The ultimate aim of the endurance training process is to improve endurance performance. An optimal training program would create sufficient training stimuli, prevent overtraining and stress-related injuries, and produce favorable adaptations toward desired outcomes at specific times. For achieving optimal adaptation to endurance training, it is necessary to adjust training stimuli according to individual's ability to adapt and tolerate training-induced load. The ability to measure and monitor positive and negative adaptations to training would ideally make a valuable contribution to the design of effective training programs. Maximal laboratory tests are used to determine training adaptation precisely. However, they are impractical and expensive to be used on weekly or even monthly basis for continuous monitoring of training adaptation. Therefore, the development of a noninvasive, inexpensive, and practical training monitoring method that provides valid information on adaptation to training is important.
Heart rate (HR) is probably the most frequently used method to quantify the training intensity and training load in running. A linear relationship exists between HR and exercise intensity (i.e., energy expenditure, oxygen uptake, running speed [RS]) over a wide range of submaximal intensities (2,8). Furthermore, it has been reported that the autonomic nervous system has a direct effect on HR and is an important factor in acute and chronic adaptation to training (7,9,15). In addition, it is widely accepted that HR at submaximal exercise decreases after endurance training (6,14,18,19). Buchheit et al. (6) observed a progressive and continued decrease in exercise HR throughout 8 weeks of endurance training (3–4 training sessions per week) in recreational endurance runners. The authors concluded that submaximal exercise HR may be an efficient method of assessing autonomic status and thus may be used to track changes in maximal aerobic RS, at least during the first 2 months of training. However, there are many well-established factors (i.e., duration and intensity of exercise, cardiac drift, dehydration, day-to-day variation, environmental factors) that influence HR response and may disturb the relationship between HR and RS in a single exercise (1,5,14). Consequently, the relationship between HR and RS needs to be assessed daily from each exercise for determining the adaptation to training.
The aim of this study was to investigate whether HR and RS from each submaximal running exercise could be used in monitoring the adaptation to endurance training. Based on the previous researches (6,14,18,19), we expected that submaximal exercise HR would decrease progressively during the training period as a consequence of an increase in cardiorespiratory fitness. Further, we hypothesized that the relationship between exercise HR and RS would be able to track changes in endurance performance during a 28-week endurance training period. We expected that if HR-RS relationship increases (HR at a given RS decreases or if RS at a given HR level increases), it means endurance performance would improve.
Experimental Approach to the Problem
To investigate the practical usefulness of the relationship between HR and RS in determining the adaptation to endurance training, 62 recreational endurance runners were trained for 28 weeks. In addition, the subjects collected training data from all training sessions and they performed a maximal running test on treadmill 4 times during the training period. The linear relationship between HR and RS accompanied by individual information on resting and maximal HR and speed at V[Combining Dot Above]O2max was selected for the basis of the HR-RS index, a novel method for submaximal noninvasive assessment of training adaptation in distance running.
A total of 62 (21 women, 41 men) recreational endurance runners (age, 21–45 years) enrolled to a 28-week marathon training study that prepared the subjects for a marathon run after the study. All subjects were healthy, nonsmokers, nonobese (body mass index <30 kg·m−2), and they did not have any diseases or use regular medication. Most of the subjects had a training background of many years and had already run at least one half or full marathon before they volunteered for this study. A total of 45 participants (15 women, 30 men) were included in the final analyses because 17 subjects dropped out due to prolonged injuries, insufficient training, or inadequate use of HR monitor or global positioning system (GPS) pod. The subjects were informed about the design of the study, with special information on possible risks and benefits, and subsequently signed an informed consent document before the start of the study. The study was approved by the Ethics Committee of the local university.
Training consisted of progressively increasing endurance training with a 14-week basic training period (BTP) during winter followed by a 14-week intensive training period (ITP) during spring and summer (Table 1). Incremental treadmill tests were performed at weeks 0, 7, 14, and 28. Training mode, duration of the training session, running distance, average HR (HRavg), and rate of perceived exertion with Borg's 1–10 scale were collected from each training session by a training diary throughout the whole experiment (3). Subjects used Suunto t6 HR monitors with Suunto GPS pod (Suunto Ltd., Vantaa, Finland) to collect the accurate HR and RS data from each training session.
