|
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! Episode here. 
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 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 Enjoy  Part 1 of my latest guide is all about physiology: ‣ Why do we care ‣ Bird’s-eye view ‣ Understanding autonomic control of the heart and more I hope you'll find it useful, enjoy the read  HRV4Training just got a new look (both on Android and iPhone) While we have made quite a few changes, some of the most important are in the homepage, which now displays:
Learn more about the only independently validated, camera-based HRV app, in our QuickStart guide: https://www.hrv4training.com/quickstart-guide.html which covers the basics of HRV, how to use the data, the various insights present in the app & more We hope you'll enjoy the new interface, and thank you for your support  Our latest paper was just accepted for publication in Frontiers in Sports and Active Living: Elite Sports and Performance Enhancement. In this paper, we show a case study of our real-time implementation of DFA alpha-1 in the HRV Logger, which you can find at this link. Learn more about the paper, here  Really enjoyed this one. You don't always get to chat about your work with one of the best ultrarunners in the world In this podcast, we talked about the basics of HRV and HRV4Training, what to expect in terms of acute changes and long term trends, and how to use the data We also touched on some of our more experimental tools for training intensity estimation such as DFA alpha 1 in the HRV Logger and deep breathing exercises with HRV4Biofeedback Full episode at this link Thank you Jason Brooks and Jason Schlarb for having me Enjoy!  In this post, I look at the long-term effects of deep breathing on heart rate variability (HRV) as measured during deep breathing practice While there is plenty of data and published literature on the acute effect of deep breathing on HRV (basically the difference between resting conditions and practice), we know much less about long-term effects. Looking at this data might help us better understand the relationship between deep breathing and long-term physiological changes (if any!) Enjoy the read  In the past few months, I've talked a lot about HRV during exercise. If this is new to you, head over to this blog post for an overview. In this post, I'd like to discuss potential issues with estimating heart rate (HR) from HRV data. And in particular, if it makes sense to do so. Why is this important? The DFA-based method for exercise intensity assessment discussed in the blogs linked above, could be used to determine at what heart rate DFA alpha 1 crosses 0.75. It is of course very practical to do so. We might not always be able to check DFA alpha 1, similarly to ventilatory or lactate thresholds, so we look at heart rate in relation to those crossing points, to use heart rate for real time guidance or training zones definition during following workouts. However, heart rate and HRV carry different information. This is rather obvious, for example resting HR changes with fitness, while HRV less so. Similarly, HRV is tightly coupled to stress responses and more sensitive to stressors, with respect to HR. Depending on context, the relationship between HR and HRV might even be the opposite of the typical increase in HR associated with a decrease in HRV. For example, during deep breathing average HR tends to increase a bit, while HRV shoots up, therefore both increasing with respect to resting conditions.  Example of increased HR and HRV during deep breathing (right) with respect to resting conditions (left). Deep breathing was carried out at 5.5 breaths/minute, while you can count about 9.5 breathing cycles in the 55 seconds recorded for the resting measurement. This is quite typical and differs from the expected relationship between HR and HRV, which normally go in opposite directions. Context matters. Curtesy of HRV4Biofeedback Back to exercise HR and HRV now. If HR and HRV carry different information (which is the whole point of using HRV and not just HR), then does it make sense to use HRV just to estimate HR? We can also translate this to morning measurements: would you use your morning HRV just to determine what's your HR when you are well recovered? I wouldn't, because once again, they carry different information and determining what's a good HR based on HRV, would not provide the same level of information (because HRV can differ at the same HR). So shall we fit a regression model or just look at the data to determine at what HR we cross DFA alpha 1 at 0.75, so that we can determine our "aerobic threshold heart rate"? Maybe. Or maybe not. If we think that this approach is solid, maybe we should just use DFA alpha 1 to determine exercise intensity, no matter the heart rate. Forgetting about the issue of being able to practically do so, would that be a better way to use the data? Obviously within individuals the relationship between HR and DFA alpha 1 at 0.75 won't differ too much on a day to day basis unless fitness or environmental factors change. So we might also get away with the more standard approach of using DFA alpha 1-derived HR, but the issue I want to highlight here is more theoretical on the validity of using this approach. These issues are exemplified by the difference that seems to be present between how HR and DFA alpha 1 behave in the context of longer efforts with cardiac decoupling. Below you can see my data on a day which shows large cardiac decoupling and no change in alpha 1 (same external load during the workout). Similarly, Bruce has shown that cardiac decoupling does not seem to affect alpha 1. While we do not have the full picture on this, it seems clear that the two signals once again carry different information, and maybe we should not just use one, to estimate the other.  Example of increased HR due to cardiac drift (warmer day), without a drop in alpha 1. Similar external load (pace) across this run. Courtesy of the HRV Logger The bottom line here is that in my opinion, while it is practical to use "methods able to identify the aerobic threshold" to determine at what heart rate that threshold is, using these instruments with the only purpose of determining the heart rate threshold might be a flawed approach or simply do not provide the full picture on underlying physiological responses.
If DFA alpha 1 confirms to be a valid approach, maybe it is simply another parameter we can use to assess training intensity, based on its own value, and not necessarily its relationship with heart rate. The Heart Rate Variability (HRV) Logger app is finally available on Google Play, including DFA alpha 1 for aerobic threshold estimation Get the app here You can find more information on the HRV Logger and answers to common questions, here Enjoy and stay aerobic  There's a new independent validation out looking at the accuracy of commercially available HRV apps and sensors Great to see the results for the lowest median error:
This is confirmation of the quality of the work we have been doing for the past 8 years, starting with early analysis of the accuracy, then our validation paper, and finally with this independently run study confirming the accuracy of the methods we have developed for both the camera based acquisition and artifact removal for RR intervals acquired from external sensors Some thoughts at this link  |
Marco ALtiniPhD in Data Science, Founder of HRV4Training, Traveler, Passionate Runner, Immigrant Archives
November 2021
|
RSS Feed