Journal of Applied Physiology
Vol. 81, No. 4, pp. 1754-1761, October 1996
METABOLISM

Combined heart rate and activity improve estimates of oxygen consumption and carbon dioxide production rates

Jon K. Moon 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 77030-2600

ABSTRACT

INTRODUCTION

METHODS

RESULTS

DISCUSSION

ACKNOWLEDGEMENTS

FOOTNOTES

REFERENCES

ABSTRACT

Moon, Jon K., and Nancy F. Butte. Combined heart rate and activity improve estimates of oxygen consumption and carbon dioxide production rates. J. Appl. Physiol. 81(4): 1754-1761, 1996.Oxygen consumption (O₂) and carbon dioxide production (CO₂) rates were measured by electronically recording heart rate (HR) and physical activity (PA). Mean daily O₂ and CO₂ measurements by HR and PA were validated in adults (n = 10 women and 10 men) with room calorimeters. Thirteen linear and nonlinear functions of HR alone and HR combined with PA were tested as models of 24-h O₂ and CO₂. Mean sleep O₂ and CO₂ were similar to basal metabolic rates and were accurately estimated from HR alone [respective mean errors were 0.2 ± 0.8 (SD) and 0.4 ± 0.6%]. The range of prediction errors for 24-h O₂ and CO₂ was smallest for a model that used PA to assign HR for each minute to separate active and inactive curves (O₂, 3.3 ± 3.5%; CO₂, 4.6 ± 3%). There were no significant correlations between O₂ or CO₂ errors and subject age, weight, fat mass, ratio of daily to basal energy expenditure rate, or fitness. O₂, CO₂, and energy expenditure recorded for 3 free-living days were 5.6 ± 0.9 ml ^· min^{1 ·} kg¹, 4.7 ± 0.8 ml ^· min^{1 ·} kg¹, and 7.8 ± 1.6 kJ/min, respectively. Combined HR and PA measured 24-h O₂ and CO₂ with a precision similar to alternative methods.

energy expenditure; human; respiration calorimetry; electronic monitor; physical activity; 24-hour free-living measurement

INTRODUCTION

NUMEROUS STUDIES have demonstrated the potential for using heart rate (HR) and physical activity (PA) to estimate free-living metabolic rates. Electronically recording HR and PA is cheaper and less intrusive than calorimetric techniques. It also provides detailed information on daily activity patterns. However, several limitations of HR and PA monitoring have prevented their widespread use in clinical and nutritional studies. Until recently, electronic monitors were often uncomfortable, lacked resolution, and had limited data-storage capacity. Rapid advances in microcircuitry have made electronic monitors smaller, cheaper, and more powerful. Commercial devices are available that continuously record HR, PA, or both at resolutions of 1 min for several days.

Individual errors in measurement of energy expenditure (EE) by HR or PA can be quite large, as much as 30% for estimates of 24-h EE (1, 4, 8, 14). Group mean errors in the range of ±5% by HR monitoring have been higher than doubly labeled water, the current standard for the measurement of free-living EE (11).

The relationships of oxygen consumption (O₂) rates to HR and PA are very different among individuals. For an individual, the relationships are complex and change with age, body composition, and fitness. Errors arise when the investigator chooses a mathematical function that does not accurately describe the nonlinear relationships of O₂ to HR and PA.

Several models have been used to provide HR-based predictions of O₂. Linear equations were used in the first attempts to predict O₂ from HR (2). A model with two linear segments that intersect at an inactive-active (FLEX) HR threshold has proven the most popular (1, 4, 14). Several continuous nonlinear equations have also been proposed, including cubic, sigmoid, and logistic functions (2, 13).

This report addresses two questions. Does a 24-h calibration period of O₂ and carbon dioxide production (CO₂) to HR and PA improve predictions of subsequent 24-h metabolic rates compared with previous efforts? Do nonlinear and discontinuous functions provide better models of the O₂-to-HR and -PA relationships than a linear function of HR?

