Energy expenditure in children predicted from heart rate and activity calibrated against respiration calorimetry

Margarita S. Treuth, Anne L. Adolph, and Nancy F. Butte

United States Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030

    ABSTRACT
Top
Abstract
Introduction
Methods
Results
Discussion
References

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 (VO2), carbon dioxide production (VCO2), 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 VO2, VCO2, 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 VO2, VCO2, 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, VO2, VCO2, 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 VO2, VCO2, 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

    INTRODUCTION
Top
Abstract
Introduction
Methods
Results
Discussion
References

HEART RATE (HR) can be used to estimate energy expenditure (EE), since a linear relationship exists between HR and oxygen consumption (VO2) 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 VO2 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.

    METHODS
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Abstract
Introduction
Methods
Results
Discussion
References

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 a VO2 peak test, ate breakfast, and then completed the body composition assessment. After receiving instructions on how to operate all necessary equipment (intercom, television/VCR, etc.) in the calorimeter, the children entered the calorimeter (~3.5 h after completion of the VO2 peak test). The child spent 24 h in the calorimeter and followed specific guidelines for periods of rest, sleep, and exercise. The next morning, the basal metabolic rate (BMR) was measured. The child exited the room after 24 h. The child was sent home with an HR watch, transmitter belt, and activity monitor. The child then returned to the MRU (an average of 3 wk later) and spent an additional 24 h in the calorimeter.

Body 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.

VO2 Peak

Fitness capacity was measured by a VO2 peak test. The treadmill protocol involved a constant speed of 2.5 miles/h at an initial 0% grade for the first 4 min. The average of minutes 3 and 4 constituted the steady state. The grade was then increased to 10%. Every 2 min thereafter, the grade was increased by 2.5% to a maximum of 22.5%, when speed was increased by 0.6 miles/h. Exercise measures (VO2, ventilation, respiration rate, and HR) and respiratory quotient (RQ) were examined during the steady-state period and at peak exercise. VO2 peak was determined by a RQ >1.0, HR >195 beats/min, and volitional fatigue. A Sensormedics 2900 metabolic cart (Yorba Linda, CA) was used to collect the respiratory gases.

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. VO2 and carbon dioxide production (VCO2) were continuously measured by a paramagnetic O2 gas analyzer (Oxymat 5E; Siemans) and a nondispersive infrared CO2 analyzer (Ultramat 5E; Siemans, Karlsruhe, Germany). The calorimeter was calibrated before each test. Three electrodes were applied to the child's skin, and HR was recorded by telemetry at 1-min intervals (DS-3000; Fukuda Denshi, Tokyo, Japan).

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 VO2 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 VO2 and 0.11 ± 0.98% for VCO2 (10). In six children not included in this study, repeat 24-h testing for VO2, VCO2, 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, VO2, VCO2, and EE measured for the entire 24 h in the calorimeter for day 1 were used to generate three regression equations as follows: 1) VO2 a(HR) + b; 2) VO2= a(HR)2 + b; and 3) VO2= a(HR)3 + b, where a is the slope and b is the intercept. The same regression equations were completed substituting either VCO2 or EE for VO2. The HR values used in the regression equations are defined as the mean of the HRs recorded every minute over the specific time periods. The nonlinear functions (HR)2 and (HR)3 were used because the previous study in adults completed in our laboratory (9) tested five nonlinear functions, and the 24-h r2 values for the nonlinear equations were better than those for linear HR.

Method 2 (awake and asleep). The 24-h period was divided into awake and asleep portions. Using graphs of EE or VO2 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 VO2, VCO2, 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 VO2 and VCO2 for both the active and inactive data. Linear equations [VO2 = (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. VO2 and VCO2 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 VO2 vs. HR, with the data separated into inactive and active periods.


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Fig. 1.   Oxygen consumption (VO2) and heart rate (HR) relationship in the room calorimeter for one subject, with separate curves for active vs. inactive data for awake portion of the day (method 3).

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 VO2, VCO2, 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 VO2, VCO2, and EE to the actual VO2, VCO2, 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 VO2, VCO2, and EE values with values predicted by the regression equations. Data are presented as means ± SD. Statistical analyses were performed using Minitab for Windows (Version 10.5) with significance set at P < 0.05.

    RESULTS
Top
Abstract
Introduction
Methods
Results
Discussion
References

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, VO2, and RQ during treadmill exercise are also presented in Table 1. VO2 peak (ml/min) ranged from 1,074 to 2,191 ml/min in the girls and from 1,006 to 1,905 ml/min in the boys. Time to exhaustion varied in the girls from 12.6 to 19.3 min (mean = 16.1 ± 2.0 min) and in the boys from 11.3 to 18.0 min (mean = 15.6 ± 2.0 min).

                              
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Table 1.   Body composition and peak exercise test in girls and boys

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 and -34 ± 908 kJ on day 2.

