Am J Physiol Endocrinol Metab
279: E1426–E1436, 2000.
special communication
Pubertal alterations in growth and body composition. V.
Energy expenditure, adiposity, and fat distribution
Received 9 November 1999; accepted in final form 24 July 2000
Roemmich, James N., Pamela A. Clark, Kim Walter,
James Patrie, Arthur Weltman, and A. D. Rogol. Pubertal alterations in growth and body composition. V. Energy
expenditure, adiposity, and fat distribution. Am J Physiol
Endocrinol Metab 279: E1426–E1436, 2000.—We determined whether activity energy expenditure (AEE, from doubly labeled water and indirect calorimetry) or physical activity [7-day physical activity recall (PAR)] was more related to
adiposity and the validity of PAR estimated total energy
expenditure (TEEPAR) in prepubertal and pubertal boys (n ⫽
14 and 15) and girls (n ⫽ 13 and 18). AEE, but not physical
activity hours, was inversely related to fat mass (FM) after
accounting for the fat-free mass, maturation, and age (partial
r ⫽ ⫺0.35, P ⱕ 0.01). From forward stepwise regression,
pubertal maturation, AEE, and gender predicted FM (r2 ⫽
0.36). Abdominal visceral fat and subcutaneous fat were not
related to AEE or activity hours after partial correlation with
FM, maturation, and age. When assuming one metabolic
equivalent (MET) equals 1 kcal 䡠 kg body wt⫺1 䡠 h⫺1, TEEPAR
underestimated TEE from doubly labeled water (TEE bias)
by 555 kcal/day ⫾ 2 SD limits of agreement of 913 kcal/day.
The measured basal metabolic rate (BMR) was ⬎1 kcal 䡠 kg
body wt⫺1 䡠 h⫺1 and remained so until 16 yr of age. TEE bias
was reduced when setting 1 MET equal to the measured
(bias ⫽ 60 ⫾ 51 kcal/day) or predicted (bias ⫽ 53 ⫾ 50
kcal/day) BMR but was not consistent for an individual child
(⫾ 2 SD limits of agreement of 784 and 764 kcal/day, respectively) or across all maturation groups. After BMR was corrected, TEE bias remained greatest in the prepubertal girls.
In conclusion, in children and adolescents, FM is more
strongly related to AEE than activity time, and AEE, pubertal maturation, and gender explain 36% of the variance in
FM. PAR should not be used to determine TEE of individual
children and adolescents in a research setting but may have
utility in large population-based pediatric studies, if an appropriate MET value is used to convert physical activity data
to TEE data.
children; adolescents; seven-day physical activity recall; total
energy expenditure; four-compartment body composition; fat
mass
Address for reprint requests and other correspondence: J. N.
Roemmich, Div. of Behavioral Medicine of Pediatrics, Rm G56,
Farber Hall-South Campus, SUNY at Buffalo, Buffalo, NY 14260
(E-mail: [email protected]).
The costs of publication of this article were defrayed in part by the
payment of page charges. The article must therefore be hereby
marked ‘‘advertisement’’ in accordance with 18 U.S.C. Section 1734
solely to indicate this fact.
E1426
in North American children and adolescents continues to rise (43), a better
understanding of the causes and effective treatment of
childhood obesity becomes increasingly important. A
decline in dietary fat, a stable energy intake, and
stable energy expenditure via basal metabolism [basal
metabolic rate (BMR)] and the thermic effect of food
(TEF) over the past four decades suggest that a reduction in physical activity energy expenditure (AEE) is
responsible for the increased number of obese youth
(43, 44).
An accurate method of assessing free-living AEE of
youth is to calculate the difference between the total
energy expenditure (TEE) measured by doubly labeled
water (DLW) and BMR measured by indirect calorimetry. However, DLW provides no information with regard to the intensity or duration of physical activity,
which may be more important in influencing the adiposity of children than AEE (20, 44). The 7-day physical activity recall (PAR) gives qualitative information
regarding the intensity, frequency, and duration of the
physical activity (36, 37).
Currently, it is unclear if the time spent in sedentary
activity (11, 27), the time spent in physical activity
(20), or AEE is most related to the adiposity of children
and adolescents. Studies using methods of varying
accuracy to assess the relationship between AEE and
body composition have found an inverse relationship
(10, 15, 45). Others have reported very weak relationships (18, 20, 21, 23). Investigations using accurate
AS THE PREVALENCE OF OBESITY
0193-1849/00 $5.00 Copyright © 2000 the American Physiological Society
http://www.ajpendo.org
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JAMES N. ROEMMICH,1 PAMELA A. CLARK,1 KIM WALTER,1
JAMES PATRIE,2 ARTHUR WELTMAN,3,4 AND A. D. ROGOL1,5
Divisions of Endocrinology and Metabolism, Departments of 1Pediatrics, 3Human Services,
4
Medicine, and 5Pharmacology and 2Health Evaluation Sciences, University of Virginia Health
Sciences Center, Curry School of Education, University of Virginia, Charlottesville, Virginia 22908
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
METHODS
Subjects
Prepubertal and pubertal boys (n ⫽ 14 and n ⫽ 15) and
girls (n ⫽ 13 and n ⫽ 18) with normal weight, height, and
weight-for-height enrolled in a longitudinal growth study
were evaluated in a cross-sectional manner. Subjects were
placed into pubertal groups based on Tanner’s criteria (40) as
assessed by examination by a pediatrician. Informed consent
from a parent and assent from the child were obtained before
entry into the study.
Energy Expenditure
BMR was measured for 30 min via indirect calorimetry
(Deltatrac; SensorMedics, Yorba Linda, CA). Subjects were
assessed upon waking. We have previously reported the
within-day test-retest reliability (intraclass correlation ⫽
0.98) of this measure in our laboratory (33).
TEE was measured by DLW. The subjects consumed a
mixed oral dose of 99.9% enrichment 2H2O (0.05 g/kg) and
10% enrichment H218O (1.5 g/kg). Urine samples were collected immediately before dosing (0800) and at 4 h, 5 h, 1 day,
6 days, and 12 days after dosing. All urine samples were
collected between 0800 and 1200 and were kept frozen at
⫺20°C in cryovials until analysis by isotope ratio mass spectroscopy [Europa Hydra 20/20 gas isotope ratio mass spectrometer (GIRMS); Metabolic Solutions, Merrimack, NH].
