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Br.
J.
Nufr.
(19781,
40,
497
497
Generalized equations for predicting body density
of
men
BY
A.
S.
JACKSON*
AND
M.
L.
POLLOCK?
Wake Forest University, WinstonSalem, North Carolina and Institute
of
Aerobics
Research, Dallas, Texas, USA
(Received
3
August
1977

Accepted
28
February
1978)
I.
Skinfold thickness, body circumferences and body density were measured in samples
of
308
and ninety
five adult men ranging in age from
18
to
61
years.
2.
Using
the sample of
308
men, multiple regression equations were calculated
to
estimate body density
using either the quadratic or log form of the sum of skinfolds, in combination with age, waist and forearm
circumference.
3.
The multiple correlations for the equations exceeded
0.90
with standard errors
of
approximately
+oao73
g/ml.
4.
The regression equations were cross validated on the second sample of ninetyfive men. The corre
lations between predicted and laboratorydetermined body density exceeded
0.90
with standard errors of
approximately
0.0077
g/ml.
5.
The regression equations were shown
to
be valid for adult men varying in age and fatness.
Anthropometry is a common field method for measuring body density (Behnke
&
Wilmore,
1974).
BroZek
&
Keys
(1951)
were the first to publish regression equations with functions
of predicting body density with anthropometric variables. Subsequently, numerous investi
gators have published equations using various combinations of skinfolds and body
circumferences.
The development of generalized equations for predicting body density from anthropo
metric equations has been found to have certain limitations. First, equations have been
shown to be population specific and different equations were needed for samples of men
varying in age and body fatness. It was shown that with samples of men differing in age,
the slopes
of
the regression lines were homogeneous, but the intercepts were significantly
different (Durnin
&
Womersley,
1974;
Pollock, Hickman, Kendrick, Jackson, Linnerud
&
Dawson,
1976).
It was further shown that the slopes
of
the regression lines of young adult
men and extremely lean world class distance runners were not parallel (Pollock, Jackson,
Ayres, Ward, Linnerud
&
Gettman,
1976).
The differences of either slopes or intercepts
resulted in bias body density estimates. A related problem has been that linear regression
models have been used to derive prediction equations, when research has shown that a curvi
linear relationship exists between skinfold fat and body density (Allen, Peng, Chen,
Huang, Chang
&
Fang,
1956;
Chen, Peng, Chen, Huang, Chang
&
Fang,
1975;
Durnin
&
Womersley,
1974).
This nonlinear relationship may be the reason for the differences in
slopes and intercepts.
Durnin
&
Womersley
(1974)
logarithmically transformed the sum of skinfolds to create
a
linear relationship with body density, but still needed different intercepts to account for
age differences. The purpose of this investigation was to derive generalized regression
equations that would provide unbiased body density estimates for men varying in age and
body composition. Efforts were concentrated
on
the curvilinearity of the relationship and
the function of age
on
body density.
*
Present address: Department of Health and Physical Education, University of Houston, Houston,
Texas,
USA.
t
Present address
:
Cardiovascular Disease Section,
Mount
Sinai Medical Center, University of Wiscon
sin, School of Medicine, Milwaukee, Wisconsin, USA.
172
498
A.
S.
JACKSON
AND
M.
L.
POLLOCK
Table
I.
Physical characteristics
of
the validations and crossvalidation samples*
Validation sample Crossvalidation sample
(n
308)
(n
95)
,.
7,
.,
