CHI

ExperimentsDesign

&

AnalysisZhaoNan20145/29StressandMultitaskinginEverydayCollegeLife:

AnEmpiricalStudyofOnlineActivityIntroductionResearchhasnotaddressedhowandtowhatextentICTusemightbeassociatedwithstressintheseyoungpeople.Characteristics:Usingamixedmethodsapproachofsensors,biosensors,anddailysurveysRESEARCHQUESTIONSToinvestigatethenatureofmultitasking,ICTusage,andhowitmightbeassociatedwithstressinthisusergroup,webreakdownthisbroadquestionintothefollowing3researchquestions.Q1.Multitaskingbehavior.Q2.StressandICTusage.Q3.Endofdayactivity,stressandICTusage.ResearchSetting&MethodologySubjects:48undergraduatesmajorsincludedcomputerscience,engineering,socialsciences,biologicalandphysicalsciences,andhumanitiesagesrangingfrom18to26TheirGPAsrangedfrom1.6to3.8ResearchSetting&Methodology“insitu”observationalstudyTimePeriod:ForsevendaysduringtheirwakinghoursMethod:Mixedmethodsdesign,including“computerlogging,thewearingofheartratemonitors,dailysurveys,andapoststudyinterview”computerlogging*OnlytimespentinthewindowthatwascurrentlyinusewasmeasuredKidlogger

:freewareWindowscomputerloggingsoftwareheartratemonitors*Heartratevariability(HRV)isconsideredavalidindicatorofmentalstressandisusedextensivelyinresearchandclinicalstudies(accordingtheareview)AdigitalHRM,thePolarRS800CXwristwatchreceiverandcheststrapsensorSurveymeasures*end-of-daysurveyPANASscaletotestthepositiveandnegativeaffectProcedureDay1ParticipantswereequippedtheLoggingsoftwareandthewristsensors.Day2-6participantswereaskedtomeetwithresearchers1-2timestomeettheresearchers,inordertodownloadthedataandconfirmthesoftwareisworkingwell.Day7Semi-structuredinterviewsResultsOf48participants,two(onefemale,onemale)wereexcludedfromtheanalysis.Forone,ourloggingsoftwarewasblockedbyanti-virussoftwareintheircomputerfromthesecondday.Anotherwaspliant,usinganotherpersonalcomputerandnotrecordingHR.HRVPolarProTrainer5Aim:TogetanHRVmeasureData:BeatsperminutesdofR-Rintervalsin15-minuteintervals

20fulldaysdatafrom9participants

32segmentsofdatarangingfrom2-5hoursfrom19participantsLogsFromComputers1350hoursofcomputerlogsfrom46participants117,559computerwindowswitchesFromSurveys306end-of-the-daysurveys“Mostparticipantsreportedintheexitinterviewsthattheweekofstudywasrepresentativeofatypicalweekinaschoolquarter;ninementionedtheweekwasatypical”Twocodersindependentlycodedthecomputerlogs,thecodedcategoriesofwebsiteswere:1.Socialmedia:Facebook,Twitter,Tumblr,Wikipedia2.Email3.Academic(relatedtocourses)4.Webinformationservices5.Gaming6.News7.Entertainment8.Business9.Shopping10.MiscellaneoussitesReasons:1.anumberofstudieshavefocusedontheuseofFacebookamongundergraduatestudents.Thesestudiesshowedconflictingresults,suggestingmoreexplorationontherelationshipbetweenFBuseandacademicactivities2.otherstudieshavereportedthatthemostfrequentlyvisitedsitesbystudentsaretheuniversity’slearningmanagementsystem,Google,emailandFB,whichfallintoeachofourchosencategories.ThisresearchfocusedonSocialmedia,(FBandOtherSM),Email,AcadandWebServFigure1Therelationshipbetween

