The predictive value of physical fitness for falls in older adults with intellectual disabilities

The predictive value of physical fitness for falls in older adultswith intellectual disabilities Alyt Oppewala,* , Thessa I.M.. Incidence of and risk factors for falls among adults with a

The predictive value of physical fitness for falls in older adults

with intellectual disabilities

Alyt Oppewala,* , Thessa I.M Hilgenkampa,b, Ruud van Wijckc,

a

Intellectual Disability Medicine, Department of General Practice, Erasmus MC, University Medical Center Rotterdam, P.O Box 2040,

3000 CA Rotterdam, The Netherlands

b Abrona, Amersfoortseweg 56, 3712 BE Huis ter Heide, The Netherlands

c

Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen, A Deusinglaan 1, 9713 AV

Groningen, The Netherlands

1 Introduction

Ahighincidenceoffallsandrelatedinjuriesisseeninpeoplewithintellectualdisabilities(ID)(Cox,Clemson,Stancliffe, Durvasula,&Sherrington,2010;Enkelaar,Smulders,vanSchrojensteinLantman-deValk,Weerdesteyn,&Geurts,2013b; Hale, Bray,&Littmann, 2007; Hsieh, Rimmer,&Heller, 2012;Sherrard, Tonge,& Ozanne-Smith,2001) Falling is not restrictedtotheelderlyinthepopulationofID(Sherrardetal.,2001),butfallriskdoesincreasewithadvancingage(Chiba

etal.,2009;Coxetal.,2010;Hsieh,Heller,&Miller,2001;Willgoss,Yohannes,&Mitchell,2010)

Anumberofpersonalandmedicalcharacteristicscanleadtoanincreasedfallrisk.InpeoplewithID,olderage,being female,moreseverelevelofID,impairedmobility,physicallyactive,backpain,arthritis,fracturehistory,cerebralpalsy,good visuo-motor capacity, good attentional focus, urinary incontinence, heart condition, epilepsy, visual impairments, polypharmacy,andbehavioralproblemshavebeenmentionedaspossibleriskfactorsforfalls(Coxetal.,2010;Enkelaar

A R T I C L E I N F O

Article history:

Received 28 January 2014

Accepted 9 March 2014

Available online 29 March 2014

Keywords:

Falls

Physical fitness

Risk factors

Intellectual disabilities

Older adults

A B S T R A C T

Ahighincidenceoffallsisseeninpeoplewithintellectualdisabilities(ID),alongwithpoor balance,strength,muscularendurance,andslowgaitspeed,whicharewell-established riskfactorsforfallsinthegeneralpopulation.Theaimofthisstudywastoassessthe predictivevalueofthesephysicalfitnesscomponentsforfallsin724olderadultswith borderlinetoprofoundID(50years).Physicalfitnesswasassessedatbaselineanddata

on falls was collected at baseline and after three years Gait speed was lowest in participantswhofellthreetimesormoreatfollow-up.Gaitspeedwastheonlyphysical fitnesscomponentthatsignificantlypredictedfalls,butdidnotremainsignificantafter correcting for confounders Falls at baseline and not having Down syndrome were significantpredictorsforfalls.ExtremelylowphysicalfitnesslevelsofolderadultswithID, possiblestrategiestocompensatefortheselowlevels,andthefindingthatfallsdidnot increasewithagemayexplainthelimitedpredictivevalueofphysicalfitnessfoundinthis study

ß2014ElsevierLtd.Allrightsreserved

* Corresponding author Tel.: +31 107032118.

E-mail address: a.oppewal@erasmusmc.nl (A Oppewal).

ContentslistsavailableatScienceDirect

http://dx.doi.org/10.1016/j.ridd.2014.03.022

0891-4222/ß 2014 Elsevier Ltd All rights reserved.

etal.,2013b;Finlayson,Morrison,Jackson,Mantry,&Cooper,2010;Hsiehetal.,2012;Willgossetal.,2010).HavingDown syndrome(DS)wasfoundtoreducetheriskforfallsandrelatedinjury(Finlaysonetal.,2010)

Nexttopersonalandmedicalcharacteristics,physicalfitnessmaybeanimportantaspectforfallsinpeoplewithID.Older adultswithIDhavepoorbalance,strength,muscularendurance,andslowgaitspeed(Hilgenkamp,vanWijck,&Evenhuis, 2012b;Oppewal,Hilgenkamp,vanWijck,&Evenhuis,2013).Inthegeneralpopulation,thesephysicalfitnesscomponents arewell-establishedriskfactorsforfalls(AmericanGeriatricsSociety,BritishGeriatricsSociety,&AmericanAcademyof OrthopaedicSurgeonsPanelonFallsPrevention,2001;Close,Lord,Menz,&Sherrington,2005;Deandreaetal.,2010;Muraki

etal.,2013;Quachetal.,2011;Stenhagen,Ekstrom,Nordell,&Elmstahl,2013;Tinetti&Kumar,2010).However,resultsfrom prospectivestudiesperformedinthegeneralpopulationmaynotapplytoolderadultswithID.Thepredictivevalueof physicalfitnessforfallsinthegeneralpopulationisrelatedtoanage-relateddecreaseinphysicalfitnessorduetodiseases Thisrelationship may beconfounded by thelifelong cognitiveimpairment of people withID This lifelong cognitive impairmentmaynegatively influencetheirmotordevelopment sincechildhood, whichmaynegatively influencetheir balance,strength,endurance,andgaitthroughouttheirlife,andnotjustatanolderage.Thislineofthinkingissupportedby thefindingthatmotorandcognitivefunctioningarefundamentallyinterrelated,withsimilardevelopmentaltrajectories andtheuseofsimilarbrainstructures(Diamond,2000).Impairmentsinphysicalfitnessmaynotnecessarilyberelatedtoan increasedfallriskinthesameamountasinthegeneralpopulationbecausepeoplewithIDmayhavedevelopeddifferent compensationstrategiesandutilizethemovertheirentirelifespan.Forexample,peoplewithDSshowmorevariabilityin gaitthanpeoplewithnormalintelligence,buttheyusethisvariabilityfunctionallytooptimizetheirmovement.Thisimplies theuseofdifferentcontrolstrategiestocompensatefortheirlimitations(Black,Smith,Wu,&Ulrich,2007;Smith,Stergiou,& Ulrich,2011).Basedonthishypothesis,thecorrelationbetweenadecreaseinphysicalfitnessandfallsmaybelessstrong

