Asia Pac J Clin Nutr 2018;27(3):592-598

592

Original Article

Anthropometry to assess body fat in Indonesian adults
Janatin Hastuti SSi, MKes, PhD1, Masaharu Kagawa BSc(Hons), PhD2, Nuala M Byrne
3
3
MAppSc, PhD , Andrew P Hills MSc, PhD, FASMF
1

Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
Institute of Nutrition Sciences, Kagawa Nutrition University, Japan3
School of Health Sciences, University of Tasmania, Australia
2

Background and Objectives: Anthropometric equations are widely used to estimate body composition however,are only appropriate for use in populations in which they have been developed and validated. The present study
developed anthropometric prediction equations for Indonesian adults and cross-validated them with selected
equations used in this population. Methods and Study Design: Six hundred Indonesian adults aged between 18–
65 years (292 males and 308 females) were divided equally into development and validation groups. Stature,body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured.
Stepwise multiple regression analysis was used to propose percentage body fat (%BF) prediction equations using
measured variables and %BF from the deuterium oxide dilution technique as the reference. The proposed prediction equations were then cross-validated using the validation group and %BF estimated from several existing
equations. Results: Proposed prediction equations showed r ranged from 0.82 to 0.86 and Standard Error of theEstimate (SEE) from 4.7 to 5.4%. Cross-validation analysis showed bias with the reference %BF between 0.2 and3.3% and Pure Error (PE) between 2.8 and 4.0%. Among the existing equations, the Durnin and Womersley
equation was applicable in females whilst the equation by Davidson et al. underestimated %BF by 6.3–6.6% andthe equation by Gurrici et al overestimated by 2.0–3.4% in both genders (p<0.01). Conclusion: The proposed
prediction equations provide better options for accurate prediction of %BF in Indonesian adults.

Key Words: body composition, percentage body fat, prediction equation, anthropometry, Indonesian adults

INTRODUCTION
Anthropometry is widely used in health assessments inboth clinical and field settings because the instruments
are portable, inexpensive, and relatively simple and noninvasive. Anthropometry has been utilized to identify thehealth risks1,2 of individuals and populations and to predict body composition.3,4 A number of studies have reported relationships between anthropometric measuresand body composition2,5-7 with studies among Asian populations indicating a higher percentage body fat (%BF) at
any given body mass index (BMI) when compared to
Caucasians.8,9 Based on studies using young Japanese andCaucasian Australians, Kagawa and colleagues10,11 reported that race or ethnicity may contribute to the difference in the relationships between %BF and anthropometric indices. For the Indonesian population, Gurrici et al9,12
reported on the BMI-%BF relationship however, associations between other anthropometric indices and %BF
were not described.
Numerous techniques are available to determine bodycomposition accurately and precisely. However the application of these techniques in both clinical and field research is limited due to cost, lack of portability plus skill
and license to operate.13-16 Even though anthropometry is
one of the viable techniques with minimum subject burden, prediction equations to estimate body composition
from anthropometry are known to be ethnic-, gender-, andage-specific.3,5,17 To date, a number of prediction equa-tions have been developed to estimate body density (BD)or %BF using anthropometric variables.5,18-21 However,most were generated from Caucasian populations with
only a few developed from Asians, including Indonesians.
To our knowledge, the only prediction equation developed from Indonesians was proposed by Gurrici et al9
using BMI however, it has not been cross-validated.
Moreover, despite the widespread use of the BMI to determine obesity, the index has several limitations including inability to differentiate body composition and theinfluence of body proportions. Different BMI-%BF relationships in various ethnic groups17,22,23 points to the needto propose %BF prediction equations using other anthropometric variables to estimate body composition of Asian
populations.
In addition, accuracy and precision of anthropometricapproaches to estimate body composition are influenced
by measurement skill, equipment, biological factors ofCorresponding Author: Dr Janatin Hastuti, Lab. of Bioanthropology and Paleoanthropology, Faculty of Medicine, Public
Health, and Nursing, Universitas Gadjah Mada. Jalan Medika,
Sekip, Yogyakarta, Indonesia, 55281.
Tel.: +62 274 552577; Fax: +62 274 552577
Email: janatin.hastuti@ugm.ac.id
Manuscript received 12 December 2016. Initial review completed and revision accepted 05 January 2017.
doi: 10.6133/apjcn.092017.02

