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Case study: Fuminori Takayama

2/20/2023

 
​This blog and case study was written by Fuminori Takayama. Fuminori is a Certified Strength and Conditioning Specialist (CSCS) with a Ph.D. in Health and Sport Sciences. He currently works as a strength and conditioning coach for athletes alongside a researcher. He is also an amateur runner. This article is a discussion of his HRV, training, sleep duration, and aerobic efficiency data for the past year. If you have any questions, comments, or inquiries, please contact Fuminori here.

Data collection

​HRV and sleep duration was collected with an Oura ring. Training data was collected with Garmin GPS watch and Polar sensor (OH1 or Verity Sense). All data was read in HRV4Training and analyzed in HRV4Training Pro in the long term. For more information about aerobic efficiency, see this article.

Contextualization of the past year's data

​As shown in Box1, this period was a training phase for a 24h race held in late May. The purpose of training is to increase volume (km). Training included long-distance running (40-80 km/session) and frequent jogging (about 15km/session). I checked HRV frequently and was careful not to increase the training load when the HRV was trending down. I felt that the approach may contribute to a stable HRV (and a slight increase in HRV). Although I was unable to break my own record from eight years before (181.690km), I was able to perform to a high standard in the race (171.760km). You can read about my approach to the race in this peer-reviewed paper.
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Read More

HRV after winning an Ironman

12/29/2022

 
Congratulations to Dan Plews for taking the win at Ironman New Zealand

Here are his last weeks of HRV data, showing:
  • an ideal response to training
  • minor sickness pre-race, recovered quickly
  • a large, sustained drop post-race

The visualization below, including daily HRV, normal range and baseline, as well as the detected trend (which combines HRV, heart rate and coefficient of variation), is part of HRV4Training Pro, which you can try here if you are using the app: https://www.hrv4t.com

all the best for your recovery, Dan
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Data interpretation issues in wearables

12/8/2022

 
Blog post by Marco Altini
More and more wearables have started capturing Heart Rate Variability (HRV) data overnight and combining it with other parameters (e.g. heart rate, sleep data, physical activity) to provide readiness or recovery advice to the user.

Despite some inconsistencies over the past years, as of the end of 2022, Oura, Whoop, and Garmin all work in a very similar way when it comes to HRV measurement. However, the way the data is used in all of these tools when building readiness or recovery scores or when providing advice to the user is often problematic and inconsistent.

Naive interpretations (higher is better), lack of a normal range (what’s a meaningful change?), and confounding your physiological response with your behavior, are common issues that limit the utility of data collected with wearables.

In my latest blog, I cover data analysis and interpretation to provide you with some useful tips and tools that should allow you to make the most of the collected data and ignore inaccurate interpretations provided by most tools out there.
Thank you for reading.

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Case study of a Japanese professional marathon runner (2h 10' PR)

11/24/2022

 
This blog and case study was written by Fuminori Takayama. Fuminori is a Certified Strength and Conditioning Specialist with the National Strength and Conditioning Association and has a Ph.D. in Health and Sport Sciences. 

He has co-authored research publications that includes case studies of HRV conditioning for elite middle-distance runners and a review of conditioning using HRV.
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If you have any questions, comments, or inquiries, please contact Fuminori here.

This work was partially supported by the research grant from Japanese Society for Running.
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Added the Subjective Score to the Correlation analysis in Pro

11/20/2022

 
In the latest update of HRV4Training Pro, we have added your subjective score to the list of parameters you can pick in the Correlation analysis.

You can try the new feature on HRV4Training Pro for free at this link, or use code SCIENCE for 20% off any package.
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What's the subjective score and how is it used in HRV4Training?

The subjective score in HRV4Training aims at capturing how you feel in response to training, and combines: perceived sleep quality, muscle soreness, motivation to train, perceived performance during training.

In the app, when providing daily advice (color-coding and message) 
in HRV4Training we combine your physiology and your subjective feel (outputs) . 

However, we do not use or include your behavior, for example your activity / training (input). This is a key difference from what you get in terms of readiness or recovery scores in most wearables. Why is that?

