A profligate purchase and an interesting stress test

After much deliberation about buying a heart rate monitor capable of recording Heart Rate Variability (HRV), I have decided to be profligate, and have bought a Polar RX800cx.  I had been vacillating between a Polar RS800cx and a Suunto t6r.  For the purpose of measuring HRV, the Polar RS800cx and the Suunto t6r have very similar capability.  Both devices record R-R interval (the interval between the ventricular contraction of consecutive heart beats), and either can be used to provide the raw data required by the Firstbeat software which I have discussed on several recent postings.  This software computes a stress/ recovery index which promises to provide a sensitive indicator of impending over-training.  This stress/recovery index is based on estimate of the balance between activity of the parasympathetic nervous system (which promotes recovery) and the sympathetic nervous system (which promotes fight or flight).

When it came to the final decision about which device to buy, I was swayed by the fact that RS800cx comes with a stride sensor that appears to provide a reliable measure of cadence.  I am very interested in assessing my cadence when running, but a discussion of the reasons for my interest will have to wait for later posting as my first objective was to see how useful R-R data might be for measuring stress levels.

 

The Polar Own Optimizer

Although I have focused mainly on First beat software in recent weeks, the software that comes with the Polar RS800cx does include a utility called Own Optimizer, which assess stress levels on wakening in the morning.  Unfortunately, the information provided in the manual provides little scientific justification for the Own Optimizer.  As far as I can gather, Own Optimizer is based largely on the changes in both heart rate and HRV in response to rising from sitting to standing.  In principle such measurements might provide a sensitive measure of the degree of withdrawal of parasympathetic nervous activity and increase in sympathetic activity associated with the mild challenge of standing-up. Therefore, I will be curious to experiment with Own Optimizer, but the output of Own Optimizer is difficult to interpret unless one has baseline data recorded while in a relaxed state. There is little point in me trying out Own Optimizer until I am fully recovered from my recent episode of illness.  Nonetheless, I hope eventually to compare Own Optimizer with the Firstbeat stress/recovery index.

 

Back to the Poincare scatter plot

Meanwhile, I exported the R-R data from my new RS800cx and subjected it to the same Poincare analysis which I had presented in my post on 26^th June.  The Poincare analysis requires the production of a scatter plot which can be produced easliy using software such as Excel.  Interpreting  the scatter plot taxes the grey cells, but the effort is probably worthwhile.

The Poincare analysis is based on a scatter plot in which each heart beat is plotted on the x-y plane with the x-coordinate equal to the interval between the preceding beat and the beat of interest, while y coordinate is equal to the interval between the beat of interest and the following beat.   A heart beat for which the preceding inter-beat interval is equal to the subsequent inter-beat interval must lie on a line that represents the equation x=y.  This line slopes upwards and to the right at 45 degrees, as shown in the figure below.  Strong parasympathetic activity is associated with large beat by beat variability in heart rate, so consecutive interbeat intervals will differ substantially in duration and the  points representing the heart beats  will lie at a substantial distance for the 45 degree line. 

Conversely, if inter-beat interval varies slowly (over a time scale long compared with the average inter-beat interval) then each heart beat will be represented by a point near the 45 degree line, though the slow variation will cause the location of the points representing the heart beats to wander along the 45 degree line.  Slow fluctuations are largely due to sympathetic nervous activity, though there is evidence that parasympathetic activity can also contribute to slow variations – however for the present purpose, let us assume that sympathetic nervous activity is mainly responsible for slow variation, resulting in a spread of points along the 45 degree line, while parasympathetic activity is responsible for rapid variation that causes a spread of points away from the 45 degree line.

 As I described on 26^th June, if there is a good balance between sympathetic and parasympathetic activity, the scatter plot should produce a cluster of points that looks like a swarm of bees the spreads out both along the 45 degree line and away from the 45 degree line. If we draw an ellipse that captures most of the points, this ellipse will be almost circular.  Conversely, if the person is stressed, so that there is an excess of sympathetic activity, the scatter plot will produce a cigar shaped cluster extending along the 45 degree line.  In the data which I had recorded using my ‘home-made’ ECG device before my recent illness, the scatter plot had exhibited a large spread away from the 45 degree line in addition to a large spread along the line.  The ellipse that embraced most of the points was pleasingly round.

 Here is a comparison of the scatter-plot for data recorded today (using my new RS800cx)  with data recorded before my recent illness.  Both sets of data were recorded while sitting in a relaxed state, breathing slowly and deeply.  The pre-illness recording was in fact made at around 6pm at the end of a day at work, while today’s recording was made in the early afternoon after a relaxing Sunday morning.  Thus, if all other circumstances were the same, today’s plot might have been expected to show an even more favorable balance between parasympathetic activity and sympathetic activity.  However, all other circumstances were not the same.  Today, I am in the fourth day of convalescence after a peculiar and debilitating illness that lasted almost 4 weeks.

 

 

Poincare scatter plot pre- and post illness

 

There are two striking differences.  First, the cluster of pink dots presenting today’s data has moved down and to the left, indicating that the average inter-beat interval was shorter.  A shorter inter-beat interval corresponds to a faster heart rate.  Before my illness, my average heart rate was around 46 beats per minute.  Today’s value was 59 beats per minute. Secondly, the ellipse that embraces the majority of the points is thinner – somewhat more like a cigar.  Today’s data reveals substantially reduced heart rate variability, especially a reduction of the parasympathetic activity that produces rapid changes in heart rate and scatters the points far from the 45 degree line.

 

Occasional parasympathetic surges

There are a few points that are outside the ellipse that embraces the general trend.  Several of these points represent heart beats for which a preceding short inter-beat interval was followed by a longer than usual inter-beat interval (causing a large displacement above the 45 degree line).  In fact these points represent a sudden drop in heart rate from around 58-62 beats per minute to 52-55 beats per minute. It appears that I was experiencing occasional surges of parasympathetic activity.  These surges were also apparent when I examined an even longer recording, so I suspect that they are not just random chance events but in fact represent some fairly consistent pattern of autonomic nervous activity.  I do not have an explanation for those occasional sudden surges of what appears to be parasympathetic activity.

 

The main conclusion

However, the main conclusion is very clear.  My recent illness has left me in a quite stressed state – at least compared to my relaxed pre-illness condition.  In fact, although today’s recording reveals a marked deterioration, it is not too bad for a 63 year old, so there is no reason for me to be too alarmed.   However, it would probably be unwise for me to resume vigorous training until the scatter plot of data recorded in a resting state returns to something more like the widely dispersed shape exhibited in my pre-illness data.

The apparent sensitivity of the Poincare scatter-plot to stress level makes me wonder how useful if might prove to be as a measure of training stress.  Once I have recovered fully, it will be interesting to compare the scatter plot following a hard training session (‘over-reaching’) compared with that following easier training sessions.  Maybe the Poincare scatter plot of data record during relaxed deep breathing will be as informative as either Polar’s Own Optimizer or the stress/recovery index computed by Firstbeat software.  Whatever the relative merits of the different ways of assessing autonomic imbalance from HRV data, it appears that HRV is a sensitive indicator of one’s internal milieu.

Snakes and ladders again

The good news is that I have recovered from the peculiar illness that has troubled me for almost four weeks.  During the past three days my symptoms have been diminishing rapidly with each passing day.  Two days ago, I did a gentle Pilates session and yesterday I went for a short, easy cycle ride, without adverse effects.  By this evening the only remaining traces of the illness were mild constriction of my upper airways (peak flow 270 litres/min compared with my usual 520-550 litres/min) and very mild diffuse musculo-skeletal aches.  I decided it was time to return to running.  Before I set out I tested my heart rate versus power on the elliptical cross trainer.  To avoid stressing myself, I did not go beyond 200 watts.  As you can see from the chart, there has been a fairly dramatic deterioration in my fitness.  Heart rate today about 11 beats higher at each level of power output, compared with 6^th June.

 

I then went for an easy 3 Km run in the woods.  After a warm up at gradually increasing pace, I timed myself over the middle Km, which I ran at a comfortable pace in the mid-aerobic zone.  I was pleased to find that this comfortable pace was 5:05 per Km and average heart rate was 133.  I then slackened the pace to a jog for the final Km.  Although I felt tired at the end, it was great to be out running in the woods again. 

The chart of heart rate v power provides graphic evidence of how rapidly fitness is lost, but in view of the severity of my illness, I was expecting at least this much deterioration, so I was neither surprised nor disappointed.  The interesting question is how quickly can I recover to my level of fitness in early June.  It took 4 weeks to lose this much fitness.  I hope I can recover to my pre-illness level by the end of summer, but  it would probably be counter-productive to push myself too hard.  I anticipate that I am going to need a few weeks of convalescence.

Getting the balance right

I have been blogging a little more frequently in the past week or so because I have been ill, and therefore not running.  I have been exploring issues related to over-training and heart rate variability on my blog because one possibility is that I had become ill because of over training.  At this stage I am puzzled.

A peculiar illness

First an outline of the illness: it started over three weeks ago with an exacerbation of my long standing inflammatory arthritis; then became an acute fever with a temperature of  101-104 degrees F. for a few days; then what appeared to be chicken pox with  fairly typical skin vesicles, and  most distressingly, severe mouth ulceration. I have largely been living on ice-cream and cool fluids for over a week.  This morning, I woke at 3:30 am with a new crop of painful vesicles in my mouth, a painful throat and a mild asthma attack.  At this stage I am a little worried that the problem will extend more deeply into my lungs, though as I sit at my desk typing this I do not feel seriously ill.  Maybe it’s just chicken pox and a few incidental problems, though since I had chicken pox as a child, it’s all a bit mysterious.

