# Heart Rate Monitoring as a Measure of Physical Activity in Children

Purpose: Using a larger sample and broader range of activities than most previous investigations of HR monitoring, this study examined the need for individual calibration of the HR-V̇O_{2} prediction equation, the effect of including low-intensity activities when establishing the HR-V̇O_{2} equation, comparisons of different methods for calculating HR-V̇O_{2} prediction equations for maximum energy expenditure (EE) variance, and the effect of these different methods when applied to free-living HR data.

Methods: Forty-three children ages 8 to 12 completed laboratory assessments of the relationship between HR and V̇O_{2}. Different methods of estimating daily EE were applied to free-living HR data collected from 37 of these children.

Results: There was significant individual variation in the HR-V̇O_{2} prediction equations. HR monitoring predicted V̇O_{2} during low-intensity activities, below most established cut points. Individual differences persisted during both high- and low-intensity activities. Although a HR-V̇O_{2} prediction equation generated from the group accounted for 85% of the variance in EE, significant improvements in prediction were achieved with individualized HR-V̇O_{2} prediction equations that took into account low-intensity activity levels.

Conclusion: Generic equations derived from group data may be suitable for some applications. However, for investigators requiring more precision, individual HR-V̇O_{2} equations significantly improve prediction.

^{1}National Institute of Child Health and Human Development, Bethesda, MD; ^{2}Miami University, Oxford, OH; and ^{3}Georgetown University, Washington, DC

Address for correspondence: Ronald J. Iannotti, Preventive Research Branch, Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, 6100 Executive Blvd., 7B05, Bethesda, MD 20892-7510; E-mail: iannottr@mail.nih.gov.

Submitted for publication October 2003.

Accepted for publication July 2004.

Physical activity (PA) has been shown to have numerous health benefits affecting both chronic and acute illness (^{14}). Unfortunately, there has been a decrease in PA in childhood and an increase in childhood obesity (^{7}). Although there is considerable effort to reverse the trend towards more sedentary childhoods, research has been impeded by methodological problems that assess daily EE in children (^{18}).

One major problem is that the most objective methods of assessing EE are expensive and labor-intensive. They either estimate total EE over a long period of time (e.g., doubly labeled water) or are too cumbersome to be used in free-living populations (e.g., indirect calorimetry) (^{18}). In contrast, minute-by-minute HR monitoring can provide details about the frequency, intensity, duration, and time of day of individual episodes of PA, and can be used to estimate daily EE. HR monitoring has been validated with EE that has been assessed by doubly-labeled water or indirect calorimetry across a range of activity levels and lifestyles (^{1,2,4,5,10,12,18}). When compared to motion sensors, HR monitors are sensitive to stationary activities that only involve the upper body; there is evidence that such low-intensity, upper body activities contribute significantly to daily EE (^{8}).

In order to estimate EE, HR can be regressed against V̇O_{2} readings obtained in the laboratory; however, the methods used to estimate EE from the HR-V̇O_{2} relationship vary across studies. For example, there is disagreement as to the nature of the relationship between HR and EE, including whether the relationship is maintained across the full range of HR or primarily exists for HR above some cut point (^{1,3,6,11,20}) or Flex-HR (^{2,4,5,10,12,19}). The Flex-HR method is limited in that its value is dependent on which activity is chosen for calibration, and it is possible to overestimate EE for activities just above the Flex-HR. Even when investigators agree that the HR-V̇O_{2} relationship is more robust above a HR cut point, there is inconsistency in how to calculate this cut point. Two studies have suggested that variations in the method of calculating Flex-HR do not have a statistically significant effect on calculation of EE. However, both studies compared EE assessments within very small, homogeneous samples; Livingstone et al. (^{11}) used a sample of eight relatively sedentary boys and Ekelund et al. (^{5}) used a sample of eight young male athletes. The current study examines HR cut points for the HR-V̇O_{2} relationship in a larger, predominantly female sample.

