Asthenopia prevalence and vision impairment severity among students attending online classes in low-income areas of western China during the COVID-19 pandemic

Abstract

Introduction: This study explored the impact of online learning during the coronavirus disease 2019 (COVID-19) pandemic on asthenopia and vision impairment in students, with the aim of establishing a theoretical basis for preventive approaches to vision health.

Methods: This balanced panel study enrolled students from western rural China. Participant information was collected before and during the COVID-19 pandemic via questionnaires administered at local vision care centres, along with clinical assessments of visual acuity. Paired ttests and fixed-effects models were used to analyse pandemic-related differences in visual status.

Results: In total, 128 students were included (mean age before pandemic, 11.82 ± 1.46 years). The mean total screen time was 3.22 ± 2.90 hours per day during the pandemic, whereas it was 1.97 ± 1.90 hours per day in the pre-pandemic period (P<0.001). Asthenopia prevalence was 55% (71/128) during the pandemic, and the mean visual acuity was 0.81 ± 0.30 logarithm of the minimum angle of resolution; these findings indicated increasing vision impairment, compared with the pre-pandemic period (both P<0.001). Notably, asthenopia prevalence increased by two- to three-fold, compared with the pre-pandemic period. An increase in screen time while learning was associated with an increase in asthenopia prevalence (P=0.034).

Conclusion: During the COVID-19 pandemic, students spent more time on online classes, leading to worse visual acuity and vision health. Students in this study reported a significant increase in screen time, which was associated with increasing asthenopia prevalence and worse vision impairment. Further research is needed regarding the link between online classes and vision problems.

Introduction

The World Health Organization announced that the coronavirus disease 2019 (COVID-19) outbreak had become an international public health emergency on 30 January 2020; on 11 March 2020, it declared that the outbreak had become a pandemic.¹ Governments and public health authorities worldwide implemented public health policies to reduce the risk of viral transmission, including strict physical distancing, severe travel restrictions, and the closure of many businesses and schools. On 25 January 2020, China's Central Government announced a nationwide travel ban and quarantine policy²; it initiated nationwide school closures as an emergency measure to prevent the spread of COVID-19.³ Thus, [greater than]220 million school-aged children and adolescents were confined to their homes; online classes were offered and delivered via the internet.⁴

Vision problems are public health challenges; among school-aged children, these problems often involve asthenopia and vision impairment. Asthenopia is defined as a subjective sensation of visual fatigue, eye weakness, or eyestrain; it can manifest through various symptoms, including epiphora, ocular pruritis, diplopia, eye pain, and dry eye.⁵ Vision impairment is defined as visual acuity (VA) of 6/12 or worse in either eye⁶; it is often caused by uncorrected refractive errors, and its estimated prevalence is 43%.⁷ Although both asthenopia and vision impairment have negative effects on students, the effects of vision impairment are greater. A previous global analysis revealed that vision impairment was present in 12.8 million children aged 5 to 15 years, half of whom lived in China.⁸ Moreover, students with vision impairment have lower scores on various motor and cognitive tests.^{9 10}

Excessive use of digital devices contributes to increases in asthenopia prevalence and vision impairment among school-aged children.^{4 11 12 13 14 15} The COVID-19 pandemic has led to increased use of digital device[-]supported online classes,^{16 17 18} which require extended exposure to those devices.^{19 20}Importantly, long durations of exposure to digital devices can contribute to many vision problems in children.¹⁴

Asthenopia and vision impairment related to the excessive use of digital devices during the COVID-19 pandemic have been investigated in developed countries and urban China.^{4 11 12} To our knowledge, no similar studies have been conducted in western rural China. Additionally, online classes are increasingly implemented in rural areas, and the use of digital devices is becoming more prevalent¹¹; thus, there is a need for research that focus on vision health in students.

