, NZ), 10, pp.1015.
Klamm, J., & Tarnow, K. G. (2015). Computer vision syndrome: a review of literature. Medsurg Nursing, 24(2), 89. 1092-0811.
Korpole, N. R., Kurada, P., & Korpole, M. R. (2022). Gender difference in ocular diseases, risk factors and management with specific reference to role of sex steroid hormones. Journal of Mid-life Health, 13(1), 20. https://doi.org/10.4103/jmh.jmh_28_22
Logaraj, M., Madhupriya, V., & Hegde, S. (2014). Computer vision syndrome and associated factors among medical and engineering students in Chennai. Annals of medical and health sciences research, 4(2), 179-185. 2141-9248.
Martínez-Mesa, J., González-Chica, D.A., Duquia, R.P., Bonamigo, R.R. & Bastos, J.L. (2016). Sampling: how to select participants in my research study?. Anais brasileiros de dermatologia, 91, pp.326-330.
Mehra, D., & Galor, A. (2020). Digital screen use and dry eye: a review. The Asia-Pacific Journal of Ophthalmology, 9(6), 491-497. https://doi.org/10.1097/APO.0000000000000328
Mohan, A., Sen, P., Shah, C., Jain, E., & Jain, S. (2021). Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: Digital eye strain among kids (DESK study-1). Indian journal of ophthalmology, 69(1), 140. https://doi.org/10.4103/ijo.IJO_2535_20
OseiFrimpong, K., & Asare, S. (2020). The Prevalence and Knowledge of University Students on Computer Vision Syndrome (CVS). IOSR Journal of Computer Engineering, 22(4), 31-41. 2278-8727.
Osman, S., Khalaf, S., Mohammed, H., El-Sebaity, D., & Osman, D. (2021). Prevalence and predictors of colour vision defects among Egyptian university students. Eastern Mediterranean Health Journal, 27(4), 399-406. 1020-3397.
Palm, P., & Risberg, E. H. (2007). Computer use, neck and upper-extremity symptoms, eyestrain and headache among female and male upper secondary school students. Scandinavian Journal of Work, Environment & Health, 33. 0355-3140.
Portello, J. K., Rosenfield, M., Bababekova, Y., Estrada, J. M., & Leon, A. (2012). Computer‐related visual symptoms in office workers. Ophthalmic and Physiological Optics, 32(5), 375-382. https://doi.org/10.1111/j.1475-1313.2012.00925.x
Rahman, Z. A., & Sanip, S. (2011). Computer user: demographic and computer related factors that predispose user to get computer vision syndrome. Int J Bus Humanit Technol, 1(2), 84-91.
Reddy, S. C., Low, C., Lim, Y., Low, L., Mardina, F., & Nursaleha, M. (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
Regmi, A., Suresh, J., & Asokan, R. (2022). Changes in work patterns during COVID-19 lockdown and its impact on the eyes and body. Clinical and Experimental Optometry, 1-7. https://doi.org/10.1080/08164622.2022.2029682
Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology, 3(1), e000146. https://doi.org/10.1136/bmjophth-2018-000146
Shi, H., Fu, J., Liu, X., Wang, Y., Yong, X., Jiang, L., Ma, S., Yin, Z., Yao, J., & Yao, X. (2021). Influence of the interaction between parental myopia and poor eye habits when reading and writing and poor reading posture on prevalence of myopia in school students in Urumqi, China. BMC ophthalmology, 21, 1-9. https://doi.org/10.1186/s12886-021-02058-3
Supiyaphun, C., Jongkhajornpong, P., Rattanasiri, S., & Lekhanont, K. (2021). Prevalence and risk factors of dry eye disease among University Students in Bangkok, Thailand. PloS one, 16(10), e0258217. https://doi.org/10.1371/journal.pone.0258217
Usgaonkar, U., Parkar, S. R. S., & Shetty, A. (2021). Impact of the use of digital devices on eyes during the lockdown period of COVID-19 pandemic. Indian journal of ophthalmology, 69(7), 1901. https://doi.org/10.4103/ijo.IJO_3500_20
Vejmelka, L., Matković, R., & Borković, D. K. (2020). Online at risk! online activities of children in dormitories: Experiences in a croatian county. International Journal of Child, Youth and Family Studies, 11(4), 54-79. https://doi.org/10.18357/ijcyfs114202019938
Victor, V. M., Parsan, A., Dookran, A., Lulkool, A., Ali, A., Mahabir, A., ... & Daniel, E. (2023). A web-based cross-sectional survey on eye strain and perceived stress amid the COVID-19 online learning among medical science students. International Medical Education, 2(2), 83-95.
Wimalasundera, S. (2009). Computer vision syndrome. Galle Medical Journal, 11(1).
Zhang, Y., Chen, H., & Wu, X. (2012). Prevalence and risk factors associated with dry eye syndrome among senior high school students in a county of Shandong Province, China. Ophthalmic epidemiology, 19(4), 226-230. https://doi.org/10.3109/09286586.2012.670742
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Bukola Oluwarinde, Nonhlanhla Tlotleng, Vusumuzi Nkosi
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Conference Proceedings of International Conference on Public Health is entirely Open Access, which means that all published content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this Proceedings journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.
Authors who publish with Conference Proceedings agree to the following terms:
- Authors retain copyright and grant the Proceedings of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this conference proceedings.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the proceeding's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this conference proceedings.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing
Published articles are licensed under a Creative Commons Attribution 4.0 International License.