Studies using laboratory assessments to calibrate the HR-V̇O_{2} relationship for each child have typically included only two assessments: HR during rest (lying and/or sitting), and HR during a single type of PA (usually on a treadmill). This presumes that the HR-V̇O_{2} relationship is constant across activities. Only a few studies have looked at the HR-V̇O_{2} relationship during other types of activities (^{5,11}). Most of these studies have been done with a small, homogeneous sample of males. In the current study of a larger, predominantly female sample, the HR-V̇O_{2} relationship is determined with a range of activities: lying, sitting, standing, standing and writing, standing and throwing, and graduated treadmill walking to exhaustion.

The major purpose of study 1 is to examine the relationship between EE (assessed with indirect calorimetry) and HR using different methods for calculating the relationship. Questions addressed include: 1) Is there a single equation for all children, or do individual variations in the HR-V̇O_{2} relationship require individualized HR-V̇O_{2} equations? 2) Does the equation for the relationship between HR and V̇O_{2} fit the full range of HR, or is there a threshold below which HR is an unreliable estimator of EE (^{13})? 3) If the relationship between HR and V̇O_{2} is not reliable across the full range of individual HR values, what is the best method for calculating the cut point? 4) Given the answers to these questions, what is the best method for calculating EE from HR data?

Study 2 applies the equations derived in study 1 to estimate EE in free-living children using 12-h HR monitoring. EE estimates from individualized equations developed in study 1 are compared to both generic and individual methods previously established in the literature.

## METHODS—STUDY 1

### Subjects.

A sample of 43 children (33 girls, 10 boys) were recruited from recreation department summer programs to participate in a laboratory visit that included an anthropometric measurement and an exercise protocol for simultaneous measurement of HR and V̇O_{2.} Females were oversampled because of the rapid decrease in PA (and corresponding increase in obesity) that commonly occurs during a girl’s transition into puberty. Parents and children gave informed written consent in compliance with university institutional review board guidelines.

### Procedures.

Laboratory visits were scheduled approximately 2 h postprandially. During the laboratory visit, written informed consent was obtained from both parent and child, parents provided demographic information, children’s anthropometric measurements were obtained, and children completed an activity protocol during which minute-by-minute HR and V̇O_{2} were assessed.

### Laboratory anthropometric measures.

Height, weight, and skinfold thicknesses were measured at the beginning of the laboratory visit. Height was measured barefoot with a stadiometer to the nearest 0.5 cm, and weight was measured to the nearest 0.1 kg with a standard physician’s beam scale. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m^{2}). Triceps, biceps, calf, subscapula, iliac and quadriceps skinfold thicknesses were assessed using Harpenden calipers.

### Laboratory assessment of HR and V̇O_{2}.

HR and V̇O_{2} were assessed with a protocol that included a range of activities. After adjusting to the ventilation mouthpiece and nose clip, the participant was instructed to rest on a horizontal gurney for 20 min. Minute-by-minute measurements were taken during the last 5 min. After adjusting the position of the ventilation tubes, measures were then taken while the participant was sitting for 5 min, standing for 3 min, writing the alphabet on a blackboard with the letters at eye level for 3 min, throwing a volleyball a distance of 6 m overhand, with both arms and at a rate of once every 4 s for 3 min, and throwing a volleyball a distance of 6 m underhand, with both arms and at a rate of once every 4 s for 3 min. Following another adjustment of the position of the ventilation tubes, a modified Balke treadmill protocol was used to increase the intensity of work to exhaustion. Each subject was required to maintain a brisk but comfortable walking speed. At 3-min intervals, either the speed of the treadmill (2.0, 2.5, 3.0, 3.2, 3.4, or 3.6 min·h^{−1}), the elevation of the treadmill (increments of 2%), or both were increased until exhaustion was reached. Oxygen uptake, carbon dioxide production, HR, blood pressure, ventilation, tidal volume, and respiratory frequency were measured throughout the assessment procedure. Oxygen uptake and CO_{2} production were measured on expired air using open circuit spirometry. Criteria for maximal exertion included reaching or exceeding two of the following states: 1) change in oxygen uptake of less than 2 mL·(kg·min)^{−1} with an increase in workload; 2) respiratory exchange rate greater than l.0; or 3) an increase of less than 5 bpm in HR over the last two work stages. HR was electronically computed from a continuous electrocardiogram.

