United States Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030
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ABSTRACT |
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The purpose of this study was to predict energy expenditure (EE)
from heart rate (HR) and activity calibrated against 24-h respiration
calorimetry in 20 children. HR, oxygen consumption (O2), carbon dioxide
production (
CO2), and EE
were measured during rest, sleep, exercise, and over 24 h by room
respiration calorimetry on two separate occasions. Activity was
monitored by a leg vibration sensor. The calibration day
(day 1) consisted of specified
behaviors categorized as inactive (lying, sitting, standing) or active
(two bicycle sessions). On the validation day (day
2), the child selected activities. Separate
regression equations for
O2,
CO2, and EE for
method 1 (combining awake and asleep
using HR, HR2, and
HR3), method
2 (separating awake and asleep), and
method 3 (separating awake into active
and inactive, and combining activity and HR) were developed using the
calibration data. For day 1, the
errors were similar for 24-h
O2,
CO2, and EE among
methods and also among HR, HR2,
and HR3. The methods were
validated using measured data from day
2. There were no significant differences in HR,
O2,
CO2, respiratory quotient,
and EE values during rest, sleep, or over the 24 h between days 1 and
2. Applying the linear HR equations to
day 2 data, the errors were the lowest
with the combined HR/activity method (
2.6 ± 5.2%,
4.1 ± 5.9%,
2.9 ± 5.1% for
O2,
CO2, and EE, respectively).
To demonstrate the utility of the HR/activity method, HR and activity
were monitored for 24 h at home (day
3). Free-living EE was predicted as 7,410 ± 1,326 kJ/day. In conclusion, the combination of HR and activity is an
acceptable method for determining EE not only for groups of children,
but for individuals.
energy metabolism; oxygen consumption; heart rate monitoring
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INTRODUCTION |
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HEART RATE (HR) can be used to estimate energy
expenditure (EE), since a linear relationship exists between HR and
oxygen consumption (O2)
during exercise. Doubly labeled water and respiration calorimetry are
very accurate methods to assess daily EE, either free-living (12) or
confined (10), but both require sophisticated instrumentation and are
expensive. The HR method is an ideal method of assessing EE, since it
is both noninvasive and inexpensive. It allows researchers in field
settings who do not have access to other, more complicated techniques
to measure daily EE. The difficulty, however, of HR monitoring is
accurately modeling the nonlinear relationship between HR and
O2 across active
and inactive periods.
Using portable HR monitors, many investigators have compared the HR
method to other techniques that assess EE. One study in 10-yr-old
children (1) utilized indirect calorimetry to validate the HR method
and reported differences of 7.6 ± 20.1% between measured and
predicted total EE. Livingstone et al. (6), who compared HR monitoring
to doubly labeled water in 7- to 15-yr-old children, found errors
ranging from 16.7 to +18.8%. Another study estimating EE from
HR reported errors of 10.4% for 24-h calorimetry and 12.3% for doubly
labeled water in children (5). In adults, similar errors ranging from
0.1 to 24.7% for daily EE have been reported (8). The consensus
appears to be that group estimates are acceptable for HR monitoring but
that individual estimates may be quite variable. Previously, Moon and
Butte (9) used a combination of HR and activity in adults to estimate
EE. The activity data were used to separate the HR data into active and inactive, which improved the estimates of EE.
This study was undertaken to improve upon the HR method for prediction of EE in children. We applied and tested the combined HR/activity method as published by Moon and Butte (9) using 24-h respiration calorimetry.
