Understanding the Role of Places and Activities on Mobile Phone Interaction and Usage Patterns

22 Pages·2017·2.22 MB·English

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Understanding the Role of Places and Activities on Mobile Phone
Interaction and Usage Pa￿erns
ABHINAVMEHROTRA,UniversityCollegeLondon,UK
SANDRINER.MU¨LLER,UniversityofCambridge,UK
GABRIELLAM.HARARI,StanfordUniversity,USA
SAMUELD.GOSLING,UniversityofTexasatAustin,USAandUniversityofMelbourne,Australia
CECILIAMASCOLO,UniversityofCambridgeand￿eAlanTuringInstitute,UK
MIRCOMUSOLESI,UniversityCollegeLondonand￿eAlanTuringInstitute,UK
P.JASONRENTFROW,UniversityofCambridge,UK
Userinteractionpa￿ernswithmobileappsandnoti￿cationsaregenerallycomplexduetothemanyfactorsinvolved.However
adeepunderstandingofwhatin￿uencesthemcanleadtomoreacceptableapplicationsthatareabletodeliverinformationat
therighttime.Inthispaper,wepresentforthe￿rsttimeanin-depthanalysisofinteractionbehaviorwithnoti￿cationsin
relationtothelocationandactivityofusers.Weconductedanin-situstudyforaperiodoftwoweekstocollectmorethan
36,000noti￿cations,17,000instancesofapplicationusage,77,000locationsamples,and487daysofdailyactivityentriesfrom
26studentsataUKuniversity.
Ourresultsshowthatusers’a￿entiontowardsnewnoti￿cationsandwillingnesstoacceptthemarestronglylinkedtothe
locationtheyareinandinminorparttotheircurrentactivity.Weconsiderbothusers’receptivityanda￿entiveness,andwe
showthatdi￿erentresponsebehaviorsareassociatedtodi￿erentlocations.￿ese￿ndingsarefundamentalfromadesign
perspectivesincetheyallowustounderstandhowcertaintypesofplacesarelinkedtospeci￿ctypesofinteractionbehavior.
￿isinformationcanbeusedasabasisforthedevelopmentofnovelintelligentmobileapplicationsandservices.
CCSConcepts:•Human-centeredcomputing HCIdesignandevaluationmethods;Empiricalstudiesinubiquitousand
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mobilecomputing;
AdditionalKeyWordsandPhrases:MobileSensing,Noti￿cations,ApplicationUsage,Context-awareComputing.
ACMReferenceformat:
AbhinavMehrotra,SandrineR.Mu¨ller,GabriellaM.Harari,SamuelD.Gosling,CeciliaMascolo,MircoMusolesi,andP.Jason
Rentfrow.2017.UnderstandingtheRoleofPlacesandActivitiesonMobilePhoneInteractionandUsagePa￿erns.PACM
Interact.Mob.WearableUbiquitousTechnol.1,1,Article1(July2017),22pages.
DOI:10.1145/nnnnnnn.nnnnnnn
1 INTRODUCTION
Mobilephonestodayhavebecomeanindispensablepartofourdailylives.Farfrombeingsimplecallinginstru-
ments,theyarenowadvancedcomputingplatformswithalways-onconnectivity,high-speeddataprocessingand
advancedsensing[21].￿esea￿ordanceshaveopenedthepossibilityofimplementingnovelcontext-awareand
personalizedapplicationsthatareabletoassistusinavarietyofday-to-daysituations.Atthesametime,they
Permissiontomakedigitalorhardcopiesofallorpartofthisworkforpersonalorclassroomuseisgrantedwithoutfeeprovidedthat
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page.CopyrightsforcomponentsofthisworkownedbyothersthanACMmustbehonored.Abstractingwithcreditispermi￿ed.Tocopy
otherwise,orrepublish,topostonserversortoredistributetolists,requirespriorspeci￿cpermissionand/orafee.Requestpermissionsfrom
[email protected].
©2017ACM. 2474-9567/2017/7-ART1$15.00
DOI:10.1145/nnnnnnn.nnnnnnn
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1:2 • Mehrotraetal.
(a) (b) (c) (d)
Fig.1. TheMyLifeLoggerapplication.
representauniqueplatformforreal-timedeliveryofinformationaboutavarietyofeventsrangingfromemails
toupdatesononlinesocialnetworks,fromadvertisementstopositivebehaviorchangeinterventions[5,20,31].
