We are releasing Manual Input in HRV4Training at the end of August, 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.
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.
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 clinical 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.
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).
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
Blog post by Marco Altini
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.
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.
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.
In our latest blog, we provide guidelines and practical tips for morning HRV measurements:
Learn more here
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:
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:
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.
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:
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.
How to use the Heart Rate Variability Logger to assess autonomic activity after exercise of different intensities and other stressors (e.g. the heat)
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)
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:
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).
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):
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.
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.
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!
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.
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.
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).
If your HRV stays suppressed after 24 hours since your workout, most likely:
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.
We've updated our Ultimate Guide to Heart Rate Variability (HRV), part 1:
You can find it here.
We hope you will find it useful, thank you for reading!
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.
For more misconceptions about HRV tracking, check out Part 4 of my Ultimate Guide To Heart Rate Variability.
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
Blog post by Marco Altini
I've enjoyed being back on the Scientific Triathlon podcast after a few years. In this episode, we discuss:
• HRV basics
• our latest research
• measurement devices
• morning vs night
• HRV vs heart rate vs readiness or recovery scores
• how to use the data (HRV-guided training, practical tips)
episode link here, thank you for listening!
I often talk about your "normal values" as the only meaningful way to make use of Heart Rate Variability (HRV) data. But what are these normal values? Why are they important?
Simply put, your normal values are a representation of your historical data. They allow you to understand if acute (daily) and chronic (weekly) HRV responses are showing meaningful changes or just small variations that you should not worry about.
Normal values make the data actionable.
Why do we need normal values? This has to do with the high day to day variability in HRV data. It might be easier to show this with an example. Let's look at the data below. This is typical HRV data, there is much variability between consecutive days.
Should the highlighted reduction trigger concerns and adjustments in our plans or not?
Technology for Heart Rate Variability (HRV) measurement at rest is getting better every day.
However, using a sensor able to measure accurately, is only the first step. In order to make use of HRV data, we need to make sure that the data is not only measured accurately, but measured at a meaningful time.
Here is where many sensors that measure automatically fail.
Finally, once we have collected accurate data at the right time, we need to be able to interpret that data, accounting for the high day-to-day variability in HRV. This last step is key to actionability.
1. Collect accurate data
2. Collect meaningful data
3. Interpret the data
Check out our latest blog to learn more.
Blog post by Marco Altini
One of the most interesting ways to analyze heart rate variability (HRV) data is to look at the amount of day-to-day variability in your HRV scores. That's what we call the Coefficient of Variation (CV HRV)
The CV HRV is different from your baseline, which is simply the average of your scores over a week. In simple terms, the CV HRV reflects how much your scores jump around on a day to day basis.
Why do we care?
Normally, the most important aspect to analyze is how your baseline is going with respect to your normal values.
A baseline within normal values shows a stable physiological condition and good adaptation (check out this blog for more information about the normal range)
However, the amount of day to day variability (the CV HRV), combined with baseline changes with respect to normal values, can provide additional insights on adaptation to training and other stressors.
The CV HRV can flag issues in response to stress, before a baseline reduction. Quoting Andrew Flatt ”the preservation of autonomic activity and less fluctuations (reduced CV HRV) seem to reflect a positive coping response ... In fact, individuals who demonstrated the lowest CV HRV during increased load showed the most favorable changes in performance"
A reduced CV HRV is often associated with coping well with training. This means also that larger fluctuations in CV HRV are signs of poor adaptation and might reflect issues in maintaining homeostatic control.
How do you use the CV HRV?
Let's look at one example.
I've discussed before how "work stress" is what drives changes in HRV for me (that's where I need to "perform"). Below you can see two similarly stable HRV trends (in the boxes), as well as my increasing subjective stress.
What about the CV HRV? Was it capturing anything differently?
Let's look at the data.
We can see how the HRV response to increased stress was still within my normal range but included a lot of jumping around (high CV), which represents a poor response, eventually leading to suppressed HRV.
Only reducing the stressor finally caused a rebound to normal.
The CV HRV had captured very well the poor response.
Learning what drives big changes in stress is probably the first step to do something about it, whenever it's possible.
Ideally, in the medium term, these are good trends we should hope to see if we are responding well to the various stressors in our lives:
Pay attention to deviations from these trends to spot potential issues in advance, which is easy to do in the HRV4Training app.
