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An updated estimate of the body dimensions of USchildrenBrian T. Paganoa, Matthew B. Parkinsonb & Matthew P. Reedc

a Mechanical Engineering, Penn State University, University Park, PA, USAb Engineering Design, Mechanical Engineering, and Industrial Engineering, Penn StateUniversity, University Park, PA, USAc Transportation Research Institute, University of Michigan, Ann Arbor, MI, USAPublished online: 19 Jan 2015.

To cite this article: Brian T. Pagano, Matthew B. Parkinson & Matthew P. Reed (2015): An updated estimate of the bodydimensions of US children, Ergonomics, DOI: 10.1080/00140139.2014.1000392

To link to this article: http://dx.doi.org/10.1080/00140139.2014.1000392

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An updated estimate of the body dimensions of US children

Brian T. Paganoa, Matthew B. Parkinsonb* and Matthew P. Reedc

aMechanical Engineering, Penn State University, University Park, PA, USA; bEngineering Design, Mechanical Engineering, andIndustrial Engineering, Penn State University, University Park, PA, USA; cTransportation Research Institute, University of Michigan,

Ann Arbor, MI, USA

(Received 23 April 2014; accepted 9 December 2014)

Anthropometric data from children are important for product design and the promulgation of safety standards. The lastmajor detailed study of child anthropometry in the USA was conducted more than 30 years ago. Subsequent demographicchanges and the increased prevalence of overweight and obesity render those data increasingly obsolete. A new, large-scaleanthropometric survey is needed. As an interim step, a new anthropometric synthesis technique was used to create a virtualpopulation of modern children, each described by 84 anthropometric measures. A subset of these data was validated againstlimited modern data. Comparisons with data from the 1970s showed significant changes in measures of width andcircumference of the torso, arms and legs. Measures of length and measurements of the head, face, hands and feet exhibitedlittle change. The new virtual population provides guidance for a comprehensive child anthropometry survey and couldimprove safety and accommodation in product design.

Practitioner Summary: This research reviews the inadequacies of available sources of US child anthropometry as a resultof the rise in the rates of overweight and obesity. A new synthesised database of detailed modern child anthropometry wascreated and validated. The results quantify changes in US child body dimensions since the 1970s.

Keywords: child anthropometry; anthropometry synthesis; human variability; child obesity; child growth; product design

1. Introduction

Accurate data on human body dimensions are critical when designers are creating products and environments for human

use. Children provide a particular challenge since their body size and shape change so quickly (Leuder and Berg Rice 2008).

Medical and public health professionals use the body dimensions of children to benchmark patients’ relative body

dimensions and growth rates (Kuczmarski et al. 2000). Statistics on child body dimensions are also used for safety,

regulation and product sizing (Snyder et al. 1975, 1977; Steenbekkers and Molenbroek 1990). Data are utilised by the

automotive industry for the creation of crash test dummies (Manary et al. 2006; Reed et al. 2009; Loyd et al. 2010) and the

regulation of child and infant restraints (Burdi et al. 1969; Reed et al. 2005; Anderson and Hutchinson 2009). Improved

design of children’s school furniture (Chung and Wong 2007; Savanur, Altekar, and De 2007; Agha 2010) and other

artefacts (Hughes and Johnson 2011; Berg Rice 2012) relies extensively on anthropometric data among other factors.

From 1975 to 1986, three detailed studies of child anthropometry were conducted at the University of Michigan under

sponsorship from the US Consumer Product Safety Commission (Snyder et al. 1975, 1977; Schneider et al. 1986). All three

studies measured children that were intended to be representative of the US population at the time of the study. The studies

resulted in reports containing descriptions of each measurement as well as plots and tables containing basic statistics and

percentile values at small age intervals. However, Snyder et al. (1977) is the only one of the three studies for which the raw

data remain available.

Snyder et al. (1977) was a continuation of a smaller study published in 1975 (Snyder et al. 1975). The primary

motivation for the new study was to gather additional information about child and infant anthropometry for use in product

safety design. Participant age ranged from two weeks to 18.99 years. The study was conducted at 105 locations around the

USA, chosen to provide an approximately random sample. No weighting of the sample was intended nor performed. The

study collected 87 traditional and functional body measures. To reduce the time required for data collection from each

participant, the measurements were divided into four separate groups, and each participant was allocated to two of them.

Figure 1 details the assignment of measurements for the study. Group I measures consisted of ‘core’ measurements that

were selected because they were known to be highly correlated with measures in the other three groups. Data were collected

on Group I measurements for all participants. Data were collected for each participant on one of the other three

measurement groups as well. Group II contained ‘body shape’ measurements. Group III contained ‘linkage and centre-of-

q 2015 Taylor & Francis

*Corresponding author. Email: parkinson@psu.edu

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gravity’ measurements. Group IV contained ‘head, face, and hand’ measurements. Thus, 42–45 measurements were

recorded for each child. After 22 months, a total of 4127 participants had been measured.

