An updated estimate of the body dimensions of US children

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by  Brian T. Pagano

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Ergonomics
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An updated estimate of the body dimensions of US
children
Brian T. Paganoa, Matthew B. Parkinsonb & Matthew P. Reedc
a Mechanical Engineering, Penn State University, University Park, PA, USA
b Engineering Design, Mechanical Engineering, and Industrial Engineering, Penn State
University, University Park, PA, USA
c Transportation Research Institute, University of Michigan, Ann Arbor, MI, USA
Published online: 19 Jan 2015.
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To cite this article: Brian T. Pagano, Matthew B. Parkinson & Matthew P. Reed (2015): An updated estimate of the body
dimensions of US children, Ergonomics, DOI: 10.1080/00140139.2014.1000392
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Ergonomics,2015
http://dx.doi.org/10.1080/00140139.2014.1000392
An updated estimate of the body dimensions of US children
BrianT. Paganoa, MatthewB. Parkinsonb* and MatthewP. Reedc
aMechanicalEngineering,PennStateUniversity,UniversityPark,PA,USA;bEngineeringDesign,MechanicalEngineering,and
IndustrialEngineering,PennStateUniversity,UniversityPark,PA,USA;cTransportationResearchInstitute,UniversityofMichigan,
AnnArbor,MI,USA
(Received23April2014;accepted9December2014)
Anthropometric data from children are important for product design and the promulgation of safety standards. The last
majordetailedstudyofchildanthropometryintheUSAwasconductedmorethan30yearsago.Subsequentdemographic
changesandtheincreasedprevalenceofoverweightandobesityrenderthosedataincreasinglyobsolete.Anew,large-scale
anthropometricsurveyisneeded.Asaninterimstep,anewanthropometricsynthesistechniquewasusedtocreateavirtual
populationofmodernchildren,eachdescribedby84anthropometricmeasures.Asubsetofthesedatawasvalidatedagainst
limited modern data. Comparisons with data from the 1970s showed significant changes in measures of width and
circumferenceofthetorso,armsandlegs.Measuresoflengthandmeasurementsofthehead,face,handsandfeetexhibited
little change. The new virtual population provides guidance for a comprehensive child anthropometry survey and could
improvesafetyandaccommodationinproductdesign.
PractitionerSummary:ThisresearchreviewstheinadequaciesofavailablesourcesofUSchildanthropometryasaresult
5 oftheriseintheratesofoverweightandobesity.Anewsynthesiseddatabaseofdetailedmodernchildanthropometrywas
1
0 createdandvalidated.TheresultsquantifychangesinUSchildbodydimensionssincethe1970s.
2
y
r Keywords:childanthropometry;anthropometrysynthesis;humanvariability;childobesity;childgrowth;productdesign
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1 1. Introduction
2
3
0 Accurate data on human body dimensions are criticalwhen designersare creating products and environments for human
5:
0 use.Childrenprovideaparticularchallengesincetheirbodysizeandshapechangesoquickly(LeuderandBergRice2008).
at Medical and public health professionals use the body dimensions of children to benchmark patients’ relative body
]
y [ dimensions and growth rates (Kuczmarski et al. 2000). Statistics on child body dimensions are also used for safety,
d b regulation and product sizing (Snyder et al. 1975, 1977; Steenbekkers and Molenbroek 1990). Data are utilised by the
de automotiveindustryforthecreationofcrashtestdummies(Manaryetal.2006;Reedetal.2009;Loydetal.2010)andthe
a
o regulation of child and infant restraints (Burdi et al. 1969; Reed et al. 2005; Anderson and Hutchinson 2009). Improved
nl
w design of children’s school furniture (Chung and Wong 2007; Savanur, Altekar, and De 2007; Agha 2010) and other
o
D artefacts (Hughes and Johnson 2011;Berg Rice 2012)relies extensivelyon anthropometricdata amongotherfactors.