Estimation of body fat in Indonesian adults

participants and choice of prediction equations. The current study therefore aimed to develop and validate %BF
prediction equations using anthropometry and deuterium
dilution technique and also to evaluate the applicability ofcommonly used existing %BF prediction equations forIndonesian adults.
PARTICIPANTS AND METHODS
ParticipantsIndonesian adults of Javanese ethnicity living in Yogyakarta Special District Province were recruited for the present study. Apparently healthy males and females aged
18-65 years and were selected among the potential participants, whilst those who had physical or mental disabilities, under medical treatment, or involved in weightreducing programs, were excluded. Written informed
consent was obtained from all potential participants prior
to their participation. Prior to the day of the measurement,
participants were reminded to fast overnight and avoid
vigorous physical exercise and excessive sweating. A
total of 600 participants (292 males and 308 females)
were included and the study was approved by the Human
Research Ethics Committee of Queensland University ofTechnology in Australia and Universitas Gadjah Mada inIndonesia.
Anthropometry
All participants underwent measurements of stature, bodyweight, eight skinfolds (triceps, subscapular, biceps, iliac
crest, supraspinale, abdominal, front thigh, and medialcalf), five girth measures (arm relaxed, arm flexed andtensed, minimum waist, gluteal, and maximum calf), andfour breadth measures (biacromial, biiliocristal, humerus,
and femur). All measurements were conducted using
standardized equipment and measurement protocol proposed by the International Society for the Advancementof Kinanthropometry (ISAK).24 All measurements wereconducted by an accredited ISAK level 1 anthropometrist
and intra-tester technical error of measurements (TEM)
calculated from the first 20 participants was within therecommended standard by ISAK.24 BMI, waist-to-hip
ratio (WHR), and waist-to-stature ratio (WSR) were calculated.
Body composition assessment
Deuterium oxide dilution was used as the reference technique to predict %BF from the measurement of total body
water (TBW). Methods for the measurement of isotope
concentration and TBW have been described in detailpreviously.25 Based on the measured TBW, fat mass (FM)and fat-free mass (FFM) were estimated using the hydration coefficient of 0.732 as proposed by Pace andRathburn.26
Statistical analysis
Participants were randomized into development and crossvalidation groups of the same sample size (n=300; 146 males
and 154 females). Between-group differences in the physicalcharacteristics and %BF were evaluated using independent
sample t-test analysis. In the development group, stepwise
multiple regression analyses were used to develop prediction
equations using %BF estimated from deuterium dilution as593