Three approaches to modeling the relationships of O₂ or CO₂ to HR and PA were evaluated. Method I modeled the relationships with a linear equation and five nonlinear equations based on HR alone. Method II combined PA with HR as independent variables in six nonlinear functions. Method III used a threshold level of PA to assign HR to separate active and inactive O₂-to-HR functions.

METHODS

The study required 5 days of 24-h HR and PA recordings within a 1-wk period. On days 1 and 5, the subjects were confined to room calorimeters in the Metabolic Research Unit of the Children's Nutrition Research Center. Days 2-4 were free living, including normal work or school with no restrictions on activity. Written informed consent was obtained under a protocol approved by the Baylor College of Medicine Review Board for Human Subject Research.

Subjects

Weight (491KL, Healthometer, Bridgeview, IL) and body composition (HA-2, EM-Scan, Springfield, IL) (15) were measured on the morning the subjects left the calorimeter after a 12-h fast. Peak O₂ (O_{2 peak}) was estimated in the calorimeter from HR and O₂ during moderate stationary cycle exercise with the equation O_{2 peak} = O₂(220  age  b)/ (HR  b), where b = 73 for the women and 63 for the men (10).

HR Recording

Activity Recording

Calorimeter Procedures

Calorimeter tests began at 0800. Meals were served at 0830, 1200, and 1700, and a snack was served at 1800. The meals provided 12.1 kJ for the men and 9.2 kJ for the women as 49% carbohydrate, 32% fat, and 19% protein. The subjects were allowed only water after 1900.

Day 1 calibration of HR and PA to metabolic rate included two sessions of compulsory activities. The morning session, which began at 0900, consisted of 30 min of supine rest, 20 min of seated rest, 5 min of seated timed respiration, 15 min of standing, and two levels of cycle exercise for 15 min at workloads intended to produce 50 and 75% of the estimated peak HR. The afternoon activities were 15-min periods of seated writing, walking around the room, and three levels of treadmill exercise at 1 m/s and 50 and 75% of peak HR, beginning at 1300. A 15-min step test was added to the afternoon exercise routine beginning with subject 4. Exercise was halted if O₂ exceeded 80% of O_{2 peak} or if the HR exceeded 180 beats/min for >2 min. The compulsory activities totaled ~4 h. For the rest of the day, the subjects were allowed free choice of activities. The subjects were not allowed to nap but chose the time they went to sleep at night. They were awakened by 0710 and remained supine for 40 min to estimate basal metabolic rates (BMRs).

On day 5, the subjects chose the type of exercise (cycle, treadmill, aerobics, etc.) and duration (at least 20 min). A minimum workload or speed was assigned to approximate moderately heavy exercise. The subjects were allowed to exceed the assigned minimums.

Free-Living Procedure

Prediction Models of O₂ and CO₂ from HR and PA

Method I: HR Regression

(1)

(2)

(3)

(4)

(5)

(6)

Method II: HR and Activity

1

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Method III: Awake Separated Into Active and Inactive Periods

(14)

O₂ and CO₂ for each minute of days 2-5 were estimated from inactive HR unless both PA and HR exceeded fixed thresholds (Eq. 14 for O₂). In Eq. 14, and denote the threshold levels for that individual. The PA threshold was calculated on day 1 as the median PA from either walking or the slowest treadmill exercise (whichever was lower). The HR threshold was the intersection of the inactive and active equations for O₂. To be classified as active, each minute required a HR above the threshold and PA had to exceed the threshold during the current minute or either of the 2 previous min in Eq. 14].

Statistical Analysis

PA* was calculated by numeric iteration with a conjugate search at a precision of 0.01 and tolerance of 5% (Excel 4.0, Microsoft, Redmond, WA). Nonlinear regression of O₂ and CO₂ on HR (Eqs. 2-6) was performed by singular-value decomposition (TableCurve 2D, Jandel Scientific, San Rafael, CA). Multiple nonlinear regression of HR and PA (Eqs. 8 and 13) used Gaussian elimination with a convergence precision of 6 and a linear significance of 0.1% (TableCurve 3D, Jandel). Regression equations were evaluated primarily by a modified F-statistic (calculated by TableCurve) that provided better discrimination than R ² because it imposed a cost on increasing model complexity (order).