Could this peculiar illness be a consequence of over-training?  At this stage I think it is very unlikely.  As I have been discussing in recent postings, there is good evidence for regarding the balance between sympathetic and parasympathetic nervous activity as a useful measure of recovery from training.  As I posted last week, my heart rate variability before I became ill indicated a good balance between parasympathetic and sympathetic activity. Since I have been ill, I have not recorded heart rate variability but simple tests such as the orthostatic test (change in heart rate on standing) indicate there has been a small shift from parasympathetic to sympathetic activity.  This is only to be expected with the degree of illness I have experienced.  Nonetheless, I can still get my heart rate down to the low 50’s by relaxed rhythmic breathing, so the evidence suggests I still have reasonably good parasympathetic control. If maintaining a good balance between parasympathetic and sympathetic activity is a sign of good recovery from training, I have been recovering well.

Could my illness be due to immune suppression produced by the steroid inhaler I use for my asthma?  My doctor thinks that is very unlikely. So the situation remains a mystery,   At this stage I do not know when I will get back to running.

 

The overall balance sheet

Though at the moment I am not well, it is also important to keep in perspective the overall balance sheet with regard to my health since I recommenced running.  Let’s start with the negative side of the balance sheet.  The one definite deterioration has been in my asthma.  Although I have suffered mild asthma since childhood, I had never needed treatment until a year or so ago.  It is possible that getting cold air into my lungs when running has exacerbated that problem. My arthritis presents a different story.  It had also been a mild problem since childhood but had started to become more of a problem as I approached middle age. In particular my right knee and the metatarsophalangeal joints in my feet were starting to be troublesome.  But contrary to expectations, those problems have greatly improved since I started running. The recent flare-up of arthritis was I fact very minor.  Most importantly for my general health, I think the decrease in my resting heart rate, from around 60 a few years ago down to the mid 40’s, is an indication that my heart is much healthier as a result of running.  So I think the balance sheet is positive.

 

Maintaining the balance

Meanwhile, I still continue to be fascinated by the question of how best to monitor training so as to maximize both my health and my running performance.  Even though there is little to suggest recent over-training, my experience of the past couple of years has demonstrated that I am now less able to cope with heavy training than forty years ago.  Maybe that is an inevitable consequence of aging.  But if I accept that, it becomes all the more important to develop good strategies for optimizing training level.

My overall conclusion is that training vigorously is almost certainly the best way to remain healthy into old age, but finding a good way to judge just how vigorously to train is the challenge.   I am also inclined to think that for an athlete of any age, the challenge is similar.  Finding the optimum balance between stress and recovery is likely to be the recipe not only for achieving for good general health but also for maintaining the consistency of training necessary to achieve one’s peak performance.  I just hope I can get back to running soon, though I might have to wait a year or two to achieve my M60 peak performances.

Do Firstbeat offer more in 2009 than Forbes and Ursula Carlile in 1959?

In recent postings I have been exploring the possibility that measurements of Heart rate Variability (HRV) might provide a useful way of detecting over-training and of adjusting training load to achieve optimum outcome. In response to a query from Ewen, on 30^th June I had looked at the question of which commercially available heart rate monitor might provide the most useful measurement of HRV.  I had attempted to compare the merits of the products offered by Polar and by Suunto.  In fact, this was a frustrating task because the material presented on the websites of the two companies does not provide enough details of the principles underlying their procedures for using HRV to monitor training load and over-training, nor adequate evidence that using their devices leads to improved outcomes of training.

However I had concluded that the most promising current approach uses software developed by the Finnish company Firstbeat Technologies.  Firstbeat software can read the data from either the Suunto t6 or the Polar RS800. Suunto have incorporated Firstbeat software into their own Training Manager software and have committed themselves more heavily to the utilization of Firstbeat software.  Some  of the measurments such as Training Effect (see below) can be read directly from Suunto HRMs  such as the t3,  t4 and t6r, during the training session.  Nonetheless, the primary input required for Firstbeat software is a record of inter-beat interval in a series of heart beats recorded under whatever circumstances one is interested in, whether than be during rest or exercise, and this data can be provided by either the Suunto t6 or Polar RS800, though the full analysis cannot be performed until after the training session is over. 

Therefore in deciding between Suunto and Polar for the purpose of measuring HRV, the major issues is likely to be reliability of the recorded data, the size of the data store and the ease with which it can be read by Firstbeat software.  I have not looked into any of these questions, though issues such as susceptibility to interference arising from power lines or nearby HRM’s worn by other athletes are addressed in a comparison of Suunto and Polar devices by PC Coach

(http://www.pccoach.com/newsletters/Nov05/speedist5.htm ) 

Note added 5 July 2009:   If you are interested in comparing other practical aspects of the Polar PS800CX and the Suunto t6r, such as convenience for use during a triathlon, or the utility of the software provided by Suunto and Polar for planning of your training sessions, Jan Musil provides an excellent comparison at:

http://runtotri.blogspot.com/2009/01/polar-rs800cx-or-suunto-t6c-that-is.html

As far as I can see, despite differences in detail, both companies provide technically sound equipment.    However my present interest is in the Firstbeat software. 

KIHU and Firstbeat

Firstbeat is a spin-off company created by members of KIHU – Research Institute for Olympic Sports, located in Jyväskylä, Finland.  KIHU researchers have conducted a number of very informative studies of HRV over the past decade.  The senior investigator in many of these investigations is Heikki Rusko, a well known exercise physiologist.  By examination of both the scientific publications produced by KIHU and by reading the material presented on the Firstbeat technologies website, it is possible to get a reasonable understanding of what the Firstbeat software has to offer, though not quite as clear a picture as I would like to have.

 

In my opinion the peer reviewed publications from KIHU provide only moderate, but nonetheless tantalizing, support for the proposal that HRV and related measures would provide a reliable estimate of training stress.  The material presented on the website provides more information about what computations the Firstbeat software performs, but many details are missing, and the quality of some of the crucial scientific evidence falls below that I would expect in a peer reviewed scientific publication.  Nonetheless, some of the material on the Firstbeat website (especially the downloadable white papers) appears to have been written by exercise physiologists rather than marketing personnel.  Maybe this makes it harder to read but ultimately, more worthwhile. So in this posting I will present a personal overview of what I regard as the most relevant outputs from the Firstbeat software

The three outputs most relevant to the scientific assessment of athletic training are:

EPOC: an estimate of oxygen debt acquired during a training session.  This provides a measure of the stress on the cardio-respiratory system resulting from the session;

Training Effect: an estimate of the potential benefit (or in some instances, degree of  overreaching)  from a training session;

Recovery index:  a measure of autonomic nervous system balance that is potentially a useful indicator of over-training.

It should be noted that Firstbeat produce three main software packages, each of which is specialized for different users:

Firstbeat ATHLETE (FBA), which calculates EPOC, Training effect and provides guidance on planning a training program.  However FBA does not provide an analysis of recovery.  While it does provide guidance that should minimize the risk of over or under-training, the planning is based on the estimated Training Effect of recent sessions rather than on a direct measurement of the degree of recovery immediately prior to the next session.   

Firstbeat SPORTS, which calculates EPOC, training effect and also provides a detailed analysis of stress and recovery.  This includes a recovery analysis based on overnight recording, and charts of stress and recovery throughout the day.  This analysis takes account of cumulative stress not only of recent training sessions, but also of other life events. This software is designed for sports professionals

Firstbeat HEALTH, which provides detailed stress and recovery analysis. It is designed for use in studies of occupational health and therefore is not oriented towards the management of an athlete’s training program.

EPOC 

It is well known that exercise creates an oxygen debt such that oxygen uptake over a period of minutes or hours after cessation of the exercise is increased compared with baseline.  This is known as Excess Post Exercise Oxygen consumption (EPOC).  This can be measured directly in a laboratory using equipment to measure respiration.  KIHU researchers report that the oxygen debt can be predicted reliably from heart rate and respiration rate (calculated from HRV) measured during the exercise.   The EPOC measurement produced by the Firstbeat software is the predicted EPOC based on heart rate recording during exercise.  As oxygen debt only accumulates when exercise is of at least moderate intensity, EPOC generally increases with time during moderate or heavy exercise but tends to decrease with time during periods of light exercise following heavier exercise.

How good is the evidence that EPOC is a good estimate of the total accumulated stress applied to the cardiovascular system?  Firstbeat show that EPOC is strongly correlated with lactate accumulation, which is quite plausible.  However, at low workloads, such as the levels proposed by Philip Maffetone during the base-building phase, lactate accumulation is minimal, and therefore one might expect that EPOC will be small, yet some training effect on the heart would be anticipated. 

The data provided by Firstbeat in their white paper on EPOC demonstrate that at a workload corresponding to 40% of VO2max, EPOC scarcely rises at all after the first 30 minutes.  Does this indicate that long slow runs do not produce stress (and hence useful training effects) on the cardio-respiratory system?   As far as I can see this issue remains unresolved,  and I would like to see more evidence.

Furthermore the calculation of predicted EPOC requires a prior estimate of HRmax and if this is inaccurate, the calculation of EPOC will be inaccurate.

Training Effect

This is an estimate of the training benefit derived from a given training session and is computed from the peak EPOC achieved during the session  taking account of the athlete’s current activity level.  Activity level is scored on a 10 point scale largely based on current weekly training load.  The underlying principle is that an athlete with a high current activity level will require a higher value of EPOC to achieve a comparable training benefit compared with a less active athlete.   This is perfectly reasonable, but on account of the crudeness of the estimate of current fitness provided by the activity level scale, I suspect that the Training Effect value is imprecise.  Training Effect itself is quantified on a scale from 1 to 5 where 1 indicates a minor training effect and 5 or more indicates over-reaching. 