While some large-scale studies of children have been conducted globally (e.g. Steenbekkers 2009), studies in the USA,

where the original studies were conducted, have focused on adults rather than children (e.g. Robinette et al. 2002). As a

result, more recent data on children in the USA are available for only a small number of measures. The National Health and

Nutrition Examination Survey (NHANES) has been conducted by the US government periodically since 1971 and

continuously since 1999 (Centers for Disease Control and Prevention 2012). Data from approximately 10,000 people of all

ages are released every two years. These data include a sampling weight for each subject, effectively describing the number

of people in the US population that each subject ‘represents’ on the basis of their demographic characteristics. However,

NHANES includes only a few measures of basic anthropometry (e.g. stature, mass); thus, the data are not directly applicable

to most design problems. The current work combines the detailed data from Snyder et al. (1977) with child data available in

NHANES to create a new, synthesised data set of detailed anthropometry that matches the current US child population on

the measures available in NHANES.

In the literature, several techniques have been used to estimate and synthesise anthropometry, notably regression

methods. These assume linear relationships between predictor measures, such as stature and body mass index (BMI; a

measure of weight-for-stature), and the measures of interest. Relationships are extracted from a detailed data set and then

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

reweighted to match the demographic distribution of the predictor data set, as correlations between anthropometric

dimensions are dependent on gender, age and other variables.

The present work implements a population synthesis approach introduced by Parkinson and Reed (2010). The method

combines linear regression models with principal component analysis (PCA), a multivariate analysis technique. Stochastic

elements that retain the residual variance from the model fit are included. This approach improves the overall predictive

ability of the method, particularly in the tails of 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

multivariate analysis technique, used here to model the covariance structure of the detailed database. PCA requires a matrix

of information with no missing data. As described earlier, the structure of the Snyder et al. (1977) data is such that only

all children(2.00 - 18.99 years)

Group IIbody shape measures

Group Icore measures

Group IIIlinkage measures

Group IVhead, face, and hand measures

Figure 1. A breakdown of the assignment of measurements in the Snyder et al. (1977) study.

- basic anthro. and demographic info. fromNHANES 1999-2008 data

Representative Predictor Dataset

- representative of target population

Detailed Dataset

- Snyder et al. (1977) anthro. data- reweighted and upsampled to match

demographics from NHANES

AnthropometrySynthesis Process

- extracts relationships betweenmeasures from the detailed data

- applies relationships to thebasic anthropometry in therepresentative predictor dataset

Virtual Populationrepresenting modern

children

Figure 2. A diagram describing the inputs and outputs for the anthropometry synthesis method.

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about half of the 87 measurements were recorded for each subject. Thus, the process described in this section was performed

separately for each of the four groups of measurements shown in Figure 1. To keep the synthesised measurements linked to

one another, core measurements from Group I were synthesised for the virtual population first, using only stature, BMI and

age as predictors. Nine measures (in addition to stature, BMI and age) were selected from the resulting synthesised data to

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. Although stature and mass were reported for each participant, BMI was not. For the purposes of the present work,

it was calculated directly from the data and added to the list of measures. This resulted in 87 2 4 þ 1 ¼ 84 measures that

were considered here. For more on the details of PCA, see Jolliffe (2004) or Shlens (2009).

The first step in the method was intended to account for changes in the distributions of demographic subgroups of the

population of American children over the past several decades. Data from Snyder et al. (1977) were reweighted such that the

distributions of demographic variables (age and gender) matched those for children from the NHANES data set from 1999

to 2008. Each individual in Snyder et al. (1977) was grouped into one of 32 bins (16 age bins ranging from 2 to 19 years for

each gender). The same process was performed for individuals from the NHANES data set. The sum of the statistical

weights of individuals in each of the NHANES bins was divided up evenly among individuals in the corresponding bins for

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

demographically similar to the representative predictor data set without the use of statistical weights. While increasing the

size of the matrices involved, it simplifies some subsequent calculations and reduces computation time.

The implementation of the method from Parkinson and Reed (2010) is summarised here. Measures that were intended to

be used as predictors, such as age in months, stature and BMI, were separated from the body segment lengths, widths and

circumferences of the detailed data set that were to be predicted. The collection of length, width, and circumference

measures were ‘centred’ such that the mean ¼ 0 and analysed using PCA on the covariance matrix. The PCA process

produced a matrix of principal components (PCs), and a matrix of loadings or scores. The subsequent analysis retained a

number of PCs sufficient to explain 99% of the variance in the original data. Linear regression was conducted to predict the

scores on the retained PCs from the predictor variables. Table 1 lists the number of subjects, predictors, predicted

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

distributed residual variance was added back into the new scores by adding a random number — selected from a normal

distribution with a mean of zero and a standard deviation equal to the square root of the residual variance from the

corresponding regression model — to each predicted score. Additionally, randomly chosen PC scores from the previously

discarded PCs were incorporated with the predicted scores to bring the new scores matrix to the same size as the original.

This adds meaningful variance back into the model from components that were not significantly related to the predictor

variables. Finally, the new scores matrix was combined with the PC matrix to transform the data from PC space back into

anthropometry space. Thus, detailed measures of length, width and circumference were predicted for each set of predictor

anthropometry from the representative data set.

The method was used to generate detailed anthropometric data for each of the 18,741 children aged 2.00 to 18.99 years

in the NHANES 1999–2008 sample. Each virtual person is described by demographic information and 84 anthropometric

measures. Because the original predictor variables and demographic information for the virtual population were extracted

from NHANES, the statistical weights associated with those NHANES data were inherited for the virtual population as

well. The synthesis process was performed separately for males and females.

Table 1. Summary details on the synthesis of the virtual population of children.

Group I Group II Group III Group IV

Gender m f m f m f m f

Predictors 3 12 12 12Synthesised measures 20 17 21 23Original detailed data set size 1737 1712 550 511 582 544 560 537Predictor data set size 9473 9268 9473 9268 9473 9268 9473 9268PCs used 6 6 3 3 3 5 11 14

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2.2. Refinement

When combined with the predictor anthropometry from the representative data set, the newly synthesised detailed

anthropometric data represented a unique population of virtual people. Due to the stochastic nature of the prediction (using

unbounded normal random variates), there is a small possibility of unlikely combinations of measures, particularly among

highly correlated measures. The PCA reduces but does not eliminate the likelihood of incompatible dimensions. Since the

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 listed in 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

several decades, these upper and lower limits of proportionality for each measure were used to eliminate synthesised virtual

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

Tables 2 and 3can be found in the final report from Snyder et al. (1977).

2.3. Comparison with NHANES

In addition to stature and body weight, the physical examination of NHANES participants included several additional body

dimensions (beyond stature, mass and BMI). These dimensions were compared with the synthesised data to assess the

success of the dimension synthesis. This comparison is particularly useful because the demographic distributions of the data

sets are identical, since each child in NHANES was used.

Upper arm length, upper arm circumference and waist circumference were available in NHANES. However, the

measures in the synthesised virtual population reflect the measurement practices of Snyder et al. (1977) and differ from the

practices that were used to make detailed measurements in NHANES. The two data sets utilised different landmark locations

for measurements and used different types of measurement tools. For example, shoulder–elbow (upper arm) length in

Snyder et al. (1977) was measured with an electronic anthropometer (a large caliper-like device) from the superior surface of

the right shoulder to the inferior surface of the forearm just below the elbow. In contrast, NHANES measures upper arm

length with a thin steel measuring tape from the uppermost edge of the acromial process to the tip of the olecranon process

(Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS) 1994). Differences in

measurement techniques and tools like these will create repeatable differences in measurements for the same individual,

called bias error. Differences in measurement technique and tools were present in all three measurements. Nevertheless, the

two sets of data have identical demographic distributions and provided a unique opportunity for comparison.

Table 2. Measure pairs (identified due to the strong correlation between them) that were compared toeliminate unlikely combinations of dimensions.

Larger Smaller

Trochanteric height Gluteal furrow heightHip height at buttocks Gluteal furrow heightIliospinale height Trochanteric heightIliocristale height Iliospinale heightKnee height Tibiale heightShoulder–elbow length Acromion–radiale lengthSuprasternale height Chest height at axillaHead height Face heightHead breadth Bitragion breadthBizygomatic breadth Frontal breadthMax seated hip breadth Bispinous breadthShoulder breadth Biacromial breadthCalf circumference Ankle circumferenceMinimum hand clearance Maximum fist breadth

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3. Results

3.1. Anthropometric trends in NHANES

The NHANES data indicate that values of certain body dimensions within each age cohort in the USA have been getting

larger in the last several decades, particularly through the 1980s and 1990s (National Center for Health Statistics 2012).

After slow and steady increases in both child stature and mass subsided in the 1960s, little change was observed until the

late 1970s. Since this time, body mass within a given age group has increased while stature remained approximately

constant (Malina 2004; Smith and Norris 2004; Roche 1995). Figure 3 shows the difference between stature, mass and BMI

in children between 1977 and the 2000s. Notice that in the mass and BMI plots, data in upper percentile ranges are

noticeably different while the lower tails of the distributions are similar. That is, both the mean and variance in body weight

are increasing as age increases.

To better illustrate the ages and percentiles at which changes in dimensions occurred, percentile values were calculated

for small age ranges on each basic measure, using data from NHANES. The results are displayed in the contour plots of

Figure 4. Darker shading signifies a greater absolute percent change between the two populations at that particular age and

percentile. Plots for males and females are similar, both showing little to no change in stature and large changes in mass and

BMI. In both cases, mass and BMI at the lowest percentiles remain relatively unchanged, and changes are largest in the

highest percentiles. In females, changes in mass and BMI appear to have reached further down into lower percentile ranges

than those in males. Differences in mass and BMI also seem to have most greatly affected children over the age of eight

years.

3.2. Synthesised anthropometry

Tables 4 and 5 present quantiles of the nine body dimensions for the synthesised population. Figure 5 shows a general

comparison of Snyder et al. (1977) with the synthesised data sets on three dimensions commonly used for design. Knee

height, maximum seated hip breadth and upper thigh circumference for a combined population of males and females are

plotted vs. age, representing measures of length, width and circumference, respectively. Of the three measures, knee height

shows the smallest change over three decades (the data from the two populations occupy the same region of the plot). Also,

the overall shape of the distributions in this plot is similar to that of stature, depicted in Figure 3. This was expected since

knee height, a measure of length, is highly correlated with stature. Plots of percent difference in knee height for males and

Table 3. Measures of length that were checked for their proportionality with stature in order toeliminate physically unlikely anthropometric proportions.

Buttock–knee length Knee height Sphyrion heightElbow–hand length Radiale–stylion length Suprasternale heightFoot length Seated eye height Tibiale heightHand length Shoulder–elbow length Trochanteric heightHip lengtha Sitting height

a Hip length ¼ iliospinale height 2 iliocristale height.

2 6 10 18

800

1200

1600

14

stature(mm)

1800

1000

1400

age (years)

20

40

60

80

100

120

mass(kg)

2 6 10 1814

age (years)

10

20

30

40

BMI(kg/m2)

2 6 10 1814

age (years)

1977 (Snyder, et al.) 1999-2008 (NHANES)2.5th50th

97.5th

2.5th50th

97.5th

Figure 3. From left to right: the 2.5th, 50th and 97.5th percentile stature, mass and BMI versus age for children from 1977 (Snyder et al.1977) and 1999–2008.

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females in Figure 6 show little to no difference between Snyder et al. (1977) and the modern virtual population of children.

These plots are similar to the plots of percent change in stature between the two time periods from Figure 4. Similarly, plots

of percent difference in the distribution of upperarm length, seen in Figures 7b and 8b, also show that there has been little to

no difference in measures of length in children since 1977 across all ages and percentiles.

Since measures of body width and circumference are correlated with BMI, maximum seated hip breadth and upper thigh

circumference were expected to show shifts similar to the BMI distribution in Figure 3. This is confirmed by Figure 5, which

compares the 2.5th-, 50th- and 95th-percentile values with their 1977 counterparts. As with BMI, the lower percentiles are

similar across the two populations, with large differences in the upper percentiles. Plots of percent difference in maximum

seated hip breath and upper thigh circumference (Figure 6) show similar results for males and females, with large changes

occurring in the upper percentile ranges and becoming most prominent after eight years of age. Differences of up to 20%

20

40

60

80

percentile(females)

20

40

60

80

percentile(males)

4 6 8 10 12 14 16 18

age (years)

4 6 8 10 12 14 16 18

age (years)4 6 8 10 12 14 16 18

age (years)

0% 10% 20% 30% 40%

percent difference between NHANES (1999-2008) and Snyder et al. (1977)

mass BMIstature

Figure 4. Contour plots for stature, mass and BMI in males and females showing the difference in anthropometry at various percentilesfor children aged 2.75 to 18.25 years between 1977 and 1999–2008.

2 6 10 1814

age (years)

upperthighcirc.(mm)

200

400

600

800

2 6 10 1814

age (years)

seatedhip

breadth(mm)

150

250

350

450

2 6 10 1814

age (years)

kneeheight(mm)

200

300

400

500

600

1977 (Snyder, et al.) 1999-2008 (virtual population)2.5th50th

97.5th

2.5th50th

97.5th

Figure 5. The 2.5th, 50th and 95th percentile knee height, maximum seated hip breadth and upper thigh circumference. The data areplotted versus age for children from Snyder et al. (1977) and the synthesized virtual population (1999–2008).

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Table

4.

Percentile

values

ofbodydim

ensionsbyagegroupformales

inthesynthesised

virtual

population.

Measure

Percentile

2.0–

3.5

years

3.5–

4.5

years

4.5–

5.5

years

5.5–

6.5

years

6.5–

7.5

years

7.5–

8.5

years

8.5–

9.5

years

9.5–

10.5

years

10.5–

11.5

years

11.5–

12.5

years

12.5–

13.5

years

13.5–

14.5

years

14.5–

15.5

years

15.5–

16.5

years

16.5–

17.5

years

17.5–

19.0

years

Stature

(mm)

5th

854

959

1013

1096

1123

1185

1235

1299

1337

1406

1445

1495

1592

1634

1634

1648

50th

937

1033

1102

1178

1233

1287

1353

1398

1454

1520

1577

1663

1734

1749

1760

1762

95th

1014

1112

1190

1249

1332

1398

1463

1512

1599

1664

1736

1801

1849

1878

1876

1889

Mass(kg)

5th

11.6

14

15.3

17.6

19

21.2

23.1

26.3

28.9

32.3

32.7

38.5

46.9

50.7

53.4

54.9

50th

14.3

17

19.1

22.1

24.4

27.9

32.2

35.3

38.1

45.3

50.6

57.1

63.8

67

71

73.4

95th

18

22.2

28.2

32.7

37

45.5

50.1

59.5

68.5

75.2

85

91.5

105.1

106.1

114.7

112.9

BMI

5th

14.7

14.1

14

13.8

13.8

14.1

14.4

14.5

15.2

15.1

15.1

16.3

16.8

17.5

18.1

18.3

50th

16.4

16.1

15.9

15.9

16.2

16.7

17.3

17.8

18.2

19.6

20

20.7

21.6

21.7

23

23.6

95th

19

18.9

20.6

21.7

22.7

24.2

25.8

27.5

28.2

30.1

31.6

31.7

34.3

34.2

35.5

36

knee

height

(mm)

