Home Print this page Email this page Users Online: 243
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 10  |  Issue : 3  |  Page : 105-109

Screen exposure time and computer vision syndrome in school-age children during COVID-19 era: A cross-sectional study


1 Department of Ophthalmology, Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh, India
2 Paediatrics, Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh, India

Date of Submission21-Nov-2021
Date of Decision30-Mar-2022
Date of Acceptance09-Jul-2022
Date of Web Publication1-Dec-2022

Correspondence Address:
Nandita Chaturvedi
Department of Ophthalmology, Government Institute of Medical Sciences, Greater Noida, Uttar Pradesh
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcor.jcor_157_21

Rights and Permissions
  Abstract 


Purpose: With the advent of COVID-19 era, teaching activities have migrated from offline to online platform. In this study, we assess whether the increased exposure to visual display terminal (VDT) devices is affecting the health of school-age children with regard to computer vision syndrome (CVS). Methods: This cross-sectional study was carried out by means of an online questionnaire. Participants were students ranging from Class 1 to Class 12. Questions were posed to participants pertaining to screen exposure time, physical activity levels, dry eye symptoms, and asthenopia symptoms. The dry eye part was adapted from the 5 Item Dry Eye Questionnaire (DEQ5 questionnaire), and the asthenopia part was adapted from the questionnaire developed by Ames et al. A total of 554 students were included in the study. The data received were statistically analyzed. Results: An increase in screen exposure time during COVID era was reported by 237 (42.8%) students. The major contributors to screen usage were online classes and assignments (94% of students). The prevalence of headache was higher in students using tablet/iPad and smartphone as the VDT device. The headache, eyeache, DEQ5 scores, and asthenopia scores were significantly correlated with screen exposure time, and a significant increase was observed in parameters from pre-COVID to COVID era. Concentration span in online classes showed a positive correlation with amount of physical activity of the student. Conclusions: Dry eye, asthenopia, and musculoskeletal symptoms of CVS have increased significantly during COVID era. Screen exposure needs to be restricted and adequate attention needs to be given to physical activity.

Keywords: Asthenopia, computer vision syndrome, COVID-19 era, dry eye, online classes, visual display terminal


How to cite this article:
Chaturvedi N, Singh P, Bhattacharya M. Screen exposure time and computer vision syndrome in school-age children during COVID-19 era: A cross-sectional study. J Clin Ophthalmol Res 2022;10:105-9

How to cite this URL:
Chaturvedi N, Singh P, Bhattacharya M. Screen exposure time and computer vision syndrome in school-age children during COVID-19 era: A cross-sectional study. J Clin Ophthalmol Res [serial online] 2022 [cited 2023 Feb 9];10:105-9. Available from: https://www.jcor.in/text.asp?2022/10/3/105/362500



Computer screen and associated visual display terminal (VDT) devices have become a household necessity. Prolonged viewing of a digital screen leads to eye strain. The American Optometric Association defines computer vision syndrome (CVS) as a complex of eye and vision problems related to the activities which stress the near vision and which are experienced in relation to, or during, the use of the computer.[1] It includes a group of visual symptoms which result from the prolonged viewing of the digital screen, when the visual task exceeds the ability of the viewer. This definition can include any digital device, also known as VDT. In addition, CVS can present with extraocular symptoms such as lower back pain, shoulder and neck pain, tingling and numbness of the fingers, and cervical stiffness.[1] Asthenopia is clinically defined as a subjective sensation of visual fatigue, eye weakness, or eyestrain.[2] With the advent of COVID-19 era, most activities have migrated from offline to online platform. This is also applicable to school teaching. In this study, we attempt to find out whether the increased exposure to VDT is affecting the health of school-age children with regard to CVS.