Variable Mean
SD
Range Mean
SD
Age (year)
Height
(m)
Weight
(kg)
Body
density (g/ml)
Fat
(%It
Lean weight (kg)
Fat weight (kg)
Sum
7
skinfolds (mm)
Log
7
skinfolds (mm)
Sum
3
skinfolds (mm)$
Log
3
skinfolds (mm)
Waist circumference (m)
Forearm circumference
(m)
32.6 10.8
74.8
11.8
17'7 8.0
1.792
0.065
1.05E6 0.0181
63.9 7.4
14'5 7'9
122.6
52.0
59'4 243
4'70 0.49
3'98 0.49
0.871 0'097
0.288 0.019
I
86
I
1.632.01
54
1
23
I
'01
611.0996
133
48
100
142
3.475'61
141
18
32272
2644'78
0.671
'25
0'220'37
33'3
77.6
18.7
62.4
15.2
124.7
59'2
1.784
1.0564
4'7
I
3'95
0.874
0.287
11.5
0.059
11.7
0.0188
8.3
6.7
7'9
53'1
0.53
25'4
036
0'
I
0'02
I
Range
1859
1.661.91
1.02591q98
133
4781
131
31222
3'435'40
10111
2'30471
0'244,'39
53102
0.681.14
*
For explanation see p.
499.
t
Fat
(%)
=
[(4.95/BD)+4.5]
IOO
(Siri,
1961)
Fat
(%).
t:
Sum
of chest, abdomen and thigh skinfolds.
METHODS
A total of
403
adult men between
18
and
61
years of age volunteered as subjects. The
sample represented a wide range of men who varied considerably in body structure, body
composition, and exercise habits. The subjects were tested in one of two laboratories
(Wake Forest University, WinstonSalem, North Carolina and Institute for Aerobics
Research, Dallas, Texas) over a period of
4
years. The total sample was randomly divided
into a validation sample consisting
of
308
men and a crossvalidation sample
of
ninetyfive
subjects. The validation sample was used to derive generalized regression equations and
were crossvalidated with the second sample. This procedure has been recommended by
Lord
&
Novick
(1968).
The physical characteristics of the two samples are presented in
Table
I.
Upon arrival at the laboratory, the subjects were measured for standing height to the
nearest
0.01
m
(0.25
in) and for bodyweight to the nearest
10
g. Skinfold fat was measured
at the chest, axilla, triceps, subscapula, abdomen, suprailiac, and thigh with
a
Lange
skinfold fat caliper, manufactured by Cambridge Scientific Industries, Cambridge, Mary
land, USA.
Recommendations published by the Committee on Nutritional Anthropometry of the
Food and Nutrition Board of the National Research Council were followed in obtaining
values for skinfold fat (Keys,
1956).
A previous study (Pollock, Hickman
et
al.
1976)
showed
that waist and forearm circumference accounted for body density variance beyond skinfold
fat, and for this reason, were included jn this study. Waist and forearm circumferences
were measured to the nearest
I
mm with a Lufkin steel tape, manufactured by the Lufkin
Rule Company, Apex, North Carolina, USA. The procedures and location of the anthro
pometric sites measured were shown and described by Behnke
&
Wilmore
(1974).
The hydrostatic method was used to determine body density. Underwater weighing was
conducted in a fibreglass tank in which a chair was suspended from a Chatillon
15
kg scale.
The hydrostatic weighing procedure was repeated six to ten times until three similar read
ings to the nearest
20
g were obtained (Katch,
1968).
Water temperature was recorded
after each trial. Residual volume was determined by either the nitrogen washout
or
helium
dilution technique. The procedure for determining body density followed the method out
Generalized body density equations
499
Table
2.
Regression analysis for predicting body density using the
sum
of
seven skinfolds in
adult men aged
1861
yearst
F, ratio Standard regression
Degrees of Sum
of
Mean for statistical certificate for
Source of variance freedom squares square significance full model
Sum
of
seven skinfolds
Full model
Skinfold fat
Linear
Quadratic
Circumferences
Age
Waist
Forearm
Residual
0.084
I
8
0.07878
(0'07757)
(0'00121)
0'00279
0.00261

