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Blog post by Marco Altini 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.
Blog post by Marco Altini
In this interview, we cover:
Thank you Kieran for having me on your channel Blog post by Marco Altini "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.  Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 5, takeaways9/30/2021
Blog post by Marco Altini 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. Finally, in part 4 we covered the most interesting aspect: individual-level data. In this final part, I will report the main takeaways of the previous four parts, as a final wrap-up.  “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 and Kieran for featuring HRV4Training and ŌURA. Find the article, here  Blog post by Marco Altini The past few months have included lots of ups and downs for many of us. Personally, I've experienced some of the worst stressors of the past years, culminating (somewhat ironically) in heart issues, which were fortunately resolved. Below is an overview of my data, showing how morning measurements of heart rate variability can capture very effectively not only the day-to-day acute stressors we are often discussing (a hard training session, the menstrual cycle, alcohol, the acute phase of sickness), but also longer term, or more chronic changes. Don't forget to look at the big picture.  Blog post by Marco Altini We have just released the latest HRV4Training update, adding support for the new generation of iPhone 13. You can find some data of our first tests, here. Enjoy another year of accurate and inexpensive physiological measurements.   Blog post by Marco Altini 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.  Blog post by Marco Altini There is a new heart rate variability (HRV) course out there, put together by Daniel Plews, one of the most prominent scientists in the field. The course is split into two parts, part 1 covering the basics and technology, and part 2, a fantastic resource covering:
Additionally, if you use HRV4Training Pro, you'll see how to use the platform in the context of the points above, as Daniel goes over many examples showing our platform. We highly recommend it, for both individuals trying to get the most out of their data, and coaches using these tools with their athletes. Check out the course here and try HRV4Training Pro for free, here  This week is HRV4Training's 8 years birthday, and we are doing a small giveaway You can win either free access to HRV4Training Pro for one year (more info on the web platform can be found at HRV4T.com) or a t-shirt (either the running singlet or regular t-shirt) To participate in the raffle, simply write a post or blog about HRV4Training either on social media (Instagram, Facebook, Twitter, etc.), or on your blog and send us a screenshot via email at hello@HRV4Training.com or let me know here We are looking forward to learning more about how you use the app and what you find useful. Deadline is next week!  Blog post by Marco Altini 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 this blog, we finally start looking at the data, and in particular at population-level differences in resting heart rate and HRV, what factors consistently show changes in resting physiology, and what we can derive from this type of analysis. We will discuss absolute values and differences between subgroups of the population with respect to:
Check out the blog, here.  Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 2, The Technology7/22/2021
Blog post by Marco Altini Check out part 2 of our series on differences between Resting Heart Rate and Heart Rate Variability (HRV). In part 1, we covered the physiology, while in this part, we detail important technological differences when measuring heart rate and HRV: ‣ Common technologies (ECG, PPG, BCG), pros and cons ‣ Sensors made for HRV ‣ Managing artifacts ‣ Timing of the measurement ‣ Takeaways Link here  Blog post by Marco Altini 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  This is a guest blog by Jim House covering the past 2 years of his personal experience using HRV4Training and HRV Logger I have been riding a mountain bike for about 30 years. I am now 62 years old and just retired and have been diagnosed with leukaemia. Rather than sitting around feeling sorry for myself I thought I needed a challenge. I live near Eastbourne in the UK and decided I would ride the South Downs Way, which is 102 miles long (90% off road), including 11000ft of climbing. I have had successes and failures in my training so I have called this blog The Good, The Bad and The Ugly and I hope you can gain something from my experiences.  Jim and his playground The GoodMy approach in the past hasn’t always been very structured, but for this challenge, I decided to do it a bit differently. In early 2020 I started using HRV4Training and got myself a training plan. From the HRV4Training screenshot below you can see just how useful the feedback was to know when to push and when to have a day off, and also the desired effect of adjusting training this way, which resulted in a stable and consistent HRV, never below my optimal range as shown in HRV4Training Pro.  The first 9 months of HRV monitoring showed consistently stable responses as I adjusted my training based on HRV4Training's feedback
Part 1 of our latest series is all about physiology, and in particular goes deep into the differences between resting heart rate and HRV: ‣ Why do we care ‣ Bird’s-eye view ‣ Understanding autonomic control of the heart and more We hope you'll find it useful, enjoy the read  We have ve updated our Ultimate Guide to Heart Rate Variability (HRV): Part 1, that you can find here:
Enjoy the read  Our latest paper, titled "Real-time estimation of aerobic threshold and exercise intensity distribution using fractal correlation properties of heart rate variability: A single-case field application in a former Olympic triathlete" 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  We are excited to renew our partnership with The Australian Institute of Sport (AIS) We wish the best to all athletes at the AIS and hope HRV4Training will make it easier to capture the athletes' response to training and lifestyle stressors, therefore enabling the coaching staff to further individualize training Learn more about the AIS, here  Blog post by Marco Altini 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:
In this post, I will provide an overview of the parameters above, trying to highlight how you can use them to better understand your body's response to training and lifestyle stressors.  Today's score and the daily advice: capture acute changesThe top part of the screen shows your daily HRV and HR, right after the measurement. Today's score typically captures well strong acute stressors. 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. Check out our ultimate guide to HRV, part 2, for some examples. Typically, strong acute stressors limit our capacity to handle additional stress, and therefore to perform optimally, both mentally and physically. Thus, the daily advice in the app will analyze your daily HRV and determine if today's score is outside of your normal range, to provide you with meaningful advice. I will cover in the next section the concept of the normal range, but the important bit to remember for now, is simply that HRV data is highly individual and has an inherently high day-to-day variability. This means that it is not meaningful to compare to others, and that in your own data there can be large fluctuations between consecutive days. 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 (what we call your normal range), and what changes do matter and might require more attention or simply truly represent a positive (or negative) adaptation to training and other stressors. This is exactly what you see in the homepage with the daily advice which is constructed from today's score and your normal range. Check out an example below, where you can see the daily HRV score below normal in the first two cases, and within the normal range in the third case:  In the three days shown above, the normal range is always the same (7.4 to 7.9 HRV). However, the daily score highlights high acute stress in the first two screenshots, as the values of 7.2 and 7.3 are below the normal range. In the third screenshot, we have an HRV of 7.8, a score close to the higher end of the normal range, which we can interpret as either lack of strong acute stressors or a positive response to stress. HRV4Training combines the daily score, normal values and your questionnaire's data to determine the color coding and daily advice message. In particular, the following parameters are used: sleep quality, muscle soreness, motivation to train, perceived performance in your last training (or a subset of these if you do not use all of these tags). More on your normal rangeYour normal range is a representation of your historical data. Currently, we use the past 60 days of your measurements to build your normal range. As soon as you start using the app, HRV4Training will start learning what day to day changes are normal for you, and what changes are outside your normal range. As you gather more data, the app will get better at this job, eventually giving you the best estimate when having all the 60 days of data. The normal range is always kept current, so that you are not stuck in older data, but at any given time, the most recent 60 days are always used. Over the years, we made a few adjustments to this method, but we believe that 60 days is a great trade-off between the following:
For heart rate and HRV, 60 days of data seems to be just right. Remember that 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. The new interface in HRV4Training should make it easier to capture changes outside of normal. Finally, if you are a Pro user, you will also see the normal values in the Baseline page, together with some of your annotations. Below you can see an example of altered physiology during the menstrual cycle (both HRV and heart rate) as well as a period of higher stress in the third screenshot (several days with suppressed HRV, below normal range).  What about the baseline?In this post, I have talked about the daily advice, daily score and normal range. However, there is one extra bit of information that can be helpful in keeping track of recent progress, the recent trend or baseline. In particular, the baseline can give us a more stable indication of how things are trending. A low daily score with a baseline within range could be less problematic than a couple of low daily scores, which will take the baseline also below normal, highlighting a more serious form of stress. Recent research on HRV-guided training for example relies on the baseline to implement changes in training. If the baseline is below the normal range, then an easier training session or rest are prescribed. Obviously, research studies are often oversimplifications of more complex processes, and while a simple rule (such as "reducing intensity when the baseline is below normal") allows researchers to analyze systematically the impact of this protocol, we might not want to ignore a daily low score either (even if the baseline is still within normal). In my view, it makes sense in general to focus on the baseline and look at the big picture, this is exactly why we now report trends (both the arrow and numerical value of the baseline) in the homepage for heart rate and HRV. Intuitively, only larger stressors will affect the baseline. However, on days in which we have a lower score, I believe it is important to use that information as well. Try to assess how you feel, also subjectively (of course!), and determine if the acute HRV drop is something transitory (maybe associated to poor sleep) or if it might be something more serious to be cautious about (for example getting sick). Finally, HRV4Training combines multi-parameter trends to help you better understand the big picture. Looking at baseline changes in HRV, heart rate and the coefficient of variation of HRV, the app can automatically determine if your recent trends are changing in a trivial way, or if the change is something to take more seriously, based on your historical data. Once the various trends have been analyzed, HRV4Training will determine your physiological response to training as one of the following categories:
You can find this analysis under Menu / Insights / HRV Trends, once you have collected at least 40 days of data  Alright, that's all for this post, I hope you are enjoying the new interface and finding it useful to better understand how your physiology is changing both at the acute and at the chronic level. Thank you for your support
Blog post by Marco Altini Below is a few months of data highlighting the relationship between the menstrual cycle, resting heart rate (HR) and perceived physical condition. For the visualization, we are using the Overview page in HRV4Training Pro, which makes it easier to spot longer term trends as it displays normal values, baseline, training load and subjective metrics, all in the same page. In particular, we can see an increase in resting heart rate in the luteal phase of the menstrual cycle, which anticipates menstruation and a reduction in perceived physical condition. This is a good example of the importance of looking at physiology + context, to better understand the full picture. As physical as well as mental stressors continuously change over time, the relationship can change. Yet, analyzing these physiological changes and properly contextualizing them (which in this case means understanding that the menstrual cycle can drive much of the change in resting physiology), can help deriving the right conclusions. The data below was collected using HRV4Training's camera-based measurement every morning. I hope you've found this short case study informative!  We have just released an update for the dashboard in HRV4Training Pro, comparing the current week to your past month for subjective and physiological data In the new version, you will find:
This way it should be easier to quantify how much each parameter has changed in the past 7 days, with respect to the previous month, and how you are trending in general See an example below, and try it for your data at http://HRV4T.com Enjoy!  In this podcast, we chat with ultrarunners Jason Brooks and Jason Schlarb about HRV and HRV4Training, what to expect in terms of acute changes and long term trends, and how to use the data We also touch 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 You can find the full episode at this link Thank you for having me and enjoy the podcast!  Below you can find a brief overview of the recent updates released for our heart rate variability tools, which we hope you have been enjoying Thank you for providing your feedback and helping us make these tools better, and more useful to you HRV4Training The latest HRV4Training update includes normal values in the Baseline page of the app for Pro users. Normal values are a representation of your historical data to help you better understand when variations in heart rate and HRV are outside your normal range, so that you can focus on important changes You can try this visualization by logging in at HRV4T.com and starting your free trial or purchasing a Pro plan (use code NORMALISGOOD for a 25% discount). You can learn more about normal values, how we build them, other features in Pro and a few case studies and examples, at this link  HRV4Biofeedback We made quite a few improvements in our biofeedback app, in particular:
If you are already using HRV4Training and are interested in trying out these deep breathing techniques, you should be able to get HRV4Biofeeback with a little discount, by using the app bundle we put together for you  We are continuing our research on the effect of deep breathing on HRV, you can see an interesting follow-up of last week's article, here, where we look at the dose-response relationship between deep breathing and session HRV changes  HRV Logger The HRV Logger now includes heart rate as a feature. This way you can also see it averaged over the feature computation window (for example 2 minutes for DFA), which makes it easier to compare with other features Bruce Rogers has been doing some comparisons of the HRV Logger and Kubios to compute DFA alpha 1, showing great agreement between the two for the Android version as well, check out some data, here  Enjoy
Blog post by Marco Altini 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  Blog post by Marco Altini The latest HRV4Training update includes normal values in the Baseline page of the app for Pro users. You can try this visualization by logging in at HRV4T.com and starting your free trial or purchasing a Pro plan (use code NORMALISGOOD for 25% off)  Overview page in HRV4Training Pro What are normal values?Normal values are a representation of your historical data. They are built using the past 60 days of data and help you better understand when variations in heart rate and HRV are outside your normal range, so that you can focus on important changes. It is normal for heart rate and HRV to fluctuate quite a bit on a day to day basis, and it is key to be able to determine what changes are just part of normal day to day variability, and what changes are important. For example, a daily score or baseline below normal values for HRV data, identifies a day or period of increased stress, something to be cautious about. On the other hand, if your score is just a little lower than yesterday, but still within normal day to day variability, that's typically nothing to worry about. HRV4Training makes it easy to differentiate between these two conditions.  