the dependent variable and anthropometric variables andcalculated indices as well as age, body weight and stature as
independent variables. Anthropometric variables weregrouped into skinfold site, sum of four skinfolds (triceps,
biceps, subscapular, and iliac crest), BMI, girths (arm relaxed, arm flexed and tensed, minimum waist, gluteal, andmaximum calf), and breadths (biacromial, biiliocristal, humerus, and femur). The precision of the prediction equation
was evaluated by the coefficient of correlation (r), coefficient of determination (r2), standard error of the estimate(SEE), and the Akaike Information Criterion (AIC). The
most optimal prediction equations were determined fromthose which had the highest r and r2, but the smallest SEE
and AIC values.
Performance of the proposed equations was evaluatedusing the validation group and the bias between the %BF
from deuterium dilution and the prediction equation wasassessed by correlation and difference using paired t-tests.
The pure error (PE) was calculated with a smaller pureerror value indicating greater accuracy of the equation.
Predicted and measured %BF values were also regressed
and Bland and Altman plots used to examine agreement
between %BF from the two methods.27 Limits of agreement were decided as mean ±1.96 x standard deviation(SD).
In addition, existing %BF estimation equations were
evaluated and compared with the values obtained fromdeuterium dilution technique. Equations evaluated in thepresent study included Durnin and Womersley,18 Davidson et al19 and Gurrici et al.9 Values were comparedusing paired t-test analysis and Bland and Altman plots.27
All statistical analyses were conducted using the SPSS
program (version 20, SPSS Inc., 2011, Chicago, IL) witha significant level of 0.05.
RESULTS
There were no significant differences in age, body weight,
stature, BMI, and %BF between the development andvalidation groups in males and females, as well as in total
samples (Table 1).
From the analyses, prediction equations using eachskinfold site, sum of four skinfolds, BMI, girth andbreadth measures, and other anthropometric indices were
proposed. All prediction equations showed comparable
correlation coefficients with the highest correlation fromthe equation using individual skinfolds (Table 2). Due toreduced participant burden, the equation using the sum offour skinfolds was preferred because equation using thesum of eight skinfolds showed only a slightly higher r2. In
this model, skinfolds at triceps and iliac crest sites
showed significant contributions in estimation of %BF. In
comparison, equations from girth and breadth measurements indicated that gluteal and waist girths as well as
humerus breadth were significant predictors with r ranged
from 0.82 to 0.86. The precision of the proposed prediction equations as shown in the SEE values were from 4.7
to 5.4% and the AIC values from 942 to 1,006.
Cross-validation analyses showed that the proposed
prediction equations were significantly correlated (p<0.01)
with %BF2HO with r ranging between 0.83 and 0.89 inmales and between 0.69 and 0.75 in females. However,significant differences were observed between the594

J Hastuti, M Kagawa, NM Byrne and AP Hills

Table 1. Characteristics of the study groups
Males

Age (years)
Body weight (kg)
Stature (cm)BMI (kg/m2)%BFD2OGroup 1(n=146)39.0±11.6
58.2±10.0
165±7.0
21.4±3.3
21.1±6.6

Group 2(n= 46)38.7±12.0
59.8±11.2
165±5.921.8±3.8
21.7± 7.5

Females
Group 1
Group 2(n=154)(n=146)39.3±10.939.5±11.2
52.2±9.452.6±9.4
154±5.2
153±5.422.1±3.7
22.6±3.932.6±7.833.9± 7.4

Total samples
Group 1
Group 2(n=300)(n=300)39.1±11.2
39.1±11.6
55.1±10.1
56.1±10.9159±8.4
159±8.5
21.8±3.522.3±3.827.0±9.3
27.9± 9.6

Group 1: development group; Group 2: cross-validation group; there were no significant differences between the mean values of group 1 and 2 inmales, females, and total sample.

Table 2. Percentage body fat prediction equations developed using anthropometric variables
Dependent variables
Skinfold sites

Regression equation%BF = 17.026 + 0.509 (triceps) + 0.342 (iliac crest) - 5.594
(G)

r
0.864

r2
0.746

SEE
4.69

AIC
926

Sum 4 skinfolds†%BF = 17.858 + 0.215 (sum of 4 skinfolds) - 6.448 (G)

0.857

0.734

4.80

942

BMI

%BF = 1.938 - 10.509 (G) + 1.388 (BMI)

0.817

0.668

5.36

1,006

Girth and breadth measures%BF = -8.545 - 4.830 (G) + 0.420 (waist girth) + 0.439(gluteal girth) - 4.830 (humerus breadth)

0.848

0.719

4.94

959

Anthropometric index%BF = -5.032 - 12.712 (G) + 0.294 (body weight) + 0.477
(WSR)

0.821

0.675

5.31

997

G: gender (1 for males, 0 for females); BMI: body mass index; WSR: waist-to-stature ratio.
†
Sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest; r: coefficient of correlation; r2: coefficient of determination;
SEE: standard error of the estimate; AIC: the Akaike Information Criterion.

