Recently I have been experimenting more with pre and post-exercise HRV measurements. Below you can find some data and thoughts.
Based on Stephen Seiler's reasearch, using pre and post-exercise HRV we can better understand the impact of different workouts.
By measuring autonomic activity (heart rate, HRV) *immediately* before and after the workout, we isolate the training stressor in a way that allows us to answer different questions, for example, the following come to mind: at what intensity does a workout require a much longer recovery time? (does it need to be hard? what about just a bit harder than "aerobic threshold"?) and what about duration? or type of exercise.
In addition, Stephen's research has shown how HRV could increase after easy exercise (below "aerobic threshold"), highlighting important differences between heart rate and HRV in terms of how they reflect autonomic control and recovery.
HRV (rMSSD) in the hours after exercise of different intensities. From Seiler, Stephen, Olav Haugen, and Erin Kuffel. "Autonomic recovery after exercise in trained athletes: intensity and duration effects." Medicine & Science in Sports & Exercise 39, no. 8 (2007): 1366-1373.
For the data presented here I used the Heart Rate Variability Logger (that I make: https://hrv.tools) with a Polar H10, but the data from Polar, especially *post* exercise, has been very low quality.
Sometimes my heart is to blame, sometimes the sensor.
I thought a single long session with the HRV Logger and an H10 was the easiest (no need to start / stop multiple recordings), but the strap gets annoying quickly maybe spot-checks with optical sensors just before and after are better (?) opinions welcome.
HEART RATE AND rMSSD PRE, POST, DURING
Let's start with rMSSD, with the caveat that post-exercise, despite very aggressive artifact removal and discarding plenty of data, I still don't trust what I have 100% for some sessions (more data will help).
I tagged workouts as easy, aerobic or hard. Aerobic means near aerobic threshold, easy means a lot easier (basically a jog), while hard means VO2max intervals This is just the training that I happened to be doing in the past 7-10 days.
Pre-exercise my data was rather similar these days. During exercise, rMSSD is near zero as soon as heart rate > 100 bpm (parasympathetic withdrawal), hence sessions are indistinguishable when using this metric (as expected, this is why it is quite useless to try to determine exercise intensity using rMSSD data collected during exercise).
post-exercise, (which is when rMSSD becomes interesting), we have a massive difference between *hard* and everything else. I recall rMSSD was still 4 ms 10 minutes after the session that day.
For easy sessions, rMSSD post-exercise is similar or even greater than rMSSD pre-exercise. This is very interesting and also in line with Stephen's findings that I have reported above.
Let's look at heart rate now. Below I have plotted the exact same data, changing only the unit, either absolute (beats per minute) or relative to my maximal heart rate (so in percentage).
We can see some differences with respect to rMSSD: for easy sessions, HR goes back to pre-training in no time. The spread of the distribution is very narrow post-exercise, which means there is almost no lag in going back to ~50 bpm.
During exercise, we have the expected differences in intensity, with heart rate tracking well the type of session. Post-exercise, I normally record for 10-15 minutes (see protocol later), hence the wider the distribution, the more delay for heart rate to re-normalize.
It's always interesting to me how the patterns in heart rate and rMSSD can differ, with rMSSD potentially increasing post easy exercise, while heart rate never reducing to pre-exercise levels.
Heart rate and rMSSD are not the same.
Since I was measuring with the HRV Logger and the Polar H10 (configured with no artifact correction, so basically collecting trash data to be post-processed in R), I could also look into DFA alpha 1.
This is relevant only for the exercise part. Alpha 1 looks at the properties of the signal, which are associated with autonomic modulation of heart rhythm, but not specifically to parasympathetic activity and indeed the data covers a broader range and is more useful than rMSSD during exercise.
However, as I have noted elsewhere, for me, this metric always provides low values (around 0.5) unless I go extremely easy (definitely easier than "aerobic threshold", whatever that is). See here my lactate data for example.