From1975to1986,threedetailedstudiesofchildanthropometrywereconductedattheUniversityofMichiganunder
sponsorshipfromtheUSConsumerProductSafetyCommission(Snyderetal.1975,1977;Schneideretal.1986).Allthree
studiesmeasuredchildrenthatwereintendedtoberepresentativeoftheUSpopulationatthetimeofthestudy.Thestudies
resultedinreportscontainingdescriptionsofeachmeasurementaswellasplotsandtablescontainingbasicstatisticsand
percentilevaluesatsmallageintervals.However,Snyderetal.(1977)istheonlyoneofthethreestudiesforwhichtheraw
data remainavailable.
Snyder et al. (1977) was a continuation of a smaller study published in 1975 (Snyder et al. 1975). The primary
motivationforthenewstudywastogatheradditionalinformationaboutchildandinfantanthropometryforuseinproduct
safetydesign.Participantagerangedfromtwoweeksto18.99years.Thestudywasconductedat105locationsaroundthe
USA,chosentoprovideanapproximatelyrandomsample.Noweightingofthesamplewasintendednorperformed.The
study collected 87 traditional and functional body measures. To reduce the time required for data collection from each
participant,themeasurementswere dividedintofourseparategroups,andeachparticipantwasallocatedtotwoofthem.
Figure 1 details the assignment of measurements for the study. Group I measures consisted of ‘core’ measurements that
wereselectedbecausetheywereknowntobehighlycorrelatedwithmeasuresintheotherthreegroups.Datawerecollected
on Group I measurements for all participants. Data were collected for each participant on one of the other three
measurementgroupsaswell.GroupIIcontained‘bodyshape’measurements.GroupIIIcontained‘linkageandcentre-of-
*Correspondingauthor.Email:[email protected]
q2015Taylor&Francis
2 B.T. Pagano et al.
Group II
body shape measures
all children Group I Group III
(2.00 - 18.99 years) core measures linkage measures
Group IV
head, face, and hand measures
Figure1. AbreakdownoftheassignmentofmeasurementsintheSnyderetal.(1977)study.
gravity’ measurements. Group IV contained ‘head, face, and hand’ measurements. Thus, 42–45 measurements were
recorded for each child. After 22 months, atotal of 4127 participants hadbeen measured.
Whilesomelarge-scalestudiesofchildrenhavebeenconductedglobally(e.g.Steenbekkers2009),studiesintheUSA,
where the original studies were conducted, have focused on adults rather than children (e.g. Robinette et al. 2002). As a
result,morerecentdataonchildrenintheUSAareavailableforonlyasmallnumberofmeasures.TheNationalHealthand
Nutrition Examination Survey (NHANES) has been conducted by the US government periodically since 1971 and
continuouslysince1999(CentersforDiseaseControlandPrevention2012).Datafromapproximately10,000peopleofall
agesarereleasedeverytwoyears.Thesedataincludeasamplingweightforeachsubject,effectivelydescribingthenumber
ofpeopleinthe USpopulationthateach subject ‘represents’ onthebasisoftheir demographic characteristics. However,
5
1 NHANESincludesonlyafewmeasuresofbasicanthropometry(e.g.stature,mass);thus,thedataarenotdirectlyapplicable
0
2
y tomostdesignproblems.ThecurrentworkcombinesthedetaileddatafromSnyderetal.(1977)withchilddataavailablein
ar NHANEStocreateanew,synthesiseddatasetofdetailedanthropometrythatmatchesthecurrentUSchildpopulationon
u
n the measuresavailable in NHANES.
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1 In the literature, several techniques have been used to estimate and synthesise anthropometry, notably regression
2
3 methods. These assume linear relationships between predictor measures, such as stature and body mass index (BMI; a
0
5: measureofweight-for-stature),andthemeasuresofinterest.Relationshipsareextractedfromadetaileddatasetandthen
0
at applied using predictor anthropometry for the target population (Robinette and McConville 1981; Kroemer 1989;
] Flannagan et al. 1998; Reed, Manary, and Schneider 1999). Prior to any modelling, the detailed data set should be
[
y reweighted to match the demographic distribution of the predictor data set, as correlations between anthropometric
b
d dimensions are dependent on gender,age andothervariables.
e
ad ThepresentworkimplementsapopulationsynthesisapproachintroducedbyParkinsonandReed(2010).Themethod
o
nl combineslinearregressionmodelswithprincipalcomponentanalysis(PCA),amultivariateanalysistechnique.Stochastic
w
o elements that retain the residual variance from the model fit are included. This approach improves the overall predictive
D
abilityofthe method, particularly inthe tailsof the distributions.
