digital eye strain epidemic amid covid-19 pandemic …

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Digital Eye Strain Epidemic Amid COVID-19 Pandemic Pratyusha Ganne 1 ; Shaista Najeeb 1 ; Ganne Chaitanya 2 ; Aditya Sharma 1 ; Nagesha CK 3 1 Department of Ophthalmology, All India InsDtute of Medical Sciences, Guntur, Andhra Pradesh, India 2 Epilepsy and CogniDve Neurophysiology Lab, Department of Neurology, University of Alabama at Birmingham, AL, USA, 35233 3 Department of Vitreo-ReDna, B W Lions Superspeciality Eye Hospital, Bangalore, India BACKGROUND Digital eye strain (DES) or computer vision syndrome encompasses a range of visual and ocular symptoms arising due to the prolonged use of digital electronic devices The corona virus disease (COVID-19) pandemic has necessitated drasDc changes in the lifestyle, one of which is increased exposure to digital devices There has been an enormous increase in the use of gadgets for online classes and entertainment during the COVID-19 pandemic The current pandemic mimics a real-life experiment to study the effects of this unprecedented increase in the use of gadgets on ocular health AIMS To esDmate prevalence of DES among students taking online classes and general public To describe the paZern of gadget usage To analyze the risk factors for increased DES during the last four months of COVID-19 pandemic in India CONCLUSION This study calls for a concerted effort to disseminate informaDon on reducing the total screen Dme and on the ergonomic use of gadgets Special care should be taken by people with previous eye diseases and those whose occupaDon demands prolonged screen exposure to avoid DES DISCUSSION Indiscriminate use of digital devices can potenDally lead to a variety of ocular and non-ocular problems like: eye strain, reDnal damage, progression of myopia, sleep disturbances, musculoskeletal problems, and behavioral abnormaliDes The limitaDons of this study include: (i) recall bias (ii) the parDcipants could have given desirable answers rather than the true answers The recommendaDons from this study include: (i) limit the total duraDon of online classes to less than 4 hours a day, give adequate breaks between classes, inculcate lectures on ergonomic use of digital devices (ii) reduce other screen related acDvity like watching television, browsing social media to compensate for the screen Dme spent on online classes or work from home. (iii) ergonomic pracDces that can ameliorate DES should be pracDced METHODS A cross-secDonal, quesDonnaire-based, online study conducted in April - July,2020 Study populaDon: Students and members of the general public aged ≥18 years were recruited. Electronic devices included televisions, computers, smart phones, e-readers, tablets, and gaming systems Survey protocol: The link to the survey was sent by emails and text messages and re- circulated thereof. Design of the quesDonnaire: Pre-validated computer vision syndrome quesDonnaire designed by Segui et al* was used to assess the level of DES symptoms. The quesDonnaire had three parts: (i) to capture the demographic details, (ii) to understand the paZern of gadget usage, (iii) to assess the degree of eye strain experienced. Grading of DES was esDmated using the frequency and intensity of 16 symptoms. Scoring was as follows: Frequency: Never (score 0), someDmes (score 1) (once a week, sporadic episodes), and always (score 2) (more than 2-3 Dmes a week). The intensity was graded as moderate (score 1) and intense (score 2). The result of (frequency X intensity) was re-coded as: 0 = 0; 1 or 2 = 1; 4 = 2 Final DES score= ∑(1-16) (frequency X intensity) DES score ≥ 6 was indicaDve of digital eye strain. A pilot study was carried out on130 parDcipants (students and the general public) StaDsDcs: Non-parametric tests of medians were used to compare the median DES score, Chi-square test to compare categorical variables, and binary logisDc regression to find the predictors of DES. RESULTS 941 responses from students of online classes (688), teachers of online classes (45), and