Example of the new Baseline page for Pro users in HRV4Training. Data can also be color-coded using different annotations, to better contextualize your trends. In particular, we can see how both HRV and heart rate go outside normal values, clearly highlighting a period of higher stress How do we build normal values?Currently, we use the past 60 days of your measurements to build your normal values. As soon as you start using the app, HRV4Training will start learning what day to day changes are normal for you, and what changes are outside your normal range. As you gather more data, the app will get better at this job, eventually giving you the best estimate when having all the 60 days of data. The normal values are always kept current, so that you are not stuck in older data, but at any given time, the most recent 60 days are always used. Over the years, we made a few adjustments to this method, but we believe that 60 days is a great trade-off between 1) not being too reactive, or adjusting your normal values too quickly for example using just the past few weeks of data 2) not getting stuck in very old measurements or in seasonal changes that might have little to do with your current status, which is what would happen when using more data. For heart rate and HRV, 60 days of data seems to be just right. How do we use normal values?Collecting high quality data using either our validated camera-measurement, a chest strap or an Oura ring, is only the first step. Once we have collected high quality data we need to be able to properly interpret it with respect to our own historical data. On a daily basis HRV4Training already compares your daily score with your normal values, you can see this comparison and a visualuzation in the homepage of the app. Additionally, you can also see the color coded advice in the History page, which also relies on normal values. The text in the homepage will also report if your score is within your normal values or not, as you can see below. What you aim for typically, is a stable physiological condition, so scores within normal on most days. Making adjustments such as trying to reduce training load or other stressors when your HRV is below your normal values, can be an effective strategy to better balance stress, and improve outcomes in the longer term. This is also the approach used in HRV-guided training.  Homepage and History page in HRV4Training. The color coding and the bar at the bottom of the homepage highlight if your daily score is within your normal values However, in the long term, once you have used the app for a few months, it can be helpful to visually look at the normal values band to better understand the full picture and analyze baseline deviations that highlight longer periods of higher stress. This is what you can do now in the app if you have a Pro account, similarly to what you already see on the web dashboard in the Overview page. Additionally, the new visualization provides color-coded annotations so that it is a bit easier to contextualize stressors in the medium-long term. See for example here annotations related to the menstrual cycle or getting sick. How can you try it?You can try Pro for free by logging in at HRV4T.com Once you have started your free trial or purchased a plan, the Baseline page of the app will update automatically. We hope you'll enjoy this update. Thank you for supporting our work. 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Marco Altini, founder of HRV4Training Blog Index The Ultimate Guide to HRV 1: Measurement setup 2: Interpreting your data 3: Case studies and practical examples How To 1. Intro to HRV 2. How to use HRV, the basics 3. HRV guided training 4. HRV and training load 5. HRV, strength & power 6. Overview in HRV4Training Pro 7. HRV in team sports HRV Measurements Best Practices 1. Context & Time of the Day 2. Duration 3. Paced breathing 4. Orthostatic Test 5. Slides HRV overview 6. Normal values and historical data 7. HRV features Data Analysis 1a. Acute Changes in HRV (individual level) 1b. Acute Changes in HRV (population level) 1c. Acute Changes in HRV & measurement consistency 1d. Acute Changes in HRV in endurance and power sports 2a. Interpreting HRV Trends 2b. HRV Baseline Trends & CV 3. Tags & Correlations 4. Ectopic beats & motion artifacts 5. HRV4Training Insights 6. HRV4Training & Sports Science 7. HRV & fitness / training load 8. HRV & performance 9. VO2max models 10. Repeated HRV measurements 11. VO2max and performance 12. HR, HRV and performance 13. Training intensity & performance 14. Publication: VO2max & running performance 15. Estimating running performance 16. Coefficient of Variation 17. More on CV and the big picture 18. Case study marathon training 19. Case study injury and lifestyle stress 20. HRV and menstrual cycle 21. Cardiac decoupling 22. FTP, lactate threshold, half and full marathon time estimates 23. Training Monotony Camera & Sensors 1. ECG vs Polar & Mio Alpha 2a. Camera vs Polar 2b. Camera vs Polar iOS10 2c. iPhone 7+ vs Polar 2d. Comparison of PPG sensors 3. Camera measurement guidelines 4. Validation paper 5. Android camera vs Chest strap 6. Scosche Rhythm24 7. Apple Watch 8. CorSense 9. Samsung Galaxy App Features 1. Features and Recovery Points 2. Daily advice 3. HRV4Training insights 4. Sleep tracking 5. Training load analysis 6a. Integration with Strava 6b. Integration with TrainingPeaks 6c. Integration with SportTracks 6d. Integration with Genetrainer 6e. Integration with Apple Health 6f. Integration with Todays Plan 7. Acute HRV changes by sport 8. Remote tags in HRV4T Coach 9. VO2max Estimation 10. Acute stressors analysis 11. Training Polarization 12. Lactate Threshold Estimation 13. Functional Threshold Power(FTP) Estimation for cyclists 14. Aerobic Endurance analysis 15. Intervals Analysis 16. Training Planning 17. Integration with Oura 18. Aerobic efficiency and cardiac decoupling Other 1. HRV normal values 2. HRV normalization by HR 3. HRV 101 |
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