RESULTS

The men were older (32 ± 5 vs. 28 ± 8 yr) and heavier (74 ± 7 vs. 59 ± 7 kg) and had a higher estimated O_{2 peak} (43 ± 10 vs. 37 ± 8 ml ^· kg^{1 ·} min¹) but a similar fat mass (14 ± 5 vs. 15 ± 2 kg) compared with the women. All 20 subjects completed the calorimeter portion (days 1 and 5) of the study. Mean record durations for HR, PA and O₂ on day 1 were 1,440 ± 0, 1,400 ± 167, and 1,440 ± 0 min, respectively. Missing PA data for one subject on day 1 occurred during sleep and did not affect the analysis. Excluding this subject, the average PA record was 1,437 ± 7 min. PA data from day 5 for two subjects could not be analyzed.

There was no significant change in the subjects' weight (P = 0.2) from day 1 to day 5. Subjects slept less (436 ± 78 min; 30.3%) on day 1 than on day 5 (489 ± 66 min; 33.9%; paired difference P = 0.02). Other differences between day 1 and day 5 measurements are summarized in Table 1. Mean ratios of 24-h O₂ to basal O₂ were 1.68 ± 0.15 (range 1.33-1.96) on day 1 and 1.69 ± 0.17 (range 1.36-1.95) on day 5.

Table 1. Mean O2, CO2, and HR for 24 h, awake, 30-min BMR and nighttime sleep in a room calorimeter and free living Calorimeter Free Living Day 1  Day 5  Day 2  Day 3  Day 4  Days 2-4 24 h   EE, kJ/min 7.38 ± 1.44  7.37 ± 1.43  7.7 ± 2  8.1 ± 1.4  7.1 ± 1.4  7.8 ± 1.6    O2, l/min 0.36 ± 0.07  0.36 ± 0.07  0.38 ± 0.1  0.4 ± 0.07  0.35 ± 0.07  0.38 ± 0.08    CO2, l/min 0.31 ± 0.06  0.31 ± 0.06  0.32 ± 0.08  0.33 ± 0.06  0.29 ± 0.06  0.32 ± 0.07    HR, beats/min 73 ± 8.5  70 ± 9.4* 76 ± 9  73 ± 8  75 ± 10  75 ± 9  Awake   EE, kJ/min 9.14 ± 1.85  9.2 ± 1.83  9.2 ± 2.7  9 ± 2.4  8.6 ± 1.8  9 ± 2.3    O2, l/min 0.44 ± 0.09  0.45 ± 0.09  0.45 ± 0.13  0.44 ± 0.12  0.42 ± 0.09  0.44 ± 0.11    CO2, l/min 0.38 ± 0.08  0.39 ± 0.08  0.39 ± 0.11  0.38 ± 0.1  0.36 ± 0.07  0.37 ± 0.1    HR, beats/min 82 ± 9  78 ± 10* 83 ± 10  81 ± 10  83 ± 10  82 ± 10    Duration, min 898 ± 53  913 ± 67  BMR   EE, kJ/min 4.29 ± 0.54  4.23 ± 0.58    O2, l/min 0.21 ± 0.03  0.21 ± 0.03    CO2, l/min 0.17 ± 0.02  0.15 ± 0.02    HR, beats/min 61 ± 11  58 ± 10* Sleep   EE, kJ/min 4.23 ± 0.65  4.21 ± 0.6  4.3 ± 0.6  4.4 ± 0.6  4.3 ± 0.6  4.3 ± 0.6    O2, l/min 0.21 ± 0.03  0.21 ± 0.03  0.21 ± 0.03  0.22 ± 0.03  0.21 ± 0.03  0.21 ± 0.03    CO2, l/min 0.17 ± 0.03  0.17 ± 0.02  0.17 ± 0.03  0.17 ± 0.03  0.17 ± 0.02  0.17 ± 0.02    HR, beats/min 57 ± 9.4  55 ± 10* 59 ± 9  57 ± 9  59 ± 10  58 ± 9    Duration, min 436 ± 78  489 ± 66* Values are means ± SD. O2, O2 consumption; CO2, CO2 production; HR, heart rate; BMR, basal metabolic rate; EE, energy expenditure. Free-living EE, O2, and CO2 were calculated from measured HR and physical activity by method III (see text). EE was calcualted from O2 and CO2 by nonprotein equation of de V Weir (3). * Significant difference between calorimeter days, P < 0.05.