2. Method
2.1. PCA-based anthropometry synthesis
Following Parkinson and Reed (2010), a diagram for the synthesis conducted in this work is shown in Figure 2. PCA, a
multivariateanalysistechnique,usedheretomodelthecovariancestructureofthedetaileddatabase.PCArequiresamatrix
of information with no missing data. As described earlier, the structure of the Snyder et al. (1977) data is such that only
Representative Predictor Dataset
- representative of target population Anthropometry
- basic anthro. and demographic info. from Synthesis Process
NHANES 1999-2008 data Virtual Population
- extracts relationships between
representing modern
measures from the detailed data children
Detailed Dataset - applies relationships to the
basic anthropometry in the
- Snyder et al. (1977) anthro. data
representative predictor dataset
- reweighted and upsampled to match
demographics from NHANES
Figure2. Adiagramdescribingtheinputsandoutputsfortheanthropometrysynthesismethod.
Ergonomics 3
abouthalfofthe87measurementswererecordedforeachsubject.Thus,theprocessdescribedinthissectionwasperformed
separatelyforeachofthefourgroupsofmeasurementsshowninFigure1.Tokeepthesynthesisedmeasurementslinkedto
oneanother,coremeasurementsfromGroupIweresynthesisedforthevirtualpopulationfirst,usingonlystature,BMIand
ageaspredictors.Ninemeasures(inadditiontostature,BMIandage)wereselectedfromtheresultingsynthesiseddatato
serve as predictors for the Groups II, III and IV data. This is consistent with the structure of the Snyder study since the
Group I measurements were selected by the original data collection team to be highly correlated with the other
measurements. Four of the 87 measures concerned the locations of centers of mass and were excluded from subsequent
analyses.Althoughstatureandmasswerereportedforeachparticipant,BMIwasnot.Forthepurposesofthepresentwork,
itwascalculateddirectlyfromthedataandaddedtothelistofmeasures.Thisresultedin8724þ1¼84measuresthat
were consideredhere.Formore on the details ofPCA, see Jolliffe (2004)orShlens (2009).
Thefirststepinthemethodwasintendedtoaccountforchangesinthedistributionsofdemographicsubgroupsofthe
populationofAmericanchildrenoverthepastseveraldecades.DatafromSnyderetal.(1977)werereweightedsuchthatthe
distributionsofdemographicvariables(ageandgender)matchedthoseforchildrenfromtheNHANESdatasetfrom1999
to2008.EachindividualinSnyderetal.(1977)wasgroupedintooneof32bins(16agebinsrangingfrom2to19yearsfor
each gender). The same process was performed for individuals from the NHANES data set. The sum of the statistical
weightsofindividualsineachoftheNHANESbinswasdividedupevenlyamongindividualsinthecorrespondingbinsfor
the detailed data set. Instead of using these new statistical weights directly during analysis, the detailed data were
upsampled by repeating the data for each individual a number of times proportional to its new statistical weight. This
process effectively created a detailed data set, made up of anthropometry from Snyder et al. (1977), that was
demographicallysimilartotherepresentativepredictordatasetwithouttheuseofstatisticalweights.Whileincreasingthe
5 size ofthe matrices involved, itsimplifies somesubsequent calculations and reduces computation time.
1
0 TheimplementationofthemethodfromParkinsonandReed(2010)issummarisedhere.Measuresthatwereintendedto
2
y beusedaspredictors,suchasageinmonths,statureandBMI,wereseparatedfromthebodysegmentlengths,widthsand
r
ua circumferences of the detailed data set that were to be predicted. The collection of length, width, and circumference
n
a measures were ‘centred’ such that the mean¼0 and analysed using PCA on the covariance matrix. The PCA process
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1 producedamatrixofprincipalcomponents(PCs),andamatrixofloadingsorscores.Thesubsequentanalysisretaineda
2
3 numberofPCssufficienttoexplain99%ofthevarianceintheoriginaldata.Linearregressionwasconductedtopredictthe
0
5: scores on the retained PCs from the predictor variables. Table 1 lists the number of subjects, predictors, predicted
0
at dimensions and retained PCs for each male and female group.