The whole point of assessing your state, either objectively via heart rate variability (HRV) or subjectively by feel, is to determine how you responded to your given circumstances. You already know the input (behavior) and are assessing the output (physiology or feel).

In other words, if I train hard or more for a few days, I want to assess how I responded (output). Including activity (input) in my assessment would mean penalizing me regardless of my body's response. For athletes (of any level), this method is particularly ineffective: it hides information.

If you train, there is no point looking at readiness or recovery scores to assess how you are responding to a given training stimulus as these scores confound your response with your behavior. Is the score low because I responded poorly, or just because I did more?

The subjective score in HRV4Training is not impacted by these limitations, as it relfects your subjective input. When combined with your physiological response (heart rate, HRV), it can give you a more comprehensive picture of your response, and help you make meaningful adjustments to your plans. 
​

​What are correlations about?

Citing Wikipedia: ​"Correlation refers to any of a broad class of statistical relationships involving dependence. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice."

In other words, looking at correlations can help us to pinpoint which parameters have a stronger impact on our physiology, and potentially make adjustments (e.g. if there is a strong negative correlation between work stress and HRV, maybe we should try to reduce work stress). 

How should I configure this analysis?

The correlation analysis in HRV4Training Pro lets you pick any timeframe between 30 days and 2 years. However, in general, we think that using a time frame between 60 and 90 days is ideal. 

Why  is that? Most likely the stressors you face will change over time, and similarly your response to certain stressors will change, therefore we believe it can be more helpful to look at these relationships in the relatively short time frame (e.g. 60-90 days), to get a better idea of what factors are influencing your physiology the most. Shorter windows (e.g. 30 days) might not have enough data, unless some really large stressor was present (for example if you go from sea level to 2000m / 6000ft of altitude, then you will certainly see a strong correlation between resting heart rate and altitude), otherwise it might be better to extend the window. On the other hand, longer windows (e.g. a whole year) might fail to capture more complex, multidimensional relationships between various training and lifestyle aspects, and your physiology.  

Finally, we would recommend to look at baseline correlations, more than day-to-day correlations. Baseline correlations are computed on the 7 days moving average of each variable, and therefore provide a more stable trend of the data. Typically, this is more insightful than to look at the individual data points, especially in the longer term.

Below you can seen an example:
  • Negative correlations are shown in yellow. Remember that negative means that for example your HRV goes up while the other parameter goes down. It does not mean that this is a bad thing! If you have a negative relationship between life stress and HRV it simply means that your HRV goes up when  your life stress goes down, as show here for my own data.
  • Positive correlations are shown in blue, in this case a higher HRV is associated with a higher value for a given parameter, for example the Subjective Score in my case
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The important part after you start looking at these correlations, is not to jump to conclusions too quickly. For example, it could be that the relation you are seeing is actually caused by another variable excluded by the analysis. However, this can be a useful starting point to explore your data, and we hope the new color-coding will make it a bit easier.

Enjoy

Push rMSSD to TrainingPeaks

11/2/2022

 
Check out the latest HRV4Training update, which allows you to choose rMSSD when configuring which parameters to send to TrainingPeaks, if you have a Pro account.

The new update is available for iOS and Android.

In this page, you can find an overview of some of the recent Pro features (and a discount code).

Enjoy and thank you for your support.

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Added the Subjective Score to the Overview page in HRV4Training Pro

11/1/2022

 
In the latest update of HRV4Training Pro (try it here), we have added your subjective score to the list of parameters you can pick in the Tags view.

You can see an example below, where the third plot shows my subjective score in the past few months, which is quite in agreement with my HRV data. In particular, we have first a slow increase over time as I am progressing well with training, eventually racing a half marathon PR, before an acute drop with sickness. 
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What's the subjective score and how is it used in HRV4Training?

The subjective score in HRV4Training aims at capturing how you feel in response to training, and combines: perceived sleep quality, muscle soreness, motivation to train, perceived performance during training.

In the app, when providing daily advice (color-coding and message) 
in HRV4Training we combine your physiology and your subjective feel (outputs) . 