HRV&StressHRVreferstothevariationsininstantaneousheartrateandR-R(intervalsbetweenconsecutivebeats).ThemendedmeasureforcalculatingHRVistousethestandarddeviation(sd)ofthenormal-to-normalheartbeatTheHRVmeasuresthefluctuationsintheautonomicnervoussystem.Thus,whenapersonisrelaxed,HRVishigher,asthebodyisnotregulatingitself.AloweringofHRVhasbeenassociatedwithincreaseinfactorsrelatedtostressResultsFig.1showsthatcomputerusageisheavyandrisesfrom2p.m.toearlymorningthenextday;thereisaconsequentsimilarriseinuseofSM,FB,andemailthroughevening.Whilestressiscomparativelylowinthemorning.Thus,ascomputerusagerises,stressrisesaswell.Q1.Multitasking:SwitchingbehaviorTheresultsforoverallusageshowthatwhenparticipantsareontheircomputers,theaveragetimeonanycomputerwindow(beforeswitchingtoanotherwindow)is47.9seconds(sd=16.47)Intermsofswitching,participantsswitchmorethan1.2timesperminuteonaveragewhentheyusetheircomputersResults1.LightUsersandLightMTshowthehighestpositiveaffect,basedonthePANASpositivescores.2.HeavyMThavesignificantlyhigherGPAsthanLightMT3.WefoundnodifferencesinHRV,anddurationsofEmail,FB,andAcadsiteusage.Q2.StressandICTuseToexaminewhatonlineactivitymightbeassociatedwithstressintheMillennials,wedevelopedamodelusingHRVasadependentmeasureAsSPSSdoesnotprovideanautomaticmodelbuildingprocedureforlinearmixedmodels,webuiltthemodelbyhandusingabackwardeliminationprocedureasinstepwiseregression,wherewestartedwithallvariablesinthemodelandtheneliminatedvariablesuntilwefoundthebestfittingmodelThemodelshowsadirectrelationshipbetweencomputerdurationandstress;astimespentonthecomputerincreases,stressincreases.Withmorewindowswitches,thehigherthestress.However,themoretimespentonFB,OtherSM,andAcad,thelowerthestress.TheoldertheagewhenparticipantsfirstadoptedtheInternet,theloweristhestress.Emaildurationwasnotsignificant.Q3.Activitythenightbefore:stress,andICTuseWecreatedthreetimeintervals:beforemidnight,12a.m.-2a.m.,andafter2a.m.Accordingtosomeoneelse’sresearch。Maleswhoendtheiractivitythelatest(after2a.m.)havethehigheststressthenextday,whereasfemaleswhoendtheiractivitytheearliest(beforemidnight)havethehigheststressthenextday(noteahigherHRVmeanslowerstress).Malesusethecomputersignificantlylaterthanfemales.Thosewhoendtheiractivitythelatest(after2a.m.)spendthelongestdurationonthecomputerthefollowingday,andalsodothemostwindowswitches.Thosewhoendtheiractivitytheearliestforthedayspendthemosttimethenextdayonacademicsites.Theparticipantswithhighestpositiveaffectthenextday(asmeasuredbythePANAS-EOD)arethosewhoendtheiractivitybetweenmidnightand2a.m.Thosewiththehighestnegativeaffect(PANAS-EOD)aretheoneswhoendtheiractivitytheearliest.ThereisnodifferenceindurationsofFBorSM.有趣的實驗結果QualitativeanalysisofinterviewsWeanalyzedthepost-studyinterviewdatawithopen-codingtoidentifythemestoexplainourparticipants'multitaskingbehaviorandtheirstress.Open-Coding概念解釋Duringopencoding,thedatathathavebeencollectedaredividedintosegmentsandthentheyarescrutinisedforcommonalitiesthatcouldreflectcategoriesorthemes.Oncethedatahavebeencategorised,thentheyareexaminedforpropertiesthatcharacteriseeachcategory.Theresearcherwillexamineandidentifythemeaningofthedataby:

askingquestions;

makingcomparisons;

lookingforsimilaritiesanddifferencesbetweenthecomments.Inthisway,similarcommentsaregroupedtogethertoformcategories.Sobasically,opencodingisaprocessofreducingthedatatoasmallsetofthemesthatappeartodescribethephenomenonthatisunderinvestigation.Onethemeweidentified,expressedbyfourparticipants,wasthatconstantswitchingwashabitual,oraroutine.“It’sjustencodedorsomething.”Relatedtothiswa