You can learn more about trends in resting physiology, in this blog post. I hope you have found this blog informative!
We've made a few changes in HRV4Training Pro
You can now select rMSSD on the Overview page. As the name implies, the Overview page gives you an overview of your physiology, training load, and subjective data (see here for some examples)
You can try Pro here: https://hrv4t.com
If you need a refresher on rMSSD and the different HRV features, I put together this blog post where I cover all of the most important ones
Our recommendation would be to keep it simple, and use only what we call HRV in the app (which is simply a transformation of rMSSD), or rMSSD, a clear marker of parasympathetic activity (i.e. a lower value with respect to your historical data is associated with higher stress)
If you use HRV4Training Pro with your athletes, clients or team, we have made some changes to the Coach Panel too, so that you can select rMSSD and quickly see the daily HRV with respect to each athlete's normal range, as shown below
This should make it easier to use the data to identify periods of higher stress, e.g. a daily score below an athlete's normal range. Pro allows you also to add some context, for example looking at annotations such as the one identifying menstruation days in the example below
We hope you'll enjoy the latest update and wish you a happy new year
Welcome to the HRV4Training team to all our new ambassadors
Full list at this link
Looking forward to working together in the next months
Thank you for your support and all the best for the new year!
Blog post by Marco Altini
In this podcast, I chat with Matt and Hanna at 80/20 endurance.
I’ve learned a lot over the years from Matt’s books, and it was a real pleasure to spend some time talking about HRV and training.
Thank you & enjoy
We have just released the latest HRV4Training update, including our new trends detection, which combines:
if you are a HRV4Training Pro user, with the latest update you will also be able to see the detected trend in the baseline page, together with your normal range.
You can try Pro for free by logging in at HRV4T.com, here.
Enjoy the update and see you next year for more.
Blog post by Marco Altini
Last week I had the opportunity to chat with Jason Koop and Corrine Malcom about heart rate variability (HRV), "readiness" scores, resting physiology, and wearables, in the context of training
If you are interested in understanding the nuances of these aspects, including their strengths and limitations, this episode is a good starting point
Don't fall for cults or for those who trivialize everything that is human physiology. Instead, strive to understand these aspects, and you'll be a better athlete or coach
Thanks again for having me!
Blog post by Marco Altini
In the second part of our latest paper, we analyzed individual stress responses to:
Using 1 year of data per person, for 28 000 people. This is in my view the most interesting part of the paper
This type of analysis allows us to answer important questions:
Can a morning measurement capture individual stress responses effectively?
Is it worth the trouble to look at HRV, or is HR enough?
What is the difference between the two, when it comes to stress responses?
Measurements and annotations (training intensities, sickness, etc.) were collected using HRV4Training, first thing in the morning Most measurements were taken with the phone camera (validation here).
Let's quickly look at our analysis framework first. How do we analyze individual stress responses? For each person, any given day there will be many stressors. However, if we take hundreds of days of data per person, and look at one stressor at a time, we can isolate the stressor and better understand its impact on resting physiology.
What did we learn?
Below are the results for training intensity (low vs high-intensity days). The change in HRV is 4.6% while for heart rate is 1.3% (with respect to a person's average). HRV is therefore more sensitive to this stressor.
The change in HRV does not reduce across age groups, indicating how HRV captures training stress equally well for older individuals, while the change in HR reduces. Additionally, women tend to have a less marked response (more about this later)
We also split the annotated intensity into four categories, as shown below. Once again we can see how HRV is more sensitive to changes in training intensity, but also how these measurements capture very well self-reported training intensities:
The change in heart rate was 1.6% between the follicular and the luteal phases, while the change in HRV was 3.2%. Once again, HRV is more sensitive. These differences might also be the reason why other stressors show somewhat less marked responses in women.
Changes in alcohol intake are 3-4 times larger than changes due to training or the menstrual cycle (6% change in heart rate and 12% change in HRV).
Not surprisingly, sickness is also a very strong stressor, similarly to alcohol intake (6% change in heart rate and 10% change in HRV).
What are the implications?
When using HRV for training guidance, lifestyle is key, and poor lifestyle or health issues will take over.