%BF2HO and the equation from both girth and breadth
measures in males (p<0.01) and the equation from skinfolds in females (p<0.05) as presented in Table 3. Meandifferences of %BF estimated from the proposed equations and the reference %BF ranged between -3.3 and-0.6% with PE between 2.8 and 4.0% in males. In females, these differences were between 0.7 and 1.0% of%BF and between 3.6 and 3.8% of PE. Bland and Altman
analysis indicated that the limits of agreement of bothmethods ranged between ±7.5 and 8.8% (Figure 1) inmales and between ±9.6 and 10.6% in females (Figure 2).
There was a tendency to underestimate %BF in lower
body fat and overestimate %BF in higher body fat in bothgenders, however males showed more clearly.
Estimated %BF from equations of Durnin and Wom-ersley,18 Davidson et al19 and Gurrici et al9 are presented
in Table 4. The results indicated significant differences(p<0.01) between the reference %BF and %BF estimatedfrom these equations, except for the Durnin and Womersley18 for females (bias of 0.5%). The equation of Davidson et al19 significantly (p<0.01) underestimated %BF
by 6.6% and 6.3% in the validation groups in males andfemales respectively, whereas the Gurrici et al9 equation
overestimated %BF by 3.4% and 2.0% in males and females respectively (p<0.01).
DISCUSSION
The present study developed and evaluated anthropometric prediction equations applicable for Indonesian adults
and demonstrated that %BF can be predicted across a

Table 3. Comparison of %BF reference and anthropometric equations in the validation groups

Males
Skinfold sites
Sum of 4 skf†
BMI
Girth & breadth
Index
Females
Skinfold sites
Sum of 4 skf†
BMI
Girth & breadth
Index*

Reference‡
Mean±SD

Mean±SD21.6±7.4
21.6±7.4
21.6±7.4
21.5±7.4
21.6±7.4

22.0±5.722.1±5.7
21.8±5.2
24.8±5.922.0±5.7-0.4±3.7-0.6±3.5-0.2±4.3-3.3±3.6-0.4±4.1

2.8±2.3
2.8±2.2
3.4±2.4
4.0±2.8
3.2±2.4

0.875**
0.886**
0.825**
0.855**
0.834**

-1.23
-1.94
-0.68
-10.55-1.15

33.9±7.433.9±7.433.9±7.433.9±7.433.8±7.4

32.9±5.933.1±6.0
33.4±5.433.2±5.6
33.0±5.3

1.0±4.9
0.8±5.0
0.4±5.4
0.7±5.1
0.7±5.2

3.7±3.3
3.6±3.4
3.6±3.5
3.8±3.6
3.8±3.6

0.751**
0.745**
0.685**
0.730**
0.707**

2.56*
2.01*
1.02
1.62
1.75

Prediction equation
Bias±SD

PE±SD

Paired sample testrt

p<0.05; **p<0.01; skf: skinfold; BMI: body mass index; SD: standard deviation; PE: pure error.
Sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest.
‡
The reference values may different from each variable due to the different dropped outliers; r: coefficient correlation; t: t values.
†Estimation of body fat in Indonesian adults

595

Figure 1. Agreement between %BF from deuterium oxide dilution methodas the reference method and %BF estimated using prediction
equations in males, prediction equations is plotted against mean %BF;a: the difference between %BF obtained from the reference and%BF predicted from skinfold site; b: %BF predicted from sum of four skinfold; c: %BF predicted from BMI, d: %BF predicted fromgirth and breadth measures; and e: %BF predicted from anthropometric index; D2O: deuterium oxide dilution method; Eq1: prediction
equation from skinfold site; Eq2: prediction equation from sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest;
Eq3: prediction equation from BMI; Eq4: prediction equation from girth and breadth measures; Eq5: prediction equation from anthropometric index prediction equations.