ArecentprospectivestudyinvestigatingriskfactorsforfallinginolderadultswithmildtomoderateIDdidnotfind balanceandgaitspeedtodifferbetweenfallersandnon-fallers.However,adultswhofellindoors,performedworseon balanceandgaittests(Enkelaaretal.,2013b).Incontrast,retrospectivestudiesdidfindstrengthandgaitimpairmentstobe associatedwithanincreasedfallriskin peoplewithID(Chibaetal.,2009;Haleetal.,2007;Hsiehetal.,2012).More knowledgeisneededtoidentifythepredictivevalueofthephysicalfitnessinpredictingfallrisk.Thiswillhelptoidentify peopleatriskandtherebythedecision-makingfortreatment

Theaimofthisstudywastoassessthepredictivevalueofbalance,gaitspeed,strength,andmuscularenduranceforfalls, overa3-yearperiod,inalargesampleofolderadultswithID

2 Methods

2.1 Studydesignandparticipants

ThisstudywaspartofthelargeDutch‘Healthyageingandintellectualdisabilities’(HA-ID)studyperformedinaconsort

ofthreeIDcareorganizationsincollaborationwithtwouniversitydepartmentsintheNetherlands(IntellectualDisability Medicine,ErasmusMC,UniversityMedicalCenterRotterdamandtheCenterforHumanMovementSciences,Universityof Groningen,UniversityMedicalCenterGroningen).Forthebaselinemeasurementsall2150olderclientswithID(50years)

ofthecareorganizationswereinvitedtoparticipate,resultinginanear-representativesampleof1050clients.Themesinthe studywerephysicalactivityandfitness,nutritionandnutritionalstate,andmoodandanxiety.Datacollectiononthese themestookplacebetweenFebruary2009andJuly2010.Detailsaboutdesign,recruitment,andrepresentativenessofthe samplehavebeenpresentedelsewhere(Hilgenkampetal.,2011).Threeyearsafterthebaselinemeasurements,follow-up dataonfallswerecollectedwithaquestionnaire

ThisstudywasapprovedbytheMedicalEthicalCommitteeatErasmusMedicalCenter(MEC2008-234andMEC2011-309) andbytheethicalcommitteesoftheparticipatingIDcareorganizations.Informedconsentwasobtainedfromallparticipantsor theirlegalrepresentativesforbothbaselineandfollow-upmeasurements;however,unusualresistancewasareasonfor abortingmeasurementsatalltimes.ThisstudyfollowedtheguidelinesoftheDeclarationofHelsinki(Helsinki,2008) 2.2 Baselinemeasurements

Oftheriskfactorsforfallsmentionedintheintroduction,thefollowingriskfactorswerecollectedintheHA-IDstudy:age, gender,levelofID,Downsyndrome(DS),mobility,physicalactivitylevels,fracturehistory,spasticityofthelegs(asanaspect

of cerebral palsy), urinary incontinence, heart condition, epilepsy, visualimpairments, polypharmacy,and behavioral problems.Inthisstudy,theseriskfactorsareusedtodescribethestudysampleandaspossibleconfoundersintheanalyses 2.2.1 Personalcharacteristics

Ageandgenderwerecollectedfromadministrativesystemsofthecareorganizations.LevelofIDwascategorizedby behavioraltherapistsorpsychologistsasborderline(IQ=70–84),mild(IQ=50–69),moderate(IQ=35–49),severe(IQ=20– 34),orprofound(IQ<20)(WorldHealthOrganization,1996).ThepresenceofDSwascollectedthroughthemedicalfiles Professionalcaregiversprovidedinformation aboutmobility(independent,walkingwithanaid,orwheelchair-bound) Physicalactivitywasmeasuredwithpedometers(NL-1000pedometer,NewLifestyles,MO,USA).Aminimumof7500steps perdaywasclassifiedassufficient

2.2.2 Medicalinformation

Professionalcaregiversprovidedinformationabouturinaryincontinence.Informationonthehistoryoffracturesandthe presenceofspasticityofthelegs,heartcondition(arrhythmiasandcoronaryheartdisease),epilepsy,visualimpairments, andpolypharmacywasretrievedfrommedicalfiles.Polypharmacywasdefinedastakingfourormoremedications.The presenceofbehavioralproblemswasobtainedfrombehavioraltherapists’records

2.2.3 Physicalfitness

BalancewasmeasuredwiththeBergBalanceScale(BBS)(Berg,1989;Berg,Wood-Dauphinee,Williams,&Maki,1992) TheBBSconsistsof14staticanddynamicfunctionalbalancetasksvaryingindifficulty,rangingfromunsupportedsittingina chairtotandemstanceandstandingononeleg.Theoriginaltestinstructionswerefollowedwithsomeaidstoenhance understandingofthetasks:twocarpetfeetandacarpetcircleonthefloor,topointoutwheretheparticipanthadtostandor turnaroundon.Walkingaidswerenotallowed.Theitemswerescoredona5-pointscalefrom0(inabilitytocompletethe task)to4(completionofthetask)points,withamaximumof56points.Ascoreof45isusedinthegeneralpopulationasa cut-offtodifferentiatebetweenthoseatriskforfalls(<45)andthosenotatriskforfalls(45)(Bergetal.,1992).Validityand reliability has been previously demonstrated in the general population (Berg, Wood-Dauphinee, & Williams, 1995; Conradssonetal.,2007;Wangetal.,2006).InthepopulationwithID,theBBSwasalsoreliable(deJonge,Tonino,&Hobbelen, 2010; Sackley et al., 2005) and feasible for older adults with borderline to moderate ID (Enkelaar, Smulders, van SchrojensteinLantman-deValk,Weerdesteyn,&Geurts,2013a;Hilgenkamp,vanWijck,&Evenhuis,2013)

