Prevalence of Dry Eye Syndrome and Associated Factors Among University Students

Öz

Objective: To investigate the prevalence of dry eye syndrome (DES) and its association with risk factors among university students. Methods: This cross-sectional study was conducted between May and November 2019 with 400 students at a foundation university. Data were collected face-to-face using a personal information form, the Ocular Surface Disease Index (OSDI), and the Young Internet Addiction Test–Short Form (YIAT-SF). Non-parametric tests, Spearman correlation, and median regression analysis were applied. Results: Mild, moderate, and severe DES were observed in 19.7%, 15.6%, and 40.0% of students, respectively. The mean YIAT-SF score was 28.27 ± 8.29, indicating moderate to high levels of internet addiction. OSDI scores differed significantly by gender, class level, eye complaints, eye disease diagnosis, and use of glasses/contact lenses (p<.05). A positive, weak but significant correlation was found between OSDI and internet addiction scores (rho = 0.227; p<.01). Median regression analysis identified internet addiction, female gender, upper class level, and presence of eye complaints as significant predictors of OSDI scores. Conclusion: DES is prevalent among university students. Internet addiction, female gender, higher class level, and eye complaints were associated with higher OSDI scores. From a public health nursing perspective, strengthening education and awareness efforts to prevent dry eyes in university students at the primary prevention level, supporting behavioral change programs to reduce screen time, identifying at-risk groups, planning targeted interventions, and expanding eye health screenings are crucial.

Anahtar Kelimeler

• dry eye syndrome
• ocular surface disease index
• internet addiction
• university students

Üniversite Öğrencilerinde Göz Kuruluğu Sendromunun Görülme Sıklığı ve İlişkili Faktörler

Öz

Amaç: Üniversite öğrencileri arasında göz kuruluğu sendromu (DES) prevalansının ve risk faktörleri ile olan ilişkisinin incelenmesidir. Yöntem: Bu kesitsel çalışma, Mayıs-Kasım 2019 tarihleri arasında bir vakıf üniversitesinde öğrenim gören 400 öğrenciyle yürütülmüştür. Veriler, kişisel bilgi formu, Oküler Yüzey Hastalıkları İndeksi (OSDI) ve Young İnternet Bağımlılığı Testi-Kısa Formu (YIAT-SF) kullanılarak yüz yüze görüşme yöntemiyle toplanmıştır. Verilerin analizinde non-parametrik testler kullanılmış, spearman korelasyon katsayısı ve Medyan regresyon analizi kullanılmıştır. Bulgular: Öğrencilerin %19.7'sinde hafif, %15.6'sında orta ve %40'ında şiddetli kuru göz sendromu saptanmıştır. Young İnternet Bağımlılığı Testi Kısa Formu sonucuna göre öğrencilerin puan ortalaması 28.27±8.29 olarak belirlenmiştir. OSDI puanları cinsiyete, sınıf düzeyine, gözle ilgili şikâyetlere, göz hastalığı tanısına ve gözlük/lens kullanımına göre anlamlı farklılık göstermiştir (p<.05). OSDI puanı ile internet bağımlılığı arasında anlamlı ve pozitif bir korelasyon saptanmıştır (rho = 0.227, p<.01). Medyan regresyon analizinde internet bağımlılığı düzeyi, kadın cinsiyeti, üst sınıf düzeyi ve göz şikâyeti varlığı OSDI için anlamlı yordayıcılar olarak belirlenmiştir. Sonuç: Üniversite öğrencilerinde kuru göz sendromunun yaygın olduğu belirlenmiştir. İnternet bağımlılığı, kadın cinsiyeti, ileri sınıf düzeyi ve gözle ilgili sağlık öyküsünün öğrencilerde OSDI puanlarını artıran önemli etkenler olduğu görülmüştür. Halk sağlığı hemşireliği açısından birincil koruma düzeyinde üniversite öğrencilerinde göz kuruluğunu önlemeye yönelik eğitim ve farkındalık çalışmalarını güçlendirilmesi, ekran süresini azaltmaya ve göz kırpmaya yönelik davranış değişikliği programlarının desteklenmesi ve riskli grupların belirlenerek hedefli müdahalelerin planlanması, göz sağlığı taramalarının yaygınlaştırılması önem taşımaktadır.