broad age range of males and females when laboratorybased body composition assessment techniques are notavailable. Examination on a separate group of samples
indicated that the proposed equations also showed good
performance.
The inclusion of gender improved the equation’s performance which may be due to the larger sample size,
however age did not significantly contribute to this model.
This finding is not consistent with previous studies by van
der Ploeg et al28 and Kagawa et al.10,32 Age may influencebody composition and the relationship between BMI and%BF since older adults may experience loss of lean mass,loss of weight and decrease in BMI but retention of fat
mass.29 Particularly in the prediction equation using skinfold thickness, van der Ploeg et al28 found that skinfold
thickness better predicted %BF in the younger age group(<30 y) and predicted %BF increased as the greater skinfold thickness was more evident in the older age group(>30 y).
In the present study, skinfold thickness at triceps andiliac crest selected from eight skinfold sites accounted for
74.6% of the variance, greater than the variance from thesum of four skinfolds (biceps, triceps, subscapular, andiliac crest) (73.4%). Using a four-compartment model todevelop an equation to predict %BF, van der Ploeg andcolleagues28 reported that subscapular, biceps, abdominal,
thigh, calf, and mid-axilla sites resulted in an SEE of
2.2% BF (r2=0.91). Difference in fat distribution and fat
mass among ethnicities are often regarded as potential
sources of inapplicability of prediction equations.9,23,30
Moreover, differences in defined sites of chosen skinfolds,
even as small as one centimeter away produced signifi-

596

J Hastuti, M Kagawa, NM Byrne and AP Hills

Figure 2. Agreement between %BF from deuterium oxide dilution methodas the reference method and %BF estimated using prediction
equations in females, prediction equations is plotted against mean %BF;a: the difference between %BF obtained from the reference and%BF predicted from skinfold site; b: %BF predicted from sum of four skinfold; c: %BF predicted from BMI, d: %BF predicted fromgirth and breadth measures; and e: %BF predicted from anthropometric index; D2O: deuterium oxide dilution method; Eq1: prediction
equation from skinfold site; Eq2: prediction equation from sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest;
Eq3: prediction equation from BMI; Eq4: prediction equation from girth and breadth measures; Eq5: prediction equation from anthropometric index prediction equations.

cant differences in the majority of skinfold measurements.31 In the present study, although the prediction
equation from skinfolds showed the best performance andwas highly correlated with the reference %BF in the
cross-validation analysis in males, a significant (p<0.05)
bias was evident in females. This may be due to genderdifferences in subcutaneous fat distribution. Kagawa etal.11 also reported gender differences - abdominal andmedial calf skinfolds had a better correlation with %BF
obtained from DXA in males, whereas triceps, iliac crest,
biceps, medial calf, and abdominal sites gave the highest
correlation value and the lowest SEE in females. 32
The skinfold prediction equation of Durnin and Womersley18 has been widely used to estimate %BF, including
to underestimate %BF in Indonesian populations.9,12,30

However, the current study found the equation had no
significant bias with %BF obtained from deuterium dilution technique, particularly for females. In the current
study, the only difference was that we replaced the suprailiac skinfold used in the Durnin and Womersley18
equation with the iliac crest skinfold taken at the centre of
the skinfold raised immediately above the marked iliocristale.24 In contrast, despite being updated specific to sex
and ethnicity and using the original Durnin and Womersley18 skinfold sites, the Davidson et al. equation19 showed
the highest bias with measured %BF in our samples. Thismight be explained by possible differences in body composition and anthropometric measurement techniques
used in the equation development.19
Although the performance of the prediction equation

Estimation of body fat in Indonesian adults

597

Table 4. Differences between %BF obtained from D2O and various prediction equations

Males%BF from DW%BF from D%BF from Gurrici
Females%BF from DW%BF from D%BF from Gurrici

