Graduate Theses and Dissertations Iowa State University Capstones, Theses andDissertations

2017

The relationship between smartphone use,symptoms of depression, symptoms of anxiety, andacademic performance in college studentsElizabeth Mae LongneckerIowa State University

Follow this and additional works at: https://lib.dr.iastate.edu/etd

Part of the Education Commons, and the Psychology Commons

This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University DigitalRepository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University DigitalRepository. For more information, please contact digirep@iastate.edu.

Recommended CitationLongnecker, Elizabeth Mae, "The relationship between smartphone use, symptoms of depression, symptoms of anxiety, and academicperformance in college students" (2017). Graduate Theses and Dissertations. 15357.https://lib.dr.iastate.edu/etd/15357

Therelationshipbetweensmartphoneuse,symptomsofdepression,symptomsofanxiety,andacademicperformanceincollegestudents

by

ElizabethMaeLongnecker

Athesissubmittedtothegraduatefacultyinpartialfulfillmentoftherequirementsforthedegreeof

MASTEROFSCIENCE

Major:Human-ComputerInteraction

ProgramofStudyCommittee:ReynolJunco,MajorProfessor

JonathanKellyRobertReason

Thestudentauthorandtheprogramofstudycommitteearesolelyresponsibleforthecontentofthisthesis.TheGraduateCollegewillensurethisthesisisgloballyaccessible

andwillnotpermitalterationsafteradegreeisconferred.

IowaStateUniversity

Ames,Iowa

2017

Copyright©ElizabethMaeLongnecker,2017.Allrightsreserved.

ii

DEDICATION

Iwouldliketodedicatethisthesistomymomforherlovingguidanceand

supportthroughoutmylife,especiallyduringthecompletionofthisthesis.

Iwouldalsoliketospeciallythankmyfriends:Alex,Mozhan,Greg,James,Meg,

Mohsen,andsomanymore.You’veallhelpedmemorethanyoucanimagine.

Love,

Elizabeth

iii

TABLEOFCONTENTS

LISTOFTABLES.........................................................................................................v

LISTOFFIGURES.......................................................................................................vi

ACKNOWLEDGEMENTS............................................................................................vii

ABSTRACT.................................................................................................................viii

CHAPTER1.INTRODUCTION....................................................................................1Background...................................................................................................1SmartphoneUsers........................................................................................2AssociatedOutcomesofSmartphoneUse...................................................4CollegeStudentsandMentalHealth............................................................7

CHAPTER2.LITERATUREREVIEW.............................................................................11

Hypothesis1:SmartphoneUseandAcademicPerformance.......................11Hypothesis2:SmartphoneUseandAnxiety................................................12Hypothesis3:AnxietyandAcademicPerformance......................................13Hypothesis4:SmartphoneUseandDepression..........................................15Hypothesis5:DepressionandAcademicPerformance................................16Summary......................................................................................................17

CHAPTER3.METHODOLOGY....................................................................................22

Participants...................................................................................................22InstrumentsandMeasures..........................................................................23

CHAPTER4.RESULTS................................................................................................25

DataCleaningandAnalyses.........................................................................25Hypothesis1:SmartphoneUseandAcademicPerformance.......................27Hypothesis2:SmartphoneUseandAnxiety................................................28Hypothesis3:AnxietyandAcademicPerformance.....................................29Hypothesis4:SmartphoneUseandDepression..........................................29Hypothesis5:DepressionandAcademicPerformance................................30

CHAPTER5.DISCUSSION..........................................................................................37

Summary......................................................................................................37Limitations....................................................................................................43Implications..................................................................................................44

REFERENCES.............................................................................................................47

iv

APPENDIXA:INFORMEDCONSENT/IRBAPPROVAL.................................................60

APPENDIXB:SURVEYINSTRUMENTS.......................................................................63

v

LISTOFTABLES

Table4.1:Hierarchicalregressionmodelexploringhowdemographics,behavioralchange,smartphoneuse,andsymptomsofanxietypredictcoursetotalpoints...........................................................................33

Table4.2:Hierarchicalregressionmodelexploringhowdemographics,behavioralchange,andsmartphoneusepredictanxiety............................34

Table4.3:Hierarchicalregressionmodelexploringhowdemographics,behavioralchange,smartphoneuse,andsymptomsofdepressionpredictcoursetotalpoints...........................................................................35

Table4.4:Hierarchicalregressionmodelexploringhowdemographics,behavioralchange,andsmartphoneusepredictdepression......................36

vi

LISTOFFIGURES

Figure1.1:Computervs.smartphoneownershipbyannualincome......................9Figure1.2:Computervs.smartphoneownershipbyethnicity................................10Figure2.1:Hypothesizedpathmodelincludingsmartphoneusage, symptomsofanxiety,andacademicperformance......................................20Figure2.2:Hypothesizedpathmodelincludingsmartphoneusage, symptomsofanxiety,andacademicperformance......................................21Figure4.1:Pathmodelincludingsmartphoneusage,symptomsofanxiety, andacademicperformance..........................................................................31Figure4.2:Pathmodelincludingsmartphoneusage,symptomsofdepression andacademicperformance..........................................................................32

vii

ACKNOWLEDGEMENTS

Iwouldliketoexpressmythankstothosewhohelpedmewithvariousaspects

ofconductingresearchandthewritingofmythesis.First,Dr.ReynolJuncoforhis

supportoverthepasttwoyears.Iwouldalsoliketothankmycommitteemembersfor

theirvaluableeffortsandcontributionstothiswork:Dr.RobertReasonandDr.

JonathanKelly.Additionally,Iwanttothankmylabmates,AlexLimandJeffRokkum,

forencouragingandsupportingme.SpecialthankstoAlexLimforhismentoringand

relentlessencouragement.Iamsogratefulforyou,bro.

Additionally,IwouldliketothankLarryRosenandJonathanPedrozafortheir

helpwithresearch.Also,thankstoKaitlinO’Brienforherhelpwiththeorganizational

andwritingaspectsofmythesis.Finally,thankyoutoJeremyHadleratISUAES

StatisticalConsultingforhishelpwithmyanalyses.

viii

ABSTRACT

Thecurrentstudyaimstoresearchtherelationshipbetweensmartphoneuse,

symptomsofanxiety,symptomsofdepression,andacademicperformance.Previous

literaturesuggeststhatsmartphoneusageisrelatedtomentalhealth(Ha,Chin,Park,

Ryu,andYu,2008;Rosen,Whaling,Rab,Carrier,andCheever,2013;Rosen,Whaling,

Carrier,Cheever,&Rokkum,2013;VanAmeringen,Mancini,&Farvolden,2003).Studies

havealsolinkedmentalhealthtoacademicperformanceincollegestudents(Eisenberg,

Golberstein,&Hunt,2009;Hysenbegasi,Hass,&Rowland,2005).Youngadultsages18-

29yearsoldaremostlikelytoownanduseasmartphonecomparedtoanyotherage

group(Anderson,2015;Smith,2015);additionally,75%ofmentalhealthdisordershave

theirfirstonsetbeforetheageof24.Therefore,thesubjectsampleforthisstudy

focusesoncollegestudents.Itisnecessarytoexaminethisrelationshiptounderstand

possiblepredictorsandproviderecommendationsonhowacademicinstitutionscan

improvestudents’well-beingandlowerriskofacademicfailure.

Students(N=216)attendingapublicuniversityinthewesternU.S.were

surveyedinageneraleducationcourseontheglobalimpactoftechnologyandaskedto

downloadtheInstantQuantifiedSelfapplicationtorecordtheirsmartphoneusage.

Regressionanalysesdeterminedthatsmartphoneusesignificantlypredictedacademic

performance,t(147)=-2.732,β=-.254,p<.01.Additionally,smartphoneusagewas

negativelypredictiveofanxietysymptoms,t(147)=-2.306,β=-.216,p<.05,

contradictorytopreviousresearchfindings;therefore,smartphoneusagemaynotbe

relatedtomentalhealthaspreviouslythought.

1

CHAPTER1.INTRODUCTION

Background

ThefirstsmartphonewasnottheAppleiPhoneasmanywouldbelieve.Inthe

beginning,smartphonedeveloperswereonlyaimedatsellingtobusinessprofessionals;

businessprofessionalshadtheneedandthemoneytopurchasethistechnology.

Therefore,thefirst‘smartphone’(thetermwasnotyetcoineduntil1997),theSIMON

PersonalCommunicator,wasproducedin1992andpatentedin1999byInternational

BusinessMachinesCorporation(IBM;Budd,Karidis,&McVicker,1999).SIMONwas

describedasanall-in-onewirelesshandsetphonewithvirtualimagedisplay,

monochrometouchscreen,pager,faxmachine,andlimited-functioncomputer(Budd,

Karidis,&McVicker,1999;Sager,2012).Thephoneretailedfor$899(about$1500in

2016afteradjustmentforinflation),andIBMonlysoldaround50,000units.

Unfortunately,SIMONwasbeforeitstime;itwasproduced,sold,andoffthemarket

beforethewebbrowsergainedpopularitysoonafter.In2002,RIMreleasedthe

Blackberry5810,aphonewithcapabilitiestocheckemailandbrowsetheweb,yetthe

Blackberry’sbiggestproblemwastherequireduseofheadphonesduringphonecalls

(Reed,2010).Thisphonewasstillonlyaimedatsellingtobusinessprofessionals;

although,amajorturningpointforthesmartphonewouldsoonbereached(Reed,

2010).

InJanuary2007,onthestageoftheMosconeConventionCenterinSan

Francisco,SteveJobspresentedtherevolutionary1stgenerationAppleiPhone.Thefirst

attemptatinfiltratingthegeneralmarketwiththesmartphoneincludedatouchscreen,

2

sharpcolordisplay,abilitytoconnecttoWi-Fi,ground-breakingintegrationofthe

mobilewebbrowser,andaccesstoasoftwaredevelopmentkitforthird-party

companieswhichlaterevolvedintotheAppStore(Apple’siPhone,2016;Reed,2010).

SellingonehundredtimesmoredevicesthantheSIMON,theAppleiPhonewasandstill

isthedevicetowhichallsmartphonesarecompared(Reed,2010).Since2007,Apple

hasreleased14additionalmodelsoftheiPhone.Evenwiththeunfortunatepassingof

SteveJobs,thecompanyhashadnoindicationofslowingdownitsinnovationor

productionofnewtechnology.

Thesmartphone’smulti-functionalityhascontributedtoitsintegrationinto

everydaylife.WiththeonsetoftheInternet’spopularity,developershavecreatedsocial

mediaplatforms,mobileapplications,andintelligentpersonalassistants(e.g.Siri),in

additiontotraditionaltextmessagingandcalling.Mobilephoneusersarenow

connectedinstantlytoanyonealmostanywhereatanytimegivingthemaccessto

informationattheirfingertips.