5th

235

268

295

322

337

358

368

389

411

431

443

468

496

505

504

514

50th

276

310

335

361

380

402

424

440

460

484

499

528

551

560

561

563

95th

319

355

378

401

424

454

468

484

514

535

565

585

603

616

610

612

seated

hip

breadth

(mm)

5th

168

176

185

194

203

209

219

220

235

248

251

265

284

292

299

301

50th

192

203

208

221

230

240

252

262

272

290

303

315

331

335

344

347

95th

215

229

252

261

276

301

316

332

357

369

383

392

417

411

434

427

upper

arm

circ

(mm)

5th

134

137

144

146

152

158

163

171

184

191

194

212

227

237

239

242

50th

160

165

168

176

181

191

203

213

221

237

251

260

272

277

291

296

95th

186

195

215

232

244

265

281

299

316

337

350

356

386

382

403

410

upper

arm

length

(mm)

5th

164

184

196

215

227

238

249

259

268

287

292

312

325

339

341

342

50th

188

208

224

241

253

265

279

290

303

320

331

349

365

369

371

374

95th

210

235

256

264

279

296

311

320

339

351

372

383

392

406

403

404

upper

thighcirc

(mm)

5th

261

269

282

295

308

320

324

349

356

379

385

417

447

463

471

481

50th

305

315

327

347

360

381

398

415

435

470

496

509

537

548

570

572

95th

354

375

425

446

467

509

531

575

615

642

669

685

726

731

755

758

Waistcirc

(mm)

5th

442

445

459

464

478

501

511

522

536

559

563

599

641

654

677

671

50th

499

509

517

535

545

573

599

618

637

680

707

726

761

764

794

797

95th

561

587

644

675

690

758

785

828

868

925

946

959

1031

1035

1062

1063

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Table

5.

Percentile

values

ofbodydim

ensionsbyagegroupforfemales

inthesynthesised

virtual

population.

Measure

Percentile

2.0–

3.5

years

3.5–

4.5

years

4.5–

5.5

years

5.5–

6.5

years

6.5–

7.5

years

7.5–

8.5

years

8.5–

9.5

years

9.5–

10.5

years

10.5–

11.5

years

11.5–

12.5

years

12.5–

13.5

years

13.5–

14.5

years

14.5–

15.5

years

15.5–

16.5

years

16.5–

17.5

years

17.5–

19.0

years

Stature

(mm)

5th

842

954

1001

1071

1134

1190

1236

1294

1355

1413

1470

1501

1499

1515

1520

1526

50th

923

1023

1090

1157

1219

1279

1343

1408

1483

1538

1576

1605

1618

1624

1626

1629

95th

1004

1095

1185

1247

1317

1386

1468

1524

1610

1648

1686

1720

1726

1731

1727

1737

Mass(kg)

5th

11

13.6

14.9

16.4

18.5

20.5

22.5

26.6

29.1

32.1

36.2

39.2

43.4

45.8

45.3

46.2

50th

13.8

16.4

18.8

20.7

24

26.5

31.4

37.1

42.9

48

52.2

54.6

57.9

59.6

58.4

60.9

95th

17.8

22.4

27.6

32.1

36.8

46.1

50

58.6

72.2

76.2

83.4

89.7

90.5

92.9

94.2

99.8

BMI

5th

14.2

13.9

13.7

13.6

13.5

13.9

14.1

14.5

14.4

15.2

16.1

16.3

17.1

18

17.5

17.9

50th

16.2

15.7

15.8

15.5

16.1

16.5

17.3

18.4

19.3

19.8

21.1

21.2

21.8

22.3

21.8

22.8

95th

19

19.5

20.7

21.1

22.6

24.5

25.9

27.1

29.9

30.6

32.1

33.5

34.6

34.6

34

36.9

Knee

height

(mm)

5th

230

271

291

308

334

346

370

388

406

434

437

454

456

458

452

455

50th

273

308

332

350

374

391

419

437

464

481

495

501

503

503

500

498

95th

313

344

378

399

423

441

462

487

521

532

541

556

555

555

552

543

Seatedhip

breadth

(mm)

5th

158

172

179

193

200

213

222

237

254

262

279

288

302

309

309

317

50th

188

199

210

218

234

248

261

284

304

313

331

337

347

357

353

364

95th

222

237

257

269

292

316

340

360

383

397

422

433

446

447

454

472

Upper

arm

circ

(mm)

5th

137

142

146

148

154

160

163

174

179

189

202

203

212

221

222

221

50th

160

166

171

176

182

191

200

213

228

232

248

253

256

262

259

266

95th

193

201

212

224

234

260

273

290

308

319

336

350

363

357

356

373

Upper

arm

length

(mm)

5th

159

183

195

207

225

232

248

258

273

285

299

304

308

313

310

313

50th

183

205

221

234

250

261

278

292

309

319

329

337

339

340

339

339

95th

211

228

246

261

279

290

304

324

345

350

361

366

369

372

370

370

Upper

thighcirc

(mm)