However, we do not use or include your behavior, for example your activity / training (input). This is a key difference from what you get in terms of readiness or recovery scores in most wearables. Why is that?

The whole point of assessing your state, either objectively via heart rate variability (HRV) or subjectively by feel, is to determine how you responded to your given circumstances. You already know the input (behavior) and are assessing the output (physiology or feel).

In other words, if I train hard or more for a few days, I want to assess how I responded (output). Including activity (input) in my assessment would mean penalizing me regardless of my body's response. For athletes (of any level), this method is particularly ineffective: it hides information.

If you train, there is no point looking at readiness or recovery scores to assess how you are responding to a given training stimulus as these scores confound your response with your behavior. Is the score low because I responded poorly, or just because I did more?

The subjective score in HRV4Training is not impacted by these limitations, as it relfects your subjective input. When combined with your physiological response (heart rate, HRV), it can give you a more comprehensive picture of your response, and help you make meaningful adjustments to your plans. 

Try Pro

We hope you will find this feature useful.

Check out HRV4Training Pro at this link or use code SCIENCE for 20% off any plan.

​Enjoy.

Ambassador Program 2023

10/12/2022

 
HRV4Training is looking for brand ambassadors worldwide

We believe in empowering individuals with the ability to measure and interpret physiological data so that training and lifestyle stressors can be better balanced, resulting in improved health and performance

Learning from athletes and coaches is an invaluable part of the journey, and we are looking forward to getting to know you and your experience
​

Are you passionate about sport and technology and have been using HRV4Training daily for at least 6 months to better balance stress? We are looking for you!

What do we offer to the ambassador and mentors?
  • Ambassadors - free HRV4Training gear: choose between a singlet or t-shirt and 1 year free access to HRV4Training Pro, our web platform;
  • Mentors - free access to HRV4Training Pro for you and up to 5 clients for one year;
  • Ambassadors and Mentors will be featured on our website and on social media (mainly IG);
  • Repost of your posts on IG stories.

What do we expect from the ambassadors and mentors?
  • Up to 4 posts per year (on your chosen platform such as IG, FB, Strava, or personal website/blog) to schedule together with our team, where you explain how you use the app; 
  • Ambassadors and Mentors should include an HRV4Training handle in their bio on social media;
  • Ambassadors should send us a photo of them wearing the singlet that we can share on our website and social media;

We are going to select up to 30 brand ambassadors. We accept applications in English, Italian and Spanish. Would you like to spread the word about HRV4Training in another language? Send us a message with your proposal for our consideration!

​​To participate in the selection process, fill in this google form. 

Deadline is October 31st, 2022
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rMSSD in the homescreen

9/21/2022

 
Blog post by Marco Altini

​
We have just released a new feature that allows you to swipe the homepage and see your daily HRV, baseline, and normal range in terms of rMSSD (normally, what we call HRV is a logarithmic transformation of this value)
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The swiping between views will be enabled if you select rMSSD under HRV View in Settings, and have a Pro plan.

Pro enables also the normal range under Baseline (see previous image), and gives you access to the web platform you can try Pro for free here:
https://hrv4t.com.

With HRV4Training Pro, you get:
  • access to the web platform to analyze data in the long term
  • rMSSD view on the homepage
  • normal range in the Baseline view
  • you support this independent business and make sure we are still here next year

Thank you and enjoy 

Using the Oura ring for morning HRV measurements

9/19/2022

 
In case you would like to use your Oura ring to measure your HRV in the morning, as opposed to the night, you can do so following the procedure below.

If you are wondering why you would want to measure HRV in the morning instead of the night, here you can find some useful pointers about the differences between these two measurement times. 

From the Oura app, tap the plus icon, and start an Unguided session:
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Make sure to set the unguided session duration to 3 minutes, otherwise the ring will not report HRV data, but only heart rate:
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At this point, select Manual Input from Settings in HRV4Training, so that you can enter your heart rate and HRV in the HRV4Training questionnaire, instead of measuring via the app.
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You can learn more about Manual Input, here.

