Listen to your heart…But what do you really want to know?

WP_20170412_004For many years measuring heart rate (HR) is one of the most popular ways for monitoring and evaluation sports exercises. It is easy to implement, it is non-invasive and it is based on scientific knowledge. In the last decades, wide availability of different types of commercial HR monitors and software made it possible for every athlete to collect continuously his/her HR data and to perform even relatively complicated analysis. It looks like today, thanks to help from HR monitoring, training process can be much more efficient and scientific. It is not so straightforward however. Though heart function is simple – to pump blood, it is a very complexly regulated machine and its connection with physical conditioning is not as simple as it may look. In this article I would like to discuss different HR measures, their possible interpretations and suitability for evaluation of the athlete’s fitness, training load, current conditions and preparedness.

Possible HR measures

For many decades coaches have been counting pulses in their training sessions and beyond. One of the great advantages of HR monitoring is that you can derive variable data basically from the same source: rate of the heart beats. I am sure that most of the readers are familiar with different HR measures. However, there are some relatively new methods and traditional approaches are now understood better . So, probably, it is worth talking about this. I will give average/normal values, where it is possible, however  keep in mind that this is just for reference.

Resting HR (HRrest).

HRrest is a minimum rate of heart beats when there are no physical workload, minimum emotional and psychological influences. It is better to measure in the standard conditions (e.g. immediately after wake up in the morning). Normal values : 60-90, however endurance athletes can have lower than 40.

HR maximum (HRmax)

HRmax is a necessary reference point for evaluation of intensity. The only problem with it is that to achieve truly HRmax subject should give”all and out” which is not always possible for elderly and non-active people. Most used theoretical calculation is: 220-age. Another formula that, in my opinion, is closer to reality may be 208-0.7x age (Tanaka, Monahan, & Seals, 2001).

HR-reserve

HRreserve, is calculated from HRmax and HRrest: HRreserv= HRmax-HRrest.                                       Since athletic heart has lower HRrest and maximum HR is generally not different between athletes and non-athletes, sportsmen have more beats in reserve when it comes to exercise. For instance, if we assume maximum HR 200 for two given individuals, one of them, who is athlete, can increase his/her HR from 40 to 200 which is 160 beats whereas non-athlete has only 130 (200-70).

HR during exercise (HRexercise)

HRexercise is usually expressed as a per cent of HRmax. Some researchers argue that intensity expressed as a fraction of HRreserve during exercise more precisely reflects fraction of maximum oxygen uptake than it does intensity expressed as per cent of HRmax.

Fraction of HRreserv= [(HRexcercise – HRrest)/ HRreserve]

Of course, there are no average values for HRexercise but there is conventional separation of HRexercise on different intensity zones. Usually it is following guidance (% of HRmax): 50-60 very easy; 60-70 easy; 70-80 moderate; 80-90 hard; 90-100 maximum. Perhaps, more scientific (though much more complicated) way is to define HR intensity zones with the reference to physiological thresholds such as: aerobic/ventilatory and anaerobic /respiratory compensation. These training zones should be established individually, during test where rate of lactate accumulation or gas exchange curve are plotted against HR (Sparks, Coetzee, & Gabbett, 2017).

Recovery HR (HRrecovery).

It shows how fast heart slows down after exercise. It can be expressed as difference between HR achieved in the last 15-30 sec of exercise and at the end of first minute of recovery. Longer periods can be used as well (usually no longer than five minutes). It is generally accepted that first rapid decrease in HR is connected with parasympathetic reactivation whereas later reduction is more work-dependent. However, some authors pointed out that even in the first seconds of recovery sympathetic influence, which depends on work intensity and duration, is present. Especially, accumulated anaerobic by-products and acidosis can play a major roles. This is, possibly, the reason why children, with their minor anaerobic contribution to exercise, have faster HRrecovery than adults (Martin Buchheit, Duche, Laursen, & Ratel, 2010). To avoid intensity bias, sub-maximal HRrecovery test may be preferable. I will talk about test later. Average values for HRrecovery after 1 min that gives me my TomTom watch are following:1-20 poor; 20-30 fair; 30-40 good; >40 excellent. Honestly, I don’t know how they came to this guidance and workload is not taken into account. There are highly cited researches which connected HRrecovery with the risk of mortality. Though they are based on ageing population, with already exiting heart problems, these data are used as “cutting points”: HRrecovery <12 after one minute and < 22 after two minutes is associated with higher risk of death (Cole , Blackstone , Pashkow , Snader , & Lauer 1999; Shetler et al., 2001).