general populaDon (208) were analyzed Table 1: Demographic profile of the parGcipants Age in years (Mean±SD) (Range) 23.4±8.2 (18-79) Gender Male (%) 481 (51.1%) Female (%) 460 (48.9%) OccupaDon General populaDon Unskilled worker 7 (0.7%) Semi-skilled worker 43 (4.6%) Skilled worker 139 (14.8%) Students 752 (79.9%) ParDcipaDon in online classes Students of online classes 688 (73.1%) Teachers of online classes 45 (4.8%) Rest of the general public 208 (22.1%) ParDcipants with eye disease (%) Total 253 (26.9%) Myopia 154 (60.8%) AsDgmaDsm 19 (7.5%) Hypermetropia 12 (4.7%) Unspecified refracDve error 46(18.2%) Seasonal allergic conjuncDviDs 6 (2.4%) Cataract, Keratoconus 3 each (1.2%) Glaucoma, reDnal detachment 2 each (0.8%) ReDniDs pigmentosa, colour blindness, dry eye, macular degeneraDon, squint, amblyopia 1 each (0.4%) RESULTS Prevalence of DES Higher among students taking online classes compared to the general public (50.6% vs. 33.2%; χ 2 =22.5, df=1, p<0.0001). The DES score was highest among students aZending online classes [median score=7, IQR=6.87-7.7] followed by teachers of online classes [median score=5, IQR=4.37-7.23] and then the rest of the general public [median score=4, IQR=4.64-6.18] [test staDsDc=22.5, df=2, p=0.0001] (Fig A) PaZern of gadget use The average daily screen Dme increased during the pandemic compared to that before the pandemic (Fig B) There was a tendency of younger parDcipants (22±5 years) to spend greater Dme with gadgets than the relaDvely older populaDon (33±17 years) (R 2 =0.066, p<0.001) (Fig C) RESULTS Greater proporDon of students taking online classes: had a screen Dme >6hours/day (χ2=33.59, df=2, p&lt;0.0001), never took breaks/ took them infrequently (χ2=8.59, df=2, P=0.014) and used gadgets in the dark (χ2=9.4, df=2, p=0.009) compared to teachers and the general public DES score was highest among students aZending online classes (p<0.0001), in those with eye diseases (p=0.001), greater screen Dme (p<0.0001), screen distance <20 cm (p=0.002), those who used gadgets in dark (p=0.017) and those who took infrequent/no breaks (p=0.018) REFERENCES v Seguí Mdel M, Cabrero-García J, Crespo A, Verdú J, Ronda E. A reliable and valid quesDonnaire was developed to measure computer vision syndrome at the workplace. J Clin Epidemiol. 2015;68:662-73 v Sheppard AL, Wolffsohn JS. Digital eye strain: prevalence, measurement and amelioraDon. BMJ Open Ophthalmol. 2018;3:e000146. v Jaadane I, Boulenguez P, Chahory S, Carré S, Savoldelli M, Jonet L, et al. ReDnal damage induced by commercial light emiyng diodes (LEDs). Free Radical Biology and Medicine 2015;84:373–84. v HAM WT, Mueller HA, Sliney DH. ReDnal sensiDvity to damage from short wavelength light. Nature. 1976;260:153–5. v Guan H, Yu NN, Wang H, Boswell M, Shi Y, Rozelle S, et al. Impact of various types of near work and Dme spent outdoors at different Dmes of day on visual acuity and refracDve error among Chinese school-going children. PLoS One. 2019;14:e0215827. v Tosini G, Ferguson I, Tsubota K. Effects of blue light on the circadian system and eye physiology. Mol Vis 2016;22:61– 72. v Borhany T, Shahid E, Siddique WA, Ali H. Musculoskeletal problems in frequent computer and internet users. J Family Med Prim Care. 2018;7:337-39. v Leung TW, Li RW, Kee CS. Blue-Light Filtering Spectacle Lenses: OpDcal and Clinical Performances. PLoS One. 2017;12(1):e0169114. v Bababekova Y, Rosenfield M, Hue JE, Huang RR. Font size and viewing distance of handheld smart phones. Optom Vis Sci. 2011;88:795-97. v Jaschinski W, Heuer H, Kylian H. Preferred posiDon of visual displays relaDve to the eyes: a field study of visual strain and individual differences. Ergonomics.1998;41:1034–49 v Feĭgin AA. Role of spectral filters for refracDon dynamics in computer users. Vestn O}almol. 2003;119:39-40. v Teran E, Yee-Rendon CM, Ortega-Salazar J, De Gracia P, Garcia-Romo E, Woods RL. EvaluaDon of Two Strategies for AlleviaDng the Impact on the Circadian Cycle of Smartphone Screens. Optom Vis Sci. 2020;97:207-217. Abstract No: EHCWOP101