​Enjoy.

Support for all iPhone 14 models

9/17/2022

 
Blog post by Marco Altini
​

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We have been working on adding compatibility for all new iPhone 14 models, and have already released updates for HRV4Training, HRV4Biofeedback (our deep breathing tool), and Camera HRV (our research app)

Below is some data collected using an iPhone 14 Pro and compared against a chest strap, the Polar H10

The protocol consisted in the following:
  • 2 minutes of regular breathing
  • 1 minute of deep breathing

HRV (rMSSD) was 61 ms via ECG (chest strap), and 62 ms via PPG (phone camera), highlighting once again the accuracy of this method. You can see the RR intervals as well, which are a perfect match between PPG from the phone camera and ECG from the chest strap

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here is an example of how we would recommend holding the phone when using three (left) or two (right) cameras.

In case you cannot cover both the flash and the camera at the same time, give priority to the camera, as it is sufficient to be nearby the flash.
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Enjoy the update!

9 years birthday giveaway

8/11/2022

 
This week is HRV4Training's 9 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 link (if publicly available) or screenshot via email at hello@HRV4Training.com

We are looking forward to learning more about how you use the app and what you find useful. ​

​Deadline is next week!
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Manual Input in HRV4Training

8/7/2022

 
Blog post by Marco Altini
We have released Manual Input in HRV4Training, a feature which allows you to enter manually your heart rate and HRV data in the morning, as part of the questionnaire. 

​Below we cover the reasoning behind this feature, and how you can use it. 
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To select manual input, go to Settings and change sensing modality. Then, simply tap "Enter Values" or "Measure" in the homescreen of the app, and the questionnaire will pop up, including an extra section where you can enter your resting heart rate and HRV as reported by another device. You can also edit your data from History, by tapping a measurement bar and opening the questionnaire for a given day.

Why Manual Input?

More and more devices are providing night HRV data based on optical measurements, with good accuracy. For example, in our recent tests comparing full night electrocardiography (ECG) against the Oura ring, Whoop band and Garmin Forerunner 955, day to day differences in HRV were very similar. 
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In the graphs above you can see a comparison of ECG data and various wearables. The normal range does not match as different amounts of data have been collected, hence here we should only look at the actual scores, for about two weeks. We can see very good agreement in day to day changes for ECG, Oura and Garmin, with a larger error for Whoop, which is however providing data that is not too far since last year's update in how they compute HRV.
So far we have provided a direct link only to Oura. However, unfortunately, this link has resulted in very unreliable data connection due to Oura's API having quite a few problems. Additionally, it seems the API has been shut down for many users, probably due to the new subscription model or using the older ring.

Similarly, other wearables do not have APIs (e.g. Whoop) or we simply do not integrate with them (e.g. Garmin, Fitbit, etc.). 

Since we are talking about just two numbers here (resting heart rate and HRV, I have covered elsewhere how the full night average is the only meaningful parameter to use when measuring during the night), Manual Input makes it really simple to avoid all issues above, and allows you to enter your data as part of the morning questionnaire, regardless of the wearable you use. 

Why should I use HRV4Training if I already have a wearable?

Simply put, HRV4Training is the only platform that provides you with an analysis of your physiology that matches how this data is used in state of the art research and applied practice.

This means analyzing your resting physiology with respect to your normal range, and providing you with feedback regarding your acute (daily) and chronic (weekly) physiological state, in response to the various stressors you face. 

While in most tools you can look at your HRV numbers, these numbers are not contextualized with respect to other events (e.g. annotations you report in our questionnaire), or even with respect to your historical data.

Is today's reduction in HRV meaningful or just part of normal day to day variability? Entering your data in HRV4Training allows you to answer this question using published methods, which aim at effectively assessing your physiological response.

In the homescreen of the app you can easily see for example your daily scores with respect to your weekly baselines and normal ranges. In particular, the normal ranges are built using the previous 2 months of data, and allow you to quickly understand your current physiological response. 

You can learn more about why you should focus on your actual physiology as we do in HRV4Training, as opposed to made up scores provided in wearables, in this blog and this podcast. 
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What data should you enter?