A holistic approach to health and performance is needed.
Strength of the stressor
To recap, changes due to training intensity and the menstrual cycle are typically 3-4 times smaller than changes due to sickness or alcohol intake.
Changes in HRV are 2-4 times larger than changes in heart rate in response to the same stressors.
When we contextualize the percentage changes reported in this paper with what we know from literature, e.g. that the smallest practical or meaningful change in heart rate is 2% and in HRV is 3%, we can see how changes in heart rate are below this threshold, and therefore smaller than normal day to day variability.
This means that heart rate is not sensitive enough unless we have very strong stressors (e.g. alcohol intake or sickness). This also means that while HRV is more sensitive, it is also less specific, as shown by the typically smaller effect sizes.
In other words: changes in heart rate are often of no practical utility (smaller than daily variability). On the other hand, higher stress will be reflected on HRV data no matter where it comes from and it might be difficult to get to the source (context is key).
We speculate that these findings might lead to new forms of HRV-guided training, where rest days are prescribed based on large changes in HR (as these capture only very strong stressors), while training intensity is modulated based on more subtle HRV responses.
You can find the full text of the paper, here.
Thank you for reading
Blog post by Marco Altini
In our recent publication, we analyzed the relationship between heart rate and HRV with respect to individual characteristics such as:
In a large sample of 28 000 individuals. What did we learn?
Some of the findings are larger-scale replications of what we knew already Consistent results with published literature that used different data collection procedures is a good first step It gives confidence in the quality of the data when we start digging a bit deeper
Women have higher resting heart rate than men, but very similar HRV In fact, at a younger age, women have a slightly higher HRV. This is of interest as a higher heart rate would normally be associated with lower HRV. The discrepancy might be due to hormonal differences.
Both underweight and overweight/obese categories show what we have called in the paper a suboptimal physiological profile, meaning that resting heart rate increases and HRV reduces when deviating from the normal range. The strongest deviation is for the obese category.
There was no correlation between resting heart rate and age, and a moderate correlation between HRV and age. This is one of the most interesting relationships, as heart rate and HRV clearly decouple and are representative of different processes (more on this later).
Physical activity level
The association between physical activity level and resting physiology is stronger for heart rate (r = 0.30, moderate effect size) than for HRV (r = 0.21, small effect size). When we break this down by age group, things get even more interesting.
The correlation between physical activity level and HRV reduces by age, getting to r = 0.13 for older individuals. Only for very young individuals (20-30 age group) there is a decent association between fitness and HRV.
Finally, we built models to determine how much variance age, sex, BMI and physical activity level could explain. Are they sufficient to get a good understanding of inter-individual differences? Not really, as they explain 19% of the variance in heart rate and only 15% in HRV.
What are the implications of these findings?
A low HRV in aging individuals might be associated with a deterioration of regulatory mechanisms. The weak link between physical activity and HRV as we age might similarly be associated with reduced baroreceptor sensitivity. On the contrary, increased stroke volume due to high levels of physical activity maintain resting heart rate low even for older age groups. In terms of explained variance, it is clear that genetic factors are key in explaining differences in heart rhythm between people.
An important implication here is that in our opinion, targeting improvements in HRV as intervention goals might not be realistic, given the strong heritability coupled with reductions with age and low explained variance associated with lifestyle factors such as physical activity level.
But there's an important caveat. In this work, we had a large sample. However, this sample is not representative of the whole population, but only of relatively healthy or health-conscious individuals. There might be more to gain for e.g. who never exercises, is overweight, etc.
This is why HRV as an absolute value is of little interest (in our opinion). On the other hand, HRV was able to capture day-to-day stressors within individuals with high sensitivity, as I will cover in a future blog.
You can find the full text of the paper, here. Thank you for reading!
Blog post by Marco Altini
Professor Maria Carrasco-Poyatos and co-authors at the University of Almería just published the latest paper on HRV-guided training, titled "Heart rate variability-guided training in professional runners: effects on performance and vagal modulation"
Thank you Maria and co-authors for using HRV4Training and for involving me in this work. You can find the paper, here, including the main highlights:
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This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
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
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
1a. Acute Changes in HRV
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
9. Samsung Galaxy
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
1. HRV normal values
2. HRV normalization by HR
3. HRV 101