Reference†
Mean±SD

Prediction equation
Mean±SDr

21.7±7.5
21.6±7.4
21.7±7.5

20.9±7.1
15.0±6.3
25.1±4.9

0.857**
0.896**
0.807**

0.8±3.9*
6.6±3.3**-3.4±4.6**

33.9±7.433.9±7.433.9±7.433.4±6.1
27.6±6.535.9±5.1

0.726**
0.768**
0.685**

0.5±5.4
6.3±4.8**-2.0±5.4**

Paired sample test
Mean diff±SD‡*p<0.05; **p<0.01; SD: standard deviation; mean diff: mean difference.
DW: %BF predicted using BD formula of Durnin and Womersley (1974) and %BF formula of Siri (1961); D: %BF predicted using formula of Davidson et al (2011); Gurrici: %BF predicted using formula of Gurrici et al (1998).
†
The reference values may different from each variable due to the different dropped outliers.
‡
Significant difference between %BF and predicted from the equations.

from BMI was not as good as others it represented thelowest bias with %BF obtained from the reference technique in cross-validation analysis indicating that thisequation is also applicable to this population. The magnitude of the BMI equation was comparable with a study
using 2C model by Kagawa and colleagues33 in Japanese
females and males with r2 values of 0.61 and 0.44 respectively. It was suggested that the use of a multicompartment model resulted in a higher correlation asreported by van der Ploeg et al28 using a 4C body composition model with r2 values of 0.84 to 0.94.
The BMI prediction equation of Gurrici et al9 was specifically designed for the Indonesian population yet overestimated %BF in our validation samples by about 3%
with greater overestimation at higher %BF in both genders. Moreover, a wide range agreement limits in theBland and Altman plots (males: between -12.4 and 5.6%,
females: between -12.6 and 8.6%) may have resulted in ahigh variation. Consequently, this equation should be
used with careful since factors such as physical activity34
and body build8,34,35 may influence the results.
The strength of the current study lies in its sampling
and validity assessment of the proposed equation. The
large sample size allows a stable estimation of the relationship between measured %BF and the predictor variables. Moreover, participants were randomly selectedacross a large age and BMI range, and representative ofthe largest ethnic group in the Indonesian populations.
Nevertheless, generalization to other populations should
be interpreted with care since differences in %BF may
exist among Indonesian sub-populations.12 The reference%BF in the current study was obtained from the deuterium oxide dilution technique considered one of the gold
standard methods for the assessment of bodycomposition16,36 which can be used in the absence of theavailability of a four-compartment model. Norgan37 indicated that laboratory-based estimates of body composition are best performed by multi-compartment methods orby two-compartment methods adjusted for populations
under investigation. In addition, the cross-validation
study of the proposed prediction equations allows advanced evaluation for the precision of the equations.37
To summarize, the present study proposed new %BF
prediction equations and based on cross-validation, equations were found to be applicable for use in Indonesian

adults. These equations may assist in both clinical andfield research settings when more complex instruments
for the assessment of %BF are not available. Limitationsof the present study include the lack of heterogeneity ofthe samples in terms of ethnicity and socio-economic
level and the use of a two-compartment model for theassessment of %BF. Further studies involving multiple
ethnicities are important to ensure precise predictionof %BF.
ACKNOWLEDGEMENTS
We thank the Directorate General of the Higher Education Republic of Indonesia for the financial support, Universitas GadjahMada, Indonesia, and Queensland University of Technology,
Australia for technical help. We are appreciative of the participants in the current study and the data collection staff. Thanks to
Ms Connie Wishart (Queensland University of Technology) for
assistance with laboratory assessment.
AUTHOR DISCLOSURESThe authors declare that this manuscript is original and there isno conflict of interests among the authors.
REFERENCES
1. World Health Organization Expert Consultation.
Appropriate body mass index for Asian populations and its
implications for policy and intervention strategies. Lancet.
2004;363:157-63. doi: 10.1016/S0140-6736(03)15268-3.
2. Abernethy P, Olds T, Eden B, Neill M, Baines L.
Anthropometry, health and body composition. In: Norton K,
Olds T, editors. Anthropometrica. New Delhi, India: CBS
Publishers; 2009. pp. 365-92.
3. Heyward V, Wagner D. Applied body compositionassessment. Champaign, IL, USA: Human Kinetics; 2004.
4. Bellisari A, Roche AF. Anthropometry and ultrasound. In:
Heymsfield SB, Lohman TG, Wang Z, Going SB, editors.
Human body composition. Champaign, IL, USA: Human
Kinetics; 2005. pp. 109-28.
5. Norton K. Anthropometric estimation of body fat. In:
Norton K, Olds T, editors. Anthropometrica. New Delhi,
India: CBS Publishers; 2009. pp. 171-98.
6. Deurenberg P, Deurenberg-Yap M, Wang J, Lin FP,
Schmidt G. Prediction of percentage body fat fromanthropometry and bioelectrical impedance in Singaporean
and Beijing Chinese. Asia Pac J Clin Nutr. 2000;9:93-8.
7. Deurenberg P, Weststrate JA, Seidell JC. Body mass index
as a measure of body fatness: age- and sex-specificprediction formulas. Br J Nutr. 1991;65:105-14.