Comfortablegaitspeed(GSC)wasmeasuredoveradistanceof5m,after3mforacceleration.Participantswalkedthree timesandtheaveragegaitspeedinm/swastheresultofthetest.Participantshadtowalkwithoutsomeonewalking alongsideorphysicallysupportingthemtoavoidinfluencingtheircomfortablespeed.Validityandreliabilityinthegeneral populationisgood(AbellanvanKanetal.,2009;Connelly,Stevenson,&Vandervoort,1996;Cooperetal.,2010;Steffen& Seney,2008;Steffen,Hacker,&Mollinger,2002).InolderadultswithID,measuringGSCwasfeasibleandreliable(ICCof0.96 forsame-dayintervaland0.93fortwo-weekinterval)(Hilgenkamp,vanWijck,&Evenhuis,2012a;Hilgenkampetal.,2013) Muscularendurancewasmeasuredwiththe30sChairstandtest(30sCS)(Rikli&Jones,2001).Participantshadtostand uprightandsitdownagainasoftenaspossiblein30swithoutusingtheirhands.Thetotalnumberofcompletestanceswas theresultofthetest.Validityandreliabilityhasbeendemonstratedinthegeneralpopulation(Jones,Rikli,&Beam,1999).In olderadultswithID,feasibilityandtest-retestreliabilitywasmoderatetogood(ICCof0.72forsame-dayintervaland0.65 fortwo-weekinterval)(Hilgenkampetal.,2012a,2013)

Grip strength (GS) was measured withthe Jamar Hand Dynanometer (#5030J1, Sammons Preston Rolyan, USA) Participantssqueezedthedynamometerwithmaximumforceinseatedposition,accordingtotherecommendationsofThe AmericanSocietyofHandTherapists(Fess&Moran,1981),threetimesforbothhandswitha1-minpausebetweenattempts Participantswereabletopracticebysqueezingarubberball,toassureunderstanding.Thebestresultswasrecorded(inkg) Theresultwasonlyrecordedifthetest instructorwasconvincedtheparticipanthadsqueezed withmaximumeffort Validityandreliabilityinthegeneralpopulationisgood(Abizandaetal.,2012;Stark,Walker,Phillips,Fejer,&Beck,2011).In olderadultswithID,measuringgripstrengthwasfeasibleandtest-retestreliabilitywasgood(ICCof0.94forsame-day intervaland0.90fortwo-weekinterval)(Hilgenkampetal.,2012a,2013)

Thephysicalfitnessassessmentwasconductedatlocationsfamiliarorclosetoparticipants.Testswereguidedbytrained testinstructors,whoallwerephysiotherapists,physicalactivityinstructors,oroccupationaltherapistswithexperiencewith peoplewithID.Tomotivatetheparticipantsweprescribed‘maximalmotivation’tothetestinstructorsforalltests.Insome cases,thismeantthatparticipantsweremotivatedtoengageinthetestsbyconstantverbalencouragementandverbal rewarding,inothercasesthetestinstructorhadtoremainverycalmandquiettomotivatetheparticipantasmuchas possibleandtopreventstressoranxiety.Thebackground,knowledge,andexperienceofthetestinstructorswereimportant forensuringthemostsuitable‘maximalmotivation’foreveryparticipant,whileregardingsafetyaswell

2.2.4 Baselinefallassessment

Afallwasdefinedasanunexpectedeventinwhichtheparticipantcomestorestontheground,floor,orlowerlevel(Lamb

etal.,2005).Professionalcaregiversprovidedinformationonhowoftentheparticipantsfellinthelastthreemonths(not fallen,1–2falls,3–5falls,6–10falls,11fallsormore)beforethebaselinemeasurements.Wechoseforarecallperiodofthree monthsbecauseweexpectedthisperiodtobelongenoughtodistinguishnon-fallersfromrecurrentfallers,becauseofthe highfallincidenceinpeoplewithID(Chibaetal.,2009)

2.3 Follow-upfallassessment

Professionalcaregiversprovidedinformationatthreeyearsafterbaselineonhowoftentheparticipantfellinthelast threemonths(notfallen,1–2falls,3–5falls,6–10falls,11fallsormore)

2.4 Statisticalanalyses

Fallsatbaselineandfollow-upwererecodedintothreecategories:‘non-fallers’,‘oneortwotimefallers’,and‘threetimes

ormorefallers’.Baselinepersonalcharacteristics,medicalinformation,falls,andphysicalfitnessweredescribedforthe

threecategoriesoffallersatfollow-up.DifferencesbetweengroupswereanalyzedwithPearson’schi-squaretestsand one-wayindependentanalysisofvariance(ANOVA),withposthoctests.Bonferronicorrectionwasusedtocorrectformultiple testing.Spearman’scorrelationcoefficientwascalculatedtoassessthecorrelationbetweenfallsatbaselineandfollow-up Simple and multiplelogistic regression analyseswere usedtoassess the predictivevalue of each physical fitness componentforfallingatfollow-up.Becausetheaimwastopredictfalling,fallsatbaselineandfollowupwererecodedinto categoriesnon-fallersandfallers(1fall)

First,thepredictivevalueofphysicalfitnessforfallswasassessedwithasimplelogisticregression,witheachphysical fitnesscomponentastheindependentvariableandfallsatfollow-upasthedependentvariable

Second,amultiplelogisticregressionwasperformedtoadjustforconfounders.Spearman’scorrelationcoefficients werecalculatedbetweenbaselinepersonalcharacteristicsandmedical informationandfallsatfollow-uptoidentify potential confounders Variables that significantly correlated with falls at follow-up were considered potential confounders.Dummyvariableswereconstructedforindependentcategoricalvariableswithmorethantwocategories Age,gender,andthepotentialconfounders wereenteredinthefirst block,andeachphysicalfitnesscomponentwas enteredin thesecondblock.MulticollinearitywascheckedwiththeVarianceInflationFactor(VIF),whichhad tobe below10forallindependentvariables(Field,2005).Resultsarepresentedasunstandardizedcoefficients(B)andtheir standarderrors(SE), representingthestrengthoftherelationbetweeneachindependentvariableandtheoutcome; oddsratios(providedasExp(B)bySPPS)anditsconfidenceintervals,representingthemagnitudeoftheinfluenceofthe predictorsandisdefinedasthechangeinoddsresultingfromaunitchangeofthepredictor;theexplainedvarianceby themodel(Cox&SnellR2andNagelkerkeR2);andthemodelchi-squarestatistic,representingthefitofthemodel (Field, 2005)

Statisticalsignificancewassetat5%(p<0.05).AnalyseswereperformedwiththeStatisticalPackageforSocialSciences (SPSS)version21(IBMCorporation,NewYork)

3 Results

3.1 Baselinecharacteristicsandfalls

Of the 1050 participants in the HA-ID study follow-up fall data were available from 724 participants Of these participants,25.5%(185participants)fellatfollow-up,ofwhom19.6%(142participants)felloneortwotimesand5.9%(43 participants)fellmorethantwotimes.ThepersonalcharacteristicsandmedicalinformationareshowninTable1,forthe threecategoriesoffallers.Duetomissingdata,thenumberofparticipantsdifferedpervariable.Fallershadmoreoften mobilityimpairments(x2