### Analysis plan.

These data were analyzed to examine four questions. First, do individual variations in the HR-V̇O_{2} relationship require individualized HR-V̇O_{2} equations? Second, is there a significant HR-V̇O_{2} relationship for low-intensity activities? Third, below what cut point does HR become an unreliable estimator of EE? Fourth, what is the best equation for predicting V̇O_{2}: a generic group equation, individualized equations based on treadmill only, individualized equations that include data from all laboratory activities, the traditional Flex-HR approach, or a combination of two equations (one for all heart rates during nontreadmill activities and one for heart rates above this range)?

Random Coefficient Modeling (RCM) using Hierarchical Linear Modeling (^{15}) was applied to determine whether there were sufficient individual differences in the HR-V̇O_{2} relationships to require individualized HR-V̇O_{2} prediction equations across the full range of HR values and for HR values above a cut point. RCM was also used to examine the need for individual HR-V̇O_{2} prediction equations for low-intensity activities and for HR values below commonly used HR cut points. Polynomial regression analyses, including the quadratic HR term, were used to compare different methods for calculating HR-V̇O_{2} prediction equations.

## RESULTS—STUDY 1

Characteristics of the male and female samples, including height, weight, BMI, skinfold thickness for each site, BP, resting HR, maximal HR, and maximal oxygen consumption, are presented in Table 1. The children ranged in age from 8 to 12 yr (mean = 10.4 yr, SD = 0.9). Descriptive results of the laboratory assessment of HR and V̇O_{2} are presented in Table 2 along with two commonly used cut point values, 40% of V̇O_{2max} and Flex-HR. Figure 1 plots the data points for the entire sample with HR along the x axis and V̇O_{2} along the y axis. Figure 2 provides the same plot for two children.

### Individual versus group HR-V̇O_{2} equations.

The first question addressed was whether a single HR-V̇O_{2} prediction equation for all children is appropriate. As can be seen in Table 2, there is considerable individual variation in HR during the different laboratory assessments as well as in potential cut points, such as the Flex-HR. These results underscore the need for individual HR-V̇O_{2} prediction equations rather than a single HR-V̇O_{2} equation for all children.

HR-V̇O_{2} correlations were calculated for each child. These zero-order correlations ranged from 0.856 to 0.988, demonstrating strong HR-V̇O_{2} relationships across the range of HR values and individual variations in this relationship. Using RCM, we tested whether the variance of individual slopes using HR to predict V̇O_{2} is significantly different from zero in the population. A significant variance in the slopes (0.0029, with χ^{2}(36) = 442.11, *P* < 0.001) indicated that there was sufficient individual variation in the HR-V̇O_{2} prediction equation to require individualized regression equations. We repeated these analyses of the subjects, controlling for age, gender, and BMI, to determine whether a conditional prediction equation might still be sufficient across individuals. Although gender (*P* < 0.05) and BMI (*P* < 0.05) were significant predictors of slopes for individual HR-V̇O_{2} prediction equations, there was still significant individual variation (χ^{2} (33) = 382.08, *P* < 0.001).

It is possible that individual differences in the HR-V̇O_{2} relationship at the low end of the HR-V̇O_{2} equation (i.e., below groupwide HR thresholds for moderate PA) are primarily responsible for the significant variance in slopes across individuals. If so, a single groupwide HR-V̇O_{2} equation could be used for HR values that correspond to moderate-to-vigorous PA (i.e., above group HR thresholds for PA). We repeated RCM, limiting the analyses to cases where HR values were above two cut points and controlling for individual age, gender, and BMI. The two groupwide cut points selected were HR values greater than 100 and 120. In all three analyses, individual slopes resulting from the HR-V̇O_{2} equation still had variances significantly different from zero in the population (for cut points of 100 and 120: χ^{2} (33) = 224.35, *P* < 0.001; and χ^{2} (4) = 134.78, *P* < 0.001, respectively). Thus we concluded that individualized HR-V̇O_{2} prediction equations significantly improve EE estimates from HR monitoring.

