University of Groningen
Digital phenotyping and the COVID-19 pandemic
Jagesar, Raj R.; Roozen, Mila C.; van der Heijden, Inge; Ikani, Nessa; Tyborowska, Anna;
Penninx, Brenda W.J.H.; Ruhe, Henricus G.; Sommer, Iris E.C.; Kas, Martien J.; Vorstman,
Jacob A.S.
Published in:
European Neuropsychopharmacology
DOI:
10.1016/j.euroneuro.2020.11.012
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Publication date:
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Citation for published version (APA):
Jagesar, R. R., Roozen, M. C., van der Heijden, I., Ikani, N., Tyborowska, A., Penninx, B. W. J. H., Ruhe,
H. G., Sommer, I. E. C., Kas, M. J., & Vorstman, J. A. S. (2021). Digital phenotyping and the COVID-19
pandemic: Capturing behavioral change in patients with psychiatric disorders. European
Neuropsychopharmacology, 42, 115-120. https://doi.org/10.1016/j.euroneuro.2020.11.012
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SHORT
COMMUNICATION
Digital
phenotyping
and
the
COVID-19
pandemic:
Capturing
behavioral
change
in
patients
with
psychiatric
disorders
Raj
R.
Jagesar
a,
Mila
C.
Roozen
a,
Inge
van
der
Heijden
b ,c,
Nessa
Ikani
d ,e ,f,
Anna
Tyborowska
d ,g ,h,
Brenda
W.J.H.
Penninx
i,
Henricus
G.
Ruhe
d,
Iris
E.C.
Sommer
b,
Martien
J.
Kas
a ,1,
Jacob
A.S.
Vorstman
j ,k ,1 ,∗aGroningenInstituteforEvolutionaryLifeSciences,UniversityofGroningen,theNetherlands bDepartmentofNeuroscience,DepartmentofPsychiatry,UniversityMedicalCenterGroningen, UniversityofGroningen,Groningen,theNetherlands
cJanssen-CilagB.V.,Breda,theNetherlands
dDepartmentofPsychiatry,DondersInstituteforBrain,CognitionandBehaviour,RadboudUniversity NijmegenMedicalCentre,Nijmegen,theNetherlands
eDepressionExpertiseCenter,ProPersonaMentalHealthCare,Nijmegen,theNetherlands
fOverwaalCentreofExpertiseforAnxietyDisorders,OCDandPTSD,ProPersonaMentalHealthCare, Nijmegen,theNetherlands
gCentreforCognitiveNeuroimaging,DondersInstituteforBrain,CognitionandBehaviour,Radboud University,Nijmegen,theNetherlands
hBehaviouralScienceInstitute,RadboudUniversity,Nijmegen,theNetherlands
iDepartmentofPsychiatryandAmsterdamNeuroscience,AmsterdamUMC,VrijeUniversiteit, Amsterdam,theNetherlands
jPrograminGeneticsandGenomeBiology,ResearchInstitute,TheHospitalforSickChildren,Toronto, Canada
kDepartmentofPsychiatry,UniversityofToronto,Toronto,Canada
Received 14July2020;receivedinrevisedform29October2020;accepted11November2020
∗Correspondingauthorat:DepartmentofPsychiatry,UniversityofToronto,Toronto,Canada. E-mailaddress:jacob.vorstman@sickkids.ca(J.A.S.Vorstman).
1Equalcontributions.
https://doi.org/10.1016/j.euroneuro.2020.11.012 0924-977X/© 2020PublishedbyElsevierB.V.
