Ambulatory Minute Ventilation Estimated From Heart Rate Monitoring in Girls

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1 Pediatric Exercise Science, , Ambulatory Minute Ventilation Estimated From Heart Rate Monitoring in Girls Douglas A. Haines and Mark E. Raizenne Models to indirectly estimate minute ventilation (VE) from heart rate (HR) monitored during normal activity were developed. VE-to-HR relationships were established from VE and HR measured in a graded cycle ergometer test performed by 99 girls, 7-14 years.of age. The regression In VE = a + (b x HR) was a better predictor of V~,when individually determined, than were generalized prediction equations. VE, estimated by applying individual VE-to-HR regressions to HR monitored over 10 daytime hours, ranged between 11.5 and 14.5 e am in-'. This is a practical method of estimating VE, but further validation of the relationships with HR under various modes of exercise are necessary to improve the prediction in everyday settings. Minute ventilation (ve), expressed as the volume of air exhaled per minute (Lmmin-I), is the common measure of ventilation rate. Few data are available regarding the ventilation rates of either children or adults as they perform their daily activities because effective techniques for directly measuring VE in nonlaboratory conditions are not presently available. However, it has been reported that heart rate (HR) may be a practical method of indirectly estimating VE because both are directly related to the metabolic demand for energy, increase markedly. with exercise, and are correlated with each other (14). Relationships between VE and HR can be established by standardized testing (12, 14). During exercise, VE increases linearly with exercise intensity, as reflected by HR, until about 60% of maximal 0, uptake, beyond which VE rises in a more accelerated manner (10). This point corresponds to HR in the range of beats per minute (bpm) in young adults. Because of this nonlinearity at higher exercise intensity, the natural logarithm (In) of VE correlates slightly better with HR than VE itself (14). Heart rate can be accurately monitored in community or occupational settings using portable recording devices (9, 12, 15). By applying v~-to-hr regressions determined in prior clinical testing to monitored HR, VE may then be indirectly estimated in everyday settings. This study developed VE-to-HR models from standardized exercise test data and examined the use of these models to indirectly estimate VE from HR monitored in children attending a summer camp. Douglas A. Haines and Mark E. Raizenne are with the Environmental Health Directorate, Department of National Health and Welfare Canada, Ottawa, Ontario KIA OL2.

2 Sample Methods Heart Rate Monitoring in Girls A residential GirlGuide summer camp located on the north shore of Lake Erie, 50 km southwest of Hamilton, Ontario, was the site for this study. The camp consisted of three consecutive 2-week sessions held in the summer of As part of this field study used to assess the relationship between respiratory health and environmental factors, exercise testing and heart rate monitoring were performed on 112 healthy girls, 7-14 years of age. Prior to camp, parents completed a questionnaire on their child's respiratory health history and signed an informed consent for her to perform the health tests. Health testing and air pollution monitoring methods have been described elsewhere (1 1). Exercise Testing The cycle ergometer test consisted of pedaling at 50 rpm for 4 minutes at each of three progressively graded workloads. The desired heart rate responses at the end of each workload were , , and bpm, respectively. Modifications of the Monark bicycle ergometer enabled a combination of greater range of seat height, shorter pedal arms, and lighter pendulum (180 grams), giving lighter resistance and finer workload gradations, which permitted testing of children as young as 7 years of age (6). Ergometer resistance scale settings were calibrated in kilograms and the workrate (WR) for each workload was expressed in kgm min- '. The ergometer resistance was set, for each workload, according to age and weight as outlined in Table 1. However, depending on deviations from the desired heart rate responses for the first and second workloads, the scale setting for the subsequent workload was adjusted up or down from those suggested in Table 1. The physical work capacity at a heart rate of 170 bpm (PWC170), which represents an index of physical fitness, was individually determined by statistical regression of exercise WR against HR. Heart rate and VE were measured at rest, immediately prior to exercise. During exercise, HR was recorded at the end of the 4th minute and ventilation was measured over the 4th minute of each workload. Heart rate was measured using the PE 3000 Sportester heart rate monitor while VE was measured using the portable Bears Vm-90 Ventilation Monitor, a pneumotachometer that was fit with a mouthpiece into which the subjects breathed while wearing a noseclip. Table 1 Recommended Ergometer Resistance Scale Settings by Age and Weight Age (~rs) Weight (kg) Scale setting for the three workloads (kg)

