Blog post by Marco Altini
I had a great time talking about stress, performance and HRV4Training with Matt Fox of Sweat Elite. You can find the podcast at this link.
Matt has spent quite some time in Kenya and Ethiopia as part of his work at Sweat Elite, and provides great insights on the lifestyle of elite athletes training there. We also discuss our own data during marathon training and the combined effect of training and lifestyle stressors on the body. You'll also learn a bit more about how HRV4Training started, recent research on HRV-guided training and the implications of a stressful lifestyle in terms of injury risk and performance.
Alright, enjoy the podcast!
In this post, I’d like to show how you can use a simple morning measurement of your resting physiology to gather useful information about your body’s response to training and lifestyle stressors.
In particular, we’ll look at two case studies using data from me and Alessandra in the two months leading to the New York City marathon, while dealing with additional non-training related stressors (work, university exams, etc.).
We’ll see how stress piles up and how the contribution of training and lifestyle choices has a cumulative impact that is reflected in your body’s physiological state (your HRV). Hopefully, the case studies will be helpful to better understand how you can apply similar principles to your own case so that you can better manage stress towards improved health and performance.
We have just released an improvement in HRV4Training Pro that lets you easily identify periods of significantly higher or lower values in the subjective tags you are tracking with HRV4Training.
In particular, the update adds normal values to the plots under the Overview page. The normal values represent the expected value for a certain parameter, given the past two months of historical data. This means that any values outside of this range will be easy to spot (for example days in which you slept much less than normal), and most importantly, you will be able to easily see when your baseline tag (7 days moving average) is outside of your normal values, highlighting how some major change is occurring. Without normal values, it can be difficult to understand if things are just fluctuating in a trivial way, or if there is a larger change that we should be more cautious about.
Let's look at a few examples. Below is the data from an athlete that has been prioritizing sleep quality, and we can see that despite some normal variation and a few data points that are particularly high or low (in this case associated to traveling), the baseline never gets outside of normal values, hence confirming that sleep is going well and should not be a major issue or the cause of any significant changes in baseline physiology (e.g. changes in HRV):
Sleep quality is rather stable despite some variability
Below is another example where we look at muscle soreness during marathon training, we can see some peaks here and there, followed by periods of recovery as we alternate long and easy runs, as well as the major impact of the race towards the end, and how long it took subjectively to go back to normal:
Muscle soreness during marathon training
And finally, here is a complete example where we can look at changes in resting physiology (HRV), training load and subjectively annotate lifestyle stress, during 2 months that include a few business trips (color coded in the first plot), periods of higher lifestyle stress (due to work and traveling, as shown in the last plot), and marathon training (plus marathon day, the peak in acute load towards the end of the second plot).
Using the latest visualization in Pro, it is easy to see when lifestyle stress was much higher than normal for this person, and how only the combination of high load (e.g. the marathon) and high stress brought HRV below normal values, showing that we had significant stress on the body (and staying in that condition for several days, with a difficulty to getting back to homeostasis quickly).
On the other hand, periods with high stress earlier where managed better, for example by reducing training load:
Overview page in HRV4Training Pro. HRV, training load and lifestyle stress are plotted during marathon preparation (and race day).
This case study above shows what we know very well already, stress is cumulative and we cannot isolate training and lifestyle stress or think that training is not affected by everything else going on at any given moment in our professional or personal life.
Yet, a simple marker such as HRV, measured in a well defined context (first thing in the morning while in a rested state), can capture stress deriving from all sources and help us make meaningful adjustments to maintain things in check.
We hope you'll enjoy the latest update.
If you have an HRV4Training account, you can try Pro for free by logging in here.
Apple Watch update: improved HRV analysis using iOS13, Watch OS6 and RR intervals available in Health
Blog post by Marco Altini
In previous posts we have shown how you can use HRV4Training to read HRV data from the Health app, convert that data (SDNN) to Recovery Points (a more readable metric), and analyze your physiology similarly to what we normally do when you measure using the phone camera or an external Bluetooth sensor.