SmartphoneUsers

AccordingtoPewresearchbyAnderson(2015)andSmith(2015),68%ofadult

Americansownasmartphone;specifically,youngadultsages18-29yearsoldaremost

likelytoownasmartphonecomparedtoanyotheragegroup.Inaparticularstudyby

Smithonsmartphoneusage(2015),100%ofyoungadultparticipantsthatowna

smartphoneusedtheirphonefortextmessagingatleastonceoverthecourseofa

week,97%usedtheInternet,93%placedphone/videocalls,91%sentemails,and91%

3

usedtheirphoneforsocialnetworking.Thepopularityofsmartphonesamongcollege-

ageadultsislikelyduetotheiropennesstonewtechnology.Collegestudentsare

generallyearlyadopters,thefirsttotrynewtechnology,andinnovators,pioneers

creatingnewwaystouseexistingtechnology(Nelson,2006;Rogers,1995).Indeed,over

85%ofcollegestudentsownasmartphone,andthenumberofsmartphoneownerswill

continuetogrow(Anderson,2015;Emanuel,2013).

By2020,projectionsindicatethatsmartphoneownerswillmorethandoubleto

6.1billion,70%oftheworld’spopulation.Manyofthesenewuserswillemergefrom

developingcountriesthroughgreaterdeviceaffordability,growingeconomies,and

young,growingpopulations(Cerwall,2016).Withthisincrease,smartphoneswill

ultimatelysurpassthenumberoffixedphonelinesworldwide(Cerwall,2016)and

quicklyapproachtheownershipofpersonalcomputers(Anderson,2015).

Smartphoneshavealsoallowedanaffordablewayforindividualsfromlower

socialeconomicstatusesandfromminoritizedbackgroundstoaccesstheInternet.In

2015,Andersonreportedonly50%ofadultswithhouseholdincomeunder$30,000

ownedapersonaldesktoporlaptopcomputer.Incomparison,atleast80%ofadults

withhouseholdincomesover$30,000ownedatleastonepersonalcomputer

(Anderson,2015).Comparisonsbetweenwhite,Hispanic,andblackhouseholdsshowed

79%,63%,and45%computerownership,respectively(Anderson,2015).However,

whenanalyzingsmartphoneownership,thedifferencesofownershipbetweenSESand

ethnicbackgroundsarereduced.Thevariationamongadultswithhouseholdincome

under$30,000comparedwithhouseholdincomeover$30,000isspreadmoreevenly

4

(seeFigure1.1)incontrasttothesharpdivideddifferencesincomputerownership

amongseparateincomes(Anderson,2015).Additionally,ethnic/racialdifferencesin

smartphoneownershiparealmostnon-existent(seeFigure1.2)withBlackownershipat

68%,Whiteat66%,andHispanicat64%(Anderson,2015).Theavailabilityand

affordabilityofsmartphoneshasshrunktheinequalitiesinInternetaccessthatthese

populationsfacecomparedtotheirhigher-incomeandWhitepeers;ultimately,Internet

accessprovidesmoreequalopportunitiesandresourcesforallpeople.

AssociatedOutcomesofSmartphoneUse

Newinnovationsintechnologycomewithnewsetsofconsequences.Wehope

modernizationenrichesthehumanrace,andinsomeways,itdoes.Smartphones,for

example,allowforthedistributionofvaluabletoolsintheformofapplications.Astudy

bySmith(2015)foundthatin2015,overhalfofsmartphoneownersusedtheirphoneto

researchhealthinformation,doonlinebanking,followbreakingnewevents,learnabout

communityactivities,anduseGPSnavigation.Additionally,53%ofsmartphoneowners

haveusedtheirphoneinanemergency(Smith,2015).Smartphoneshavealsoprovided

anopportunitytodistributeapplicationstohelpwithbehavioralproblems.Cognitive

behavioralpractitionersfindthatincorporatingtechnologyintotheirtherapiescanhelp

withinterventionsforpeoplewithfewerresourcesbyextendingthescopeoftherapy

outsidetheoffice(Laneetal.,2011).

Developershavecreatedappstohelpcorrectbehavioralproblemssuchas

medicationnonadherence(Dayeretal.,2013),alcoholabuse(Dulin,Gonzalez,&

5

Campbell,2014),andunhealthyhabitsaffectingwell-being(Laneetal.,2011).

Specifically,medicationnonadherenceappshelpwithpatientsrequiringdaily

treatment.Nonadherenceisacostlyandcommonproblem,butnewsmartphone

adherenceappsofferinexpensiveandeasysolutions(Dayeretal.,2013).Thehighest

ratedadherenceappsprovidebasicreminderfeaturesandadvancefunctionalityto

correctthisbehavioralissue(Dayeretal.,2013).However,researchersstressthe

importanceofusingtheseself-helpapplicationsinconjunctionwithprofessional

involvement;individualsmayeventuallylosemotivationtousethesecognitive

behavioraltherapy(CBT)tools(Dayeretal.,2013).Generally,onlyabout38%ofall

downloadedappsareopenedmorethanonceafteramonth,andthisfiguresharply

decreasesto4%afterayear(Farago,2011).

Whilecertainsmartphoneappsmayhelpcorrectbehavioralproblems,

smartphoneusecanbecomeabehavioralissueinitself.Ina2015studyconductedby

Smith(2015),46%ofsmartphoneusersreportedthattheyfelttheycouldnotlive

withouttheirphone,30%reportedtheyfelttheirsmartphonewasa“leash”,restricting

theirfreedom,and19%felttheirphonewasafinancialburden.Additionally,Rosenand

hiscolleagues(2013)createdaninstrumentmeasuringmediaandtechnologyusageand

attitudes(MTUAS)inwhichtheysurveyedcollegestudentsover18ontheirtechnology

habitsandbeliefs.Theyfoundthatthetimespentusingasmartphonewaspositively

relatedtoanxietyaboutnotcheckinginoftenenoughwithtechnology(Rosen,Whaling,

Carrier,Cheever,&Rokkum,2013).InasecondstudybyRosen,Whaling,Rab,Carrier,

andCheever(2013),resultsfromtheMTUASshowedthathavingnegativeattitudes

6

abouttechnology(e.g.,technologymakeslifemorecomplicated,technologymakes

peoplewastetoomuchtime,technologymakespeoplemoreisolated)predictedmore

clinicalsymptomsofdepression.Thismayindicatethatquantitativeuseofsmartphones

maynotalwaysbeproblematicforusers;however,smartphoneusepairedwith

negativeattitudesandfeelingsofdependenceandanxietyabouttechnologymay

increasenegativeoutcomesassociatedwithsmartphoneuse,specifically,smartphone

users’riskforanxietyanddepression(Rosen,Whaling,Rab,Carrier,&Cheever,2013;

Thomée,Härenstam,&Hagberg,2011).

Anxietyanddepressioncanhavenegativeeffectsonmood(AmericanPsychiatric

Association,2013),motivation/interest(AmericanPsychiatricAssociation,2013),sleep

(Reynoldsetal.,1983;Tsuno,Besset,&Ritchie,2005),physicalhealth(Kawachi,

Sparrow,Vokonas,&Weiss,1994;Keenan-Miller,Hammen,&Brennan,2007),andself-

esteem(Battle,1978;Sowislo&Orth,2013).Eachofthesesymptomscannegatively

impactlifesatisfactionandacademicachievement(Andrews&Wilding,2004;

Koivumaa-Honkanenetal.,2004;Stein&Heimberg,2004,VanAmeringen,Mancini,&

Farvolden.2003).Inparticular,collegestudentswithdepressionstrugglemorewith

academicperformancethantheirhealthiercounterparts,havingquantifiablenegative

effectsontheirGPA(Andrews&Wilding,2004;Hysenbegasi,Hass,&Rowland,2005).

Hysenbegasi,Hass,andRowland(2005)collectedacademic,health,andproductivity

datafromuniversitystudentsandfoundthatdiagnoseddepressionwasassociatedwith

halfalettergradedeficitinoverallGPA.AndrewsandWilding(2004)alsoconducteda

studyinvolvingundergraduatesanddeterminedthatdepressionpredictedadecreasein

7

courseexamperformancebetweenfirstandsecondyears.Infact,29%ofcollege

freshmeninAndrewsandWilding’s(2004)studydevelopedanxietyordepression

beforetheendoftheirfirstyear.However,36%ofstudentswithpriordiagnoseswere

recoveredbytheendoftheirinitialyear,indicatingthatwhilethetransitionfrom

secondarytohighereducationcanprovidenewstressorsforstudents,opportunitiesfor

new,positiverelationshipsandbetterunderstandingofmentalhealthissuescanalso

developinthisenvironment(Andrews&Wilding,2004).

CollegeStudentsandMentalHealth

Theyearsspentincollegesignifydramaticlifechanges.Collegestudentsare

pushedintoindependencewhilemakingsomeoftheirfirstcareerdecisionssuchas

declaringamajorlinkedtoaspecificoccupationalarea(Pascarella&Terenzini,2005).

Coincidentally,75%ofmentalhealthdisordershavetheirfirstonsetbeforetheageof

24,resultinginproblematicoutcomeswithinacademic,social,andoccupationalaspects

oflife(Breslau,Lane,Sampson,&Kessler,2008;Ettner,Frank,&Kessler,1997;Kessler,

Walters,&Forthofer,1998;Kessleretal.,2005).Infact,4.4%ofhighschoolgraduates

failtoevenentercollegebecauseofmentalhealthissuesandapproximately2.6%of

collegedropoutsattributetheirdeparturetomentaldisorders(Breslau,Lane,Sampson,

&Kessler,2008).Traditionalcollege-agedstudentsarepassingthroughacrucialstagein

adulthumandevelopment;therefore,effectivetreatmentcouldpromotelong-term

healthbenefitsifimplementedearly.

8

Postsecondaryinstitutionsofferanidealopportunityforstudentstoidentifyand

treatmentaldisorders.Campusesprovideresidences,socialnetworks,andmental

healthservicestostudentsinaconcurrent,convenientlocation;thisisidealformental

healthtreatmentsincesupportnetworksimprovephysicalandpsychologicalwell-being

throughstressbuffersandotherdirectmeans(Thoits,2011).Forexample,social

connectionsmayprovideasenseofbelongingandacceptance(Thoits,2011).Withthis

acceptance,astudentmayfeelconnectedtoanetworkofcommunicationaswellasa

senseofsecuritythathis/herneedswillbemetbytheirsupportsystem(Thoits,2011).

Unfortunately,manyadultswithmentaldisordersdonotreceiveappropriate

treatmentbecauseofpublicstigma,unavailabilityofhealthcareservices,andfinancial

barriers(Wangetal.,2005).However,mostcollegecampusesprovidefreeorhighly

subsidizedhealthcareservices,butcollegestudentsstilloftendonotseektreatment

becauseofstigmatization,lackofperceivedneedforhelp,orunawarenessofavailable

services(Eisenberg,Golberstein,&Gollust,2007).

Consequently,understandingthestressorsincollegestudents’livesiscriticalfor

providingsuccessfultreatmentandsupport.Ifcampushealthproviderscanuncover

additionalfactorsattributedtomentaldisordersincollegestudents,theywillbeableto

increaseself-awarenessofpossiblementalhealthissuesandgivemorecomprehensive

solutionsforalleviatingstress.Meanwhile,collegeinstitutionsneedtoimprovethe

marketingstrategiesfortheirmentalhealthservicesbecauseseekinghelpshouldnotbe

perceivedasweakormeaningless.