Heart rate variability (HRV).

HRV becomes increasingly popular nowadays. It measures variation in HR. If, for example, person has average HR 60 beats per minute this doesn’t mean that time between every two beats is exactly 1 sec. Sometimes it may be 1.1 sec and sometimes 0.9. HRV may be important because it reflects balance between parasympathetic (generally slow down HR) and sympathetic (generally increases HR) systems. Generally, higher HRV is considered as a positive sign in physical conditioning.  HR fluctuates in different frequencies: very low, low and high. High frequencies oscillations presumably reflect  parasympathetic influences whereas very low—sympathetic and low – both. There are many methods to analyse HRV which are divided on two main groups : time-domain and frequency domain methods (for review see (“Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,” 1996)). Coach and/or sport scientist needs HR monitor which can measure beat-to-beat intervals and computer software which can produce analysis. One of the most simple and, as some researchers argue, most reliable for sport purposes method is RMSSD (root mean square of the successive differences). It is time-domain method for analysing high frequency osculations. RMSSD analysis does not demand a plenty of time for collecting data ( 1 min may be enough, though usually data is collected for 3-5 min) and it can be easily done in Excel (Plews, Laursen, Stanley, Kilding, & Buchheit, 2013). Further in this article, I will always keep in mind RMSSD when I talk about HRV. HRV can be measured during sleep, after wake up, during exercise and after exercise. Average RMSSD values (mean-SD) are 42-15 msec (Nunan, Sandercock, & Brodie, 2010). Athletes usually have higher values.

HR acceleration.

This is relatively new index. It reflects how fast heart can achieve necessary rate after start of exercise. It is hypothesised that the faster HR acceleration is beneficial for better oxygen supply at the first stages of the exercise. Thus fitter athletes have faster HR acceleration. Though it is probably true, it is not easy to measure HR acceleration practically because such test demands highly standardised workload and, more importantly, the same base-line HR values, which would be difficult to control in reality. For these reasons and due to lack of research on this measure I am not going to discuss it further in this article.

Long-term heart adaptations to exercise (assessing fitness).

During exercise body requirement in oxygen, fuel, as well as removal metabolic by-products increase dramatically. In order to cope with that demand blood flow raises up to 7 times (Joyner & Casey, 2015) thus heart needs to pump much more blood than in rest. As a consequence, athlete’s heart has to undergo some necessary adaptations. It is bigger in mass and volume, it is less stiff (that allows better filling) and in wide range of intensities it beats slower. To understand why the latter is the case we need to consider cardiac output and stroke volume.

Cardiac output which is a product of HR and stroke volume (the amount of blood ejected in one beat). At rest cardiac output is the same for athletes and non-athletes but during exercise sportsmen can achieve much higher values. This is due to larger stroke volume. As it was mentioned, athletic heart is bigger, more powerful and has better filling properties than ordinary heart. In addition endurance athletes have higher blood volume and better venous return during exercise. All these facilitate stroke volume. Elite athletes have 1.7 times  at rest and up to 2 times during exercise higher stroke volume than sedentary men (Joyner & Casey, 2015). Thus at rest or during sub-maximal exercise, when there is no need for maximal cardiac output, athlete’s heart beats slower. Endurance athletes may have HR at rest lower than 40 whereas for normal population it is around 70. In its turn, lower HR allows more time for heart filling thus eventually increasing stroke volume.  During the same sub-maximal exercise better adapted subject has generally lower HR than person who adapted less. Though it is necessary to note that such exercise should be sport-specific thus you cannot compare rower and runner by giving them sub-maximal running test.