598

J Hastuti, M Kagawa, NM Byrne and AP Hills

8. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians aredifferent from Caucasians and from each other in their bodymass index/body fat per cent relationship. Obes Rev. 2002;
3:141-6. doi: 10.1046/j.1467-789X.2002.00065.x.
9. Gurrici S, Hartriyanti Y, Hautvast JGAJ, Deurenberg P.
Relationship between body fat and body mass index:
Differences between Indonesians and Dutch Caucasians. Eur
J Clin Nutr. 1998;52:779-83.
10. Kagawa M, Kerr D, Binns CW. New percentage body fat
prediction equations for Japanese males. J Physiol
Anthropol. 2006;25:275-9.
11. Kagawa M, Kerr D, Uchida H, Binns CW. Differences in
the relationship between bmi and percentage body fat
between Japanese and Australian-Caucasian young men. Br
J Nutr. 2006;95:1002-7.
12. Gurrici S, Hartriyanti Y, Hautvast JGAJ, Deurenberg P.
Dufferences in the relationship between body fat and bodymass index between two different Indonesian ethnic groups:
The effect of body build. Eur J Clin Nutr. 1999;53:468-72.
13. Ross R, Janssen I. Computed tomography and magnetic
resonance imaging. In: Heymsfield S, Lohman T, Wang Z,
Going S, editors. Human body composition. Champaign, IL,
USA: Human Kinetics; 2005. pp. 89-108.
14. Lohman T, Chen Z. Dual-energy x-ray absoptiometry. In:
Heymsfield S, Lohman T, Wang Z, Going S, editors. Humanbody composition. Champaign, IL, USA: Human Kinetics;
2005. pp. 63-78.
15. Going S. Hydrodensitometry and air displacement
plethysmography. In: Heymsfield S, Lohman T, Wang Z,
Going S, editors. Human body composition. Champaign, IL,
USA: Human Kinetics; 2005. pp. 17-34.
16. Schoeller D. Hydrometry. In Heymsfield S, Lohman T,
Wang Z, Going S,editors. Human body composition.
Champaign, IL, USA: Human Kinetics; 2005. pp. 35-50.
17. Deurenberg P, Deurenberg-Yap M. Validity of bodycomposition methods across ethnic population groups. Acta
Diabetol. 2003;40:S246-9. doi: 10.1007/s00592-003-0077-z.
18. Durnin JVGA, Womersley J. Body fat assessed from total
body density and its estimation from skinfold thickness:
Measurements on 481 men and women aged from 16 to 72
years. Br J Nutr. 1974;32:77-97.
19. Davidson LE, Wang J, Thornton JC, Kaleem Z, SilvaPalacios F, Pierson RN et al. Predicting fat percent byskinfolds in racial groups: Durnin and Womersley revisited.
Med Sci Sports Exerc. 2011;43:542-9. doi: 10.1249/MSS.
0b013e3181ef3f07.
20. Peterson MJ, Czerwinski SA, Siervogel RM. Development
and validation of skinfold-thickness prediction equations
with a 4-compartment model. Am J Clin Nutr. 2003;77:18691. doi: 10.1093/ajcn/77.5.1186.
21. Deurenberg P, Deurenberg-Yap M. Validation of skinfold
thickness and hand-held impedance measurements for
estimation of body fat percentage among SingaporeanChinese, Malay, and Indian subjects. Asia Pac J Clin Nutr.
2002;11:1-7.