116 R.R.Jagesar,M.C.RoozenandI.vanderHeijdenetal. KEYWORDS Covid-19; Psychiatricdisorders; Socialbehavior; Digitalphenotyping; Mobilepassive monitoring Abstract
TheCOVID-19pandemichasledtounprecedentedsocietalchangeslimitingusinourmobility andourabilitytoconnectwithothersinperson.Theseunusualbutwidespreadchangesprovide auniqueopportunityforstudiesusingdigitalphenotypingtools.Digitalphenotypingtools,such asmobilepassivemonitoringplatforms(MPM),provideanewperspectiveonhumanbehavior and holdpromiseto improve humanbehavioralresearch. However, there iscurrently little evidencethatthesetoolscanreliablydetectchangesinbehavior.ConsideringtheConsidering theCOVID-19pandemicasahighimpactcommonenvironmentalfactor westudiedpotential impactonbehaviorofparticipantsusingourmobilepassivemonitoringplatformBEHAPPthat wasambulatorytrackingthemduringtheCOVID-19pandemic.WepooleddatafromthreeMPM studiesinvolvingSchizophrenia(SZ),MajorDepressiveDisorder(MDD)andBipolarDisorder(BD) patients (N =12). Wecompared the data collectedon weekdaysduring threeweeksprior andthreeweekssubsequenttothestartofthequarantine.Wehypothesizedanincreasein communication and adecrease inmobility. We observed asignificant increase in the total time spentoncommunicationapplications(median 179and243minper weekrespectively,
p=0.005),andasignificantdecreaseinthenumberofuniqueplacesvisited(median6and 3visitsperweekrespectively,p=0.007),whilethetotaltimespentathomedidnotchange significantly(median64and77hperweek,respectively,p=0.594).Thedataprovidesaproof ofprinciplethatdigitalphenotypingtoolscanidentifychangesinhumanbehaviorincitedbya commonexternalenvironmentalfactor.
© 2020PublishedbyElsevierB.V.
1.
Introduction
Socialdistancing-andpubliclockdownmeasures,aimedat controlling the COVID-19 pandemic have significantly lim-itedourmobilityingeneraland,morespecifically,our abil-itytomeetotherpeople.Arguably,thepandemicandthe ensuingpoliciestocontainitsspreadcanbeconsideredas auniquesociologicalexperimentinwhichoneshared envi-ronmentalfactorhasincitedwidespreadbehavioralchanges intheentirepopulation.
These unusual circumstances provide a unique oppor-tunity to examine upcoming behavioral research tools in thefieldofdigital phenotyping.Digitalphenotypingis de-fined as the “moment-by-moment quantification of the individual-level human behavioral phenotype in situ using data from personal digital devices” (Torous et al., 2016, p. 2). Onespecific strategy of digital phenotyping is mo-bile passive monitoring (MPM), whereby data is collected through the smartphone without requiring any active in-put fromthe owner (excepting informed consent and in-stallation). MPM generates objective behavioral data col-lectedinreallifesettings,andisthereforethoughttohold promiseforimprovementofbehavioralresearchingeneral
(Insel,2017).However,empiricalevidence tocorroborate theclaimthatMPMcanreliablydetectbehavioralchanges isscarce(Morenoetal.,2020).
InthisstudyweexploitedthefactthattheCOVID-19 pan-demicandensuingcontainmentpoliciescanbeexpectedto have exerteda substantialimpactonbehavior. These cir-cumstances allowed us toexamine the ability of MPM to detect changes in social behaviors at the group level. To this end, we used MPM data collected in ongoing studies ofindividualswithapsychiatricdiagnosis,priorand subse-quenttotheintroductionofsocialdistancingandlockdown measures inthe Netherlands (fromhereonreferred toas
‘quarantinemeasures’).Wehypothesizedthatthe introduc-tionofquarantinemeasuresonMarch12th2020wouldlead toadecreaseinmobilityandanincreaseinmobile commu-nication,andthatthesechangeswouldbereliablycaptured byMPMdatacollectedbyindividualsmartphones.
2. Experimentalprocedures
2.1. Studydesign
ThestudywasconductedusingBEHAPP,anMPMplatformthrough whichwe aimto investigateand classifycommunicationand ex-plorationpatternsinhumansusingobjectivedatagathering tech-niques(https://behapp.org).Afterinformedconsentand installa-tion,BEHAPPpassivelycollectsdataviathesmartphoneof partic-ipants.Thedatacollectedrelatestovariousaspectsofbehavior, includingpatternsofmobility,communicationanddiurnalrhythm. Comparedtotraditionalmethods,thepotentialnoveltyofMPMis theobjectivenatureofthedataaswellastheirunprecedented res-olution,andthattheycanbeprospectivelycollectedinanatural “real-life” settingofparticipants.