The subjects were asked to maintain the same training volume as before the study (3–6 times per week) during BTP. The intensity of training was mostly below the lactate threshold (LT), which was individually determined for each subject from the incremental treadmill test (12). The training was periodized into 4-week mesocycles, where 3 hard weeks of training was followed by an easier recovery week. The training consisted mainly of running but occasionally included also other endurance sports, such as cycling, Nordic walking, and cross-country skiing. Furthermore, the runners were asked to complete strength training 1–2 times per week. Intensive training period included higher running training volume (prolonged duration of the training sessions) and intensity compared with BTP. During ITP, the ratio between hard training and recovery weeks was 2:1. Training intensity increased in a progressive manner. During the hard weeks, the runners were programmed to perform 2 intensive training sessions, in which the intensity was in the beginning of ITP between LT and respiratory compensation threshold (RCT). Thereafter, the intensity was progressively increased above RCT at the end of the ITP. The other endurance training sessions during hard training weeks were performed below LT. Furthermore, the subjects were asked to complete 1 strength training session per week throughout the ITP.
Incremental Treadmill Test
Incremental treadmill test was performed by running in the laboratory conditions. Peak RS (Speak), maximal oxygen uptake (V[Combining Dot Above]O2max), RCT, and LT were determined from the test (12). The treadmill tests were run to repeat during the same time of the day (morning and afternoon), and the subjects were advised to avoid eating 2 to 3 hours before the treadmill tests. The treadmill test started at 7 km·h−1 for women and 8 km·h−1 for men, and the speed increased by 1 km·h−1 every third minute until volitional exhaustion. The incline was kept at 0.5° throughout the whole test. Heart rate (Suunto t6; Suunto Ltd.) and oxygen consumption (Oxygen Mobile; Viasys Health Care GmbH, Würzburg, Germany) were measured during the whole test. Heart rate and V[Combining Dot Above]O2 were averaged from the last minute of each load for the analyses. Blood samples (20 μl) were taken from fingertip at the end of each load to analyze blood lactate concentrations (La) (Biosen S_line Lab + lactate analyzer; EKF Diagnostic, Magdeburg, Germany). Speak was considered as the speed at exhaustion. If the subject could not complete the whole 3-minute load until exhaustion, Speak (km·h−1) was calculated as follows: S (km·h−1) + t (s)/(150 [s] × 1 [km·h−1]), where S = speed of the last completed stage and t = running time at exhaustion during the last run subtracted by 30 seconds. Corresponding speed at RCT was determined as SRCT. V[Combining Dot Above]O2max was determined as the highest 60 seconds of average V[Combining Dot Above]O2 value during the test.
Heart Rate-Running Speed Index
The basis of the HR-RS index equation is the linear relationship between HR and RS. Heart rate-running speed index represents the absolute difference between the theoretical and actual RS. The equation of the HR-RS index includes average speed and HR from a submaximal running exercise. In addition, individual standing and maximal HR (HRstanding and HRmax) as well as RS corresponding to V[Combining Dot Above]O2max or HRmax (Speak) from a baseline maximal test (e.g., 3,000 m or Cooper test) are needed. HRstanding was calculated from resting HR (HRrest) with an equation HRstanding = HRrest + 26 (based on observations of Hynynen et al. (11)). HRrest was measured in the beginning of the study using nocturnal HR recording. HRrest was considered as the lowest average value of 50 consecutive heartbeats achieved during the nocturnal HR recording. With these points of reference, a novel equation 1 was created:
where k represents slope and is counted with an equation 2 according to HRstanding, HRmax, Speak, and a speed of standing (Sstanding) (Figure 1). Because Sstanding is 0 km·h−1, it is not included in the following equation:
Heart rate-running speed index was calculated from every continuous-type running exercise during the 28-week experiment performed on the flat with correct HR data, running distance, and duration. Accuracy of data was visually confirmed using Suunto Training Manager 22.214.171.124 software (Suunto Ltd.). The data were then averaged to a time-dependent factor of average HR-RS index per week to achieve reliable comparison in a group of recreational runners with various amounts of running exercises per week. In addition, HR-RS index was calculated from the incremental treadmill tests at a speed of 10 km·h−1, which corresponded to the subjects' representative speed of low-intensity training.
