Digital Eye Strain Epidemic Amid COVID-19 Pandemic …
Transcript of 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<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