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METHODS |
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Subjects
Healthy girls (n = 10) and boys (n = 10) were recruited from the local Houston area to participate in the study. All children were in the age range 8-12 yr and included 15 Caucasian, one African-American, and three Hispanic children. Individuals with cardiovascular disease, anemia, diabetes, significant renal or hepatic disease, hypothyroidism, and musculoskeletal problems were excluded. All subjects (and their parents) provided written informed consent to participate in this study, which was approved by the Institutional Review Board for Human Subject Research for Baylor College of Medicine and Affiliated Hospitals.Study Protocol
Children were admitted to the Metabolic Research Unit (MRU) at the Children's Nutrition Research Center at 8:00 AM, after a 12-h overnight fast. After admission procedures, the child underwent aBody Composition
Total body composition was assessed by total body electrical conductivity (TOBEC; EM-SCAN, Springfield, IL). The conductive mass of each subject was determined, and the percent body fat, fat-free mass (FFM), and fat mass were calculated. The procedure requires the subject to lie in a supine position for ~5 min.O2 Peak
Dietary Analysis
During the calorimetry tests, the children were fed a balanced diet designed to approximate 55% carbohydrate, 30% fat, and 15% protein. The children's energy intake was based on the child's predicted BMR based on the age-appropriate Schofield (13) equation (13.34 × weight + 692.6) multiplied by a factor of 1.6 to account for activity while in the calorimeter. All food not consumed was reweighed. The total caloric intake and percentage of calories from carbohydrate, protein, and fat were analyzed using the Minnesota Database System (version 2.8; NDS, Minneapolis, MN).Calorimeter Measurements
Measurements of 24-h EE were taken in a room respiration calorimeter. The calorimeter design characteristics and calibration have been previously described in detail (10). The rooms were equipped with a bed, desk, chair, lamp, toilet, sink, television/VCR, video games, motion sensor, and telephone.On day 1, the children participated in
three 10-min, inactive periods dedicated to lying still, sitting, and
standing, respectively. Two 20-min exercise sessions were conducted on
stationary bicycles (CombiCycle Ex80; COMBI, Tokyo, Japan) at workloads
approximating 40 and 60% of the child's
O2 peak. The
subject was awakened the following morning (0630) after a 12-h
overnight fast, asked to void, and returned to sleep. After the child
was confirmed awake, the BMR began at 7:20 AM and was measured for 40 min. The child was monitored both visually and by the activity sensor
(<50 counts) and was required to lie still for the entire measurement period. Sleeping metabolic rate and 24-h EE were calculated by the de
Weir equation (4). All measures of EE were expressed as kilojoules per
day.
On day 2, the schedule was different
from day 1 in that the children were
allowed freedom of activity except for a requirement to complete two
20-min exercise sessions, in the mode (aerobics, bicycle) and intensity
of their choosing. The BMR was repeated. Errors from 24-h infusions for
these calorimeters were reported as 0.34 ± 1.24% for
O2 and 0.11 ± 0.98%
for
CO2 (10). In six
children not included in this study, repeat 24-h testing for
O2,
CO2, and EE gave
r2 values of
0.96, 0.92, and 0.96, respectively.
HR and Activity Monitor (Field Days)
HR was recorded using the Polar Vantage-XL monitors (Polar Night Vision, Helsinki, Finland), which can store HR at 1-min intervals. A transmitter band was worn around the chest, and the wristwatch stored the HR (receiver). Activity was monitored by a leg vibration sensor (Mini-mitter 2000, Sun River, OR), and a sensor was taped to the leg midway between the top of the patella and the hip. In some children, the HR was also stored simultaneously by the unit. The children were asked to wear the monitor for 48 h, from which a complete 24-h period (day 3) was extracted for analysis.Data Calculations
Method 1 (combined awake and asleep). HR,Method 2 (awake and asleep). The 24-h
period was divided into awake and asleep portions. Using graphs of EE
or O2 plotted against time,
sleep was determined by the drop in EE and minimal movement (<50
counts) detected by the activity sensor. Three regression equations
using HR, (HR)2, or
(HR)3 for
O2,
CO2, and EE were generated
separately for the awake and asleep data. The results were then
combined for the 24-h period.
Method 3 (HR and activity). The awake
period was divided into active and inactive segments. Prediction
equations were then developed for
O2 and
CO2 for both the active and
inactive data. Linear equations
[
O2 = (a × HR + b)], with different
coefficients for a and
b depending on whether the minutes
were defined as active or inactive, as described by Moon and Butte (9),
were generated.