Values are expressed as mean ± SD and 95% confidence interval for mean. The change in HR-RS index was calculated as absolute differences and the change in physiological performance variables as relative differences between the measurement points. The data were analyzed with SPSS software (SPSS Statistics version 17.0; SPSS, Inc., Chicago, IL, USA). The normal distribution of the data was estimated with Q-Q plots test. Repeated-measures analysis of variance was used for statistical testing, followed by Bonferroni as a post hoc test. Pearson product moment correlation coefficient was used to determine the relationship between the HR-RS index and training adaptation. The p ≤ 0.05 criterion was used for establishing statistical significance.
Training volume in hours per week did not differ between the 2 training periods, but in kilometers per week, it was significantly higher in ITP than in BTP (Table 2). The relative amount of moderate- and high-intensity training was significantly higher during ITP than in BTP (Table 2).
Endurance performance characteristics (V[Combining Dot Above]O2max, Speak, and SRCT) increased significantly throughout the whole experiment (Table 3). Submaximal HR in the treadmill test at 10 km·h−1 decreased significantly throughout the whole experiment (161 ± 16, 153 ± 17, and 148 ± 18 b·min−1, respectively, in PRE, WEEK 14, and POST, p < 0.001 in all cases).
HR-RS index calculated from running exercises (HR-RS indexexerc) and from the treadmill tests (HR-RS indextreadmill) at the speed of 10 km·h−1 increased throughout the experiment, without significant differences between the training periods (Table 3). The change in HR-RS indexexerc (ΔHR-RS indexexerc) correlated significantly with the changes of Speak and SRCT (ΔSpeak, ΔSRCT) throughout the experiment (Table 4). The only exception was a nonstatistically significant correlation between ΔHR-RS indexexerc and ΔSRCT during ITP. ΔHR-RS indextreadmill correlated significantly with ΔSpeak and ΔSRCT during BTP and from PRE to POST but not during ITP. In addition, ΔHR-RS indexexerc correlated with relative changes of V[Combining Dot Above]O2max (in ml·kg−1·min−1), but only between PRE and POST measurements (r = 0.49, p = 0.001). Figure 2 illustrates examples of how HR-RS data from daily exercises were related with the changes in the running performance. There were no differences between sexes in any variables related to the change of training adaptations and HR-RS index.
HR-RS index was created to be a simple, inexpensive, and practical method for monitoring the adaptation to endurance training in running. The results of this study showed that the change in HR-RS index calculated from exercises and treadmill tests correlated significantly with the changes of Speak and SRCT during the 28-week training period. The change in HR-RSexerc index also correlated significantly with the relative changes in V[Combining Dot Above]O2max (in ml·kg−1·min−1) between PRE and POST measurements. Therefore, the main finding of this study was that HR-RS index may be an efficient method of monitoring changes in endurance running performance.
In addition to the improvements in running performance, a significant improvement was also observed in HR-RS index calculated from exercises and treadmill tests during the training periods. A significant positive correlation between ΔHR-RS index (calculated from exercises and treadmill tests) and the change in endurance performance (ΔSpeak, ΔSRCT) suggest that HR-RS index can be used in monitoring the adaptation to endurance training. HR-RS index improves if HR at a given RS decreases or if RS at a given HR level increases. Therefore, the improvement in HR-RS index reflects the changes in cardiorespiratory fitness and endurance performance.
In various previous studies, the endurance training has resulted into decreased exercise HR (6,18). Scharhag-Rosenberger et al. (18) reported that submaximal running HR decreased significantly during the beginning of the 12 months of endurance training with constant training intensity, but seemed to plateau after the ninth week of training among previously untrained subjects. The authors suggested that exercise HR does not seem to be an appropriate parameter to indicate fitness changes in long-term training studies of several months (18). Buchheit et al. (6) observed also a progressive and continued decrease in exercise HR throughout 8 weeks of endurance training (3–4 training sessions per week) in recreational endurance runners. The authors concluded that submaximal exercise HR may be an efficient method of assessing autonomic status and thus may be used to track changes in maximal aerobic RS, at least during the first 2 months of training. In this study, the submaximal HR decreased and HR-RS index increased significantly throughout the whole 28 weeks of training. The correlations between the changes in HR-RS and running performance (ΔSpeak, ΔSRCT) in both training periods suggest that HR-RS index indicates fitness changes also during prolonged training among women and men. Using only submaximal exercise HR in monitoring training adaptation does not serve possibility to compare exercises with different intensities in an appropriate way because exercise intensity has an effect on the relation between HR and RS. During high-intensity running exercises, HR increases more than speed causing lower S/HR ratio compared with low-intensity exercises. HR-RS index takes into account this by including individual background factors (HRmax, HRstanding, Speak, Sstanding) in the equation and calculating theoretical RS for whole intensity scale (from rest to maximal performance). And further, HR-RS index represents the absolute difference between the theoretical and actual RS.
