Training Prescription Guided by Heart Rate Variability

Blog post by Marco Altini and Alejandro Javaloyes. You can reach Alejandro via email here, and also follow him on twitter.

In other blog posts, we’ve talked about how to use HRV data on a day to day basis, and how to look at the big picture, meaning at medium and long term trends in HRV baseline, with respect to your historical data, as clearly displayed in HRV4Training Pro. 

The idea, is always to use the data in the best way possible, so that you can understand how your body is responding to your training plan, and make adjustments (for example by providing the most appropriate training stimuli in a timely manner, when your body is ready to take it, so that positive adaptation will occur and you will be able to improve performance). HRV allows us to capture such body response to the input we provide (training), but the challenge remains to decide how to act on this information. 

As more studies investigate different protocols to prescribe training based on your own individual physiological responses (read: HRV), a clearer picture is emerging. In this post, we cover the latest study by Alejandro Javaloyes and co-authors, titled Training Prescription Guided by Heart Rate Variability in Cycling and published in the International Journal of Sports Physiology and Performance in which the authors prescribed training based on HRV in a group of cyclists. Alejandro has been kind enough to provide us with a comment on his current and future work, which is reported below. ​

In this post you will also learn how to apply the same strategy to your own training plan, using HRV4Training Pro, which you can try for free at this link or get 15% off any subscription using code SCIENCE until end of the week. ​​

What’s the study about?

The purpose of this study was to examine the effect of training prescription based on HRV in road cycling performance. After 4 weeks baseline measurements, 17 well-trained cyclists were split into two groups, HRV-guided and traditional periodisation group. The training program lasted another 8 weeks, and performance measures were taken before and after the 8 weeks in both groups.

HRV measurements were performed at home and without direct supervision (finally, like the rest of us do!). Now to the interesting bit: how was HRV used to guide training? First, the authors computed the Smallest Worthwhile Change (SWC). What’s the SWC again? We  talk about the SWC when we want to identify changes in a metric that are not only due to chance or some confounding factor, but are a true representation of an underlying change in performance or adaptation in your physiology. This is what we call your “normal values” in HRV4Training and HRV4Training Pro, see image below. In simple terms, the SWC is a statistical representation of your historical data, the green-ish band in our screenshot below. 

Then, the authors computed a 7 days moving average of ln rMSSD, this is nothing different than your Recovery Points baseline in HRV4Training. When the 7 days moving average was outside of the SWC (baseline outside of your normal values), the prescribed training intensity was reduced, so from high or moderate it would go to easy or rest. In particular, getting a little technical, the SWC was built as half a standard deviation from the mean of the rMSSD and updated continuously throughout the study, so the normal values would stay up to date, exactly like we do in HRV4Training (we use 0.75 standard deviations in Pro, hence our normal values are a little wider).

Se an example of how you could implement the same protocol using HRV4Training Pro, which means holding back when your baseline (blue line) goes below your normal values (green-ish band). 

What did the authors find? ​

The HRV guided group improved peak power output (by 5%)  and 40 minutes time trial performance (by 7%), while the traditional periodisation group did not improve in any metric. The authors conclude that daily training prescription based on HRV could result in a better performance enhancement than a traditional periodization in well-trained cyclists. As the author state "Our hypothesis for this greater adaptation to training for HRV-G is in line with the idea of performing high intensity training when the athlete is in optimal conditions to perform it. Therefore, these differences in PPO changes may be due to a better timing in the programming of high intensity training” - which is what we were talking about at the beginning of this post. 

Another interesting aspect in my opinion is the following, also reported in the paper "Daily variation of HRV was significantly greater for TRAD instead of HRV-G. In addition, the standard deviation was also greater for TRAD group. This result suggests that maintaining HRV values within an optimal range during the training process could result in greater increases in performance”.

This to me links also back to the fact that the coefficient of variation of your HRV tends to be smaller when you are responding well to a certain training block (there is less day to day variability), while HRV is a bit all over the place if you are struggling, and this is why for example in our HRV trends analysis, part of HRV4Training’s Insights, we combine HRV, resting heart rate and CV to determine your response to training, as discussed here. ​For more resources on this topic, I highly recommend Andrew Flatt's blog.

What does this mean for you?

As usual, it is important to fully understand the protocol and interpret the results in the context of such protocol and study aim. I still find online debates on the topic of “HRV works or not”, which doesn’t mean anything, as HRV is simply a way to capture physiological stress. What “works or doesn’t work” is how we act on such information with the aim to improve performance. Regardless of performance, I would argue that being a little more aware about our own physiological stress deriving from not only training but also lifestyle, is only an advantage, but of course I am terribly biased on the topic. 