￿eadventofmobilesensinghasprovidedagreatopportunityforresearchersandpractitionerstoinvestigate
users’mobileinteractionbehavior.Mobileappsarenowcapableofrecordinginformationaboutusers’interactions
withmobilephonesandthesurroundingcontext(e.g.,location,activity,audioenvironment,andcollocationwith
otherdevices).￿ankstotheavailabilityofthisinformation,researchershavebeenabletoconductseveralstudies
onavarietyofaspectsofhuman-smartphoneinteraction.Forexample,studieshaveinvestigatedthediversityin
appusagebehaviorofindividuals[14],thecharacterizationofmobileusageinruralandurbansocieties[13]
orindi￿erentsocio-economicgroups[32]. Othershavefocussedonthein￿uenceofpersonality[10,11],the
associationbetweensocialcontextandappusagepa￿erns[26],andusers’motivationsforusingdi￿erenttypes
ofapps[18].Moreover,somestudieshavealsofocusedonunderstandingusers’appusagepa￿ernsforpredicting
theirfutureinteractionwithapps[27,38].
Anotherparticularaspectthathasa￿ractedthea￿entionofresearchersgivenitspracticalimportanceisthe
characterizationofthereactionofuserstonoti￿cationsandthedesignofmobilenoti￿cationmanagementsystems.
Forexample,somestudieshaveinvestigatedthefactorsthatin￿uenceusers’a￿entivenessandreceptivityto
noti￿cations[24,30]andhowthesearein￿uencedbycontext[28],content[23],andthecomplexityofanongoing
task[24].Otherprojectshaveaimedatanticipatingusers’a￿entiveness[30]andreceptivity[22]tonoti￿cations
bylearningtheirbehavioralpa￿erns.
However,existingworkhasnotconsideredtheimpactoftheexternalfactors,suchasthetypeoflocations
usersareinandtheactivitiestheyarecurrentlycarryingout,ontheirinteractionwithnoti￿cationsandapp
usage behavior. A deep understanding of these factors would enable us to improve users’ experience and
the e￿ectiveness of noti￿cations as well as applications (e.g., marketing and positive behavior intervention
applications). Keychallengesforsuchastudyincludedatacollectionatavery￿negranularity,whichmight
requirefrequentinputsfromusers,andtheextractionofhigh-levelinformationfromrawsensordatawiththe
goalofassigningsemanticstoit.
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Tobridgethisgap,inthispaperwepresentthe￿rstin-situstudyoftheimpactoflocationandactivitieson
users’interactionwithmobilenoti￿cationsandapplications. Overaperiodoftwoweeks,wecollectedmore
than36,000noti￿cations,17,000instancesofapplicationusage,77,000locationsamples,and487daysofdaily
activityentries(i.e.,2984activityinstances)from26studentsataUKuniversity.Usingthisdata,weinvestigate
users’interactionwithmobilenoti￿cationsandappswhentheyareperformingdi￿erentactivities,whenthey
visitdi￿erenttypesoflocations,andlocationswithdi￿erentcharacteristicssuchasbeingboringvs.exciting,sad
vs.happy,inactivevs.busy,lazyvs.productive,distressingvs.relaxing,andnaturalvs.urban.
￿ekeycontributionsand￿ndingsofthisworkcanbesummarizedasfollows:
Wediscussanin-depthstudyoftherelationshipsbetweenusers’activitiesandinteractionwithnoti￿ca-
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tionsandapps.
Wediscussaquantitativeevaluationofusers’interactionwithnoti￿cationsandappswhentheyareat
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di￿erenttypesoflocations;thisisimportantfromadesignperspective,sinceitallowsustounderstand
howcertaintypesofplacesarelinkedtospeci￿ctypesofinteractionbehaviorasabasisofthedevelopment
ofintelligentapplications.
Wepresentanextensiveinvestigationoftheimpactoflocationcharacteristicsonusers’a￿entiveness
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andreceptivitytonoti￿cationsandappusagepa￿ern.Weconsiderdi￿erentcharacterizationofplaces
(e.g.,urban,productiveandsoon)inrelationtousers’interactionwithnoti￿cations.Again,besidesthe
inherentintellectualinterest,the￿ndingsmightbeusedinthedesignofpersonalizedapplicationsbased
onlyontheknowledgeofthecurrentlocation.
￿roughouranalysisweuncovervariousnewinsightsaboutthephoneusageandnoti￿cationinteraction
behaviorsofusersandalso,quiteimportantly,con￿rmsome￿ndingsofpreviousstudies.Morespeci￿cally,the
mainnovel￿ndingsofouranalysisare:
Participantsweremorereceptivetonoti￿cationswhiletheywereexercisinganddoingroutinetasks.
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Overallcommunicationappswereusedthemost,exceptwhileparticipantsweregoingtosleep,whichis
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whentheymostlyusedlifestyleapps.
Participantsweremorereceptivetonoti￿cationswhentheywereatcollege,inlibraries,onstreets,and
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theywereleasta￿entivewhilebeingatreligiousinstitutions.
￿eappusagewashighestwhileparticipantswereatcollegeorinlibraries.
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Participantsusedmostlymusicandreadingappswhilewaitingatbusstopsandtrainstations.
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Participantsweremorea￿entivetonoti￿cationsatproductiveplacescomparedtolazyplaces.
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Participantswerelessreceptivetonoti￿cationsatnaturalplacescomparedtourbanplaces.