Wearables use slightly different methods and sensing locations to measure your HRV. These differences make it so that the data is not interchangeable, but the relative changes, which are the only aspect that really matters, are. Hence, it is important that if you wear a wearable, you try to use always the same device and place it on the same arm, finger, etc, since the relative position of the sensor with respect to the heart, can cause differences in pulse rate variability.

If you use a Whoop band, enter your night resting heart rate and HRV as found in the app. If you use an Oura ring, enter your average night heart rate (not what they report as resting heart rate, which is the lowest, enter the average instead) and your average HRV. If you use a Garmin, enter your night average HRV and your resting heart rate (Garmin uses the 30 minutes of the night in which it was the lowest).   
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Do you need a wearable?

If you do not have a wearable and you have been measuring your physiology in the morning daily, you probably do not need a wearable and can save some money. 

If you are struggling with compliance for your morning measurement and would like to collect data passively, a wearable can help.

In any case, check out this blog to better understand some of the important differences between morning and night measurements of resting physiology.

I hope you'll find the update useful, thank you

Building a meaningful daily advice

7/25/2022

 
Blog post by Marco Altini
​

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When providing daily advice (color-coding and message) in HRV4Training we combine your physiology and your subjective feel (outputs) . However, we do not use or include your behavior, for example your activity / training (input).

this is a key difference from what you get in terms of readiness or recovery scores in most wearables. Why is that?

The whole point of assessing your state, either objectively via heart rate variability (HRV) or subjectively by feel, is to determine how you responded to your given circumstances. You already know the input (behavior) and are assessing the output (physiology or feel).

In other words, if I train hard or more for a few days, I want to assess how I responded (output). Including activity (input) in my assessment would mean penalizing me regardless of my body's response. For athletes (of any level), this method is particularly ineffective: it hides information.

If you train, there is absolutely no point looking at readiness or recovery scores to assess how you are responding to a given training stimulus as these scores confound your response with your behavior. Is the score low because I responded poorly, or just because I did more?

This approach not only provides you with poor information about your actual response, but fools you to believe the tool works. You go hard or do more, and they tell you you need to recover. In fact, you might be doing very well and be ready for another big training block.

This is not to say that your behavior does not matter: it is key context you can use to understand what could be driving changes. However, it should not be used to determine your response (output). You want to learn about the output of the system (physiological or subjective response) given the input (behavior and other).

There are many nuances that are worth understanding a bit better if we want to make good use of available technology. Hopefully, this explains a bit why it is worth assessing your physiology and feel, while you can ignore most (all?) made-up scores. 

Case study of a Paralympic Medal-Winning Cyclist

7/21/2022

 
Blog post by Marco Altini

In their recent paper, Dajo Sanders, David Spindler, and Jamie Stanley show a really well-presented case study of the impact of different stressors (heat, psychological, training) on resting HR and HRV (as well as self-reported parameters such as mood and motivation).

In the figure below, showing resting heart rate and HRV in relation to different annotations (for example health issues or training in the heat), we can clearly see how HRV is often more sensitive to stress, as it is associated with longer-lasting suppressions. This is in line with what we have reported in our recent analysis as well.
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Resting heart rate and HRV data was collected using HRV4Training for one minute in the morning, as we covered last week in our article on guidelines for morning measurements.

Finally, note how the authors report daily values with respect to the smallest worthwhile change, what we call the normal range in the app. Comparing daily values to our normal range is the only meaningful way to assess if daily values are different from what is expected when no stressors have a large impact on our resting physiology. Small variations within our normal range should not concern us or lead to any changes.


You can enable the normal range in the Baseline view of HRV4Training once you login at HRV4T.com and start your free trial or purchase a Pro subscription.

​You can use code SCIENCE at checkout for a 15% discount on Pro.
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How Should You Measure Your Morning Heart Rate Variability? (HRV)

6/23/2022

 
In our latest blog, we provide guidelines and practical tips for morning HRV measurements:
  • When should you measure?
  • How should you breathe?
  • Should you lie down, sit or stand?
  • For how long should you measure?
  • What about artifacts?