### Can HR be used to estimate EE during low-intensity activities?

A number of investigators have suggested that the HR-V̇O_{2} relationship is not maintained across the full range of HR values (i.e., at lower levels of EE). If this were the case, it would not be necessary to examine the HR-V̇O_{2} relationship in activities other than the treadmill; HR levels below those assessed during the treadmill activity would be ignored, and EE could be estimated with a substitute value, such as the resting metabolic rate (RMR) (^{2,4,5,10,12,19}).

Controlling for individual differences as well as age, gender and BMI, RCM was used to examine whether there was a significant relationship (slope) between HR and V̇O_{2} during the resting stage (lying and sitting) or during tasks involving low-intensity activity (standing, standing and writing, and standing and throwing). As expected, the mean slope of the HR-V̇O_{2} prediction equation was not significantly different from zero for the resting stage; *t*(301) = 0.05. However, the relationship between HR and V̇O_{2} was significant during tasks involving a range of upper body activities; *t*(394) = 6.33, *P* < 0.0001. It would appear that HR monitoring during low-intensity activities, including activities primarily limited to upper body activity, does provide information about EE.

The question remains, however, whether the HR values generated from the treadmill activity effectively covered the full range for which there is a significant HR-V̇O_{2} relationship. To answer this question, the HR-V̇O_{2} equation was examined for HR below the minimum value measured during the treadmill assessment for each individual. Even when restricted to values not commonly used to calibrate the HR-V̇O_{2} prediction equation, there was a significant relationship, *t*(600) = 15.21, *P* < 0.0001.

### Is there a threshold below which a HR-V̇O_{2} prediction equation is inappropriate?

For those investigators interested in using a groupwide threshold for estimating EE with HR monitoring, we attempted to identify the HR level below which the HR-V̇O_{2} relationship is no longer significant. RCM controlling for age, gender, and BMI was used to examine whether there was a significant slope when the HR was below 105 bpm and repeated in 5-bpm intervals. This rate was selected as a starting point because it approximates the mean of a commonly used Flex-HR (Table 2). At 95 bpm, the HR-V̇O_{2} prediction equation still had a significant slope, *t*(351) = 4.78, *P* < 0.0001. When the analysis was repeated for HR values below 90 and controlled for age, gender and BMI, the HR-V̇O_{2} prediction equation no longer had a significant slope, *t*(256) = 1.79, *P* > 0.05.

### What is the optimum equation for predicting EE?

Although it is clear that there is a significant relationship between HR and V̇O_{2} at HR levels below that measured during the treadmill activity and below cut points frequently used in the literature, it is still possible that the HR-V̇O_{2} prediction equation generated from the treadmill data is sufficient to predict the V̇O_{2} for the full range of potential HR values, including those for low-intensity activities. Five potential methods for generating HR-V̇O_{2} prediction equations were identified: 1) a single, generic equation based on the treadmill HR data from the entire sample and applied to all children; 2) individualized HR-V̇O_{2} prediction equations generated with treadmill data; 3) the Flex-HR method (^{2,4,5,10,12,19}), using RMR for all HR values below the Flex-HR and the treadmill equation for all HR values above; 4) individualized HR-V̇O_{2} prediction equations calculated on the full range of activities (resting, low-intensity, and treadmill); or 5) two individualized equations, one based on treadmill data to predict V̇O_{2} values above the HR range of the nontreadmill activities, the other based on all nontreadmill activities to predict V̇O_{2} values in this lower range. In all five methods, the HR-V̇O_{2} prediction equations included both linear and quadratic terms of HR. To determine whether there was a significant difference between the five different HR-V̇O_{2} prediction methods, the five methods were compared based on SEE and the percentages of variance explained by each method. A smaller SEE value and more variance indicate better prediction. The results of these analyses are presented in Table 3.