0.01612
0.01
684
0.03939
0,07757
0'00121
0'00279
0~00261
L
Log transformation of seven skinfolds
Full model
4
0.08425 0~02106 421.20*

Log skinfold fat
(I)
0.07706 0.07706 1541.20*

0.64
(I)
0.00284 0.00284 56.80*
0.13
Age



0.38


0.23
Residual
303
0.01605
0~00005



Circumferences
(2)
0.00435 0.00435 87~*
Waist

Forearm


*
P
<
0'01.
t
For details, see Table
I.
lined by Goldman
&
Buskirk
(1961).
Body density was calculated from the formula of
Broiek, Grande, Anderson
&
Keys
(1963)
and fat percentage according to Siri
(1961)
(see Table
I).
In
a
factor analysis study,
it
was shown (Jackson
&
Pollock,
1976)
that skinfolds measured
the same factor; therefore, the skinfolds were summed. The sum of several measurements
provides
a
more stable estimate of subcutaneous fat. A second sum consisting of
chest, abdomen and thigh skinfolds was also derived. These three skinfolds were selected
because of their high intercorrelation with the sum of seven and
it
was thought that they
would provide
a
more feasible field test. The sum of skinfolds were also logarithmically
transformed
so
that they could be compared with the work of Durnin
&
Womersley
(1974).
Regression analysis (Kerlinger
8t
Pedhazur,
1973)
was used to derive the generalized
equations. Polynomial models were used to test if the relationship between bodydensity
and the sum
of
skinfolds was curvilinear. 'Stepdown' analysis was used to determine if
age, and then age in combination with the circumference measurements, accounted for
additional bodydensity variance beyond that attributed
to
the sum of skinfolds. The cross
validation procedures recommended by Lord
&
Novick
(1968)
were followed to determine
if the equations derived
on
the validation sample accurately predicted the body density of
the crossvalidation sample.
RESULTS
Table
I
shows that basic results derived from the validation and crossvalidation samples
including natural log transformations of the sum of skinfolds. The standard deviations
and ranges showed that the men differed considerably in both age and body composition.
Tables
z
and
3
show the regression analysis using the sum of seven and sum of three skin
500
A.
s.
JACKSON
AND
M.
L.
POLLOCK
Table
3.
Regression analysis for predicting body density using the
sum
of
three skinfoldst
Source of variance
Full model
Skinfold fat
Linear
Quadratic
Circumferences
Age
Waist
Forearm
Residual
Full model
Log skinfold fat
Circumferences
Age
Waist
Forearm
Residual
Sum of
squares
F, ratio Standard regression
Mean for statistical certificate for
square significance full model
Sum of three skinfolds
0.01691 338.20*
0~04000 800.00*
0.07943 1588.60*
000055
I
I.OO*
0'00220
44'00*
0.0011~ 23.40.






1.11
0.12
0.43

0.31
0.19

0.01571
0~00005

Log
transformation
of
three skinfolds
0.08415 0~02104 420.80*

007614
0.07674 1534.80~ 0.62
om~248 0.00248 49'60*
0.11
0.00493 0.00493 98.60*

0.41
0.23
0.01626
0~00005



 



*
P
<
0'01
t
For details,
see
Table
I.
folds respectively. The correlation between the sum of three and seven skinfolds was
0.98;
thus, the regression analyses for these variables were nearly identical. The full model
consisted of either the linear and quadratic or the log transformed sum of skinfolds in
combination with age, and body circumferences. The multiple correlations for these full
models were nearly identical, ranging from
0.915
to
0.9
18.
Regression equations for the
full models may be found in Table
4.
Since the full models were significant, the stepdown analysis was conducted to determine
if
each variable accounted for
a
significant proportion of bodydensity variance. The first
analysis within the full model was to determine if the relationship between skinfold fat and
body density was linear or quadratic. This was found to be quadratic which supported the
findings of other investigators (Allen
et al.
1956;
Chen
et
al.
1975;
Durnin
&
Womersley,
1974).
Durnin
&
Womersley
(1974)
used a log transformation to form a linear relationship
between skinfold fat and body density. For this reason, only the linear relationship with
log transformed skinfolds was used.
Age was the next variable entered into the regression model and it accounted for a signifi
cant proportion of bodydensity variance beyond the logtransformed
or
quadratic form
of skinfolds. Waist and forearm circumference were the last two variables entered into the
full model and these measures accounted for a significant proportion of bodydensity
variance beyond age and skinfold fat.
The standardized regression coefficients for the full model are presented in Tables
2
and
3.
The magnitude of these weights represented the relative importance of each variable with
the effects of the other variables held constant. These statistics showed that the linear and
quadratic components accounted for most of the body density variance. The negative
weighting of the sum of skinfolds and positive weighting of the squared sum
of
skinfolds
represent the quadratic relationship between body density and the sum of skinfolds. The
Generalized body density equations
501
Table
4.
Generalized regression equations
for
predicting body density
(BD)
of
adult men
ages
I
861
years*
Anthropometric Eauation
variables
S,P,
age
S,Sz,
age,
C
log
S,
age
log
S,
age,
C
S,S1,
age
(5)
Regression equation no. R
Sum of seven skinfolds
BD
=
1.1
IZOOOOO0.00043499
(X1)+0.00000055
I
0.902
BD
=
1~10100000