Deuterium analysis was completed after a 72-h equilibration
with standard hydrogen gas over a Pt catalyst. The data are
reported as delta vs. Vienna standard mean ocean water
(VSMOW). 18O analyses were completed on GIRMS after
24-h equilibration with CO2. Differences in 2H and 18O in the
pre- and postdose urine samples were determined using the
unprocessed mass spectrometric data as previously described
(31). The unprocessed mass spectrometic (enrichment) data
were standardized by expressing it as a fraction of the initial
dose as suggested by the consensus report of the International Dietary Energy Consultancy Group (31). Linear regression was used to determine the slope and intercept of the
linear relationship between the time in days and the normalized 2H and 18O data. The pool sizes for 2H2O (ND) and H218O
(NO) were estimated from the reciprocals of the regression
intercepts. The intercept of the regression line was the NDto-NO ratio (ND/NO). The data points were analyzed, and the
observed outliers in the data were reanalyzed. The fractional
turnover rates of 2H and 18O were determined from the slope
of the regression line. Any ND /NO lying outside the range of
1.015 and 1.06 were reanalyzed. If the regression outliers or
ND/NO were confirmed upon reanalysis, the data were not
used. The mean daily rate of CO2 production (mol/day) was
calculated by the revised equations of Speakman et al. (39).
The mean daily energy expenditure was calculated by multiplying the rate of CO2 production by 127.5 kcal/mol CO2,
the energy equivalent of the typical Western diet that will
produce a respiratory quotient of 0.85, with 15% of energy
from protein oxidation (31).
Quality control data: Precision data. Quality control data
were obtained from Metabolic Solutions. A method validation
is repeated every 6 mo to establish the precision and accuracy
for the 2H and 18O measures using internal laboratory standard assigned values vs. international standards (VSMOW).
The data are reported as delta VSMOW. To determine interday 18O precision, seven standards ranging in value from
about ⫺9 to 200 delta were analyzed multiple times over
multiple days. Each of the seven standards must have a
relative standard deviation (RSD ⫽ coefficient of variation ⫻
100) ⱕ2.5. Interday 2H precision was determined by measuring seven standards ranging in value from about ⫺50 to
3,000 delta multiple times over multiple days. For each
standard, RSD must be ⱕ2.5 except for the ⫺50 delta standard whose RSD must be ⬍10. To determine intraday 18O
and 2H precision, within each run, four quality control standards are analyzed 5 to 10 times, and their mean values must
fall within the RSD range. The 18O means and RSD limits (in
parentheses) were ⫺8.69 per mil (1.99), 36.05 per mil (1.33),
100.89 per mil (0.81), and 210.10 per mil (0.46). The 2H
means and RSD limits were ⫺53.03 per mil (⬍15), 564.74 per
mil (⬍2), 1,447.03 per mil (⬍2), and 3,022.35 per mil (⬍2). If
the interday or intraday precision data did not meet specifications, the analysis run was not accepted, and the samples
were either reassayed or prepared again (Metabolic Solutions).
Quality control data: Accuracy data. During the method
validation studies, two international standards [International Atomic Energy Agency (IAEA) 18O and 2H standards]
were measured in multiple runs on multiple days. The IAEA
range (95% confidence interval) for 18O is typically ⫾3–5
delta per mil and for 2H typically ⫾5–10 delta per mil. The
instruments were accepted as performing properly when the
18
O and 2H results were within ⫾0.5 delta of their respective
IAEA standards. For both 18O and 2H, the intraday accuracy
was maintained through the secondary standards (standards
1–4) because of the scarcity of the IAEA standards.
In a comparison of 18 prominent laboratories assaying
DLW, Metabolic Solutions Laboratory had an error of 5.9%.
The median error of all laboratories was 6.6% (32).
AEE was estimated by subtracting the BMR from TEE. As
discussed by Goran et al. (20) and Crocker and Algina (9),
estimates based on the difference between two scores were
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measures of activity, energy expenditure, and body
composition are required to determine the relationships among them.
Although accurate methods are necessary to describe
interactions between energy expenditure and adiposity
and to establish the efficacy of behavioral and lifestyle
interventions to increase the energy expenditure of
children, few valid methods of estimating energy expenditure exist. The DLW method is accurate and valid
but often is prohibitively expensive and cumbersome
because the children are required to collect several
urine samples over a 2-wk period (17). Additionally, a
prolonged world-wide shortage of H218O has made the
measurement of TEE by DLW impossible for even
small groups of children. Self-report measures such as
PAR have been developed for this purpose. It is simple,
inexpensive, and well accepted by children. The physical activity times and intensities obtained by PAR
have been used to estimate TEE (3, 4, 36, 38). However,
no study has compared TEE from DLW (TEEDLW) with
TEE estimated from PAR (TEEPAR). Therefore, the
purpose was to determine whether AEE, total activity
time, or the number of hours spent in activities of
various intensities was most related to criterion estimates of body composition and abdominal fat distribution of prepubertal and pubertal boys and girls. We
also determined the agreement between TEEDLW and
TEEPAR and examined the influence of age, gender,
TEE, and percentage of body fat (%fat) on the degree of
agreement.
E1427
E1428
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
subject to error propagation. As such, true scores, which take
into account the test-retest reliability of TEE and BMR
measures, were calculated (9). True scores improve the precision of an estimate that is based on the difference between
two measures but does not change the group mean data (20).
When calculating the “true” scores, a Pearson correlation
coefficient of 0.8 was used for BMR data and 0.6 for TEE data
(20). The true scores were used to estimate AEE as follows:
(true TEE ⫻ 0.90) ⫺ true BMR. The 10% correction of TEE
was to account for the energy expenditure of the thermogenic
effect of food (29).
Seven-Day PAR
Body Composition
Body composition was estimated using a four-compartment model described by Lohman (25). We have described
and validated the use of this model in our laboratory (34).
Body density was measured by underwater weighing and
corrected for residual lung volume by nitrogen washout. The
body density was corrected for the total body water by deuterium oxide dilution and bone mineral content by dualenergy X-ray absorptiometry (QDR 2000; Hologic, Waltham,
MA).
Body Fat Distribution
As previously described (33), the abdominal subcutaneous
fat (ASF) and visceral fat (AVF) were measured at the L4-L5
intervertebral space with magnetic resonance imaging using
a Siemens Vision 1.5T scanner. Tissue areas were assessed
using a Dixon imaging sequence phase corrected for magnetic
field inhomogeneities. A standard spin-echo pulse sequence
was used for the in-phase image. The out-of-phase image was
acquired by shifting the 180° refocusing pulse by 1.12 ms.