Our heart has autonomic neural regulation which is provided by sympathetic and parasympathetic (vagal) systems. Parasympathetic impulses down-regulate HR whereas sympathetic influence up-regulate it. Since athletic heart normally beats slower than non-athletic, it is generally accepted that athletes have more profound parasympathetic influence than ordinary people. Thus, when doing different HR measures, we are expecting following adaptations for fitter subjects (Borresen & Lambert, 2008):

1. Lower HRrest due to better parasympathetic regulation and higher stroke volume.

2. Higher HRV at rest due to better balance between sympathetic and parasympathetic system.

3. Lower HR for the same exercise because relative intensity is lower and stroke volume is higher.

4. Faster HRrecovery after the same exercise due to better parasympathetic influence.

5. Faster HRV recovery after the same exercise due to better autonomic balance.

HR for assessing intensity and workload

Intensity

HR is a good estimation of aerobic intensity in continuous exercise. The idea is simple: if intensity is higher, muscles need more blood/oxygen therefore heart pumps at higher rates. It is not so simple in intermittent exercise, like for example, sport games, as well as for assessing anaerobic workload and resistance/plyometric activities. HR has some inertia hence it does not always come up with the rapid changes of intensity. Sometimes maximal bursts of activity are relatively short and HR does not have enough time to achieve its highest level. Nevertheless HR measure may provide useful information for sports game’s coaches. Head coach of Spanish football team was really surprised when I showed him his player’s HR graphs during training session. He found that he is talking too much between exercise and players HRs are significantly falling down during these brakes. Or HR measured during 4 min of small-side games showed huge variability between players because some of them work really hard during these games whereas other not. Of course, during conditioning training sessions players perform exercises where HR measures give useful information about aerobic strain. Commercially available HR monitors provide coaches with the graphical representation of the HR which can reflect relative intensity of the game periods, as well as a percentage of time spent in different HR zones. It can be very helpful in planning and analysing training load. Some researches argue that to gain improvements in aerobic fitness players have to spent at least 7% of training time in high-intensity zone (>90% HR max) (Manzi, Bovenzi, Impellizzeri, Carminati, & Castagna, 2013). And, finally, unusually low or high HR combined with the high perceived difficulty of exercise (RPE) can warn coach that player is fatigued or unwell.

Quantifying  internal workload

As in opposite to external work, which can be measured as the distances , speeds and accelerations, internal work means physiological strain imposed on athlete during an exercise. This can be different for the same external work for different athletes. There are suggestions to use HR for quantifying internal workload during training sessions. It is the easiest way to collect physiological data and there is logical connection between HR and exercise’s difficulty. Nevertheless to establish scientifically based relationship between HR and internal workload is not a simple task. It is clear that relative internal difficulty of exercises is not strictly proportional to HR ( e.g. 1 min exercise with HR 200 probably more than two times harder than 1 min of HR 100 exercise). So there is a need to find more reasonable proportions.

Sally Edwards suggested to divide HR on five intensity zones separated by 10 per cents of HR max and to give “ weighting coefficient” for each zone: 50-60% – 1 : 60-70 % – 2 : 70-80% – 3 : 80-90%-4; 90-100% – 5 (Foster et al., 2001). So now exercise with HR 200 beats/min would be 5 times harder than 100 beats/min (assuming that 200 ,in this case, is a HRmax). That is probably much closer to reality. Nevertheless this method looks as oversimplification for me because there is no scientific ground for it, neither for weighting coefficients nor for HR zones definition.

Eric Banister proposed Training Impulse (TRIM) for quantification of internal workload. This impulse is based on exercise average HR and its duration. Sum of TRIMs at specific HRs may be used if training session has multiple exercises and intensities. Banister proposed special formula, for dealing with the non-linear relationship between HR and exercise physiological strain:

TRIMP(men) = sum of (D x HRr x 0.64e 1.92xHRr)

TRIM(women) sum of ( D x HRr x 0.86e1.67xHRr)

Where:

D is the duration in minutes at particular HR

HRr is the particular HR as a fraction of HRreseve

This formula was developed based on the experimentally observed relationship between heart rate and rate of lactate accumulation in incremental test. It was established for average athlete. To improve precision of this method, HR – blood lactate function can be calculated individually for every athlete (Manzi, Iellamo, Impellizzeri, D’Ottavio, & Castagna, 2009). In addition, HR zones may be specified with the reference to lactate thresholds that makes them more scientifically grounded (Stagno, Thatcher, & van Someren, 2007). However, in my opinion, there is a fundamental flaw in this method. That is relationship between blood lactate, HR and internal load in intermittent activities. In continuous exercise, though blood lactate itself is not the reason for fatigue, it, like alarming signal, can indicate that fatigue is coming. It is because course of blood lactate accumulation coincides with course of build-up in metabolic by-products and homoeostasis’s disturbances. All these processes influence increase in HR hence some logical construction can be build.