  Methods Top


This was a questionnaire based cross sectional study. It was carried out in accordance with the tenets of the Helsinki Declaration and was approved by the Institutional Ethics Committee. Students ranging from class 1 to class 12 and attending online classes recruited from our contacts, irrespective of their place of residence, were invited to answer the questionnaire online. Younger students were assisted by their parents in filling the form. Students with other conditions leading to symptoms mimicking eye strain and headache were excluded. These exclusion criteria were corneal scar, squint, ocular allergy, improper vision with glasses(amblyopia) and fits. Sample size was calculated as 360 with prevalence being 69.5%. A total of 42 questions were posed to the students. The questions were close ended and semi close ended type. Questions were asked regarding demographic data, screen exposure patterns, physical activity levels, concentration span and sleep.The two parts of the questionnaire were adapted from already validated and utilised questionnaires present in the public domain. At our end a pilot survey was administered to 50 participants to assess the questionnaire for suitability. Questions pertaining to dry eye symptoms were adapted from DEQ5 questionnaire.[3] Ocular discomfort, its severity, dryness of eyes, its severity and excessive watering of eyes were the parameters used to assess dry eye. Score of 6 or more was considered positive for dry eye. Questions pertaining to asthenopia symptoms were adapted from the questionnaire originally developed by Ames et al.[4] Tiredness of eyes, aching eyes, ocular irritation, burning of eyes were some of the parameters assessed for asthenopia. Score of 20 or more was considered significant for asthenopia. These questions were asked in pre Covid and during Covid format. Consent was obtained from the parents for utilising the data thus obtained. The questionnaire utilised is attached as a supplementary material. The responses received were collated in a google sheet. The data obtained was consolidated and analysed.

Statistical analysis

Statistical analysis was carried out using Epi Info version 7 and version 20.0 (Statistical Package for Social Sciences; IBM Corp; 2011. IBM SPSS Statistics for Windows Version 20.0. Armonk, NY: IBM Corp.). Demographic data such as age and gender, class in which they are studying, and purpose of device usage were recorded as explanatory variables. Type of device used, daily screen exposure time, posture while using the device, and level of physical activity were the exposure variables utilized. Concentration span in online classes, DEQ5 score, and asthenopia score were calculated for pre-COVID-19 era and COVID-19 era. These were the outcome variables. Descriptive analysis was performed for the explanatory and outcome variables and expressed as percentages. Chi-square test was used to study the relationship between exposure and outcome variables, and paired t-test was used to assess for change from pre-COVID to COVID era. P value was calculated in each case. Logistic regression was carried out for DEQ5 score ≥6 and asthenopia score ≥20.


  Results Top


The questionnaire was administered to 800 students. A total of 791 responses were received of which 237 were excluded. The predominant cause for exclusion was ocular allergy (107 students), followed 76,33,33,20 students having amblyopia, corneal scar, fits and squint respectively.Several candidates had more than one exclusion factor. Finally 554 students were included in the study. This gave us an effective response rate of 69.3%. Of the total respondents 342 (61.7%) were male and 212 (38.2%) were female. Mean age of students was 12.46 ± 3.57 years and median age was 13 years. The most frequently used VDT device was smartphone, accounting for by 364 (65.7%) students. Other devices used were laptop/notebook, tablet/iPad and desktop, with frequencies of 22%, 8% and 4% respectively.

The majority of students utilised their online device for the purpose of online classes and assignments (94%). Other common uses were watching videos (44.9%), gaming (27.4%) and social media (19.7%). Majority of students (47.1% n=261) used the device while sitting on the bed, 38.6% (214) used table & chair whereas 9.4% (52) used the device while lying in bed. The remaining 4.9% (27) students used other postures including sitting on floor, sitting in garden.

Regarding their concentration in online classes as compared to offline classes in regular school 259 (46.8%) students reported having a decreased concentration level in online classes. An increase in physical activity was reported by 133 (24%) students whereas 241 (43.5%) students reported a decrease in physical activity as compared to pre Covid era. Association between decreased physical activity levels and decreased concentration in online classes was found to be statistically significant by chi square test with p value of 0.0005 and Odds Ratio (OR) of 1.83. With respect to the relationship between type of device used and headache 56.5% (26 0f 46) students using tablet/iPad and 55.2% (201 of 364) students using smartphone developed headache symptoms whereas only 34.7% (43 of 124) students using laptop and 30% (6 of 20) students using desktop developed headache symptoms.