Learn more here
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Determining your normal range

6/2/2022

 
Blog post by Marco Altini
To make sense of changes in physiology (in particular, HRV and heart rate), we need to interpret them with respect to what we call your "normal range".

In the scientific literature, this is called the smallest worthwhile change (or SWC). In this short blog, I will cover our reasoning when it comes to how much data we should include to determine your normal range: a key aspect that will determine how changes in HRV are interpreted to provide you with useful advice.

If you are new to the concept of the normal range or SWC, please check out this blog post first.

How much data should we include?

In the context of analyzing relative changes over time, for example to identify periods of higher stress, there are two important trade-offs to consider when it comes to resting heart rate and HRV:


  • using a short time window (e.g. a few weeks) would be too reactive: a period of poor health would become your normal quickly. We want to prevent this from happening, as even long periods of poor health (due to sickness or injury for example) should not be considered your normal state
  • using a long time window we would be stuck in seasonality, and not change dynamically as our physiology does over a year. This is also something that we need to avoid, as we need to compare your physiology today with relevant and current historical data, and not with your data many months ago

In the scientific literature, for practical reasons, often one month is used to determine the normal range. However, we need to realize that scientific studies typically face obvious constraints (e.g. time and budget) and as such, might be trying to shorten the time required to capture an individual's normal range.

I would like to argue that this is too short and ineffective to capture longer-term decouplings between baseline (weekly average) and your normal range. Let's look at an example:

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In the data above, towards the end of the 3 months, we can see a few bad weeks where HRV is quite suppressed.

Let's look at the first graph first. If we were to build the normal range using only a month of data, the normal range would change too quickly: it would always include the baseline (blue line) despite a very large change in daily scores. In other words, if we use short windows, we are almost always within normal range.


Now moving to the second graph. When using 60 days of data to build the normal range, we can see how the normal range decouples more effectively from the baseline and daily values. In this case, we can clearly see that we are in a negative phase of suppressed daily and baseline HRV, with respect to our normal range.

In HRV4Training we use 60 days of data for these reasons. Obviously, ​there are always trade-offs to make and no choice is perfect, but in our experience, 2 months is an ideal time frame when looking at HRV data: you are not too reactive and can capture acute drops, you don't get stuck in very old data and seasonal changes.

You can try Pro for free by logging in here. Once you are logged in, the Baseline page in the app will also show your normal range, as shown below. Use code SCIENCE for 15% off.

​Enjoy
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Improved VO2max model for hilly runs

5/14/2022

 
Blog post by Marco Altini

We have just released a new version of HRV4Training for iPhone and Android, which deploys our latest VO2max model. 

In particular, this model better accounts for hilly terrain and provides more accurate VO2max estimates for runners that train on hills.

Normally, the relationship between running speed and heart rate is used to estimate VO2max, as we cover in more detail here. 
Intuitively, a lower heart rate at the same speed, means that you are getting fitter. However, this relationship falls apart when we run on trails, or include much elevation gain in our runs. Below is an example of my own data, where you can see a few things:
  • until the end of 2017, I lived in a hilly city (San Francisco). Then, I moved to a flat one (Amsterdam)
  • the only real change in VO2max was in 2016-17, when I started with polarized training (details here)
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In the last part of the graph you can see how my interest shifted toward trail and ultrarunning (very few flat runs). As a result, VO2max estimates show a large decrease. In fact, little change is present in speed over heart rate when looking only at these flat runs (yellow line).

This makes it hard to track progress in aerobic efficiency or VO2max (or whatever you want to call how your heart rate changes at a given speed). 


With the newly released model, we are able to better account for these changes, and provide a more accurate estimate. 

To build this, we modeled the difference in estimated VO2max for the same person when running flat vs hilly, in relation to the average grade of the runs (N = 10 000).

There are of course still limitations, for example no knowledge of how technical is the terrain, but it should be an improvement.


Below are results showing the original estimate (which was a match with testing in the lab), then the poor performance of the model on hilly runs, and the updated results today, for me (right) and Alessandra (left).