O2 and
CO2 were predicted from the
inactive HR equation unless both the activity and HR exceeded fixed
thresholds defined for each individual. The activity threshold was
determined as the level (registered from the leg sensor) elicited
during random movement while the child was in the calorimeter. At this
time, the child was packing his/her belongings or was standing and
getting ready to exercise on day
1. The HR threshold was determined by examining the intersection of the active and inactive curves. To assign
an HR to the active equation, the HR for the current minute had to
exceed the HR threshold, and the activity for the current of either of
the two previous minutes had to exceed the activity threshold. Figure
1 illustrates a plot for one child's 24-h
calorimetry of
O2 vs. HR,
with the data separated into inactive and active periods.
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The errors between the predicted and actual data for
day 1 were computed for the three
equations. The best equation (lowest errors) was then used for all
subsequent analysis. The activity and HR data from the free-living day
(day 3) were then entered into the
derived regression equations (method
3) for each child to estimate free-living
O2,
CO2, and EE. The activity
and HR data from the validation day in the calorimeter
(day 2) were also entered into the
derived regression equations for each child to compare the predicted
O2,
CO2, and EE to the actual
O2,
CO2, and EE. Errors were
then computed as the difference between the actual and predicted mean
values for each subject over the 24-h, basal, and sleep periods.
Statistical Analysis
All variables were compared from day 1 to day 2 using paired t-tests. All regressions were completed using Microsoft Excel (Version 7.0a; Microsoft, Redmond, WA). The Bland-Altman (2) technique was used to compare measured calorimetry ![]() |
RESULTS |
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Subject Characteristics
Twenty children completed the study. For the girls, the weight-for-height percentile was 61 ± 29. For the boys, the percentile was 65 ± 33. Within each group, the range was from the 5th to 95th percentile. The body mass index was 18.8 ± 4.0 and 17.4 ± 2.4 kg/m2 for the girls and boys, respectively. The body composition results (Table 1) from the TOBEC scan revealed a wide range in percent body fat (9.2-36.7% and 6.0-33.4% for the girls and boys, respectively). There were no differences between the girls and boys for percent body fat, fat mass, and FFM. The data for steady-state and peak HR,
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Respiration Calorimetry
Energy intake. There were no significant differences in the percentage of calories from protein (14.4 ± 0.7 vs. 14.2 ± 0.9%), carbohydrate (55.6 ± 1.7 vs. 56.2 ± 1.7%), or fat (31.5 ± 1.6 vs. 31.2 ± 1.4%) in these children between the 2 days in the calorimeter. Energy intake for the calorimeter on day 1 (6,979 ± 983 kJ/day) did not significantly differ from day 2 (6,782 ± 1,427 kJ/day). Energy intake was not significantly different from 24-h EE in the calorimeter either for day 1 or day 2, with a mean energy balance of 92 ± 816 kJ on day 1 andEE. Table
2 gives the values for HR,
O2,
CO2, and EE for the 2 days in
the calorimeter. There were no significant differences for HR,
O2,
CO2, and EE during the BMR or
sleep period between days 1 and
2. There were also no significant
differences in 24-h HR,
O2,
CO2, and EE between
days 1 and
2. Twenty-four-hour EE for the group
ranged from 5,284 to 9,008 kJ/day for day
1 and from 5,054 to 9,251 kJ/day for
day 2. The physical activity level (PAL, 24-h EE/BMR) was not different between days (1.31 ± 0.11 on
day 1 and 1.31 ± 0.13 on
day 2).