[] The resulting linear models were used to predict PC scores for each subject in the NHANES sample. Normally
y
b distributedresidualvariancewasadded back intothe new scores by addinga random number —selectedfromanormal
d
e distribution with a mean of zero and a standard deviation equal to the square root of the residual variance from the
d
oa correspondingregressionmodel—toeachpredictedscore.Additionally,randomlychosenPCscoresfromthepreviously
nl discardedPCswereincorporatedwiththepredictedscorestobringthenewscoresmatrixtothesamesizeastheoriginal.
w
o This adds meaningful variance back into the model from components that were not significantly related to the predictor
D
variables.Finally,thenewscoresmatrixwascombinedwiththePCmatrixtotransformthedatafromPCspacebackinto
anthropometryspace.Thus,detailedmeasuresoflength,widthandcircumferencewerepredictedforeachsetofpredictor
anthropometry fromthe representativedata set.
Themethodwasusedtogeneratedetailedanthropometricdataforeachofthe18,741childrenaged2.00to18.99years
intheNHANES1999–2008sample.Eachvirtualpersonisdescribedbydemographicinformationand84anthropometric
measures.Becausetheoriginalpredictorvariablesanddemographicinformationforthevirtualpopulationwereextracted
from NHANES, the statistical weights associated with those NHANES data were inherited for the virtual population as
well.The synthesisprocesswas performed separately for males andfemales.
Table1. Summarydetailsonthesynthesisofthevirtualpopulationofchildren.
GroupI GroupII GroupIII GroupIV
Gender m f m f m f m f
Predictors 3 12 12 12
Synthesisedmeasures 20 17 21 23
Originaldetaileddatasetsize 1737 1712 550 511 582 544 560 537
Predictordatasetsize 9473 9268 9473 9268 9473 9268 9473 9268
PCsused 6 6 3 3 3 5 11 14
4 B.T. Pagano et al.
2.2. Refinement
When combined with the predictor anthropometry from the representative data set, the newly synthesised detailed
anthropometricdatarepresentedauniquepopulationofvirtualpeople.Duetothestochasticnatureoftheprediction(using
unboundednormalrandomvariates),thereisasmallpossibilityofunlikelycombinationsofmeasures,particularlyamong
highlycorrelatedmeasures.ThePCAreducesbutdoesnoteliminatethelikelihoodofincompatibledimensions.Sincethe
synthesis procedure is conducted a large number of times, increasing the possibility of manifestations of this issue, the
dimensions for each individual within the candidate population were examined programmatically. When unrealistic
combinations were identified, the complete set of dimensions associated with that individual or set of predictors was
synthesised again using the same procedure. The highly correlated measurement pairs that were used to check for
incompatible dimensions are listedin Table 2.
As a further check, the data from Snyder et al. (1977) were used to compute body segment proportions relative to
stature. Assuming that the proportions of body segment lengths of children have not changed dramatically in the past
severaldecades,theseupperandlowerlimitsofproportionalityforeachmeasurewereusedtoeliminatesynthesisedvirtual
children with highly unlikely proportions. Five percent of the range of each proportion was added to the maximum and
subtracted from the minimum observed proportion values to account for the difference in sample size and increased
physical size between Snyder et al. (1977) and the synthesised virtual population. The measures that were examined for
checks on length proportionality are listed in Table 3. Buttock–knee length, which is also affected by body mass, is
included with measures of length since that is the dominant relationship. Detailed descriptions of the measures listed in
Tables2 and 3can be found inthe final report from Snyder et al. (1977).