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Page 1: Digital Eye Strain Epidemic Amid COVID-19 Pandemic …

DigitalEyeStrainEpidemicAmidCOVID-19Pandemic

PratyushaGanne1;ShaistaNajeeb1;GanneChaitanya2;AdityaSharma1;NageshaCK3

1DepartmentofOphthalmology,AllIndiaInsDtuteofMedicalSciences,Guntur,AndhraPradesh,India

2EpilepsyandCogniDveNeurophysiologyLab,DepartmentofNeurology,UniversityofAlabamaatBirmingham,AL,USA,35233

3DepartmentofVitreo-ReDna,BWLionsSuperspecialityEyeHospital,Bangalore,India

BACKGROUND

•  Digitaleyestrain(DES)orcomputervisionsyndromeencompassesarangeofvisualand

ocularsymptomsarisingduetotheprolongeduseofdigitalelectronicdevices

•  The corona virus disease (COVID-19) pandemic has necessitated drasDc changes in the

lifestyle,oneofwhichisincreasedexposuretodigitaldevices

•  There has been an enormous increase in the use of gadgets for online classes and

entertainmentduringtheCOVID-19pandemic

•  The current pandemic mimics a real-life experiment to study the effects of this

unprecedentedincreaseintheuseofgadgetsonocularhealth

AIMS

•  ToesDmateprevalenceofDESamongstudentstakingonlineclassesandgeneralpublic

•  TodescribethepaZernofgadgetusage

•  To analyze the risk factors for increased DES during the last fourmonths of COVID-19

pandemicinIndia

CONCLUSION

•  This study calls for a concertedeffort to disseminate informaDon

on reducing the total screen Dme

and on the ergonomic use of

gadgets

•  Special care should be taken bypeople with previous eye diseases

and those whose occupaDon

demands p ro longed s c reen

exposuretoavoidDES

DISCUSSION

•  IndiscriminateuseofdigitaldevicescanpotenDallyleadtoavarietyofocularandnon-ocular

problems like: eye strain, reDnal damage, progression of myopia, sleep disturbances,

musculoskeletalproblems,andbehavioralabnormaliDes

•  The limitaDons of this study include: (i) recall bias (ii) the parDcipants could have given

desirableanswersratherthanthetrueanswers

•  TherecommendaDonsfromthisstudyinclude:

•  (i) limit the totalduraDonofonlineclasses to less than4hoursaday, giveadequate

breaksbetweenclasses,inculcatelecturesonergonomicuseofdigitaldevices

•  (ii)reduceotherscreenrelatedacDvitylikewatchingtelevision,browsingsocialmediato

compensateforthescreenDmespentononlineclassesorworkfromhome.

•  (iii)ergonomicpracDcesthatcanameliorateDESshouldbepracDced

METHODS

•  Across-secDonal,quesDonnaire-based,onlinestudyconductedinApril-July,2020•  Study populaDon: Students and members of the general public aged ≥18 years were

recruited. Electronic devices included televisions, computers, smart phones, e-readers,

tablets,andgamingsystems

•  Survey protocol: The link to the surveywas sent by emails and textmessages and re-

circulatedthereof.

•  Design of the quesDonnaire: Pre-validated computer vision syndrome quesDonnaire

designedbySeguietal*wasusedtoassessthelevelofDESsymptoms.ThequesDonnaire

hadthreeparts:(i)tocapturethedemographicdetails,(ii)tounderstandthepaZernof

gadgetusage,(iii)toassessthedegreeofeyestrainexperienced.

•  Grading of DES was esDmated using the frequency and intensity of 16 symptoms.

Scoringwasasfollows:Frequency:Never(score0),someDmes(score1)(onceaweek,

sporadicepisodes),andalways(score2)(morethan2-3Dmesaweek).Theintensity

was graded asmoderate (score 1) and intense (score 2). The result of (frequency X

intensity)wasre-codedas:0=0;1or2=1;4=2

•  FinalDESscore=∑(1-16)(frequencyXintensity)DESscore≥6wasindicaDveofdigitaleyestrain.

•  Apilotstudywascarriedouton130parDcipants(studentsandthegeneralpublic)•  StaDsDcs:Non-parametrictestsofmedianswereusedtocomparethemedianDESscore,

Chi-squaretesttocomparecategoricalvariables,andbinarylogisDcregressiontofindthe

predictorsofDES.

RESULTS

•  941responsesfromstudentsofonlineclasses(688),teachersofonlineclasses(45),and

generalpopulaDon(208)wereanalyzed

Table1:DemographicprofileoftheparGcipants

Ageinyears(Mean±SD)(Range) 23.4±8.2(18-79)

GenderMale(%) 481(51.1%)Female(%) 460(48.9%)

OccupaDonGeneralpopulaDon

Unskilledworker 7(0.7%)Semi-skilledworker 43(4.6%)Skilledworker 139(14.8%)

Students 752(79.9%)

ParDcipaDoninonlineclasses

Studentsofonlineclasses 688(73.1%)Teachersofonlineclasses 45(4.8%)Restofthegeneralpublic 208(22.1%)

ParDcipantswitheyedisease(%)

Total 253(26.9%)Myopia 154(60.8%)AsDgmaDsm 19(7.5%)Hypermetropia 12(4.7%)UnspecifiedrefracDveerror 46(18.2%)

SeasonalallergicconjuncDviDs 6(2.4%)

Cataract,Keratoconus 3each(1.2%)Glaucoma,reDnaldetachment 2each(0.8%)

ReDniDspigmentosa,colourblindness,dryeye,maculardegeneraDon,squint,amblyopia 1each(0.4%)

RESULTS

PrevalenceofDES

•  Higheramongstudentstakingonlineclassescomparedtothegeneralpublic(50.6%vs.