22. Deurenberg P, Deurenberg-Yap M. Differences in body-composition assumptions across ethnic groups: Practicalconsequences. Curr Opin Clin Nutr Metab Care. 2001;4:
377-83.
23. Deurenberg P, Yap M, van Staveren W. Body mass index
and percent body fat: A meta analysis among different
ethnic groups. Int J Obes. 1998;22:1164-71.
24. International Society forthe
AdvancementofKinanthropometry.
International
Standards
for
Anthropometric Assessment. Canberra, Australia: ISAK;
2006.
25. Hastuti J, Kagawa M, Byrne NM, Hills AP. Development
and validation of anthropometric prediction equations for
estimation of body fat in indonesian men. Asia Pac J Clin
Nutr. 2013;22:522-9. doi: 10.6133/apjcn.2013.22.4.14.
26. Pace N, Rathbun EN. Studies on body composition, III. The
body water and chemically combined nitrogen content inrelation to fat content. J Biol Chem. 1945;158:685-91.
27. Bland JM, Altman DG. Measuring agreement in method
comparison studies. Stat Methods Med Res. 1999;8:135-60.
doi: 10.1177/096228029900800204.
28. van der Ploeg GE, Gunn SM, Withers RT, Modra AC. Useof anthropometric variables to predict relative body fat
determined by a four-compartment body composition model.
Eur J Clin Nutr. 2003;57:1009-16. doi: 10.1038/sj.ejcn.
1601636.
29. Baumgartner RN. Age. In: Heymsfield SB, Lohman TG,
Wang Z, Going SB, editors. Human body composition.
Champaign, IL: Human Kinetics; 2005. pp. 259-70.
30. Küpper J, Bartz M, Schultink JW, Lukito W, Deurenberg P.
Measurements of body fat in Indonesian adults: comparison
between a three-compartment model and widely usedmethods. Asia Pac J Clin Nutr. 1998;7:49-54.
31. Hume P, Marfell-Jones M. The importance of accurate site
location for skinfold measurement. J Sport Sci. 2008;26:
1333-40. doi: 10.1080/02640410802165707.
32. Kagawa M, Kuroiwa C, Uenishi K, Mori M, Hills A, Binns
C. New percentage body fat prediction equations forJapanese females. J Physiol Anthropol. 2007;26:23-9.
33. Kagawa M, Binns CW, Hills AP. Body composition andanthropometry in Japanese and Australian Caucasian males
and Japanese females. Asia Pac J Clin Nutr. 2007;16:31-6.
34. Gurrici S, Hartriyanti Y, Hautvast JGAJ, Deurenberg P.
Differences in the relationship between body fat and bodymass index between two different Indonesian ethnic groups:
the effect of body build. Eur J Clin Nutr. 1999;53:468-72.
35. Deurenberg P, Deurenberg-Yap M, Wang J, Lin F, Schmidt
G. The impact of body build on the relationship between
body mass index and percent body fat. Int J Obes. 1999;23:
537-42.
36. Ramírez E, Valencia M, Moya-Camarena S, Alemán-Mateo
H, Méndez R. Four-compartment model and validation of
deuterium dilution technique to estimate fat-free mass in
Mexican youth. Nutrition. 2009;25:194-9. doi: 10.1016/j.nut.
2008.08.007.
37. Norgan N. Laboratory and field measurements of bodycomposition. Public Health Nutr. 2005;8:1108-22.