5th

260

278

290

302

311

333

346

371

387

407

443

458

473

491

477

490

50th

305

320

336

352

373

395

418

455

485

509

535

543

558

568

560

574

95th

363

393

426

445

480

542

578

604

655

679

709

746

759

759

771

784

Waistcirc

(mm)

5th

426

440

458

462

484

493

513

526

561

571

603

615

631

651

639

650

50th

489

508

517

531

550

571

601

645

673

695

724

731

746

755

742

750

95th

564

598

637

661

684

745

786

828

908

917

952

990

995

998

991

1030

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can be seen in maximum seated hip breadth and up to 30% in upper thigh circumference. Differences in these measures

decrease towards lower percentiles. These trends are consistent with those observed in plots of differences in upper arm

circumference and waist circumference for the same data that can be found in Figures 7b and 8b. All of these percent

difference plots are much more consistent with the patterns seen in the plots of percent difference in mass and BMI from

Figure 4 than they are with those of stature in the same figure. These observations suggest that there have been large changes

in measures of body width and circumference in children since 1977 and that those changes have been concentrated in upper

percentile levels in children over the age of eight years.

The trends and observations discussed above are most consistent for measures of the torso, legs and arms, but measures

of the head, face, hands and feet did not exhibit similar trends. Synthesised measurements in these areas of the body showed

little tono change across all ages and percentiles for both male and female 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 be found in Figure 9, give a more detailed look at the differences between the two distributions.

These plots show that in all three measures and for both males and females the two curves for NHANES detailed data and

the virtual population are slightly offset; this is most likely a product of the bias error in the measurements. Otherwise, the

two curves are nearly identical, differing only slightly in peak 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

0% 10% 20% 30% 40%

percent difference between the virtual population (1999-2008) and Snyder et al. (1977)

max seated hip breadth upper thigh circumference

20

40

60

80

knee height

percentile(females)

20

40

60

80

percentile(males)

4 6 8 10 12 14 16 18

age (years)

4 6 8 10 12 14 16 18

age (years)

4 6 8 10 12 14 16 18

age (years)

Figure 6. Contour plots for knee height, maximum seated hip breadth and upper thigh circumference in males and females showing thedifference in anthropometry at various percentiles for children aged 2.75 to 18.25 years between 1977 and 1999–2008.

Ergonomics 9

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between the two data sets at any given age and percentile. Mean percent difference for any one of the six plots is

always between 2% and 4%. However, in all measures for both genders, it is clear that percent error is low and mostly

uniform across the graph, indicating that differences are most likely a result of repeatable bias error.

Regardless of the measurement strategy used within the representative target population (e.g. NHANES), the

synthesised data will be typical of data measured using the procedures of the detailed database. The predictive model

is based on the detailed database, and so the generated data, unless modified post hoc, will be similar to those that

created the model. With most of the differences between the actual and predicted data sets attributable to bias error, it

can be concluded that the synthesis process is likely to have reasonably estimated the distributions of other body

dimensions.

4. Discussion

This paper presents the first effort to generate estimates of the detailed body dimensions of US children since the 1970s. The

synthesised virtual population can be used in much the same way as other fully detailed anthropometric data sets. Designers

should be fully aware of the assumptions under which the study was designed to avoid inappropriate use of the data. For

factors involving the distribution of demographics, the data should be used like NHANES anthropometric data. The

0% 10% 20% 30% 40%

percent difference between datasets

upper arm circumference waist circumference

20

40

60

80

upper arm length

percentile

20

40

60

80

percentile

4 6 8 10 12 14 16 18

age (years)

4 6 8 10 12 14 16 18

age (years)

20

40

60

80

4 6 8 10 12 14 16 18

age (years)

percentile

(a)

NHANES(1999-2008)

vs.Snyder et al.

(1977)

(b)virtual pop.

(1999-2008)vs.

Snyder et al.(1977)

(c)

NHANES(1999-2008)

vs.virtual pop.

(1999-2008)

Figure 7. Contour plots showing the percent difference between three data sets (a–c) at various percentiles of upper arm length, upperarm circumference and waist circumference in males aged 2.75 to 18.25 years.

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synthesised population, when used with statistical weights, has the same gender and age distribution as NHANES in that it

represents children from the USA in the distributions that were observed in the 2000 US census. The synthesised population

should always be used with statistical weights.

However, factors involving the body measures themselves (not including the distributions of body measurements) are

more reliant on the assumptions, measurement techniques and study design of Snyder et al. (1977). Detailed descriptions,

illustrations and photos for most measures can be found in the final report from Snyder et al. (1977). Definitions of

anthropometric measures can change between data sets, and the specific details on how a measurement is made and which

body landmarks are used can be critical to the success of a design.

The synthesis approach is limited by the linearity of the analysis methods and by the assumption of normality in the

regression residuals. Visual examination and appropriate statistical tests (e.g. Shapiro–Wilk tests of normality) supported

the use of these models, but it is possible that more complex modelling approaches would yield more accurate results. The

method assumes that the relationships between the predictor variables and the outcome measures (or, rather, the PC-

transformed outcome measures) are the same in the modern population as in the measured population. This assumption

seems reasonable, given that nearly all individuals in the modern population lie within the range of stature and body weight

in Snyder et al. (1977).