Data were analyzed for 55 of the 60 free-living days planned for the study. At least two free-living records were obtained for each subject. Daily free-living records of HR and PA in each 24-h period averaged 1,337 ± 192 (93%) and 1,389 ± 140 min (96%), respectively. Most often, the subjects experienced interrupted HR recording during sleep on the first night. Further instructions to the subject reduced data loss on subsequent nights. Data lost during sleep had little effect on subsequent analyses. Total awake and sleep records averaged 950 ± 104 and 448 ± 129 min, respectively.

Metabolic Rate Measurements

Predictions of O₂ and CO₂ from HR by Eq. 3 were compared with measured O₂ and CO₂ on day 5. Mean errors for sleep O₂ and CO₂ were 0.2 ± 0.8 and 0.4 ± 0.6%, respectively. Errors were higher awake (Table 2) and over 24 h (5.5 ± 5.6 and 7.5 ± 6.7%, respectively) for both O₂ and CO₂. The range of errors between 24-h predictions and measurements of O₂ and CO₂ was 16.8 to 4.5 and 21 to 3.5%, respectively. Results from Eq. 6 (sigmoid) were similar. There was no significant difference between Eq. 3 and Eq. 6 prediction residuals during the awake period for O₂ (P = 0.37) or CO₂ (P = 0.15; Fig. 2A). The nonlinear sigmoid regression algorithm failed to converge to solutions for one subject on O₂ and for another on CO₂.

Table 2. Awake O2 and CO2 estimate errors relative to measurements in a room calorimeter HR (method I, Eq. 3) HR and Activity (method II, Eq. 11) HR Functions Assigned by Activity (method III, Eq. 14)  O2  CO2  O2  CO2  O2  CO2 Mean ± SD  5.9 ± 8.3   7.8 ± 10.7   4.4 ± 6.6   6.9 ± 6.5   3.4 ± 4.5   4.6 ± 3.6  Range  21.9 to 9.2  23.7 to 22.6  13.8 to 10.6  15.3 to 4.7  10.6 to 4.6  11.7 to 3.6 Values are in percent. See text of description of methods I, II, and III and Eqs. 3, 11, and 14.

We concentrated further analysis on awake predictions for all three methods because sleep prediction errors were sufficiently low in method I. For comparison to HR predictions, we calculated linear regressions of day 1 O₂ during supine rest and BMR to day 1 sleep and compared these with day 5 sleep. Mean prediction errors were 1.0 ± 8.1 and 8.8 ± 5.1% for supine O₂ and CO₂, respectively, and 2.3 ± 6.5 and 0.4 ± 6.7% for BMR O₂ and CO₂, respectively. Of the four predictions, only the supine CO₂ prediction of sleep was significantly different from that measured (P < 0.001).

Method II. HR and PA* were combined in regressions against O₂ and CO₂ with Eqs. 8-13. Regressions were calculated for awake periods because predictions of sleep were satisfactory by method I. Equation 11 was judged the best based on the F-statistic and R ² (0.92).

O₂ and CO₂ calculated from Eq. 11 were compared with measured O₂ and CO₂ on day 5 for 18 subjects. While the subjects were awake, the respective differences in mean O₂ and CO₂ averaged 4.4 ± 6.6 and 6.0 ± 6.5%, respectively (Table 2). The range of errors was nearly as large as with method I: O₂, 13.4 to 10.6% and CO₂, 15.3 to 4.7%. Mean O₂ values predicted from method II were 0.014 l/min greater than those from method I (P = 0.054).

Method III. Eighteen subjects averaged 897 ± 53 min awake on day 5, with 115 ± 37 min classified as active by the HR and PA thresholds described in Method III: Awake Separated Into Active and Inactive Periods. Prediction equations for active awake were obtained in the calorimeter from walking, cycling, treadmill exercise, and the step test. Steady-state exercise averaged 11.0 ± 1.9 min (Fig. 3).