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Derivation of Prediction Equations: Day 1
A comparison of equations 1, 2, and 3 revealed comparable errors in predicting 24-h
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Predictions From the Calorimeter: Day 2
Table 4 illustrates the errors in predicting 24-h
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Correlations between the active and inactive slopes of the linear HR
prediction equations for O2
and
CO2 and age were completed. Significant correlations were found between the active
O2 and
CO2 slopes and age (both
r = 0.42, P < 0.05). Significant correlations
were also observed between the inactive
O2 and
CO2 slopes and age
(r = 0.20 and
r = 0.21, respectively,
P < 0.05).
Predictions From Free-Living HR and Activity: Day 3
Free-living EE was predicted from HR and activity using method 3 equations. In other words, the free-living HR and activity data while the children were at home were entered into the derived equations from the 24-h calorimetry. The total duration for the free-living day was 1,424 ± 28 min, with 855 ± 82 min spent awake and 569 ± 78 min asleep. Awake HR was 99 ± 7 beats/min, whereas asleep HR was 74 ± 5 beats/min. The predicted ![]() |
DISCUSSION |
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In this study, we sought to predict EE from HR and activity in children
calibrated against 24-h respiration calorimetry. Each child completed
one calibration period and one validation period each of 24-h
calorimetry and one free-living day. Three approaches were taken to
predict EE. For method 1, the entire
24-h period was predicted from the same regression equation using HR.
In method 2, the 24-h period was
predicted after partitioning the 24-h period into awake and asleep; and
for method 3, the 24-h period was
predicted after partitioning the awake period into active or inactive
curves, using activity to define the active and inactive minutes.
Initially, for method 1, three
equations were tested of varying complexity, namely, linear HR,
(HR)2, and
(HR)3. Estimates of 24-h
O2,
CO2, and EE were similar
among equations. However, because linear HR was the simplest equation
and gave similar errors, it was chosen for further analysis.
Method 3 proved the most accurate way
to predict EE. The validation day (day
2) results indicated that using both HR and activity
provided the lowest errors and standard deviations. The errors were
2.6 ± 5.2%,
4.1 ± 5.9%, and
2.9 ± 5.1% for
O2,
CO2, and EE, respectively. Therefore, with 24-h respiration calorimetry used to obtain calibration and validation data, EE was predicted from HR and activity in children
with low error.
The errors found in this study improved upon previous investigations
that have examined the HR/O2
relationship and prediction of EE. Our average errors were lower than
those that have been found in children (1, 5), young adults (8), and
the elderly (11). Previous studies that used doubly labeled water
(5-7, 11) reported that group estimates of EE by the HR method are suitable, but errors are greater for individuals. The low standard deviations in our study indicate that this method is acceptable for
predicting EE in individuals. The significant correlations observed
between the slopes of the active and inactive linear HR prediction
equations for
O2 and
CO2 and age indicate that regression equations must be developed on an individual basis.
One possible explanation for the improvement may be the division of the
awake data into active vs. inactive. When 24-h EE was predicted using
linear HR in our study, the errors were lower with day
2 separated into active and inactive conditions
(2.9 ± 5.1%) vs. the entire day combined (
6.1 ± 7.9%; Table 4). This held true for 24-h
O2 and
CO2, as well as when the
different equations for HR, namely
HR2 and
HR3 (data not shown), were used.
The errors were markedly greater for sleep predicted using
method 1 (combined awake and asleep) vs. methods 2 and
3. Thus the errors for 24-h EE were
approximately one-half when using HR and activity compared with the
other methods. In a study of adults (9), combining HR and activity also
yielded lower errors for 24-h
O2 (
3.4 ± 4.5)
compared with a method using HR3
without activity measures (
5.9 ± 8.3). Thus we recommend the use of an activity monitor in conjunction with an HR monitor when designing studies to predict EE.
In this study, we utilized a sensor taped to the leg to measure
activity. However, there may be several types of activities that
elevate HR but do not involve leg movement. This might have some
potential impact on the activity and HR method. In future studies, it
might be worthwhile to add another sensor to the arm to capture all
movement in the children and therefore possibly improve estimates of
O2,
CO2, and EE.