5
1 2.3. Comparison with NHANES
0
2
y Inadditiontostatureandbodyweight,thephysicalexaminationofNHANESparticipantsincludedseveraladditionalbody
r
a
u dimensions (beyond stature, mass and BMI). These dimensions were compared with the synthesised data to assess the
n
Ja successofthedimensionsynthesis.Thiscomparisonisparticularlyusefulbecausethedemographicdistributionsofthedata
21 sets are identical,sinceeach child in NHANES was used.
03 Upper arm length, upper arm circumference and waist circumference were available in NHANES. However, the
5: measuresinthesynthesisedvirtualpopulationreflectthemeasurementpracticesofSnyderetal.(1977)anddifferfromthe
0
at practicesthatwereusedtomakedetailedmeasurementsinNHANES.Thetwodatasetsutiliseddifferentlandmarklocations
]
[ for measurements and used different types of measurement tools. For example, shoulder–elbow (upper arm) length in
y
b Snyderetal.(1977)wasmeasuredwithanelectronicanthropometer(alargecaliper-likedevice)fromthesuperiorsurfaceof
d
e the right shoulder tothe inferior surface ofthe forearm justbelow the elbow.Incontrast, NHANES measures upper arm
d
oa lengthwithathinsteelmeasuringtapefromtheuppermostedgeoftheacromialprocesstothetipoftheolecranonprocess
wnl (Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS) 1994). Differences in
Do measurement techniques and tools like these will create repeatable differences in measurements for the same individual,
calledbiaserror.Differencesinmeasurementtechniqueandtoolswerepresentinallthreemeasurements.Nevertheless,the
twosetsofdatahaveidenticaldemographicdistributionsandprovidedauniqueopportunityforcomparison.
Table2. Measurepairs(identifiedduetothestrongcorrelationbetweenthem)thatwerecomparedto
eliminateunlikelycombinationsofdimensions.
Larger Smaller
Trochantericheight Glutealfurrowheight
Hipheightatbuttocks Glutealfurrowheight
Iliospinaleheight Trochantericheight
Iliocristaleheight Iliospinaleheight
Kneeheight Tibialeheight
Shoulder–elbowlength Acromion–radialelength
Suprasternaleheight Chestheightataxilla
Headheight Faceheight
Headbreadth Bitragionbreadth
Bizygomaticbreadth Frontalbreadth
Maxseatedhipbreadth Bispinousbreadth
Shoulderbreadth Biacromialbreadth
Calfcircumference Anklecircumference
Minimumhandclearance Maximumfistbreadth
Ergonomics 5
Table 3. Measures of length that were checked for their proportionality with stature in order to
eliminatephysicallyunlikelyanthropometricproportions.
Buttock–kneelength Kneeheight Sphyrionheight
Elbow–handlength Radiale–stylionlength Suprasternaleheight
Footlength Seatedeyeheight Tibialeheight
Handlength Shoulder–elbowlength Trochantericheight
Hiplengtha Sittingheight
aHiplength¼iliospinaleheight2iliocristaleheight.
3. Results
3.1. Anthropometrictrendsin NHANES
TheNHANESdataindicatethatvaluesofcertainbodydimensionswithineachagecohortintheUSAhavebeengetting
larger in the last several decades, particularly through the 1980s and 1990s (National Center for Health Statistics 2012).
Afterslowandsteadyincreasesinbothchildstatureandmasssubsidedinthe1960s,littlechangewasobserveduntilthe
late 1970s. Since this time, body mass within a given age group has increased while stature remained approximately
constant(Malina2004;SmithandNorris2004;Roche1995).Figure3showsthedifferencebetweenstature,massandBMI
in children between 1977 and the 2000s. Notice that in the mass and BMI plots, data in upper percentile ranges are
noticeablydifferentwhilethelowertailsofthedistributionsaresimilar.Thatis,boththemeanandvarianceinbodyweight
are increasingas age increases.