The change in HR-RS indexexerc also correlated significantly with ΔV[Combining Dot Above]O2max (ml·kg−1·min−1) between PRE and POST measurement points, but not during BTP or ITP. It has been previously reported that cardiovascular autonomic function is more closely related to endurance running performance than to oxygen uptake (10,13). The findings of this study support these findings because a higher correlation was observed between the changes in HR-RS index and ΔSpeak than HR-RS index and ΔV[Combining Dot Above]O2max. It is remarkable because maximal aerobic RS is thought to be a superior predictor of endurance performance (4,17). These findings support observations that HR-RS index may be used to track changes in maximal endurance performance.
The reliability of the HR-RS index is highly dependent on the accuracy of the measurement of distance or speed in exercises. The speed determined by the GPS receiver is within 0.2 m·s−1 of the true speed measured for 45% of the values with a further 19% lying within 0.4 m·s−1 (20). Global positioning system data loggers are therefore accurate for the determination of speed overground on relatively straight courses. It has been reported that internal factors such as level of dehydration, body temperature, and cardiac drift may disturb the relationship between HR and RS in a single exercise, especially in high-intensity and prolonged exercises (1,5,14). External factors such as running surface, ascent/descent during the exercise, wind, air temperature, humidity, and time of day have also effect on the relationship between HR and RS in a single exercise. Hot and humid environments, windy conditions, hilly terrain, and the hypoxic conditions at higher altitudes can all increase exercise HR, and thus decrease HR-RS index (14,16). To overcome these limitations, HR-RS index was also calculated from the treadmill tests with standard protocols and environmental conditions. One may assume that HR-RS index is more reliable when it is calculated from treadmill running at constant velocity than when it is calculated from outdoor exercises because the conditions and RS can be standardized in laboratory but not in outdoor conditions. However, similar correlations were observed between the changes in endurance performance characteristics and HR-RS index calculated from treadmill running at 10 km·h−1 and when the HR-RS index was calculated from the outdoor exercises. The reason for this may be that only one measurement was included when the HR-RS index was calculated from the treadmill test and the average value of 2–4 exercises were used in outdoor HR-RS index. Consequently, it seems to be that by using 1-week average value of HR-RS index, the effect of external and internal factors can be minimized or decreased (see trendlines in Figure 2). This may also help to avoid possible misinterpretation because of day-to-day variation in HR (14) or abnormal HR and/or RS of one single exercise (e.g., because of special environmental conditions). Lambert et al. (14) concluded that under noncompetitive conditions, HR-RS relationship is fairly constant but HR is not an accurate indication of RS during competition. It is possible that the same phenomenon exists in laboratory tests, which could explain generally weaker correlation between the change of HR-RStreadmill and running performance despite standardized conditions.
It seems that HR-RS index serves the most valid information about the training adaptation when exercise conditions, duration, and intensity of exercise have been standardized, and HR-RS index data have been averaged for a longer period. The method compares the current training status to pretraining status and, therefore, does not take into account the possible changes in HRstanding, HRmax, or Speak caused by endurance training. If training has led to positive training adaptation, the current training status is better than pretraining status that exists as a positive HR-RS index. The significant correlations between ΔHR-RS index and the change in endurance capacity (ΔSpeak, ΔSRCT, ΔV[Combining Dot Above]O2max) show that ΔHR-RS index may be used in monitoring the training adaptation. This, however, is shown in this study to be true after a minimum of 14 weeks of training. Further investigations are required for evaluating the use of HR-RS index during shorter time periods and to examine additional aspects of HR-RS index.