​We both had an accurate estimate when living in The Netherlands, then got a reduction in VO2max simply because of the different efficiency (relationship between pace and heart rate) when running on hills in Italy, and then finally you can see the prediction of the new model, which is close to the original one.

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​Enjoy!

How to use the Heart Rate Variability Logger to assess autonomic activity after exercise of different intensities and other stressors (e.g. the heat)

5/12/2022

 
Blog post by Marco Altini


​
We have made some changes to the Heart Rate Variability Logger app on iPhone (http://hrv.tools) to make it easier to compare pre and post-exercise data.

The goal of these measurements is to assess the impact of intensity (or other stressors, such as the heat for example).

Measuring heart rate and HRV before and after a workout, we isolate the training stressor in a way that allows us to assess and compare autonomic control. I have discussed these aspects in greater detail in my blog here.

This approach, is based on Stephen Seiler's research and could be a practical way to determine if training was executed according to prescription or if the intensity or the addition of other stressors, caused a greater autonomic disruption (and therefore a need for more recovery)
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With the HRV Logger you can take measurements before and after exercise, and run the same comparisons shown below, directly in the app (the Compare tab is available only on iPhone).

Make sure to configure the app as below:
  • 30" windows
  • 25% artifact correction

I would like to add a note about artifacts here and how the Compare view allows you to filter them out even when the RR intervals timeseries is impacted. Post-exercise, these days I have many ectopic beats. You can see below that some of them remain even after artifact correction (the spikes in the second recording, right end side).
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​When we analyze the data, we can get rid of features that have been computed using the Outlier removal button, in the Compare tab.

First, select the recordings to compare. Then, you will see histograms showing the distribution of the data, for the selected feature (typically I would look at heart rate and rMSSD). You can also see the averages (e.g. rMSSD = 68.1 pre-run and 10.8 post-run):

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As mentioned earlier, there were some artifacts in the RR intervals, that might impact rMSSD. If you toggle the Outlier removal button, you will get a cleaner picture without the need to export and re-process the data. Here for example rMSSD post-exercise becomes 8 ms.

As per Stephen's research, easy training should show almost no change in rMSSD post-exercise with respect to pre-exercise. This can be a useful test to assess if your sessions are truly easy (below aerobic threshold).

You want those bars to be really close or overlapping.
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Additionally, you can assess the impact of other stressors, such as the heat. Despite running very slowly and trying to keep intensity low when I recorded the data above, it is clear from the data change in autonomic activity that the heat for me is a large stressor, apparently as large (or larger) than high-intensity training.

Enjoy.

Fitter Radio Podcast

4/11/2022

 
Blog post by Marco Altini


​
I had a nice chat with Bevan discussing how to collect high quality and meaningful HRV data, how to interpret that data, our latest research with HRV4Training, and more

You can find it at this link, thank you for listening!
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Brand new HRV4Training

4/11/2022

 
We have just released the latest version of HRV4Training, which brings a complete re-design of the app.

It should be easier now to better understand if your resting physiology is within your normal range.

That's what matters the most.

You can find a quick start guide, here.

You can use HRV4Training either with your phone camera (a method that has been validated and independently validated), or with an Apple Watch, Oura ring, Scosche Rhythm24 or Bluetooth strap.

All sensing modalities are valid as long as you follow a few simple best practices.

Thank you for your support.

Enjoy!
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Heart Rate Variability (HRV) after hard workouts

4/5/2022

 
Blog post by Marco Altini

A common misconception is that HRV should dictate how you can perform. However, this is not the case. For example, delayed onset muscle soreness (DOMS) and nervous system recovery are on a different schedule.

In this blog, I would like to discuss what it means when your HRV is still within normal after a hard workout, and what you should expect after such sessions.

This can be confusing at times, but there is nothing better than a good (within normal range) HRV after a hard session. Why would this be the case?

A good HRV after a hard session shows that you were able to quickly bounce back. This is a sign of good fitness and an highlights how an adequate training stimulus was applied.