Anahtar Kelimeler

• kuru göz sendromu
• oküler yüzey hastalığı indeksi
• internet bağımlılığı
• üniversite öğrencisi

Kaynakça

1. Abdulmannan, D. M., Naser, A. Y., Ibrahim, O. K., Mahmood, A.S., Alkrad, J.A., Sweiss, K., Alrawashdeh, H.M., Kautsar, A.P. (2022). Visual health and prevalence of dry eye syndrome among university students in iraq and jordan. BMC Ophthalmology, 22: 265. https://doi.org/10.1186/s12886-022-02485-w
2. Abu-Ismail, L., Abuawwad, M.T., Taha, M.J., Khamees, A., Abu Ismail, D.Y., Sanwar, M., Al-Bustanji, Y., Nashwan, A., Alameri, O.H., Alrawashdeh, H.M., Serhan H.A., Abu-Ismail, J. (2023). Prevalence of dry eye disease among medical students and its association with sleep habits, use of electronic devices, and caffeine consumption: a cross-sectional questionnaire. Clinical Ophthalmology, 17: 1013–1023. https://doi.org/10.2147/OPTH.S397022
3. Al-Dossary, SK. (2024). Environmental and occupational triggers of dry eye symptoms in the asha region of saudi arabia: a cross-sectional study. Clinical Ophthalmology, 18 : 2427–2438. https://doi.org/10.2147/OPTH.S474832
4. Allwihan R, Alhalwani A, Khojah M, Abduljawad J, Albedaiwi T, Bazhair R, Alkhayyat A, Alsolami M, Anbari L. (2024). The impact of electronic device use on dry eye disease symptoms based on age and gender: a cross-sectional study in health science university students. The Open Ophthalmol Journal, 18: e18743641330774. http://dx.doi.org/10.2174/0118743641330774240909052623
5. Aljammaz, HM., Aleithan, WM., Albalawi, AM., Aljayani, RT., Aljayani, RT., Aljammaz, MM., Alenezi, SH., Alreshidi, S., Hashem, F., Alali, NM., ALBalawi, HB. (2023). Prevalence and risk factors for symptomatic dry eye disease based on mcmonnies questionnaire among medical students, saudi arabia; a cross-sectional study. International Journal of General Medicine, 16: 123-134. https://doi.org/10.2147/IJGM.S410790
6. Awasthi, U. (2020). Impact of prolonged digital screens exposure on ocular surface. Delhi Journal of Ophthalmology 31(1):26-30. https://doi.org/10.7869/djo.563
7. Aykutlu, H.C., Aykutlu, M.Ş. (2024). Association between problematic internet use, digital game addiction, and digital eye strain among adolescents: a cross-sectional clinical study. Neuropsychiatric Investigation, 62(4):122-127.
8. Bahkir FA., Grandee SS. (2020). Impact of the COVID-19 lockdown on digital device-related ocular health. Indian Journal of Ophthalmol. 68(11):2378–2383. https://doi.org/10.4103/ijo.IJO_2306_20