The primary purpose of this study was to assess screen time, asthenopia prevalence, and vision impairment progression during the COVID-19 pandemic among students in western rural China. To achieve this goal, we first conducted a general descriptive analysis of student characteristics and screen time trends before and during the pandemic. We then investigated the prevalence of asthenopia and progression of vision impairment. Finally, we explored factors influencing the prevalence of asthenopia and progression of vision impairment before and during the pandemic.

Methods Setting

This study focused on areas that were broadly representative of rural western China because of limited resources. Thus, the study was conducted in Shaanxi and Ningxia regions in western China. In 2019, the per capita gross domestic product in Shaanxi Province was US$10 167; this is similar to that in Ningxia Autonomous Region (US$8236).²¹

Sample selection

Vision data were acquired from local vision care centres (VCs), which had been established by the Center for Experimental Economics in Education at Shaanxi Normal University, in cooperation with county-level organisations such as the local education ministries and hospitals.

Before the pandemic, VC screenings were performed in each county, except during summer and winter vacations. Staff conducted one to two screenings per week (covering 2 to 4 schools); they completed one round of screening in one town each month. In practice, approximately 1 year is needed to complete one round of vision screening for all eligible children in a particular county. The second round and subsequent rounds of vision screening were performed using a similar workflow. After the completion of vision screening, students who required further assessment were referred to the VC for full eye and refractive examinations. This study included students who had visited the VC 3 months before the beginning of the COVID-19 pandemic.

During the pandemic, VC staff could not attend schools to perform vision screenings. To maintain vision screening services for students, we telephoned all students who had visited the VC before the pandemic. Participants in this panel study were students who participated in data collection before and during the COVID-19 pandemic.

Data collection

We conducted two cycles of surveys in the VC. The first survey cycle was conducted from October to December 2019 (before the pandemic); the second survey cycle was conducted among a group of students who visited the VC for follow-up from July to December 2020 (during the pandemic), based on their enrolment in the study before the pandemic. The same information was collected during the two survey cycles. During the vision screening process, VC staff administered questionnaires to students for collection of the following information: sex (male=1), age, ethnicity (Han=1), residence (non-rural=1), only-child status (yes=1), parental education (parents with [greater than or equal to]12 years of education=1), and parental migration status (one or both out-migrated=1; defined as one or both parents worked away from home during the semester). Household assets were calculated by summing the values of 13 items owned by the family, in accordance with the China Rural Household Survey Yearbook.²²

The survey also included the collection of information regarding screen time and asthenopia. Students completed a previously described, self-administered questionnaire concerning mean time spent throughout the day on near activities (including computer and smartphone use, television viewing, and studying/homework after school). Reports of time spent on near activities during different parts of the day were categorised as screen time while learning and screen time while playing. Information regarding asthenopia was collected via three questions focused on ocular discomfort: whether the student had experienced dry eyes (yes=1), eye pain and swelling (yes=1), and eye fatigue and watery eyes (yes=1). Asthenopia was defined as the presence of at least one of these three types of vision health problems (yes=1).²³ Furthermore, information regarding VA was collected when students visited the VC. The optometrist in the VC conducted a VA test to measure the clarity of each student's vision. All students completed VA tests without refractive correction; students with spectacles completed VA tests with their routine method of vision correction.

The questionnaire regarding asthenopia was developed and reviewed by a group of health experts from Shaanxi Normal University and Zhongshan Ophthalmic Center, a well-known ophthalmology institution in China. The included questions were constructed to ensure that they could be clearly understood by students aged 9 to 17 years with the aid of trained VC staff. These three questions can serve as good indicators of symptoms representing different degrees of asthenopia in students, and they have been used in previous research.²³

Visual acuity assessment

Visual acuity was assessed using Early Treatment Diabetic Retinopathy Study tumbling-E charts (Precision Vision, La Salle [IL], United States). In an indoor area with sufficient light, VA was separately assessed for each eye without refraction at a distance of 4 m. Students were first examined using a 6/60 line; if they correctly identified the orientation of at least four of five optotypes, they were examined using a 6/30 line, followed by a 6/15 line and a 6/3 line. In this manner, the VA for an eye was defined as the lowest line on which four of five optotypes were correctly identified. If the participant could not read the top line at a distance of 4 m, they were tested at a distance of 1 m, and the VA result was divided by 4.

