Heart Rate Monitoring

Introduction
Heart rate during exercise provides a measure of cardiac load, (Borresen and Lambert, 2007) and can be refined to assess anaerobic tolerance. Anaerobic tolerance (or threshold) is defined as the amount of exercise oxygen uptake above which aerobic metabolism is supplemented by anaerobic metabolism, (Weltman, 1995) and correlates well with performance in endurance sports. (Groslambert et al., 2004)
Although blood lactate measurement is a gold standard criterion (Nikooie et al., 2009), it involves multiple instances of blood sampling, making non-invasive alternatives such as ventilation and heart rate monitoring potentially more viable.

The heart rate deflection point, which coincides with the lactate threshold, could be potentially predictive of anaerobic threshold as explored in previous studies involving long distances. In fact, it has been proposed that anaerobic threshold occurs at 85% of maximum heart rate. There has also been significant correlation between deflection heart rate and heart rate at ventilatory threshold. However, it should be noted that the direction of heart rate deflection (upward or downward) has been a controversial issue with various theories proposed, such as decreased stoke volume, increased beta-receptor sensitivity, and increased myocardial wall thickness from training. Another theory also described decreased blood pH stimulating increased sympathetic discharge and corresponding decreased parasympathetic discharge. (Carey, 2008)
A heart rate monitoring and data collection system such as the Zephyr Bioharness could prove very useful in sports science research due to its portability, ease of use and accuracy. The Zephyr Bioharness can also be used in other real-life scenarios, such as in mine rescue operations, NASA zero gravity experiments, fire training and monitoring of extreme sporting activity. (Technologies, 2011) As such, we seek to assess the potential application of the Zephyr Bioharness in estimating anaerobic tolerance, and confirming it as a reliable instrument in on-site heart rate monitoring.
Methods
Test subjects
Eleven sports studies students from the University of Hertfordshire, aged 19-24, were involved in the study (height 178.1 ± 7.7cm, weight 71.5 ± 7.5kg) which took place at Sports Village De Havilland, Hertfordshire, UK. All subjects were in good health, with no known respiratory or cardiac disease, and were instructed to abstain from food 2 hours prior to the test. The subjects were also told to abstain from alcohol or caffeine 24 hours prior to the test to avoid confounding results of the physical tests. Ethical approval had been obtained before the study was carried out.
Equipment
The Zephyr Bioharness (Version 1, ISM; Maryland, USA) can potentially monitor vital signs remotely using Bluetooth technology. These include heart rate, ECG, respiration, skin temperature, posture and relative activity. For this study, only velocity and heart rate in comparison to varying levels of physical exertion were measured.
Test conditions
Each subject was tight-fitted with one Zephyr Bioharness (to the lower chest) and one polar heart rate monitor, and made to do a series of 20-metre walks, jogs and sprints in accordance to a bleep test. Water was applied to heart rate monitors to stimulate the chest strap electrodes which sense electrical stimulation from the heart. The multistage shuttle run was timed according to a played CD (Coachwise, UK), and was used in accordance with a hand held timer. Data in the form of real-time heart rate was gathered on a Toshiba Protege laptop
A 10-minute walking test was conducted to familiarise subjects to the testing environment. Subjects then completed a six-stage shuttle run test with speeds ranging from 8-11 km/hour, lasting for about 6-20 seconds. After 3 minutes of active recovery, the subjects completed the sprint test, with 6 sprints over a distance of 20 metres and 30 seconds of active recovery in between each sprint. Subjects were assessed after the entire test was completed, with the entire procedure conducted in the same order on both test days.
Statistical analysis
Heart rate beats with a difference of 20 beats/min between the two bioharnesses were removed from the raw data. Standard deviation was used to measure variability by measuring the distance between the individual score and the mean (Mean ± SD). Typical error was used to provide an average measurement of error for each individual’s results. Pearson’s data was used to provide correlative data significance to analyse if the Zephyr Bioharness was reliable through different stages of testing.
Results
A straight line was found to best describe the heart rate-work rate relationship as the log-linear method significantly overestimated anaerobic threshold. (Carey, 2008) Heart rate was also plotted against velocity as per the Conconi test, in an attempt to elucidate a heart rate deflection point. However, due to the small sample size and lack of controlled velocity per subject in the tests, no evident deflection point was noted although an upward trend of heart rate with increased physical velocity was observed.
Walking velocity (4-6 km/hr) showed a positive change in mean heart rate (increase in 0.29 beats/min) compared to the multi-stage shuttle run and sprint exercise, which showed a decrease in heart rate (-0.41 and -0.80 beats/min respectively). The Pearson correlation coefficient showed that walking and multi-stage shuttle run velocity had strong positive correlation to heart rate (0.99 and 0.94 respectively) unlike sprint velocity (0.22). However, there was significantly high typical error (TE) in the sprint portion of the study compared to the walk and multi-stage shuttle run portions (38.31 vs 1.52 and 4.81 respectively).