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Whileatlazy,distressingandnaturalplacesparticipantsusedtheirphoneslesscomparedtoproductive,
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relaxingandurbanplacesrespectively.
￿isstudyhasledtomanyinterestinginsightsthatarediscussedindetailinthefollowingsections.Firstof
all,peoplepaylessa￿entiontonoti￿cationswhentheyarepreparingtogotobed(ortheyareinbedbefore
sleeping)andwhileexercising.Peopleacceptmostnoti￿cationsthataredeliveredwhiletheyaredoingexercise
androutinetasks.Whenpeopleareonstreets,orincollege,universityandresidentialareas,theynotonlypay
morea￿entiontowardsnoti￿cationsbutalsoacceptmostofthem.Also,peoplearemorea￿entivetonoti￿cations
atplacesthatarecharacterizedas“productive”.￿eyacceptmorenoti￿cationsatproductiveandurbanplaces.
Furthermore,overallphoneusagedurationaswellasusageofspeci￿cappsvarywhenpeoplevisitspeci￿ctypes
ofplacesandwhileperformingspeci￿cactivities. Overallappusageishighestwhilepeoplearerelaxing,at
collegeorinlibraries,whereasitislowestwhilepeoplearedoingexercise,routinetasksorgoingtosleep,and
whentheyareatgyms,religiousinstitutionsorinparkingplaces.
Webelievethatthepotentialapplicationsofthisworkaremany. Firstofallthe￿ndingsofthispapercan
be used as a basis for the development of predictive applications that rely on the analysis of users’ current
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locationsandnotonlyontheirpastbehavioralpa￿erns.Morespeci￿cally,thisisparticularlyimportantinthe
bootstrappingphaseofintelligentapplicationsthatarebasedonlearningalgorithmsthatrequirealargehistory
ofpastinteractionswiththephonesinordertomakeaccuratepredictionaboutusers’behavior.Examplesinclude
noti￿cationmanagementsystems,andpre-cachingandpre-launchingmechanismsformobileapplications.
2 RELATEDWORK
Inthissectionwereviewtherelatedworkintwokeyareas,namelythestudiesaboutthecharacterizationof
users’interactionwithnoti￿cationsandthoseaboutusers’appusagebehavior.
2.1 UnderstandingUsers’InteractionwithNotifications
Inrecentyearstheareaofmobileinterruptibilityhasreceivedincreasinga￿ention.Previousstudieshaveexplored
variousaspectsofusers’interactionwithmobilenoti￿cations[4,6,7,12,24,29,35].Inparticular,in[35]Sahami
etal.showthatusersdealwitharound60noti￿cationsperday,andmostoftheseareviewedwithinafewminutes
ofarrival.Additionally,bycollectingthesubjectivefeedbackfrommobileusers,theauthorsdemonstratethat
usersassigndi￿erentimportancetonoti￿cationstriggeredbyapplicationfromdi￿erentcategories.Atthesame
time,Pielotetal.[29]showthatpersonalcommunicationnoti￿cationsarerespondedtoquickestbecauseofsocial
pressureandtheexchangeoftimecriticalinformationthroughcommunicationapplications(i.e.,Whatsapp).
Ontheotherhand, asIqbaletal. suggestin[19], usersarewillingtotoleratesomedisruptioninreturnfor
receivingnoti￿cationsthatcontainvaluableinformation.Similarly,in[24]Mehrotraetal.showthatnoti￿cations
containingimportantorusefulcontentareo￿enaccepteddespitethedisruptioncausedbythem.
Moreover,otherstudieshavealsoinvestigatedhowusers’a￿entivenessandreceptivitytonoti￿cationsare
in￿uencedbytheircontext[28,30]andcontent[16,22,23]. In[28],theauthorsshowthatthea￿entiveness
ofuserscanbedeterminedbycontextualfactorsincludingactivity,locationandtimeofday. ￿eyproposea
mechanismthatreliesonthesecontextmodalitiestopredictopportunemomentsfordeliveringnoti￿cations.
In[30]Pielotetal.proposeamodelthatcanpredictwhetherauserwillviewanoti￿cationwithinafewminutes
withaprecisionofapproximately81%. Ontheotherhand,in[16]theauthorsshowthatusers’receptivityis
in￿uencedbytheirgeneralinterestinthenoti￿cationcontent,entertainmentvalueperceivedinitandaction
required by it, but not the time of delivery. In [23] Mehrotra et al. suggest to use contextual information,
sender-recipientrelationshipandapplicationcategorythattriggeredthenoti￿cationfordeterminingtheuser’s
interruptibility.Inanotherstudy[22],theauthorsdemonstratethatusers’receptivitytonoti￿cationsisin￿uenced
bytheirlocationandthecontentofnoti￿cationsdelivered.￿eauthorsproposeasystemthatreliesonmachine
learningalgorithmsfortheautomaticextractionofrulesthatre￿ectuser’spreferencesforreceivingnoti￿cations
indi￿erentsituations.