The HR-V̇O_{2} prediction equation derived from the treadmill data had a SEE of 5 and accounted for 85% of the variance in the observed V̇O_{2} readings for all activities. Thus, reasonably good prediction of EE can be obtained from a general HR-V̇O_{2} prediction equation applied to all children. However, each subsequent iteration of individualized HR-V̇O_{2} prediction equations accounted for significantly more of the variance in the observed V̇O_{2} readings (Table 3). In addition, including data from the low-level activities in the equation improved prediction: 96% of the variance was accounted for by the combination of two individualized equations, one for low-level activity and one for moderate to vigorous activity.

## DISCUSSION—STUDY 1

Consistent with previous research, there were significant individual differences in the HR-V̇O_{2} prediction equations, suggesting that individual equations for each child improve prediction. BMI and gender also contributed to the prediction equation; however, these individual characteristics had minimal effects in reducing individual differences in the HR-V̇O_{2} prediction equation. Individual variations in the HR-V̇O_{2} prediction equation were not attributable to variation in low-intensity activities. Significant individual variations in slope persisted when HR values were restricted to those above commonly used cut point values. These results suggest that individual equations significantly improve prediction.

One limitation of these analyses is that the calibration equations were used to predict V̇O_{2} during a prescribed set of activities in a laboratory setting. Furthermore, the equations that were used to predict V̇O_{2} for the same set of activities were used to derive the prediction equation. The question remains as to whether there is any practical consequence when these different predictive equations are applied to the distribution of activity levels and corresponding HR in a free-living population. That is, the laboratory analyses do not address whether HR-V̇O_{2} prediction equations developed in the laboratory also produce significantly different estimates of free-living EE. In order to answer this question, a second study was performed to apply these equations to HR representative of a typical day.

## METHODS—STUDY 2

### Subjects.

Thirty-seven of the children from study 1 (27 girls, 10 boys) participated in study 2.

### Free-living HR monitoring.

Toward the end of the study 1 laboratory visit, children and parents were instructed on how to use a Polar Vantage XL heart rate monitor. Appointments were then made for three separate days of recording, including two weekdays and one weekend day; for each day of recording, parents were instructed to attach the HR monitor immediately after the child awoke in the morning. The goal was to obtain 12 h of minute-by-minute HR data for each day scheduled. HR data was uploaded to a laptop computer during a home visit on the evening of the same day.

### Analysis plan.

The focus of the analyses was to compare different methods for estimating daily EE (specifically, V̇O_{2}) by applying the HR-V̇O_{2} equations generated in study 1 to the free-living HR data gathered in study 2. The mean estimated daily EE for each child was then compared using paired *t*-tests.

## RESULTS—STUDY 2

### Subjects.

The characteristics of the study 2 sample are displayed in Table 4. Girls who did not complete study 2 differed from girls who completed the study on one of 13 characteristics; girls who did not complete study 2 had significantly lower biceps skinfold readings; *t*(24) = −3.17, *P* < 0.01.

### Estimating EE from free-living HR data.

There were two criteria for acceptable HR data. First, at least 90% of the data had to be free of artifacts, i.e., HR < 40 or >220 (^{16}). Second, each child had to have at least 12 h of data without artifacts. Less than 1% of the data included artifacts. After artifacts were removed, the mean minutes of observations per child were 1813 (SD = 408).

One question is whether, during a typical day, a sufficient time is spent in low-level activity to have an appreciable effect on daily EE. It was established in study 1 that there is a significant relationship between HR and V̇O_{2} for HR below Flex-HR. Approximately 55% of free-living HR were below the Flex-HR. The cut point used in the two-equation solution in study 1 was the highest HR during low-level activities; approximately 73% of free-living HR fell below this cut point. That is, the traditional method of estimating daily EE (i.e., applying the treadmill HR-V̇O_{2} equation to all HR above the Flex-HR) ignores the potential HR-V̇O_{2} relationship for 55% of the HR values. Furthermore, the low-level activity cut point suggests that it may be inappropriate to apply the treadmill HR-V̇O_{2} equation for an additional 18% of the free-living HR values.