0% 10% 20%

percent difference between datasets30% 40%

upper arm circumference waist circumference

20

40

60

80

upper arm length

percentile

percentile

20

40

60

80

4 6 8 10 12 14 16 18

age (years)

4 6 8 10 12 14 16 18

age (years)

20

40

60

80

4 6 8 10 12 14 16 18

age (years)

percentile

(a)

NHANES(1999-2008)

vs.Snyder et al.

(1977)

(b)virtual pop.

(1999-2008)vs.

Snyder et al.(1977)

(c)

NHANES(1999-2008)

vs.virtual pop.

(1999-2008)

Figure 8. Contour plots showing the percent difference between three data sets (a–c) at various percentiles of upper arm length, upperarm circumference and waist circumference in females aged 2.75 to 18.25 years.

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The most important limitation of the Snyder et al. (1977) reference data is the lack of children identified as of Hispanic

ethnicity. If children who are identified as Hispanic have different relationships between, for example, BMI and waist

circumference, the relationships from Snyder et al. (1977)) would exhibit bias relative to the current population.

The method presented in this work could be applied to other populations for which up-to-date anthropometric data are

not available. The large cost of conducting extensive studies of anthropometry could be alleviated, to an extent, by

occasionally collecting data on only demographic and basic anthropometric information. In these instances, these basic data

can be used in conjunction with older detailed data sets to create a useful virtual population with highly detailed

anthropometry.

These new body dimension estimates will have considerable value for the design of products for children, but the

availability of this virtual population should be considered only a stop-gap measure. A new, detailed study of child body

dimensions in the USA is needed. Such a study should include oversampling of minorities defined by race and ethnicity and

should include three-dimensional surface anthropometry.

5. Conclusions and recommendations

The new synthesised virtual population of modern US children can be used in univariate and multivariate studies of design

accommodation. They indicate that there have been large changes in measures of body width and circumference in children

since 1977 and that those changes have been concentrated in upper percentile levels in children over the age of eight years.

These data have the potential to be used to improve the safety, comfort and accommodation levels of products and

environments created for children and the safety standards that govern their design. The large changes since 1977 in many

of the synthesised child measures suggests that designers of products for children should examine whether the current

guidelines based on Snyder et al. (1977) are adequate.

Disclosure statement

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation.

Funding

This research was partially funded by the National Science Foundation under Awards No. 0846373 and 1131467.

References

Agha, S. R. 2010. “School Furniture Match to Students’ Anthropometry in the Gaza Strip.” Ergonomics 53 (3): 344–354.

upper arm circumference (mm) waist circumference (mm)upper arm length (mm)150 250 350 450 100 200 300 400 500

males

females

400 600 800 1000 1200 1400

Snyder et al. (1977)NHANES (1999-2008)virtual population (1999-2008)

Figure 9. Probability density plots comparing the distributions of three data sets for upper arm length, upper arm circumference andwaist circumference in male and female children.

B.T. Pagano et al.12

Dow

nloa

ded

by [

] at

05:

03 2

1 Ja

nuar

y 20

15

Anderson, R. W., and T. P. Hutchinson. 2009. “Optimising Product Advice Based on Age when Design Criteria Are Based on Weight:Child Restraints in Vehicles.” Ergonomics 52 (3): 312–324.

Berg Rice, V. J. 2012. “Designing for Children.” In Handbook of Human Factors and Ergonomics, edited by G. Salvendy. 4th ed.Hoboken, NJ: Wiley.

Burdi, A. R., D. F. Huelke, R. G. Snyder, and G. H. Lowrey. 1969. “Infants and Children in Adult World of Automobile Safety Design.Pediatric and Anatomical Considerations for Design of Child Restraints.” In ASME Meeting BHF-10, number 69-BHF-10, AmericanSociety of Mechanical Engineers.

Centers for Disease Control and Prevention. 2012. National Health and Nutrition Examination Survey (NHANES). Hyattsville, MA:National Center for Health Statistics.

Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). 1994. National Health and NutritionExamination Survey III anthropometric procedure videos. Hyattsville, MD: U.S. Department of Health and Human Services, Centersfor Disease Control and Prevention.

Chung, J. W. Y., and T. K. S. Wong. 2007. “Anthropometric Evaluation for Primary School Furniture Design.” Ergonomics 50 (3):323–334.

Flannagan, C., M. Manary, L. Schneider, and M. Reed. 1998. “Improved Seating Accommodation Model with Application to DifferentUser Populations.” In Proceedings of the SAE International Congress & Exposition. 1358, 43–50. Warrendale, PA: SAE.

Hughes, E., and P. W. Johnson. 2011. “Child Computer Use and Anthropometry.” Proceedings of the Human Factors and ErgonomicsSociety 1716–1719.