A good coach (or even a thoughtful athlete) might perhaps be able to estimate the value of a training session just as well on the basis of experience, but many of us are not wise judges of how hard we are training, and we do not all have access to a wise and experienced coach.  In principle, keeping a log of Training Effect values for the week’s sessions would probably be a more sensible guide to how effectively we are training that keeping a log of total miles or Km run  each week, yet many of us are inclined to draw psychological support from our weekly mileage total. 

However, just as a log of weekly mileage has its limitations as a measure of training, so does a log of Training Effect.  Not only is it likely to be an imprecise estimate of effects on the heart,  but in addition, Training Effect does not take adequate account of other organs of the body, especially the musculo-skeletal system.  It is highly likely that long slow distance training strengthens bones, tendons and ligaments (and conversely that running excessively long distances creates risk of musculo-skeletal injury) yet the computation of Training Effect appears to under-estimate either the benefits or risks of long slow distance running.  Furthermore, the calculation of Training Effect does not take account of the benefits derived from strength or flexibility exercises.   So it might be a little more useful that a log of weekly mileage, but it does not reflect all of the important benefits (and risks) from a  training program, and it would be foolish to plan a program guided only by measurments of Training Effect.

Recovery Analysis

The potentially most useful analysis provided by the Firstbeat software is the recovery analysis based on an overnight estimate of stress and recovery.  The measurement is based on recorded heart beat during a four hour period of sleep.  A quantity known as the recovery index is computed from heart rate, heart rate variability (HRV) and estimated breathing rate derived from HRV. 

Firstbeat do not say exactly how this computation is performed, but a white paper presented on the website states that the procedure  was derived by fitting a mathematical model to a large amount of data collected in many studies.  In this context, the mathematical model is an equation that estimates the balance between sympathetic and parasympathetic nervous activity using physiological information such as HRV.   The principle of deriving an equation that predicts a physiological variable of interest from the values of related physiological variables is well established in exercise physiology.  For example Daniels’ famous equation for predicting VO2 from speed and duration of a run is a quadratic equation that was derived by determining the coefficients of the quadratic equation that gave the best fit to the observed data in a large number of individuals.  It is likely that Firstbeat have used a different type of equation, based on more complex mathematics, for the prediction, but the principle of fitting observed data to a mathematical model is likely to be similar. 

Potentially the biggest limitation of this type of approach is that even if the prediction works well for the average value in a group, it might not be accurate for the individual.  This issue is most clearly illustrated by the linear equations that have been proposed to predict HRmax from age. In some instances, the prediction is quite inaccurate.   The relationship between HRmax and age is a notorious example of an unreliable mathematical model of physiological data.  Nonetheless, I would like to see more data demonstrating the reliability of the model used by Firstbeat to estimate the recovery index.

Firstbeat quite rightly point out that the absolute value of the  recovery index is not very meaningful.  What is required is a measure of change from baseline within the individual.  However they do not present clear evidence for the consistency of changes from baseline within individuals who develop over-training syndrome.   The most relevant supportive data comes for a study by Hynynen and colleagues comparing over-trained with non over-trained  athletes (  Hynynen, E., Uusitalo, A., Konttinen, N. & Rusko H. (2006). Heart rate variability during night sleep and after awakening in overtrained athletes. Medicine and Science in Sports and Exercise 38(2): 313-317).

However the data relevant to the recovery index was not included in the published peer reviewed paper reporting the study but is only available in a white paper on the Firstbeat website.   Ironically, the published article actually concluded that overnight HRV did not distinguish between the over-trained and non-over-trained athletes, whereas measurement of change in HRV on rising did.   In contrast, the table of data presented in the Recovery white paper on the Firstbeat website does indicate that the stress/recovery index computed from overnight values by the Firstbeat software distinguished between the well recovered and poorly recovered state in 7 athletes.  Unfortunately, the white paper provides no indication of how this data was selected. 

I am intrigued by the possible utility of the recovery index and would be very interested to try this out myself.  However for the time being I will persist with my own amateur system described in my post on 26th June.  My system provides me with the ability to study the shape of the ECG  T wave as well as HRV.  In my analysis, the most informative estimate  of balance between sympathetic and parasymatheic activity is provided by the Poincare analysis of HRV.  This analysis assesses the ratio of high frequency variation (presumed closely associated with recuperative parasympathetic activity) to low frequency variation (predominantly determined by sympathetic activity)  by comparing the length of the two axes of the ellipse which I presented in my posting on 26th June.  Unfortunately, the interpretation of the Poincare analysis is not quite as simple as described in my posting on 26th June.  It is possible that the computations done by Firstbeat software are more reliable.  On the other hand, the evidence presented by Firstbeat is scarcely any more convincing than the data on ECG T waves presented by Forbes and Ursula Carlile to the Australian  Sports Medicine Association  in 1959.  As I described in my post on 30^th June, the report by Forbes and Ursula Carlile demonstrated that in selected cases, flattening of the T waves corresponds very closely to deterioration in performance due to over-training.  The presentation of data on individual subjects or small groups of subjects can look very impressive, but what I would like to see is evidence showing how well the Firstbeat procedure works for an unselected sample of athletes.

Conclusion

The available evidence does suggest that HRV measurements can provide a useful assessment the quality of training and might detect over-training.  I think that for any athlete who can afford the cost, and is prepared to interpret the data thoughtfully, a Suunto t6 with Firstbeat software (or maybe a Polar RS800 with Firstbeat software) would be a worthwhile investment.  However I am disappointed that half a century after the thought provoking presentation by Forbes and Ursula Carlile to the Australian Sports Medicine Association in 1959, it is still difficult to find publicly accessible data that would allow an objective evaluation of the reliability of measurements of autonomic nervous system function for the purpose of detecting over-training.

Should you buy a HRM that measures HRV?

In response to my recent post on over-training and Heart Rate Variability (HRV),  Ewen asked if I had an opinion about which Heart Rate Monitor with capacity to measure HRV might be best.  I have not yet purchased such a device.  Before I offer my tentative thoughts on what might be the best device to buy, it is worth a brief deviation back to the Australia of my childhood in the 1950’s.

A trip back to Australia in the 1950’s

In those days, Australia dominated the world in several sports, but especially in swimming.  The really memorable character was Dawn Fraser, who won gold in the 100m freestyle in Melbourne (1956), Rome (1960) and Tokyo (1964).  Among the men, two of the greats were John Konrads and Murray Rose. A feature of the Rome Olympics was the battle between Konrads and Rose, with Rose winning gold in the 400m freestyle and Konrads in the 1500m.  Konrads held the world 400m record at the time.   During his career he broke multiple world records over distances from 220 yards to 1650 yards.  

What has this got to do with measuring over-training?  Following my recent posting on Heart Rate Variability, Mystery Coach sent me a very interesting report which Forbes Carlile and his wife Ursula presented to the Australian Sports Medicine Association in April 1959, entitled ‘T wave changes in strenuous exercise’.  Forbes Carlile was in those days a giant figure in swimming coaching, in Australia and internationally.  Carlile and his wife had recorded over 500 ECGs from swimmers, cyclists and oarsmen, in many cases performing recordings at different points in the season and relating these recordings to changes in performance

The main conclusion of the report was that stressful training or racing produces a decrease in amplitude or sometimes complete inversion of  T waves in the ECG.  Carlile and his wife reported:  ‘In general the sportsmen with a relatively light training load gave a series of practically unchanged electrocardiograms whereas those who were training strenuously frequently showed T wave changes in all leads.’

The pictures of the ECG traces were dramatic.  For me, one of the fascinating contrasts was between the recordings for John Konrads and those for several other top level swimmers.  The three recordings for Konrads were done at the beginning of hard training at the end of November 1958, and then again immediately before and after a 440 yd race on 28^th January 1959.  Unlike the pattern seen in the other top level athletes, Konrads’  T wave amplitude increased during hard training, and remained unaffected by the race. However for several other top-level swimmers, their T waves showed quite perceptible flattening during periods of intense training.  In these instances, the decrease in T wave amplitude was associated with deterioration in performance.

Carlile and his wife concluded: ‘we suggest that serial electrocardiograms offer a practical and scientific means of guiding the sportsman in his training.’  Examination of the ECG traces provided in their report made it very difficult to disagree.  Though in light of the fact that Dawn Fraser bestrode Australian swimming like a colossal cheeky Amazon at the time, one wonders about the use of the word ‘sportsman’ – but the 1950’s were of course over before another famous but slightly cheeky Australian woman, Germaine Greer, turned not just our T waves, but our attitudes upside down with ‘The Female Eunuch’.

What has happened to T waves since 1959?

In fact we now know quite a lot more about the things that produce a change in T waves.  T waves are the most labile feature of the ECG and can be affected by many stresses on the body.  One unifying feature is that T wave amplitude is diminished when the sympathetic nervous system is overactive.  By performing scans of the heart after administering a radioactive tracer substance called I¹²³-MIBG , which competes with noradrenaline to bind to the receptors on the surface of cells in the heart that mediate the effects of the sympathetic nervous system, it is possible to show that over-activity of the sympathetic nervous system is associated with suppression of the T waves. 

Thus, in principle, T wave suppression appears to be a good candidate to assess the form of over-training characterized by excess sympathetic activity.   There is of course a problem that anxiety also causes over-activity of the sympathetic nervous system, and can cause suppression of T waves.  Therefore assessment of T waves would only be useful if interpreted in light of other features affecting the physical and mental state of the athlete. 