Images were acquired with a slice thickness of 6 mm, matrix
of 256 ⫻ 256, and a ratio of repetition time to echo time of
575/15 ms. For postacquisition processing, a magnetic field
inhomogeneity map was calculated from the in-phase and
out-of-phase images, which assumes that there are unequal
amounts of fat and water in each pixel. This map was then
Statistics
Univariate 2 (gender) ⫻ 2 (maturation) ANOVA and covariance were used to compare group means for physical
characteristics, energy expenditure, and physical activity.
Simple and partial correlation analyses were used to examine the associations among gender, maturation, AEE, activity hours, body composition, and body fat distribution. Forward stepwise regression was used to determine the
combination of variables that most accurately predicted fat
mass (FM) and fat distribution in our sample, with careful
attention to multicollinearity among variables. TEEPAR was
compared with TEEDLW using the Bland and Altman (5)
method. Ordinary least squares (OLS) regression analyses
were used to test if the slopes and intercepts of the energy
expenditure-adiposity relationships differed among the subject groups and to predict the bias in TEE as a linear function
of age, gender, TEE, and percentage body fat (%fat). Goodness of the model fit was based on an internal bootstrap
validation (22). The bootstrap method determines the precision of a statistic. Much more confidence can be placed on the
value of R2 from a bootstrap technique, where the model has
been simulated, in our case, 400 times. The R2 that is obtained from the bootstrap provides a more accurate estimate
of the amount of variation in the response that we would
expect to be able to explain if the original regression model
were fit to a new sample of data. OLS was also used for the
extra sum of squares (SS) F-test (28). Initially, TEE was
predicted as a linear function of BMR, height, and weight.
The five PAR variables (hours spent in sleep and light,
moderate, hard, and very hard activities) were then added to
the OLS model to determine if the combined information of
these five variables significantly reduced the error sum of
squares, where F ⫽ {[SS error(reduced model) ⫺ SS error(full model)]/
⌬df }/mean squared error(full model), where df indicates degrees
of freedom.
RESULTS
Physical Characteristics and Energy Expenditure
As shown in Table 1, the boys were taller (P ⫽ 0.02)
and had a smaller FM (P ⫽ 0.04) and percentage body
fat (%fat, P ⫽ 0.002) and a greater TEEDLW (P ⫽ 0.01)
than the girls. As expected, the pubertal boys and girls
were older (P ⬍ 0.001), taller (P ⬍ 0.001), heavier (P ⬍
0.001), and had a greater FM (P ⫽ 0.002), fat-free mass
(FFM; P ⬍ 0.001), AVF (P ⫽ 0.05), ASF (P ⫽ 0.07), and
TEEDLW (P ⬍ 0.001) than the prepubertal boys and
girls. The pubertal boys had a greater BMR than the
other groups (gender by maturation interaction, P ⫽
0.04). The interaction effect for TEEDLW was P ⫽ 0.07.
After covariance for FFM, BMR remained significant
(P ⫽ 0.05), whereas TEEDLW was significant only for
the main effect of gender (P ⫽ 0.055). AEE was not
related to FFM (r ⫽ ⫺0.04, P ⫽ 0.74) and therefore was
not covaried for FFM.
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The time spent in physical activity at different intensities
was assessed with PAR (3, 4, 36, 38). All subjects were
interviewed by the same investigator (Roemmich) to recall
the time spent in sleep [1 metabolic equivalent (MET)] and in
moderate (4 METs, as strenuous as walking), hard (6 METs,
an intensity between walking and running), and very hard
(10 METs, as strenuous as running) activities. The greatest
amount of time each day is spent in light activities (1.5
METs, less strenuous than walking, not including sleep) and
was determined by subtraction. A MET is a multiple of the
resting energy expenditure. Before the interview, the subjects were given instructions on how to properly answer the
interviewer’s questions. The interview began with the previous day and worked back the preceding 6 days. To aid recall,
the subjects were prompted for activities in the morning,
noon, and evening of each day. TEE was calculated by multiplying the number of hours at each intensity by its respective MET value, assuming that 1 MET was equivalent to 1
kcal 䡠 kg body wt⫺1 䡠 h⫺1. The data are presented as kilocalories per day rather than kilocalories per kilogram body
weight per day because the farther the resting metabolism
differs from 1 kcal 䡠 kg body wt⫺1 䡠 h⫺1, the more estimates of
energy expenditure reflect the body weight than the metabolic rate (1). Procedures for estimating TEEPAR have been
described previously (3, 36).
used to unwrap phase shifts induced by the inhomogeneities
using a region growing technique and was used to correct for
phase error in the opposed phase image. Adding or subtracting the in-phase and opposed phase images resulted in the
water and fat images, respectively. The fat- and water-based
tissue areas were determined using MedX Software (Sensor
Systems, Sterling, VA).
