Dry eye syndrome in academia: from behavioural strategies to AI-oriented prevention
https://doi.org/10.21516/2072-0076-2026-19-2-43-46
Abstract
Purpose of the study was to assess the prevalence of dry eye syndrome (DES) among ITMO University students and staff using the OSDI questionnaire, determine the effectiveness of interactive behavioral interventions, and consider the prospects for using artificial intelligence (AI) in DES prevention in the academic environment. Material and methods. A total of 470 people aged 18 years and older completed the initial survey using the OSDI scale. 281 participants (59.8 %) had DES symptoms. Forty-five people with an OSDI score ≥ 13 were randomized to the intervention (n = 21) and control (n = 24) groups. The intervention group received the Eye.Kit and informational support via a Telegram channel. After 14 days, participants repeated the survey. Results. Both groups showed a reduction in DES symptoms. The reduction in the mean OSDI score in the intervention group (from 21.5 to 12.8, p < 0.001) was more significant than in the control group (from 22.8 to 19.4, p = 0.041). The difference between the groups in the final symptoms level was statistically significant (p = 0.023), indicating a higher effectiveness of the behavioral intervention. Conclusion. DES is widespread in the academic environment. Behavioral intervention with elements of digital communication demonstrated a statistically significant reduction in DES symptoms. The implementation of AI can improve the personalization of prevention through lifestyle analysis, monitoring of blink patterns, and adaptation of the learning environment.
About the Authors
E. P. BryantsevaRussian Federation
Ekaterina P. Bryantseva — Master’s Degree, ITMO University; ophthalmologist, Surgut Regional Clinical Hospital.
49, bldg. A, Kronverksky Ave, St. Petersburg, 197101; 24, Energetikov St., Surgut, Khanty-Mansiysk Autonomous Region, 628408
A. O. Ukina
Russian Federation
Anastasia O. Ukina — Master’s Degree, ITMO University; ophthalmologist, Gatchina Clinical Interdistrict Hospital.
49, bldg. A, Kronverksky Ave, St. Petersburg, 197101; 15a, bldg. 1, Roshchinskaya St., Gatchina, Leningrad Region, 188300
References
1. Kozina E.V., Sinyapko S.F., Gololobov V.T., et al. Preclinical diagnosis of dry eye syndrome have medical students. Pacific Medical Journal. 2015; (3): 42–5 (In Russ.).
2. Alqurashi A, Almaghrabi H, Alahmadi M, et al. The severity of dry eye symptoms and risk factors among university students in Saudi Arabia: a cross-sectional study. Sci Rep. 2024 Jul 2; 14 (1): 15149. doi: 10.1038/s41598-024-65297-6
3. Wróbel-Dudzińska D, Osial N, Stępień PW, Gorecka A, Żarnowski T. Prevalence of dry eye symptoms and associated risk factors among university students in Poland. Int J Environ Res Public Health. 2023 Jan 11; 20 (2): 1313. doi: 10.3390/ijerph20021313
4. Al-Mohtaseb Z, Schachter S, Shen Lee B, Garlich J, Trattler W. The relationship between dry eye disease and digital screen use. Clin Ophthalmol. 2021 Sep 10; 15: 3811–20. doi: 10.2147/OPTH.S321591
5. Fjaervoll K, Fjaervoll H, Magno M, et al. Review on the possible pathophysiological mechanisms underlying visual display terminal-associated dry eye disease. Acta Ophthalmol. 2022 Dec; 100 (8): 861–77. doi: 10.1111/aos.15150
6. Kovalevskaya M.A., Antonyan V.B., Sergeeva M.I. Possibilities of dry eye syndrome therapy in various types of ametropia. Russian ophthalmological journal. 2023; 16 (2): 22–7 (In Russ.). https://doi.org/10.21516/2072-0076-2023-16-2-22-27
7. Zulkarnain B, Budiyatin AS, Aryani T, Loebis R. The effect of 20–20–20 rule dissemination and artificial tears administration in high school students diagnosed with computer vision syndrome. J Pengabdi Kpd Masy. 2021 Mar; 7 (1): 24. doi:10.22146/jpkm.54121
8. Graham AD, Wang J, Kothapalli T. et al. Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes. Sci Rep. 2025; 15: 13378. https://doi.org/10.1038/s41598-025-96778-x
9. Pratama R, Suhanda R, Aini Z, Nurjannah N, Geumpana TA. Application of artificial intelligence technology in monitoring students’ health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM). Narra J. 2024 Aug; 4 (2): e644. doi: 10.52225/narra.v4i2.644
10. Huang JJ, Channa R, Wolf RM, et al. Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations. NPJ Digit Med. 2024 Jul 22; 7 (1): 196. doi: 10.1038/s41746-024-01197-3. Erratum in: NPJ Digit Med. 2024 Aug 23; 7 (1): 220. doi: 10.1038/s41746-024-01229-y
11. Xiao Y, Hu Y, Quan W, et al. Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery. Ann Transl Med. 2021 May; 9 (10): 830. doi: 10.21037/atm-20-8065
12. Neroev V.V., Zaytseva O.V., Petrov S.Yu., Bragin A.A. Artificial intelligence in ophthalmology: the present and the future. Russian ophthalmological journal. 2024; 17 (2): 135–41 (In Russ.). https://doi.org/10.21516/2072-0076-2024-17-2-135-141
Review
For citations:
Bryantseva E.P., Ukina A.O. Dry eye syndrome in academia: from behavioural strategies to AI-oriented prevention. Russian Ophthalmological Journal. 2026;19(2):43-46. (In Russ.) https://doi.org/10.21516/2072-0076-2026-19-2-43-46
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