[4,n=694]=17.26,p=0.002)andepilepsy(x2

[2,n=636]=17.27,p<0.001)

Fallsatbaselinewassignificantlycorrelatedtofallsatfollow-up(rs=0.13,p<0.001).Atbaseline,22.6%experiencedat leastonefallinthreemonths.Comparedtofallsatbaseline,107participants(15.5%)fellmoreoftenatfollow-upand94 participants(13.6%)felllessoftenatfollow-up.Theremaining488participants(70.8%)wereinthesamecategoryoffallsat follow-upastheywereatbaseline

Theresultsofthebaselinephysicalfitnessassessmentforthethreecategoriesoffallersarepresentedinthefinalpartof

Table1.Becausenotallparticipantsperformedallphysicalfitnessteststhenumberofparticipantsdifferedacrossthetests Comfortablegaitspeed(GSC)(F[2,503]=5.71,p=0.004)wassignificantlydifferentacrossthethreecategories.Posthoctests revealedthatGSCwassignificantlylowerinparticipantsthatfellthreetimesormorethaninparticipantsthatdidnotfall (p=0.005)

3.2 Predictivevalueofphysicalfitnessforfalls

Simplelogisticregressionanalysesshowedthataslowercomfortablegaitspeedwasasignificantpredictorforfalls,with

anoddsratioof0.47,95%CI[0.24,0.95](model1,Table2 Noneoftheotherphysicalfitnesscomponentssignificantly predictedfalls

Olderage(rs=0.10, p=0.008),epilepsy(rs=0.13,p=0.001),polypharmacy (rs=0.11, p=0.005)andfallsat baseline (rs=0.31,p<0.001)weresignificantlypositivelycorrelatedwithfallsatfollow-up.HavingDS(rs= 0.12,p=0.004)was significantlynegatively correlatedwithfallsatfollow-up Togetherwithgender, thesecharacteristicswereenteredas confoundersinthemultiplelogisticregressionmodels.Multiplelogisticregressionanalysesrevealedthatgaitspeeddidnot significantlyaddtothepredictionoffallsafteradjustmentsforconfounders(model2,Table2 Fallsatbaselinewasa significantpredictorforfalls.NothavingDSwasasignificantpredictorintheregressionmodelswithcomfortablewalking speedandmuscularenduranceasphysicalfitnesscomponent.TheCox&Snellexplainedvariancesofthefinalmultiple logisticregressionmodelsrangedfrom6.8%to11.5%,andtheNagelkerkeexplainedvariancerangedfrom10.4%to17.3% (Table2

4 Discussion

Weassessedthepredictivevalueofbalance,gaitspeed,strength,andmuscularenduranceforfalls,overa3-yearperiod,

in724olderadultswithintellectualdisabilities(ID).Fallsatfollow-upoccurredin25.5%oftheparticipants.Gaitspeedwasa

significant predictorfor falls,however, afteradjustment for confounders, gaitspeed didnot significantly add to the predictionoffalls.OnlyfallsatbaselineandnothavingDownsyndrome(DS)weresignificantpredictorsforfalls One-fourthoftheparticipantsexperiencedafallinthethreemonthspriortothefollow-upfallassessment,ofwhom 19.6%felloneortwotimesand5.9%fellthreetimesormore.OtherstudiesregardingfallsinthepopulationofIDfound percentagesoffallers,rangingfrom25%to46%duringoneyear(Coxetal.,2010;Enkelaaretal.,2013b;Finlaysonetal.,2010; Hsiehetal.,2012)and70%duringfiveyears(Grantetal.,2001).Ourlowerpercentageoffallersmaybeduetotheshorter timeperiodand/orapossibleunderestimationduetoretrospectivedatacollection(Ganz,Higashi,&Rubenstein,2005) ParticipantswithDSfellless.ThisisinlinewiththeresultsofFinlaysonetal.(2010),whofoundalowerriskforfallsand relatedinjuriesforpeoplewithDS.Apossibleexplanationmaybethestability-enhancinggaitpatternofpeoplewithDS, implyinganeffectivestrategy.However,weonlyfoundthisresultintheregressionmodelswithcomfortablewalkingspeed andmuscularenduranceasthephysicalfitnesscomponent.Thislimitsthegeneralizabilityofthisresulttoothersubgroups thanthoseperformingthesetests

Inourstudy,fallsatbaselinewereanimportantpredictorforfuturefalls.Thishasalsobeenfoundtobeanimportant predictorforfallsinthegeneralpopulation(AmericanGeriatricsSocietyetal.,2001;Closeetal.,2005;Deandreaetal.,2010; Tinetti&Kumar,2010).Itisthereforeimportanttoprovidefallpreventionprogramsforpeoplewhohavealreadyfallenonce,

tolimittheriskofrecurrentfalls.Unfortunately,todatenofallpreventionguidelinesareavailableforthepopulationof adultswithID.However,arecentstudyshowedthata10-sessionobstacleexercisetrainingwaseffectiveinreducingthe numberoffalls,alongsidewithimprovementsinbalance,gaitcapacityandspeed,inadultswithmildtoprofoundIDwitha highfallrisk(VanHanegem,Enkelaar,Smulders,&Weerdesteyn,2013).Thisresultimpliesthat,althoughphysicalfitness

Table 1

Baseline personal characteristics categorized according to follow-up fall data.

n Non-fallers (n = 539) 1–2 fallers (n = 142) 3 fallers (n = 43) Personal characteristics

Age 724 Years (m  sd) 60.7  7.8 61.5  6.8 63.2  8.0

Gender 724 Female 261 (48.4%) 74 (52.1%) 25 (58.1%)

Male 278 (51.6%) 68 (47.9%) 18 (41.9%) Level of ID 707 Borderline 13 (2.4%) 5 (3.5%) 0

Mild 104 (19.3%) 33 (23.2%) 7 (16.3%) Moderate 263 (48.8%) 72 (50.7%) 24 (55.8%) Severe 100 (18.6%) 22 (15.5%) 7 (16.3%) Profound 47 (8.7%) 5 (3.5%) 5 (11.6%) Down syndrome 619 Yes 80 (14.8%) 11 (7.7%) 1 (2.3%)

Mobility 694 Independent 407 (75.5%) 105 (73.9%) 24 (55.8%)