In this study, VA levels were calculated and compared using the logarithm of the minimum angle of resolution (logMAR) scale, which is a linear scale with regular increments that offers a reasonably intuitive interpretation of VA measurement.²⁴ In this study, vision impairment was defined as logMAR [greater than or equal to]0.3 (ie, VA of 6/12 or worse) in either eye.

Statistical methods

This balanced panel study compared student data between two periods (before and during the COVID-19 pandemic). Mean screen time, asthenopia prevalence, and vision impairment progression were compared among students usingt tests, after stratification according to various demographic and behavioural factors. Fixed-effects logistic and regression models were used to explore factors influencing the prevalence of asthenopia and progression of vision impairment before and during the pandemic. Fixed-effects models were adjusted for sex, age, ethnicity, rural or non-rural residence, only-child status, parental migration status, parental education level, household assets, screen time while learning, and screen time while playing. All analyses were performed using Stata Statistical Software, version 14.1 (StataCorp, College Station [TX], United States). All tests were two-sided, and P values [lesser than]0.05 were considered statistically significant.

Results

This study included 128 students from western rural China (mean age before pandemic, 11.82 ± 1.46 years; mean age during pandemic, 12.32 ± 1.54 years; 80 girls [62.5%] and 48 boys [37.5%]). All participants had vision impairment and were attending online classes (Table 1).

Table 1. Screen time before and during the coronavirus disease 2019 pandemic, stratified according to student characteristics (n=128)

During the pandemic, screen time significantly increased because of enrolment in online classes. The mean total screen time during the pandemic was 3.22 hours per day, compared with 1.97 hours during the pre-pandemic period (P[lesser than]0.001). The mean screen time while learning during the pandemic was 1.70 hours per day, compared with 0.90 hours during the pre-pandemic period (P[lesser than]0.001); the mean screen time while playing during the pandemic was 1.52 hours per day, compared with 1.33 hours during the pre-pandemic period (P=0.019). Additionally, rural students had significantly greater screen time while learning during the pandemic, compared with the pre-pandemic period (P[lesser than]0.001); there was no such difference among non-rural students (Table 1).

The prevalence of asthenopia and progression of vision impairment significantly differed between the pandemic and pre-pandemic periods. The prevalence of asthenopia during the pandemic was 55% (71/128), whereas it was 27% (35/128) during the pre-pandemic period (P[lesser than]0.001). The mean logMAR VA was worse during the pandemic compared with the pre-pandemic period (0.81 vs 0.65; P[lesser than]0.001). The prevalence of asthenopia was higher during the pandemic than during the pre-pandemic period, regardless of the characteristics used to stratify participants. The mean logMAR VA was worse during the pandemic than during the pre-pandemic period, although the difference being insignificant among participants with non-Han ethnicity and participants in the top quartile of household assets (Table 2).

Table 2. Asthenopia prevalence and visual acuity (in logarithm of the minimum angle of resolution [logMAR]) before and duringthe coronavirus disease 2019 pandemic, stratified according to student characteristics (n=128)

Fixed-effects logistic models for asthenopia revealed that screen time while learning was associated with asthenopia prevalence, and the probability of asthenopia increased by 24.6% for each 1-hour increase in screen time while learning (95% confidence interval [CI]=1.02-1.53; P=0.034). Additionally, older age (odds ratio [OR]=2.073, 95% CI=1.13-3.81, P=0.019), Han ethnicity (OR=2.405, 95% CI=1.22-4.74; P=0.011), and only-child status (OR=0.488, 95% CI=0.21-1.13; P=0.095) were factors associated with asthenopia; screen time while playing was not (Table 3).