EE. Table 2 gives the values for HR, VO2, VCO2, and EE for the 2 days in the calorimeter. There were no significant differences for HR, VO2, VCO2, and EE during the BMR or sleep period between days 1 and 2. There were also no significant differences in 24-h HR, VO2, VCO2, 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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Table 2.   Calorimetry data (days 1 and 2) and predicted free-living data (day 3) in children (n = 20)

Derivation of Prediction Equations: Day 1

A comparison of equations 1, 2, and 3 revealed comparable errors in predicting 24-h VO2, VCO2, and EE (Table 3). Equation 1 (linear HR) produced either lower or similar errors when the data were compared between actual and predicted measures for day 1. The r2 values for the three equations were slightly lower with linear HR (Eq. 1) for VO2, VCO2, and EE for the 24 h (Table 3). Because the prediction with linear HR was similar to the (HR)2 and (HR)3 equations and since linear HR is a simpler mathematical equation, all subsequent analyses used HR (Eq. 1). Additionally, basal and sleep prediction equations were developed, and the errors were calculated (data not shown).

                              
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Table 3.   Errors and r2 in predicting 24-h VO2, VCO2, and EE for day 1 in the calorimeter using three methods

Predictions From the Calorimeter: Day 2

Table 4 illustrates the errors in predicting 24-h VO2, VCO2, and EE using linear HR for the three methods. For method 1, the errors were lower for VO2 than for VCO2 or EE for the 24-h, basal, and sleep periods. A Bland-Altman (2) plot (Fig. 2) depicts the difference between measured 24-h EE on day 2 and predicted EE from linear HR (Eq. 1, method 2). The errors for method 2 were on average 5 ± 7% for 24-h VO2 and EE, with the lowest mean errors obtained during sleep (-1.2 ± 5.4% for EE). A reduction in both the mean errors and standard deviation (for VO2) was seen with method 3. This combined activity and HR method gave individual errors ranging from -12.0 to +5.8% (mean = -2.6 ± 5.2) for 24-h VO2 and -11.3 to +5.5% (mean = -2.9 ± 5.1%) for 24-h EE. The difference between measured 24-h EE on day 2 and the predicted EE using HR and activity is illustrated with a Bland-Altman (2) plot (Fig. 3). The rank order from the lowest to highest mean errors for 24-h VO2, VCO2, and EE is method 3, method 2, and method 1. Method 1 yielded approximately twice the errors of method 3. It should be noted that the basal and sleep errors were identical between methods 2 and 3 (Table 4). The awake period was further refined for method 3 by separating the data into active and inactive; activity was then combined with HR to predict EE.

                              
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Table 4.   Errors (%) in predicting 24-h, basal, and sleep VO2, VCO2, and EE for day 2 in the calorimeter


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Fig. 2.   Bland-Altman plot of the difference between the measured 24-h energy expenditure (EE) in the calorimeter (day 2) and the predicted EE from linear HR (Eq. 1, method 2). Solid line represents the mean difference between the measured and predicted 24-h EE values. The 2 dashed lines represent upper and lower limits of agreement, calculated as the mean difference ± 2 SD.


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Fig. 3.   Bland-Altman plot of the difference between the measured 24-h EE in the calorimeter (day 2) and the predicted EE from HR and activity (method 3). Solid line represents the mean difference between the measured and predicted 24-h EE values. The 2 dashed lines represent upper and lower limits of agreement, calculated as the mean difference ± 2 SD.

Correlations between the active and inactive slopes of the linear HR prediction equations for VO2 and VCO2 and age were completed. Significant correlations were found between the active VO2 and VCO2 slopes and age (both r = 0.42, P < 0.05). Significant correlations were also observed between the inactive VO2 and VCO2 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 VO2, VCO2, and EE for day 3 are shown in Table 5. The 24-h EE values ranged from 5,364 to 10,134 kJ/day. The predicted 24-h EE was 496 ± 922 and 569 ± 1,126 kJ/day higher than days 1 and 2 in the calorimeter, respectively. The free-living PAL (24-h EE/BMR) was calculated to be 1.4 ± 0.2. The PAL was not significantly different whether the BMR from day 1 or day 2 was used in the calorimeter. Activity EE, calculated as 24-h EE - (24-h EE × 0.1 + BMR), was similar whether day 1 BMR data (1,372 ± 975 kJ/day) or day 2 BMR data (1,435 ± 1,059 kJ/day) were used.

    DISCUSSION
Top
Abstract
Introduction
Methods
Results
Discussion
References

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 VO2, VCO2, 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 VO2, VCO2, 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/VO2 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 VO2 and VCO2 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 VO2 and VCO2, 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 VO2 (-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  VO2, VCO2, 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 VO2 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.

    ACKNOWLEDGEMENTS

We thank the children who participated in the study and Anh Nguygen, Firoz Vohra, and Maurice Puyau for technical assistance.

    FOOTNOTES

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.

    REFERENCES
Top
Abstract
Introduction
Methods
Results
Discussion
References

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Am J Physiol Endocrinol Metab 275(1):E12-E18
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