Walking-aid 63 (11.7%) 22 (15.5%) 12 (27.9%) a

Wheelchair 52 (9.6%) 4 (2.8%) b

5 (11.6%) Physical activity 191 Steps/day (m  sd) 6830.1  3772.9 8003.5  4115.0 5682.7  2207.8 Medical information

Fracture history 626 39 (7.2%) 12 (8.5%) 8 (18.6%)

Spasticity legs 632 Unilateral 13 (2.4%) 4 (2.8%) 4 (9.3%)

Bilateral 39 (7.2%) 5 (3.5%) 2 (4.7%) Urinary incontinence 694 231 (42.9%) 60 (42.3%) 23 (53.5%)

Heart condition 640 16 (3.0%) 7 (4.9%) 0

Epilepsy 636 85 (15.8%) 29 (20.4%) 18 (41.9%) a

Visual impairments 627 121 (22.5%) 20 (14.1%) 8 (18.6%)

Polypharmacy 649 225 (41.7%) 77 (54.2%) 21 (48.8%)

Behavioral problems 148 (27.5%) 39 (27.5) 10 (23.3)

Falls at baseline

Falls in 3 months before baseline? 689 None 436 (80.9%) 82 (57.7%) 15 (34.9%)

1 or 2 times 70 (13.0%) 36 (25.4%) 10 (23.3%)

3 times 13 (2.4%) 11 (7.7%) 16 (37.2%) Physical fitness

47.4  10.3 47.3  8.7 43.7  11.8 Comfortable gait speed 506 m/s (m  sd) n = 386 n = 92 n = 28

0.996  0.346 0.935  0.323 0.784  0.362 c

Grip strength 512 kg (m  sd) n = 389 n = 94 n = 29

24.4  10.4 24.4  10.3 22.8  8.8 Muscular endurance 383 m  sd n = 296 n = 68 n = 19

9.4  3.2 10.1  3.8 7.7  2.9

m = mean; sd = standard deviation; n = number of participants; ID = intellectual disability.

a

Observed value significantly higher than expected value.

b

Observed value significantly lower than expected value.

c Significantly different from non-fallers.

wasnotpredictiveforfallsinthisstudy,itcanbeeffectiveinreducingfallrisk.Moreresearchisneededtoestablishthe beneficialeffectsofexercisetrainingonfallsanddevelopfallpreventionguidelinesforadultswithID

Gaitspeedwaslowestinparticipantswhofellthreetimesormoreatfollow-up.However,gaitspeeddidnotremaina significantpredictoraftercorrectingforconfounders.Therefore,thepredictivevalueofphysicalfitnessforfallsfoundinthe generalpopulation(AmericanGeriatricsSocietyetal.,2001;Closeetal.,2005;Deandreaetal.,2010;Murakietal.,2013;

Table 2

Results of the simple (model 1) and multiple (model 2) logistic regression analyses for the predictive value of the physical fitness for falls.

B (SE) Exp(B) [95% CI] Model characteristics Balance

Model 1

BBS 0.01 (0.01) 0.10 [0.97, 1.02] n = 271, C&S R 2

= 0.001, Constant 1.00 (0.62) 0.37 Nagelkerke R 2 = 0.001,x2 = 0.145 Model 2

Block 1 Age 0.01 (0.02) 1.01 [0.97, 1.06] n = 271, C&S R 2

= 0.068, Female 0.30 (0.31) 1.35 [0.73, 2.46] Nagelkerke R 2

= 0.104,x2

= 19.21 **

Down syndrome 0.25 (0.54) 0.78 [0.27, 2.25]

Epilepsy 0.58 (0.41) 1.79 [0.80, 4.03]

Polypharmacy 0.18 (0.32) 1.20 [0.64, 2.23]

Falls at baseline 1.15 (0.34) ** 3.14 [1.63, 6.07]

Block 2 BBS 0.01 (0.01) 1.01 [0.98, 1.04]

Constant 3.00 (1.64) 0.05 Comfortable gait speed

Model 1

GSC 0.75 (0.36) *

0.47 [0.24, 0.95] n = 409, C&S R 2

= 0.011, Constant 0.50 (0.34) 0.60 Nagelkerke R 2

= 0.016,x2

= 4.47 *

Model 2

Block 1 Age 0.01 (0.02) 0.99 [0.96, 1.03] n = 409, C&S R 2 = 0.115,

Female 0.09 (0.26) 1.09 [0.66, 1.80] Nagelkerke R 2

= 0.173,x2

= 49.86 **

Down syndrome 1.38 (0.51) **

0.25 [0.09, 0.69]

Epilepsy 0.51 (0.32) 1.66 [0.88, 3.12]

Polypharmacy 0.24 (0.27) 1.27 [0.75, 2.14]

Falls at baseline 1.32 (0.28) **

3.73 [2.14, 6.40]

Block 2 GSC 0.49 (0.41) 0.62 [0.28, 1.37]

Constant 0.57 (1.30) 0.56 Muscular endurance

Model 1

30sCS 0.01 (0.04) 0.99 [0.91, 1.08] n = 301, C&S R 2

< 0.001, Constant 1.16 (0.42) **

0.31 Nagelkerke R 2

< 0.001,x2

= 0.05 Model 2

Block 1 Age 0.001 (0.02) 1.00 [0.96, 1.04] n = 301, C&S R 2

= 0.096, Female 0.11 (0.30) 1.12 [0.63, 2.01] Nagelkerke R 2 = 0.146,x2 = 30.29 **

Down syndrome 1.26 (0.58) *

0.28 [0.09, 0.89]

Epilepsy 0.29 (0.40) 1.33 [0.61, 2.90]

Polypharmacy 0.19 (0.31) 1.20 [0.65, 2.22]

Falls at baseline 1.43 (0.33) **

4.17 [2.17, 8.01]

Block 2 30sCS 0.01 (0.05) 0.99 [0.91, 1.09]

Constant 1.53 (1.49) 0.22 Grip strength

Model 1

GS 0.01 (0.01) 0.99 [0.96, 1.01] n = 405, C&S R 2

= 0.003, Constant 0.92 (0.30) **

0.40 Nagelkerke R 2

= 0.004,x2

= 1.04 Model 2

Block 1 Age 0.00 (0.02) 1.00 [0.97, 1.04] n = 405, C&S R 2

= 0.076, Female 0.05 (0.28) 1.05 [0.61, 1.81] Nagelkerke R 2 = 0.116,x2 = 32.12 **

Down syndrome 0.55 (0.44) 0.58 [0.25, 1.36]

Epilepsy 0.50 (0.32) 1.65 [0.89, 3.07]

Polypharmacy 0.15 (0.27) 1.16 [0.69, 1.96]

Falls at baseline 1.21 (0.27) **

3.34 [1.96, 5.70]

Block 2 GS 0.01 (0.02) 0.99 [0.96, 1.02]

Constant 1.46 (1.21) 0.23 Model 1: simple logistic regression excluding potential confounders; model 2: multiple logistic regression including potential confounders.