The primary question is whether the two individualized equations that were most successful in estimating EE using lab data produce significantly different estimates for EE using free-living HR data. The five methods for predicting EE in study 1 were applied to the full range of HR data to estimate daily EE (Table 5). Application of these five methods to free-living HR data results in significantly different estimates of daily EE. The two best solutions from study 1, individual single equations derived from all activities, and the individual two-equation method with a low-level activity cut point, differed significantly from each other and from the traditional Flex-HR solution.

## DISCUSSION

The results of study 1 suggest that individual calibrations of the HR-V̇O_{2} equation significantly improve prediction. Introduction of low-level activities into the calibration process further improves prediction; there was a significant HR-V̇O_{2} relationship within the range of HR values during low-level activity. This significant relationship persisted below the commonly used Flex-HR cut point. Furthermore, results from the two-equation method suggest that the HR-V̇O_{2} equation derived from treadmill data does not adequately describe the HR-V̇O_{2} relationship during low-level activities. In study 2, fully 73% of the HR values in the free-living sample were within the range of the low-level activity equation; therefore, using the treadmill equation alone may result in an overestimation of daily EE. These findings have practical implications for assessing daily EE and for making dietary and physical activity recommendations for youth at risk for obesity and health-related problems.

HR monitoring of children is frequently used to estimate free-living EE, to identify patterns of PA, and to validate other methods for assessing EE and PA such as accelerometers and self-report. There is still some disagreement as to how best to calculate the HR-V̇O_{2} prediction equation. Excluding the use of labor-intensive and burdensome 24 h assessment of EE as a means of calibrating the HR monitor (^{20}), previous efforts have tended to be limited to adult samples, relatively small, homogeneous samples of children (usually males), or a set of activities that do not include a range of low-intensity activities (^{1-6,10-12,17,19,21}). Study 1 addresses some of the problems of previous studies with a larger sample of children that includes an oversampling of girls and a procedure for calculating the HR-V̇O_{2} prediction equation that adds a range of low-intensity activities to the treadmill test. Although study 1 was not designed to include a representative sample at each age group, all of the major findings remained significant when analyses controlled for age, gender, and BMI.

Flex-HR cut points are frequently used to identify the HR above which there is a significant HR-EE relationship (^{1,5,6,11}). Flex-HR values are usually calculated as between the highest RHR and the minimum HR during exercise, such as a treadmill test or equivalent. Consistent with previous work, there was no significant relationship between HR and V̇O_{2} during resting periods. However, HR was a significant predictor of EE during low-intensity activities. Furthermore, HR predicted EE below the threshold for HR during treadmill activities and well below a commonly used Flex-HR cut point. This is strong evidence for a HR-V̇O_{2} relationship during low-intensity activities. One limitation to these conclusions is that HR and V̇O_{2} were assessed using primarily upper body activities. However, these HR were fairly low, below the level of most cutoffs used in research estimating EE and below HR generated from a fairly slow rate of walking. These findings are consistent with those of Bitar et al. (^{3}), who included at least one low-intensity physical activity in their procedures. They found that the best equation for predicting EE included values below the Flex-HR, rather than an equation limited to HR above the Flex-HR. Further research is needed to identify whether these findings are limited by the activities selected for evaluation in this protocol. However, it should be noted that the sample used in this study is larger, and includes a larger proportion of females than many of the previous studies in this area.

An important question for the researcher is: How much is gained by using individualized HR-V̇O_{2} prediction equations? In addition, is it necessary to calibrate the HR-V̇O_{2} prediction equation with a range of activities, or is the treadmill test or its equivalent sufficient? The good news for the investigator wishing to use a single cut point or single HR-EE prediction equation is that in this sample the single, general equation derived from the group treadmill data accounted for 85% of the variance in V̇O_{2} across all tasks. These results are similar to those of Sarton-Miller et al. (^{17}), who used change in HR and V̇O_{2} as the predictive and outcome elements in their equation, and found that a single regression equation based on group data accounted for approximately 84% of the variance. We are not suggesting that the equation derived from this sample can be used for other samples. Group differences in Flex-HR and the HR-V̇O_{2} relationship due to age, gender, body composition, etc. suggest that investigators would still need to calculate a HR-V̇O_{2} prediction equation for their sample by testing a representative subsample (^{11,19}).