9. Carpenito, L. J. (2024). Kuru göz riski. F. Erdemir & G. Türk (Ed.), Hemşirelik tanıları el kitabı (ss. 439–442). Nobel Tıp Kitabevleri. [in Turkish]
10. Coles-brennan, C., Sulley, A., Young, G. (2019). Management of digital eye strain. Clinical and Experimental Optometry, 102(1): 18-29. https://doi.org/10.1111/cxo.12798
11. Colligris, B., Alkozi, H. A. and Pintor, J. (2014). Recent developments on dry eye disease treatment compounds. Saudi Journal of Ophthalmology, 28(1): 19–30. https://doi.org/10.1016/j.sjopt.2013.12.003
12. Condori-Meza, I. B., Dávila, L. A., & Guzmán, A. (2021). Problematic internet use associated with symptomatic dry eye disease in medical students from Peru. Clinical Ophthalmology, 2021(15): 4355-4362. https://doi.org/10.2147/OPTH.S334156
13. De Medeiros-Araujo, J.N., Rocha-Botarelli, F., de Lima-Fernandes, A.P.N., Dantas A.C., Silva, A. B. (2024). Content validity of the nursing diagnosis proposal ocular dryness in adult patients admitted to the intensive care unit. Enfermeria Global 23 (1): 388- 403. https://doi.org/10.6018/eglobal.561191
14. Gayton, J. L. (2009). Etiology, prevalence, and treatment of dry eye disease. Clinical Ophthalmology, 2009(3): 405–412. https://doi.org/10.2147/opth.s5555
15. Gowrisankaran, S. &Sheedy, J. E. (2015). Computer vision syndrome: a review. Work, 52(2): 303–314. https://doi.org/10.3233/WOR-152162
16. Grubbs, J. R., Tolleson-Rinehart, S., Huynh, K. & Davis, R. M. (2014). A review of quality of life measures in dry eye questionnaires. Cornea, 33(2): 215–218. https://doi.org/10.1097/ICO.0000000000000038
17. Hasan, Z. A. I. Y. (2022). Dry eye syndrome risk factors: a systemic review. Saudi Journal of Ophthalmology, 35(2): 131–139. https://doi.org/10.4103/1319-4534.337849
18. IBM Corp. (2020). IBM SPSS Statistics for Windows, Version 27.0 [Computer software]. Armonk, NY: IBM Corp.
19. Ji, H., Yang, Y., Lu, Y., Kong, X., Yang, G., Liu, J., Yang, Y., Wang, X., Ma, X. (2023). Prevalence of dry eye during the COVID-19 pandemic: A systematic review and meta-analysis. PLOS ONE, 18(12): e0288523. https://doi.org/10.1371/journal.pone.0288523
20. Jiang, W., Yang, L., & Liang, S. (2024). The relationship between sex hormone levels and ocular surface parameters in girls with idiopathic central precocious puberty. Frontiers in Endocrinology, 15: 1429154. https://doi.org/10.3389/fendo.2024.1429154
21. Kaya, H. (2019). Evaluation of the relationship between asthenopic complaints and internet addiction in university students. Pamukkale Medical Journal, 12(3): 561–567. https://doi.org/10.31362/patd.568645
22. Khajoei Nejad, F., Rafati, F., Rafati, S., Dastyar, N. (2023). The association between sexual function, quality of marital relationship and associated factors in women with a history of ectopic pregnancy: a cross-sectional study in iran. BMC Women’s Health, 23: 506 https://doi.org/10.1186/s12905-023-02635-2
23. Kırağ, N. & Temel, A. B. (2016). Frequency of dry eye symptoms and related factors in elderly individuals. Journal of Anatolia Nursing and Health Sciences, 19(4): 243–251.
24. Kutlu, M., Savcı, M., Demir, Y., Aysan, F. (2016). Turkish adaptation of the short form of the young internet addiction test: validity and reliability study in university students and adolescents. Anatolian Journal of Psychiatry, 17(Suppl. 1), 69–76. https://doi.org/10.5455/apd.190501
25. Muntz, A., Turnbull, P. R. K., Kim, A. D., Gokul, A., Wong, D., Tsay, T. S.-W., Zhao, K., Zhang, S., Kingsnorth, A., Wolffsohn, J. S., & Craig, J. P. (2022). Extended screen time and dry eye in youth. Contact Lens and Anterior Eye, 45: 101594. https://doi.org/10.1016/j.clae.2021.101541
26. Patel, H., Jain, R., Kamath, M.A., Bappal, A. (2023). A study on correlation of computer vision syndrome and dry eye disease and knowledge regarding its associated factors amongst health professionals. Indian Journal of Ophthalmol, 71(4):1441–1445. https://doi.org/10.4103/IJO.IJO_2776_22
27. Pawlikowski, M., Altstötter-Gleich, C. and Brand, M. (2013). Validation and psychometric properties of a short version of young’s internet addiction test. Computers in Human Behavior, 29(3): 1212–1223. https://doi.org/10.1016/j.chb.2012.10.014