I'll keep looking into this when I use the chest strap, mostly to see if I can find any difference between sessions closer to what I consider my aerobic threshold and harder sessions, or relationships between alpha 1 and post-exercise rMSSD. Food for thought.
Still going back and forth on some of the details, similarly to the tools section, it's messy. There is unfortunately no way the lab protocols used by Stephen can be replicated in real life (for example, people had to wait for 4 hours post-workout).
I started with 10 minutes long measurements pre-exercise, then training and then 30 minutes long measurements post-exercise. This quickly converted into 10' pre and 10-15' post. This means that we can only capture the acute change in the first few minutes, more than the recovery trend, which is a pity, but otherwise unfeasible.
I tried to minimize talking and allowed drinking water however, other things mess up the data quickly: swallowing for example, is problematic for HRV. As a result, in the 10-15 minutes of post-exercise data, much of it gets discarded, which is why I grouped the data in pre and post instead of showing any dynamic component in the post-exercise data. Still valuable I believe.
For breathing, no control (self-paced). Measuring with the Polar H10 the whole time is easy, but if we were to do spot checks, you could shower and then measure again, instead of e.g. sitting in the living room while being disgusting (also here, opinions are welcome).
This type of data collection is challenging, but despite the limitations and shorter data collections after exercise (with respect to lab studies), I think there is plenty of useful information to investigate if we were to pull it off with a large user-generated study.
I will collect and think some more.
Thank you for reading. Stay in touch.
Technology for Heart Rate Variability (HRV) measurement at rest is getting better every day. From our own approach using the phone camera in HRV4Training (validated, and independently validated), to the Oura ring, Polar Vantage V2 and Scosche Rhythm24 (plus of course, good chest straps like Polar H10), it has become really easy to capture high-quality HRV data.
In a recent blog, I discussed the importance of using an accurate tool, sampling at the right time, and interpreting your data with respect to your normal values.
I have often argued that provided you know what you are doing, morning and night measurements are equivalent in the long run. This does not mean that there are no differences on a daily basis (we'll get to that), but it means that these are both reliable way to assess baseline physiological stress.
Check out an example of a few months of my own data, with annotations, here
Application of interest
We first need to define our application of interest.
Both heart rate and HRV can be used for different applications and measured under different conditions. In this blog, our application of interest is determining chronic physiological stress level, which derives from combined strong acute stressors (e.g. a hard workout, intercontinental travel) and long-lasting chronic stressors (e.g. work-related worries, etc.).
By measuring the impact of various stressors (e.g. training or lifestyle) on our resting physiology (HR and HRV), we can make meaningful adjustments that can lead to better health and performance.
Back to morning vs night. Apart from my anecdotal data above showing a very good match between the two, what does the research say?
In the latest paper looking at this exact question, "Evaluation of nocturnal vs. morning measures of heart rate indices in young athletes", Christina Mishica and co-authors report that "heart rate and RMSSD obtained during nocturnal sleep and in the morning did not differ".
We can even look at the data for both heart rate and rMSSD, a marker of parasympathetic activity (the same feature used in HRV4Training or in the Oura ring), which indeed show great agreement:
You can find the full text of the paper here.
As I have tried to cover in this and other blog posts, morning and night measurements can be used to capture baseline physiological stress in response to acute and chronic stressors. Both methods have been used in different studies resulting in the same outcomes in terms of the relationship between HRV, training load, and recovery (see an overview here).
While long term trends will be similar between these two methods, there are a few differences to keep in mind, mainly linked to these aspects:
What are the takeaways here?
In my opinion, when it comes to the data, there is no advantage in using one method or the other. However, if you prefer to wear something over the night, get a device that does so. If you prefer not to wear something during the night and just to take a measurement in the morning, then go that way.
If your athlete can’t be bothered to take a morning measurement, get a device that tracks HRV during the night. If you are not sure this is for you, you can use your phone camera and invest as little as 10$ in measuring your physiology daily with an independently validated HRV app such as HRV4Training.