E1429
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
Table 1. Physical characteristics, four-compartment model body composition, body fat distribution, aerobic
fitness, and energy expenditure of the subjects
Age,† yr
Tanner stage
Height,*† cm
Weight,† kg
Body fat,* %
Fat mass,*† kg
Fat-free mass,† kg
Abdominal visceral fat,† cm2
Abdominal subcutaneous fat, cm2
TEEDLW,*† kcal/day
BMR,‡ kcal/day
Activity energy expenditure, kcal/day
Prepubertal Boys
(n ⫽ 14)
Pubertal Boys
(n ⫽ 14)
Prepubertal Girls
(n ⫽ 13)
Pubertal Girls
(n ⫽ 18)
10.9 ⫾ 1.0
1.0
143.1 ⫾ 4.7
34.8 ⫾ 6.9
19.3 ⫾ 7.3
7.0 ⫾ 4.3
27.8 ⫾ 3.7
44.0 ⫾ 20.9
68.2 ⫾ 60.5
2,174 ⫾ 63
1,245 ⫾ 41
712 ⫾ 51
13.4 ⫾ 1.2
3.4 ⫾ 0.2
162.2 ⫾ 12.1
52.0 ⫾ 11.4
18.4 ⫾ 6.3
9.6 ⫾ 4.4
42.5 ⫾ 10.0
62.8 ⫾ 21.6
105.3 ⫾ 74.3
2,555 ⫾ 67
1,626 ⫾ 78
673 ⫾ 56
10.2 ⫾ 1.4
1.0
136.6 ⫾ 8.9
34.7 ⫾ 7.9
23.3 ⫾ 5.9
8.3 ⫾ 3.3
26.4 ⫾ 5.3
47.5 ⫾ 19.9
100.9 ⫾ 61.9
2,123 ⫾ 57
1,217 ⫾ 30
693 ⫾ 31
12.8 ⫾ 1.9
3.6 ⫾ 0.2
158.1 ⫾ 9.1
51.2 ⫾ 9.0
25.5 ⫾ 6.7
13.2 ⫾ 5.0
37.8 ⫾ 6.1
49.4 ⫾ 11.8
136.0 ⫾ 82.4
2,237 ⫾ 62
1,359 ⫾ 38
654 ⫾ 35
Seven-Day PAR
The time spent in activities of various intensities, as
assessed by PAR, are shown in Table 2. The boys (P ⫽
0.02) and prepubertal subjects (P ⫽ 0.03) spent more
time sleeping. The prepubertal boys spent less time in
light activities and more time in very hard activities
than the other groups (gender by maturation interaction P ⫽ 0.01 for both variables). Prepubertal girls
spent less time in hard activities (gender by maturation interaction, P ⫽ 0.08) and activity hours (sum of
moderate, hard, and very hard activity hours; gender
by maturation interaction, P ⫽ 0.002) than prepubertal
boys and pubertal girls.
Relationships Among AEE, Physical Activity,
and Adiposity
AEE-adiposity relationships. AEE was inversely related to FM of all subjects and boys (Fig. 1) and both
the prepubertal (r ⫽ ⫺0.41, P ⫽ 0.03) and pubertal (r ⫽
⫺0.34, P ⫽ 0.05) groups. The slope of the AEE/FM
relationship was greater (P ⫽ 0.02, Fig. 1) for the boys
(⫺0.0125 kg FM/kcal AEE, or ⫺27.7 kcal AEE/kg FM)
than the girls (⫺0.0073 kg FM/kcal AEE, or ⫺5.3 kcal
AEE/kg FM) but did not differ between pubertal groups
Table 2. Hours of activity as assessed by the 7-day
physical activity recall
Sleep*†
Light activity‡
Moderate
activity
Hard activity
Very hard
activity‡
Total activity
time‡
Prepubertal
Boys
Pubertal
Boys
Prepubertal
Girls
Pubertal
Girls
10.1 ⫾ 0.3
11.8 ⫾ 0.5
9.4 ⫾ 0.3
13.2 ⫾ 0.3
9.4 ⫾ 0.2
13.6 ⫾ 0.3
9.0 ⫾ 0.2
13.0 ⫾ 0.3
0.6 ⫾ 0.2
0.7 ⫾ 0.2
0.6 ⫾ 0.1
0.6 ⫾ 0.1
0.5 ⫾ 0.2
0.4 ⫾ 0.1
0.9 ⫾ 0.2
0.8 ⫾ 0.2
0.8 ⫾ 0.2
0.3 ⫾ 0.1
0.1 ⫾ 0.1
0.4 ⫾ 0.1
2.1 ⫾ 0.4
1.4 ⫾ 0.2
1.0 ⫾ 0.2
2.0 ⫾ 0.3
Values are means ⫾ SE. Units are h/day. Total activity time, sum
of moderate, hard, and very hard activity. * Significant gender effect.
† Significant maturation effect. ‡ Significant gender ⫻ maturation
interaction.
(P ⫽ 0.56). AEE was not related to FFM (r ⫽ ⫺0.04,
P ⫽ 0.74). AEE was inversely related to ASF of all
subjects and boys (Fig. 1) and the prepubertal (r ⫽
⫺0.41, P ⫽ 0.04) and pubertal (r ⫽ ⫺0.40, P ⫽ 0.03)
groups. AEE was not related to AVF of either gender
(Fig. 1) or pubertal group. The slope of the AEE/ASF
and AEE/AVF relationships did not differ between
genders or pubertal groups.
Physical activity-adiposity relationships. The recalled time (h/day) spent in light activity was directly
related (Table 3) to AVF of all subjects (r ⫽ 0.25, P ⫽
0.07) and boys (r ⫽ 0.39, P ⬍ 0.05). The recalled time
spent in very hard activity was inversely related to FM
of all subjects (r ⫽ ⫺0.25, P ⬍ 0.05), boys (r ⫽ ⫺0.36,
P ⫽ 0.06), and prepubertal subjects (r ⫽ ⫺0.42, P ⬍
0.05), AVF of all subjects (r ⫽ ⫺0.24, P ⫽ 0.08) and
boys (r ⫽ 0.39, P ⬍ 0.05), and ASF of all subjects (r ⫽
⫺0.29, P ⬍ 0.05) and prepubertal subjects (r ⫽ ⫺0.41,
P ⬍ 0.05). The total recalled activity time was inversely
related to AVF of boys (r ⫽ ⫺0.43, P ⬍ 0.05) and
prepubertal (r ⫽ ⫺0.38, P ⬍ 0.05) groups. The recalled
time spent in moderate or hard intensity activity was
not significantly related to any of the adiposity variables. AEE was not related to the hours spent in light,
moderate, hard, and very hard activity or the total
activity hours.
Partial correlation. After partial correlation to statistically remove the effects of confounding variables
(FM, pubertal maturation, and age), neither AEE nor
the physical activity variables were related to AVF and
ASF. After adjustment for FFM, pubertal maturation,
and age, AEE continued to explain a significant
amount of variance in FM of all subjects (r ⫽ ⫺0.35,
P ⫽ 0.009) and the boys (r ⫽ ⫺0.58, P ⫽ 0.003), and
very hard activity continued to explain a significant
amount of variance in FM of all subjects (r ⫽ ⫺0.25,
P ⫽ 0.06).
Regression models. Forward stepwise regression was
used to determine the combination of energy expenditure and physical activity variables that most accurately predicted FM, AVF, and ASF. To maintain a
proper subject-to-variable ratio, the independent vari-
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Values are means ⫾ SD; n, no. of subjects. TEEDLW, total energy expenditure (TEE) by the doubly labeled water (DLW) method; BMR,
basal metabolic rate. * Significant gender effect. † Significant maturation effect. ‡ Significant gender ⫻ maturation interaction.