Screen time exposure per day reported in pre Covid era was 2 hours or less in 275 (49.60%) students and 3 – 5 hours, 6 – 8 hours and more than 8 hours in 205 (37%), 61(11%) and 13 (2.40%) students respectively. In contrast during Covid era only 88 (15.90%) students reported screen time exposure limited to 2 hours or less; whereas all the other screen exposure groups reported an increase in percentage [Table 1]. A total of 237 (42.8%) students reported an increase in screen exposure time during Covid era as compared to the pre covid state.
Table 1: Change in screen time from pre-COVID to COVID era

Click here to view


A total of 22 students out of 31 (71%) with screen exposure time of 8 hours or more, complained of having headache or eyeache. This percentage decreased with decreasing screen exposure time, being 64% (86 of 135), 46% (137 of 300) and only 33% (29 of 88) in the 6 – 8 hour, 3 – 5 hour and 2 hour or less screen exposure groups respectively [Figures 1-4]. A total of 92 students reported increase in headache/ eyeache complaints. The correlation between increased screen exposure time and increased headache/ eyeache was found to be statistically significant by chi square test (p <<0.0001; OR 3.19).

A total of 170 (30.7%) students reported an increase in occurrence of neck or shoulder pain during Covid era. Statistical association between increased screen exposure time and increased neck or shoulder pain was found to be significant by chi square test (p = 0.0004; OR 1.94). Students scores for tiredness of eyes were compared in pre Covid and Covid era. The mean score in pre Covid time was 0.93 and during Covid era was 1.64. The difference in scores was found to be significant statistically by paired t test (p<0.0001).

The mean score for eye strain symptoms reported in pre Covid era was 0.82 and in the Covid era was 1.25. This difference was found to be significant statistically by paired t test (p value <0.0001). DEQ5 scores were compared between pre Covid era and Covid era. Mean score in pre Covid era was 3.13 and during Covid era was 4.37. The difference in scores was significant by paired t test with p value <0.0001. DEQ5 scores of 6 or more indicating dry eye disease were observed in 20.5% (18 of 88) students with screen time of 2 hours or less per day. This percentage increased with increasing screen time and 30% (90 of 300) students in 3 – 5 hour exposure group, 45.9% (62 of 135) students in 6 – 8 hour exposure group and 51.6% (16 of 31) students with screen time of 8 hours or more per day had DEQ5 scores of 6 or more. Positive association between increased screen exposure time and increased DEQ5 scores from pre Covid to Covid era was was found by chi square test (p <0.00001; OR 2.7).

Mean asthenopia score in pre Covid era was 5.30 and in Covid era was 8.03. The difference in scores was significant by paired t test with p value <0.0001. Asthenopia scores of 20 or more were observed in only 3.5% (3 of 88) students with daily screen time of 2 hours or less. In contrast 20% (6 of 31) students with screen time of more than 8 hours had asthenopia scores of 20 or more. Positive correlation between increased screen exposure time and increased Asthenopia scores from pre Covid to Covid era was found by means of chi square test and this correlation was significant statistically (p <0.00001; OR 3.7).

A logistic regression analysis was carried out to study the effect of several variables on dry eye score. The dichotomous variable 'DEQ5 score ≥ 6' was used as the dependant variable. The results are depicted in [Table 2]. The overall model was found to be a good fit. The values for Cox and Snell R Square and Nagelkerke R Square were 0.067 and 0.093 respectively. The results indicate that type of device used is a significant contributor towards dry eye (OR 1.22, p 0.050) and likelihood of having DEQ5 score ≥ 6 increased by 22% progressively for laptop, tablet/iPad and smartphone respectively. Duration of screen exposure per day was also a significant contributor (OR 1.75, p <0.001). Odds of having DEQ5 score ≥ 6 increased by 75% when the duration of daily screen exposure increased to 6 –8 hours. The variable change in exercise level during Covid era contributed significantly to the model as well (OR 0.48, p 0.004). The likelihood of having DEQ5 score ≥ 6 decreased by 52% in students who had increased levels of physical exercise during Covid era.
Table 2: Logistic regression table for dry eye questionnaire score ≥6