Table 3. Fixed-effects logistic analysis of factors associated with asthenopia before and during the coronavirus disease 2019 pandemic (n=128)

Fixed-effects regression models showed that residence in a non-rural area (OR=-0.200, 95% CI=-0.355 to -0.046; P=0.011) and only-child status (OR=-0.099, 95% CI=-0.197 to 0.000; P=0.049) were factors associated with logMAR VA. The probability of worse logMAR VA increased by 0.200 in non-rural areas, compared with rural areas. However, screen time while learning and screen time while playing were not associated with vision impairment (Table 4).

Table 4. Fixed-effects regression analysis of factors associated with visual acuity (in logarithm of the minimum angle of resolution [logMAR]) before and during the coronavirus disease 2019 pandemic (n=128)

Discussion

The global spread of the COVID-19 pandemic has affected the education of [greater than]1.5 billion children and adolescents worldwide.²⁵ The participants in our study were representative of this important population. They demonstrated declines in VA and vision health during the pandemic, in relation to the excessive use of digital devices; these findings were consistent with the results of previous studies.^{19 26}

All students in our study were attending online classes during the pandemic. We observed an increase in the mean daily time spent on digital devices between the pre-pandemic and pandemic periods; these results are consistent with international findings that screen time was greater during the pandemic than before the pandemic.¹⁹ Notably, we found that total screen time and screen time while learning significantly changed among rural students but not among non-rural students; these results are also consistent with previous findings.¹⁹ This difference presumably occurred because, compared with rural students, non-rural students were more likely to use digital devices and online classes before the pandemic.

We observed a significant difference in asthenopia prevalence among students in low-income areas of western China before and during the pandemic; this finding supports the results of previous studies.^{26 27} Although the risk of asthenopia reportedly increases with screen time,²⁸ there is no published literature concerning changes in asthenopia among students in relation to the COVID-19 pandemic. Similar to previous studies,¹⁴we found that the prevalence of asthenopia was approximately twofold greater among students aged 13 to 17 years than among those aged 9 to 12 years. Furthermore, Moon et al²⁶ reported that symptoms of dry eye diseases were more common among older children than among younger children. Older children spend more time using digital devices, leading to a higher prevalence of asthenopia.²⁹

This study showed significant progression of vision impairment in relation to the pandemic; similarly, a study in eastern China revealed that students had worse vision during the pandemic, compared with their vision at pre-pandemic examinations.⁴ However, screen time has not been associated with vision impairment among students. Furthermore, evidence regarding the impact of digital devices use on vision impairment has been inconsistent,^{30 31} with computer screen time made students' vision worse while television viewing had no effect. We speculate that the association will become clearer as school-aged children spend increasing amounts of time using these devices.

This study had three important limitations. First, the screen time data were retrospectively collected through a self-reporting mechanism, which may have led to recall bias. However, considering the resource and measurement limitations that researchers encountered during the pandemic, self-reported recall was regarded as the optimal method for collection of screen time data in the present study. Second, the selection of students with poor vision may lead to underestimation of screen time effects on the general population, and the results should be generalised with caution. Third, the study was not designed to accurately distinguish between vision impairment caused by intrinsic factors and vision impairment caused by pandemic-related eye strain.

Our findings provide new evidence regarding the effects of increased screen time on asthenopia and vision impairment among students in western rural China during the pandemic; they can also serve as a basis for future research. Although pandemic-related school closures are temporary, the increasing popularity of online classes may accelerate the overall acceptance of digital devices. The use of online learning approaches is associated with multiple vision problems, which merit attention in future studies.

Conclusion

The present study demonstrated that asthenopia and vision impairment among students in western rural China were also affected by the pandemic; these findings provide critical insights regarding the effects of the pandemic on vision health in rural students. Moreover, the findings highlight important issues related to childhood vision health during the pandemic; parents, teachers, and eye care providers should consider evidence-based measures to avoid asthenopia and vision impairment in children. The current pace of economic and technological development is leading to increased use of digital devices and online learning approaches, but vision problems in rural China have not received sufficient consideration. Thus, there is a critical need for greater efforts to monitor VA and vision health among students in this region.