I do not know whether athletes at the Australian Institute of Sport still have serial ECG’s to assess training stress but I suspect this is unlikely.   Since the 1990’s the emphasis has shifted from the shape of the ECG waveform to heart rate variability, but the fact that non-invasive assessment of the  effects of the autonomic nervous system on the heart has been possible for half a century, yet there is no widespread accepted procedure, makes me cautious in offering any advice.

Back to the measurement of HRV

Despite promising findings regarding the use of HRV to adjust training schedules (as reported by Kiviniemi and colleagues in the study I described on my blog posting yesterday), the situation is complex, so I think that investment in a HRM capable of recording HRV is a speculative investment.  They are not cheap, though if you can afford it and regard it as an interesting tool for investigation rather than a certain answer to the question of how to adjust training load, then I think it might be worthwhile. 

The two companies that have invested extensively in HRV technology are Suunto and Polar.  As a person with a wish to understand the underlying physiology, I find the websites of both companies very frustrating.  Both companies have clearly recognized that there is no simple measurement that applies to all individuals under all circumstances and both have developed ways of calculating training stress that takes account of the characteristics and situation of the individual.

Polar RS800

As mentioned in my blog recently, for assessment of over-training, Polar appear to place the main emphasis on 5 measurements performed on standing up from relaxed resting. This is a variant of the traditional orthostatic test, and involves assessment of changes in heart rate and heart rate variability.  In my post yesterday I gave a link to the Polar website.  It might also be useful to read the second part of this document prepared by the Heart Rate Monitor Shop in which they describe the OwnOptimizer test performed using Polar RS800.

http://www.heartratemonitor.co.uk/Manuals/RS800/ch09.html#N119D3

My main concern about Polar’s OwnOptimizer is that it does not employ data based on the body’s response to a training session, and I am not sure how easy it is to derive estimates of autonomic function during training or during other activities of daily living from the Polar RS800.

Note added 30 June 09 (22:00): I have discovered that FirstBeat Technolgies software can read the data from a Polar RS800.  Therefore, it appears that the various useful computations that I attribute to the Sunto T6 when used in conjuntion with the First Beat Technologies software  might also be achieved using the Polar RS800.  I am frustrated by the fact that neither the Polar website or Suunto website make it clear that the capability of their devices might be mproved by use of Firstbeat Technologies software.

 

Suunto t6

The Suunto t6, when used  in conjunction with software developed by Firstbeat Technologies appears to provide useful information about autonomic function at any time of the day or night.  As far as I can see the recommended way to detect over-training is from overnight recordings.  The software measures what are described as ‘stress reactions’ during sleep, and if these continue throughout the night, the athlete is at risk of over-training.  The software also produces two potentially informative quantities related to stress during training: training effect (an estimate of the stress on the body arising from training session) and EPOC (an estimate of the body’s additional oxygen requirement post exercise, estimated from HRV measurements). 

Useful information about the Suunto t6 is provide by Eddie Fletcher, a indoor rowing coach with international credentials and a clientele that includes international indoor rowing champions.

 http://www.fletchersportscience.co.uk/ 

He has written some interesting articles for Peak Performance. The following article from PP 237 is available on his website:

http://www.fletchersportscience.co.uk/uploads/img4668277a5a6191.pdf

Emma Snowsill (Triathlon gold medal winner in Beijing) uses Suunto t6c red arrow.

 

A tentative recommendation

If I had to choose between Polar and Suunto, I would choose Suunto t6 (with the FirstBeat Technolgies software – though at this stage I am uncertain whether or not the Polar RS800 might also yield similar information when used in conjuction with FirstBeat technolgies software ).   However, because my own personal approach is to try to understand the physiology, for the time being, I am inclined to continue to use my own amateur set-up.  With my set up I can also examine the waveform of the ECG.  I am still inclined to think that the size and shape of the T wave might be quite informative (despite the lack of clearcut conclusions subsequent to the report by Forbes and Ursula Carlile fifty years ago). However, my set-up does not allow wireless recording, so it is only useful for resting and standing measures.  I think that Suunto (when used in conjunction with Firstbeat software) is probably on the right path with assessment of autonomic nervous system function during sleep, every day activities and training.  I aim to post some more information on these issues in my blog over the next few weeks.

Additional Edit (30 June): As I explore the Firstbeat Technologies website ( http://www.firstbeat.fi/ ) I am starting to get a clearer understanding of which devices  can be used to perform the various measurements (all derived from HRV data) that have been developed by Firstbeat Technologies, which is a spin-off from the Research Institute for Olympic Sports, Jyväskylä, Finland

My current understanding is as follows:

Suunto t3 and t4 provide a ‘real time’ read out of Training Effect.

Suunto t6 with Suunto training manager software can provide more detailed analysis including Training Effect and  EPOC

It appears that Firstbeat have provided Suunto with the relevant software for incorporation in the Suunto Products.

Furthermore, I understand that data from either Suunto t6 or Polar RS800 can be read directly by Firstbeat Technologies software and used to compute Training Effect, EPOC and several other physiological variables. 

In my experience, the Firstbeat Technologies website is clear and informative, whereas I found it harder to glean the facts from either the Polar or Suunto websites.

Over-training, free radicals and HRV

Since taking up running again in middle age I have been very aware that my capacity for training appears to be greatly reduced compared to 35-40 years ago.  Once it seemed I could push the weekly mileage up to Lydiard’s recommended 100 miles per week with relatively little specific build-up.  I suspect that was because my general base fitness used to be high as a result of a range of sporting activities in childhood.  However, nowadays, if I push the weekly training volume above 55 Km per week I develop accumulating tiredness.  I have therefore been intrigued as to what it is that causes the accumulation of fatigue, and in my attempt to understand this I have explored the concept of over-training is some detail. 

Identifying over-training

The central features of over-training are relatively easy to define: accumulating fatigue, deteriorating performance, loss of motivation, a range of abnormalities of the autonomic nervous system and various biochemical and hormonal abnormalities.  However despite the range of abnormalities, it has so far proven difficult to identify a reliable laboratory test for the over-training syndrome. 

Purine metabolism and free radicals

Among the tests that make the most sense to me are tests of abnormal purine metabolites generated by the breakdown of the high energy molecule, ATP –adenosine triphosphate.  (Adenosine belongs to the goup of chemicals known as purines). In the process of releasing the energy stored in its so called ‘high energy phosphate bond’ to provide the energy for muscle contraction (and many other energy consuming processes within the body) ATP loses a phosphate group and becomes ADP  -  adenosine diphosphate .   The ADP can be re-used, but some of it gets broken down to simpler molecules and unless it is salvaged, it is excreted from the body in the form of uric acid.   The crucial issue with regard to damage to tissue is that intermediate steps in the metabolic pathway from adenosine to uric acid  result in the creation of ‘free radicals’.  Free radicals are highly reactive molecules that can cause damage by oxidation of various intra-cellular molecules.  In principle, this might happen in both heart muscle and in skeletal muscle and hence it is of potential interest to an athlete concerned about possible cumulative damage to either heart or skeletal muscle.  Free radical damage is especially likely to occur in older runners, but should not  be completely ignored by younger runners.

The fact that energy metabolism can lead to the creation of free radicals is the reason anti-oxidants have been popular among health food enthusiasts, though unfortunately there is no convincing evidence that consuming anti-oxidant supplements does any good and indeed might even do harm.  So I simply eat a sensible amount of food rich in anti-oxidants. 

This speculative relationship between purine metabolism and over-training has been given some substance by a recent study by Zielinski and colleges from Poznan in Poland (Eur J Appl Physiol May 29, 2009, Epub ahead of print). They examined levels of various metabolites of adenosine in the blood of young athletes (average age 22 years) and found substantial accumulation of these metabolites after exercise, that varied in magnitude at different phases of the training cycle.  It would be very premature to conclude that a rise in purine metabolites after exercise is a sign of over-training but nonetheless, does provide some grounds for further exploration of the idea that free radical damage may contribute to over-training, and maybe might even sometimes  result in irreversible changes. Whatever the mechanism of damage, over-training is clearly something to be avoided, by both old and young athletes. 

The central conundrum of training

The conundrum is that fitness arises via super-compensation for minor degrees of tissue damage produced by subjecting the body to stress.  Without stressing the body, and then allowing a recovery phase in which super-compensation occurs, we cannot become fit.  Optimal training requires the right balance between stress and recovery.

To a large extent we must listen to our bodies, and take things a little more easily when we experience accumulating fatigue, but it is tantalizing to ask whether or not there might be some physiological measurement to guide us.  So far no reliable biochemical or  hormonal measure has been identified and in any case, for the amateur athlete, regular laboratory testing is impractical.   However, in an era in which heart rate monitors are widely available, it has become feasible to measure the function of the autonomic nervous system, which controls many bodily functions including heart rate.

The autonomic nervous system

The autonomic nervous system governs the way in which we respond to threat or stress, and is sensitive to a very wide range of signals from within the body.  It governs short term responses such as the need to increase heart rate to deliver blood to exercising muscles, and also to ensure blood pressure is adequate to supply the brain.  But it also takes account of the body’s longer term needs, and it apparently acts to prevent us from over-exerting ourselves.  In general terms, all is well provided there is a good balance between the activity of the sympathetic nervous system, which promotes fight or flight, and the parasympathetic system which promotes relaxation and recuperation. 