5 Tobetterillustratetheagesandpercentilesatwhichchangesindimensionsoccurred,percentilevalueswerecalculated
1
0 for small age ranges on each basic measure, using data from NHANES. The results are displayed inthe contour plots of
2
y Figure4.Darkershadingsignifiesagreaterabsolutepercentchangebetweenthetwopopulationsatthatparticularageand
r
a
u percentile.Plotsformalesandfemalesaresimilar,bothshowinglittletonochangeinstatureandlargechangesinmassand
n
a BMI. In both cases, mass and BMI at the lowest percentiles remain relatively unchanged, and changes are largest in the
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21 highestpercentiles.Infemales,changesinmassandBMIappeartohavereachedfurtherdownintolowerpercentileranges
3 than thoseinmales. DifferencesinmassandBMIalsoseem tohave mostgreatly affected childrenover theage ofeight
0
5: years.
0
at
]
[
y 3.2. Synthesised anthropometry
b
d Tables 4 and 5 present quantiles of the nine body dimensions for the synthesised population. Figure 5 shows a general
e
d
a comparison of Snyder et al. (1977) with the synthesised data sets on three dimensions commonly used for design. Knee
o
nl height,maximum seated hipbreadthand upper thigh circumference for a combined population of males and females are
w
o plottedvs.age,representingmeasuresoflength,widthandcircumference,respectively.Ofthethreemeasures,kneeheight
D
showsthesmallestchangeoverthreedecades(thedatafromthetwopopulationsoccupythesameregionoftheplot).Also,
theoverallshapeofthedistributionsinthisplotissimilartothatofstature,depictedinFigure3.Thiswasexpectedsince
kneeheight,ameasureoflength,ishighlycorrelatedwithstature.Plotsofpercentdifferenceinkneeheightformalesand
120 40
1800
100
1600
80 30
stature 1400 mass BMI
(mm) (kg) 60 (kg/m2)
1200
20
40
1000
20
800
10
2 6 10 14 18 2 6 10 14 18 2 6 10 14 18
age (years) age (years) age (years)
97.5th 97.5th
50th 1977 (Snyder, et al.) 50th 1999-2008 (NHANES)
2.5th 2.5th
Figure3. Fromlefttoright:the2.5th,50thand97.5thpercentilestature,massandBMIversusageforchildrenfrom1977(Snyderetal.
1977)and1999–2008.
6 B.T. Pagano et al.
percent difference between NHANES (1999-2008) and Snyder et al. (1977)
0% 10% 20% 30% 40%
stature mass BMI
80
60
percentile
(males)
40
20
80
60
percentile
(females)
40
5 20
1
0
2
y
ar 4 6 8 10 12 14 16 18 4 6 8 10 12 14 16 18 4 6 8 10 12 14 16 18
u
n age (years) age (years) age (years)
a
J
1
2 Figure4. Contourplotsforstature,massandBMIinmalesandfemalesshowingthedifferenceinanthropometryatvariouspercentiles
03 forchildrenaged2.75to18.25yearsbetween1977and1999–2008.
5:
0
at femalesinFigure6showlittletonodifferencebetweenSnyderetal.(1977)andthemodernvirtualpopulationofchildren.
]
[ TheseplotsaresimilartotheplotsofpercentchangeinstaturebetweenthetwotimeperiodsfromFigure4.Similarly,plots
y
b ofpercentdifferenceinthedistributionofupperarmlength,seeninFigures7band8b,alsoshowthattherehasbeenlittleto
d
e no difference inmeasuresof length in children since 1977 across allages andpercentiles.
d
oa SincemeasuresofbodywidthandcircumferencearecorrelatedwithBMI,maximumseatedhipbreadthandupperthigh
wnl circumferencewereexpectedtoshowshiftssimilartotheBMIdistributioninFigure3.ThisisconfirmedbyFigure5,which
Do comparesthe2.5th-,50th-and95th-percentilevalueswiththeir1977counterparts.AswithBMI,thelowerpercentilesare
similaracrossthetwopopulations,withlargedifferencesintheupperpercentiles.Plotsofpercentdifferenceinmaximum
seatedhipbreathandupperthighcircumference(Figure6)showsimilarresultsformalesandfemales,withlargechanges
occurringintheupperpercentilerangesandbecomingmostprominentaftereightyearsofage.Differencesofupto20%
600 800
450
500 seated upper
knee 600
hip 350 thigh
height
400 breadth circ.