Errors of estimated O₂ and CO₂ during awake periods were 3.4 ± 4.5 and 4.6 ± 3.6%, respectively (Table 2). During active periods, errors in estimated O₂ and CO₂ were 2.2 ± 8.1 and 2.3 ± 8.4%, respectively. One subject had substantially higher predicted O₂ and CO₂ (26 and 28%, respectively) during a single exercise session. Means ± SD for active periods recalculated with this subject excluded were lower (O₂, 3.9 ± 4.1%; CO₂, 4.1 ± 3.6%). Inactive mean estimations contributed the most error (O₂, 4.3 ± 5.5%; CO₂, 6.3 ± 5.4%). We did not find any significant relationships between awake O₂ or CO₂ prediction errors and subject fitness (estimated O_{2 peak}), age, weight (Fig. 2B), lean tissue mass, or 24-h EE/BMR (from day 1 data). Twenty-four-hour measured O₂ and O₂ predicted by method III are compared for one subject in Fig. 4.

Free-living measurements. Sleep estimates of O₂ and CO₂ were obtained with HR³ (Eq. 3) developed in method I, and predictions for the awake periods used method III (Eq. 14). None of the differences between days 2 and 3 (weekdays) and day 4 (weekend) were significant. The mean 24-h O₂ and CO₂ values on all 3 free-living days were 0.01 ± 0.06 and 0.008 ± 0.06 l/min greater, respectively, than on day 1 in the calorimeter (Table 1).

DISCUSSION

Method III achieved improved precision in O₂ estimates by the classification of awake HR to active and inactive functions by using PA. The active HR function was calculated as a linear function with measurements made during exercise (Eq. 14). Inactive periods were estimated with a cubed function of HR during periods with low PA levels. For much of the day, our subjects confined themselves to relatively sedentary activities characterized by a limited range of O₂ over a moderate range of HR. Most individuals displayed a range of HR with both higher and lower O₂ that was matched by the two functions in the model (Fig. 1B).

Dauncey and James (2) were the first to evaluate HR monitoring against 24-h measurements of O₂ in a room calorimeter. They tested several models for the O₂-HR relationship, including linear, logistic, and power functions, and recommended that future efforts explore nonlinear equations to describe O₂ to HR as continuous functions. They also looked forward to the development of room calorimeters capable of faster measurements.

We found the 24-h calibration period to be valuable in two respects. First, it correctly weighted the predominant sedentary periods in the regression algorithms. Second, it allowed us to define sections of lower O₂ and moderate HR that did not correspond to any compulsory activities or exercise. These O₂-HR combinations appeared throughout the day but were most prolonged during recovery from exercise.

Large calorimeters can now be operated with response times of <5 min (9) and have been used by others for calibration of EE-HR relationships (8). We scheduled 15 min for compulsory activities to allow time for the calorimeter and subject to reach a steady state. An average of 11.0 ± 1.9 min of steady-state data were analyzed from each 15-min interval (Fig. 3).

Mean 24-h O₂ on day 5 was 0.36 l/min or 1.7 times the measured basal O₂ . Twenty-four-hour O₂ and CO₂ in the calorimeter (days 1 and 5) averaged 5.4 and 3.9%, respectively, lower than the mean free-living O₂ and CO₂ (days 2-4). Mean indexes of physical activity (PAI = 24-h EE/BMR) in the calorimeter (1.68 ± 0.15, range 1.33-1.96) were similar to free-living days (1.73 ± 0.31, range 1.3-2.7), although the free-living range was greater. The free-living PAI for the men (1.78 ± 0.26) was not significantly higher than that for the women (1.68 ± 0.36; P = 0.4). The PAI values for both sexes were within the ranges reported by others (1, 8, 12). Schulz and Schoeller (12) reported a higher PAI for a large number of men (1.84 ± 0.31) and women (1.7 ± 0.23) and noted that these values were higher than the recommended dietary allowances.