Once again, if you like wearables, they can clearly capture high-quality HRV data as we sleep. Just make sure to use one that gives you the full night of data, as otherwise HRV measurements won't be reliable (as I discuss in greater detail here). My recommendation would be the Oura ring for this exact reason: most other sensors will provide automatically collected sporadic data points (e.g. 5 minutes during the night, like the Apple Watch does), which unfortunately are noisy and do not reflect underlying physiological stress very well.
As an alternative, you can get the same data with a morning measurement taken with HRV4Training, the only validated camera-based app. Pretty simple and cost effective, as long as measuring in the morning works in your daily routine.
This is great news as we all have different reasons to use one method or the other (cost, preference for passive measurements, etc.), but as long as we use valid tools, the same physiological processes can be captured over time.
This consistency will help us move forward in many areas of research in which these sensors or apps are currently deployed.
Stay in touch.
In this podcast, I chat with Matt and Hanna at 80/20 endurance
I’ve learned a lot over the years from Matt’s books, and it was a real pleasure to spend some time talking about HRV and training
Thank you & enjoy
I have recently joined the editorial board of IEEE Pervasive Computing Magazine.
Here you can find our first article, written with Lucy Dunne, and covering future trends in wearable technology
Enjoy the read
The Use Case for Heart Rate Variability with Marco Altini and Corrine Malcolm | Koopcast Episode 106
Last week I had the opportunity to chat with Jason Koop and Corrine Malcom about heart rate variability (HRV), "readiness" scores, resting physiology, and wearables, in the context of training
If you are interested in understanding the nuances of these aspects, including their strengths and limitations, this episode is a good starting point
Don't fall for cults or for those who trivialize everything that is human physiology. Instead, strive to understand these aspects, and you'll be a better athlete or coach
Thanks again Jason!
What Is Behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living
Excited to share our latest paper on heart rate and heart rate variability (HRV):
This one has been in the making for a long time, and brings it all together: developing an accurate and affordable method to measure resting physiology, analyzing data at a scale that goes far beyond what we can do in the lab, and as a result, new, valuable insights
Give it a read, here
Professor Maria Carrasco-Poyatos and co-authors at the University of Almería just published the latest paper on HRV-guided training, titled "Heart rate variability-guided training in professional runners: effects on performance and vagal modulation"
Thank you Maria and co-authors for using HRV4Training and for involving me in this work. You can find the paper, here, including the main highlights:
Heart rate variability (HRV) trends over long periods of time (e.g. from weeks to months) are one of the most interesting and complex aspects to analyze when it comes to resting physiology
While day-to-day (or acute) changes reflect well stressors such as training intensity, the menstrual cycle, sickness, alcohol intake, or travel in the day(s) before the measurement, in the long term things are quite different
In this post, I will cover our approach to trends analysis in HRV4Training, and cover some of the features in the app that should help you make sense of the data in the longer term
Learn more, here
How does the body respond to stress?
Below I look at heart rate variability (HRV), heart rate, and glucose in response to two very different weeks (N = 1).
High vs low stress:
Some context first
Last summer in July I had a strong negative stress response (cumulative stressors), resulting in arrhythmia and concerns for my health I've talked about this before, but here I want to focus on what happened to glucose during that week.
Coincidentally, I was wearing a continuous glucose monitor (CGM) since the previous week and noticed that after meals, my glucose was spiking really high, near 200 mg/dL, consistently.
Very interesting to see poor regulation at work so clearly.
As usual, I was also monitoring my resting physiology (HRV and HR) using HRV4Training (morning measurements), and saw quite a dip in HRV, as well as a minor change in heart rate This is the type of stress response I often discuss (see for example my guide here)
Physiologically, we know that high stress is associated acutely and chronically with elevated glucose in the bloodstream and reduced parasympathetic activity Pretty neat to see it with simple measurements and currently available technology.
