How many days of monitoring predict physical activity and sedentary behaviour in older adults?

pages

English

Documents

2011

Écrit par
Swartz , Cashin , Strath , Hart

Publié par
biomed

Lire un extrait

Obtenez un accès à la bibliothèque pour le consulter en ligne En savoir plus

Découvre YouScribe et accède à tout notre catalogue !

Je m'inscris

Découvre YouScribe et accède à tout notre catalogue !

Je m'inscris

pages

English

Documents

2011

Lire un extrait

Obtenez un accès à la bibliothèque pour le consulter en ligne En savoir plus

Publié par

biomed

Publié le

01 janvier 2011

Nombre de lectures

517

Langue

English

The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Methods Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. Conclusions The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of differences between days suggests that day of the week cannot be completely ignored in the design and analysis of PA studies that involve < 7-day monitoring protocols for these instruments. More days of accelerometer data were needed to determine typical sedentary behaviour than PA level in this population of older adults.

Voir

Publié par

biomed

Publié le

01 janvier 2011

Langue

English

Hart
etal
.
InternationalJournalofBehavioralNutritionandPhysicalActivity
2011,
8
:62
http://www.ijbnpa.org/content/8/1/62

RESEARCH

OpenAccess

Howmanydaysofmonitoringpredictphysical
activityandsedentarybehaviourinolderadults?
TeresaLHart
1
,AnnMSwartz
2
,SusanECashin
2
andScottJStrath
2*

Abstract
Background:
Thenumberofdaysofpedometeroraccelerometerdataneededtoreliablyassessphysicalactivity
(PA)isimportantforresearchthatexaminestherelationshipwithhealth.Whilethisimportantresearchhasbeen
completedinyoungtomiddle-agedadults,dataislackinginolderadults.Further,datadeterminingthenumber
ofdaysofself-reportsPAdataisalsovoid.Thepurposeofthisstudywastoexaminethenumberofdaysneeded
topredicthabitualPAandsedentarybehaviouracrosspedometer,accelerometer,andphysicalactivitylog(PAlog)
datainolderadults.
Methods:
Participants(52oldermenandwomen;age=69.3±7.4years,range=55-86years)woreaYamax
DigiwalkerSW-200pedometerandanActiGraph7164accelerometerwhilecompletingaPAlogfor21consecutive
days.Meandifferenceseachinstrumentandintensitybetweendaysoftheweekwereexaminedusingseparate
repeatedmeasuresanalysisofvarianceforwithpairwisecomparisons.Spearman-BrownProphecyFormulaebased
onIntraclassCorrelationsof.80,.85,.90and.95wereusedtopredictthenumberofdaysofaccelerometeror
pedometerwearorPAlogdailyrecordsneededtorepresenttotalPA,lightPA,moderate-to-vigorousPA,and
sedentarybehaviour.
Results:
Resultsofthisstudyshowedthatthreedaysofaccelerometerdata,fourdaysofpedometerdata,orfour
daysofcompletingPAlogsareneededtoaccuratelypredictPAlevelsinolderadults.Whenexaminingtimespent
inspecificintensitiesofPA,fewerdaysofdataareneededforaccuratepredictionoftimespentinthatactivityfor
ActiGraphbutmoreforthePAlog.Toaccuratelypredictaveragedailytimespentinsedentarybehaviour,fivedays
ofActiGraphdataareneeded.
Conclusions:
Thenumberdaysofobjective(pedometerandActiGraph)andsubjective(PAlog)dataneededto
accuratelyestimatedailyPAinolderadultswasrelativelyconsistent.Despitenostatisticaldifferencesbetweendays
fortotalPAbythepedometerandActiGraph,themagnitudeofdifferencesbetweendayssuggeststhatdayofthe
weekcannotbecompletelyignoredinthedesignandanalysisofPAstudiesthatinvolve<7-daymonitoring
protocolsfortheseinstruments.Moredaysofaccelerometerdatawereneededtodeterminetypicalsedentary
behaviourthanPAlevelinthispopulationofolderadults.