3 144 - Haines and Raizenne Heart Rate Monitoring On each of 24 days, 4 or 5 randomly chosen children, to a total of 112 girls, wore portable heart rate monitors (PE 3000 Sportester) that recorded heart rate each minute for up to 12 hours. The receivers were sealed so that the children could participate in all camp activities including swimming. The children being monitored were instructed not to get closer than 5 meters from each other in order to avoid signal interference between the monitoring systems. Statistical Analysis Descriptive statistics were computed which include means and standard deviations (SD) of the subject physical characteristics as. well as the exercise test parameters of each workload performed. Individual VE-to-HR regression functions were determined by In VE~~ = ai + (bihrij) (1) where ai and bi represent the intercept and slope for the ith child, In VE.. is the natural logarithm of minute ventilation for the ith child recorded at the jtpworkload, and the corresponding-heart rate measurement is described by HRij. Common regression models for VE (i.e., ai = a and bi = b for all i) were determined by forward stepwise regressions (SAS Stepwise Procedure, Options = MAXR) (13). Included in the analysis for In VE were HR, age, height, wei ht, body surface area (BSA) where BSA = weight0425 height0,72f (4), and PWC 170lKG. In order to examine differences befween the minute ventilations estimated from individual {VE-ind) and common (VE-com) regression functions described above, VE-ind/V~-corn ratios at heart rates of 80, 110, 140, and 170 bpm were calculated for each subject. Means and percentile distributions of these ratios at each heart rate were generated. For every subject monitored, average HR over each 10-min time interval between 09:OO and 18:59 hours was calculated. VE was then individually estimated by using each subject's 10lmin HR averages and her own VE-to-HR regression function. Heart rate and VE profiles over 10 hours were then plotted for the overall sample. Subject Characteristics Results From the respiratory health history questionnaire completed by the parents of 112 girls, 5 girls whose parents responded "yes" to both "Has a doctor ever said that this child has asthma?" and "Does she still have asthma?" were considered asthmatics. These 5 girls as well as 3 girls with incomplete exercise test data and 5 with incomplete heart rate monitoring data were excluded from analysis, leaving an overall sample of 99 girls. Means and standard deviations for the physical characteristics of the subjects are presented in Table 2. Included in this table are the physical work capacity values derived from the exercise test. Exercise Minute Ventilation/Heart Rate Relationships The exercise test results presented in Table 3 show that HR increased from an average of 93 bpm at rest to 169 bpm at the end of the third workload. The heart

4 Heart Rate Monitoring in Girls Table 2 Subject Characteristics (N = 99) Mean SD Age (yrs) Height (cm) Weight &kg) BSA (m ) PWC170 (kgm - min-') PWCI 70lKG (kgm - kg-'. min-l) Table 3 Exercise Test Work Rate, Heart Rate, and Minute Ventilation at Each Workload WR (kgm. rnin-l) HR (b~m) VE (L - min-l) Workload Mean SD Mean SD Mean SD Preexercise rates at each workload were within the targeted ranges. VE also increased with each workload from 8.9 emi in-' at rest to 35.7 ern in-' at the third workload. The coefficient of determination (I?') for VE-to-HR regressions derived for each individual was equal to or exceeded 0.90 in 85 of the 99 subjects; the lowest RZ was Including other variables did not improve the models and were not considered further. The best one, two, and three variable common regression models were as follows: In VE = HR BSA R2 =.80 (3) In VE = HR weight PWC170lKG R2 =.83 (4) Height but not age was a predictor in the models. However, their inclusion only increased R2 to.84 and were not considered further. \j~-ind/ir~-com ratios, expressed as percents, were calculated using individual regression functions (Equation 1) as the numerator and each of the three common regression functions (Equations 2,3, and 4) as the denominator. Means