0.00041
I
50
(X,)
+
0~00000069
(
X,),
z
0916
0~00028826 (X3)
0.0002~63I (X3)0'0059239 (X4)+0.0190632
(X,)
BD
=
1'213940'03101 (log X,)0~00029 (X3) 3 0.893
BD
=
1.176150.02394 (log
X,)OQOOZZ (X,)
4 0'917
0.0070
(X4)+0'02120
(X,)
Sum of three skinfolds
BD
=
1.10938000~0008267 (X,)+0~0000016
(X,)a
5
0'905
0.0002574
(Xs)
BD
=
1.09907500.0008~09 (X2)+0~0000026
(A',),
6
0.918
BD
=
1,188600.03049 (log X,)0~00027
(X,)
7
0.888
00002017 (X3)0*005675
(X,)
t
0.018586
(X,)
BD
=
1.157370.02288 (log X,)000019
(X,)
8
0,915
0.0075
(xd)+O'OZ23
(XE.)
SE
0.0078
0'0073
0.0082
0.0073
0.0077
om72
0.0083
0.007
3
s,
Sum of skinfolds;
C,
circumference;
X,,
sum of chest, axilla, triceps, subscapula, abdomen, suprailium
and front thigh skinfolds;
X,,
sum of chest, abdomen and thigh skinfolds; X3, age;
X4,
waist circumference;
X,,
forearm circumference.
*
For details, see Table
I.
Table
5.
Crossvalidation
of
generalized equations
on
the calibration sample (n
95)
Range of
SE
A
,
\
Variables Equation no.*
ryyf
SET
Age$ Fat§
S,S2,
age
I
0.915 0.0078 0~00640~0085 0~00660~0092
S,
Sa,
age,
C
2
0.915 00077 000570Oog4 000670.0084
log
S,
age 3 0.914 0.0078
oao550.0085
0.00540.oog1
log
S,
age,
C
4 0.913 0.0078 0.00610.0098 0~0064~~oog1
S,
S2,
age
5
0.917 0~x177 0~00660m83
0~00574~0087
S,
S2,
age,
C
6
0920
0.0076 0006600092 oa~580~0087
Sum
of
seven skinfolds
Sum of three skinfolds
Log
S,
age 7 0.904
0.0085
0.00640.0112
0~00474'0102
log
S,
age,
C
8
0.910
0.0082
OfX3570~0100
OC€&O~oOg7
s,
sum of skinfolds;
C,
circumference;
ruvt,
correlation between predicted
(y')
and laboratory determined
*
For
details, see Table
4.
t
SE
=
2/[~(J"Y)a/~1.
2
Age (years) categories
;
<
19.9, ZOe299, 39'039'9, 40'049'9,)
50'0.
3
Fat
(%)
categories: <9.9, 10.014.9, 15c19.9, 20.0249,)
25.0.
b)
body
density.
positive weighting for waist and negative weighting for forearm is consistent with the results
reported by Katch
&
McArdle
(1973).
Table
4
lists selected raw score equations and the equation's multiple correlation and
standard error. The high multiple correlations are due partially to the heterogeneous sample
studied. However, the standard errors are low and well within the values reported by other
502
A.
s.
JACKSON
AND
M.
L.
POLLOCK
1.100
1.095
1.090
1.085
1.080
1.075
1.070
1.065
2
1,060
.
1.055
1.050
1.045
8
1.040
1.035
1,030
1.025
1.020
1.015
1.010
.
0