Background
easytoadministerhoweveraresubjecttoerrorand
Physicalactivity(PA)isasporadicandcomplexbeha-recallbias[3].ObjectivemeasuresofPAandsedentary
viourtomeasureandissubjecttointer-andintra-indi-behaviour,suchaspedometersandaccelerometers,have
vidualvariability[1].Ithasalsobeensuggestedthatshownpromisewhenusedtoassesshabitualbehaviour.
sedentarybehaviourisanimportant,independentbeha-Determiningthenumberofdaystoreliablyassesshabi-
viourtoaccountforduetoitsrelationwithhealth[2].tualPAandsedentarybehaviourandminimizingparti-
Self-reportmethodstoassessPAandsedentarybeha-cipantburdenremainsachallenge.
vioursuchaslogs,questionnaires,orsurveys,areoftenThenumberofdaystoreliablypredicthabitualPA
behaviourforyoungandmiddle-agedadultshasbeen
*Correspondence:sstrath@uwm.edu
examinedusingpedometersandaccelerometers.Aspart
2
DepartmentofHumanMovementSciences,UniversityofWisconsin-
ofayear-longpedometerself-monitoringstudyinadults
Milwaukee,Milwaukee,Wisconsin,USA
Fulllistofauthorinformationisavailableattheendofthearticle
(meanage=38±10years),itwasdeterminedthatfive
©2011Hartetal;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreativeCommons
AttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,andreproductionin
anymedium,providedtheoriginalworkisproperlycited.

Hart
etal
.
InternationalJournalofBehavioralNutritionandPhysicalActivity
2011,
8
:62
http://www.ijbnpa.org/content/8/1/62

Page2of7

consecutivedaysorsixrandomlyselecteddaysofdatalimited.Thereforethepurposeofthisstudywasto
wereneededtoproduceanintraclasscorrelation(ICC)examinethenumberofdataacquisitiondaysneededto
of0.80[4].Datafromsevendaysofconsecutiveped-reliablypredictPAandsedentarybehaviourusingaped-
ometermonitoringinadultmen(meanage=49.1±ometer,accelerometer,orPAloginanolderadult
16.2years)andwomen(meanage=44.8±16.9years)population.Secondly,weaimtoprovideanindicationof
suggestedthatthreedaysofmonitoringproducedanwhetherestimatesofPAandsedentarybehaviourvary
ICCof0.80orgreater[5].Similarresultswerereporteddependingonwhichdaysoftheweekareexamined..
whenassessingPAandsedentarybehaviourusinganTheresultsfromthisstudywillprovideusefulinforma-
accelerometerinmiddle-agedadults.ToreliablypredicttionregardingtheuseofPAassessmentmethodology
21daysofmonitoring,itwassuggestedthatthreetofortheolderadultpopulation.
fourdaysofaccelerometermonitoringwereneededto
achieve80%reliabilityfortotalPAaswellasmoderate
Methods
andvigorousintensityPA,andsevendaysofmonitoring
ParticipantsandProcedures
wereneededtopredictsedentarybehaviour[6].Participantsincluded52oldermenandwomen(mean±
Together,thesedatasuggestthataminimumofthreestandarddeviationage=69.3±7.4years,range=55-86
daysofobjectivemonitoringareneededtoreliablypre-years)whowererecruitedaspartofalargerongoing
dictPAbehaviour,whilesevendaysofobjectivemoni-trialexaminingobjectivelydeterminedphysicalactivity
toringareneededtopredicttimespentinsedentaryprofilesofcommunitydwellingolderadults.Participants
behaviourinayoungtomiddle-agedpopulation.woreaYamaxDigiwalkerSW-200(YamasaCorporation,
DespitethepopularityofestimatingPAandsedentaryTokyo,Japan)pedometerandanActiGraphmodel7164
behaviourstodeterminePAorSBprevalenceorrela-(formerlyCSIandMTI;ActiGraphLLC,Pensacola,FL)
tionshipswithvariousaspectsofhealth,thereremainaaccelerometerconcurrentlywhilecompletingaphysical
numberofgapsintheliteraturefocusingonthenumberactivitylog(PAlog)duringallwakinghours(excluding
ofdataacquisitiondaysneededtoreliablypredictPAshoweringandswimming)for21consecutivedays.
andsedentarybehaviours.First,whilestudieshaveTable1containsdemographicinformationforallparti-
reportedconsistentresultswithregardstonumberofcipants.Ethicalapprovalforthisstudywasgrantedby
daysofmonitoringusingobjectivemethodsofPAtheUniversityofWisconsin-MilwaukeeInstitutional
assessmentinayoungtomiddle-agedpopulation,dataReviewBoard.
onolderadultsislacking.Second,thenumberofdays
ofdataacquisitionneededtoreliablypredictPAbeha-
Instruments
viourfromsubjectivemethodsisvoidintheliterature.
ActiGraphAccelerometer
Finally,comprehensive,concurrentcomparisonsacrossTheActiGraph(model7164)usedinthisstudyisoneof
subjectiveandobjectivemethodsofPAandsedentarythemostwidelyusedaccelerometersinPAresearch.
behaviourassessmentwithinthesamepopulationareThissamemodelhasbeenusedforobjectivePA