However, if there is too much stress and too little opportunity for recovery, the action of the sympathetic system tends to become dominant – this leads to an over-training syndrome dominated by excessive sympathetic activity.  Potential markers for this include increased resting heart rate, an exaggeration of the normal increase of heart rate on rising from lying to standing (‘the orthostatic test’), and a loss of the high frequency variability (HRV) in heart rate, generated by parasympathetic input to the heart.

However, the body can react to cumulative stress even more dramatically by producing a excessive surge of parasympathetic activity that has the opposite effects.  When this happens acutely, the result is dizziness due to lack of blood reaching the brain, or even an outright faint.  When the excess parasympathetic activity occurs on a more sustained time scale, the result is the parasympathetic form of the over-training syndrome.  It is probable that this represents compensation by the body, possibly driven by a governor located in the inferior aspect of the frontal cortex of the brain that is responsible for regulating the parasympathetic system , to protect us from ourselves. 

The reason for laying out all these speculations is to dispel the idea that it is likely that any simple measure of heart rate or heart rate variability will prove to be a universally useful indicator of the over-training syndrome. 

1998 – a new heart rate test!

Among the FAQs on the website of Polar, the company that pioneered the manufacture of wireless heart rate monitors, is an article entitled ‘The new heart rate based test gives a pre-warning of an overtraining condition’.  This describes a test based on measuring heart rate variability on waking and after rising to maintain a standing position for several minutes. 

(http://www.polar.fi/support/faqs?product=&category=Training)

The test was developed by Dr. Arja Uusitalo, at the Research Institute for Olympic Sports in Jyväskylä, Finland.  The article on the Polar website proclaims optimistically: ‘The most demanding task for the coach and the athlete is to find out the cause of the overtraining condition and how to control it. What makes it easier, is that a new test will tell whether the condition was fatigue, caused by an acute stress situation, or an athletic burn-out as a result of too heavy training.’  The data on which that article was based was published in Dr Uusitalo’s PhD thesis in 1998.  

What has happened since 1998? 

Surely if the optimism implied by the article had been fully justified, many of us would have by now invested in an advanced Polar HRM and use this test to monitor our training.  In fact, since 1998, Dr Uusitalo, together with her colleagues from the Institute for Olympic Sports in Jyväskylä, has published a number of important articles on HRV and over-training.  The findings are only moderately supportive of the value of HRV measurements, though overall, I interpret these articles as providing support for the hypothesis that HRV is potentially a useful indicator of over-training.  However, it would be far too simplistic to expect a single test, such as that proposed by Dr Uusitalo in 1998, to provide a reliable answer in all situations.   In light of the complexity and variability of the over-training syndrome, one might predict that any test of HRV would have to be interpreted in light of individual characteristics and circumstances.

What do Polar offer in 2009?

Polar now offer a test procedure called the Own Optimizer which is based on five heart rate and heart rate variability measurements: two of the five values are calculated at rest, one while standing up and two while standing. It is not clear to me exactly what these five measurement are, though it appears likely that both the orthostatic test (change in heart rate on standing) and change in HRV on standing are included.  Unfortunately, Polar present very little evidence regarding the utility of Own Optimizer.  On the Polar discussion forum, a moderator named Mico refers to evidence from a study of  endurance training guided individually by daily heart rate variability measurements, performed by Antti Kiviniemi and colleagues from Oulu in Finland  (Eur J Appl Physiol. 101(6):743-751, 2007) [the reference given on the Polar website was not quite accurate, but this appears to be the relevant study]

Training guided by HRV

The study by Kiviniemi reports a comparison between a 4 week training program guided by HRV and a pre-defined training program   The predefined program entailed two sessions at low intensity and four at high intensity each week, for the 4 weeks.   The HRV guided  training program was based on individual changes in high-frequency HRV,  measured every morning.  If there was an increase or no change in HRV, the athlete performed high-intensity training on that day. If there was significant decrease in HRV (below reference value or a decreasing trend for 2 days), low-intensity training or rest was prescribed. 

VO2max improved significantly from 56 to 60 ml/min/Kg in the HRV guided group, but only showed a non-significant increase from 54 to 55 ml/min/Kg in the group who followed the predefined program.  Furthermore, running velocity in a treadmill test  increased by a significantly greater amount in the HRV guided group than in the predefined training group.  The authors concluded that cardio-respiratory fitness can be improved effectively by using HRV for daily training prescription.

The report by Kiviniemi is intriguing and indeed a cause for optimism.  However, it needs to be interpreted in light of the many other studies of HRV and training (or over-training) that have been published in the past decade. I will attempt to review some of the studies that I think tell an interesting story, in future postings on my blog, though at this stage, the overall conclusion is that HRV might potentially be useful  to monitor training, but no reliable simple test has yet been developed, and the data must be interpreted in light of individual circumstances.

Heart rate variability

Heart rate variability, as the name implies, is variability in the duration between consecutive beats.  In the ECG, it is variability in the time interval between consecutive R waves in the QRS complex that represents the electrical events of ventricular contraction.  In general, the heart beats faster during inspiration and more slowly during expiration.  These variations are governed by the autonomic nervous system, which is responsible for the regulation of many of the body’s internal organs and in particular, coordinates the fight-or-flight reactions that prepare our body to deal with challenging situations.  There are two distinct divisions of the autonomic systems: the sympathetic system which tends to accelerate the heart and the parasympathetic system which produces deceleration. 

Almost certainly HRV is a crucial importance to the athlete, though the details are still a subject of debate.  There are three main issues:   1) loss of HRV  is potentially a useful indicator of the stress associated with training, and there is evidence suggesting that adjusting training schedules according to changes on HRV can increase the quality of the training and diminish the risk of over-training; 2) loss of HRV is a fairly reliable indicator of sudden cardiac death in individuals with heart disease and some evidence indicates that it has similar implications even when there is no other evidence of heart disease; 3) a resilient heart with high HRV might in fact function more efficiently and hence improved HRV might itself contribute to improved performance.   In the next few posts I will attempt to examine all three of these issues, and also to address the question of how HRV might best be increased by training.

Before examining the evidence that training might have either beneficial or deleterious effects on HRV, I decided  to look at HRV in my own heart.  Both Polar and Suunto manufacture hear rate monitoring equipment that provides an estimate of HRV, but I am still undecided about the value of investing in such equipment.   Furthermore, I thought it would be interesting to examine not only heart rate variability, but also the shape of the various peaks and troughs that make up the ECG.  I rigged up a system with a lead attached to each forearm, and fed the signal to some electrically isolated amplifiers, before digitizing it so that I could subject it to some detailed analyses. (Isolation of the amplifiers is crucial for safety).  The trace recorded with this ad hoc system in not a clinical ECG, but nonetheless corresponds quite closely to lead 1 in a clinical 12 lead ECG.  Lead 1 represents the difference in voltage between left and right arms.

 

My  ECG

The ECG reflects the flow of the electrical currents that produces contraction of the heart muscle.  The current flow produces currents in the surrounding body tissues and provides a signal detectable on the body surface.  The magnitude and average direction of the current flow determines the size and shape of the various peaks in the ECG.  For our present purposes, the feature of greatest important is the rhythm, and in particular, the variability of the interval between beats.  However, a substantial number of athletes have minor abnormalities of the shape of the waveform.  Follow-up studies indicate that these abnormities are generally benign and do not create a high risk of heart attack, though the issue of distinguishing clearly between normal variation seen in athletes and pathological variation is still a somewhat vexed question.  Therefore, I was interested to examine the waveform to see whether or not I have significant abnormalities.  I am an amateur in the interpretation of ECG’s, so if a cardiologist happens to read this, I would be delighted to hear whether or not I have interpreted things correctly. 

ECG and inter-beat interval

The figure on the left shows the ECG during two successive heart beats, while the figure on the right shows the variation in inter-beat interval over a period of about 80 seconds.  The waveform looks fairly normal to me (as an amateur). 

Atrial contraction

The P wave reflects spread of the electrical signal from its generation at the sinoatrial node, though the walls of the right and left atria, and in my recording is about 80 milliseconds wide and has a height of 0.08 mV.  After an interval of around 195 millisec, the P wave is followed by a QRS complex.  The PR interval is the time between sino-atrial node firing and the firing of the AV-node which initiates ventricular contraction.  It is an important indicator of transmission of the electrical signal from atria to ventricles. The normal value is around 200ms.  Very short values might indicate a potentially serious conduction abnormality known as the Wolfe-Parkinson-White syndrome, while long values might indicate blockage of transmission, so it is reassuring that the duration of my PR interval seem fairly normal.   Between P wave and QRS complex the trace should be fairly flat, though in my case there is a slight downwards slope.  I do not know if this has any significance. 

Ventricular contraction

The QRS complex reflects the spread of the electrical signal that causes depolarization of the muscle in the walls of the ventricles.  The depolarization of the muscle cell membrane produces the muscular contraction that ejects the blood from the ventricles.  The negative Q wave represents the spread of electrical signal downwards and left-wards to the apex of the heart and the large Q wave reflects the spread around the lateral walls upwards and predominantly rightwards from the apex. In some leads of the ECG, R is followed by a small negative deflection known as the S wave, though due to the direction of current flow at this time, the S wave often small in lead 1, and in my ECG, it is scarcely discernible at all.    In a healthy heart the width of the QRS complex should be less than 120 ms.  In my case it is only about 60 ms.

Ventricular repolarization

The final peak of interest is the T wave, a hump occurring about 350 ms after the end of the QRS complex in my trace.  This denotes re-polarization of the heart muscle, making it ready to contract again.  Prolongation of the QT interval occurs in a number of pathological conditions and can also be an unintended side effect of various medications. Because it tends to be shorter at high heart rates, it is usual to estimate a corrected value known as QTc which allows for variation in the heart rate.  In my recording QTc is about 300 ms.  The usual value is around 420 ms, so I do not have any evidence of a pathologically long QTc.