Author contributions

Concept or design: All authors.

Acquisition of data: Y Ding, H Guan, K Du.

Analysis or interpretation of data: Y Ding, H Guan, K Du, Y Shi.

Drafting of the manuscript: Y Ding, Y Zhang, Z Wang.

Critical revision of the manuscript for important intellectual content: H Guan, Y Shi.

All authors contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

Conflicts of interest

As an International Editorial Advisory Board member of the journal, Y Shi was not involved in the peer review process. Other authors have disclosed no conflicts of interest.

Acknowledgement

We thank Dr Wenting Liu, Dr Jiaqi Zhu, and staff from the Center for Experimental Economics in Education of Shaanxi Normal University, China for their valuable contributions.

Funding/support

H Guan received funding for this study from the National Natural Science Foundation of China (Grant No.: 7180310) and Soft Science Project of Shaanxi Province (Grant No.: 2023-CX-RKX-127). Y Ding received funding for this study from the Fundamental Research Funds for the Central Universities (Grant No.: 2020CSWY018). This study was supported by the 111 Project (Grant No.: B16031). The funders had no role in designing the study, collecting, analysing or interpreting the data, or in drafting this manuscript.

Ethics approval

This study protocol was approved by Sun Yat-sen University, China (Registration No.: 2013MEKY018) and all procedures followed the principles of the Declaration of Helsinki. Permission was obtained from the local boards of education in the study area, as well as the principals of all participating schools. All participating children provided oral assent before baseline data collection, and legal guardians provided written informed consent for their children to be enrolled in the study.

References

1. World Health Organization. WHO timeline—COVID-19. 2020. Available from: https://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19. Accessed 13 Sep 2021.

2. Li D, Liu Z, Liu Q, et al. Estimating the efficacy of quarantine and traffic blockage for the epidemic caused by 2019-nCoV (COVID-19): a simulation analysis. medRxiv [Preprint]. 25 Feb 2020. Available from: https://doi.org/10.1101/2020.02.14.20022913. Accessed 13 Sep 2021.

3. Wang G, Zhang Y, Zhao J, Zhang J, Jiang F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. Lancet 2020;395:945-7.

4. Wang J, Li Y, Musch DC, et al. Progression of myopia in school-aged children after COVID-19 home confinement. JAMA Ophthalmol 2021;139:293-300.

5. Kowalska M, Zejda JE, Bugajska J, Braczkowska B, Brozek G, Malińska M. Eye symptoms in office employees working at computer stations [in Polish]. Med Pr 2011;62:1-8.

6. Cumberland PM, Peckham CS, Rahi JS. Inferring myopia over the lifecourse from uncorrected distance visual acuity in childhood. Br J Ophthalmol 2007;91:151-3.

7. Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol 2012;96:614-8.

8. Resnikoff S, Pascolini D, Mariotti SP, Pokharel GP. Global magnitude of visual impairment caused by uncorrected refractive errors in 2004. Bull World Health Organ 2008;86:63-70.

9. Jan C, Li SM, Kang MT, et al. Association of visual acuity with educational outcomes: a prospective cohort study. Br J Ophthalmol 2019;103:1666-71.

10. Roch-Levecq AC, Brody BL, Thomas RG, Brown SI. Ametropia, preschoolers’ cognitive abilities, and effects of spectacle correction. Arch Ophthalmol 2008;126:252-8.

11. Zhang Z, Xu G, Gao J, et al. Effects of e-learning environment use on visual function of elementary and middle school students: a two-year assessment—experience from China. Int J Environ Res Public Health 2020;17:1560.