It is not so in intermittent activities. As it was mentioned before, HR in such exercises not always reflects their real physiological strain in the same manner as it does in continuous running, thus relationship between HR and internal workload based on lactate curve, derived from continuous incremental test, may be not applicable (or, at least, not precise) in sport games (Impellizzeri, Rampinini, & Marcora, 2005).

There is another method which is suggested for calculation of TRIM. Though it based on psychological measurement (Borg’s RPE scale) and probably doesn’t look as scientific as complicated Banister’s formula, nevertheless it is probably not less reliable. At least, both methods showed pretty good correlation between them (Alexiou & Coutts, 2008; Foster, et al., 2001). This calculation is really simple: internal training load can be defined as product of RPE (from 1 to 10) and session duration in minutes. The advantage of RPE is that it reflects overall difficulty of training, including resistance, anaerobic and psychological strains.

HR for assessing preparedness, fatigue and exercise prescription

One of the most difficult and important task in coaching practice is to evaluate athletes current conditions and their preparedness for competitions. This is necessary for planning and monitoring a workload. There are many physiological, psychological and training factors that should be taken into account. One of the most challenging problem is to give athlete optimal workload and, at the same time, don’t over train him/her.

Fatigue is inseparable part of the training process. It may be in form functional overreaching and chronic overtraining. Former is considered as part of positive adaptation which is followed, after tapering, by super compensation and improvement in performance. The latter is negative condition and is followed by sustainable performance impairment, psychological apathy and even drop-out. Distinguish between these two conditions before overtraining is already happened is not an easy task. The idea is that HR measures may provide some additional information on this matter.

From different physiological methods, HR is probably most simple way which is available for every coach. However, disadvantage of HR measures is that on day-to-day basis they may fluctuate for many reasons sometimes independently from what we are interested most – athlete’s physical conditions. In addition, even connection HR indices with athlete’s current physical state are not straight forward and are poorly understood at the moment.

Essentially, assessing physical form through HR indices we are looking at autonomic balance. We are hoping that disturbance in this balance, reflected in HR fluctuations, warn us about fatigue and/or overtraining thus we can adjust training load. So basically, we are expecting that HR indices show enhanced or maintained autonomic balance if athlete is in a good shape and an opposite tendency if his/her conditions deteriorate. However, as we see further, it is not always the case. My analysis of literature revealed very contradicting results. Just give you a few examples.

In their review Stanley et al. gave examples how training prescription based on HR autonomic induces were superior to traditional planning  (Stanley, Peake, & Buchheit, 2013). Participants in these studies were moderately trained subjects thus applicability of the conclusions to the high-level performance is questionable.

Lamberts et al. trained 14 good-level cyclists for 4 weeks. Those, who had faster HRrecovery during the training, later showed a tendency to perform better in 40 km trial. Authors made a conclusion that HRrecovery may be a valuable tool to assess training status (Lamberts, Swart, Capostagno, Noakes, & Lambert, 2010).

In their review of literature L.Bosquet et al. concluded that fluctuations in HR indices related to training interventions are small and fell within possible day-to- day variations. Thus their correct interpretations requires comparison with other signs and symptoms (Bosquet, Merkari, Arvisais, & Aubert, 2008).

Basically, the same conclusion was made by Bellenger and colleagues. In their meta-analysis, they found  that HR indices may change in the same manner in overreaching and non-fatigue states thus additional measures are needed to understand athlete’s conditions (Bellenger et al., 2016).

Thorpe et al. investigated whether HR measures may be helpful to track morning fatigue in elite football players during competitive season. They concluded that other methods such as: perceived rating of fatigue, delayed onset of muscle soreness (DOMS) and sleeping quality are clearly superior and more helpful. (Thorpe et al., 2016)

So, why literature is so inconclusive?