One advantage of our study is the use of 24-h calorimetry to both generate and verify our prediction equations. A recent study (11) in elderly individuals also used calorimetry (84 h) to calibrate but used doubly labeled water to validate the prediction of EE by the HR method. Although Bitar et al. (1) also used room calorimetry for validation, the calibration activities were measured by a metabolic cart, and activity was not incorporated into the regression equations. Other studies in adults have also utilized single bouts of prescribed activities and validated the measurements with room respiration calorimetry (3, 14). Room calorimetry has the advantage of being able to measure sleeping EE. The separation of sleep from the 24 h contributes to improving the prediction of 24-h EE (Table 4) since sleep constitutes such a large portion of the day.
The differentiation of the data into active vs. inactive, as previously described by Moon and Butte (9), is a novel approach in children. We chose a time period when the child was moving around the calorimeter, i.e., packing his/her belongings, bending down, standing up. The HR at this time (104 ± 8 beats/min) was below the rate elicited by the lowest-intensity cycle exercise (129 ± 16 beats/min). The active vs. inactive criterion could be made with this type of activity. Our results indicate that the activity data during that time period worked well as a threshold, since the errors were lowest for method 3. The "active" vs. "inactive" periods can also be compared between days. The period of time for day 1 defined as active was 138 ± 56 min, with 730 ± 125 min classified as inactive. For day 2, there were 151 ± 99 min defined as active and 732 ± 105 min defined as inactive. For the free-living day (day 3), there were 318 ± 184 and 513 ± 236 min categorized as active and inactive, respectively. This indicates that the calorimeter days were relatively similar in terms of activity, whereas more time was spent active on the free-living days.
There are some disadvantages to these types of measurements in children. We requested that the children wear the activity and HR monitors for 48 h. However, we extracted from that period 1,440 consecutive minutes of complete data. In very few cases did we obtain 48 complete hours of data. Also, the data processing is quite significant. In addition, many children participated in structured sports (soccer, swimming) and had to remove the monitors while exercising. This could lead to an underestimation of free-living EE, since the high HRs elicited during the physical activity would not be registered on the monitors. In future studies, noting the time and activity when the monitor is removed would be beneficial. Despite these limitations, however, investigators conducting field studies in children should be encouraged to conduct studies utilizing HR and activity to assess free-living EE when other techniques, such as doubly labeled water, are not available.
The PAL (24-h EE/BMR) is an index of level of activity. The predicted
free-living PAL (1.40 ± 0.2) was not much higher than that obtained
in the calorimeter. Again, this may be due to removal of the monitors
during sports. The average PAL was 1.3 while in the calorimeter in
these children. Low PALs are reflective of the confinement of
calorimeters, even though two 20-min exercise sessions were included in
the protocol. During the remainder of the day, the children typically
watched television, played video games, or participated in arts and
crafts. However, there were wide ranges in PALs in the calorimeter
(1.13-1.48 on day 1 and 1.07-1.60 on day 2) and
predicted free living (1.03-1.82). This corresponds well with the
O2 peak data, which indicate
that the children varied in fitness levels.
In conclusion, the combination of HR and activity is an acceptable method for determining EE not only for groups of children but for individuals. Future studies should focus on verifying the existing protocol in even younger children (preschool) or children with metabolic disorders such as obesity or diabetes.
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ACKNOWLEDGEMENTS |
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We thank the children who participated in the study and Anh Nguygen, Firoz Vohra, and Maurice Puyau for technical assistance.
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FOOTNOTES |
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This study was supported by United States Department of Agriculture/Agricultural Research Service Cooperative Agreement 58-6250-6-001. The contents of this publication do not necessarily reflect the views or policies of the US Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.
Address for reprint requests: M. S. Treuth, USDA/ARS Children's Nutrition Research Center, Dept. of Pediatrics, Baylor College of Medicine, 1100 Bates St., Houston, TX 77030.
Received 18 December 1997; accepted in final form 2 March 1998.
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