Similar to policies in other countries throughout the world, theDutch quarantine strategywas aimedat clearing thepublic space inorder to prevent spreadof theSARS-CoV-2 coronavirus (CoronaviridaeStudyGroupoftheInternationalCommitteeon Tax-onomyofViruses,2020).Thestrategy,whichcameintoeffecton March12,2020,isbasedonmeasuressuchas,butnotlimitedto: 1)themandatoryclosureofthehospitality,sportsandeducational sectors;2)abanongatheringsofgroups3)amandatoryhaltof ac-tivitiesforprofessionswhichrequirecloseproximitytoothers(e.g. hairdressersanddrivinginstructors);and4)voluntary,butstrongly recommended,stayandworkfromhomeorders(Ministryof Gen-eralAffairs,theNetherlands,2020a,2020b).
Totestthehypothesisofbehavioralchangescausedby quaran-tinemeasures,wecomparedMPMdatacollectedonweekdays dur-ingthreeweekspriorandthreeweekssubsequenttothestartof
Table1 Demographicdataofparticipatingsubjects.In or-dertopreservetheprivacyoftheparticipantswereporton agebracketsandomitcohortdatafromtheoverview. Demographicdata
SubjectID Diagnosis Sex Agebracket
1 MDD M 20–29 2 SZ M 30–39 3 BD F 20–29 4 SZ M 30–39 5 MDD F 40–49 6 MDD F 50–59 7 MDD F 50–59 8 MDD M 50–59 9 MDD F 50–59 10 MDD F 40–49 11 MDD M 40–49 12 MDD F 50–59 13 MDD F 60–69
thequarantine.Wechose,a-posteriori,toreportonthefourouter mostweeksexcludingthetwoweeksinbetweenasweobserved thistransitionalperiodtointroducetoomuchnoiseinthe behav-ioralpatternsofoursubjects.Someparticipantsalreadychanged theirbehaviorinanearlystadium,whileothersneededmoretime to adjust. For similar reasons we chose to focus on weekdays only.Intypicalcircumstances,weekdayandweekendstatesshow strongdifferenceswhich,whencombined,leadtodiminishedsignal strength.Thus,alloutcomemeasureswerecalculatedfrom week-daysbeforeandafterMarch12,2020.Outcomesfromweeks9and 10(24-02-2020–06-03-2020)andweeks13and14 (23-03-2020–03-04-2020)werecombinedas‘pre-quarantine’andpost-quarantine’ states,respectively.
2.2. Participants
WepooleddatafromthreeongoingMPMstudiesintheNetherlands: NESDA,TheNetherlandsStudyofDepressionandAnxiety,acohort studyondepressionandanxietydisorders(Penninxetal.,2008); SMARD,astudyonSmartphonebasedMonitoringandcognition Mod-ificationAgainstRecurrenceofDepression;andHAMLETT,(Handling AntipsychoticMedicationLong-termEvaluationofTargeted Treat-ment,Begemannetal.,2020),atrialwhichcomparesdifferent an-tipsychotictreatmentstrategies(continuation,dose-reductionand discontinuation)inpatientswithschizophrenia-spectrumdisorders. From thesethree studies, we identified 13individuals (Table 1) withBEHAPPdataavailablefortheexacttimeframespanningthe fourtargetweeks(weeks9,10,13and14).Allsubjectswere en-rolledintherespectivestudiesbasedonalifetimediagnosis;ten subjectswithremittedMajorDepressiveDisorder(MDD),twowith Schizophrenia(SZ)andonewithBipolarDisorder(BD).Allpatients consentedtosharingtheirdataaspartofongoingMPMstudiesand theapplicationofMPMasadatacollectiontechniquewasapproved byethicscommittees.Furthermore,permissionwasgrantedbyall threestudiesforthisexploratorystudyinthecontextofthe COVID-19pandemic.
2.3. Measurements
Mobilecommunicationwasmeasuredby1)thetotaltimespent us-ingcommunicationapps(inminutes);andmobilitywasmeasured by2)thetotaltimespentathome(inhours);and3)thetotal num-berofuniqueplacesthatwerevisited.