28. Phadatare, S. P., Momin, M., Nighojkar, P., Askarkar, S. and Singh, K. K. (2015). A comprehensive review on dry eye disease: diagnosis, medical management, recent developments, and future challenges. Advances in Pharmacological Sciences, 2015: 704946. https://doi.org/10.1155/2015/704946
29. Prescott, C.R. (2021). Increased screen time and dry eye: another complication of covid-19. Eye Contact Lens. 47(8):433-434. https://doi.org/10.1097/ICL.0000000000000820
30. Rabie, E.A.E.G.A., Elrazkey, J.Y., Ahmed, H.A. (2024). Empowering vision: the impact of nursing-led educational program on patients with dry eye syndrome. BMC Nursing 23: 693. https://doi.org/10.1186/s12912-024-02318-9
31. Sağlan, R., Atay, E., Demirtaş, Z., Öcal, E. E., Arslantaş, D. and Ünsal, A. (2017). Evaluation of internet addiction and dry eye disease among secondary and high school students. Eurasian Journal of Family Medicine, 6(3), 117–126.
32. Savur, Y.İ., Yar, K. (2024). Computer vision syndrome: an overview. Çukurova Medical Student Journal. 4(2):39-43.
33. Shapiro, S. S. & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4): 591–611. https://doi.org/10.1093/biomet/52.3-4.591
34. Sheppard, A.L. & Wolffsohn, J.S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ Open Ophthalmol,16;3(1):e000146. https://doi.org/10.1136/bmjophth-2018-000146
35. Simavlı, H., Önder, H. İ., Bucak, Y. Y., Erdurmuş, M., Güler, E. & Hepsen, İ. F. (2014). Relationship between ocular surface disease index, dry eye tests, and demographic properties in computer users. Turkish Journal of Ophthalmology, 44(2): 115–118. https://doi.org/10.4274/tjo.72473
36. Stapleton, F., Alves, M., Bunya, V. Y., Jalbert, I., Lekhanont, K., Malet, F., Na, K., Schaumberg, D., Uchino, M., Vehof, J., Viso, E., Vitale, S., Jones, L. (2017). TFOS DEWS II epidemiology report. The Ocular Surface, 15(3): 334–365. https://doi.org/10.1016/j.jtos.2017.05.003
37. Tamimi, A., Sheikhzadeh, F., Ezabadi, S. G., Islampanah, M., Parhiz, P., Fathabadi, A., Poudineh, M., Khanjani, Z., Pourmontaseri, H., Orandi, S., Mehrabani, R., Rahmanian, M., Deravi, N. (2023). Post-LASIK dry eye disease: a comprehensive review of management and current treatment options. Frontiers in Medicine, 11, 1057685. https://doi.org/10.3389/fmed.2023.1057685
38. Tear Film & Ocular Surface Society (TFOS). (2017). TFOS DEWS II dry eye report: pathophysiology of dry eye disease. The Ocular Surface, 15(3): 438–510. https://doi.org/10.1016/j.jtos.2017.05.011
39. Tripathi A, Agarwal R, Kharya P. (2022). Dry eye disease related to digital screen exposure in medical students. The Pan-American Journal of Ophthalmology, 4(1): 35. https://doi.org/10.4103/pajo.pajo_16_22
40. Turkish Statistical Institute. (2024, August 27). Survey on information and communication technology (ICT) usage in households and by individuals. https://data.tuik.gov.tr/Bulten/Index?p=Survey-on-Information-and-Communication-Technology-(ICT)-Usage-in-Households-and-by-Individuals-2024-53492&dil=2
41. Wróbel-Dudzińska, D., Osial, N., Stępień, P. W., Gorecka, A., & Żarnowski, T. (2023). Prevalence of dry eye symptoms and associated risk factors among university students in poland. International Journal of Environmental Research and Public Health, 20(2):1313. https://doi.org/10.3390/ijerph20021313
42. Young, K. S. (1998). Caught in the net: How to recognize the signs of internet addiction and a winning strategy for recovery. John Wiley & Sons.
43. Zarban, N. A., Alghamdi, M. S., Alshamrani, A. S., Alzahrani, F. A., & Alzahrani, H. A. (2024). Prevalence and risk factors of dry eye disease in association with electronic device use among university students in western saudi arabia. Cureus, 16(1): e51554. https://doi.org/10.7759/cureus.51554
44. Zhang, X., Jeyalatha, M. V., Qu, Y., He, X., Ou, S., Bu, J., Wang, J. & Liu, Z. (2017). Dry eye management: targeting the ocular surface microenvironment. International Journal of Molecular Sciences, 18(7): 1398. https://doi.org/10.3390/ijms18071398
45. Zhang, Y., Chen, H. and 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