12. Wong CW, Tsai A, Jonas JB, et al. Digital screen time during COVID-19 pandemic: risk for a further myopia boom? Am J Ophthalmol 2021;223:333-7.

13. Kim J, Hwang Y, Kang S, et al. Association between sexposure to smartphones and ocular health in adolescents. Ophthalmic Epidemiol 2016;23:269-76.

14. Mohan A, Sen P, Shah C, Jain E, Jain S. 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 J Ophthalmol 2021;69:140-4.

15. Guan H, Yu NN, Wang H, et al. Impact of various types of near work and time spent outdoors at different times of day on visual acuity and refractive error among Chinese school-going children. PLoS One 2019;14:e0215827.

16. Sultana A, Tasnim S, Hossain MM, Bhattacharya S, Purohit N. Digital screen time during the COVID-19 pandemic: a public health concern. Available from: https://f1000research.com/articles/10-81. Accessed 13 Sep 2021.

17. Nigg CR, Wunsch K, Nigg C, et al. Are physical activity, screen time, and mental health related during childhood, preadolescence, and adolescence? 11-year results from the German Montorik–Modul Longitudinal Study. Am J Epidemiol 2021;190:220-9.

18. Schmidt SC, Anedda B, Burchartz A, et al. Physical activity and screen time of children and adolescents before and during the COVID-19 lockdown in Germany: a natural experiment. Sci Rep 2020;10:21780.

19. Aguilar-Farias N, Toledo-Vargas M, Miranda-Marquez S, et al. Sociodemographic predictors of changes in physical activity, screen time, and sleep among toddlers and preschoolers in Chile during the COVID-19 pandemic. Int J Environ Res Public Health 2020;18:176.

20. Bates LC, Zieff G, Stanford K, et al. COVID-19 impact on behaviors across the 24-hour day in children and adolescents: physical activity, sedentary behavior, and sleep. Children (Basel) 2020;7:138.

21. National Bureau of Statistics of China, PRC Government. China Statistical Yearbook 2020. Available from: http://www.stats.gov.cn/tjsj/ndsj/2020/indexch.htm. Accessed 14 Sep 2021.

22. National Bureau of Statistics of China, PRC Government. China Statistical Yearbook 2013. Beijing, China: China State Statistical Press; 2013.

23. Seguí Mdel M, Cabrero García J, Crespo A, Verdú J, Ronda E. A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace. J Clin Epidemiol 2015;68:662-73.

24. Yi H, Zhang L, Ma X, et al. Poor vision among China’s rural primary school students: prevalence, correlates and consequences. China Econ Rev 2015;33:247-62.

25. United Nations International Children’s Emergency Fund. Don’t let children be the hidden victims of COVID-19 pandemic. Available from: https://www.unicef.org/press-releases/dont-let-children-be-hidden-victims-covid-19-pandemic. Accessed 6 Oct 2020.

26. Moon JH, Kim KW, Moon NJ. Smartphone use is a risk factor for pediatric dry eye disease according to region and age: a case control study. BMC Ophthalmol 2016;16:188.

27. Moon JH, Lee MY, Moon NJ. Association between video display terminal use and dry eye disease in school children. J Pediatr Ophthalmol Strabismus 2014;51:87-92.

28. Rechichi C, De Mojà G, Aragona P. Video game vision syndrome: a new clinical picture in children? J Pediatr Ophthalmol Strabismus 2017;54:346-55.

29. Mowatt L, Gordon C, Santosh AB, Jones T. Computer vision syndrome and ergonomic practices among undergraduate university students. Int J Clin Pract 2018;72:e13035.

30. Terasaki H, Yamashita T, Yoshihara N, Kii Y, Sakamoto T. Association of lifestyle and body structure to ocular axial length in Japanese elementary school children. BMC Ophthalmol 2017;17:123.

31. Fernández-Montero A, Olmo-Jimenez JM, Olmo N, et al. The impact of computer use in myopia progression: a cohort study in Spain. Prev Med 2015;71:67-71.