Heart Rate Variability (HRV) response to training and lifestyle: a case study
In this post, I’d like to show some data to highlight a few important aspects when analyzing your heart rate variability (HRV) data.
In particular, I’d like to cover some misconceptions about the relationship between training and HRV as well as the importance of lifestyle and psychological aspects (context!).
Tools of the trade
- Data collection: HRV4Training for iOS, camera-based measurement.
- Data analysis: HRV4Training Pro, Overview page.
New to Heart Rate Variability? Check out our Ultimate Guide covering measurements, data analysis, case studies, and misconceptions. Keep an eye on my Twitter as well for updates on these topics
The Ultimate Guide to Heart Rate Variability (HRV): Part 1
Measurement setup, best practices, and metrics.
What are we going to look at?
We’ll use my own data collected between January and April 2018, so 3 months in which I went from best shape of my life to injured and then back to training regularly post-injury, but in poor shape (detrained).
We’ll look at:
- The basics: acute response to high-intensity training
- The more interesting stuff: positive long term adaptation to high intensity and high volume training
- Context: negative response to poor lifestyle choices following an injury
- Feedback loop vs fitness marker: assessing full (physiological) recovery
- Putting it all together
1. The basics: acute response to high-intensity training
What’s an acute stressor? Acute stressors are events that affect your physiology in the immediate future. Think about an intense workout, an intercontinental flight, a night out with too many drinks, high caffeine intake, etc. — anything that has an effect on your physiology which lasts from a few minutes up to 24–48 hours.
Acute stressors are typically the easiest phenomena to interpret and reproduce, and looking at data in the context of acute stressors can help understanding how your physiology works. Looking at acute changes can also help in gaining confidence in the tools we use, as these changes should be captured and reproduced more easily.
It’s important to remember that physiology is complex, and while acute stressors and the resulting HRV changes are often repeatable and easy to understand, there might be other factors behind the relationships that we are seeing (or not seeing) in our data. No stressor acts in isolation, there’s always something going on with our lifestyle, training, health, etc.
The rationale behind monitoring recovery using HR or HRV is that heavy training shifts the autonomic nervous system towards a sympathetic drive, which is reflected in higher HR and lower HRV within 24 to 48 hours after training. To analyze this relationship between training and physiology, we can first compute day to day differences in resting HRV for a person. Subsequently, we can analyze the change in HRV on days following training of different intensities.
Here is, for example, my data for the past 3 months:
As you can see from the figure above, there is quite a strong relationship between intense workouts and reductions in HRV on the following day. This is a typical acute stressor, and the reduction in HRV can be used to quantify recovery and understand if we need an extra day off.
However, this relationship does not tell us much about long term adaptations to a training block or training program, that’s what we will learn by looking at medium and long term trends (point 2.).
The view that training should cause a dip in HRV is in my opinion very simplistic, HRV is a measure of physiological stress — or even better: a measure of how we are adapting and responding to stress.
While higher stress is typically highlighted by a reduction in HRV, positive adaptation to stress (think for example about an intense training block) should result in a stable or increased HRV.
Let’s look at some data from a different angle to clarify these points.
2. The more interesting stuff: positive long term adaptation to high intensity and high volume training
HRV trends over long periods of time (e.g. from weeks to months) are one of the most interesting and complex aspects to analyze. While day to day acute changes reflect rather well training load in the day(s) before the measurement, which is one of the principles behind using HRV to quantify recovery needs (as we have seen in the previous section), in the long term things get much less linear. However, analyzing long term trends is a very powerful way to better understand adaptation to training and determine if it is the case to implement changes to our planning.
Due to the availability of more practical tools, much research has been carried out in the field in the last few years, showing consistently a few aspects that are applicable across a wide range of sports and athletes. Hence, in this section, we’ll try to look at such aspects so that you can learn what to look for in your data:
- HRV baseline with respect to normal values
- HRV coefficient of variation (CV)
Let’s start with the HRV baseline (7-days moving average, shown as the blue line below) with respect to the normal values (historical data, shown as a green-ish band below):
When the baseline is within normal values, we have a stable condition, typically a sign of good adaptation in response to training and lifestyle stressors.
Do you see the difference between this analysis and the previous one (the acute changes)? During the period shown above, most likely harder sessions still triggered lower scores the day after, but we need to look at the big picture. You should not expect your HRV to reduce if you are responding well to stress in the medium / long term. On the contrary, a positive adaptation is shown as a stable HRV or even increasing HRV for a few weeks, as shown above.
Let’s introduce another useful parameter that can tell us even more about adaptation and response to training and lifestyle stressors when combined with what we have just seen in terms of HRV baseline and normal values: the HRV coefficient of variation (CV).
The CV is a measure of the variability in our daily values. Analyzing both the baseline HRV value and its variation can provide more insights compared to looking at the baseline only. Let’s see how these two measures differ with a simple example. If your baseline HRV is 8, it could be that for the entire week you scored 8 every day. It could also be that your HRV was jumping around, for example, 6 10 6 9 7 10, etc. — still averaging 8. In the first case, there is no variation in your HRV, while in the second case, CV is quite high as your HRV varies a lot on a day to day basis. This variation in daily HRV is a very good parameter to look at, as it is also representative of adaptation to a new stressor.
In recent research, a reduction in CV has often been reported as a measure of better adaptation. This makes sense since we expect more day to day changes if we are not coping very well with training and therefore our HRV jumps around more (for example when we start a new training phase with more high-intensity work). On the other hand, other research has shown that a reduction in CV has been associated with increased risk of non-functional overreaching. This also makes sense since less variation might mean that there is more stress and an inability to respond to stress (flat HRV). How do we explain these conflicting results? I believe a multiparameter approach can easily help us in making this interpretation easier, by looking at both baseline HRV and CV, for example, better adaptation will be associated with reduced CV with a rising or stable baseline HRV. On the other hand, poor adaptation and increased risk of non-functional overreaching will be associated with a big dip in both CV and baseline HRV (the body is basically unable to react to anything at that point).
Let’s look at the CV for the data shown above:
The CV is shown color-coded (the actual value can be displayed under Insights / Resting Physiology in HRV4Training Pro). Here we have a series of similar values, which mean reduced CV (always interpreted relative to your historical data), and once again positive adaptation.
3. Context: negative response to poor lifestyle choices following an injury
Training is hardly the only stressor in anyone’s life, no matter if you are a professional athlete or just went for your first run yesterday. There’s work, family, expectations, etc. — we need to deal with a lot more than just training, and it all affects us physiologically.
In mid-February, I suffered an injury. This was the second time I had a big setback while preparing a marathon, in just a few months, despite being injury free for many years before. If you’ve been there you know what follows, you get a little depressed, start being less careful with your diet and alcohol intake, fitness goes down, etc. — you got my point.
What did my data show at this point? I am barely training (I am doing some cross-training cycling, not shown here), and yet my HRV keeps going down:
Of course, HRV reduces: mentally I am in the wrong place, I drink a glass of wine too many, eat less healthy, I am less motivated to work. HRV shows objectively how poorly I am dealing with the current situation.
What about the CV? We can see already from the plot above that my data is all over the place, a sign of poor response to stress. Here is the color-coded version of the same plot:
I first go through a phase with high CV, much day-to-day variability showing an inability to deal well with daily stressors. Once my data finally stabilizes (reduced CV, more similar day-to-day values), it’s because I am way below normal values, hence another sign of poor adaptation and need for recovery (or lifestyle changes!).
It makes no sense to look at your HRV data decontextualized. HRV represents your response to training and lifestyle stressors, and you need to look at how things are changing with respect to those stressors, as shown in HRV4Training Pro for example.
During this period I went for a few days to Lisbon, I had planned a half marathon there but couldn’t race because of the injury. However, as I could bike and Amsterdam was being pretty horrible (50 km/h winds and rain all day didn’t get me in the mood for cycling), I extended my trip a little and took the opportunity to ride a few days over there. You can see below how breaking the depressing routine to spend a few days with friends in sunny Lisbon triggered a few days of baseline within normal values, pretty much the only positive response in a month:
Once again: context. Travel is not necessarily bad for you, it all depends on what was going on in that specific situation.
If you plot your HRV over time or look at your Apple Watch data on Health and don’t understand what it is about, no surprise: it’s all decontextualized and it makes no sense to look at data that way. Start here.
4. Feedback loop vs fitness marker: assessing full (physiological) recovery
Another misconception when looking at HRV data is that HRV should be linked to your fitness. I disagree.
HRV should be used as a continuous feedback loop, so that in the long term, what you optimize is performance (by for example providing the right stressor at the right time, as shown by research on HRV-guided training), and not HRV itself. This is different from for example even just resting heart rate, which typically is highly correlated with changes in cardiorespiratory fitness and reduces as you get more fit. This is also why HRV is not a good predictor of fitness (we don’t even use it in our VO2max estimate, as explained here).
My recommendation is to avoid using HRV as a marker of fitness, use it instead to measure your body’s response to stress and how you are adapting. Relative changes with respect to your baseline are what matters, regardless of what is your baseline value.
To measure your progress and fitness, there are way better metrics, check out, for example, our aerobic endurance and cardiac decupling analysis described here:
Or lactate threshold and running time estimates described here:
Estimating running performance
Insights from data acquired in unconstrained free living settings
and used here:
Serena’s sub-4 marathon
How to use HRV4Training to monitor adaptation to training and adjust things on the go: a case study.
Alright, back to HRV.
HRV is all about responses to stressors and adaptation. This is also what we see in my data post-injury:
As my injury is behind me and I can run again, mentally I am in the right place. I get back into better eating and less drinking, and my HRV gets back to what are my normal values. Similarly, the CV reduces, which makes me confident that things are going in the right direction, regardless of the fact that I am terribly unfit due to lack of training.
Here is the detected trend in HRV4Training Pro which automatically combines HRV and CV trends (plus HR trend and contextual information such as training load), showing a good summary of all that I’ve discussed up to now: good adaptation to high load at the beginning, poor adaptation to lifestyle choices following my injury in the central part and finally getting back into coping well with training and positive adaptations post-injury:
5. Putting it all together
At the beginning of this post, I have introduced acute stressors and analyzed the relation between acute changes in HRV and training intensity when analyzed systematically over a few months. The data shows that training of higher intensity results in a reduction in HRV the following day, highlighting how HRV can be used as a valuable tool to measure training load and recovery. While this kind of relationship between training load and HRV was already shown multiple times in literature, my data showed how the relationship can be easily captured in totally uncontrolled settings, outside of the laboratory environment using HRV4Training’s camera-based measurement.
Validating known relationships is a first important step in trying to better understand complex relations between not only training, but also lifestyle and other stressors affecting our physiology, recovery, and performance. As we’ve mentioned before, remember that physiology is complex, and while acute stressors and the resulting HRV changes are often repeatable and easy to understand, there might be other factors behind the relationships that we are seeing (or not seeing) in our data, as no stressor acts in isolation.
As you start collecting some data, before you look at implementing changes, try to look at how your body is responding to different forms of acute stress as shown in section 1, and see if you can capture the same relationships highlighted in this post. The more data, the more annotations, and context around your data, the more you will be able to understand.
Research up to today is clear on the need for a multiparameter approach. Looking at daily HRV alone will not be sufficient to understand long term trends and overall physical condition. Make sure to look at how your baseline is changing with respect to your normal values, and also keep an eye on the CV, that we discussed several times in this post, especially in sections 2 and 3.
In section 4, I have shown how HRV is not necessarily a parameter to optimize towards a certain value, hoping to achieve improvements over months or years, but it is a parameter to continuously act upon (and sure, if your lifestyle is terrible and you are sleep deprived, drink too often, etc - there is something to do also in terms of your HRV baseline, but the point I am making here is for the health conscious person).
HRV provides a feedback loop between training and lifestyle stressors and our body’s ability to cope with such stressors, so that in the long term what we will be optimizing is not HRV itself, but performance. By providing the right stimulus at the right time, when the body can adapt best to the provided stimulus, we will be able to optimize training and improve performance. Holding back when the HRV baseline is below normal values is the main strategy used by recent research on HRV-guided training, showing consistently improved performance with respect to a structured training plan that ignores our own, individual response to the stimulus (learn more here).
Last few words
There are no quick fixes for lifestyle changes and physiological adaptations.
It takes time to collect data, understand how you respond to different stressors (lifestyle or training) and finally to learn how to make meaningful changes that have positive long term impact on your health and performance.
Take that time. Looking at the data can be eye-opening.
Measure every day for a few weeks, gather contextual data (subjective parameters, lifestyle, etc. — can be easily gathered in the HRV4Training app using the questionnaire that pops up after the measurement), then look at how things have evolved and start to implement meaningful changes.
I hope this case study can be a good starting point to identify useful ways to look at your data using HRV4Training Pro.
Take it easy.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology and human performance.