Age (in years), gender (male = 0, female = 1), Down syndrome (no = 0, yes = 1), epilepsy (no = 0, yes = 1), polypharmacy (no = 0, yes = 1), falls at baseline (non-faller = 0, faller = 1).

B = unstandardized coefficient; SE = standard error; Exp(B) = odds ratio; CI = confidence interval; R 2

= explained variance; C&S R 2

= Cox & Snell explained variance;x2

= model chi-square statistic; BBS = Berg Balance Scale; GSC = comfortable gait speed; 30sCS = 30s Chair stand; GS = grip strength.

* p < 0.05.

** p < 0.01.

Quachetal.,2011;Stenhagenetal.,2013;Tinetti&Kumar,2010),andtherelationbetweenphysicalfitnessandfallsfoundin retrospectivestudieswithpeoplewithID(Chibaetal.,2009;Haleetal.,2007;Hsiehetal.,2012),wasnotconfirmedinour study.ThisresultisinlinewiththeresultsofEnkelaaretal.(2013b),whoalsofoundthatbalanceandgaitmeasures(Berg BalanceScale,Timedupandgotest,Functionalreach,Singlelegstance,Tenmeterwalkingtest,Comfortablegaitspeed)did notpredictfallsinolderadultswithID.However,inthestudyofEnkelaaretal.(2013b),theparticipantswhofellindoors weresignificantlyolderandperformedsignificantlyworseonthebalanceandgaitteststhanthosewhofelloutdoors Possibleexplanationsforthelimitedpredictivevalueofphysicalfitnessforfallsaredescribedbelow.Inordertofinda predictivevalueofphysicalfitnessforfalls,thenumberoffallsshouldincreasewithagealongwithadecreaseinphysical fitness.Wefoundthatthenumberoffallersdidnotincreasewithage.Inaddition,physicalfitnessofpeoplewithIDseemsto

belowacrossthelifespan.Inpreviousstudies,wefoundthatthebalance,strength,muscularendurance,andgaitspeedof olderadultswithIDwerecomparableto,orevenworsethan,thatofagegroups20–30yearsolderinthegeneralpopulation (Hilgenkampetal.,2012b;Oppewaletal.,2013).Lowfitnesslevelsareseenatyoungeragesaswell(Golubovic,Maksimovic, Golubovic,&Glumbic,2012;Lahtinen,Rintala,&Malin,2007;Salaun&Berthouze-Aranda,2012).Duetotheselifelonglow physicalfitnesslevels,theage-relateddecreaseinphysicalfitnessand(related)increaseinfallsmaybelesspronouncedin olderadultswithIDthaninthegeneralpopulation.Thismaylimitthepredictivevalueofphysicalfitnessforfallsinthis population,whichisinlinewithourresults.Nevertheless,anexerciseprogrammayreducetheriskoffalling

Inaddition,thepredictivevalueofphysicalfitnessforfallsmaybelimitedinpeoplewithIDbecausetheymayhave developedcompensationstrategiestodealwiththeirlowphysicalfitness.Forexample,ithasbeenfoundthatpeoplewith

DSandWilliamssyndromehaveslowergaitspeed,shorterstepandstridelengths,widerbaseofsupport,andalongerstance anddoublesupportphasethanpeoplewithnormalintelligence,whichmaybeacompensatorystrategytomaintainstability andposturalcontrol(Hocking,Rinehart,McGinley,&Bradshaw,2009;Horvat,Croce,Zagrodnik,Brooks,&Carter,2012; Rigoldi,Galli,&Albertini,2011;Smith&Ulrich,2008).Furtherresearchisneededtoestablishtherelationshipbetweenthese gaitpatternsofpeoplewithIDandfalls.Thiswouldprovideinformationabouttheeffectivenessofthesecompensation strategiesandidentifyareasforfallprevention

Thisstudyhadsomelimitations.First,ourpowertofindsignificanteffectsofpersonal,medical,andphysicalfitness factorsinpredictingfallsmayhavebeenlimitedbecausethenumberofparticipantswhofellthreetimesormoreat

follow-upwasrelativelysmall.Thiscouldbeanalternativeexplanationforthefactthatriskfactorsforfallsfoundinotherstudies withpeoplewithID,werenotidentifiedasriskfactorsinourstudy.Second,itwasrecommendedinpreviousstudiestomake

adistinctionbetweenfallersandrecurrentfallers(Lambetal.,2005),whilethisstudycombinedbothfallersandrecurrent fallersinonegroup(oneortwotimefallers).Unfortunately,wecouldnotsplitthisgroupintofallersandrecurrentfallers duetothestructureofthequestionnaireusedinthisstudy.Third,weretrospectivelycollectedfalls,whichislessreliable thanprospectivelymonitoringfalls(Ganzetal.,2005).Last,becausenotallparticipantsperformedallphysicalfitnesstests, theselectionofparticipantsdifferedforeachregressionanalysis(withthat specifictest asindependentvariable) The selectionofparticipantsthatperformedthephysicalfitnesstestswasthefunctionallymoreablepartofthestudysample (Hilgenkampetal.,2012b;Oppewaletal.,2013).Thislimitsthegeneralizabilityofourresultstotheentirepopulationof olderadultswithID.However,tokeepoursamplesizeaslargeaspossible,wechosetonotonlyselectthoseparticipantswho performedalltests

Inconclusion,wefoundthatbalance,comfortablegaitspeed,strength,andmuscularendurancewerenotsignificant predictorsforfallsinolderadultswithID.Nonage-relatedincrease infalls,extremelylowphysicalfitnesslevels,and possiblestrategiestocompensatefortheselowlevels,mayexplainthelimitedpredictivevalueofphysicalfitnessfoundin thisstudy.Thelowexplainedvarianceofourregressionmodelsshowthatmoreresearchisneededregardingtheroleof physicalfitnessandotherriskfactorsforfalls.ThiswillhelpindevelopingfallpreventionprogramsforolderadultswithID Conflictofinterest

None

Acknowledgements

Theauthors thankthemanagement andprofessionals ofthecare organizations,Abrona (Huister Heide),Amarant (Tilburg)andIpsedeBruggen(Zoetermeer),involvedintheHA-IDconsortfortheircollaborationandsupport.Wealsothank allparticipants,theirfamilymembersandcaregiversfortheircollaboration.Thisstudywascarriedoutwiththefinancial supportofZonMw(No.57000003)

References

Abellan van Kan, G., Rolland, Y., Andrieu, S., Bauer, J., Beauchet, O., Bonnefoy, M., et al (2009) Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force Journal of Nutrition, Health & Aging, 13(10), 881–889.