2.2 UnderstandingUsers’AppUsageBehavior
Previous studies have investigated the association between users’ app usage behavior and various socio-
economic[13,14,32]andpsychological[10,11,26]factors.Othershavefocussedonusers’motivationsforusing
di￿erenttypesofapps[8,18].
Morespeci￿cally, in[32], Rahmatietal. presentastudythatinvestigateshowuserswithacertainsocio-
economicstatusinstallanduseapps.￿eir￿ndingscon￿rmthein￿uenceofsocio-economicstatusonphone
usage. In[11], theauthorsshowthatthereisasigni￿cantassociationbetweenusers’personalitytraitsand
phoneusage.Furthermore,quiteinterestingly,in[18]Hinikeretal.provideevidencethatusers’motivationsfor
engagingwithtechnologycanbedividedintoinstrumentalandritualistic.
In[8],Bohmeretal. presentalarge-scalestudywiththegoalofunderstandingusers’appusagepa￿erns
basedontheircontext.￿e￿ndingsofthisstudydemonstratethatusersspendaroundanhoureverydayusing
theirphones,buttheiraveragesessionusinganapplicationlastsforaminute.Overall,communicationappsget
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Group Feature Description
Arrivaltime Timeatwhichanoti￿cationarrivesinthenoti￿cationbar.
Removaltime Timeatwhichanoti￿cationisremovedfromthenoti￿cationbar.
Noti￿cation
Senderapplication Nameandpackageoftheapplicationthattriggersthenoti￿cation.
AppName Nameoftheapplication.
ApplicationUsage LaunchTime Timeatwhichtheapplicationislaunchedandappearedintheforeground.
BackgroundTime Timeatwhichtheapplicationuseisendedanditismovedfromforeground
tobackground.
Lock/unlockevent Timeatwhichthephonewaslockedandunlocked.
PhoneInteraction
Screeninteraction Typeofinteraction(i.e.,singleclick,longclickandscroll),timeandthename
offoregroundapplications(includinghomescreen)withwhichtheinteraction
happened.
Location Geo-locationoftheplacesvisited.
Context DailyActivity Typeandtimedurationofdi￿erentactivitiesperformedinaday.￿eseactiv-
itiesincludesleep,eat,work,physicalexercise,socialactivityandrelaxation.
Table1. Descriptionoffeaturesfromthedataset.
usedmost,exceptwhenusersaretraveling,inwhichcasetheyaremorelikelytousemultimediaapps.In[15],
Ferreiraet.al.conductedastudyshowingthatappusagebehaviorofusersisstronglyin￿uencedbytheirsocial
andspatialcontext. Also,Xuetal.[37]exploitthenetworktra￿cfromapps(basedonHTTPsignatures)to
demonstratethatappusageisin￿uencedbyspatialandtemporalfactorsincludinggeographicalareasandtime
oftheday.￿eir￿ndingsalsoshowthatcertainappshaveanonnegligiblelikelihoodofco-occurrence.
An open question in this area remains the impact of locations and activities on users’ interaction with
noti￿cations. ￿epresentstudyaimstobridgethisgapbyinvestigatingthee￿ectsofthesefactorsonusers’
a￿entivenessandreceptivitytonoti￿cationsaswellasontheirappusagebehavior.
3 METHODOLOGY
Inthissectionwepresentourapproachforinvestigatingthein￿uenceofdailyactivities,typeandcharacteristics
ofvisitedlocationsonusers’appusage,andnoti￿cationinteractionbehavior.
3.1 LifeLoggerApp
Giventheaimsoftheproposedinvestigation,wedesignedandcarriedoutanin-the-wild study[34]tocollect
users’data.Morespeci￿cally,wedevelopedanAndroidappcalledMyLifeLogger (showninFigure1).￿eapp
performs continuous sensing in the background to log users’ interaction with noti￿cations, app usage, and
context.Table1providesadescriptionofthefeaturescapturedbyMyLifeLogger.
￿eappreliesonAndroid’sNoti￿cationListenerService[1]andUsageStatsManager[2]totracenoti￿cations
andapplicationusage. Moreover, theappallowsuserstoprovidetheirdailyactivityschedules, forwhicha
remindernoti￿cationistriggeredeverynightat9pm(localtime).AsshowninFigure1(b),usersweregivenalist
ofsixpossibledailyactivities:
Eat:timeperiodwhenauserishavingfood.
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Sleep:timeperiodforwhichauserslept.1
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Work:timeperiodwhenauserisengagedinanactivityinvolvingmentalorphysicale￿ort.Sinceour
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participantsarestudents,thisactivitywouldmostlyconsistoforberelatedtostudying.
1Itisworthnotingthatparticipantsdidnotreceiveanystandardde￿nitionoftheseactivitiesbeforethestudy.￿erefore,forexample,some
participantsmighthaveinterpretedsleepingasbeinginbed.