Firstly, mentioned above significant HR variations may be partly responsible. Coefficient of variation for HRrest and HRV rest is 10 and 12 % respectively, however it is much higher for exercise and post-exercise measures (e.g. 60% for HRV exercise)(M. Buchheit, 2014). To deal with such variations, data should be collected more often. Some authors suggested using seven days rolling average HRV and HRrest data instead of single day values for exercise prescription (Le Meur, Pichon, et al., 2013). Although this may reduce variations, nevertheless planning today’s training based on data collected a few days ago may put in question the main goal of these methods – online monitoring and planning. In addition, not all athletes can and willing to collect morning HR data consistently.

Second reason may be that HR induces of autonomic balance are respond differently in already highly trained individuals compare to average athletic population (Borresen & Lambert, 2008). High level athletes can already have “fine tuned” heart autonomic regulation and interplay between different influences during different training and competition periods is much more complex than it can be for fitness enthusiasts.

Thirdly, methodological issues in HR measuring are very important. When and how they were collected, what type of analysis was used and what changes should be considered as meaningful can make a huge differences in their interpretations.

And finally, studies, which claimed superiority of HR measures for exercise prescription, often used individual approach for HR participants whereas others were prescribed collectively. Individual approach is clearly better thus that might bias the results.

The very idea that HR should be “better” if athlete is in a good form is questionable. For example, increase in HRexercise and decrease in HRrecovery do not necessary indicate impair in form (M Buchheit, Simpson, Al Haddad, Bourdon, & Mendez-Villanueva, 2012). Sometimes when athlete is on the peak of his preparedness some healthy level of increasing sympathetic influence is present-“sympathetic mobilisation” (M. Buchheit, 2014)) , and that may make some or all HR induces look worse . In opposite, when athlete is overtrained parasympathetic system can “put brakes” on heart and to limit its ability to pump at higher rates thus sportsman may be fatigued despite having relatively good HR characteristics. For example, though in long-term faster HRrecovery means positive adaptations, on particular period this does not necessary mean that athlete is in a good form. Sometimes in opposite; faster HRrecovery combined with higher RPE may signal overreaching (Le Meur, Buchheit, Aubry, Coutts, & Hausswirth, 2016). In another study Le Meur et al. found that overreached triathletes had significantly lower HRexercise than controls (Le Meur, Hausswirth, et al., 2013). Decrease in HRV may be influenced by both positive and negative adaptations.

To overcome uncertainty whether decrease in HRV may be consequence of positive sympathetic mobilisation rather than negative decrease in vagal modulation, new index was suggested: Ln RMSSD/R-R interval ratio (Plews, Laursen, Kilding, & Buchheit, 2012). When sympathetic mobilisation is present, HRrest is higher thus R-R interval (average time between two beats) is smaller and Ln HRV/R-R ratio will be bigger. This may smooth HRV decrease or even overturn it. However, even this new index remains individual for every athlete and should be considered carefully (Plews, et al., 2013).

In summary:

From all HR indices, morning HR and HRV, HRexercise and HRrecovery are most suitable for analysis of physical form and preparedness. They should be implemented together with other methods.

HRV after exercise (HRV recovery) and, especially, HRV during exercise have high variability and multiple influences such as: blood pressure, baroreflex activity and metabolic disturbance. This, probably, makes their application for assessment of autonomic balance impractical (M. Buchheit, 2014).

Based exclusively on HR indices it is difficult to draw conclusion about athlete’s current condition.

Practice

Test

Test’s design is a very important issue when it comes to HR indices. It should be valid, reliable, not time-consuming and not physically demanding. Recently, 5 to 5 test, which can measure HRexercise, HRrecovery and HRV recovery in one sub-maximal exercise, was suggested (M. Buchheit, 2014). This test consists of 5 min sub-maximal running (cycling or rowing) below aerobic threshold and 5 min recovery. Last 30 seconds of exercise gives us HRexercise , HRrecovery can be estimated from 1-st minute of the recovery, and HRV recovery can be calculate during last 3 min of rest. Such test may be easily incorporated into warm-up, it is less intensity-dependent and psychologically non-demanding. HRrecovery measured in the earlier period of recovery (during 1-st minute) better reflects parasympathetic influence than later periods which are more intensity-dependent (Martin Buchheit, Papelier, Laursen, & Ahmaidi, 2007). Due to limited usefulness and demand of extra time, collecting HRV recovery may be excluded. This will make test shorter (6 min).