FacebookMessenger),BEHAPP registersappusagelogswhich in-cludesthenameoftheappanddurationofuseperevent.The to-taltimespentusingcommunicationappsiscalculatedbyfiltering thelogforallinstanceswithappsbelongingtothecommunication categoryandsubsequentlysummingtheusagedurationvalues.The categorizationschemeisbasedongeneralappcategoriesas speci-fiedintheGooglePlayStore(GooglePlayStoreTeam,2020).
Outcome2)and3)arederivedfrompatternsofmobilityandare derivedfrom pre-processedgeolocationdata. Ourpre-processing procedure,knownas‘staypointdetection’, isbasedonprevious workbyZhengetal.(2009).Inshort,thisprocedureconvertsraw geolocationdataintoalistofstaypoints,whichforma chronolog-icaloverviewofplaceswhereparticipantswerestationary, includ-ingthetimespentstationaryateachpoint.Here,wedefinedastay pointasanylocationwitharadiusof350m,whereparticipants re-mainedforatleast30min.Additionally,byclusteringstaypoints anddeterminingwherethemajorityofnightlyhoursarespentboth theuniquenumberofplacesandthehomelocationofthe partic-ipantcanbederived(Jongsetal.,2020).Thus,wecalculated2) thetotaltimespent athomebysummingthetimespentforall staypointswhichbelongedtothe‘home’category,and3)thetotal numberofuniqueplacesvisitedbycountingthenumberofunique staypointsinthegeolocationdata.
2.4. Statistics
Forourquantitativeoutcomemeasures,theWilcoxonsigned-rank test was applied as a non-parametric statistical hypothesis test usedtocomparetworepeatedmeasurementsonsimilarindividuals (namely,beforeandafterthestartofthequarantinemeasures). Wecompensatedformultipletesting(threetests)byapplyingthe Bonferronicorrectionwithp<0.017.
3.
Results
Prior to the analysis we carried out two quality control steps:Firstwe examined generaldataloss,retaining only participants withatleast fourout offivedays ofdata on a weekly basis. One patient (SZ) exceededthis threshold andwasthereforeexcludedfromthesample.Consequently, datafromN=12wereincludedintheanalysesofthe com-municationoutcomemeasure.Secondly,weverifiedthe res-olution of location data, retaining nine participants with sufficientlocationdataavailabletoextractthetwolocation databoundoutcomemeasures(N=9).Themedianagewas 49;38%maleand62%female.
Results from our primary outcomes are depicted in
Fig. 1a–c. When comparing behavior post to prior to the startofquarantine,therewasastatisticallysignificant in-crease in the total timespent using communication apps (median179and243minperweekrespectively,p=0.005) aswellasthetotalnumberofuniqueplacesvisited(median 6and3perweekrespectively,p=0.007).Thetimespent athomewasnotsignificantlydifferent(median64and77h perweek,respectively,p=0.594).
The significant decline in the unique number of places thatwerevisitedpromptedfurther(post-hoc)investigation intotheunderlying geolocationdata.Fig.2depictsan ag-gregatedoverviewofalluniquestaypointsinrelationtothe homelocationofeachparticipantforbothtimeframes.The figureillustrateshowthedistancestraveledfromhome
de-118 R.R.Jagesar,M.C.RoozenandI.vanderHeijdenetal.
Fig.1 a–c:OverviewofoutcomemeasuresbeforeandaftertheintroductionofquarantinemeasuresonMarch12,2020.1a:Time spentusingcommunicationappsinminutesperweek,1b:Thenumberofuniqueplacesvisitedperweek,1c:Thetotaltimespent athomeinhoursperweek.
Fig.2 Aggregateduniquestaypointoverview.Inorderto pre-serve the privacy of participants, latitude and longitude in-formationincludingscaleareomittedfrom thefigureandall uniquestaypointsarecenteredaroundamidpointof0.0.
creasesharplywithlessoveralldensityatthecenter mean-ingthatpatientsspentlesstimeoutsideoftheirhomes dur-ingthefirstweeksofquarantine.
4.