One feature that is of great clinical importance is the ST segment.  This is depressed when the heart muscle is deprived of oxygen and can be elevated once the heart muscle has been damaged.  The ST segment should be flat, but in my case it rises steadily for about 100 ms preceding T wave.  As far as I am aware, this upward slope of the ST segment is quite common in athletes, and possibly denotes thickening of the ventricle walls.  The point at which ST elevation of depression is usually measured is 60 millisec after the end of the QRS complex.  At this point, my trace shows no evidence of either depression of elevation, so I am inclined to interpret this picture as indicating a healthy though perhaps slightly hypertrophic heart.

HRV

Now to the important issue for our present discussion.  My average heart rate during this recording was 46 beats per minute, which I think  is fairly typical of a fit athlete when sitting down, trying the relax but nonetheless, with my mind focused on making sure the equipment is continuing to function.  However even more striking is the variation of the heart rate.  As can be seen from the right hand figure, over the course of about 80 seconds the inter-beat interval varied from 1.1 sec (HR 54 bpm) to 1.5 sec (HR 40 bpm).  The normal variability in beat-to-beat interval is less than 10%.  So I definitely have substantially increased heart rate variability.

Even  more interesting is the time-scale of the variation. It can be seen from the right hand figure, that the dominant fluctuation are rapid (ie high frequency) fluctuation occurring on a time is around 2-4 beats (i.e over a period of 4 seconds corresponding to a frequency of  0.25 cycles/sec.)  These high frequency fluctuations are characteristic of the parasympathetic nervous system, which in general is associated with relaxation and recovery. 

However, closer inspection also reveals underlying variation on a time scale of around 30-40 beats (40-50 seconds) corresponding to a frequency of 0.02 cycles per second.  Slow fluctuations on this time scale are characteristic the action of the sympathetic nervous system – the part of the autonomic nervous system that promotes fight or flight.  So I am encouraged to observe evidence of a balance between parasympathetic and sympathetic activity, but with a particularly strong parasympathetic component.

 

Sympathetic-parasympathetic balance: the Poincare plot

There is an intriguing though somewhat complex way of quantifying the relative contributions of parasympathetic and sympathetic nervous system to hear rate variability, known as the Poincare plot.  The Poincare plot is a scatter plot in which each heart beat is represented by a point in a two dimensional plane, with the value of the  inter-beat interval preceding that beat is plotted on the horizontal axis and the value of the following  inter-beat interval on the vertical axis.   This scatter plot is shown on the figure below, for the same data as is shown in the figure above.  The points look a bit like a swarm of bees scattered around a line that runs diagonally upwards at 45 degrees. 

Sympathetic - parasympathetic balance: the Poincare plot

 

The interpretation requires some concentrated thinking but is worth the effort.  Imagine that the heart rate variability is produced entirely by sympathetic nervous system activity.  Such fluctuations occur on a slow time scale – typically varying substantially over a period  40 beats or more. Therefore for consecutive beats, the inter-beat interval is almost unchanged.  The points representing those near identically spaced consecutive heart beats must lie near the 45 degree line.  However, if there is a slow drift in values of inter-beat interval over time, due to fluctuation in the input from the sympathetic nervous system, then the points will wander up and down the 45 degree line.

On the other hand, if the fluctuations are driven by the parasympathetic nervous system, they occur on a time scale of a few beats, and the beat by beat variation will be large.  Therefore many heart beats will be represented by points that lie far away from the 45 degree line

If we draw an ellipse that is just large enough to include most of the heart beats (allowing that there will usually be few outliers that buck the general trend) then this ellipse will have one  axis pointing along the 45 degree line and the other axis at right angles to this.  The length of the axis along the 45 degree line represents the amount of sympathetic drive and the length axis at right angles to this represents parasympathetic drive. 

In individuals a high risk of heart attack the ellipse is a long thin cigar shape lying along the 45 degree line.  For individuals with a good balance between parasympathetic and sympathetic input, the ellipse is almost round. I was delighted to find that for me, the ellipse is fat and almost round.

 

Summary

So in summary, I am quite pleased with what my little experiment showed.  As far as I can tell, the shape of my ECG trace is near normal, apart from the upward slope of the ST segment that I understand is relatively common in athletes.  My heart rate is slow, but even more importantly, there is a large variability, driven by a good balance between parasympathetic and sympathetic activity.  I am re-assured that I appear to be at low risk of a heart attack.

 

I suspect that the substantial HRV is the product of my training.  In future posts I will examine the evidence regarding the types of training that are most likely to improve HRV and also explore the question of whether measurement of HRV does in fact provide not only an estimate of likely risk of a heart attack, but also might be a useful indicator of the over-training syndrome, and hence provide a useful way to adjust one’s training schedule.

High intensity v low intensity training for the heart

My post on 20^th June looked at the evidence  that training can produce both cardiac hypertrophy and increased blood supply to the heart muscle – the combination  of features that distinguish healthy hypertrophy for the unhealthy hypertrophy seen in some cases of cardiovascular disease.  The evidence from studies of pigs on treadmills and novice runners following a moderately demanding aerobic program is that several months of aerobic training can produce a substantial increase in the mass of the left ventricle – eg a 15% increase in mass after 6 months training in Rodriguez’s study of healthy but previously untrained young men (Am J Cardiol. 97:1089-92, 2006). This increase was associated with increased ventricular diameter and increased thickness of the muscular walls of the heart.  There was an associated increase in VO2max, a direct measure of aerobic capacity and a strong predictor of performance over middle and long distances.

Naylor’s study of elite athletes also demonstrated an increased ventricular mass after 6 months training in elite athletes (J Physiol 563; 957-963, 2005), but the increase was less than in the novices studied by Rodriguez and there was a disconcerting observation that despite pre-existing hypertrophy from previous years of training, at the beginning of the study (after a 6 week lay-off) the elite athletes had evidence of slower filling of their ventricles, which would reduce the capacity to utilize the additional muscle mass effectively.

 The contrast between the studies by Rodriguez and Naylor demonstrates that the benefits of a training program vary depending on the prior training status of the athletes.  Consequently, it is difficult to provide a clear answer to a very simple question: what form of training is likely to be most beneficial for improving cardiac function.

 The alternative to examining the results of studies of training programs is to examine what we know about the mechanism of hypertrophy.  Unfortunately, rapidly growing knowledge about the mechanisms by which the body responds to training has revealed just how complex these mechanisms are.  On account of the scope for unpredictable interactions between many variables, prediction of the final outcome on the basis of simple theory is unreliable.   My own view is that the most sensible approach is to combine what we know about mechanisms with the evidence from studies of training, and test that against one’s own experience – since  no two individuals are identical in genes and experience and therefore each person has to find out what works for him or her.

 

Speculation based on theory

First we need to ask what variable is of greatest interest.  For the middle and long distance runner, the most important demand on the heart is to deliver a large volume of blood bearing oxygen – the capacity to do this is known as cardiac output – the volume of blood delivered per minute.  This is the product of heart rate and stroke volume.  From the point of view of aerobic performance, the ultimate measure  is VO2 max, the maximum rate of utilization of oxygen. This is calculated by multiplying  cardiac output by oxygen extraction fraction.  Oxygen extraction fraction is a property of the skeletal muscles determined by capillary density and density of mitochondria in the skeletal muscle.  But for the present purpose we are concerned about training the heart.  Therefore, the trainable quantity if greatest interest for our present discussion is stroke volume.

The acute effect of ventricular filling

 Stroke volume is determined largely by the diameter of the ventricles but also by the efficiency of filling of the ventricles and the power to eject blood from the ventricles.  One of the important features of the function of cardiac muscle is the fact that stretching immediately prior to contraction produces a more powerful contraction – this is the Frank-Starling principle. As heart rate and cardiac output rise in response to demand for oxygen in the muscles, the return of blood from the periphery rises, greater stretching occurs during filling, and a more powerful contraction is produced.  In a trained athlete, stroke volume normally increases as the  cardiac output, and therefore the amount of blood returned to the heart, increases, reaching its maximum when heart rate reaches its maximum. 

In the early phases of training, increase in blood volume leads to greater filling and more powerful contraction.  Incidentally, either dehydration or the forcing of fluid into body tissues that accompanies an increase in blood pressure, decreases the volume of blood returned to the heart, so stroke volume falls and heart rate needs to rise higher to compensate to maintain a given cardiac outpt. VO2 max will be truncated because maximum heart rate does not change substantially. 

 The long term effects of ventricular filling

 Not only does increased cardiac filling promote an immediate rise in force of contraction, but the stretching of the heart muscle at the end of the filling phase (diastole) acts as a trigger to hypertrophy, apparently via the Akt signaling within the heart muscle cell, which ultimately leads to both the generation of additional contractile proteins and also the parallel development of capillaries, as discussed in my blog a few days ago.  This hypertophy will lead to an increase in both the diameter  of the ventricles and also the thickness of the walls of the ventricles, as demonstrated in the study by Rodrigues et al (Am J Cardiol. 97:1089-92, 2006).

So the most efficient form of training for increasing stroke volume and for the associated development of capillaries supplying the heart muscle is likely to be fairly vigorous exercise that produces a large amount of filling of the ventricles during diastole.  It would be expected that the  greatest benefit per unit of time spent training will be gained by training near VO2 max – though of course the overall picture must take into account the risks  associated with training at this level.  We will return to that issue again in the future.

