The aim of our study was to evaluate the influence of running an ultramarathon over a distance of 100 miles on the heart rate and the HRV, as well as the potential use of these parameters to predict the status of recovery to full physical capacity following this exertion.
In consistency with published data (8, 11, 12, 13, 14), the presented study found an increase in the heart rate during the first hours after the UM (U2). This is commonly accepted to be due to a shift of the autonomic nervous system in favour of sympathetic activity (16, 17). This sympathetic dominance leads to a decline of the SDNN value, which describes the cooperation between the sympathetic and parasympathetic nerves (18, 19) and is consistent with our data. In fact, an increase in vagal heart control, as seen in our measurements of RMSSD between U1 and U2, often reflects an improvement in fitness, while the athlete's fatigue and impaired performance is often associated with reduced vagal HRV, which is evident when comparing RMSSD values between U1 and U3 (Table 1)(20, 21).
Yet, the main finding of the current study is shown in the comparison of the parameters of autonomic nervous system seven days before the run (U1) with the measurements seven days after the run (U3). While the baseline heart rate at U1 did not show significant differences to the measurements at U3, it can be seen that there is still no full return to baseline of the HRV at (U3), shown by lower average values for the SDNN, RMSSD and pNN50 (graphs 1-4). First of all, these results seem to indicate that even 7 days after the 100 miles, the athletes have not yet fully recovered from the effort in terms of vegetative parameters. A similar result was shown by Velenzano, et al. (2016) who examined an ultra endurance swim athlete covering a distance of 78.1km, whose HRV was still lower after 16 hours of recovery compared to his initial HRV (22). The study by Chambers et all. (2010) showed that the heart rate response to renewed steady- state exercises after a 90 km marathon takes almost a month to return to the same initial values. (23). Also Nicolas et all. (2011) were able to show that stress and recovery values after a 24-hour race (100km) take 2 weeks to return to baseline level (24). Our data imply, that in terms of recovery after a 100 miles run, HRV seems to be more accurate in the prediction of recovery, than the absolute heart rate. Although these findings stand in contrast to the findings of Fazackerley et al., whose runners achieved a return of HRV to baseline within two days after 64km run, this difference might be explained by the shorter distance covered or superior physical fitness of the athlete`s reported in the Fazackerley study.
Although the HR and HRV seem to be useful to evaluate recovery after such an extreme exertion, none of the physiological parameters, neither the baseline HR, nor the baseline HRV seem to predict the finishing time.
When analysing the data to factors influencing the finishing times, the presented data showed that only the age of the runners had an effect.
Because of the low number of participants in this study, the following might only be speculated. The low pre- race RMSSD mean of non-finishers, which is also referred to as the body's rate of recovery (6, 25) might be interpreted as a better ability of the finishers to regenerate in advance. The fact that RMSSD in U3 was higher in the non- finishers than in the finishers is probably justified with the considerably lower running distance covered by the non- finishers (table 3).
The presented data imply that none of the parameters predicting post- exertional recovery can be used to predict finishing times or the ability to finish an ultramarathon as such. It can only be speculated that there are not only physiological factors influencing the finishing of an ultra-marathon, but also psychological and environmental factors that mainly influence the coping with such a large distance.
Limitations of this study include the low number of subjects, which could limit the ability to detect statistical significance, and the lack of further post-run control to establish an exact time to return to baseline.
A prospective study with more patients, multiple follow-ups, and the capture of multiple predictors such as mental health, exercise status, or weather conditions, could help to quantify the differences between finishers and non-finishers, as well as the impact of other factors on the run time.
There are no potential sources of conflict of interest in our study.