5 146 - Haines and Raizenne Table 4 Individual to Common Minute Ventilation Ratios, Expressed as Percents, at Selected Heart Rates Percentiles HR Ratio* (bprn) Mean SD P95 P90 P75 P50 P25 PI0 PO O *VE-ind/?i~-corn where VE-corn was derived using the following: 1 : In VE = HR : In VE = HR BSA : In VE = HR weight PWC170lKG and percentile distributions of these ratios at each heart rate are presented in Table 4. Differences between the median and the 25th and 75th percentiles ranged from 16 to 30 % for ratios derived using Equation 2 as the denominator, and from 9 to 25 % for ratios derived using both Equations 3 and 4. Heart Rate Monitoring Heart rate monitoring between 09:OO and 1859 hours resulted in 98% data capture with negligible outlier values. Mean 10-min HR averages ranged between 102 and 115 bpm (Figure 1). The SD about this curve did not vary substantially over time. VE ranged between and 14.5 erni in-' (Figure 2), and the coefficient of varjation averaged 27 % (range: %) for daytime VE derived from individual VE-to-HR regressions. Discussion Heart rate alone generally explained more than 90% of the variation in individual VE regressions derived from exercise test data (Equation 1). This is similar to results reported by Shamoo et al. (14), who also noted that regression coefficients differed between treadmill and outdoor exercise modes in the same adult subjects. Anderson and Godfrey (1) also reported that heart rates were generally lower on the treadmil! than those expected for ergometer testing in children. Therefore, comparing VE and HR results obtained during cycle ergometer, treadmill and

6 Heart Rate Monitoring in Girls l l l l l ; l l l l l ; l ' l ' l ; l ' l ' l ; ' l l ' l ~ ' l l l l l " I 1 I ; I I l I I ~ I I I I I ; 1 I l I 1 9:00 10:OO 11:OO 12:OO 13:OO 14:OO 15:OO l6:oo 17:OO 18:OO 19:OO Time (houra) Figure 1 - Overall average heart rate of summer camp subjects t t1ti11'11111t :OO 1O:OO 11:OO 12:OO 13:OO 14:OO 15:OO l0:oo 17:OO 18:OO 1O:OO Time (hours) F i e 2 - Overall average $E estimated from heart rate monitored in summer camp subjects.

7 148 - Haines and Raizenne other tests approximating conditions encountered in the field may be necessary to validate VE-to-HR relationships and to improve the VE prediction from HR monitored during no~mal daily activities. The common VE function (Equation 2) is similar to the function reported by Colucci (In VE = 0.019HR ) which was derived from existing literature data for 12-year-old children (3). The present model only explained 73 % of the variation in VE between subjects. The addition of BSA (Equation 3), or weight and PWC170/KG (Equation 4) improved the models to 80 and 83%, respectively. The mean VE-~~~IVE-CO~ ratios calculated in this study suggest high concordance between VE estimated from individual and common functions. ow ever, the standard deviations were large (17-36%) and common functions estimated VE within 9-30% of individual functions in the percentile ranges of subjects. This variability suggests that better predictions may be obtained from individually determined VE-to-HR relationships. Given larger and more heterogeneous samples, it may be possible to quantify consistent differences in VE-to-HR relationships related to age, sex, size, and fitness level, and thus to develop useful generalized prediction equations. The profile of heart rate obtained in this study is representative of overall light to moderate exertion in children attending a nonathletic summer camp. Over long periods, physical exertion is the major factor influencing heart rate, but other factors such as psychological stress could also raise heart rate (8). When estimating VE from monitored HR, the assumption was made that VE-to-HR relationships, which were derived from exercise test data under steady-state conditions, also applied to field conditions. However, everyday activity patterns are usually intermittent, non-steady-state, and have changing exertion (2). It has been reported that compared with a non-steady-state progressive exercise test, HR and VE can be 5-10% higher in a steady-state test at the same workloads in. children (5, 7). Therefore, estimating VE from heart rate monitoring, using VE-to-HR relationships developed from steady-state exercise tests, may result in periodic overestimation of ventilation. Combining HR monitoring with diary records that characterize the physical activities performed may provide additional information that could improve the prediction of VE in everyday settings (14). In summary, heart rate monitoring appears to be a practical method of estimating. ventilation indirectly. However, the best predictions are obtained when the VE-to-HR relationships are determined individually. Further research examining the effects of age, sex, size, and fitness levels in larger heterogeneous samples are necessary to develop useful generalized prediction equations. Additional validation of the VE-to-HR relationships under various modes of exercise and physical activity are necessary to improve the prediction of VE in everyday conditions. References 1. Anderson, S.D., and S. Godfrey. Cardio-respiratory response to treadmill exercise in normal children. Clin. Sci. 40: , Astrand, P.O., and K. Rodahl. Textbook of Work Physiology (3rd ed.). New York: McGraw Hill, Colucci, A.V. Comparison of the doseleffect relationship between NO2 and other air