"If you've ever wondered how to tap into the secrets of how that pump in your chest can help you to train faster, harder and longer Marco is just the man to listen to."
“We've been using Marco's app for a while to understand how to better regulate training and recovery and in this episode we do a deep dive on the how and why”
I’ve really enjoyed talking to ultrarunners Jay and Tris about HRV, thanks for having me!
In this interview, we cover:
Thank you Kieran for having me on your channel
Excited to announce that I am joining the editorial board of IEEE Pervasive Computing Magazine
I will take a role as editor for the Wearables Department together with Lucy Dunne
Our first editorial should be out soon sometime soon
“HRV reflects your physiological responses to all stressors, not just training stress,” says Marco Altini
“Tracking HRV allows us to better understand our own response to training and lifestyle stressors, so that we can make meaningful adjustments towards improved health and performance"
Thank you Men's Fitness Mag for featuring HRV4Training and ŌURA. Find the article, here
Last week I had a nice chat with Dr. Greg Wells for his podcast. We talked wearables, validity, what’s measured (HRV), what’s estimated (sleep), how to use HRV data, and more
You can find the episode here. Thanks again Greg for having me
Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 4, individual-level data
In part 1 of this series, I covered the basic physiology of heart rhythm regulation. In part 2, I discussed the technology required for these measurements, why some sensors can be trusted, and why others can be used just for resting heart rate, and not for HRV. In part 3, we started looking at the data, with an analysis of population-level differences in resting heart rate and HRV.
In this blog, we finally get to the most interesting aspect: individual-level data. Needless to say, both resting heart rate and HRV become a lot more useful when tracked over time within individuals, and this is exactly what I’ll be showing here. I’ll also try to highlight some of the differences between these two parameters, so that you can better understand what the data means when tracked in response to strong acute stressors (e.g. training, sickness, alcohol intake, the menstrual cycle) and in the longer run (e.g. changes in fitness).
You can find the blog, here.
The goal of this post is to provide some clarity and general considerations on heart rate variability (HRV), readiness and wearables. I will try to clarify why comparing HRV and readiness scores is of little use and what you should be comparing (if anything) for a more meaningful assessment of how these devices work. Most importantly, we will see how you can benefit from the data for both HRV and readiness.
What are we talking about?
HRV is a measure of physiological stress. For today's wearables and apps, it typically represents parasympathetic activity due to how it is measured (at rest, while sleeping or first thing in the morning) and computed (relying on high frequency changes captured by rMSSD). This means that a lower HRV with respect to your historical data, is associated with higher stress.
Readiness is a made up construct that most apps or wearables provide. The goal of readiness is to combine multiple parameters (one of them typically is HRV), to determine your level of recovery or ability to tackle the day (whatever that means in your case).
Why does this matter?
Due to the novelty of some of these metrics for consumers, issues in science communication, and whatnot, there is much confusion on either of them, to the point that often I see people comparing HRV from one wearable with readiness from another. While understandable (the tools are supposed to do the same thing, measure our recovery), this is like comparing apples with pears, it does not make much sense.
This is an important aspect to address because wearables and apps can be extremely helpful in better understanding physiological responses to the various stressors we face, but not all devices are equal, nor differences between the output of one or the other device necessarily mean that they cannot be trusted.
Excited to finally share something I've been working on for the past year and a half, together with a great team at Oura
"The promise of sleep: a multi-sensor approach for accurate sleep stage detection using the Oura ring"
Full text here
Short thread, here
I had a nice chat yesterday with Michael @ x3training about heart rate variability (HRV) biofeedback
It was my first time trying to cover this topic, I hope you’ll find it useful
Episode link here
HRV4Biofeedback app here
Part 1 of my latest guide is all about physiology:
‣ Why do we care
‣ Bird’s-eye view
‣ Understanding autonomic control of the heart
I hope you'll find it useful, enjoy the read