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Exercise:timeperiodwhenauserisperforminghealthand￿tnessactivities.
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Social:timeperiodwhenauserissocializingwithothers.
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Relaxation:timeperiodduringwhichauserisbeingfreefromtensionandanxiety.
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Itisworthnotingthatparticipantswereabletoselectonlyoneactivityforaspeci￿ctimeinterval.Moreover,
theywereallowedtoenterotheractivitiesbyselectingtheother option,throughwhichtheycouldinputan
activitynameasfreetext. Mostactivitiesregisteredthroughtheother activityoptionwererelatedtoroutine
taskssuchaslaundry,cooking,ge￿ingready,packing,supermarketandsoon.￿erefore,wecreatedanother
categoryforchoresandmappedtheroutinetasksenteredthroughtheother activityoptiontothisnewactivity.
￿eMyLifeLoggerappalsocollectedadditionaldataaboutothercontextualfeatures(suchasmovement,call
andSMSlogs)aswellasmood-relatedquestionnaires. However,wedonotdiscussthoseaspectsofthedata
becausetheyarenotusedfortheanalysispresentedinthispaper.
3.2 RecruitmentoftheParticipants
MyLifeLoggerwaspublishedonGooglePlayStore2andadvertisedto￿rst-yearundergraduatestudentsataUK
University.Itwasinstalledby28studentsand26studentscompletedthestudybykeepingtheapprunningon
theirphoneforaminimumoftwoweeks.￿eseparticipantscomefrombothsexes(16malesand10females),
withtheagespanbetween18and27years(mean =19.46andstandardde�iation =2.18). ￿estudentswere
enrolledin15di￿erentcoursesand27%(n=7)werenon-British.Allparticipantswhocompletedthestudywere
given£25.
3.3 EnsuringPrivacyCompliance
InordertoallowtheMyLifeLoggerapplicationtomonitornoti￿cationsandappusage,theuserhastogiveexplicit
permissionasrequiredbytheAndroidoperatingsystem.Moreover,theapplicationalsoshowsaconsentform
detailingtheinformationthatiscollected.￿isensuresthattheusergoesthroughatwo-leveluseragreement
andiscompletelyawareofthetypeofinformationcapturedbytheapplication.
Itisworthnotingthatwereceivedethicalapprovalforthestudy,includingallproceduresandmaterials,from
thePsychologyResearchEthicsCommi￿eeattheUniversityofCambridge.
3.4 Dataset
Weanalyzedthedataof26userswhoparticipatedforaminimumperiodoftwoweeks.￿edatasetcorresponding
totheseusersincludes36,106noti￿cations,17,680instancesofapplicationusage,77,306locationsamples,and
487daysofdailyactivityentries(i.e.,2,984activities).
3.4.1 CharacterizingLocations. Inordertoperformouranalysisontheimpactoflocationonnoti￿cation
responseandapplicationusage,wecannotjustrelyonsensor(i.e.,GPS)dataasitonlyprovidesthecoordinates
ofaplaceratherthanitstypeandcharacteristics.￿erefore,wemanuallycategorizedthelocationsbyclustering
themintosigni￿cantplaces,andthencharacterizedthesigni￿cantplacesbyhavingcodersrateeachplaceon
severaldimensions(e.g.,thedegreetowhichaplaceisinactivevsbusy).
IdentifyingSigni￿cantPlaces.Firstofall,wediscardthelocationsampleswithmorethan50metersaccuracy
sothattheestimatedlocationclustersareofbe￿erquality.Wethen￿ndthelocationsamplesthatwerecollected
whileusersweremovingandwealsodiscardthem.Inordertoinfersuchlocationpoints,wecomputethespeed
oftheuserbyusingthedistanceandthetimebetweenthelastandthecurrentlocationpoints.Ifthespeedis
2h￿ps://play.google.com/store/apps/details?id=com.nsds.mystudentlife
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lessthanacertainthreshold(i.e.,5kmperhour)weconsiderthatlocationreadingwascollectedwhentheuser
wasnotmoving.
Now,weusethelocationclusteringapproachpresentedin[36]forgroupingthe￿lteredlocationsamples.We
iterateoveralllocationsamplesandforeachlocationpointwecreateanewclusteronlyifthedistanceofthis
locationfromthecentroidofeachexistingclusterismorethan200meters.Otherwise,weaddthislocationto
thecorrespondingclusterandupdateitscentroid.Finally,weconsiderallcentroidsassigni￿cantplaces.
IdentifyingPlaceTypeandCharacteristics.Placescanbedescribedintermsofobjectiveinformation,such
aswhetheralocationisindoororoutdoor,orinaresidentialorindustrialarea,andalsointermsofinformation
thatrelatestousers’a￿ectiveappraisalsofthem.Despitepeople’sexperiencesofcoursebeingsubjective,they
on average agree on a variety of characteristics such as liveliness, pleasantness, and naturalness, to name a
few[17,25].Atthesametime,werecruitedcodersbelongingtothesamedemographicsoftheparticipants(e.g.,
studentstatus,averageage,evengenderdistribution)toreducethesubjectivenessinplaceratings.Moreover,we
computedthesimilarityintheratingstoensurethattheyarereliable.