33.2%;χ2=22.5,df=1,p<0.0001).

•  TheDES scorewashighest among students aZendingonline classes [median score=7,

IQR=6.87-7.7] followed by teachers of online classes [median score=5, IQR=4.37-7.23]

and then the rest of the general public [median score=4, IQR=4.64-6.18] [test

staDsDc=22.5,df=2,p=0.0001](FigA)

PaZernofgadgetuse

•  TheaveragedailyscreenDmeincreasedduringthepandemiccomparedtothatbefore

thepandemic(FigB)

•  TherewasatendencyofyoungerparDcipants(22±5years)tospendgreaterDmewith

gadgetsthantherelaDvelyolderpopulaDon(33±17years)(R2=0.066,p<0.001)(FigC)

RESULTS

•  Greater proporDon of students taking online classes: had a screen Dme >6hours/day

(χ2=33.59, df=2, p&lt;0.0001), never took breaks/ took them infrequently (χ2=8.59, df=2,

P=0.014)andusedgadgets in thedark (χ2=9.4,df=2,p=0.009)compared to teachersand

thegeneralpublic

•  DESscorewashighestamongstudentsaZendingonlineclasses (p<0.0001), in thosewith

eyediseases(p=0.001),greaterscreenDme(p<0.0001),screendistance<20cm(p=0.002),

those who used gadgets in dark (p=0.017) and those who took infrequent/no breaks

(p=0.018)

REFERENCESv  SeguíMdelM, Cabrero-García J, Crespo A, Verdú J, Ronda E. A reliable and valid quesDonnairewas developed to

measurecomputervisionsyndromeattheworkplace.JClinEpidemiol.2015;68:662-73v  Sheppard AL,Wolffsohn JS. Digital eye strain: prevalence,measurement and amelioraDon. BMJ Open Ophthalmol.

2018;3:e000146.v  JaadaneI,BoulenguezP,ChahoryS,CarréS,SavoldelliM,JonetL,etal.ReDnaldamageinducedbycommerciallight

emiyngdiodes(LEDs).FreeRadicalBiologyandMedicine2015;84:373–84.v  HAMWT,MuellerHA,SlineyDH.ReDnalsensiDvitytodamagefromshortwavelengthlight.Nature.1976;260:153–5.v  Guan H, Yu NN,Wang H, BoswellM, Shi Y, Rozelle S, et al. Impact of various types of near work and Dme spent

outdoorsatdifferentDmesofdayonvisualacuityandrefracDveerroramongChineseschool-goingchildren.PLoSOne.2019;14:e0215827.

v  TosiniG,FergusonI,TsubotaK.Effectsofbluelightonthecircadiansystemandeyephysiology.MolVis2016;22:61–72.

v  BorhanyT,ShahidE,SiddiqueWA,AliH.Musculoskeletalproblemsinfrequentcomputerandinternetusers.JFamilyMedPrimCare.2018;7:337-39.

v  Leung TW, Li RW, Kee CS. Blue-Light Filtering Spectacle Lenses: OpDcal and Clinical Performances. PLoS One.2017;12(1):e0169114.

v  BababekovaY,RosenfieldM,HueJE,HuangRR.Fontsizeandviewingdistanceofhandheldsmartphones.OptomVisSci.2011;88:795-97.

v  JaschinskiW,HeuerH,KylianH.PreferredposiDonofvisualdisplaysrelaDvetotheeyes:afieldstudyofvisualstrainandindividualdifferences.Ergonomics.1998;41:1034–49

v  FeĭginAA.RoleofspectralfiltersforrefracDondynamicsincomputerusers.VestnO}almol.2003;119:39-40.v  TeranE,Yee-RendonCM,Ortega-Salazar J,DeGraciaP,Garcia-RomoE,WoodsRL.EvaluaDonofTwoStrategies for

AlleviaDngtheImpactontheCircadianCycleofSmartphoneScreens.OptomVisSci.2020;97:207-217.

AbstractNo:EHCWOP101