Most importantly, you should not expect your HRV to sink after a hard workout. If that is the case, it does not mean that you did a "good workout" (other common misconception), but it means that you dealt poorly with the workout and could not bounce back within a reasonable time.
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Elite athletes hardly ever see dips in their HRV post hard workouts. Are they not training hard or not training enough? unlikely. However, their autonomic nervous system recovers much faster than in other trained individuals, as we can see from the figure below (full paper here).
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If your HRV stays suppressed after 24 hours since your workout, most likely:
  • training was too hard for your current fitness level
  • ​the training stimulus was novel
  • non-training related stressors played a role

These are key points highlighted by Andrew Flatt in his research. 

When it comes to recovery, HRV is one piece of the puzzle. Having recovered quickly from a nervous system point of view means you have the capacity to assimilate the stimulus. Additionally, a stable HRV highlights how you are also dealing well with non-training related stressors (even more important).

Yet, there should be no surprise if your daily HRV does not correlate with muscle soreness or feel (whatever that means) or performance. HRV is your response: keep it stable.

If today you are resting and your HRV looks good, you are in an ideal situation. 

For any feedback on this blog, feel free to reach me here.

The Ultimate Guide to Heart Rate Variability (HRV): Part 1

3/31/2022

 
We've updated our Ultimate Guide to Heart Rate Variability (HRV), part 1:
  • short background
  • similarities and differences between morning and night data
  • best practices for your measurements
  • HRV metrics
  • a note on normal values

You can find it here.

We hope you will find it useful, thank you for reading!
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Heart Rate Variability (HRV) and training load

3/31/2022

 
Blog post by Marco Altini
​

It is a common misconception that HRV should track training load, reducing when training load is higher.

In particular, studies looking at the relationship between HRV (and other metrics) with training load over time, look at how these metrics correlate. However, the whole assumption that you should find the metric that “correlates the most” with training load, makes very little sense.

Why? Because you are already measuring training load. What is the point of having another metric that gives you the exact same information? By definition, if a metric is perfectly correlated to training load (positively or negatively), then it is a useless metric.

If HRV had a perfectly negative correlation with training load, It would not add any information to the training and recovery equation. Ironically, these studies would interpret the metric with the highest negative correlation with training load as the best metric (!).

On top of this, HRV is all about individual responses. A non-relationship at the group level does not tell us anything at the individual level. Maybe a few people responded very well and had increased HRV. Other people had a suppression in response to the same load. This is exactly why we monitor.

It makes sense to analyze group-level data in response to acute stressors (see for example our paper here where we look at training, sickness, alcohol intake and the menstrual cycle) However, in the long run, acute and chronic responses differ. As such, a group level analysis does not tell us anything about the individual response.

The notion that increased load should trigger a reduction in HRV is very simplistic. We can have stable or increased HRV when increasing load (a sign of positive adaptation) and decreased HRV with reduced load because of other stressors (travel, work). Check out this blog for more information on HRV trends.

How should we use HRV and training load information then?

If our training load is increasing and our HRV stays within normal or increases, that’s great, it means we are responding well to stress. This is confirmation that we can take the load, maybe even increase it a little more. In general, HRV should not negatively correlate with load.

​By measuring your resting physiology first thing in the morning, you can understand how you are responding to training (and other stressors), and use that information as part of your decision-making process.

If you are coping well with stress, HRV will not be decreased.
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For more misconceptions about HRV tracking, check out Part 4 of my Ultimate Guide To Heart Rate Variability.

Heart Rate Variability and performance

2/24/2022

 
In the latest study using HRV4Training to look at the relationship between acute changes in HRV (your daily value, with respect to your normal range) and performance, Justin DeBlauw and co-authors have found that "Daily HRV monitoring provides valuable insight that an individual’s peak power and speed may be compromised during cycling performance"

In particular, performance metrics such as peak power and speed, were lower when performing time trials on a day in which HRV was outside of the participant's normal range

Check out the full study, titled "Association of Heart Rate Variability and Simulated Cycling Time Trial Performance", at this link
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    This blog is curated by
    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
    2​b. 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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