(mm)
(mm) (mm)
250 400
300
200 150 200
2 6 10 14 18 2 6 10 14 18 2 6 10 14 18
age (years) age (years) age (years)
97.5th 97.5th
50th 1977 (Snyder, et al.) 50th 1999-2008 (virtual population)
2.5th 2.5th
Figure5. The2.5th,50thand95thpercentilekneeheight,maximumseatedhipbreadthandupperthighcircumference.Thedataare
plottedversusageforchildrenfromSnyderetal.(1977)andthesynthesizedvirtualpopulation(1999–2008).
Ergonomics 7
17.5–19.0years 16481762188954.973.4112.918.323.6365145636123013474272422964103423744044815727586717971063
16.5–17.5years 16341760187653.471114.718.12335.55045616102993444342392914033413714034715707556777941062
15.5–16.5years 16341749187850.767106.117.521.734.25055606162923354112372773823393694064635487316547641035
14.5–15.5years 15921734184946.963.8105.116.821.634.34965516032843314172272723863253653924475377266417611031
13.5–14.5years 14951663180138.557.191.516.320.731.7468528585265315392212260356312349383417509685599726959
12.5–13.5years 14451577173632.750.68515.12031.6443499565251303383194251350292331372385496669563707946
15 11.5–12.5years 14061520166432.345.375.215.119.630.1431484535248290369191237337287320351379470642559680925
20 n.
nuary pulatio 10.5–11.5years 13371454159928.938.168.515.218.228.2411460514235272357184221316268303339356435615536637868
a o
J p
03 21 virtual 9.5–10.5years 12991398151226.335.359.514.517.827.5389440484220262332171213299259290320349415575522618828
5: d
y [] at 0 nthesise 8.5–9.5years 12351353146323.132.250.114.417.325.8368424468219252316163203281249279311324398531511599785
aded b nthesy 7.5–8.5years 11851287139821.227.945.514.116.724.2358402454209240301158191265238265296320381509501573758
o i
Downl ormales 6.5–7.5years 1123123313321924.43713.816.222.7337380424203230276152181244227253279308360467478545690
groupf 5.5–6.5years 10961178124917.622.132.713.815.921.7322361401194221261146176232215241264295347446464535675
e
sbyag 4.5–5.5years 10131102119015.319.128.21415.920.6295335378185208252144168215196224256282327425459517644
n
mensio 3.5–4.5years 95910331112141722.214.116.118.9268310355176203229137165195184208235269315375445509587
di
ofbody 2.0–3.5years 854937101411.614.31814.716.419235276319168192215134160186164188210261305354442499561
evalues ercentile 5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th
ntil P
Table4.Perce Measure Stature(mm) Mass(kg) BMI kneeheight(mm) seatedhipbreadth(mm) upperarmcirc(mm) upperarmlength(mm) upperthighcirc(mm) Waistcirc(mm)
8 B.T. Pagano et al.
17.5–19.0years 15261629173746.260.999.817.922.836.94554985433173644722212663733133393704905747846507501030
16.5–17.5years 15201626172745.358.494.217.521.834452500552309353454222259356310339370477560771639742991
15.5–16.5years 15151624173145.859.692.91822.334.6458503555309357447221262357313340372491568759651755998
14.5–15.5years 14991618172643.457.990.517.121.834.6456503555302347446212256363308339369473558759631746995
13.5–14.5years 15011605172039.254.689.716.321.233.5454501556288337433203253350304337366458543746615731990
12.5–13.5years 14701576168636.252.283.416.121.132.1437495541279331422202248336299329361443535709603724952
nuary 2015 population. 10.5–11.5–11.512.5yearsyears 13551413148315381610164829.132.142.94872.276.214.415.219.319.829.930.6406434464481521532254262304313383397179189228232308319273285309319345350387407485509655679561571673695908917
a
03 21 J dvirtual 9.5–10.5years 12941408152426.637.158.614.518.427.1388437487237284360174213290258292324371455604526645828
y [] at 05: synthesise 8.5–9.5years 12361343146822.531.45014.117.325.9370419462222261340163200273248278304346418578513601786
aded b inthe 7.5–8.5years 11901279138620.526.546.113.916.524.5346391441213248316160191260232261290333395542493571745
Downlo orfemales 6.5–7.5years 11341219131718.52436.813.516.122.6334374423200234292154182234225250279311373480484550684