Abizanda, P., Navarro, J L., Garcia-Tomas, M I., Lopez-Jimenez, E., Martinez-Sanchez, E., & Paterna, G (2012) Validity and usefulness of hand-held dynamometry for measuring muscle strength in community-dwelling older persons Archives of Gerontology and Geriatrics, 54(1), 21–27.

American Geriatrics Society, British Geriatrics Society, & American Academy of Orthopaedic Surgeons Panel on Falls Prevention, (2001) Guideline for the

Berg, K O (1989) Measuring balance in the elderly: Preliminary development of an instrument Physiotherapy Canada, 41(6), 304–311.

Berg, K O., Wood-Dauphinee, S L., Williams, J I., & Maki, B (1992) Measuring balance in the elderly: Validation of an instrument Canadian Journal of Public Health, 83(Suppl 2), S7–S11.

Berg, K O., Wood-Dauphinee, S L., & Williams, J I (1995) The Balance Scale: Reliability assessment with elderly residents and patients with an acute stroke Scandinavian Journal of Rehabilitation Medicine, 27(1), 27–36.

Black, D P., Smith, B A., Wu, J., & Ulrich, B D (2007) Uncontrolled manifold analysis of segmental angle variability during walking: Preadolescents with and without Down syndrome Experimental Brain Research, 183(4), 511–521.

Chiba, Y., Shimada, A., Yoshida, F., Keino, H., Hasegawa, M., Ikari, H., et al (2009) Risk of fall for individuals with intellectual disability American Journal on Intellectual and Developmental Disabilities, 114(4), 225–236.

Close, J C T., Lord, S L., Menz, H B., & Sherrington, C (2005) What is the role of falls? Best Practice & Research Clinical Rheumatology, 19(6), 913–935.

Connelly, D M., Stevenson, T J., & Vandervoort, A A (1996) Between- and within-rater reliability of walking tests in a frail elderly population Physiotherapy Canada, 48(1), 47–51.

Conradsson, M., Lundin-Olsson, L., Lindelof, N., Littbrand, H., Malmqvist, L., Gustafson, Y., et al (2007) Berg balance scale: Intrarater test-retest reliability among older people dependent in activities of daily living and living in residential care facilities Physical Therapy, 87(9), 1155–1163.

Cooper, R., Kuh, D., Hardy, R., Mortality Review, G., Falcon, & Teams, H A S (2010) Objectively measured physical capability levels and mortality: Systematic review and meta-analysis British Medical Journal, 341, c4467.

Cox, C R., Clemson, L., Stancliffe, R J., Durvasula, S., & Sherrington, C (2010) Incidence of and risk factors for falls among adults with an intellectual disability Journal of Intellectual Disability Research, 54(12), 1045–1057.

de Jonge, P A., Tonino, M A M., & Hobbelen, J S (2010) Instruments to assess risk of falls among people with intellectual disabilities Paper presented at the International congress of best practice in intellectual disability medicine.

Deandrea, S., Lucenteforte, E., Bravi, F., Foschi, R., La Vecchia, C., & Negri, E (2010) Risk factors for falls in community-dwelling older people: A systematic review and meta-analysis Epidemiology, 21(5), 658–668.

Diamond, A (2000) Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex Child Development, 71(1), 44–56.

Enkelaar, L., Smulders, E., van Schrojenstein Lantman-de Valk, H., Weerdesteyn, V., & Geurts, A C (2013a) Clinical measures are feasible and sensitive to assess balance and gait capacities in older persons with mild to moderate intellectual disabilities Research in Developmental Disabilities, 34(1), 276–285.

Enkelaar, L., Smulders, E., van Schrojenstein Lantman-de Valk, H., Weerdesteyn, V., & Geurts, A C (2013b) Prospective study on risk factors for falling in elderly persons with mild to moderate intellectual disabilities Research in Developmental Disabilities, 34(11), 3754–3765.

Fess, E E., & Moran, C (1981) Clinical assessment recommendations Indianapolis, USA: American Society of Hand Therapists Monograph.

Field, A (2005) Discovering statistics using SPSS (2nd ed.) London: SAGE Publications Ltd.

Finlayson, J., Morrison, J., Jackson, A., Mantry, D., & Cooper, S A (2010) Injuries, falls and accidents among adults with intellectual disabilities Prospective cohort study Journal of Intellectual Disability Research, 54(11), 966–980.

Ganz, D A., Higashi, T., & Rubenstein, L Z (2005) Monitoring falls in cohort studies of community-dwelling older people: Effect of the recall interval Journal of the American Geriatrics Society, 53(12), 2190–2194.

Golubovic, S., Maksimovic, J., Golubovic, B., & Glumbic, N (2012) Effects of exercise on physical fitness in children with intellectual disability Research in Developmental Disabilities, 33(2), 608–614.

Grant, H J., Pickett, W., Lam, M., O’Connor, M., & Ouellette-Kuntz, H (2001) Falls among persons who have developmental disabilities in institutional and group home settings Journal of Developmental Disabilities, 8(1), 57–73.

Hale, L., Bray, A., & Littmann, A (2007) Assessing the balance capabilities of people with profound intellectual disabilities who have experienced a fall Journal of Intellectual Disability Research, 51(Pt 4), 260–268.

Helsinki (2008) World Medical Association Declaration of Helsinki, ethical principles for medical research involving human subjects Retrieved from: http:// www.wma.net/en/30publications/10policies/b3/

Hilgenkamp, T I., Bastiaanse, L P., Hermans, H., Penning, C., van Wijck, R., & Evenhuis, H M (2011) Study healthy ageing and intellectual disabilities: Recruitment and design Research in Developmental Disabilities, 32(3), 1097–1106.