E1430
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
ables were limited to those considered in our a priori
hypothesis: gender, maturation, and AEE and those
variables that were significantly related to the dependent variable in the simple correlation analyses. As
shown in Table 4, increased FM was best estimated
with increasing values of pubertal maturation, lower
values of AEE, and the female gender. Very hard
activity hours and BMR did not add to the prediction of
FM. No energy expenditure or physical activity variable significantly predicted AVF. Only FM entered into
the model to predict AVF (R2 ⫽ 0.11, P ⫽ 0.01) and
ASF (R2 ⫽ 0.66, P ⬍ 0.001).
Validity of TEE by the 7-Day PAR
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Fig. 1. Relationship between activity energy expenditure (AEE) and
fat mass (FM, A), abdominal subcutaneous fat (ASF, B), and abdominal visceral fat (AVF, C). The slope of the AEE-FM relationship was
greater in the boys (⫺27.7 kcal/kg) than the girls (⫺5.3 kcal/kg) but
did not differ between pubertal groups. Solid line, regression for all
subjects; short dashed line, regression for boys; long dashed line,
regression for girls. Slopes of the AEE-ASF and AEE-AVF relationships were not dependent on gender or maturation. F, Prepubertal
boys; E, prepubertal girls; Œ, pubertal boys; ‚, pubertal girls.
BMR of children and adolescents does not equal 1
kcal 䡠 kg body wt⫺1 䡠 h⫺1. Conversion of PAR data to
energy expenditure data utilizes multiples of the resting energy expenditure, which is assumed to be 1
kcal 䡠 kg body wt⫺1 䡠 h⫺1 (3, 36). However, this was less
than (P ⬍ 0.001) the mean measured BMR (1.35 ⫾ 0.03
kcal 䡠 kg body wt⫺1 䡠 h⫺1). As shown in Fig. 2, BMR did
not approach 1 kcal 䡠 kg body wt⫺1 䡠 h⫺1 until about 16 yr
of age, and as such BMR expressed in kilocalories per
kilogram body weight per hour was lower (P ⬍ 0.001)
in the pubertal subjects (Table 5).
TEE bias when assuming 1 MET equals 1 kcal 䡠 kg
body wt⫺1 䡠 h⫺1. When we assumed that 1 MET equals
1 kcal 䡠 kg body wt⫺1 䡠 h⫺1, TEEPAR was greater in the
pubertal than prepubertal subjects (Table 5). As shown
in Fig. 3A, TEEPAR underestimated the criterion
TEEDLW by 555 kcal/day, with a large range between
the lower (⫺358 kcal/day) and the upper (1,457 kcal/
day) limit of agreement (mean bias ⫾ 2 SD). There was
an inverse relationship (r ⫽ ⫺0.65, P ⬍ 0.001) between
TEE bias and the mean of TEEDLW and TEEPAR. TEEPAR bias (TEEDLW ⫺ TEEPAR) was lowest in the pubertal girls (gender by maturation interaction P ⫽ 0.008;
Table 5). As shown in Table 6, estimates of TEEPAR
prediction bias were minimized with increasing values
of age (P ⬍ 0.001) and percentage of body fat (P ⫽
0.007). The influence of age was dependent on the
gender (age ⫻ gender: P ⫽ 0.056).
TEE bias when setting 1 MET equal to the measured
BMR. Setting 1 MET equivalent to the measured BMR
and then using multiples of the measured BMR to
calculate TEE [TEEPAR(1 MET ⫽ BMR)] reduced the mean
TEE bias to 60 ⫾ 51 kcal/day but did not perform well
for the individual child (Fig. 3B). The lower and upper
limits of agreement were ⫺723 and 843 kcal/day. The
bias was dependent on the mean TEE (Fig. 3). The bias
in TEEPAR(1 MET ⫽ BMR) was modestly inversely related
to the age of the children, and the full model did not
significantly predict the bias (Table 6). As shown in
Table 5, TEEPAR(1 MET ⫽ BMR) was greater in the pubertal than in the prepubertal subjects (P ⬍ 0.001) and in
the boys than in the girls (P ⫽ 0.006). The prediction
bias [TEEDLW ⫺ TEEPAR(1 MET ⫽ BMR)] was greater in
the prepubertal girls than in prepubertal boys and
pubertal girls (gender by maturation interaction, P ⫽
0.08; Table 5).
E1431
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
Table 3. Summary of correlations between body composition or fat distribution and the time spent in activities
of various intensity as assessed by a physical activity recall
Light
All subjects
Boys
Girls
Prepubertal
Pubertal
Very Hard
Total Activity
FM
AVF
ASF
FM
AVF
ASF
FM
AVF
ASF
0.21
0.21
0.08
0.31
0.03
0.25
0.39*
⫺0.06
0.41
⫺0.05
0.21
0.13
0.24
0.24
0.15
⫺0.25*
⫺0.36
0.02
⫺0.42*
⫺0.01
⫺0.24
⫺0.39*
0.01
⫺0.38
⫺0.03
⫺0.29*
⫺0.25
⫺0.28
⫺0.41*
⫺0.19
⫺0.06
⫺0.26
0.15
⫺0.26
0.02
⫺0.20
⫺0.43*
0.12
⫺0.38*
⫺0.04
⫺0.15
⫺0.16
⫺0.14
⫺0.20
⫺0.17
The time (h/day) spent in moderate and hard intensity activities was not significantly related to any of the body composition or fat
distribution variables. The time spent in light, moderate, hard, and very hard activities and the total activity time were not related to activity
energy expenditure (AEE). FM, fat mass; AVF, abdominal visceral fat; ASF, abdominal subcutaneous fat. * P ⱕ 0.05.
combined information of these five variables reduced
the sum of squares error. The combined information of
the five PAR variables did not add [F(5,58), P ⫽ 0.15] to
the prediction of TEE (Table 7).
DISCUSSION
Physical activity is prescribed for reducing the adiposity of children and adolescents, but it is unclear
whether AEE, total activity time, or activity intensity
is most important for altering their body composition
or whether normal, free-living AEE and physical activity have measurable influences on the adiposity of
normal-weight youth. Also unclear is whether physical
activity data such as those obtained from the PAR can
be used to accurately estimate the total daily energy
expenditure of youth. We used criterion measures to
examine 1) the associations among AEE, qualitative
aspects of daily physical activity, body composition,
and body fat distribution and the influence of pubertal
maturation and gender on these relationships and 2)
the validity of the 7-day TEEPAR to estimate TEEDLW.