The myoglobin effect

However one additional point needs to be made. If training is to be above the lactate threshold, then each effortful interval must be relatively brief – but not too brief, because of the phenomenon of buffering by myoglobin. At the beginning of an effortful interval, oxygen attached to myoglobin in the muscles can meet the metabolic needs for a period of a minute or so, so the demand for cardiac output does not reach a peak until about two minutes after the start of the effort.  Therefore, one might expect that intervals of three or four minutes duration would proved the best value for time spent (though alternatively one might do shorter intervals if the rest period is very short (eg 10-20 sec) so that myoglobin is only partially  replenished during the rest period).

 Matching observation to theory

How does observation match theory?  There are very few studies that have directly compared the changes in stroke volume after a program of high intensity interval training compared with lower intensity aerobic training. The only one I know of is by Helgerud and colleagues from Trondheim in Norway (Med Sci Sports Exerc. 39(4):665-71; 2007). They randomly allocated 40 moderately trained male participants (with initial VO2 max around 60 ml/min/kg)  to one of four training groups for 3 sessions per week for 8 weeks:

 1) long slow distance (LSD) (70% maximal heart rate);

2) lactate threshold (85% HRmax);

 3) 15:15 interval running (15 s of running at 90-95% HRmax followed by 15 s of active resting at 70% HRmax); a session included 47 x15 s effort intervals.

4) 4 x 4 min of interval running (4 min of running at 90-95% HRmax followed by 3 min of active resting at 70%HRmax).  

The amount of work in each session was adjusted to that the total oxygen consumption was similar is all four groups. 

The two interval training programs resulted in a significantly greater improvement of VO2max (5.5% for 15:15 and 7.2% for 4 x 4 min intervals than the low intensity aerobic and lactate threshold sessions. Furthermore stroke volume increased by approximately 10%  after each of the high intensity interval programs.  Thus, it appears that compared with low intensity aerobic or lactate threshold training, high intensity interval training produces greater improvements in VO2 max  and parallel increases in stroke volume, in accord with expectation based on theoretical considerations.

 High intensity is best, but in moderation

Thus the most efficient from of training for producing an increase in stroke volume and VO2 max appears to be high intenirty interval training.   This certainly does not mean that a training program should consist entirely of high intensity sessions for two reasons.  First, it is necessary to take account of the need to train the leg muslces as well. Increasing capillaries and mitochondria in leg muscles, and also developing the ability to withstand eccentric contractions of the leg muscles for the duration of the intended race are also important aspects of optimizing racing performance.  At least for long races (half-marathon and marathon) training the leg muscles to cope with multiply repeated eccentric contractions at each footfall is crucial, and this requires a substantial training volume.  The second issue is avoiding too much stress on the heart.  The crucial issue here is maintaining heart rate variability (HRV).  HRV can be improved by training, but both excessive volume and excessive intensity of training can impair HRV.  I will examine this issue in more detail in my next posting.

 Nonetheless the simple conclusion with regard to increasing cardiac output is that both medium intensity aerobic training (as employed in the study by Rodriguez, considered in my post on 20^th June) and high intensity interval training can produce benefits, but high intensity interval training is the more efficient.

Hypertrophy and the supply of blood to heart muscle

There are two main reasons why a runner might be concerned about the best way to train the heart. First, to improve running performance and second, to increases life expectancy or at least minimize the risk, albeit small, of heart attack during or after a race. As discussed in recent postings there are at least four aspects of heart structure and function that respond to training: blood supply, muscle hypertrophy, efficient fuel metabolism and heart rate variability (HRV). The relevance of HRV to perhaps less easy to appreciate, but as discussed in my post two days ago, decreased HRV appears to be a risk factor for sudden cardiac death.

In today’s post I want to examine cardiac muscle hypertrophy and improved blood supply. The reason for considering these two types of adaptation together is that the most important feature that distinguishes the healthy cardiac hypertrophy for the unhealthy hypertrophy that can occur in patients with cardiovascular disease, is the concurrent development of both muscle hypertrophy and blood vessels in healthy ‘athletes heart’, in contrast to hypertrophy without increased blood supply in pathological conditions. There is strong evidence for mutual interaction between the processes that promote normal development of muscle and blood vessels in the heart.

Back to the mini-pigs

Before examining the evidence from studies of human athletes, it is worth returning to the study of Yucatan mini-pigs which I mentioned a week ago. Pig heart has many similarities to human heart, but it is possible to do much more comprehensive investigations of changes in the pig heart. In that study by White and colleagues (J Appl Physiol 85:1160-1168, 1998) the pigs underwent an aerobic training program similar in volume and intensity to that typical of a long distance runner’s base-building program. The pigs were trained to run on a treadmill at a heart rate in the range 70-80% of maximum. In the first week they ran for 30 min on 5 days per week. The daily duration was increased by 5 min per day each week in the first 8 weeks and thereafter they continued to run for 70 min per day 5 days a week until 16 weeks.

During the first three weeks, the density of capillaries supplying blood to heart muscle increased, and then in the remaining weeks these capillaries apparently enlarged to become arterioles, so that by the end of 16 weeks, the cross sectional area of blood vessels had increased by 37%, and coronary blood flow had increased by 22%.

Capillary transfer reserve, which was assessed by measuring the increase in transport of diffusible molecules from blood to muscle when vessels were maximally dilated by administering a vasodilating drug, increased steadily throughout the study and at 16 weeks was 59% greater than at baseline. This demonstrates not that not only was the resting blood flow increased be training, but the capacity to increase the delivery of oxygen and nutrients to the heart under conditions of high demand was increased even more dramatically.

Left ventricular mass (relative to total body mass) had increased by 16% at 8 weeks and 24% at 16 weeks. VO2max increased by almost 40% in the first 8 weeks and thereafter only increased slightly. Thus a 16 week program of aerobic training produced an initial rapid development of new capillaries. Very little new sprouting of capillaries occurred after 3 weeks, but the total cross sectional area of blood vessels continued to increase, apparently reflecting the conversion of capillaries to arterioles, while muscle hypertrophy continued throughout.

Studies of humans

Many studies have revealed that athletes exhibit cardiac hypertrophy relative to sedentary controls, but there are remarkable few longitudinal studies that allow an estimate the magnitude of the effect of a particular training program on cardiac structure and function. Furthermore, it is necessary to consider the issue of the stage in an athletes career. One might expect the greatest gains in the early years, though of course this must be set against the expectation that training volume and intensity should be less in the early stages of a running career.

Moderate intensity training in novices

In a study by Rodriguez et al Am J Cardiol.;97:1089-92, 2006), 23 sedentary men in their late 20’s and early 30’s undertook a 6 month program of moderate-intensity aerobic training (1 hour/day, 3 times/week). This program achieved a 14.5% increase VO2 max; a 4 beat per minute decrease in average resting HR; a 15% increase in left ventricular mass index; and approximately a 6% increase in thickness of both the septum separating the ventricles and the posterior ventricular wall (assessed by Doppler echocardiography). Somewhat surprisingly there was not a statistically significant increase in stroke volume. Nonetheless, this study clearly demonstrates substantial left ventricular hypertrophy and also an associated increase in VO2 max during the type of 6 month aerobic program that might be recommended for novice who has recently taken up running.

Intense training in elite athletes

Perhaps of more relevance to committed athletes is a study of changes during the 6 months training commencing at the end of a 6 week off-season, in a cohort of 22 young elite athletes, by Naylor and colleagues from University of Western Australia (J Physiol 563; 957-963, 2005). The athletes (all rowers) had a mean age of 20, suggesting a prior career of 3-5 years duration. At the beginning of training after the off-season they had a mean left ventricular mass of 235 gm compared with 178 gm in a group of matched recreationally active control subjects. After three months training (twice daily, 6 or 7 days per week), the athletes had increased their left ventricular mass even further to 253 gm and it then remained stable around this level (with a value of 249 gm at 6 months). Thus it appears that there is a cumulative increase in hypertrophy over years of training, at least in young athletes, and training in the new season can produce a further increase around 7% in the first 3 months followed by a plateau period extending out to 6 months (ie a lesser relative increase than the 15% reported by Rodriguez in the 6 month program in novice subjects).

However the even more interesting measurement in the elite athletes studied by Naylor was left ventricular flow propagation velocity, an indicator of speed of left ventricular filling. At the beginning of the season, this quantity was less in the athletes than in the control subjects. Rapid filling is required to make the most of the benefits of greater contractility. It appears that a hypertrophic heart is of little value if it is not regularly exercised. However left ventricular filling rate improved throughout the 6 months of training, to a level marginally higher than that in the non-athlete control subjects. Thus by the end of the 6 month training period the athletes had hearts that were not only larger than the controls but also filled at least as rapidly.

It should be noted that the rowers studied by Naylor engaged in a mixed training program including both aerobic and resistance training. It was once believed that aerobic training produced predominantly an enlargement of the ventricular diameter, and hence stroke volume (so called eccentric hypertrophy), while resistance training produced thickening of the ventricular walls (concentric hypertrophy). More recent evidence indicates that enlarged diameter does predominate slightly in endurance-trained athletes whereas increased wall thickness predominates slightly in resistance- and static-trained athletes, but the differences are not dramatic (Barbier et al, Herz 31, 531-543, 2006).

Mechanical mechanism of hypertrophy

The mechanism of hypertrophy can be investigated at various levels. At the level of large scale mechanical processes it is well understood that increased filling of the chambers of the heart will stretch the vessel walls and this will lead to increased tension in the muscular walls which will in turn result a more powerful contraction (the Frank Starling Principle) – similar to the greater power of skeletal muscles during plyometric exercise. This greater stretching acts as a stimulus to hypertrophy.