For investigators requiring more precision, individual HR-V̇O_{2} equations improve prediction. Individual equations generated from a treadmill or equivalent activity result in a significant improvement over a single equation based on group data; in this sample, individual HR-V̇O_{2} prediction equations derived from the treadmill data accounted for 88% of the variance in V̇O_{2} across all activities. This is higher than the typical equation derived using the Flex-HR method (^{9}). Single individualized HR-V̇O_{2} prediction equations based on HR values from the full range of activities accounted for 95% of the variance in V̇O_{2} readings. The greatest precision, accounting for 96% of the variance, resulted from two individualized HR-V̇O_{2} prediction equations, one for HR in the range of the nontreadmill activities, and one for HR above this range. For investigators hoping to get maximum precision from the HR-V̇O_{2} prediction equation, there is a clear benefit to individualized HR equations. Calibrating the HR-V̇O_{2} equation with activities that vary in intensity improves the precision of these estimates.

In study 1, the two best methods—individualized equations using the full range of laboratory HR values, and the two-equation solution—were significantly better than the Flex-HR method in estimating EE within the laboratory protocol. In study 2, these two methods produced significantly different estimates of daily EE than the Flex-HR method. The Flex-HR estimate of daily EE was approximately 7% higher than that of the two-equation solution, and differed by 8% from the individualized equation solution. Since the equations for calculating EE from the upper HR values uniformly rely on the treadmill data across methods, these differences in estimates of daily EE must reflect significant differences in impact of lower-intensity activities. Some investigators have suggested various adjustments to the Flex-HR method to get a better estimate of daily EE, such as increasing the Flex-HR by 10 (^{19}), effectively reducing the estimate of daily EE by limiting the range of HR values to which the treadmill equation is applied.

Correlations between daily EE calculated by the different methods were quite varied (Table 5). Uniformly high correlations would have suggested consistency across individuals in the relative importance of low-level activity, even though the absolute values differed. However, the inconsistent correlations indicate considerable individual variation in the contribution of low-level activity to daily EE.

Exclusive reliance on the HR-V̇O_{2} treadmill equation for estimating EE appears to result in an overestimation of daily EE. The findings from study 1 and study 2 have implications for research and clinical applications. For example, from a research perspective, misestimation and individual variations in the impact of low-level activity on estimates of daily EE could significantly affect hypothesized relationships among EE, PA, diet, and body composition. From a clinical perspective, the differences could influence recommendations regarding dietary intake. For a child at risk of obesity who is believed to be very active, a 2800-kcal diet might be recommended to maintain weight. However, a 7% error in estimated daily EE would result in a daily recommendation that is almost 200 kcal higher than it should be. This error is sufficient to cause regular weight gain.

Based on a number of factors and constraints (e.g., purpose of assessment, sample size, budget, subject burden, subject retention, subject adherence to the protocol), investigators can decide whether a HR-V̇O_{2} equation derived from group data and applied to the entire sample is satisfactory compared to individualized prediction equations. Once an investigator decides to use individualized prediction equations, it appears that the optimum method would be to use two HR-V̇O_{2} prediction equations derived from a range of laboratory activities, with separate equations for HR values above or below the HR cut point identified with nontreadmill activities or their equivalent.

This project was supported by a Research Challenge Award from the Ohio Board of Regents and by the National Institute of Child Health and Human Development. The authors would like to thank Wayne Brooks, Emily Cole, Paco Labrador, Sara Levine, Rachel Mitchell, Caroline Thomas, Adam Van Auken, Kalina Van Every, and Tracy Whittaker for their assistance with the project.

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**Keywords:**

ASSESSMENT; METHODOLOGY; ENERGY E-PENDITURE; E-ERCISE; ADOLESCENCE