These studies were completed in a group of children
Table 4. Results from forward stepwise regression
analyses using fat mass as the dependent variable
Fat Mass, kg
Coefficient
Coefficient SE
Constant
Pubertal
maturation
stage
AEE, kJ/day
Gender
13.95
2.59
1.36
⫺0.002
⫺2.16
0.39
0.001
1.06
P
Progressive R2
0.001
0.006
0.05
0.21
0.31
0.36
Gender data were coded as girls ⫽ 0, boys ⫽ 1.
Fig. 2. Measured basal metabolic rate (BMR) vs. age. Solid horizontal line, assumed BMR of 1 kcal 䡠 kg⫺1 䡠 h⫺1 suggested to be used when
calculating the total energy expenditure from physical activity duration and intensity data; dashed line, regression for all subjects. F,
Prepubertal boys; E, prepubertal girls; Œ, pubertal boys; ‚, pubertal
girls.
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.6 on June 17, 2017
Validation of a BMR prediction equation. The Food
and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU)
BMR prediction equation (46) has been cross-validated
and accurately predicts BMR of children and adolescents (13, 14). We found that the FAO/WHO/UNU
prediction equation produced a mean BMR prediction
bias of 1.0 kcal/day, with a lower and an upper limit of
agreement of ⫺285 and 286 kcal/day, respectively (Fig.
4). There was a direct relationship between the bias
and mean BMR due to an underprediction in several
pubertal boys.
TEE bias when setting 1 MET equal to the predicted
BMR. When BMR estimated from the FAO/WHO/UNU
prediction equation was used to calculate TEEPAR
[TEEPAR(1 MET ⫽ estimated BMR)], the mean bias was 53 ⫾
50 kcal/day (Fig. 3C), with lower and upper limits of
⫺711 and 817 kcal/day. The bias was inversely related
to the mean TEE (Fig. 3). TEEPAR(1 MET ⫽ estimated BMR)
was greater in the pubertal subjects than prepubertal
subjects (P ⬍ 0.001) and greater in boys than girls (P ⫽
0.004; Table 5). For TEEPAR(1 MET ⫽ estimated BMR), the
bias was greater (gender by maturation interaction,
P ⫽ 0.01) in the prepubertal girls than prepubertal
boys and pubertal girls (Table 5).
Utility of PAR data beyond BMR and physical activity data for predicting TEE. OLS regression and the
extra sum of squares F-test (see Statistics) were used
to determine if PAR information added to the prediction of TEE beyond BMR and physical characteristic
data. As shown in Table 7, estimates of TEE were
predicted with increasing BMR and height and lower
body weight. PAR variables (hours spent in sleep and
light, moderate, hard, and very hard activities) were
then added to the OLS model to determine if the
E1432
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
Table 5. BMR, predicted TEE, and prediction bias of the subject groups when multiples of the assumed,
measured, and estimated BMR are used to calculate the energy expenditure
⫺1
⫺1
BMR,† kcal 䡠 kg 䡠 h
TEEPAR,† kcal/day
TEEPAR(1 MET ⫽ BMR),*† kcal/day
TEEPAR(1 MET ⫽ estimated BMR),*† kcal/day
TEEDLW ⫺ TEEPAR,‡ kcal/day
TEEDLW ⫺ TEEPAR(1 MET ⫽ BMR),† kcal/day
TEEDLW ⫺ TEEPAR(1 MET ⫽ estimated BMR),‡ kcal/day
Prepubertal Boys
Pubertal Boys
Prepubertal Girls
Pubertal Girls
1.52 ⫾ 0.04
1,452 ⫾ 81.8
2,159 ⫾ 105
2,208 ⫾ 105
722 ⫾ 95
15 ⫾ 116
⫺34 ⫾ 119
1.27 ⫾ 0.03
2,016 ⫾ 117
2,591 ⫾ 145
2,489 ⫾ 95
539 ⫾ 91
⫺37 ⫾ 94
66 ⫾ 78
1.52 ⫾ 0.08
1,224 ⫾ 78
1,769 ⫾ 64
1,799 ⫾ 76
899 ⫾ 75
353 ⫾ 53
324 ⫾ 90
1.13 ⫾ 0.05
2,058 ⫾ 130
2,283 ⫾ 133
2,318 ⫾ 95
179 ⫾ 106
⫺46 ⫾ 95
⫺81 ⫾ 78
Values are means ⫾ SE. TEEPAR, TEE by the physical activity recall (PAR) method assuming 1 metabolic equivalent (MET) ⫽ 1
kcal 䡠 kg⫺1 䡠 h⫺1; TEEPAR(1 MET ⫽ BMR), TEE by the PAR method using multiples of the measured BMR as 1 MET; TEEPAR(1 MET ⫽ estimated BMR),
TEE by the PAR method using multiples of the estimated BMR as 1 MET. * Significant gender effect. † Significant maturation effect.
‡ Significant gender ⫻ maturation interaction.
obese children. Maffeis et al. (27) found that the percentage of body fat was directly related to sedentary
activity time in children but, in contrast to Goran et al.
and the present study, not related to activity time or
AEE. Goran et al. suggest that long bouts of exercise by
children promote an active lifestyle and reduce the
snacking associated with sedentary activities. However, in adults, high intensity exercise of shorter duration can produce greater reductions in the percentage
of fat than exercise bouts of low intensity and longer
duration (7, 41, 42). The conflicting results may be a
function of methodological differences. For AEE, Goran
et al. used methods similar to the present study,
whereas Maffeis et al. (27) estimated AEE from heart
rate and BMR, which overestimate TEE of obese children (26). Different tools [recall (present study), questionnaire (20), heart rate (27)] were used to assess the
time spent in physical activity. We used a criterion
four-compartment body composition model to estimate
FM (34). Goran et al. used a dual-energy X-ray absorptiometry model, and Maffeis et al. used skinfold thicknesses. These methods do not agree with the criterion
body composition model and do not predict well for the
individual child (34), which would affect the relationship between activity and body composition variables.
In addition, previous investigations (20, 27) studied
children only. We included both children and adolescents in our study. However, analyzing the data by
pubertal maturation group produced similar results in
the present study in that AEE was a significant predictor of FM in both prepubertal (r ⫽ ⫺0.41, P ⫽ 0.03)
and pubertal (r ⫽ ⫺0.34, P ⫽ 0.05) groups. Further
detailed investigations are necessary to determine the
relative importance of the energy cost of activity vs. the
exercise duration for modifying FM of children.