8 Heart Rate Monitoring in Girls pollutants. In: Air Pollution by Nitrogen Oxides, T. Schneider and L. Grant (Eds.). Amsterdam: Elsevier Scientific, 1982, pp DuBois, D., and E.F. DuBois. The measurement of the surface area of man. Arch. Intern. Med , Gadhoke, S., and N.L. Jones. The responses to exercise in boys aged 9 to 15. Clin. Sci. 37: , Gauthier, R., D. Massicotte, R. Hermiston, and R. Macnab. The physical work capacity of Canadian children, aged 7 to 17, in A comparison with CAHPER (Nov.-Dec.): 4-9, Godfrey, S., C.T.M. Davies, E. Wozniak, and C.A. Barnes. Cardio-respiratory response to exercise in normal children. Clin. Sci. 40: , Jung, K., and J. Schulze. Sports-medical studies on parachute jumpers with particular reference to the behaviour of heart rate. Biotelem. Patient Monitg. 9: , Karvonen, J., J. Cwalbinska-Moneta, and S. Saynajakangas. Comparison of heart rates measured by ECG and microcomputer. Phys. Sportsmed. 12:65-69, Lamb, D.R. Physiology of Exercise (2nd ed.). New York: MacMillan, Raizenne, M.E., R.T. Burnett, B. Stern, C.A. Franklin, and J.D. Spengler. Acute lung function responses to ambient acid aerosol exposures in children. Environ. Health Persp. 79: , Raizenne, M.E., and J.D. Spengler. Dosimetric model of acute health effects of ozone and acid aerosols in children. In: Atmospheric Ozone Research and its Policy Implications, T. Schneider et al. (Eds.). Amsterdam: Elsevier Science, 1989, pp SAS Institute Inc. SAYSTAT Guide forpersona1 Computers. Version 6Edition. Cary, NC: Author, Shamoo, D.A., S.C. Trim, D.E. Little, W.S. Linn, and J.D. Hackney. Improved quantification of air pollution dose rates by improved estimation of ventilation rates. In: Proceedings of the First International Symposium on Total Exposure Assessment Methodology: A New Horizon (Las Vegas, NV, 1989). Pittsburgh, PA: Air and Waste Mgmt. Assoc., 1990, pp Treiber, F.A., L. Musante, S. Hartdagan, H. Davis, M. Levy, and W.B. Strong. Validation of a heart rate monitor with children in laboratory and field settings. Med. Sci. Sports Exerc. 21 : , Acknowledgment The authors would like to thank Dr. Richard Burnett for his statistical assistance and Dr. Judith Leech for her comments regarding this manuscript.

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