Onaverage,53signi￿cantplaces(perperson)wereidenti￿edinthetwo-weekstudyperiod.Foreachparticipant
weselectedthetoptenplacesinwhichtheyhadspentmosttime.Werecruitedfourindependentcoderswho
wereundergraduatestudentsthemselvesbutnotparticipantsinthestudy.First,theyweretrainedinpersonand
providedwithadetailedhandbookonthelocationcodingprocess.A￿ertheirtraining,theywereprovidedwith
alistofcoordinates(i.e.,longitudeandlatitude)oftherespectiveplaces,andtheywereaskedtocategorizeand
evaluatetheseplacesforthegivencharacteristicsbylookingatthemusingGooglemaps.
Inordertocategorizetheplacetype,werelyonGoogle’sPlaceTypes[3]asthepossiblecategories.Moreover,
coderswereprovidedwithanoptiontoenteranadditionalplacetypeincaseitwasnotpresentinthegivenlist.
Incaseswhereaplacetypewasunclear,codersusedan“unclear”categorytodenoteanambiguousplace.Finally,
eachplacetypethatwasclassi￿edbyfourcoderswasmergedbyoneoftheauthors.Itisworthnotingthatwe
￿lteredoutthecategoriesthatappearedrarelyornotatall.Inordertoperformthis￿ltering,weensuredthat
placesofeachcategorywerevisitedatleastoncebyaminimumof50%oftheparticipants.
Inordertoidentifythecharacteristicsofsigni￿cantplaces,thesewerealsocodedfor24descriptivecharacter-
isticsthatcapturedtheambienceoftheplace,andthetypesofpeoplethatwouldvisittheplace.Inthiswork,we
focusedonthefourcharacteristicratingsthatdescribetheambianceoftheplace.Speci￿cally,wefocusedonthe
degreetowhichtheplacewas:inactive-busy,lazy-productive,distressing-relaxing,andnatural-urban.￿esefour
characteristicswereratedona7-pointLikertscale. ￿eseratingsfromthefourcoderswerethenmergedby
computingtheirmean. Finally,themergedratingswerecenteredandrescaledfroma1to7scaletoa-3to3
scale.Wethentransformthesefroma7-pointscaletoa3pointsscale(-1to1)byusingtheseranges:-3to-0.5as
-1,-0.5to0.5as0,and0.5to3as1.￿isturnedthecontinuousvariableintoacategoricaloneandmakesitlikely
thatwehaveenoughdataforalllevels.Levels 1,0,1representthenegative,neutral,andpositivevalueofthe
�
locationcharacteristicrespectively.Forinstance,theseratingsforinactive-busycharacteristicswouldconvertto
inactive,neutralandbusy.
3.4.2 AppCategories. Inordertoinvestigateusers’behaviorforinteractingwithspeci￿capps,wecategorized
all apps using the categories de￿ned on the Google Play store. Overall, the apps belong to 11 categories,
namelyreading,￿tness,business,photography,communication,game,lifestyle,music,social,tools,andtravel
applications. However,appsofcertaincategoriesarenotusedbyallparticipants. ￿erefore,weconsiderthe
typeofappsthatwereusedbyallparticipants. Consequently,wecameupwiththefollowingsixcategories:
communication,lifestyle,music,reading,social,andtravelapplications.
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3.5 ￿antitativeMeasuresforNotificationandPhoneUsage
In this section we discuss the metrics used in this study for quantifying users’ behavior in terms of their
interactionswithnoti￿cationsandapps. Weusetwometricsforquantifyinginteractionwithnoti￿cations–
Noti￿cationReceptivityandNoti￿cationSeenTime,andonemetricforinteractionwithapp–AppUsageTime.
￿eseareclassicindicatorswidelyadoptedforthistypeofstudiesbytheubiquitouscomputingcommunity(see
forexample[16,30]).￿ede￿nitionsofthesemetricsarereportedbelow.
Noti￿cationReceptivity:theuser’swillingnesstoreceiveanoti￿cation.￿ismetricrepresentshow
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willingauseristoreceiveinterruptions. Highreceptivity(i.e.,moreclicks)indicatesincreaseinthe
willingnessoftheusertobeinterruptedandviceversa.Inordertoinfertheresponsetoanoti￿cation,we
checkwhetherthecorrespondingapp(whichtriggeredthenoti￿cation)waslauncheda￿ertheremoval
timeofthatnoti￿cation.Weareawarethatourapproachhaslimitations,becausesomenoti￿cationsthat
donotrequirefurtheractionmightnotbeclickedratherjustseenanddismissedbytheuser.