Click here to view


A similar logistic regression analysis was constructed to study the relationship of the above covariates with asthenopia scores. The dichotomous variable 'Asthenopia Score ≥ 20' was used as the dependant variable. The analysis results are depicted in [Table 3]. The overall model was found to be a good fit. Cox and Snell R Square and Nagelkerke R Square were 0.078 and 0.155 respectively. Gender (OR0.51, p 0.015), change in exercise level during Covid era (OR 0.28, p 0.001) and daily screen exposure duration (OR 2.10, p <0.001) were the significant variables contributing to the model. The results suggest that males were 49% less likely to have Asthenopia Score≥ 20. Also the likelihood of having Asthenopia Score≥ 20 decreased by 72% in students who had increased levels of physical exercise during Covid era. Duration of screen exposure was also an important factor and the odds of developing the studied outcome increased by 2.10 when daily screen exposure increased to 6 – 8 hours. In this model type of device used was not a contributory factor.
Table 3: Logistic regression table for asthenopia score ≥20

Click here to view



  Discussion Top


Human eyes need to adjust themselves in order to see objects from different distances, such as by changing the size of pupil, lengthening or shortening the lens to change eye focus, and contracting extra-ocular muscles to coordinate the two eyes.[5] Due to the pixel structure of characters on a digital screen the eyes need to repeatedly refocus on the screen. Therefore, constant focusing and refocusing is required. These constant changes take place multiple times a day when a computer user stares at a computer screen for hours. This in turn stresses the eye muscles leading to ocular fatigue and discomfort causing headache.[6] The symptoms of CVS can be related to accommodation (blurred vision for near objects, headache, tired eyes, and eyestrain) and related to dryness (burning sensation, foreign body sensation, itching, watering, intolerance to light).[7] During COVID era, screen exposure time for students has increased significantly. In our study, we saw that screen exposure had increased in 42.8% of students. A study conducted in Tamil Nadu, India,[8] reported increased screen exposure in as many as 93.6% of respondents. This difference in percentage may be attributable to the difference in mean age of respondents. In our study, participants were school students with a mean age of 12.46 ± 3.57 years, whereas in the Tamil Nadu study, the mean age of participants was 27.4 years with occupation related increase in screen exposure. This increased screen exposure has concurrently resulted in increased dry eye scores. Mean dry eye score increased by 1.24 from pre-COVID to COVID era. Similar to this, a study in Japan[9] also showed an increase in dry eye disease during COVID era and this was associated with increased screen time. In our study headache symptoms were more prevalent in students using smartphones and tablets as compared to those using laptop or desktop. The smaller font size in mobile devices resulting in closer viewing distance and better screen resolution provided by desktop devices may be responsible for the difference. Similar findings were seen in a study by Mohan et al.[10] where smartphone usage was associated with increased symptoms of eye strain. During the peak of COVID era, there were severe restrictions on outdoor activity which adversely impacted the level of physical activity in children. A decrease in physical activity was reported by 43.5% students during Covid era as compared to pre Covid era and was positively correlated with decreased concentration span in online classes. Since 42.8% of students in our study reported an increase in screen exposure time during COVID era, we can associate it with decreased physical activity and consequently poorer concentration spans. Similar results were reported in a systematic review by Donnelly et al.[11] where findings suggested a beneficial impact of physical exercise on cognitive function and concentration in children.
Figure 1: Type of Device Used

Click here to view
Figure 2: Relationship between screen time and headache/ eyeache symptoms

Click here to view
Figure 3: Relationship between screen time and deq5 score

Click here to view
Figure 4: Relationship between screen time and asthenopia score

Click here to view


Moreover, complaints of headache and eyeache showed a steady increase with increasing screen exposure time. In our study we found a positive correlation between increased screen exposure time from pre-COVID to COVID era and increased complaints of neck and shoulder pain. This was in concurrence with a study carried out in Pakistan[12] that reported increased musculoskeletal symptoms with increased duration of VDT usage. DEQ5 scores and asthenopia scores also worsened in COVID era with more students having DEQ5 scores ≥6 and asthenopia scores ≥20 during COVID era as compared to pre-COVID era. Both were also positively correlated with the increased screen exposure durations during COVID era. Similar findings were reported in a study carried out in Australia that found increased asthenopia symptoms in computer users with long duration of screen time.[13] The most important contributors to dry eye were usage of smartphones and screen exposure of more than 6 h. Increased levels of physical exercise had a protective effect on risk of dry eye. This could be associated with the automatic abstinence from screen time while engaged in physical exercise. The most important contributor to asthenopia was screen exposure of more than 6 h. Male gender and increased levels of physical exercise had a protective effect against developing asthenopia.