9

Figure1.1:C

ompu

terv

s.sm

artpho

neowne

rshipbyann

ualincom

e(Smith

,2015)

10

Figure1.2:C

ompu

terv

s.sm

artpho

neowne

rshipbyethnicity(Smith

,201

5)

11

CHAPTER2.LITERATUREREVIEW

Hypothesis1:SmartphoneUseandAcademicPerformance

Researchexploringtherelationshipbetweensmartphoneuseandacademic

performanceisstillevolvingfromtheearlystages.However,inthestudiesthathave

beenpublished,therelationshipbetweenthesevariableshavebeennegative.Lepp,

Barkley,andKarpinski(2015)determined,aftercontrollingforknownpredictorsofGPA

(demographicvariables,self-efficacyforself-regulatedlearning,self-efficacyfor

academicachievement,andactualhighschoolGPA),thatcellphoneusewasnegatively

andsignificantlyrelatedtocollegeGPAinasampleof536undergraduates.

Additionally,JuncoandCotten(2012)revealedthatbrowsingFacebookand

textingwhiledoingschoolworkwerenegativelyassociatedwithoverallcollegeGPA.

Collegestudentswereaskedtocompleteasurveyontheirinformationand

communicationtechnology(ICT)usage,multitaskinghabits,andtechskills(Junco&

Cotten,2012).Theresearchersalsoreceivedeachsubject’scollegeandhighschool

gradepointaverages(Junco&Cotten,2012).Fromthesedata,theyfoundthattheir

hierarchicallinearregressionmodelpredictingcollegeGPAfromdemographics,high

schoolGPA,Internetskill,andICTmultitaskingwassignificant(F(18,1623)=28.274,p<

0.001,AdjustedR2=0.232;Junco&Cotten,2012).Therefore,theyconcludedthat

Facebookuseortextingwhilecompletingschoolworkmayoverloadstudents’capacity

forcognitiveprocessingandimpedeacademicperformance.

Furthermore,Rosen,Carrier,andCheever(2013)determinedfromasampleof

263USstudents(11-25yearsold),thosewhousedFacebookwhileworkingorstudying

12

hadloweroverallGPAsthanstudentswhoavoidedmultitaskingwithICTs.Ingeneral,

thesestudiesfocusontheuseoftechnologywhilestudyinganditsrelationshipwith

academicperformance,attributingthisconnectiontotheimpairmentmultitaskingmay

causeonstudents’cognitiveload.Thedivisionofattentionbetweenstudyingandother

irrelevanttaskssuchascheckingorusingasmartphonemaybetheunderlying

mechanismfortherelationshipbetweenthesetwovariables.Therefore,thefirst

hypothesisforthecurrentstudyis:

Hypothesis1:Smartphoneuserelatestoacademicperformance.

Hypothesis2:Smartphoneuseandanxiety

Thesecondhypothesisofthisstudyconnectstheindependentvariablein

Hypothesis1(smartphoneuse)tosymptomsofanxiety.Previousresearchonthe

relationshipbetweenphoneuseandanxietyislimitedbutthosethatexistshowsome

significantresults.Forexample,inthestudybyHa,etal.(2008),studentsfroma

technicalhighschoolwereaskedtoparticipateinasurveyonexcessivecellphoneuse;

thesurveyincludedquestionsabout“controldifficulty,apersistentneedforconnection

withothers,andspecificcommunicationpatternsviacellularphone”(Ha,Chin,Park,

Ryu,&Yu,2008,p.783).Theresearchersusedtheupperandlower30%ofscoresfrom

thesurveytoclassifyusersintoexcessiveandlow-usercategories.Theresearchers

foundthatexcessivemobilephoneusersreportedlowerself-esteem,higher

interpersonalanxiety,anddifficultyinexpressionofemotionthanlow-usage

comparisongroup(Ha,Chin,Park,Ryu,&Yu,2008).However,theseresultswere

13

correlationalratherthanpredictive;therefore,theresearcherswereonlyableto

determinethatatrendexistsbetweenthevariablesbutwereunabletoshowa

predictivemodeloftheirrelationship.

AnadditionalstudybyJenaroetal.(2007)foundthataftercomparingmeans

(chi-squared)betweencellphoneheavy-usersandlight-users,cellphoneheavy-users

weremorelikelytosufferfromsomaticcomplaints,insomnia,socialdysfunction,

anxiety,anddepressionthanlight-users.Theresearchersalsoperformedalogistic

regressionanalysisonthevariablesaswell.Theyfoundthattheirmodelpredictingthe

likelihoodofbeingaheavyorlightcellphoneuserwassignificant(chi-square=39.854,

df=6,p<.001)andanxietysubscore(BeckAnxietyInventory)wassignificantly

predictiveofthislikelihood(standardizedbeta=.291,p<.05;Jenaroetal.,2007).

However,theydidnottestthelinearrelationshipbetweencontinuousmeasuresofcell

phoneusageandmeasuresofanxietysymptoms.Thecurrentstudyintendstoanalyze

linearregressionstoshowasimilarrelationshipaspreviousstudies.Consequently,the

secondhypothesisofthisstudystates:

Hypothesis2:Smartphoneuserelatestosymptomsofanxietyinstudents.

Hypothesis3:Anxietyandacademicperformance

Researchshowsthatanxietyinfluencestestperformance,academic

performance,anddropoutratesinyoungadults.Regardingtestperformanceand

academicperformanceinstudentswithanxiety,DesideratoandKoskinen(1969)

determinedthatinasampleof94collegefreshmenwomen,thosewithdebilitative

14

anxiety(anxietythatinterfereswithperformance)earnedlowergradepointaverages

thanthosewithfacilitativeanxiety(anxietythatmayincreaseorimproveperformance).

Kessleretal.(1995)reportedsignificanteffectsofanxietydisordersonfailureto

completehighschool(oddsratio=1.4,p<.05),failuretoentercollege(oddsratio=1.4,

p<.05),andfailuretocompletecollege(oddsratio=1.4,p<.05)inasampleofover

5,000participantsages15to54.InaretrospectivestudybyVanAmeringenetal.

(2003),about24%ofpsychologicalpatientswhodroppedoutofschoolreportedleaving

schoolprematurelybecauseoftheiranxietydisorder.Inparticular,studentswith

anxietydisordersavoidpost-secondaryeducationtopreventfacingsocialand

communicationdemands(VanAmeringen,Mancini,&Farvolden,2003).Socialanxiety

typicallyinvolvesthefearthatanindividualwillbehumiliatedorembarrassedinsocial

orperformancesituations;thisfearcaninspireavoidanceintheindividualssuffering

fromsuchanxiety(VanAmeringen,Mancini,&Farvolden,2003).

Therefore,itisimportantforcollegestudentstobuildhealthysocial

relationshipsattheirinstitution;researchshowsthatwhenstudentsbuildsocialcapital

(benefitsderivedfromsocialrelationshipsi.e.emotionalsupport,diverseideas;Ellison

etal.,2011),thispromotesasenseofconnectiontotheirinstitutionleadingtogreater

academiccommitmentandacademicperformance(Pascarella&Terenzini,2005;Tinto,

1993).Consequently,inastudybyBrookandWilloughby(2015),researchersfoundthat

inapathanalysisof942Canadianuniversitystudents,socialanxietyhadasignificant

andnegativerelationshipwithacademicachievement.Theirfindingsemphasizethe

15

importanceofsocialcapitalinrelationtoacademicoutcomes.Therefore,thethird

hypothesisstates:

Hypothesis3:Symptomsofanxietyinstudentsrelatetoacademicperformance.

Hypothesis4:Smartphoneuseanddepression

Factorscontributingtosmartphoneusearenowthepressuresofdaily

obligationsfromwork,school,andpersonallife.Smartphoneuseanddemandsfor

achievementwerealsoidentifiedasdirectsourcesofstressandmentalhealth

symptoms(Thomée,Härenstam,&Hagberg,2001).Thomée,Härenstam,andHagberg

(2001)investigatedtherelationshipbetweencellphoneusage,socialsupport,and

symptomsofdepressioninasampleofover4,000youngadults.Socialsupportcould,in

fact,buffertheeffectsofstressonindividuals(Cohen,1998).However,theresearchers

foundthatfrequencyofphoneusehadnoassociationwithperceivedaccesstosocial

support(Thomée,Härenstam,&Hagberg,2001).Theyalsoconcludedthathigh

quantitativemobilephoneuse(11+phonecallsandtextsaday)wasrelatedto

symptomsofdepression(Thomée,Härenstam,&Hagberg,2001).

Inaddition,studiesbyRosenetal.(2013)showedthatanxietyaboutnot

checkingtextmessagesandsocialnetworkingweresignificantpredictorsofdepression

(Rosen,Whaling,Rab,Carrier,&Cheever,2013;Rosen,Whaling,Carrier,Cheever,&

Rokkum,2013).Accordingly,thefourthhypothesisis:

Hypothesis4:Smartphoneuserelatestosymptomsofdepressioninstudents.

16

Hypothesis5:Depressionandacademicperformance

Academicengagement,firstdefinedbyAstin(1984)whichincludesaspectsof

“physicalandpsychologicalenergythatthestudentdevotestotheacademic

experience”(pg.297).Morerecently,researchsuggestsquantifyingengagementasthe

timeandeffortstudentsspendoneducationalactivitiesthatarelinkedtopositive

academicoutcomessuchasgradesandretention(Kuh,2009).Suchactivitiesinvolve

interactionswithfaculty,interactionswithpeers,involvementinco-curricularactivities,

andinvestmentinthecollegeacademicexperience(Kuh,2009;Pascarella&Terenzini,

2005).Kuh,Crouce,Shoup,Kinzie,&Gonyea(2008)foundthatinagroupofover6,000

collegestudentsfrom18differentbaccalaureateinstitutions,studentengagement

measuresincludingtimespentparticipatinginco-curricularandeducationally

purposefulactivitiesaccountedfor13%ofthevarianceinfirst-yearstudentGPA.

Lackofstudentengagementcouldbequantifiedasadiminishedinvolvement

andinvestmentintheacademicexperience.Creatingengagedstudentsrequiresthe

supportofthecampuscommunity,instructors,andthestudentsthemselves.However,

itcanbedifficulttoengageorsupportastudentexperiencingsymptomsofdepression

suchasirritability,overwhelmingfear,lossofenergy,difficultyconcentrating,andloss

ofinterestinthingstheyotherwisefindpleasurable(Eisenberg,Golberstein,&Hunt,

2009).

Eisenberg,Golberstein,andHunt(2009)concludedthatanhedonia(losing

pleasureinpreviouslyrewardingactivities)significantlyandnegativelypredictedcollege

GPAevenaftercontrollingforotherdepressivesymptomsincludingfeelingtired,

17

undergoingsleepproblems,andpoorappetite.Additionally,Hysenbegasi,Hass,and

Rowland(2005)foundthatdiagnoseddepressionincollegestudentsrelatestoahalf

gradepointdeficitinoverallGPA.

Thosewithdepressionarealsolikelytoexperiencefeelingsofloneliness(Wei,

Russell,&Zakalik,2005).Lonelinesshasalsobeenshowntoindicatealackofsocial

skills,specificallyrelationshipformationandmaintenanceskills,requiredtodevelop

importantinterpersonalrelationshipsnecessaryforsocialandacademicengagement

(Jones,Hobbs,&Hockenbury,1982;Wittenberg&Reis,1986).Ifstudentslivingwith

depressionandanhedonialosemotivationtoparticipateinextra-orco-curricular

activities,theirsocialandacademicengagementwillsuffer,likelyresultinginacademic

performancedeficits.Consequently,thefinalhypothesisofthisstudyis:

Hypothesis5:Symptomsofdepressioninstudentsrelatetoacademicperformance.