Noti￿cationSeenTime(Noti￿cationAttentiveness):thetimefromthenoti￿cationarrivaluntilthe
•
time the noti￿cation was seen by the user. ￿is metric re￿ects the user’s a￿entiveness towards new
noti￿cations.Inordertodetectthemomentatwhichanoti￿cationisseen,weusetheunlockeventof
thephoneandassumethatallnewlyavailablenoti￿cationsinthenoti￿cationbarareseenwhentheuser
unlocksthephone.Incaseanoti￿cationarriveswhentheuserisalreadyusingthephone(i.e.,thephone
isunlocked),theseentimeofthisnoti￿cationwouldbeconsideredequaltozero.Todetectthelockand
unlockeventsweusethePhoneInteractiondata(discussedinTable1).Itisworthnotingthatweremoved
allnoti￿cationinstancesthatwerenotrespondedtowithin2hours.Asarecentstudy[29]demonstrated
thatpeoplereceivenoti￿cationseveryhour(frommorningtolatenight),whicharehandledwithinafew
minutes.￿erefore,weuse2-hourthresholdforthemaximumseentimeto￿lteroutnoti￿cationsthat
arrivedwhentheuserwasawayfromthephoneorsleeping.
AppUsageTime:durationforwhichanapplicationwasinforeground.Morespeci￿cally,itisthetime
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intervalbetweenthelaunchofanapplicationandtheinstantoftimewhenitwassenttothebackground.
We compute these metrics for each user when they are performing speci￿c activities and when they are
atcertaintypesofplaces. Wethenaggregatethesemetricstocomputetheiraveragevalues. Finally,weuse
statisticalteststocomparethedi￿erenceinusers’interactionwhenperformingdi￿erentactivitiesatdi￿erent
typesofplaces.
ItisworthnotingthatwhilecomputingAppUsageTimefordi￿erentactivities,wenormalizetheappusage
valuebydividingitbythetimespentbytheuserengaginginthecorrespondingactivity.Similarly,tocompute
theAppUsageTimefordi￿erenttypesofplaces,wenormalizetheappusagevaluebydividingitbythetime
spentbytheuseratthecorrespondingplace.￿isstepisnecessaryinordertoavoidbiasesduetotherelative
timesspentinagivenlocationorwhileengaginginacertainactivity.￿erefore,theuseofanon-normalized
AppUsageTimemetriccouldproducebiasedresults.
3.6 Procedure
Wewanttoinvestigatethein￿uenceofvariables,includingdailyactivities,typeandcharacteristicsofvisited
locations,onthenoti￿cationandphoneusagebehaviorofusers,soweconsiderthemastheindependentvariables.
Ontheotherhand,ourdependentvariablesshouldrepresentthenoti￿cationandphoneusagebehaviorofusers.
￿erefore,weusethethreenoti￿cationandphoneusagemetrics(Noti￿cationReceptivity,Noti￿cationSeenTime,
andAppUsageTime)asdependentvariables. ￿eanalyseswereperformedforeachpairofindependentand
dependentvariables.Weperformaone-wayANOVAforquantifyingthedi￿erencesinthedependentvariables
(i.e.,Noti￿cationReceptivityandNoti￿cationSeenTime)thatrepresentsnoti￿cationinteractionbehavior.However,
forquantifyingthevariabilityinthedependentvariable(i.e.,AppUsageTime)thatrepresentappusagebehavior,
PACMonInteractive,Mobile,WearableandUbiquitousTechnologies,Vol.1,No.1,Article1.Publicationdate:July2017.
UnderstandingtheRoleofPlacesandActivitiesonMobilePhoneInteractionandUsagePa￿erns • 1:9
ANOVA (p<0.05) 90 ANOVA (p<0.05)
40
Seen Time (mins)123000 ● ● Receptivity (%)678000 ● ● ● ● ● ●
● ● ● ● ● 50 ●
0
work sleep chores relaxation social eat exercise work sleep chores relaxation social eat exercise
Daily Activity Daily Activity
(a) (b)
Fig.2. Roleofdailyactivityininfluencingtheuser’s(a)a￿entivenessand(b)receptivity.Di￿erentcolorindicatesstatistically
significantdi￿erences(p<0.05).Intheseplotsthedotsrepresentthemeansandthebarsrepresentthestandarddeviations.
Activity work chores social exercise Activity work chores social exercise
sleep relaxation eat sleep relaxation eat
0.08
0.015
0.06
Density0.04 Density0.010
0.02 0.005
0.00 0.000
0 25 50 75 0 25 50 75 100
Seen Time (mins) Receptivity
(a) (b)
Fig.3. Probabilitydistributionsofusers’average(a)a￿entivenessand(b)receptivitywhileperformingdi￿erentdailyactivities.