Intuitively, AEE would also seem to be a function of
FFM. However, we found that FFM was not related to
AEE of youth. There were also no group differences in
AEE even though the pubertal boys and girls had a
greater body weight and FFM than the prepubertal
children (Table 1). This would suggest that the pubertal boys and girls participated in less physical activity
because the energy cost for activity should be greater
given their greater body weight. Goran et al. (20) suggest that the weak relationship between FFM or phys-
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.6 on June 17, 2017
and adolescents of normal height, weight, growth velocity, and weight for height.
AEE of normal-weight late preadolescent and adolescent boys and girls has not been reported widely and
is rather variable between studies. Similar to the
present study (Table 1), Craig et al. (8) reported an
AEE of 650 kcal/day for 10-yr-old girls (after a 10%
adjustment for TEF taken by us). Bratteby et al. (6)
reported a much larger AEE (adjusted for TEF) of
1,226 and 875 kcal/day for 15-yr-old boys and girls,
respectively. The TEE and BMR data in the present
study are similar to those of previous studies of youth
(6, 12, 19).
FM was inversely related to AEE (Fig. 1) and time
spent in “very hard” activities but not the total activity
time or light (sedentary) activity time (Table 3). The
robustness of the inverse relationship between AEE
and FM was tested in two ways. Partial correlation
demonstrated that the relationship remained significant after adjusting for confounding variables (see RESULTS). Second, from forward stepwise regression (Table 4), AEE contributed to the prediction of FM. Others
have also reported an inverse relationship between
AEE and adiposity (10, 15, 45). AEE is important in the
determination of energy balance, since it is more modifiable than BMR or TEF (44). Our results imply that
increasing AEE helps limit the accrual of FM of children, but more so in boys than girls (Fig. 1). Girls
appear to require a greater AEE to maintain the same
FM as boys (Fig. 1), as noted by the greater (P ⫽ 0.02)
AEE-FM slope of the boys than girls, and girls maintain a greater FM than boys from late childhood
through adolescence despite similar amounts of AEE
(Table 1). Although our data do not lend a simple
explanation for this gender difference, it may be due to
differences in circulating sex steroid hormone concentrations that occur during puberty and their effect on
the accrual and distribution of body fat (35). As shown
in Table 4, increases in FM were best predicted by
being more pubertally mature, the female gender, and
lesser amounts of AEE. FM was independent of the
intensity or the total activity time of recalled physical
activities (see RESULTS).
In contrast, Goran et al. (20) found FM to be more
inversely related to total activity time than AEE in
E1433
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
Table 6. Ordinary least squares regression used to
predict the bias in TEE as a linear function of age,
gender, percentage of body fat, and TEE
Prediction Model When
Assuming 1 MET ⫽ 1
kcal 䡠 kg⫺1 䡠 h⫺1
Intercept,
kcal/day
Age, yr
Gender
TEE, kcal/day
Fat, %
Age ⫻ Gender
Regression*
Prediction Model When
Using Multiples of the
Measured BMR as 1 MET
Coefficient
P
Coefficient
P
2,719.44
⫺161.98
⫺1,216.67
0.09
⫺21.92
106.62
⬍0.001
⬍0.001
0.14
0.68
0.007
0.056
⬍0.001
2
R ⫽ 0.32*
1,310.70
⫺87.54
⫺1,208.01
⫺0.09
1.08
93.30
0.04
0.04
0.17
0.71
0.89
0.10
0.09
R2 ⫽ 0.02*
The relative impact of subcomponents of energy expenditure and time spent in activities of various intensities on the abdominal fat distribution of children may
provide useful clinical information. An accumulation of
both AVF (30) and ASF (16) has been associated with
reduced insulin sensitivity and increased serum lipid
concentrations, the antecedents of type 2 diabetes and
cardiovascular diseases that occur during childhood
and adolescence. Thus it is important to understand
the factors associated with the early development of
these conditions. Neither AVF nor ASF was inversely
related to AEE after adjusting for confounding variables (see RESULTS). The lack of relationship between
these energy expenditure and activity variables and
abdominal adiposity may be due to the relatively small
amount of AVF and ASF in the normal-weight youth
studied. Evaluation of children with elevated AVF
and ASF may provide better insight into factors associated with increased abdominal adiposity in childhood
obesity.
Fig. 3. Bias in total energy expenditure (TEE) measured by doubly
labeled water (TEEDLW) minus TEE estimated by physical activity
recall (TEEPAR) vs. the mean of TEEDLW and TEEPAR. A: plot when
the BMR is assumed to be 1 MET (1 kcal 䡠 kg body wt⫺1 䡠 h⫺1). B: plot
when the measured BMR is used to calculate the TEE. C: plot when
the estimated BMR is used to calculate the TEE. F, Prepubertal boys;
E, prepubertal girls; Œ, pubertal boys; ‚, pubertal girls.
ical activity and AEE in children is due to the DLW
method combining both weight-bearing and nonweight-bearing physical activity, including sedentary
activities. In addition, interindividual differences in
nonexercise activity thermogenesis such as fidgeting,
restlessness, and other types of nonexercise activity
(24) may confound the relationships among AEE and
FFM, activity intensity, and activity duration.
Fig. 4. Bias in BMR measured by calorimetry (BMRcal) minus the
BMR estimated by the Food and Agriculture Organization/World
Health Organization/United Nations University (FAO/WHO/UNU)
equation (BMRFAO) vs. the mean BMR of both methods. F, Prepubertal boys, E prepubertal girls; Œ, pubertal boys; ‚, pubertal girls.
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.6 on June 17, 2017
Gender data were coded as girls ⫽ 0, boys ⫽ 1. * R2 from bootstrap
validation technique.
E1434
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
Table 7. Ordinary least squares regression used to
predict TEE as a linear function of BMR, weight,
height, and PAR data and extra sum of squares
F-test to determine if PAR data significantly
reduced the error sum of squares
Reduced Model
Coefficient
P
Coefficient
P
43.03
0.87
⫺8.21
9.31
0.90
⬍0.001
0.03
0.004
⫺137,046
0.95
⫺9.39
8.39
5,683.23
5,736.59
0.23
⬍0.001
0.02
0.01
0.23
0.23
5,700.24
5,739.90
0.23
0.23
5,771.94
0.23
R2 ⫽ 0.71
R2 ⫽ 0.66
F(5,58) ⫽ [(SS error(reduced model) ⫺ SS error(full model))/⌬ df]/MS error⫽ [(1,603,446 ⫺ 1,369,820)/5]/27,396 (P ⫽ 0.15), where SS is
the sum of squares, df is degrees of freedom, and MS is the mean
squared error.
