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: 

• HRV & training adaptations
• HRV guided training
• interpreting changes
• Threshold detection

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

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:

• Sex
• Fitness level
• Ageing
• Body mass index

Check out the blog, here.

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

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

The 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). 

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:

• Stable physical condition
• Coping well with training
• Maladaptation to the current stimulus
• Accumulated fatigue

You can find this analysis under Menu / Insights / HRV Trends, once you have collected at least 40 days of data

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:

• The percentage of days the daily advice was green, yellow or red in the past 7 days

• The percentage of days the daily advice was green, yellow or red in the previous month

• The relative change in percentage, for each parameter included in the radar plot

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!

HRV4Biofeedback
We made quite a few improvements in our biofeedback app, in particular:

• Real-time panel to monitor heart rate and HRV during deep breathing sessions
• Spot check measurements, which you can use to monitor any potential changes before or after your deep breathing sessions
• Select different inhale to exhale ratios

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

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.

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.

The Heart Rate Variability (HRV) Logger app is finally available on both the Apple Store and Google Play, including DFA alpha 1 to analyze exercise intensity with respect to the aerobic threshold

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). If alpha 1 is above 0.75, the intensity is considered low, or below the aerobic threshold

How can you try it?
The HRV Logger app requires an external sensor, and for this specific analysis, we highly recommend a Polar H7 or 10 for its accuracy. Garmin dual straps should also work well. 

Learn more and get the app at this link

Enjoy and stay aerobic​