+

'
+*
















investigators (Katch
&
McArdle,
1973;
Pascale, Grossman, Sloane
&
Frankel,
1956;
Pollock, Hickman
et
af.
1976;
Sloan,
1967;
Wilmore
&
Behnke,
1969;
Wright
&
Wilmore,
1974)
who used more homogeneous samples.
The 'raw score' equations were applied to the anthropometric results
of
the cross
validation sample. The crossvalidation analysis is presented in Table
5.
The product
moment correlation between laboratory determined and estimated body density were all
higher than
0.90,
and the standard errors were within the range found with tte validation
sample results.
The crossvalidation sample was then reduced first, to five age categories, and next,
to
levels
of
body fat content by five fat
(%)
categories. The ranges
of
standard errors for these
different categories are also presented
in
Table
5.
With the exception
of
the
log
equations,
none
of
the standard
errors
exceeded
O.OIOO
g/ml. Since these standard error estimates
were based on sample sizes that varied from ten to thirtythree cases, more variability was
expected. These analyses showed that the regression equations accurately predicted body
density for samples differing in age and fatness.
DISCUSSION
The findings
of
several studies (Durnin
&
Womersley,
1974;
Pollock, Hickman
ct
d.
1976)
showed that regression equations were population specific. The application
of
regres
sion equations derived
on
one sample, but applied
to
other samples that differed in age and
fatness, produced biassed body density estimates. The findings of this study showed that
some of this bias may be attributed to the use
of
linear regression models because the
Generalized body density equations
503
relationship between skinfold fat and body density was quadratic. This is shown by the
‘scattergram’ between the sum of seven skinfolds and body density which is presented as
Fig.
I.
Both linear and quadratic regression lines are provided. The differences between the
two regression lines showed where the largest bias prediction errors would occur. This was
at the ends
of
the bivariate distribution. For example, the fat
(yo)
differences between the
linear and quadratic sum of seven skinfold equations for
250
and
40
mm of skinfold fat
were
2.9
and
1.3
fat
(yo)
respectively, while the difference was only
0.5
fat
(yo)
for
150
mm.
In
a previous study (Pollock, Jackson
et
al.
1976),
it was found that the slopes of the
regression lines of lean worldclass distance runners and young adult men were not parallel.
The prediction of the bodydensity of the lean runner with linear equations derived on
a
sample of young adult men systematically underestimated the body density of these lean
subjects. This source of systematic error is documented by the differences between the linear
and quadratic regression lines shown in Fig.
I
and confirms the need for quadratic equations.
Jt has been shown that the intercepts of the regression lines of young adult men and older
(+
35
years) and fatter men were different (Pollock, Hickman
et
al.
1976).
Since the relation
ship between bodydensity and skinfold fat was quadratic, the differences in intercepts
could be partly due to the use of linear regression equations. The results reported by
Durnin
&
Womersley
(1974)
showed, however, that age was also responsible for the inter
cept differences. Durnin
&
Womersley
(1974)
used
a
logarithmic transformation of the
sum of four skinfolds. This transformation changed the quadratic relationship between
body density and the sum of skinfolds, in the ‘raw score’ form, into
a
linear relationship.
With male subjects who ranged from
16
to
59
years of age, they reported that the slopes for
samples divided by
10
year intervals were parallel, but had different intercepts. This would
result in biassed estimates due to age differences, thus Durnin
&
Womersley
(1974)
pro
vided five different equations which had the same slope, but different intercepts.
The finding of this study, that age accounted for
a
significant proportion of bodydensity
variation beyond that attributed to quadratic or logarithmic sum of skinfolds agreed with
the findings reported by Durnin
&
Womersley
(1974).
They suggested that this age
relationship may be due to
a
higher proportion of total body fat being situated internally
and a decrease in the density of fatfree mass. The decrease in fatfree mass was primarily
attributed to skeletal changes (Durnin
&
Womersley,
1974).
In the present study, the use
of age as an independent variable accounted for intercept difference, and eliminated the
need for several different ageadjusted equations. The crossvalidation results documented
the accuracy
of
a
generalized equation for samples differing in age and fatness. The standard
errors found in these analyses are within the range reported by Durnin
&
Womersley
(1974).
Using
209
men who varied in age from
16
to
72,
Durnin
&
Womersley
(1974)
reported standard errors that ranged from
0.0059
to
0.0
I
17
g/ml for prediction equations
derived for similar age groups.
The multiple correlations for the generalized equations derived with the logarithmic
or
quadratic sum of skinfolds were nearly identical. The results of the crossvalidation analysis
suggested that the quadratic equations were more accurate. The standard errors tended to
be lower for the total sample and less variable for the total sample and for the different
age and fat
(Oh)
categories. This was expecially true for the sum of three skinfolds.
The generalized equations provided valid and accurate bodydensity estimates with adult
men varying in age and fatness. The crossvalidation of equations is important because one
is not certain that equations developed with one sample will predict body density with the
same accuracy when applied to the data of a different sample. The best evidence
is
pro
vided by the standard error when the equation is crossvalidated on the second sample. The
standard errors for the crossvalidation analysis were low and nearly identical to the
standard errors found with the validation sample. This provided the strongest evidence
504
A.
s.
JACKSON
AND
M.
L.
POLLOCK
that the generalized equations were accurate and valid
for
use with adult men varying in
age and body density.
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H., Peng,
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T.,
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T.
F.,
Chang, C.
&
Fang, H.
S.
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Metabolism
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&
Wilmore, J. H.
(1974).
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Body Build and Composition.
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T.
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S.
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A.
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M.
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Goldman,
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F.
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E.
R.
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S.
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L.
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