(full model)
An important issue in pediatric weight regulation
research is the ability to accurately estimate total
free-living energy expenditure in field settings. The
PAR has been used to estimate TEE (3, 4, 36, 38) but
has not yet been validated in youth against an accurate
method such as DLW. The PAR underestimated TEE
(Fig. 3) and more so in the prepubertal than pubertal
groups (Table 5). However, comparing Table 2 with
previous studies (37), this was not a function of the
children recalling less physical activity but rather a
function of assuming that the resting energy expenditure of a child was similar to the adult value of 1
kcal 䡠 kg⫺1 䡠 h⫺1. As shown in Table 5, the measured
BMR was ⬎1 kcal 䡠 kg⫺1 䡠 h⫺1 in all four groups. Thus
multiplying by 1 kcal 䡠 kg⫺1 䡠 h⫺1 resulted in a large
underestimation of TEEPAR, and this constant should
not be used in pediatric research. The prepubertal boys
and girls, whose measured BMR was much greater
than the assumed constant, had the greatest underestimation in TEE, whereas the pubertal girls, whose
measured BMR most closely approached the assumed
constant, had the smallest TEE bias. BMR did not
approach 1 kcal 䡠 kg⫺1 䡠 h⫺1 until ⬃16 yr of age (Fig. 2).
Using multiples of the measured BMR to calculate
TEE [TEEPAR(1 MET ⫽ BMR)] reduced the mean underprediction bias by 89% (from 555 to 60 kcal/day; Table
5 and Fig. 3). After correction for the metabolic rate,
the prepubertal girls had a larger bias than any other
group (P ⫽ 0.08; Table 5), suggesting that they did not
recall activities and/or activity intensities as well as
the children in the other groups. These results are
consistent with previous findings that the validity of
PAR improves with age (37), with the caveat that it
only occurs in girls, because the prepubertal and pubertal boys had similar prediction errors. The mean
prediction errors for the pubertal girls and prepubertal
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.6 on June 17, 2017
Intercept, kcal/
day
BMR, kcal/day
Weight, kg
Height, cm
Sleep, h
Light activity, h
Moderate
activity, h
Hard activity, h
Very hard
activity, h
Full Model
and pubertal boys of 15–46 kcal/day are well within an
acceptable range. However, when we used the measured BMR to calculate the energy expenditure, PAR
still did not perform well for an individual child (Fig. 3)
and had large ⫾2 SD limits of agreement [784 kcal/day
for TEEPAR(1 MET ⫽ BMR)].
Although the mean prediction bias is less when using multiples of the measured BMR, measuring BMR
may be a limitation in field settings and population
studies where PAR estimates of TEE would seem most
beneficial. Thus we determined if TEE prediction bias
could also be diminished when a BMR prediction equation is used that might permit more accurate estimates
of TEE in the field. Cross-validation of the FAO/WHO/
UNU prediction equation (46) produced a mean bias of
1.0 kcal/day with 2 SD limits of agreement of 286
kcal/day (Fig. 4). Similar to the results when the measured BMR was used, the mean bias was acceptable in
all groups but the prepubertal girls (Table 5). The
mean bias for all subjects was reduced to 52 kcal/day;
however, once again, PAR did not estimate TEE well
for an individual boy or girl (Fig. 3).
After accounting for the percentage of body fat, age,
and gender, TEE bias when assuming 1 MET ⫽ 1
kcal 䡠 kg⫺1 䡠 h⫺1 was no longer related to TEE (Table 6).
The inverse relationship between age and TEE bias is
likely a result of BMR approaching the assumed 1
MET ⫽ 1 kcal 䡠 kg⫺1 䡠 h⫺1 in the older children (Fig. 2).
The inverse relationship between the percentage of
body fat and TEE bias is likely a result of the older girls
having the greatest percentage of body fat (Table 1)
and a BMR that most closely approximates 1
kcal 䡠 kg⫺1 䡠 h⫺1 (Table 5).
PAR information did not add to the prediction of
TEE beyond that provided by BMR, height, and body
weight, suggesting that TEE is best predicted from
these other variables (Table 7). Although the PAR does
not accurately predict TEE for a given boy or girl, it
still has utility to provide important qualitative information regarding the frequency, type, and intensity of
activity of children that is not provided by DLW. As
discussed above, future research must continue to include accurate measures of both physical activity and
AEE to tease apart the importance of sedentary activity (27), physical activity (20), and AEE (present study)
for modifying adiposity of children.
In summary, AEE, but not total activity time, is
inversely related to FM in children and adolescents.
This is likely because 1) AEE is the most modifiable
component of TEE, 2) energy balance is more a function of AEE than activity time, and 3) despite an
increased incidence of obesity, the energy intake of
children and adolescents has not changed perceptibly
over the past four decades. AEE is not related to FFM
in children and adolescents. The lack of relationship
with FFM may be a function of the non-weight-bearing
physical activity, including fidgeting and other sedentary activities included in AEE. The underestimation
of TEE by PAR in youth is due to the assumption that
1 MET for a child is equal to 1 kcal 䡠 kg⫺1 䡠 h⫺1, when the
actual 1 MET value for a child may be as great as 2
ACTIVITY AND FAT DISTRIBUTION IN CHILDREN
We are indebted to Sandra Jackson and the nursing staff at the
University of Virginia General Clinical Research Center who provided patient care, Katy Nash for study coordination, Judy Weltman
and Dr. Laurie Wideman for collecting the BMR data. We also
acknowledge the subjects for their enthusiasm for the research
program for the past four years.
This work was supported in part by National Institutes of Health
Grants HD-32631 (A. D. Rogol) and MO1 RR-00847 (University of
Virginia), the Genentech Foundation for Growth and Development
(P. A. Clark), and the University of Virginia Children’s Medical
Center (J. N. Roemmich).
New address for J. N. Roemmich: Div. of Behavioral Medicine of
Pediatrics, Rm. G56, Farber Hall-South Campus, SUNY at Buffalo,
Buffalo, NY 14260.
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