Summary

Consideringthecurrentresearchconnectingsmartphoneusagetomentalhealth

aswellasmentalhealthtoacademicperformance,thisstudyaimstoevaluatethelink

betweenallthreevariables.Therefore,themainresearchquestionis:whatisthe

relationshipbetweencollegestudents’smartphoneusage,mentalhealthsymptoms,and

academicperformance?

Thefirststudytoresearchtheconnectionbetweenallthreevariablespreviously

developedastructuralequationmodeldepictingtherelationshipbetweentexting,

anxiety,satisfactionwithlife,andcollegeGPA(Lepp,Barkley,&Karpinski,2013).

18

However,Lepp,Barkley,andKarpinski’s(2013)modelshowedanxietyandGPAas

mediatingfactorsbetweentextingandsatisfactionwithlife.Thecurrenthypothesized

model(Figure2.1)forthisstudyplacesdepressionandanxietyasmediatingfactors

betweensmartphoneuseandacademicperformance.Thecurrentstudyalsointendsto

includedepressionasafactorsincepreviousliteraturedemonstratessignificant

relationshipsbetweensymptomsofdepressionandacademicperformance(Andrews&

Wilding,2004).

Inaddition,Lepp,Barkley,andKarpinski(2013)usedmeasuresofself-reported

smartphoneusageintheirstudy.However,Junco(2013)foundthatwhenstudents

reporttheamountoftimetheyspendonFacebook,mostsignificantlyoverestimatethe

actualtimetheyspendonthesocialmediasite.Smartphoneusagetypicallyincludes

timespentonFacebook,othermobileapplications,texting,and/orcalling.Combining

theamountoftimespentusingmultiplesmartphonefeaturesleavesgreaterroomfor

errorinself-reporting.Therefore,thisstudywillbethefirsttouseactualsmartphone

datatoresearchtherelationshipbetweenactualsmartphoneuse,mentalhealth

symptoms,andacademicperformance.

Previousliteraturesuggeststhatsmartphoneusageisrelatedtosymptomsof

anxietyanddepression(Ha,Chin,Park,Ryu,andYu,2008;Rosen,Whaling,Rab,Carrier,

andCheever,2013;Rosen,Whaling,Carrier,Cheever,&Rokkum,2013;VanAmeringen,

Mancini,&Farvolden,2003).Studieshavealsolinkedmentalhealthsymptomswith

pooracademicperformanceincollegestudents(Eisenberg,Golberstein,&Hunt,2009;

Hysenbegasi,Hass,&Rowland,2005).Itispossiblethatmentaldisordersarethelink

19

betweensmartphoneuseandacademicperformance,soitisnecessarytoexaminethis

relationshiptounderstandpossiblepredictorsandproviderecommendationsonhow

academicinstitutionscanimprovestudents’well-beingandlowerriskofacademic

failure.Therefore,thisstudyaimstolinkpreviousfindingsontherelationshipbetween

smartphoneuse,anxiety,depression,andGPAinordertoobtaindatatoultimately:(1)

modernizestudents’approachtowardstechnologybyteachingthembetterwaystouse

smartphonesand(2)createawarenessabouttherelationshipbetweensmartphoneuse,

mentalhealth,andacademicperformancetoinfluencepolicychangesatacademic

institutionsregardingmentaldisordersandtheirtreatment.

20

Sym

ptom

s of

anxi

ety

Aca

dem

ic

perf

orm

ance

Sm

artp

hone

us

e

Hyp

othe

sis 1

Figure2.1:H

ypothe

sized

pathmod

elinclud

ingsm

artpho

neusage,sym

ptom

sofanxiety,and

acade

mic

perfo

rmance.

21

Sym

ptom

s of

depr

essi

on

Aca

dem

ic

perf

orm

ance

Sm

artp

hone

us

e

Hyp

othe

sis 1

Figure2.2:H

ypothe

sized

pathmod

elinclud

ingsm

artpho

neusage,sym

ptom

sofd

epression,and

academ

icpe

rform

ance.

22

CHAPTER3.METHODOLOGY

Participants

Duringthespring2016semester,students(N=216)attendingapublicuniversity

inthewesternU.S.weresurveyedinageneraleducationcourseontheglobalimpactof

technology.Participantswereofferedextracredittowardstheircoursegradeasan

incentive.Inadditiontothesurvey,studentswereaskedtodownloadtheInstant

QuantifiedSelfapplication(Emberify.com).Themobileapplicationrecordedthenumber

oftimesaparticipantunlockedtheirphoneaswellastheamountoftimetheirphone

wasinuse(unlocked).Atotalof179surveysalongwithusagedatawerecompletedand

collectedforanoverallresponserateof82.9%.

Afterremovalofincompleteresponses,specialcases(seeDataCleaningand

Analyses),andoutliers,158participantsremained.Amongthissample,60(38.0%)were

maleand98(62.0%)werefemale.Thesubjectswerecomposedof85(53.8%)

Hispanic/Latino/Spanishstudents,27(17.1%)Asianstudents,22(13.9%)

White/Caucasianstudents,19(12.0%)Black/African-Americanstudents,and5(3.2%)

studentswhomidentifiedasOther.Theiragesrangedfrom21to67yearsold(M=

26.28,SD=6.11).ParticipantssuppliedtheirhomeZIPcode(postalcode)thatwas

transformedintoestimatedmedianincomebasedontheU.S.censusfigures(U.S.

CensusBureau,2010)andrangedfrom$25,028to$153,621(M=$54,913.15,SD=

$20,797.33).Allparticipantswererequiredtobe18yearsorolderandowna

smartphone.

23

InstrumentsandMeasures

StudentswereaskedtoanswerquestionsfromtheGeneralizedAnxietyDisorder

7-itemscale(GAD-7;Löweetal.,2008),PatientHealthQuestionnaire(PHQ-9;Kroenke,

Spitzer,&Williams,2001),andaskedtoapproximatetheiroverallGPAattheuniversity.

TheGeneralizedAnxietyDisorderscaleprovidesabrief7-itemquestionnairefor

findingpotentialcasesofGAD.Inasamplestudyinvolvingthegeneralpublic,theGAD-7

demonstratedacceptableinternalconsistencywithaCronbach’salphaof.89(Löweet

al.,2008,pg.268).Inanotherstudyincludingadultmentalhealthpatients,datafrom

thescalehadacceptableinternalconsistencywithCronbach’salphaof.92aswellas

acceptableoneweektest-retestreliability(intraclasscorrelation=.83;Spitzer,Kroenke,

Williams,&Lo,2006,pg.1094).Thementalhealthpatientsamplestudyalsodisplayed

evidenceofconvergentconstructvaliditywhencomparedwiththeBeckAnxiety

Inventory(r=.72)andtheanxietysubscaleoftheSymptomChecklist-90(r=.74;

Spitzer,Kroenke,Williams,&Lo,2006,pg.1094).

The9-itemPatientHealthQuestionnaireisashortenedversionofthefullPatient

HealthQuestionnaireaimedatdetectingDSM-IV(DiagnosticandStatisticalManualof

MentalDisorders,4thedition)depressivedisorders.Datafromtheadministrationofthis

surveyinstrumentdemonstratesreliabilitythroughinternalconsistencyamongprimary

carepatientsandobstetricsandgynecology(OBGYN)patients(Cronbach’salpha=.86-

.89;Kroenke,Spitzer,&Williams,2001,pg.608).ThePHQ-9wasalsofoundtohave

convergentconstructvaliditywiththe12-itemGeneralHealthQuestionnaire(GHQ-12)

andBriefBeckDepressionInventory(Brief-BDI);PHQ-9scoresweresignificantlyand

24

positivelycorrelatedwithGHQ-12(r=.59;p<.0001)andBDI(r=.73;p<.0001;Martin,

Rief,Klaiberg,&Braehler,2006,pg.75).

ThePHQ-9andGAD-7scaleswereadministeredaspartofalargersurveythat

alsoincludedestimatedGPA,coursegrade,demographicitems,MediaandTechnology

UsageandAttitudesScale(MTUAS;Rosenetal.,2013),BarrattImpulsivityScale(BIS-11;

Patton,Stanford,&Barratt,1995),andotherquestionsthatwereincludedforfuture

analyses.Studentswerealsorequiredtodownloadanapp,InstantQuantifiedSelf

(Emberify.com),whichwasusedtomeasuretheminutesthatthesmartphonestayed

unlockedeachday.Participantsreportedtheirapplicationdatabyexportingthedata

filefromtheirphoneortypingtheresultsfromtheirphoneandsendingthefiletothe

primaryinvestigator.Overall,atleast21daysofsmartphoneusageinformationwas

collectedforeachparticipant.Inaddition,theapplicationhadtobekeptopeninthe

backgroundtoprovideaccurate,completedailydata;however,someusersmistakenly

closedit.Therefore,anyunlocks2.5standarddeviationsbeloweachsubject’smean

unlockswereremoved.

25

CHAPTER4.RESULTS

DataCleaningandAnalysis

ThesurveydataweredownloadedandsavedasanSPSS(IBM)file,andthe

Instantmonitoringdataweredownloadedaswellfromstudentsmartphones.Subjects

wereeachassignedarandomizedIDnumberandtheirsurveyandInstantdatawere

manuallymatchedbytheirIDnumbersinordertomaintainanonymityofthe

participants.

Totestthehypothesizedmodel,apathanalysiswasconductedthroughmultiple

regressionsinSPSS.First,thedataweretestedformultivariatenormality,collinearity,

andoutliersthroughdiagnosticsinSPSS.Thesamplestartedwith179subjectswhoboth

respondedtothesurveyquestionsandcompletedtheInstantdatacollection

requirements.Sixteenparticipantswereremovedfromthesamplebecauseofmissing

responsesandduplication.Onewasremovedbecauseofmissingincomedata.An

additionalfivesubjectswereidentifiedasoutlierswithGAD-7scoresabovethe95%

confidenceintervalandwereremoved.Thefinalsamplecontained158subjects.

ReliabilityanalysesfortheGAD-7andPHQ-9showedacceptableinternal

consistencyforthecurrentstudy’ssample.TheGAD-7showedaCronbach’salphaof

.865andthePHQ-9showed.858.Concludingthatthequestionsforeachscale

appropriatelymeasuredthesameitem.

Afteranalyzingapost-surveyabouteachparticipant’sexperiencewiththe

Instantapp,manyofthesubjectsreportedthattheychangedtheirsmartphoneusage

behaviorsbecauseoftheapplication.TheInstantappallowsfortheusertoseta

26

notificationtoindicatewhenacertainlevelofusagewasreached.Therefore,some

participantsengagedthisfeatureandmayhaveusedtheirsmartphonedifferentlythan

normal.Otherparticipantsalsofeltthattheywerebeing“watched”bytheapplication

andthereforeindicatedthattheychangedtheirbehaviortolessenorevenincrease

theirsmartphoneusage.Consequently,thisbehavioralchangewasincludedasacontrol

variable.