Decision making

When deciding whether to use particular test or measure in real life or not, practitioner always has to weight it usefulness and necessity against comfort of the players and coaches. Training and recovery processes must not be distracted by scientific toys. In my opinion, despite some promising results, practical application of HR measures for understanding athlete current form and exercise prescription remains difficult. Especially this is true for  team sports. To be meaningful, HR measures should be collected often, in highly standardised conditions and constantly be compared against other physiological and psychological measures. The same nature of the sport games with their very variable and unpredictable activities, multi-intensity training, complicated psychological influences and busy player’s schedule makes these methods problematic for implementation in reality. Maybe it is better to allow players a nice sleep instead of bothering them with HR measures in every morning.

This doesn’t mean that I am sceptical about  HR measures. Actually I use them a lot. HR may provide valuable information which can add to overall picture. However I want to emphasise that coach should understand what he/she wants to derive from HR data and all limitations of its application. Otherwise, instead of being useful scientific help, counting pulse on every occasion turns out to be annoying and distracting procedure. I hope that following conclusions may be helpful for coaches and athletes.

Conclusion

1. In a long term, adaptation to exercise may result in decreasing HRrest, HRexercise and increasing in HRV as well as speed of HRecovery. This is due to decreasing in relative difficulty of exercise and adaptation of cardio-vascular system itself. Coach may consider such changes as positive effect of training intervention.

2. Though HR may provide useful information about aerobic strain, evaluation of training workload (especially in intermittent exercise) based exclusively on HR is questionable because it does not reflect comprehensively all particularities of training intensity.

3. Calculating TRIM trough HR is relatively complicated and possibly provides no advantages compare to TRIM calculating through RPE.

4. Usage HR indices for assessing athlete current form, preparedness, recovery and exercise prescription is possible only with combination of other methods such as RPE, sleeping quality, mood questioner, DOMS assessment, physical tests etc. HR measures do not comprehensively reflect such aspects of fatigue/recovery as energy restoration and muscles damage.

5. When using for mentioned above aims, HR indices should be measured consistently and in standard conditions.

6. Meaningful changes should be compared against natural day-to-day variability.

7.Significant deviation from average values rather than particular tendency (e.g. higher HRrest or slower HRrecovery) should be considered as possible signs of impairment in form for high-level athletes. These average values are specific for every athlete. Training period and intensity have to be taken into account.

 

Reference

Alexiou, H., & Coutts, A. J. (2008). A comparison of methods used for quantifying internal training load in women soccer players. International Journal of Sports Physiology and Performance, 3(3), 320-330.

Bellenger, C. R., Fuller, J. T., Thomson, R. L., Davison, K., Robertson, E. Y., & Buckley, J. D. (2016). Monitoring athletic training status through autonomic heart rate regulation: a systematic review and meta-analysis. Sports Medicine, 46(10), 1461-1486.

Borresen, J., & Lambert, M. I. (2008). Autonomic control of heart rate during and after exercise : measurements and implications for monitoring training status. Sports Med, 38(8), 633-646.

Bosquet, L., Merkari, S., Arvisais, D., & Aubert, A. E. (2008). Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature. Br J Sports Med, 42(9), 709-714.

Buchheit, M. (2014). Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol, 5(73).

Buchheit, M., Duche, P., Laursen, P. B., & Ratel, S. (2010). Postexercise heart rate recovery in children: relationship with power output, blood pH, and lactate. Applied Physiology, Nutrition, and Metabolism, 35(2), 142-150.

Buchheit, M., Papelier, Y., Laursen, P. B., & Ahmaidi, S. (2007). Noninvasive assessment of cardiac parasympathetic function: postexercise heart rate recovery or heart rate variability? American Journal of Physiology-Heart and Circulatory Physiology, 62(1), H8.