R ² values of all HR functions to O₂ and CO₂ during sleep were low (0.15-0.21). PA signals were minimal because slight motions fell below the threshold of the PA sensor. Yet, predictions of day 5 O₂ and CO₂ from HR³ were quite good, with mean errors of 0.2 ± 0.8 and 0.4 ± 0.6%, respectively. O₂ during sleep was also adequately predicted by regressions across subjects with O₂ measured during supine rest (mean error 1.0 ± 8.1%) or basal conditions (2.3 ± 6.5%). Mean O₂ throughout sleep was approximately the same as the BMR. Prediction of CO₂ from supine rest was less accurate because subjects had just eaten.

Free-living sleep duration (450 ± 89 min) was similar to sleep duration in the calorimeter, although the range of free-living sleep times was large: 0-607 min. Periods of awake, sleep, and activity were easily identified from the HR and PA records without the need of a diary or notes from the subject. We suggest that HR or PA be recorded for 24 h even when sleep is predicted by standardized equations, especially in subjects whose sleep may be frequently interrupted or truncated or who cannot be expected to provide a reliable record.

When HR alone was used to estimate O₂ and CO₂ (method I), the nonlinear equations provided significantly better fits to both 24-h and awake data. HR³ (Eq. 3) was the most efficient of the nonlinear functions as measured by the F-statistic. Equation 6 (sigmoid) had the highest mean R ². We preferred the HR³ equation in further computations because it was efficient and simple.

Computation of combined nonlinear regressions of PA and HR (method II) required much more effort than any of the models used in method I. F-statistics were also calculated for this method, and Eq. 10 was the most efficient. Mean day 5 prediction errors for Eq. 10 were similar to Eq. 3, whereas the range of errors decreased.

Method III was developed from the FLEX technique and the recommendations of Haskell et al. (5), who demonstrated improved correlations with multiple linear regressions of exercise O₂ on combined HR and PA compared with regression based on HR or PA alone. The authors proposed that PA be used to assign 1-min HR values to separate active and "nonactive" equations. We employed separate functions to model active and inactive periods while the subjects were awake.

Because the model in method III was discontinuous, we were concerned that abrupt and nonphysiological changes in estimated O₂ might be produced when the HR shifted from one function to the other. We controlled shifts between the two functions by adding hysteresis to the PA criteria. Two minutes of PA above the PA threshold were required from among the current minute and 2 preceding min to allow a shift from the inactive to the active function. We also identified the threshold HR as the intersection of the inactive and active functions. This value was set as the minimum HR allowed for the active function. Either HR or PA was required to be below their assigned thresholds to shift from the active function to inactive function.

The range of errors in predicting day 5 awake O₂ by method III was from 10.6 to 4.6% (16% overall). Only a few recent studies have compared HR techniques with room calorimetry, and all used the FLEX method. Reported overall error ranges were 20 and 35% for adult studies (1, 14) and only 10% in children (4). In addition, 24-h HR monitoring has been validated against the doubly labeled water method, with error ranges of 40% or more (9, 13, 7). Doubly labeled water has performed well in comparisons with calorimetry. In a published summary of several validation studies, labeled-water tests in adults had a weighted mean error < 1% and a mean SD of 7% (12).

In method III, an average of 251 ± 196 min were above the threshold HR but below the PA threshold and were thus classified as inactive. Reclassifying all these points as active, as would occur with a method employing HR alone, would have increased the awake mean O₂ and CO₂ to produce estimation errors of 5.3 ± 8.6 and 3.5 ± 7.4%, respectively. One subject's O₂ and CO₂ were clearly overestimated by this simulated adjustment (errors: O₂, 30%; CO₂, 24%). With this subject removed, the mean estimation errors were still elevated (O₂, 4 ± 6.6%; CO₂, 2.4 ± 5.9%). The threshold HR was similar to mean 24-h HR for most subjects and lower than during any exercise.

