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 30th 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 30th 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.

Advertisements

One Response to “Do Firstbeat offer more in 2009 than Forbes and Ursula Carlile in 1959?”

  1. Ewen Says:

    Thanks Canute. The recovery analysis (Firstbeat SPORTS) would seem to be most useful. There was a Suunto stand at the Gold Coast Marathon Expo, but the person I spoke to could only say that the t6c could measure HRV. It’s odd that the HRV ability of the unit isn’t even mentioned in the brochure.

    I’m almost tempted to purchase one (and the software) – especially if more data becomes available about the accuracy of the over-training measurement. I’ll keep reading!

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s


%d bloggers like this: