The Ultimate Guide to Heart Rate Variability (HRV): Part 2
You measured, now what?
This is the second part of my series of educational posts on heart rate variability (HRV). You can find the other posts at these links:
- Part 1: Measurement setup, best practices, and metrics.
- Part 2: You measured, now what? (on interpreting your data, this post!)
- Part 3: Show me the data (case studies)
- Part 4: Common misconceptions (strength training, night data, etc. -coming soon)
Let’s dive right into it.
Part 2: You measured. Now what?
Once you start collecting some data, the most important thing to remember is that you are unique.
This means that you need to always interpret your HRV data with respect to your historical data, and there is absolutely no point comparing your HRV to another person. Your normal might be different from your friend’s normal (most likely it will be), and that means nothing. This difference in the average baseline is most likely due to genetics and other factors hardly modifiable (at least if we consider health-conscious individuals, if your lifestyle or health is not good, then you can also improve your baseline — but even in this case, the most meaningful way to use your data is to look at how things are changing over time).
Once we have understood that we should only look at relative changes over time, with respect to our historical data (our previous recordings), one of the most important and delicate points comes up: fluctuations between consecutive days. Let’s look into this point in more detail.
Fluctuations between consecutive days
HRV data has an inherently high day to day variability. This means that there can be large fluctuations between consecutive days, which is different from parameters that you might be more familiar with (for example your heart rate or your body weight).
What are the implications? To make effective use of the data, we need to be able to determine what changes are trivial, or just part of normal day to day fluctuations, and what changes do matter and might require more attention or simply truly represent a positive (or negative) adaptation to training and other stressors. In the next sections, I will cover in more detail all of these aspects.
Normal is good
HRV analysis requires a mindset shift. As discussed in Part 1, we first need to understand the nature of the data and the constant re-adjusting of the autonomous nervous system, and therefore take all the necessary steps to acquire a reliable measurement. This is typically addressed by the morning routine: the importance of context, limiting external factors, measuring as soon as you wake up and in the same body position every day — with the only viable alternative being as previously introduced, the night measurement. Then, we need to shift from a “higher is better” to a “normal is better” mentality, as physiologically speaking, being in a stable condition is typically a good sign.
The inherent variability of HRV measurements is something that your app or software of choice, needs to deal with. This is something we have spent a lot of time researching and designing in HRV4Training, starting with the way the daily advice is built.
A software that interprets any HRV increase as a good sign, or any HRV decrease as a bad sign, is failing to correctly represent the fact that there are normal variations in physiology, and that only variations outside of this normal range, should trigger concern or more attention or simply be interpreted as actual changes.
You can see some examples below, where an individual’s historical data is highlighted by a light blue band, which makes it clear when HRV is significantly suppressed (for example when the daily score or baseline is below the band). Here we have two examples showing HRV changes in response to sleep and work-stress issues (more examples later).
Start by observing
I’d recommend just to measure and collect data for several weeks (or even months!) so that you can start getting a feel of how you respond to various stressors and how the data reflects your choices (in terms of both training and lifestyle) as well as how it relates to subjective parameters. There are various ways you can analyze your data without necessarily making changes, here are the two most common methods:
- Acute changes (day to day variability in response to strong stressors)
- Medium to long term trends (long term responses and adaptation to larger stressors)
Let’s look at both of them in more detail.
Acute changes: what happens after a strong stressor?
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. — which is why HRV is such a useful tool to keep things in check, no matter what sport you do, there will be stress.
Let’s look at some data, for example, changes in HRV due to traveling and alcohol intake:
These are simple examples of how you can analyze your response to acute stressors in isolation, for example in the HRV4Training app, which provides this analysis for you:
Let’s look at training as well, from an acute-response point of view. 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, a paper we published a few years back showing that measurements taken first thing in the morning, in unsupervised free-living settings (real life, not the lab!), clearly capture the different recovery demands of training of various intensities, across a broad population:
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 (as well as other lifestyle stressors). That’s what we will learn by looking at medium and long term trends.
One last important point to discuss here. 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.
Long term changes: how are you responding and adapting to training and lifestyle stressors?
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.
Positive and negative responses
In particular, in this section, I will focus on the most important parameter, the HRV baseline (7-days moving average, shown as the blue line below) with respect to the normal values (historical data, shown as a light blue band below):
See how much variability we have on a day to day basis? The gray bars (or actual daily scores) jump a lot, this is why we should rely on statistical representations that are able to provide a more clear representation of what is going on. In our software, we show your historical data as a band that is built using the past 60 days of data and highlights where your data is expected to be if there were no major disruptions. The baseline, or 7 days average, is normally within this band, showing that it’s all good, and we can proceed as planned. When the daily scores or the baseline are below the band, then it means we have significant stress, as we are outside of the range that is considered normal for our own physiology.
Let’s look at an example. Physiological stress comes from different sources, all having an impact on our ability to deal with additional stress and therefore of maintaining or improving our health and performance. In the figure below we can see how both running a marathon and a few days out of the ordinary for new years trigger a baseline change below normal values, a clear sign of high stress and difficulty to cope.
On the other hand, in the middle part, we have consistently high HRV, even with high training load (not shown). Let’s clarify this point with another example:
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.
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.
Last year 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.
This is the whole point of HRV monitoring, it’s naive to think that the only thing that matters is training, and no matter what sport you do (or do not do), measuring your response to stress can help you get back on track.
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, 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 an increase in 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.
Make sure to collect data properly, as explained in Part 1, and you’ll be able to see useful trends over time.
Now let’s make some adjustments
So far we have seen how HRV data can be used to better understand how we are responding and adapting to various stressors. While this is a key first step towards improved health and performance, we have not made any changes yet.
In my opinion, HRV should be used as a continuous feedback loop, so that in the long term, what you improve is health and 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 (or even better, submaximal heart rate during exercise), 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).
How do you integrate HRV with your training plan? Should you have a plan at all?
Let’s revise the basics: HRV, in particular, rMSSD or a transformation of rMSSD such as HRV4Training’s Recovery Points, are simply a way to capture parasympathetic activity, or in other words, level of physiological stress. As we apply stress to trigger certain adaptations, measuring our body’s response to such stressors, as well as to all other forms of stress we are affected from (e.g. simply life happening, work stress, family, etc.), is very helpful as it can provide objective feedback and help us make meaningful adjustments.
The simpler adjustments are probably just being a little more honest with ourselves, and slowing down from time to time, especially when our body is already too stressed.
The example I’ve just highlighted is something we all understand quite well, higher stress as shown by lower HRV highlights how it might be a good idea to take it easy and avoid excessive stress which might lead to overtraining or slower recovery, hindering improvements in performance.
But what is sometimes confusing is what shall we do when it’s all good? Should you push it all the time because your HRV is within normal values, often shown in apps as a green light?
Of course not.
The fact that your body is in a (physiologically speaking) normal state, is what you should aim for. Normal is good. However, this does not mean that every time HRV looks good you should go hard. The point I’m trying to make, which I’ve discussed also in this podcast with Molly and Peter at the Consummate Athlete, as well as in this one with Mikael at the scientific triathlon) is that you first need to have a plan, then you can make adjustments based on how you respond to such plan, which is something HRV and physiological measurements can allow you to do, by providing feedback on your individual specific physiological response to your training plan.
Normal values, or in other words a green light, should give you confidence that everything is going well and in general, you are coping well with your current training and lifestyle. Yet, if your training plan says you are due for a rest day, take it. If you are due for a low-intensity workout, do it.
Small adjustments such as flipping an intense workout scheduled for tomorrow are another way to make better use of these measurements, however, it is important to understand that HRV and physiological measurements are tools for awareness, which allow you to understand how you respond to a particular plan, not to replace your plan entirely.
Research on HRV-guided training
Now that we know that we should start with a plan, the question remains of what is the best strategy to adjust our plan in order to improve performance.
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).
As more studies investigate different protocols to prescribe training based on your own individual physiological responses (read: HRV), a clearer picture is emerging. In particular, two studies on runners and cyclists have shown consistently improved performance for the HRV-guided group, with respect to standard periodization.
In both studies, 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). This is simply what I have called above the normal values, so 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.
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 (if that’s the goal because we could also try to have a better-balanced lifestyle, not a bad idea).
In my view, when it comes to performance, as long as you do not decrease it, using HRV to reduce load or intensity when your body is highly stress can be extremely beneficial to avoid injuries as well, as shown here — stress is a multifaceted issue after all). Regardless of performance, I would argue that being a little more aware of 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 7 years:
- It is pointless to measure HRV once or twice and think you have a baseline or you know what’s a person’s HRV. Many studies, especially before today’s technology, would measure HRV once then perform a several months study and measure HRV once again, to look at differences. This is absolute nonsense (see figure below) in my 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 optimize in the long term (what we want to optimize is performance!). In the studies above, 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, 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 individualize it. This approach has shown performance improvements both in runners (see Vesterinen et al, discussed here) and cyclists (here), 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).
The principle is therefore quite simple, as summed up by Javaloyes et al. in their paper: “Our hypothesis for this greater adaptation to training for the HRV guided group is in line with the idea of performing high-intensity training when the athlete is in optimal conditions to perform it … Therefore, these differences may be due to better timing in the programming of high-intensity training”
In HRV4Training, 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 training and muscle soreness), and provide daily advice based on the same principle.
This being said, these are all methods and procedures that you can implement yourself or potentially find in other software, as they are all clearly described here and in research papers.
Now that you have seen how you can interpret your measurements by looking at relative changes over time you should be able to make the most of your data. I’ve also discussed a few strategies to integrate HRV in your training plan and the latest research on HRV-guided training.
Most importantly, the data above should clearly show how important context is. It makes little sense to analyze HRV just in relation to training or to measure it without properly contextualizing data. This is why HRV4Training includes a simple questionnaire after the measurement and provides the visualizations shown above to let you explore your data in relation to all other factors (travel, subjective parameters, training load, etc.).
In the next posts, we’ll look at more data with a few extra case studies and also learn about some common misconceptions:
- Part 3: Show me the data (case studies)
- Part 4: Common misconceptions (strength training, night data, etc. -coming soon)
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering and is currently enrolled in a M.Sc. 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.