Buchheit, M., Simpson, M., Al Haddad, H., Bourdon, P., & Mendez-Villanueva, A. (2012). Monitoring changes in physical performance with heart rate measures in young soccer players. European Journal of Applied Physiology, 112(2), 711-723.

Cole , C. R., Blackstone , E. H., Pashkow , F. J., Snader , C. E., & Lauer , M. S. (1999). Heart-Rate Recovery Immediately after Exercise as a Predictor of Mortality. New England Journal of Medicine, 341(18), 1351-1357. doi: 10.1056/nejm199910283411804

Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., . . . Dodge, C. (2001). A new approach to monitoring exercise training. The Journal of Strength & Conditioning Research, 15(1), 109-115.

Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Circulation, 93(5), 1043-1065.

Impellizzeri, F. M., Rampinini, E., & Marcora, S. M. (2005). Physiological assessment of aerobic training in soccer. Journal of Sports Sciences, 23(6), 583-592.

Joyner, M. J., & Casey, D. P. (2015). Regulation of increased blood flow (hyperemia) to muscles during exercise: a hierarchy of competing physiological needs. Physiological reviews, 95(2), 549-601.

Lamberts, R. P., Swart, J., Capostagno, B., Noakes, T. D., & Lambert, M. I. (2010). Heart rate recovery as a guide to monitor fatigue and predict changes in performance parameters. Scand J Med Sci Sports, 20(3), 449-457.

Le Meur, Y., Buchheit, M., Aubry, A., Coutts, A. J., & Hausswirth, C. (2016). Assessing Overreaching With HRR: What is the Minimal Exercise Intensity Required? International Journal of Sports Physiology and Performance, 1-14.

Le Meur, Y., Hausswirth, C., Natta, F., Couturier, A., Bignet, F., & Vidal, P. P. (2013). A multidisciplinary approach to overreaching detection in endurance trained athletes. Journal of Applied Physiology, 114(3), 411-420.

Le Meur, Y., Pichon, A., Schaal, K., Schmitt, L., Louis, J., Gueneron, J., . . . Hausswirth, C. (2013). Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med Sci Sports Exerc, 45(11), 2061-2071.

Manzi, V., Bovenzi, A., Impellizzeri, M. F., Carminati, I., & Castagna, C. (2013). Individual training-load and aerobic-fitness variables in premiership soccer players during the precompetitive season. The Journal of Strength & Conditioning Research, 27(3), 631-636.

Manzi, V., Iellamo, F., Impellizzeri, F., D’Ottavio, S., & Castagna, C. (2009). Relation between individualized training impulses and performance in distance runners. Med Sci Sports Exerc, 41(11), 2090-2096.

Nunan, D., Sandercock, G. R., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short?term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407-1417.

Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2012). Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. European Journal of Applied Physiology, 112(11), 3729-3741.

Plews, D. J., Laursen, P. B., Stanley, J., Kilding, A. E., & Buchheit, M. (2013). Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports Medicine, 43(9), 773-781.

Shetler, K., Marcus, R., Froelicher, V. F., Vora, S., Kalisetti, D., Prakash, M., . . . Myers, J. (2001). Heart rate recovery: validation and methodologic issues. Journal of the American College of Cardiology, 38(7), 1980-1987.

Sparks, M., Coetzee, B., & Gabbett, T. J. (2017). Internal and External Match Loads of University-Level Soccer Players: A Comparison Between Methods. J Strength Cond Res, 31(4), 1072-1077.

Stagno, K. M., Thatcher, R., & van Someren, K. A. (2007). A modified TRIMP to quantify the in-season training load of team sport players. J Sports Sci, 25(6), 629-634.                                                                         Stanley, J., Peake, J. M., & Buchheit, M. (2013). Cardiac parasympathetic reactivation following exercise: implications for training prescription. Sports Medicine, 43(12), 1259-1277.

Tanaka, H., Monahan, K. D., & Seals, D. R. (2001). Age-predicted maximal heart rate revisited. J Am Coll Cardiol, 37(1), 153-156.

Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson, G., Drust, B., & Gregson, W. (2016). Tracking Morning Fatigue Status Across In-Season Training Weeks in Elite Soccer Players. International Journal of Sports Physiology and Performance, 11(7), 947-952. doi: 10.1123/ijspp.2015-0490