Mobileoperatingsystemwasalsoincludedasacontrolvariableafteritwas

discoveredthatAndroidusershadsignificantlymoreunlocksperdaythaniOSusers,

t(156)=6.814,p<.001,significantlyfewerminutesperunlock,t(156)=-4.822,p<.001,

butsimilarnumberofminutes,t(156)=-.490,p>.05.WhileAndroidusersunlocktheir

phonemore,theylikelyuseitfewerminutesperunlockbecauseofwidgetprompts.For

example,Androidusersmayhaveaccesstomoreinformationontheirlockscreenvia

applicationwidgets(acomponentofaninterfacethatenablesausertoperforma

functionoraccessaservice),invitingthemtochecktheirphonemoreoften,butthey

usetheirphonelessperunlockbecausetheydon’tneedasmuchadditionalinformation

asiOSusersbecauseoftheprecedinginformationprovidedbytheAndroidwidgetson

thelockscreen.

Demographicvariableswerealsocheckedforimportanteffectsinrelationto

smartphoneuse,mentaldisorders,andacademicperformance.Forexample,previous

researchhasnotedaneffectofgenderonsmartphoneuse;femalesgenerallyuse

smartphonesmorethanmales(Jenaroetal.,2007;Tan,Pamuk,&Dönder,2013).These

demographicvariableswereusedascontrolvariablestoshowthatanyrelationship

27

betweentheprimaryvariables(smartphoneuse,depression/anxiety,andGPA)were

independentfromanyconfoundingfactors.

Blockedmultipleregressionswereperformedtoexaminethepathmodeland

hypotheses.UsingtheStatisticalPackagefortheSocialSciences(SPSS),hierarchical

multipleregressionswereperformedamongthose158participantstotestall

hypotheses.Thefirstblockforallthemodelsincludedcontrolvariablessuchas

smartphoneusagebehavioralchanges,mobileoperatingsystem,anddemographic

variables:gender,ethnicity,medianincome,andage.Thesecondblockforallthe

hypothesizedmodelsincludedsmartphoneuseasanindependentvariable.Thefirst,

third,andfifthhypothesizedregressionmodelsusedacademicperformanceasthe

dependentvariable.Thesecondusedanxietyasthedependentvariable,andthefourth

modeluseddepressionasitsdependentvariable.Thethirdhypothesizedregression

modelalsoincludedanxietyasanindependentvariableinanadditionalthirdblock,and

thefifthmodelincludeddepressioninitsthirdblock.

Hypothesis1:Smartphoneuseandacademicperformance

Totestthefirsthypothesis,severaldemographicvariableswerecontrolledto

examinevarianceexplainedbysmartphoneuseonacademicperformance.The

demographicvariablescontrolledinthefirsthypothesisandallsubsequenthypotheses

weregender,participantsunderoroverage30years,ethnicity,indicationof

smartphoneusebehavioralchanges,mobileoperatingsystem,andmedianincome.

28

Aftercontrollingforthesevariables,hypothesis1wastestedwithaseriesof

hierarchicalmultipleregressionsinwhichestimatedGPAwasthedependentvariable

andmeandailyminutesspentonsmartphonewastheindependentvariable.Thisinitial

modelanditspredictorswereshowntonotbesignificant.Totalcoursepointsisalsoan

indicatorofacademicperformance,whichprovedtobeamoreaccuraterepresentation

sincetotalGPAforeachstudentwasestimatedbytheparticipantsthemselves;total

coursepointswerereportedbytheinstructorofthecourse.Afterchangingthe

dependentvariable,thesecondseriesofregressionanalysesalsoprovedtonotbe

significant.Dailysmartphoneunlocksisalsoameasureofsmartphoneuse,sothemodel

wastestedwithunlocksastheindependentvariable.

Theregressionmodelforstep1(thecontrolvariables)wasnotstatistically

significant,F(9,148)=1.672,R2=.092,p>.05.However,theoverallmodelincludingthe

controlvariablesandmeanunlockswasstatisticallysignificant,F(10,147)=2.317,R2=

.136,p<.01.Thestandardizedbetaweightformeanunlockalsoprovedsignificantin

thefinalversionofthemodel,t(147)=-2.732,β=-.254,p<.01.Hypothesis1was

supportedinthatmeandailyunlockswerepredictiveofcourseperformance.

Hypothesis2:Smartphoneuseandanxiety

Thesecondhypothesisstatedthatsmartphoneusageisrelatedtosymptomsof

anxiety.Usingthesamecontrolvariablesasthemodelinhypothesis1,thefirststepof

theregressionmodelwasnotstatisticallysignificant,F(9,148)=1.610,R2=.089,p>.05.

29

Afterincludingstep2,theoverallmodelwassignificant,F(10,147)=2.023,R2=.121p<

.05,andmeanunlockswaspredictiveofanxietysymptoms,t(147)=-2.306,β=-.216,p

<.05.However,thedirectionofthecoefficientwasnotaspreviouslyreportedinprior

studies.Hypothesis2waspartiallysupported.

Hypothesis3:Anxietyandacademicperformance

Hypothesis3statedthatsymptomsofanxietyarerelatedtoacademic

performance.Step1oftheregressionmodelwasnotstatisticallysignificant,F(9,148)=

1.672,R2=.092,p>.05.Afterincludingstep2,meanunlocks,themodelwasstatistically

significant,F(10,147)=2.317,R2=.136,p<.05.Afterstep3,symptomsofanxiety,the

overallmodelwasstillsignificant,F(11,146)=2.161,R2=.140,p<.05.Although,GAD-7

scorewasnotpredictiveoftotalcoursepoints,t(146)=-.806,β=-.066,p>.05.

Therefore,hypothesis3wasnotsupported.

Hypothesis4:Smartphoneuseanddepression

Hypothesis4statedthatsmartphoneuseisrelatedtosymptomsofdepression.

Step1oftheregressionmodelwasstatisticallysignificant,F(9,148)=2.025,R2=.110,p

<.05.Theoverallmodelwasalsostatisticallysignificant,F(10,147)=2.035,R2=.122,p

<.05.However,meanunlockswerenotpredictiveofdepression,t(147)=-1.414,β=-

.132,p>.05.Hypothesis4wasnotsupported.

30

Hypothesis5:Depressionandacademicperformance

Thefinalhypothesispredictedthatsymptomsofdepressionarerelatedto

academicperformance.Step1oftheregressionmodelwasnotstatisticallysignificant,

F(9,148)=1.672,R2=.092,p>.05.Afterincludingstep2,meanunlocks,themodelwas

statisticallysignificant,F(10,147)=2.317,R2=.136,p<.01.Afterstep3,symptomsof

depression,theoverallmodelwasstillstatisticallysignificant,F(11,146)=2.499,R2=

.158,p<.01.However,PHQ-9scorewasnotpredictiveoftotalcoursepoints,t(146)=-

1.966,β=-.159,p=.05.Hypothesis5wasnotsupported.

31

Sym

ptom

s of

anxi

ety

Aca

dem

ic

perf

orm

ance

Sm

artp

hone

us

e

-.254

** (-

.268

**)

...9 39

e 1 =

.9

38

e 2 =

.9

27

Figure4.1:Pathmod

elinclud

ingsm

artpho

neusage,sym

ptom

sofanxiety,and

acade

micpe

rform

ance,N

=159

.

Varia

blese

1ande 2re

presen

tthe

errorvarianceorth

epo

rtionofth

evaria

nceofth

emod

elth

atisdue

to

extraneo

usvariablesand

measuremen

terror.

32

Sym

ptom

s of

depr

essi

on

Aca

dem

ic

perf

orm

ance

Sm

artp

hone

us

e

-.254

** (-

.275

**)

e 1 =

.9

37

e 2 =

.9

18

Figure4.2:Pathmod

elinclud

ingsm

artpho

neusage,sym

ptom

sofd

epression,and

acade

micpe

rformance,N

=

158,Variablese

1ande 2re

presen

tthe

errorvarianceorth

epo

rtionofth

evaria

nceofth

emod

elth

atisdue

to

extraneo

usvariablesand

measuremen

terror.

33

34

35

36

37

CHAPTER5.DISCUSSION

Summary

Thepurposeofthisstudywastoinvestigatetherelationshipbetween

smartphoneuse,symptomsofanxiety,symptomsofdepression,andacademic

performanceaftercontrollingfordemographicvariables,behavioralchanges,and

mobileoperatingsystem.Withthegrowingpopularityofsmartphonetechnology

amongyoungadults,itisimportanttounderstandpredictivefactorsofpooracademic

performance,depression,andanxietytopreventnegativeoutcomes.

Thefirsthypothesiswassupportedinthatmeannumberofdailysmartphone

unlockswasnegativelypredictiveofcoursetotalpoints.Itcouldbepossiblethatthose

whousetheirphonemoremayhaveamoredifficulttimeregulatingtheirusage.Poor

self-regulationcouldalsoaffectacademicperformancenegatively.Self-regulationis

neededtoorganizeandcontrolone’sownlearninginandoutsideoftheclassroom

(Zimmerman,1990).Afteradditionalanalyses,thecurrentstudyfoundthatsmartphone

minuteswerepositivelyrelated(r=.182,p<.05)andcoursetotalpointswere

negativelyrelated(r=-270,p<.01)tothenon-planningportionoftheBarratt

ImpulsivenessScalewhichincludesquestionsonself-controlandcognitivecomplexity;

higherscoreontheBISnon-planningsubscalemeanslessself-controlandcognitive

complexity.Self-regulationisanimportantskillforyoungadultstolearnbecauseit

couldhaveanegativeeffectonacademicoutcomes.Itwouldbeimportantforfurther

38

analysestoinvestigatetherelationshipbetweenallthreevariables:self-regulation,

academicachievement,andsmartphoneuse.

Itisalsopossiblethatthosewithpoorself-regulationoftenmultitask.Checkinga

smartphonewhilecompletingothertasksespeciallythoserelatedtoacademicscouldbe

detrimentaltostudents’cognitiveactivity.PreviousresearchbyJuncoandCotten

(2012)foundthatFacebookuseortextingwhilecompletingschoolworkmayoverload

students’capacityforcognitiveprocessingandimpedeacademicperformance.

Additionally,Rosen,Carrier,andCheever(2013)attributethisrelationshiptothe

impairmentmultitaskingmaycauseonstudents’cognitiveload.However,thecurrent

studyhasnowayofknowingexactlywhentheparticipantsusedtheirphoneandifthey

weremultitasking.So,furtheranalyseswouldneedtoincludemorequestionsabout

multitaskingandtechnologytotestthistheory.

Thesecondhypothesisofthecurrentstudywaspartiallysupported.Smartphone

unlockswererelatedtoGAD-7score.However,thisrelationshipwasshowntobe

negative,contrarytopreviousresearch(Jenaroetal.,2007;Ha,Chin,Park,Ryu,&Yu,

2008).Onepossibleexplanationisthatunlockingorcheckingasmartphonecanreduce

symptomsofanxiety.Perhapsthesocialconnectionsprovidedbyasmartphonehelp

createasupportsystemforeachuser.Usershaveaccesstoself-helpapplications,social

networks,andothersourcesofsocialbuffering.Socialrelationshipshaveshowntobe

negativelyrelatedtostressviasocialbuffering(effectivesocialsupportnetworkslessen

theadversepsychologicalconsequencesofstress;Aneshensel&Stone,1982).Itwould

beimportanttodetermineinfuturestudieswhethersmartphoneusageprovides

39

effectivesocialbuffering,thuspossiblyexplainingthenegativerelationshipbetween

smartphoneuseandanxietysymptoms.