Table1Participantdemographicsandphysicalactivitybehaviourdata
All(N=52)
Age(years)
69.3(7.4)

BodyMassIndex(kg/m
2
)

WaistCircumference(cm)

RestingSystolicBloodPressure(mmHg)

RestingDiastolicBloodPressure(mmHg)

Accelerometer(counts/day)

PALog(MET-min/day)

27.0(23.9,29.3)

94.5(83.9,101.0)

128(14)

7(7)9

250550(116260)

1107(358)

Pedometer(steps/day)
5589(3941,8971)
Note.Dataarepresentedasmean(standarddeviation)ormedian(interquartilerange).

Male(n=13)
68.7(10.2)

27.0(24.6,30.2)

100.0(92.0,111.0)

127(13)

74(9)

278430(152809)

1147(496)

8971(3894,9260)

Female(n=39)
69.6(6.3)

27.0(23.6,29.4)

90.3(81.6,100.4)

128(14)

78(8)

241257(102040)

1094(307)

5490(3892,7592)

Hart
etal
.
InternationalJournalofBehavioralNutritionandPhysicalActivity
2011,
8
:62
http://www.ijbnpa.org/content/8/1/62

monitoringintheNationalHealthandNutritionExami-
nationSurvey(NHANES)[7].Detailedtechnicalspecifi-
cationfortheActiGraphareprovidedelsewhere[8,9].
TheprimaryoutputsfromtheActiGraphareactivity
counts,whichrepresentrawaccelerationsthathave
beenfiltered,digitized,integratedandrescaled.Detected
activitycountsaresummedovereachepoch(i.e.,com-
monlyaminuteinlengthforadults)[10].Thesumof
theactivitycountsinagivenepochisrelatedtoactivity
intensityandcanbecategorized(e.g.,sedentary,light,
moderate,vigorous)basedonvalidatedactivitycount
cutpoints[8,9].Forthecurrentstudy,theActiGraph
waswornontherightsideofthewaistonanelastic
belt,accordingtomanufacturerspecifications.
YamaxDigiwalkerSW-200Pedometer
TheYamaxDigiwalkerSW-200isanelectronicped-
ometerwithahorizontal,spring-suspendedleverarm
whichmovesupanddownwithverticalaccelerationsof
thehip.Whenaccelerationsare
≥
0.35g,theleverarm
makesanelectricalcontactandonestepisrecorded.
TheYamaxdisplaysthenumberofstepstakenduringa
givenperiodoftimeduringwhichthemonitorisworn
withadisplayoutputrangeof0-99,999steps.The
Yamaxattachestothewaistlineofpantsortoabeltat
themidlineofeitherthigh.TheYamaxDigiwalkeris
consideredtobethecriterionpedometerforfree-living
PAresearchstudies[11].
PhysicalActivityLog
ParticipantsrecordedactivitiesonthePAlogwhichhas
beenusedbyothersinpaststudies[12]attheendof
eachday.Alistofactivitieswasprovidedforthepartici-
pantonthedailylogsheetandthebroadcategories
included:householdactivities,lawn/gardenactivi

Voir