Here,probabilitiescanbecomputedasmultiplicationofdensitywitha￿entivenessandreceptivityvaluesrespectively.
weperformatwo-wayANOVA,becauseappcategoryisconsideredasanotherindependentvariableapartfrom
activityandlocationbasedindependentvariables.Itisworthnotingthatweremovedthelevelsofindependent
variablesthatdidnothaveobservationsfromatleast50%oftheparticipants.
4 UNDERSTANDINGTHEROLEOFDAILYACTIVITY
Inthissectionweprovideaquantitativeevaluationoftherelationshipbetweendailyactivityandtheusers’
behaviorintermsofnoti￿cationinteractionandappusage.
￿ekey￿ndingsofthissectionare:
Peopleareleasta￿entivetonoti￿cationswhiletheyarepreparingtogotobed(orusingthephone
•
inbed)andduringthetimetheyexercise.
Peoplearemorereceptivetonoti￿cationswhiletheyareexercisinganddoingchores(i.e.,routine
•
tasks).
Overallappusageishighestwhilepeoplearerelaxingandlowestwhentheyareengagedinchores,
•
doingexerciseandgoingtosleep.
Usageofspeci￿cappsisassociatedtousers’dailyactivities.
•
PACMonInteractive,Mobile,WearableandUbiquitousTechnologies,Vol.1,No.1,Article1.Publicationdate:July2017.
1:10 • Mehrotraetal.
Application Category
● communication ● lifestyle ● music ● reading ● social ● travel
ANOVA
App Category (p<0.05)
%)3 ● Daily Activity (p<0.05)
me ( ● ● Interaction (p<0.05)
Ti
e 2 ●
g
a
s ●
pp U1 ● ● ● ● ● ● ●● ●
A0 ● ● ● ●●● ● ● ●●●● ●●● ● ●●●●● ● ● ●●●●●
work eat sleep social chores relaxation exercise
Daily Activity
Fig.4. Relationbetweendailyactivityandapplicationusage.Inthisplotthedotsrepresentthemeansandthebarsrepresent
thestandarddeviations..
4.1 A￿entivenessandReceptivity
Toinvestigatetherelationshipbetweendailyactivityandusers’a￿entivenessandreceptivitytonoti￿cations,
weperformtwoseparateone-wayANOVAsthatquantifythedi￿erencesintheuser’s(i)a￿entivenessand(ii)
receptivitytonoti￿cationswhiletheyperformdi￿erentactivities.￿eresultsofthe￿rstanalysis(i.e.,e￿ects
ona￿entiveness)showthatthereisasigni￿cante￿ectofdailyactivitiesontheuser’sa￿entiveness,withF =
4.788,p <0.05.Inorderto￿ndwhichdailyactivitiesa￿ectusers’a￿entiveness,weperformaTukeypost-hoc
test(byse￿ing� equalto0.05).AsshowninFigure2(a),thetestrevealsthattheseentimeislongest(i.e.,low
a￿entiveness)whennoti￿cationsarrivewhiletheuserissleeping.However,thereisnosigni￿cantdi￿erence
ina￿entivenessofuserswhiletheyareengagedinanyotherdailyactivity,exceptthattheyareslightlyless
a￿entivewhileexercising.
￿eresultsofthesecondanalysisshowthatthereisalsoasigni￿cante￿ectofdailyactivitiesontheusers’
receptivitytonoti￿cations,withF =2.947,p <0.05.AsshowninFigure2(b),aTukeypost-hoctest(byse￿ing�
equalto0.05)revealsthatusers’aremostreceptivetonoti￿cationswhentheyareperformingchoresorphysical
exercisecomparedtootheractivities.
Moreover,inordertoinvestigatethediversityinusers’a￿entivenessandreceptivity,inFigure3wepresentthe
probabilitydistributionofusers’averagea￿entivenessandreceptivitywhiletheyareperformingdi￿erentdaily
activities.￿eresultsdemonstratethatthereissomevariabilityinbotha￿entivenessandreceptivitybetween
users.Forinstance,someusersareconsiderablylessa￿entivetonoti￿cationswhileexercisingcomparedtoother
users.Users’receptivityvariesacrossallactivities.
4.2 AppUsageTime
Inordertoinvestigatetherelationshipbetweendailyactivitiesandtheappusage,weperformatwo-wayANOVA
byse￿ingappusagetimeasdependentvariable(DV),anddailyactivityandappcategoryasindependentvariable
(IVs). Here,weusetwoIVsaswecanquantifyboththee￿ectofdailyactivityonoverallappusagetimeand
thee￿ectofdailyactivityoftheuseofspeci￿capps.￿eresultsdemonstratethatalle￿ectswerestatistically
signi￿cantatthe0.05signi￿cancelevel.￿emaine￿ectfordailyactivityyieldedF =7.45(p <0.05),indicatinga
signi￿cantdi￿erenceintheuser’soverallappusagetime(allcategoriesconsideredtogether)whileperforming
PACMonInteractive,Mobile,WearableandUbiquitousTechnologies,Vol.1,No.1,Article1.Publicationdate:July2017.