Jolliffe, I. 2004. Principal Component Analysis. 2nd ed. New York, NY: Springer.Kroemer, K. H. 1989. “Engineering Anthropometry.” Ergonomics 32 (7): 767–784.Kuczmarski, R. J., C. Ogden, L. M. Grummer-Strawn, K. Flegal, S. S. Guo, R. Wei, Z. Mei, L. R. Curtin, A. F. Roche, and C. L. Johnson.

2000. “CDC Growth Charts: United States.” Vital Health Stat 314. Hyattsville, MD: National Center for Health Statistics.Leuder, R., and V. J. Berg Rice, eds. 2008. Ergonomics for Children: Designing Products and Places for Toddlers to Teens. Boca Raton,

FL: Taylor & Francis.Loyd, A. M., R. Nightingale, C. R. Bass, H. J. Mertz, D. Frush, D. Clark, C. Lee, J. R. Marcus, S. Mukundan, and B. S. Myers. 2010.

“Pediatric Head Contours and Inertial Properties for ATD Design.” Stapp Car Crash Journal 54: 167–196.Malina, R. M. 2004. “Secular Trends in Growth, Maturation and Physical Performance: A Review.” Anthropological Review 67: 3–31.Manary, M. A., M. P. Reed, K. D. Klinich, N. L. Ritchie, and L. W. Schneider. 2006. “The Effects of Tethering Rear-Facing Child

Restraint Systems on ATD Responses.” In The Annual Proceedings—Association for the Advancement of Automotive Medicine,pp. 397–410. Association for the Advancement of Automotive Medicine.

National Center for Health Statistics. 2012. Health, United States, 2011: With Special Feature on Socioeconomic Status and Health.Hyattsville, MD: National Center for Health Statistics.

Parkinson, M., and M. Reed. 2010. “Creating Virtual User Populations by Analysis of Anthropometric Data.” International Journal ofIndustrial Ergonomics 40: 106–111.

Reed, M. P., S. M. Ebert-Hamilton, M. A. Manary, K. D. Klinich, and L. W. Schneider. 2005. “A New Database of Child Anthropometryand Seated Posture for Automotive Safety Applications.” SAE Technical Paper 2005-01-1837, Society of Automotive Engineers,Warrendale, PA.

Reed, M., M. Manary, and L. Schneider. 1999. “Automobile Occupant Posture Prediction for Use with Human Models.” SAETransactions: Journal of Passenger Cars 108: 1857–1866.

Reed, M. P., M. M. Sochor, J. D. Rupp, K. D. Klinich, and M. A. Manary. 2009. “Anthropometric Specification of Child Crash DummyPelves through Statistical Analysis of Skeletal Geometry.” Journal of Biomechanics 42 (8): 1143–1145.

Robinette, K., S. Blackwell, H. Daanen, and M. Boehmer. 2002. “Civilian American and European Surface Anthropometry Resource(CAESAR), Final report.” Technical report, United States Air Force Research Laboratory: Wright-Patterson Air Force Base, Dayton,OH.

Robinette, K., and J. McConville. 1981. “An Alternative to Percentile Models.” Society of Automotive Engineers, SAE Technical Paper810217. 938–946.

Roche, A. F. 1995. “Executive Summary of Workshop to Consider Secular Trends and Possible Pooling of Data in Relation to theRevision of the NCHS Growth Charts.” Technical Report, National Center for Health Statistics, U.S. Department of Health andHuman Services, Hyattsville, MD.

Savanur, C. S., C. R. Altekar, and A. De. 2007. “Lack of Conformity between Indian Classroom Furniture and Student Dimensions:Proposed Future Seat/Table Dimensions.” Ergonomics 50 (10): 1612–1625.

Schneider, L., R. Lehman, M. Pflug, and C. Owings. 1986. “Size and Shape of the Head and Neck from Birth to Four Years.” TechnicalReport UMTRI-86-2, Consumer Product Safety Commission.

Shlens, J. 2009. “A Tutorial on Principal Component Analysis.” Technical Report, Systems Neurobiology Laboratory, Salk Institute forBiological Studies.

Smith, S. A., and B. J. Norris. 2004. “Changes in the Body Size of UK and US Children over the Past Three Decades.” Ergonomics47 (11): 1195–1207.

Snyder, R. G., L. W. Schneider, C. L. Owings, H. Reynolds, D. H. Golomb, and M. A. Schork. 1977. “Anthropometry of Infants, Childrenand Youths to Age 18 for Product Safety Design.” Technical Report UM-HSRI-77-17, Consumer Product Safety Commission.

Snyder, R. G., M. L. Spencer, C. L. Owings, and L. W. Schneider. 1975. “Physical Characteristics of Children as Related to Death andInjury for Consumer Product Design and Use.” Technical Report UM-HSRI-BI-75-5, Consumer Product Safety Commission.

Steenbekkers, L. 2009. “Anthropometry of Children 2 to 13 Years of Age in the Netherlands.” In Contemporary Ergonomics 1984–2008:Selected Papers and an Overview of the Ergonomics Society Annual Conference, pp. 335–340.

Steenbekkers, L. P. A., and J. F. M. Molenbroek. 1990. “Anthropometric Data of Children for Non-specialist Users.” Ergonomics 33 (4):421–429.

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