Anotherpossibilityfortherelationshipbetweenphoneuseandanxietyisthat

reducingsmartphoneusagecouldincreaseanxiety.Forexample,studentswhomade

behavioralchangestotheirphoneuseoftenstatedthattheyknewtheyusedtheir

phone“toomuch”butweremoreawareofitbyusingtheapp(i.e.,“Iwassoworried

thatIusedmyphonewaytoomuch”,“IwassurprisedhowmuchIspenttimeonmy

phoneandwhatappsIusedthemost.Although,Ididknowbutitwasawake-upcall.”).

Thisawarenesscouldhaveaffectedtheirusagesothattherelationshipbetween

smartphoneunlocksandGAD-7scoreswasnegative.Itisfeasiblethatthosewho

changedtheirbehaviorsweremoreawareorperhapssensitivetothefactthattheyuse

theirphonemoreoften;additionalanalysesshowedthatparticipantswhomade

behavioralchangesactuallyhadhigheranxietyscoresthanthosewhodidnotchange

theirbehavior,t(156)=-2.216,p<.05.Perhapsparticipantswhoaccessedtheirphone

less,inherentlydidsotoavoidstressfuloranxiousfeelings.Butbyavoidingtheir

anxiousfeelingsandusingtheirphonelessthantheywantedorneeded,theyultimately

increasedtheiranxietysymptoms.Forexample,Cheever,Rosen,Carrier,&Chavez

(2014)foundthatmoderatetohighmobilephoneusershadincreasedanxietywhen

theirdevicewasabsent.Futureresearchwouldneedtoinvestigatethisrelationship

further.

Thethirdhypothesiswasnotconfirmed.Anxietydidnotpredictacademic

performance.Althoughpreviousresearchconcludedthatanxietyrelatestoacademic

40

outcomes(Desiderato&Koskinen,1969;VanAmeringen,Mancini,&Farvolden,2003),

itcouldbethatthestudentsinthecurrentstudyhadasimilaramountofdebilitativeas

theydidfacilitativeanxietythushavingacumulativeeffectofnorelationshipto

academicperformance.Facilitativeanxietyactsasamotivatortoperformbetterwhile

debilitativeanxietyinterfereswithperformance(Desiderato&Koskinen,1969).

Essentially,anxietycanhavepositiveandnegativeeffectsonacademicperformance.

Additionalstudieswouldneedtoseparatethesetwotypesofanxietyusingascalesuch

astheAchievementAnxietyTest(Alpert&Haber,1960)whichmeasures

positive/negative(facilitative/debilitative)feelings,attitudes,andexperiencesabout

takingcourseexaminationstodetermineifeitherhasamediatingrelationshipbetween

smartphoneuseandacademicperformance.

Hypothesis4wasalsonotconfirmed;smartphoneusewasnotrelatedto

depressionsymptomseventhoughpastresearchshowedapositiverelationship

betweenthesevariables(Rosen,Whaling,Rab,Carrier,&Cheever,2013;Rosen,

Whaling,Carrier,Cheever,&Rokkum,2013;Thomée,Härenstam,&Hagberg,2001).

ThestudybyThomée,Härenstam,andHagberg(2001)concludedthatphoneusehad

noassociationwithperceivedaccesstosocialsupportandthathighquantitativemobile

phoneuse(11+phonecallsandtextsaday)wasrelatedtosymptomsofdepression

(Thomée,Härenstam,&Hagberg,2001).However,theresearchersonlyaccountedfor

textingandcallingandhowtheyrelatetodepression.Itcouldbethatsincephonesnow

accesstheinternetandsocialnetworks,phoneusersnowfeelmoresocialsupport

throughtechnologyandconsequentlysmartphoneusenolongerrelatestosymptomsof

41

depression(Shaw&Gant,2002).Or,maybeactualmeanunlocksisabettermeasurefor

mobilephoneusagethanonlynumberofdailytextsorandcalls.Additionalresearch

shouldinvestigatethepossiblechangeinperceivedsocialsupportandtechnologyuse

toestablishnewideasaboutattitudestowardssmartphonesandtheirrelationshipto

mentalhealth.

Thefinalhypothesiswasnotconfirmed;thecurrentstudyshowedthat

depressionwasnotrelatedtoacademicperformance.Perhapsstudentsarelearning

newwaystocopewithdepression,andstigmatizationaboutmentalhealthhas

decreased.Sevenandahalfpercent(12)ofstudentsfromthecurrentstudyreported

moderatelyseveretoseveredepressionsymptoms,19.5%(31)reportedmoderate

symptoms,and27%(43)reportedmildsymptoms.The95%confidenceintervalforthe

currentstudy’ssampleis5.73–7.29meaningthatin95%ofsamplecases,our

confidenceintervalwouldcontainthepopulationmean.Thisdistributionofdepression

symptomsamongcollegestudentswaslowerthanpreviousfindings;forinstance,

Garlow,etal.(2008)founda95%confidenceintervalof10.03–10.85forPHQ-9scores

intheirsampleof729collegestudents,suggestingnooverlapbetweentheoriginal

populationsineachstudy.However,itisalsopossiblethatthecurrentstudy’ssample

wasanabnormalorinaccuraterepresentationofdepressionprevalenceincollege

students.Furtheranalyseswouldneedtoinvestigatethenumberofstudentsdiagnosed

withdepressionorreceivingtherapyfordepressiontohelpdistinguishifstudentsare

findinghelpfortheirsymptoms.

42

ThestudybyThomée,Härenstam,andHagberg(2001)foundthatsmartphone

dependencyanddemandsforachievementwereidentifiedasdirectsourcesofstress

andmentalhealthsymptoms.However,“smartphonedependency”maybearelative

termtoeachpersonandcannotbeaccuratelyquantified;therefore,perhapsitisthe

perceptionofone’ssmartphoneusagethatdeterminesthepredictivequalityofmental

healthsymptoms.Amorenegativeperceptionofsmartphoneusagepairedwithhigh

smartphoneusewouldberelatedtohigheranxietyordepression.Thesenegative

thoughtscouldprobablycreatemoreanxietyorstressfortheuserespeciallyifthey

actuallyusedtheirphone“toomuch”.Forexample,thestudybyRosen,Whaling,Rab,

Carrier,andCheever(2013),concludedthathavingnegativeattitudesabouttechnology

(e.g.,technologymakeslifemorecomplicated,technologymakespeoplewastetoo

muchtime,technologymakespeoplemoreisolated)predictedclinicalsymptomsof

depression.Thismayindicatethatexcessivequantitativeuseofsmartphonesmaynot

alwaysbeproblematicforusers;however,excessiveusepairedwithnegativeattitudes

andfeelingsofdependenceandanxietyabouttechnologymayincreasenegative

outcomesassociatedwithsmartphoneuse,specifically,smartphoneusers’riskfor

anxietyanddepression(Rosen,Whaling,Rab,Carrier,&Cheever,2013;Thomée,

Härenstam,&Hagberg,2011).Futureresearchshouldanalyzetherelationshipbetween

attitudesandperceptionsofsmartphoneuse,mentalhealthsymptoms,andactual

smartphoneusagebyhypothesizinganotherpathmodeltodeterminethesignificance

ofthislink.

43

Whilethepreviousliteraturedoessupporteachhypothesis,theconclusionof

thisstudystatesthatsincethelastthreehypotheseswerenotsupported,symptomsof

depressionandsymptomsofanxietydonotmediatetherelationshipbetween

smartphoneuseandacademicperformance.However,itwouldbeimportantto

developfurtherresearchintotheserelationshipsandincludeadditionalfactorssuchas

attitudesabouttechnology,help-seekingbehaviorsinstudentswithmentalhealth

disorders,andperceivedsocialsupporttoconcludeiftheyplayasignificantpartinthe

pathmodelproposedbythecurrentstudy.

Limitations

Thisstudycontainsanumberoflimitations.Whilepreviousliteraturehasshown

thatcollege-ageadultsareatgreaterriskofmentalhealthdisordersandhavehigher

smartphoneusage,thissamplepopulationmaynotberepresentativeofthegeneral

populationofyoungadults.Forexample,thecurrentstudyincludedanagerangeof

participantsthatisnotwithinthetypicalrangeoftraditional-agedcollegestudentsand

waspredominatelyfemaleandHispanic/Latino/Spanishdescent.

Third,usersreportedpossibleskewedorinaccurateappdatabecauseof

multiplereasons.Forexample,someparticipantsreportedmultipleusersontheir

phone(i.e.achildorfriend).Someuserssetusagelimitsforthemselveswhichnotified

themwhentheyreachedacertainnumberofminutes.Otherusersunknowinglyclosed

theapplication,butforittocollectusagedata,itneededtobeconstantlyopeninthe

background.However,thenoveltyofthisdatacollectionmethodcouldshedlightonthe

44

accuracywithwhichweestimatetheamountoftimespentonourphones.Perhaps

someusersoverestimatetheirusage,thusinflatingtheeffectsofcertainfactorsin

previousresearchsuchasmentalhealthsymptoms.

Fourth,thestudyalsoincludedsomeself-reportedsurveydata.WhiletheGAD-7

andPHQ-9haveshownevidenceofconstructvalidityandreliability,theymaynothave

beentrulysymbolicofeachsubject’sexperience.Participantswerereassuredoftheir

anonymitythroughoutthestudy.However,theseindividualsreceivedcourseextra

creditforcompletingthesurvey,itispossiblethatthismethodofcompensationcould

haveinfluencedtheintegrityoftheirresponses.

Lastly,thisstudyincludednon-directionalhypotheseseventhoughmostofthe

previousliteratureshowedstrongdirectionalrelationshipsbetweenthevariablesused

inthisstudy.However,sincetheactualmeasurementofsmartphoneusagewasnovelin

comparisontopreviousresearch,itwasappropriatetoassumenon-directional

hypothesestopreventanybiases.Additionally,thisstudyiscorrelationalandtherefore

notcausal.

Implications

Aftercreatingandanalyzingapathmodel,wecanbegintounderstandthe

relationshipbetweensmartphoneuse,mentalhealth,andacademicperformance.If,in

fact,theeverydaypressuresofbeingastudentintheageofdigitaltechnologyare

45

enoughtocausepsychologicalsymptomsleadingtopooreracademicperformance,

instructors,parents,andstudentsmustconsidernewapproachestosmartphoneuse

andinstitutionsneedtopromotehelp-seekingbehaviorintheirstudents.

However,resultsshowedthattheonlystatisticallysignificantpredictorof

academicperformancewassmartphoneuse.Smartphoneusealsonegativelypredicted

symptomsofanxiety.So,smartphoneusagemaynotberelatedtomentalhealthas

previouslythought.Therefore,wecannotdefinitivelyconcludethatsmartphoneusage

isdirectlyindicativeofpoormentalhealth,butperhapsnegativeattitudesabout

smartphoneusagecouldbeattributedtomentalhealthsymptoms;possibly,achangeof

perceptionabouttechnologycouldalleviatethesesymptoms.