Errors in HR estimation of metabolic gas exchange may arise from inadequate calibration data, poor modeling of the O₂ and CO₂ relationships to HR, and uncompensated effects on HR, stroke volume, and oxygen extraction. Day-to-day intraindividual variations in the O₂-HR characteristic may exceed 10% (7). Over longer periods, the O₂-HR relationship is altered by growth, aging, and changes in fitness or body composition. Our results confirmed the importance of calibrating O₂ to HR for each individual. Frequent recalibration may be needed in studies that involve physical training or changes in weight.

None of the differences between individuals in weight, lean tissue mass, O_{2 peak}, age, or PAI helped to explain errors in prediction of O₂ and CO₂. The effect of weight on methods I and III is shown in Fig. 2. Lean tissue mass is correlated with weight and gives similar results. All the subjects were healthy sedentary to moderately active adults from 20 to 40 yr so that age was unlikely to be related to growth, overall health, or activity. The lack of a relationship between O_{2 peak} or PAI and measurement error suggests that the 24-h period of calibration compensated for the variation in levels of habitual activity.

Mean day 5 prediction errors for O₂ and CO₂ were lower for method III than for methods I and II. One explanation might be that HR was elevated because of anxiety or some other cause during onset and recovery from imposed exercises on day 1. Method III eliminated data during recovery from bicycle and treadmill exercise. HR recorded during the steady-state portion of exercise may have been more closely driven by metabolic demand. Li et al. (8) observed a similar phenomenon that led to their recommendation that calibration be performed with exercises that match a subject's normal activities.

Predicted mean O₂ and CO₂ on day 5 for all three methods were significantly lower than the mean O₂ and CO₂ as measured by the calorimeters. Mean 24-h and awake HR and PA values were significantly lower on day 5 than on day 1 (Table 1). However, there was no significant difference in EE, O₂, CO₂, or the ratio of 24-h O₂ to BMR O₂ between day 1 and day 5. The subjects slept longer on day 5 than on day 1, but awake HR was lower, as well as 24-h HR. Confinement to the calorimeter, exposure to an unusual workout, and more intense scrutiny during calibration may have caused anxiety and elevated HR on day 1, leading to an underestimation on day 5.

Li et al. (7) proposed many other sources of short-term intraindividual differences in O₂-to-HR relationships, including infection, sleep (quality and duration), temperature, emotion, alcohol/caffeine/cigarette consumption, or meal timing. Most of these factors were eliminated in this study. The calorimeter temperature differed <1°C between day 1 and day 5. The subjects were not permitted alcohol or cigarettes, and caffeine consumption was minimal. Meals and meal times were identical. None of the subjects had abnormal body temperatures when they entered the calorimeters, and no one complained of illness at any time during the study.

All three methods underestimated CO₂ by a greater percentage than for O₂ but with lower SDs. In addition to the HR and exercise effects discussed above, CO₂ may have differed on day 5 relative to day 1 because of altered rates of substrate oxidation. The mean 24-h respiratory quotient (CO₂/O₂) was slightly higher on day 5 (0.86) than on day 1 (0.85; P = 0.016). There were no significant differences in respiratory quotient during sleep or BMR measurement. Relative differences in O₂ and CO₂ may have occurred because subjects altered their substrate intakes by choosing to eat different portions of their meals.

Most equations to estimate EE from O₂ and CO₂ can be reduced to linear combinations of the two parameters, with O₂ making a much larger contribution than CO₂. For example, in the nonprotein equation of de V Weir (3), the contribution of O₂ is 3.5 times greater than that of CO₂. Measurement errors from O₂ and CO₂ contribute to errors in EE in the same proportion. Thus estimates of EE are unlikely to be improved by measurement of CO₂ from HR. However, if CO₂ is of interest separately, it might be beneficial to supplement the HR recording with a dietary record.

Conclusions

ACKNOWLEDGEMENTS

The authors thank Anne Adolph and Maurice Puyau for assistance in programming and data analysis. We are grateful to Dr. Gerald Spurr for his critical review of the manuscript.

FOOTNOTES

Address for reprint requests: N. F. Butte, Baylor College of Medicine, 1100 Bates St., Houston, TX 77030-2600 (E-mail: nbutte{at}bcm.tmc.edu).

Received 1 August 1995; accepted in final form 6 May 1996.

REFERENCES