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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  In the past few months, we’ve talked a lot about HRV during exercise. In this post, I’ll try to keep it simple and address some of the main motivations behind this approach, as well as provide practical tips and tools for the ones interested in trying it out. Keep reading, at this link  Stressed?? Why HRV is the Coolest Thing You Should Be Tracking (ft. HRV4Training - Marco Altini)24/2/2021
 What is it? The Heart Rate Variability Logger app I discussed a few posts ago has been extended to use the Apple Watch as a display, so that you can see in real-time HRV features as you exercise, including the DFA alpha 1 What's the DFA alpha 1? A non-linear HRV feature which can be used to estimate the aerobic threshold. In particular, when DFA alpha 1 is below 0.75, it means the intensity is higher than the aerobic threshold (so it defines "low intensity work", as typical in a polarized training program) When can you try it? The HRV Logger app is available on the Apple Store. It requires an external sensor, and for this specific analysis, we highly recommend a Polar H10 for its accuracy. The Apple Watch extension will be available in a few days The science: https://lnkd.in/gvsrXaK The app: https://lnkd.in/g7Ua2tq Enjoy!  Thank you Kristof for having me on your show, it was great to meet you and discuss all things sports science, entrepreneurship, and heart rate variability. Check it out at this link  "Until recently no mobile-based product could display DFA a1 in real-time using an off the shelf consumer device" ... well, that has changed Check out how you can use the Heart Rate Variability Logger app to estimate the aerobic threshold non-invasively, in the British Journal of Sports Medicine: "From laboratory to roadside: Real-time assessment and monitoring of the aerobic threshold in endurance-typed sports" Thank you Thomas and Bruce for involving me in this project, I've really enjoyed working with you and making your scientific work more accessible via this app More to come... 
Thank you Ben for putting this together and showcasing how HRV4Training can be used to adjust training on a day to day basis
The recording of yesterday's live interview on Wild Ginger Running is available at this link. Thank you Jen and Marcus for the invite, it was a really fun chat with great questions. Enjoy!  In my latest blog post, I go over the basics of heart rate variability (HRV) biofeedback and show changes in baseline physiology (resting HR, HRV) potentially linked to practicing deep breathing at resonant frequency consistently for the past month Learn more, here Get breathing!  Grateful for another opportunity to chat about monitoring heart rate variability (HRV), the work we do HRV4Training, and what it can be used for Thank you Ben and Sam for the chat! Episode here🎙  |
Marco ALtiniPhD in Data Science, Founder of HRV4Training, Traveler, Passionate Runner, Immigrant Archives
June 2021
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