Hilgenkamp, T I., van Wijck, R., & Evenhuis, H M (2012a) Feasibility and reliability of physical fitness tests in older adults with intellectual disability: A pilot study Journal of Intellectual & Developmental Disability, 37(2), 158–162.

Hilgenkamp, T I., van Wijck, R., & Evenhuis, H M (2012b) Low physical fitness levels in older adults with ID: Results of the HA-ID study Research in Developmental Disabilities, 33(4), 1048–1058.

Hilgenkamp, T I., van Wijck, R., & Evenhuis, H M (2013) Feasibility of eight physical fitness tests in 1050 older adults with intellectual disability: Results of the healthy ageing with intellectual disabilities study Intellectual and Developmental Disabilities, 51(1), 33–47.

Hocking, D R., Rinehart, N J., McGinley, J L., & Bradshaw, J L (2009) Gait function in adults with Williams syndrome Experimental Brain Research, 192(4), 695– 702.

Horvat, M., Croce, R., Zagrodnik, J., Brooks, B., & Carter, K (2012) Spatial and temporal variability of movement parameters in individuals with Down syndrome Perceptual and Motor Skills, 114(3), 774–782.

Hsieh, K., Heller, T., & Miller, A B (2001) Risk factors for injuries and falls among adults with developmental disabilities Journal of Intellectual Disability Research, 45(Pt 1), 76–82.

Hsieh, K., Rimmer, J., & Heller, T (2012) Prevalence of falls and risk factors in adults with intellectual disability American Journal on Intellectual and Developmental Disabilities, 117(6), 442–454.

Jones, C J., Rikli, R E., & Beam, W C (1999) A 30-s chair-stand test as a measure of lower body strength in community-residing older adults Research Quarterly for Exercise and Sport, 70(2), 113–119.

Lahtinen, U., Rintala, P., & Malin, A (2007) Physical performance of individuals with intellectual disability: A 30 year follow up Adapted Physical Activity Quarterly, 24(2), 125–143.

Lamb, S E., Jorstad-Stein, E C., Hauer, K., Becker, C., Prevention of Falls Network, E., & Outcomes Consensus, G (2005) Development of a common outcome data set for fall injury prevention trials: The Prevention of Falls Network Europe consensus Journal of the American Geriatrics Society, 53(9), 1618–1622.

Muraki, S., Akune, T., Oka, H., Ishimoto, Y., Nagata, K., Yoshida, M., et al (2013) Physical performance, bone and joint diseases, and incidence of falls in Japanese men and women: A longitudinal cohort study Osteoporosis International, 24(2), 459–466.

Oppewal, A., Hilgenkamp, T I., van Wijck, R., & Evenhuis, H M (2013) Feasibility and outcomes of the Berg Balance Scale in older adults with intellectual disabilities Research in Developmental Disabilities, 34(9), 2743–2752.

Quach, L., Galica, A M., Jones, R N., Procter-Gray, E., Manor, B., Hannan, M T., et al (2011) The nonlinear relationship between gait speed and falls: The Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly of Boston Study Journal of the American Geriatrics Society, 59(6), 1069–1073.

Rigoldi, C., Galli, M., & Albertini, G (2011) Gait development during lifespan in subjects with Down syndrome Research in Developmental Disabilities, 32(1), 158– 163.

Rikli, R E., & Jones, C J (2001) Senior fitness test manual USA: Human Kinetics.

Sackley, C., Richardson, P., McDonnell, K., Ratib, S., Dewey, M., & Hill, H J (2005) The reliability of balance, mobility and self-care measures in a population of adults with a learning disability known to a physiotherapy service Clinical Rehabilitation, 19(2), 216–223.

Salaun, L., & Berthouze-Aranda, S E (2012) Physical fitness and fatness in adolescents with intellectual disabilities Journal of Applied Research in Intellectual Disabilities, 25(3), 231–239.

Sherrard, J., Tonge, B J., & Ozanne-Smith, J (2001) Injury in young people with intellectual disability: Descriptive epidemiology Injury Prevention, 7(1), 56–61.

Smith, B A., & Ulrich, B D (2008) Early onset of stabilizing strategies for gait and obstacles: Older adults with Down syndrome Gait Posture, 28(3), 448–455.

Smith, B A., Stergiou, N., & Ulrich, B D (2011) Patterns of gait variability across the lifespan in persons with and without down syndrome Journal of Neurologic Physical Therapy, 35(4), 170–177.

Stark, T., Walker, B., Phillips, J K., Fejer, R., & Beck, R (2011) Hand-held dynamometry correlation with the gold standard isokinetic dynamometry: A systematic review Physical Medicine and Rehabilitation: The Journal of Injury, Function, and Rehabilitation, 3(5), 472–479.

Steffen, T M., & Seney, M (2008) Test-retest reliability and minimal detectable change on balance and ambulation tests, the 36-item short-form health survey, and the unified Parkinson disease rating scale in people with parkinsonism Physical Therapy, 88(6), 733–746.

Steffen, T M., Hacker, T A., & Mollinger, L (2002) Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds Physical Therapy, 82(2), 128–137.

Stenhagen, M., Ekstrom, H., Nordell, E., & Elmstahl, S (2013) Falls in the general elderly population: A 3- and 6-year prospective study of risk factors using data from the longitudinal population study ‘Good ageing in Skane’ BMC Geriatrics, 13, 81.

Tinetti, M E., & Kumar, C (2010) The patient who falls: ‘‘It’s always a trade-off’’ Journal of the American Medical Association, 303(3), 258–266.

Van Hanegem, E., Enkelaar, L., Smulders, E., & Weerdesteyn, V (2013) Obstacle course training can improve mobility and prevent falls in people with intellectual disabilities Journal of Intellectual Disability Research.

Wang, C Y., Hsieh, C L., Olson, S L., Wang, C H., Sheu, C F., & Liang, C C (2006) Psychometric properties of the Berg Balance Scale in a community-dwelling elderly resident population in Taiwan Journal of the Formosan Medical Association, 105(12), 992–1000.

Willgoss, T G., Yohannes, A M., & Mitchell, D (2010) Review of risk factors and preventative strategies for fall-related injuries in people with intellectual disabilities Journal of Clinical Nursing, 19(15–16), 2100–2109.

World Health Organization (1996) ICD-10 guide for mental retardation Geneva, Switzerland: World Health Organization.