Strengths and limitations of the study

A key strength of the present study was the large sample size and good response rate along with the detailed analysis of data obtained. The main limitation of our study was that no Ophthalmological measurements were carried out and the symptoms were self-reported by subjects. The symptoms not appreciated by the subjects may have remained unreported, the same may occur due to recall bias. For establishing causal relationship, more firmly randomized controlled trials should be conducted.


  Conclusions Top


Our study highlights the increased prevalence of CVS in school-going children during the current COVID era. Online classes, while being essential in the present scenario, are also putting the ocular health of children under duress. Shorter duration of online exposure along with adequate attention to physical exercise is required to strike a balance between imparting education and maintaining the well-being of students. Based on the findings of our study it is suggested that keeping daily online activity confined to under 6 h and using larger screen devices such as desktop or laptop may be effective in reducing the burden of CVS.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Loh K, Redd S. Understanding and preventing computer vision syndrome. Malays Fam Physician 2008;3:128-30.  Back to cited text no. 1
    
2.
Ichhpujani P, Singh RB, Foulsham W, Thakur S, Lamba AS. Visual implications of digital device usage in school children: A cross-sectional study. BMC Ophthalmol 2019;19:76.  Back to cited text no. 2
    
3.
Chalmers RL, Begley CG, Caffery B. Validation of the 5-Item Dry Eye Questionnaire (DEQ-5): Discrimination across self-assessed severity and aqueous tear deficient dry eye diagnoses. Cont Lens Anterior Eye 2010;33:55-60.  Back to cited text no. 3
    
4.
Ames SL, Wolffsohn JS, McBrien NA. The development of a symptom questionnaire for assessing virtual reality viewing using a head-mounted display. Optom Vis Sci 2005;82:168-76.  Back to cited text no. 4
    
5.
Ranasinghe P, Wathurapatha WS, Perera YS, Lamabadusuriya DA, Kulatunga S, Jayawardana N, et al. Computer vision syndrome among computer office workers in a developing country: An evaluation of prevalence and risk factors. BMC Res Notes 2016;9:150.  Back to cited text no. 5
    
6.
Yan Z, Hu L, Chen H, Lu F. Computer vision syndrome: A widely spreading but largely unknown epidemic among computer users. Comput Hum Behav 2008;24:2026-42.  Back to cited text no. 6
    
7.
Portello JK, Rosenfield M, Bababekova Y, Estrada JM, Leon A. Computer-related visual symptoms in office workers. Ophthalmic Physiol Opt 2012;32:375-82.  Back to cited text no. 7
    
8.
Bahkir FA, Grandee SS. Impact of the COVID-19 lockdown on digital device-related ocular health. Indian J Ophthalmol 2020;68:2378-83.  Back to cited text no. 8
[PUBMED]  [Full text]  
9.
Mineshita Y, Kim HK, Chijiki H, Nanba T, Shinto T, Furuhashi S, et al. Screen time duration and timing: Effects on obesity, physical activity, dry eyes, and learning ability in elementary school children. BMC Public Health 2021;21:422.  Back to cited text no. 9
    
10.
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.  Back to cited text no. 10
[PUBMED]  [Full text]  
11.
Donnelly JE, Hillman CH, Castelli D, Etnier JL, Lee S, Tomporowski P, et al. Physical activity, fitness, cognitive function, and academic achievement in children. Med Sci Sports Exerc 2016;48:1197-222.  Back to cited text no. 11
    
12.
Borhany T, Shahid E, Siddique WA, Ali H. Musculoskeletal problems in frequent computer and internet users. J Family Med Prim Care 2018;7:337-9.  Back to cited text no. 12
[PUBMED]  [Full text]  
13.
Junghans BM, Azizoglu S, Crewther SG. Unexpectedly high prevalence of asthenopia in Australian school children identified by the CISS survey tool. BMC Ophthalmol 2020;20:408.  Back to cited text no. 13
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Methods
Results
Discussion
Conclusions
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed1004    
    Printed20    
    Emailed0    
    PDF Downloaded99    
    Comments [Add]    

Recommend this journal