Back to our study, a few things here to highlight that we consider important and also emerged from our applied work in the field over the past 6 years:

• It is pointless to measure HRV once or twice and think you got a baseline or you know what’s a person’s HRV. Many studies especially before today’s technology was available would measure HRV once then perform a several months study and measure HRV once again, to look at differences. This is absolute nonsense in our opinion given the day to day variability in these metrics as well as the fact that HRV should be used as a continuous feedback loop, not as some marker to optimise in the long term (what we want to optimise is performance!). In the study, the authors used 4 weeks of measurements to determine what’s a person HRV, this is a bit like our normal values window in HRV4Training, and continuously updated this window of normal values (what they call SWC), exactly as we do. In this way, you always know what’s a person normal range at a given time, and based on the current baseline, can easily implement changes. 
• Looking at medium and long term trends, and in particular at where your daily data (either daily score or daily baseline) stands with respect to your historical data seems to be the way to go. This makes a lot of sense as comparing your daily scores with respect to your historical data is a simple statistical way to determine when a daily score or baseline is significantly different from what is normal for you (e.g. lower, highlighting more stress), and therefore this seems an optimal moment to adjust training so that we can truly individualise it. This approach has shown performance improvements both in runners (see Vesterinen et al, discussed here) and cyclists (current study), and if we abstract a little from the exact methodologies, what both studies are saying here is that you should hold back when your HRV is significantly lower than your normal. Using your baseline instead of your daily score to make the decision, seems a better way as you probably are capturing stronger forms of stress (as they affect an entire week of data, not just a day).

Again this is all done automatically in HRV4Training, where we compare your daily score to your normal values, combine it with your own subjective score (a combination of sleep quality, motivation, perceived performance during the last trainings and muscle soreness), and provide daily advice. 

Using HRV4Training Pro, you can also look easily at baseline changes with respect to normal values, and apply the same method described in this study, to your training. You can find this analysis in the Overview page of our web platform.

​That’s all from me. I’ll let Alejandro take it from here

What’s next?

Hi there! My name is Alejandro Javaloyes. I’m a researcher at the Sports Research Centre of Miguel Hernandez University (Spain). As Marco said before, we are currently working with HRV to optimize training prescription.

The mentioned study was carried out by following a predefined traditional periodization and HRV-guided training during a relatively short time (8 weeks). Our next step is to compare the HRV-guided training against a block periodization model during the same time frame (8 weeks of intervention) in highly trained cyclists. Block periodization has emerged as a greater methodology than traditional programmes that favours greater increments in fitness and performance. Furthermore, its effectiveness is supported by a large body of research in many endurance sports like running or cycling. Block training consists of training cycles of highly concentrated workloads (see studies by Vladimir Issurin). Our idea is to focus the training blocks to develop the anaerobic threshold and peak power output because the competitive situations that have a major impact on the result of a race are performed around the anaerobic threshold (i.e mountain passes and log time-trials) and above (sprint finals and short time trials). When comparing this methodology against HRV-guided training in trained runners, Nuuttila et al (2017) reported similar increments in running performance for both groups, with greater increments in resting HRV and serum testosterone concentration in HRV-G.
In highly trained cyclists, with less room for further improvements, the training prescription guided by HRV measurements could lead to better timing for prescribing high-intensity training than a predefined training, and consequently, greater increments in performance than a predefined training. We have promising results on that and we hope to publish them during the next months.

This studies of HRV-guided training were carried out in short time spans (8 weeks of training), however, the possibility of this kind of day-to-day training programs during long periods of time (i.e. complete seasons) or its usefulness against other periodization models remained partially unanswered in cycling. This sport is often considered one of the hardest due to a large amount of training and racing: over 30.000 km and up to 80-90 days of competition for professional road cyclists. The implementation of HRV in cycling (especially in highly-trained level) could play a key role in managing the stress-recovery process and optimizing adaptation to training. In this regard, one of our next studies will be to determine if the trends in HRV are associated with a good performance during competitions or in the power profile (i.e an increment in the greatest 1 to 60-min power output).

These two research projects are focused on high performance, but one answer that we keep in mind is if this kind of monitoring and training individualization can be taken to the field of health. In this regard, we are implementing HRV records in people with pathologies that follow an exercise program to improve their health. Currently, we are working with people that have suffered heart attack or coronary disease and people undergoing bariatric surgery, and we hope that in the future this measures could help in individualizing the training prescription making it more effective.

Thanks for the invitation Marco, its been a pleasure!