Additionally,sincesmartphoneusestillshowstobenegativelyrelatedto

academicperformance,researchersshouldcontinuetoinvestigatepossiblemediating

factorssuchasself-regulationandmultitasking.Academicinstitutions,parents,and

individualsstillneedtoencouragebettersmartphoneusagehabitsuntiladditional

researchcanaddtothesefindings.However,tochangethisbehavior,wemustnot

encouragenegativethoughtsabouttechnologyforthatmayonlyincreaseanxietyand

stress.Aspreviouslystated,excessiveusepairedwithnegativeattitudesandfeelingsof

dependenceandanxietyabouttechnologymayincreasenegativeoutcomessuchasrisk

foranxietyanddepression(Rosen,Whaling,Rab,Carrier,&Cheever,2013;Thomée,

Härenstam,&Hagberg,2011).Therefore,academicinstitutionsandparentscouldteach

studentshelpful,positivewaystousetheirsmartphonesforschool,work,orhome.

Academicinstitutionscouldcreateinitiativesforinstructorstoincorporatesmartphone

46

technologyasanotherteachingstrategyintheirclassroomstoshowstudentswaysto

usethistechnologytohelptheirlearning.Parentscansetanexamplefortheirchildren

byshowingthemthatputtingtheirphoneawayduringimportanteventscanbehelpful

ratherthanstressful.Additionally,thisisanopportunitytoexplaintheconsequencesof

multitaskingoncognitiveprocessesandtoinspirethosewithmentalhealthissuesto

usetheirsmartphoneasanothertooltocopewithstress.

47

REFERENCES

Alpert,R.,&Haber,R.N.(1960).Anxietyinacademicachievementsituations.The

JournalofAbnormalandSocialPsychology,61(2),207.

AmericanPsychiatricAssociation.(2013).Diagnosticandstatisticalmanualofmental

disorders(DSM-5).APAPress,Washington,D.C.

Anderson,M.(2015).Technologydeviceownershipin2015.Washington,D.C.Retrieved

fromhttp://www.pewinternet.org/2015/10/29/technology-device-ownership-

2015

Andrews,B.,&Wilding,J.M.(2004).Therelationofdepressionandanxietytolife-stress

andachievementinstudents.BritishJournalofPsychology,95(4),509-521.

Retrievedfrom

https://www.researchgate.net/profile/John_Wilding/publication/8192220_The_

relation_of_depression_and_anxiety_to_life-

stress_and_achievement_in_students_British_Journal_of_Psychology_95_509-

521/links/54aaf1a50cf25c4c472f7072.pdf

Aneshensel,C.S.,&Stone,J.D.(1982).Stressanddepression:Atestofthebuffering

modelofsocialsupport.Archivesofgeneralpsychiatry,39(12),1392-1396.

Apple'siPhone:Adefinitivehistoryinpictures.(2016).TheTelegraph.Retrievedfrom

http://www.telegraph.co.uk/technology/2016/02/25/apples-iphone-a-

definitive-history-in-pictures/

Astin,A.W.(1984).Studentinvolvement:Adevelopmentaltheoryforhigher

education.JournalofCollegeStudentPersonnel,25(4),297-308.Retrievedfrom

48

https://www.researchgate.net/profile/Alexander_Astin/publication/220017441_

Student_Involvement_A_Developmental_Theory_for_Higher_Education/links/00

b7d52d094bf5957e000000.pdf

Battle,J.(1978).Relationshipbetweenself-esteemanddepression.Psychological

Reports,42(3),745-746.

Breslau,J.,Lane,M.,Sampson,N.,&Kessler,R.C.(2008).Mentaldisordersand

subsequenteducationalattainmentinaUSnationalsample.Journalof

PsychiatricResearch,42,708-716.Retrievedfrom

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2748981/

Brook,C.A.,&Willoughby,T.(2015).Thesocialtiesthatbind:socialanxietyand

academicachievementacrosstheuniversityyears.JournalofYouthand

Adolescence,44(5),1139-1152.doi:10.1007/s10964-015-0262-8

Budd,R.A.,Karidis,J.P.,&McVicker,G.(1999).U.S.PatentNo.5,970,418.Washington,

DC:U.S.PatentandTrademarkOffice.

Cerwall,P.(2016).Ericssonmobilityreport.Retrieved

fromhttps://www.ericsson.com/assets/local/mobility-

report/documents/2016/ericsson-mobility-report-november-2016.pdf

Cheever,N.A.,Rosen,L.D.,Carrier,L.M.,&Chavez,A.(2014).Outofsightisnotoutof

mind:Theimpactofrestrictingwirelessmobiledeviceuseonanxietylevels

amonglow,moderateandhighusers.ComputersinHumanBehavior,37,290-

297.Retrievedfrom

49

https://pdfs.semanticscholar.org/4f08/5ac5d84435d9d868042c16c926199dc419

5a.pdf

Cohen,S.(1988).Psychosocialmodelsoftheroleofsocialsupportintheetiologyof

physicaldisease.Healthpsychology,7(3),269.Retrievedfrom

http://repository.cmu.edu/cgi/viewcontent.cgi?article=1260&context=psycholog

y

Dayer,L.,Heldenbrand,S.,Anderson,P.,Gubbins,P.O.,&Martin,B.C.(2013).

Smartphonemedicationadherenceapps:Potentialbenefitstopatientsand

providers.JournaloftheAmericanPharmacistsAssociation:JAPhA,53(2),172–

181.http://doi.org/10.1331/JAPhA.2013.12202

Desiderato,O.,&Koskinen,P.(1969).Anxiety,studyhabits,andacademicachievement.

JournalofCounselingPsychology,16(2p1),162.Retrievedfrom

http://psycnet.apa.org.proxy.lib.iastate.edu/journals/cou/16/2p1/162.pdf

Dulin,P.L.,Gonzalez,V.M.,&Campbell,K.(2014).Resultsofapilottestofaself-

administeredsmartphone-basedtreatmentsystemforalcoholusedisorders:

Usabilityandearlyoutcomes.SubstanceAbuse:OfficialPublicationofthe

AssociationforMedicalEducationandResearchinSubstanceAbuse,35(2),168–

175.http://doi.org/10.1080/08897077.2013.821437

Eisenberg,D.,Golberstein,E.,&Gollust,S.E.(2007).Help-seekingandaccesstomental

healthcareinauniversitystudentpopulation.MedicalCare,45(7),594-601.

Retrievedfromhttp://www-

50

personal.umich.edu/~daneis/papers/hmpapers/help-seeking%20--

%20MC%202007.pdf

Eisenberg,D.,Golberstein,E.,&Hunt,J.B.(2009).Mentalhealthandacademicsuccess

incollege.TheBEJournalofEconomicAnalysis&Policy,9(1).Retrievedfrom

http://www-

personal.umich.edu/~daneis/symposium/2010/ARTICLES/eisenberg_golberstein

_hunt_2009.pdf

Ellison,N.B.,Steinfield,C.,&Lampe,C.(2011).Connectionstrategies:Socialcapital

implicationsofFacebook-enabledcommunicationpractices.Newmedia&

society,13(6),873-892.

Emanuel,R.C.(2013).TheAmericancollegestudentcellphonesurvey.CollegeStudent

Journal,47(1),75-81.

Ettner,S.L.,Frank,R.G.,&Kessler,R.C.(1997).Theimpactofpsychiatricdisorderson

labormarketoutcomes.IndustrialandLaborRelationsReview,51,64-81.

Retrievedfromhttp://people.terry.uga.edu/mustard/courses/e8420/Ettner-

ILR.pdf

Farago,P.(2011).iOS&Androidappschallengedbytrafficacquisitionnotdiscovery

[Weblogpost].Retrievedfromhttp://blog.flurry.com/?Tag=App%20Store

Garlow,S.J.,Rosenberg,J.,Moore,J.D.,Haas,A.P.,Koestner,B.,Hendin,H.,&

Nemeroff,C.B.(2008).Depression,desperation,andsuicidalideationincollege

students:resultsfromtheAmericanFoundationforSuicidePreventionCollege

ScreeningProjectatEmoryUniversity.Depressionandanxiety,25(6),482-488.

51

Retrievedfrom

https://www.researchgate.net/profile/Charles_Nemeroff/publication/22765242

4_Depression_desperation_and_suicidal_ideation_in_college_students_Results_

from_the_American_Foundation_for_Suicide_Prevention_College_Screening_Pr

oject_at_Emory_University/links/0912f5034d77f3f24a000000/Depression-

desperation-and-suicidal-ideation-in-college-students-Results-from-the-

American-Foundation-for-Suicide-Prevention-College-Screening-Project-at-

Emory-University.pdf

Ha,J.H.,Chin,B.,Park,D.-H.,Ryu,S.-H.,&Yu,J.(2008).Characteristicsofexcessive

cellularphoneuseinKoreanadolescents.Cyberpsychology&Behavior,11(6),

783–784.http://doi.org/10.1089/cpb.2008.0096

Hysenbegasi,A.,Hass,S.L.,&Rowland,C.R.(2005).Theimpactofdepressiononthe

academicproductivityofuniversitystudents.JournalofMentalHealthPolicyand

Economics,8(3),145.Retrievedfrom

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.424.663&rep=rep1&

type=pdf

Jenaro,C.,Flores,N.,Gómez-Vela,M.,González-Gil,F.,&Caballo,C.(2007).Problematic

internetandcell-phoneuse:Psychological,behavioral,andhealthcorrelates.

AddictionResearch&Theory,15(3),309–320.

http://doi.org/10.1080/16066350701350247

Jones,W.H.,Hobbs,S.A.,&Hockenbury,D.(1982).Lonelinessandsocialskill

deficits.JournalofPersonalityandSocialPsychology,42(4),682.Retrievedfrom

52

https://www.researchgate.net/profile/Don_Hockenbury/publication/16120577_

'Loneliness_and_Social_Skills_Deficits'/links/55a9571508aea3d086803b00.pdf

Junco,R.(2013).Comparingactualandself-reportedmeasuresofFacebook

use.ComputersinHumanBehavior,29(3),626-631.Retrievedfrom

http://www.reyjunco.com/wordpress/pdf/JuncoFBActualvsSRCHB2013.pdf

Junco,R.,&Cotten,S.R.(2012).NoA4U:Therelationshipbetweenmultitaskingand

academicperformance.Computers&Education,59(2),505-514.Retrievedfrom

https://pdfs.semanticscholar.org/1d78/db709e2e148f52d80d5cc057011f02dbd6

5e.pdf

Kawachi,I.,Sparrow,D.,Vokonas,P.S.,&Weiss,S.T.(1994).Symptomsofanxietyand

riskofcoronaryheartdisease:Thenormativeagingstudy.Circulation,90(5),

2225-2229.Retrievedfrom

http://circ.ahajournals.org/content/90/5/2225.full.pdf

Keenan-Miller,D.,Hammen,C.L.,&Brennan,P.A.(2007).Healthoutcomesrelatedto

earlyadolescentdepression.JournalofAdolescentHealth,41(3),256-262.

Retrievedfromhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2034364/

Kessler,R.C.,Berglund,P.,Demler,O.,Jin,R.,Merikangas,K.R.,&Walters,E.E.(2005).

Lifetimeprevalenceandage-of-onsetdistributionsofDSM-IVdisordersinthe

NationalComorbiditySurveyReplication.ArchivesofGeneralPsychiatry,62,593-

602.Retrievedfromhttp://happierhuman.wpengine.netdna-cdn.com/wp-

content/uploads/2014/06/P12.-Lifetime-Prevalence-and-Age-of-Onset-

53

Distributions-of-DSM-IV-Disorders-in-the-National-Comorbidity-Survey-

Replication.pdf

Kessler,R.C.,Foster,C.L.,Saunders,W.B.,&Stang,P.E.(1995).Socialconsequencesof

psychiatricdisorders,I:Educationalattainment.TheAmericanjournalof

psychiatry,152(7),1026.Retrievedfrom

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.477.1539&rep=rep1

&type=pdf

Kessler,R.C.,Walters,E.E.,&Forthofer,M.S.(1998).Thesocialconsequencesof

psychiatricdisorders,III:Probabilityofmaritalstability.AmericanJournalof

Psychiatry,155,1092-1096.

Koivumaa-Honkanen,H.,Kaprio,J.,Honkanen,R.,Viinamäki,H.,&Koskenvuo,M.

(2004).Lifesatisfactionanddepressionina15-yearfollow-upofhealthy

adults.SocialPsychiatryandPsychiatricEpidemiology,39(12),994-999.

Kroenke,K.,Spitzer,R.L.,&Williams,J.B.W.(2001).ThePHQ-9.JournalofGeneral

InternalMedicine,16,605–613.http://doi.org/10.1046/j.1525-

1497.2001.016009606.x

Kuh,G.(2009).Thenationalsurveyofstudentengagement:Conceptualandempirical

foundations.NewDirectionsforInstitutionalResearch.,2009(141),5-20.

Retrievedfrom

http://my.tru.ca/__shared/assets/NSSE_Conceptual_and_Empirical_Foundation

s_Kuht23614.pdf

54

Kuh,G.D.,Cruce,T.M.,Shoup,R.,Kinzie,J.,&Gonyea,R.M.(2008).Unmaskingthe

effectsofstudentengagementonfirst-yearcollegegradesandpersistence.The

JournalofHigherEducation,79(5),540-563.Retrievedfrom

http://rhartshorne.com/fall-2012/eme6507-rh/cdisturco/eme6507-

eportfolio/documents/kuh,%20cruce,%20shoup,%20gonyea.pdf

Lane,N.D.,Mohammod,M.,Lin,M.,Yang,X.,Lu,H.,Ali,S.,...&Campbell,A.(2011).

Bewell:Asmartphoneapplicationtomonitor,modelandpromotewellbeing.5th

InternationalICSTConferenceonPervasiveComputingTechnologiesfor

Healthcare,23-26.Retrievedfrom

https://pdfs.semanticscholar.org/3f66/fb999124767352e1ee823609671be1abf0

d8.pdf

Lepp,A.,Barkley,J.E.,&Karpinski,A.C.(2013).Therelationshipbetweencellphone

use,academicperformance,anxiety,andsatisfactionwithlifeincollege

students.ComputersinHumanBehavior,31,343–350.

http://doi.org/10.1016/j.chb.2013.10.049

Lepp,A.,Barkley,J.E.,&Karpinski,A.C.(2015).Therelationshipbetweencellphoneuse

andacademicperformanceinasampleofUScollegestudents.SageOpen,5(1).

Retrievedfrom

http://journals.sagepub.com/doi/full/10.1177/2158244015573169

Löwe,B.,Decker,O.,Müller,S.,Brähler,E.,Schellberg,D.,Herzog,W.,&Herzberg,P.Y.

(2008).ValidationandstandardizationoftheGeneralizedAnxietyDisorder

55

screener(GAD-7)inthegeneralpopulation.MedCare,46(3),266–274.

http://doi.org/10.1097/MLR.0b013e318160d093

Martin,A.,Rief,W.,Klaiberg,A.,&Braehler,E.(2006).ValidityoftheBriefPatient

HealthQuestionnairemoodscale(PHQ-9)inthegeneralpopulation.General

HospitalPsychiatry,28(1),71–77.

http://doi.org/10.1016/j.genhosppsych.2005.07.003

Nelson,M.R.(2006).Digitalcontentdeliverytrendsinhighereducation.Educause

CenterforAppliedResearch,2006(9).Retrievedfrom

http://net.educause.edu.proxy.lib.iastate.edu/ir/library/pdf/ERB0609.pdf

Pascarella,E.T.,&Terenzini,P.T.(2005).Howcollegeaffectsstudents:Athirddecade

ofresearch.SanFrancisco,CA:Jossey-Bass.

Patton,J.H.,Stanford,M.S.,&Barratt,E.S.(1995).FactorstructureoftheBarratt

impulsivenessscale.JournalofClinicalPsychology,51(6),768–774.

http://doi.org/10.1002/1097-4679(199511)51:6<768

Reed,B.(2010).Abriefhistoryofsmartphones.Networkworld.Retrievedfrom

http://www.networkworld.com/article/2869645/network-security/a-brief-

history-of-smartphones.html

Reynolds,C.F.,Shaw,D.H.,Newton,T.F.,Coble,P.A.,&Kupfer,D.J.(1983).EEGsleep

inoutpatientswithgeneralizedanxiety:apreliminarycomparisonwith

depressedoutpatients.PsychiatryResearch,8(2),81-89.

Rogers,E.(1995).DiffusionofInnovations(4thed.).NewYork:TheFreePress.

56

Rosen,L.D.,Carrier,L.M.,&Cheever,N.A.(2013).Facebookandtextingmademedo

it:Media-inducedtask-switchingwhilestudying.ComputersinHuman

Behavior,29(3),948-958.Retrievedfrom

https://pdfs.semanticscholar.org/a449/623136ff47e87ec67a1a6f2980ef108c328

d.pdf

Rosen,L.D.,Whaling,K.,Rab,S.,Carrier,L.M.,&Cheever,N.A.(2013).IsFacebook

creating‘iDisorders’?Thelinkbetweenclinicalsymptomsofpsychiatricdisorders

andtechnologyuse,attitudesandanxiety.ComputersinHumanBehavior,29(3),

1243-1254.Retrievedfrom

https://www.researchgate.net/profile/Mark_Carrier/publication/235991153_Is_

Facebook_creating_''iDisorders''_The_link_between_clinical_symptoms_of_psyc

hiatric_disorders_and_technology_use_attitudes_and_anxiety/links/564f949008

aeafc2aab3eb62.pdf

Rosen,L.D.,Whaling,K.,Carrier,L.M.,Cheever,N.A.,&Rokkum,J.(2013).TheMedia

andTechnologyUsageandAttitudesScale:Anempirical

investigation.ComputersinHumanBehavior,29(6),2501–2511.Retrievedfrom

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338964/

Sager,I.(2012).BeforeiPhoneandAndroidcameSimon,thefirstsmartphone.

Bloomberg.Retrievedfromhttp://www.bloomberg.com/news/articles/2012-06-

29/before-iphone-and-android-came-simon-the-first-smartphone

Shaw,L.H.,&Gant,L.M.(2002).IndefenseoftheInternet:Therelationshipbetween

Internetcommunicationanddepression,loneliness,self-esteem,andperceived

57

socialsupport.Cyberpsychology&behavior,5(2),157-171.Retrievedfrom

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/63277/10949310275

3770552.pdf%3Bjsessionid%3D033CFECB45C479AEB6AE2516407D2049?sequen

ce%3D1

Smith,A.(2015).USsmartphoneusein2015.Washington,D.C.Retrievedfrom

http://www.pewinternet.org/files/2015/03/PI_Smartphones_0401151.pdf

Sowislo,J.F.,&Orth,U.(2013).Doeslowself-esteempredictdepressionandanxiety?A

meta-analysisoflongitudinalstudies.PsychologicalBulletin,139(1),213.

Retrievedfrom

https://www.researchgate.net/profile/Ulrich_Orth/publication/228064767_Doe

s_Low_Self-Esteem_Predict_Depression_and_Anxiety_A_Meta-

Analysis_of_Longitudinal_Studies/links/09e415140f47fc4c8a000000.pdf

Spitzer,R.L.,Kroenke,K.,Williams,J.B.W.,&Lo,B.(2006).Abriefmeasurefor

assessingGeneralizedAnxietyDisorder.ArchivesofInternalMedicine,166,

1092–1097.http://doi.org/10.1001/archinte.166.10.1092

Stein,M.B.,&Heimberg,R.G.(2004).Well-beingandlifesatisfactioningeneralized

anxietydisorder:Comparisontomajordepressivedisorderinacommunity

sample.JournalofAffectiveDisorders,79(1),161-166.

Tan,Ç.,Pamuk,M.,&Dönder,A.(2013).Lonelinessandmobilephone.Procedia-Social

andBehavioralSciences,103,606–611.

http://doi.org/10.1016/j.sbspro.2013.10.378

58

Thoits,P.A.(2011).Mechanismslinkingsocialtiesandsupporttophysicalandmental

health.Journalofhealthandsocialbehavior,52(2),145-161.Retrievedfrom

https://pdfs.semanticscholar.org/43ed/664c940c43602c7dc2457a65bb5829ce38

9b.pdf

Thomée,S.,Härenstam,A.,&Hagberg,M.(2011).Mobilephoneuseandstress,sleep

disturbances,andsymptomsofdepressionamongyoungadults-aprospective

cohortstudy.BMCPublicHealth,11(1),66.http://doi.org/10.1186/1471-2458-

11-66

Tsuno,N.,Besset,A.,&Ritchie,K.(2005).Sleepanddepression.JournalofClinical

Psychiatry,66(10),1254-1269.

U.S.CensusBureau(2010).2010censusdata.Retrievedfrom

https://www.census.gov/2010census/data/

VanAmeringen,Mancini,&Farvolden.(2003).Theimpactofanxietydisorderson

educationalachievement.JournalofAnxietyDisorders,17(5),561-571.Retrieved

fromhttp://rlrw.bnu.edu.cn/NewsImage/2012514104629.pdf

Wang,P.S.,Lane,M.,Olfson,M.,Pincus,H.A.,Wells,K.B.,&Kessler,R.C.(2005).

Twelve-monthuseofmentalhealthservicesintheUnitedStates:resultsfrom

theNationalComorbiditySurveyreplication.ArchivesofGeneral

Psychiatry,62(6),629-640.Retrievedfrom

https://www.researchgate.net/profile/Harold_Pincus/publication/7802231_Twe

lve-

month_use_of_mental_health_services_in_the_United_States_results_from_th

59

e_National_Comorbidity_Survey_Replication/links/0fcfd50d4e78b078ec000000.

pdf

Wei,M.,Russell,D.W.,&Zakalik,R.A.(2005).Adultattachment,socialself-efficacy,

self-disclosure,loneliness,andsubsequentdepressionforfreshmancollege

students:Alongitudinalstudy.JournalofCounselingPsychology,52(4),602.

Retrievedfromhttp://public.psych.iastate.edu/wei/manuscript/transition.pdf

Wittenberg,M.T.,&Reis,H.T.(1986).Loneliness,socialskills,andsocial

perception.PersonalityandSocialPsychologyBulletin,12(1),121-130.

Zimmerman,B.J.(1990).Self-regulatedlearningandacademicachievement:An

overview.Educationalpsychologist,25(1),3-17.Retrievedfrom

https://ciel.viu.ca/sites/default/files/self_regulated_learning_and_academic_ac

hievement_an_overview_0.pdf

60

APPENDIXA:INFORMEDCONSENT/IRBAPPROVAL

61

62

63

APPENDIXB:SURVEYINSTRUMENTS

(Spitzer,Kroenke,Williams,&Lo,2006)

64

(Kroenke,Spitzer,&Williams,2001)