Discussion
Thisstudyreportsontheabilityofmobilepassive monitor-ing,anovel digital phenotypingstrategy, toreliably mea-surebehavioralchangesingroupsofindividuals.Tothisend, weconsideredtheCOVID-19pandemicasaunique sociolog-icalexperiment inwhichthe behaviorofthe entire popu-lationisinfluencedbyacommonenvironmentalfactor.We expectedthattheinitiationofthequarantinewouldinduce adecreaseinmovementpatternsaswellasanincreasein communicativebehaviors.Ourobservationswereconsistent withtheseexpectations, indicating that MPMcan reliably detectsuchbehavioralchanges,evenina relativelysmall samplesetasusedinthecurrentanalysis.
Inaccordancewithourexpectations,weobserveda sig-nificantincreaseinthetotaltimespentoncommunication applications and a significant decrease in the number of uniqueplacesvisited,while thetotal timespentat home didnotchange significantly asexaminedbeforeand after thequarantine. Thedecreaseinuniqueplacesvisited sug-geststhat patients wereless likelytoexplore newplaces
Totaltimespentathomeincreased,butthedifferencewas not significant.Although we expecteda prioritoalsosee an increase in this measure, it is important to note that time spent at home prior to the pandemic was already quiteelevatedinthiscohortofmentallyaffectedsubjects, leavinglimitedroomforfurtherincrease.Indeed, observa-tionsreportedinotherstudiesindicatethatthispopulation is inclined to spend more time at home compared to in-dividuals without psychiatric illness (Schuch et al., 2017;
Stubbsetal.,2016).
Ourexploratorystudywaslimitedinsamplesizedueto theunplannednatureofouranalyses,andbecausethe spe-cific time-frame ofour studyreduced thenumber of sub-jects fromthe ongoing studies with dataavailable during thatexacttimeframe.Wehave addressedthisbylimiting thenumberoftests(three)basedonaprioriformulated hy-pothesesandtheapplicationofconservativecorrectionfor multiple testing. Theinclusion ofhealthycontrolsubjects wouldhave allowedustocomparetheimpactofthe pan-demic onthosewithandthosewithout psychiatricillness, butthesedataweresimplynotavailablewithintheselected timeframe.However,thefactthatourobservationswere obtained in a group of individuals with psychiatric illness doesnotdiminishtheconclusionofthisstudy.
In conclusion, we provide a proof of principle thatthe MPMclassofdigitalphenotypingtoolscanreliablyidentify relevantchangesinhumanbehaviorwhenincitedbya com-monexternalenvironmentalfactor.
Role
of
funding
source
Thisresearchreceivednoexternalfunding.
Contributors
RRJ, MCR,MJKandJASVconceptualizedanddesignedthe study. RRJ and MCR performed the data analysis. IH, NI, AT, BWJHP, HGRandIECS wereinvolved in the implemen-tation, execution and/or patient recruitment of the clin-ical studies. MJK and JASV are the founders of the BE-HAPPmobilepassivemonitoringservice.RRJbuiltand main-tained theBEHAPPmobilepassive monitoringservice.RRJ and JASV wrote the first version of the manuscript. All authors reviewed the manuscript prior to submission and theirfeedbackwasimplementedtothefinalversionofthe manuscript.
Conflict
of
Interest
Authorsdeclarenoconflictsofinterest.
Acknowledgments
We thank Shiromani Janki MD,PhD for heradvice and in-put during the data analysis and reporting phases of this researchinitiative.
tionModificationAgainstRecurrenceofDepression(SMARD) studywasfundedbytheHersenstichting(#HA2015.01.07)to H.G.Ruhe,C.J.Harmer,M.Kas,&J.Vorstman.
HAMMLET:The HAMLETTprojectis financedbyZonMW, #348041003
NESDA: The infrastructure for the NESDA study (www. nesda.nl) has been funded through the Geestkracht pro-gram ofthe NetherlandsOrganisationfor HealthResearch andDevelopment(ZonMw,grantnumber10-000-1002)and by participatinguniversities andmental health care orga-nizations(AmsterdamUniversity MedicalCenters(location VUmc),GGZinGeest,LeidenUniversityMedicalCenter, Uni-versityMedicalCenterGroningen,UniversityofGroningen, Lentis, GGZFriesland,GGZDrenthe, RobGiel Onderzoek-centrum).
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