Underlying molecular mechanisms

However perhaps more interesting is the mechanism at the level of the molecular processes that go on within the muscle cells. As is the case in many biological processes, effects occurring at the cell surface (eg mechanical effects such as stretching or the binding of messenger molecules) initiate a complex cascade of signaling within the cells. This intracellular signaling usually involves activation of enzymes that add phosphate groups to other proteins thereby changing their shape and function, and these proteins then act on others creating a cascade of effects. The signaling processes lead to the expression of genes (that is, the translation of the DNA code) to produce new proteins, which might themselves be either additional signaling molecules, or the proteins that carry out the primary function of the cell – e.g. contractile proteins.  Thus the  end result of the cascade of signals is the production of proteins that carry out the main functions of the cell. 

One of the pathways involved in cardiac muscle hypertrophy is the Akt pathway (Shiojima & Walsh, Genes Dev. 20: 3347-3365, 2006). Akt is activated by various extra-cellular stimuli. One of the mechanisms of activation is via insulin-like growth factor (IGF). A comparison of professional soccer players (who trained for 10 hours per week) with non-athletes, revealed that IGF formation is associated with cardiac hypertrophy (Neri Serneri et al, Circ. Res. 89;977-982, 2001). The authors concluded that increased cardiac IGF is likely to be a major contributor to cardiac hypertrophy in athletes. Akt is a signaling molecule that itself acts on three different pathways: pathways involving GSK-3, m-TOR, and FOXO. [The names of signaling molecules should be read in the same spirit as Jabberwocky, or perhaps, like the names of fundamental particles in physics - some names have an understandable serious origin but others sound like a private joke between the scientists who created them]. GSK-3 regulates cardiac muscle hypertrophy; m-TOR regulates growth of small blood vessels (possibly be the well known vascular endothelial growth factor, VEGF). Akt switches off FOXO, which plays a role in protein degradation.

A crucial feature is that transient bursts of Akt signaling promote concurrent development of both muscle fibres (hypertrophy) and also development of capillaries. Concurrent hypertrophy and increase in capillaries is the characteristic of healthy hypertrophy. However paradoxically sustained Akt activity can result in the hypertrophy without accompanying development of small blood vessels. This is the characteristic feature of pathological hypertrophy seen in several form of heart disease. Although I am not aware of any evidence that excessive training can lead to hypertrophy without increased blood supply to the heart muscle, the observation that sustained Akt activation can have potentially pathological effects points to the need for the body to have mechanisms that protect against excessive training. My own speculative hypothesis is that the over-training syndrome, which acts to discourage an athlete from continuing with an excessive training routine, might indeed be a defensive mechanism invoked by the body to protect us from ourselves. I will return to this theme in a later post.

Practical conclusions

So in conclusion, the main goals of a distance runner, namely improving blood supply to heart muscle and producing the hypertrophy associated with an increase in stroke volume, can be met simply an aerobic program along the lines undertaken by the Yucatan mini-pigs studied by White and Bloor. However, the study by Naylor of elite athletes in the six months following the off-season demonstrates the complexity of the relationship between prior training history and the improvement in cardiac function. In particular, the evidence indicates that despite the persistence of previously acquired hypertrophy, deterioration in ventricular filling speed during a 6 week off-season might offset any advantages of the pre-existing hypertrophy until many months after the resumption of vigorous training. There is not a great deal of evidence to demonstrate superiority of one training regimen over another for improving cardiac function, but nonetheless, I will review what evidence there is, in the near future.

Heart rate variability: is the debate between Noakes and Ekblom about the wrong question?

In my recent post about training the heart I listed three aspects of heart structure and function that respond to training: blood supply; muscle hypertrophy; and fuel metabolism. There is a fourth which is less easy to understand, but might actually be the most important. So bear with me.  We will get back the the reality of every-day training before too long.

The fourth trainable aspect is heart rate variability (HRV); the variability in heart rate that occurs on a time scale of 2 to 20 seconds. It is well established that the heart rate varies breath by breath, inceasing during inspiration and decreasing during expiration, mainly due to input from the parasympathetic nervous system during expiration. Furthermore it is established that loss of this heart rate variability is a predictor of risk of sudden cardiac death. Finally, many stressors, including hard training or racing cause a reduction in heart rate variability and the degree of this reduction is correlated with the length of time it takes for the body to recover.  It should be noted that many different types of stress, possibly involving signals from different systems within the body, can produce a reduction in HRV.  It appears that a healthy heart requires resilience

Back to the central governor

For the past decade Tim Noakes in Capetown (J Appl Physiol 106: 347, 2009 and 106:341, 2009.) and Bjorn Ekblom from the Karolinska Institute in Stockholm (J Appl Physiol 106:339-341, 2009) have debated the issue of the central governor – a proposed governor in the brain that acts to limit power output so as to protect the heart. The debate between Noakes and Ekblom centres on the fact that even at a power output around VO2 max, there is good evidence that under many circumstances there is substantial coronary reserve (ability to increase coronary blood flow) and also ability to increase cardiac work. Ekblom has invoked this evidence to oppose Noakes theory of the central governor [Ekblom, Scand J Med Sci Sports 2000: 10: 119–122; J Appl Physiol 2007; 102:781-786 ] As far as I can see, Ekblom has got the best of the argument, at least in the eyes of many professional physiologists, though many athletes are more sympathetic to Noakes.

In my view, both Ekblom and Noakes might be asking the wrong question. If indeed it is loss of heart rate variability rather than simple deprivation of oxygen that puts the heart at risk, an effective governor will be geared up to identify serious decrease in HRV and at that point, limit the body’s power output.

Why might HRV be the key?

This seems plausible for several reasons. First of all, most deaths during or after a marathon do not occur when the heart is likely to be acutely deprived of oxygen, but rather following cumulative stress (Journal of the American College of Cardiology, vol 28, pp 428-431, 1996).  Cumulative stress decreases HRV. Secondly, the fact that decrease of HRV predicts recovery time from a race or training suggests that the brain not only keeps track of HRV but sets this in the context of how much longer the race will be. This would explain the observation that it is possible to sprint at the end of a marathon even when it was impossible even to increase pace slightly after hitting the wall a few miles earlier.

Why is it hard to improve max HR by training ?

This proposal provides a simple answer to the question of why peak heart rate is virtually non-responsive to training and tends to decrease with age, thereby resulting in decreasing VO2max and decreasing aerobic performance. A rise in HR is associated with an increase in sympathetic input; a decrease in parasympathetic input; and a decrease in HRV. At a certain heart rate, it would be dangerous to decrease parasympathetic input any further. As the tissues of the heart become stiffer with age, this might be expected to occur at a lower HR. This raises the crucial issue of whether training can prevent this deterioration. I will return to that issue later.

Nerves supplying the heart

To propose that Noakes and Ekblom have been arguing about an irrelevant measurement is a rather a bold claim to make, so you might reasonably expect me to provide some better evidence. The first body of evidence concerns the nerve supply to the heart. There is an internal nerve supply in the heart (Arora et al , Am J Physiol Regul Integr Comp Physiol 285: R1212–R1223, 2003), and also an external input from the two main divisions of the autonomic nervous systems: the sympathetic system which makes the heart beat faster and the parasympathetic system that slows the heart down

Sympathetic input rises under stressful circumstances while parasympathetic input increases during relaxation or recovery. Sympathetic input to the heart causes slow oscillations in heart rate (on a time scale of 7-20 second2); parasympathetic input causes more rapid oscillations on a time scale of 2-7 seconds. There is a great deal of evidence that a preponderance of slower parasympathetic oscillations predicts a longer life. Spinal cord stimulation which is likely to send parasympathetic signals to the heart tends to overcome the dangerous effects of oxygen deprivation in a diseased heart, and can be an effective treatment for angina (Foreman et al Cardiovascular Research 47;367–3750: 2000).

The heart talks to the brain

Furthermore the passage of nerve messages between heart and brain is not one way. The nerves in the heart send messages back to the brain, providing a means of informing the brain about the current variability and stress level. However the internal cardiac nerves cannot by themselves produce the heart rate variability that appears to be a feature of health. In a dog with the sympathetic and parasympathetic input removed surgically, coordinated rapid contraction of the heart occurs but HRV is abolished (Murphy et al Am J Physiol Regulatory Integrative Comp Physiol 266:1127-1135, 1994). There is a large body of evidence indicating that HRV predicts the amount of extra oxygen that will be required after a hard training run – and this does not appear to be simply a matter of the brain estimating the accumulation of lactate, at least when the heart has been under substantial stress (Dixon, Cardiovasc Res. 26(7):713-9,2000).  Suunto, who manufacture a heart rate monitor that can measure HRV, claim that loss of HRV might be a useful indicator of over-training. I think the evidence regarding detection of over-training is complex, but there appears to be a degree of truth in Suunto’s claim.

What does this mean for planning a training program?

If some or all of these speculations are correct, the crucial issue for the athlete is whether HRV can be improved by training. The good news is yes (eg Dixon, Cardiovasc Res. 26(7):713-9, 1992). In the near future I will return to the discussion of how we can best train the heart, and at that point I hope to provide a good answer to this question.

Meanwhile, with regard to Ewen’s comment after my post on Saturday suggesting that he is limited by his muscles, not his heart, I agree, but I would propose that it might be that his current strenuous training and relativley unremitting program might have led to a reduction in HRV. Hence, his central governor is acting to stop him pushing himself too near to VO2max. Ewen, do you have a HRM that will provide you with HRV values? I apologize in advance if I am shooting off with an unfounded hypothesis.