The practical implications from this study are that athletes and coaches can be confident in monitoring changes in endurance performance during training in the novel method. HR-RS index provides daily information about the adaptation to endurance training from different kinds of running exercises without the need to repeat laboratory tests during training. HR-RS index does require a baseline maximal field test (e.g., 3,000 m, Cooper test) for determining HRmax and RS corresponding to V[Combining Dot Above]O2max but after that normal running training with training information collections (RS, HR) is adequate for monitoring changes in endurance performance during training. With the help of modern technology, like HR monitors and smartphones with GPS systems, it is able to get easily required information from every continuous-type running exercises for determining HR-RS index. The novel method is more practical and it provides daily information on adaptation to training for athletes and coaches compared with impractical and expensive maximal laboratory tests, which provide usually information on the adaptation a few times per year. Further, HR-RS index enables faster changes in training programs if training has led to undesirable outcomes and help to achieve better adaptation to endurance training.
Funding of the study was provided by the Finnish Funding Agency for Technology and Innovation (TEKES). The authors wish to thank the participating subjects for their collaboration. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
1. Achten J, Jeukendrup AE. Heart rate monitoring: applications and limitations. Sports Med 33: 517–538, 2003.
2. Astrand P, Rodahl K. Textbook of Work Physiology. New York, NY: McGraw Hill, 1986.
3. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14: 377–381, 1982.
4. Bosquet L, Leger L, Legros P. Methods to determine aerobic endurance. Sports Med 32: 675–700, 2002.
5. Boudet G, Albuisson E, Bedu M, Chamoux A. Heart rate running speed relationships during exhaustive bouts in the laboratory. Can J Appl Physiol 29: 731–742, 2004.
6. Buchheit M, Chivot A, Parouty J, Mercier D, Al Haddad H, Laursen PB, Ahmaidi S. Monitoring endurance running performance
using cardiac parasympathetic function. Eur J Appl Physiol 108: 1153–1167, 2010.
7. Carter JB, Banister EW, Blaber AP. Effect of endurance exercise on autonomic control of heart rate. Sports Med 33: 33–46, 2003.
8. Conconi F, Ferrari M, Ziglio PG, Droghetti P, Codeca L. Determination of the anaerobic threshold by a noninvasive field test in runners. J Appl Physiol Respir Environ Exerc Physiol 52: 869–873, 1982.
9. Hautala AJ, Makikallio TH, Kiviniemi A, Laukkanen RT, Nissila S, Huikuri HV, Tulppo MP. Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects. Am J Physiol Heart Circ Physiol 285: H1747–H1752, 2003.
10. Hautala AJ, Makikallio TH, Kiviniemi A, Laukkanen RT, Nissila S, Huikuri HV, Tulppo MP. Heart rate dynamics after controlled training followed by a home-based exercise program. Eur J Appl Physiol 92: 289–297, 2004.
11. Hynynen E, Konttinen N, Kinnunen U, Kyrolainen H, Rusko H. The incidence of stress symptoms and heart rate variability during sleep and orthostatic test. Eur J Appl Physiol 111: 733–741, 2011.
12. Iwaoka K, Hatta H, Atomi Y, Miyashita M. Lactate, respiratory compensation thresholds, and distance running performance
in runners of both sexes. Int J Sports Med 9: 306–309, 1988.
13. Kiviniemi AM, Hautala AJ, Kinnunen H, Tulppo MP. Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol 101: 743–751, 2007.
14. Lambert MI, Mbambo ZH, St Clair Gibson A. Heart rate during training and competition for long-distance running. J Sports Sci 16: S85–S90, 1998.
15. Mazzeo RS. Catecholamine responses to acute and chronic exercise. Med Sci Sports Exerc 23: 839–845, 1991.
16. Noakes TD. Fluid replacement during exercise. Exerc Sport Sci Rev 21: 297–330, 1993.
17. Paavolainen LM, Nummela AT, Rusko HK. Neuromuscular characteristics and muscle power as determinants of 5-km running performance
. Med Sci Sports Exerc 31: 124–130, 1999.
18. Scharhag-Rosenberger F, Meyer T, Walitzek S, Kindermann W. Time course of changes in endurance capacity: A 1-yr training study. Med Sci Sports Exerc 41: 1130–1137, 2009.
19. Skinner JS, Gaskill SE, Rankinen T, Leon AS, Rao DC, Wilmore JH, Bouchard C. Heart rate versus % VO2max
: Age, sex, race, initial fitness, and training response_HERITAGE. Med Sci Sports Exerc 35: 1908–1913, 2003.
20. Witte TH, Wilson AM. Accuracy of non-differential GPS for the determination of speed over ground. J Biomech 37: 1891–1898, 2004.