groupf 5.5–6.5years 10711157124716.420.732.113.615.521.1308350399193218269148176224207234261302352445462531661
e
sbyag 4.5–5.5years 10011090118514.918.827.613.715.820.7291332378179210257146171212195221246290336426458517637
n
mensio 3.5–4.5years 9541023109513.616.422.413.915.719.5271308344172199237142166201183205228278320393440508598
di
ofbody 2.0–3.5years 84292310041113.817.814.216.219230273313158188222137160193159183211260305363426489564
evalues ercentile 5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th5th50th95th
ntil P
Table5.Perce Measure Stature(mm) Mass(kg) BMI Kneeheight(mm) Seatedhipbreadth(mm) Upperarmcirc(mm) Upperarmlength(mm) Upperthighcirc(mm) Waistcirc(mm)
Ergonomics 9
percent difference between the virtual population (1999-2008) and Snyder et al. (1977)
0% 10% 20% 30% 40%
knee height max seated hip breadth upper thigh circumference
80
60
percentile
(males)
40
20
80
60
percentile
(females)
40
5 20
1
0
2
y
r
ua 4 6 8 10 12 14 16 18 4 6 8 10 12 14 16 18 4 6 8 10 12 14 16 18
n
a age (years) age (years) age (years)
J
1
2
3 Figure6. Contourplotsforkneeheight,maximumseatedhipbreadthandupperthighcircumferenceinmalesandfemalesshowingthe
0 differenceinanthropometryatvariouspercentilesforchildrenaged2.75to18.25yearsbetween1977and1999–2008.
5:
0
at
]
y [ can be seen in maximum seated hip breadth and up to 30% in upper thigh circumference. Differences in these measures
d b decrease towards lower percentiles. These trends are consistent with those observed in plots of differences in upper arm
de circumference and waist circumference for the same data that can be found in Figures 7b and 8b. All of these percent
a
o differenceplotsaremuchmoreconsistentwiththepatternsseenintheplotsofpercentdifferenceinmassandBMIfrom
nl
w Figure4thantheyarewiththoseofstatureinthesamefigure.Theseobservationssuggestthattherehavebeenlargechanges
o
D inmeasuresofbodywidthandcircumferenceinchildrensince1977andthatthosechangeshavebeenconcentratedinupper
percentile levels inchildrenover the age of eight years.
Thetrendsandobservationsdiscussedabovearemostconsistentformeasuresofthetorso,legsandarms,butmeasures
ofthehead,face,handsandfeetdidnotexhibitsimilartrends.Synthesisedmeasurementsintheseareasofthebodyshowed
little tono change across allages andpercentiles for both male andfemale children.
3.3. Validation
Initial visual inspections of data from both the Snyder and synthesised data sets plotted against age showed that the
distributions had similar overall shapes and occupied the same regions of the graphs. Probability density plots of both
distributions,which can befound inFigure9,give amoredetailed look atthedifferencesbetweenthe twodistributions.
TheseplotsshowthatinallthreemeasuresandforbothmalesandfemalesthetwocurvesforNHANESdetaileddataand
thevirtualpopulationareslightlyoffset;thisismostlikelyaproductofthebiaserrorinthemeasurements.Otherwise,the
two curves are nearlyidentical,differing only slightlyinpeak density.
The shape of the density curves in Figure 9 differ from the approximately normal distributions seen in some
measures of adult anthropometry. This is primarily due to the added element of natural growth that is present in the
progression of every child’s body dimensions. Thus, the differences in the distributions of individuals in the lowest
percentiles of the youngest portion of the population and individuals in the highest percentiles of the oldest portion of
the population are most visible. Figures 7c and 8c provide a better way to examine the differences between the two
distributions at all ages and percentiles. These contour plots examine the percent difference between the two data sets at
many different percentiles that were calculated for many small age intervals. Lighter shades indicate less difference