VelocityDescriptive Heart Rate DataReliability Heart Rate Data
Bio 8
Mean ± SD
(beats/min)Bio 2
Mean ± SD
(beats/min)Change in mean
(beats/min)TEPearson correlation coefficient
Walk 4 – 6 km/hr99.97±14.4999.68 ±14.660.29
1.52
0.99
MSSR 8-11 km/hr155.85±20.46156.26±19.85-0.414.810.94
Sprint >18 km/hr156.82±20.71157.62±20.29-0.8038.310.22
Discussion
A low typical error of 1.52 and 3.48 in the walking test and multi-stage shuttle run respectively demonstrate potential reliability of the heart rate data, unlike the high typical error of 38.31 in the sprint test where individual results showed significant variation. This makes deriving maximum heart rate, and therefore calculating anaerobic tolerance, difficult. There was a strong positive Pearson correlation of heart rates measured by both Bioharnesses (>0.85) with an estimated standard error of <- 7 beats/min.
An increase in resting heart rate was also seen with an increase in physical velocity, as participants segued from the slow walking test to the multi-stage shuttle run, to the sprint test. However, a deterioration of reliability was observed with increased testing intensity, as mentioned earlier and seen from the plateau in the Conconi graph (Figure 1). This makes the Bioharness valid only under physical activities of low velocity, such as in walking tests.
Bioharness technology has been previously suggested to be sensitive to intensity in other studies (Wolfe et al., 2004). This was attributed to vigorous movement during activities of higher velocity such as the multi-stage shuttle run and sprint tests in this study, making the Bioharness improperly secured to the test subject. If the Bioharness could be tightly secured throughout the entire range of velocities tested without compromising optimal performance of the test subjects, it could be a valid method to monitor heart rate.(Burke and Whelan, 1987)
There also exists a possibility of the Bioharness providing erroneous results in physical tests which involve a change in direction of movement. (Welk, 2002) Human error in taking timings of the three tests, variation in extent of movement among individual test subjects during tests of higher velocity, as well as improper strapping of the Bioharness, could also contribute to experimental error. (Brage et al., 2005)
It is important to note that peak performance of the physical tests is linked to subject motivation (Dickstein et al., 1990), which may therefore require a larger and more specific group of test participants. The small sample size of study also decreases reliability of the test, due to intra-group variation and possible confounding factors in subject non-compliance to alcohol, food and caffeine abstinence.
Further physiological testing involving more variables besides heart rate, could be incorporated into future tests, with a larger pool of test subjects and automated timers, thereby decreasing the impact of human error. Measurement of individual organ performance, the heart for example, may not be reflective of resultant cardiopulmonary and musculoskeletal performance. (Rao et al., 2012) Heart rate recovery is an indirect marker of autonomic function and could be included in a future study measuring post-activity heart rate. This would reflect the body’s capacity to respond to exercise, (Borresen and Lambert, 2007) providing a follow-up from the current study which merely measures real-time heart rate and allow for an integrated observation of the effects of exercise on the individual.
A heart rate performance curve, which is non-linearly related to work load, can be used too if it can be shown to be fairly uniform upon validation of the heart rate turn point. This occurs at maximal lactate steady state, but has yet to be substantiated with data from large-scale studies. (Hofmann and Pokan, 2010) A controlled velocity experiment for each individual could also have been conducted to better calibrate the Conconi graph, thereby allowing for the observation of a heart rate deflection point.
In conclusion, this study has been shown to be inadequate in proving the reliability of the Bioharness as an effective heart rate monitoring device. More robust testing is needed before the Bioharness is recommended as an on-site testing equipment for sporting professionals.
References
BORRESEN, J. & LAMBERT, M. I. 2007. Changes in heart rate recovery in response to acute changes in training load. Eur J Appl Physiol, 101, 503-11.
BRAGE, S., BRAGE, N., FRANKS, P. W., EKELUND, U. & WAREHAM, N. J. 2005. Reliability and validity of the combined heart rate and movement sensor Actiheart. Eur J Clin Nutr, 59, 561-70.
BURKE, M. J. & WHELAN, M. V. 1987. The accuracy and reliability of commercial heart rate monitors. Br J Sports Med, 21, 29-32.
CAREY, D. 2008. A comparison of different heart rate deflection methods to predict the anaerobic threshold. european journal of sports science, 8, 315-323.
DICKSTEIN, K., BARVIK, S., AARSLAND, T., SNAPINN, S. & KARLSSON, J. 1990. A comparison of methodologies in detection of the anaerobic threshold. Circulation, 81, II38-46.
GROSLAMBERT, A., GRAPPE, F., BERTUCCI, W., PERREY, S., GIRARD, A. J. & ROUILLON, J. D. 2004. A perceptive individual time trial performed by triathletes to estimate the anaerobic threshold. A preliminary study. J Sports Med Phys Fitness, 44, 147-56.
HOFMANN, P. & POKAN, R. 2010. Value of the application of the heart rate performance curve in sports. Int J Sports Physiol Perform, 5, 437-47.
RAO, R. P., DANDURAN, M. J., LOOMBA, R. S., DIXON, J. E. & HOFFMAN, G. M. 2012. Near-infrared spectroscopic monitoring during cardiopulmonary exercise testing detects anaerobic threshold. Pediatr Cardiol, 33, 791-6.
TECHNOLOGIES, Z. 2011. Application notes and white papers [Online]. Available: http://www.zephyr-technology.com/resources/whitepapers [Accessed 2 June 2012.
WELK, G. 2002. Physical Activity Assessment for Health-Related Research, USA, Human Kinetics Publishers.
WELTMAN, A. 1995. The blood lactate response to exercise, Champaign, Illingworth, R.
WOLFE, B. L., LEMURA, L. M. & COLE, P. J. 2004. Quantitative analysis of single- vs. multiple-set programs in resistance training. J Strength Cond Res, 18, 35-47.