With the release of iOS13 and Watch OS6, Apple provides RR intervals directly in the Health app, which we can use to compute rMSSD, Recovery Points and signal quality, just like we do with the validated camera based measurement or using external sensors. In this post, we'll look at the quality of the data as well as provide instructions for you to use the Apple Watch with our app.
Let's start with the practical aspects and then move to data quality.
How to use the Apple Watch with HRV4Training
Due to the fact that RR intervals can only be accessed by apps via the Health app, you need to follow these steps in order to gather meaningful data:
If you do not get your data in Health right after using the Breathe app, try to synch your Apple Watch and it will show up a few seconds afterwards.
Always remember that context is key, so while the Apple Watch writes somewhat random HRV numbers also during the day or night, that data could be affected by artifacts, and it is always decontextualized.
To properly interpret physiology, data must be acquired under standard, reproducible conditions, and the best way to do so is with a measurement as soon as you wake up, or with a night long measurement (not just a minute or two over a night). Only in this way, you'll be able to determine how you are responding and adapting to training and lifestyle stressors, as shown in this post and in this case study.
If you have used already HRV4Training with your Apple Watch, then you do not have to do anything different, but we will be able to provide you with a better analysis of your parasympathetic activity, as we can now compute directly the rMSSD feature and Recovery Points, instead of estimating it from SDNN.
Comparison with chest straps
Data was acquired using the Apple Watch and a Polar H7 (previously validated with respect to ECG here) connected to a different device running the HRV Logger app, which is an app that simply records everything coming from the sensor plus additional features.
During data acquisition, we collected data a few minutes while breathing freely, and a few minutes while deep breathing, to elicitate higher HRV due to RSA. You will see in the plots below visually the effect of deep breathing as we get greater swings in RR intervals. Then, we built a simple app to read the Apple Watch RR intervals from Health, so that we could compare them to what we collected with the Polar chest strap.
A final note on data synchronization: data cannot be perfectly synchronized because it is not timestamped by the sensors. What we can do is either to log real time and then to split data in windows based on when data was collected, then compute HRV features on these windows or to sum up RR intervals over time. For this analysis we went with the second option and also tried to visually align the data streams.
What can we derive from these data? You can see clearly almost perfect correlation between Polar H7 and Apple Watch for all conditions (relaxed vs paced breathing as highlighted by bigger oscillations in RR intervals or instantaneous heart rate), meaning that the sensor works really well in this modality.
Heart rate variability: rMSSD
As features, we will look only at rMSSD, the only feature we really care about. rMSSD is a clear marker of parasympathetic activity and the main feature we use for our analysis in HRV4Training, similarly to what other apps do as well. Additionally, the sports science community seems to have settled on this feature for several reasons (apart from the clear physiological link, as mathematically it captures fast changes that are due to how the vagus nerve modulates heart rhythm, there are also practical implications, as it is easy to acquire, easy to compute and reliable over short time windows and less controlled conditions), and therefore we'll stick to it.
What we expect given the data above is to see extremely close values between the Polar H7 chest strap and Apple Watch data.
For the plot below, I computed rMSSD for each time window:
Results are very good considering normal variation in physiology and limitations in data synchronization.
What are Recovery Points? A more human friendly HRV score, based on rMSSD. For more information, read this.
How accurate is the Apple Watch in measuring HRV? Very accurate, provided you stay completely still and use the Breathe app to take a measurement.
When should I use the Breathe app to take a measurement? First thing in the morning.
How much time do I have after measuring with the Breathe app, to fill in my tags in HRV4Training? You have three hours. When you tap 'read from Health' we always check only the last three hours, and see if we can find any HRV scores in the Health app, then take the last one. For this reason, we highly recommend reading data right after you have measured.
Should I use the Watch or the camera? Up to you. We consider both methods equivalent, and it is entirely based on your preference that you should make the call. What matters the most is that you are consistent over time, hence simply use what you consider the easiest and most practical method for you.
HRV4Training is looking for brand ambassadors worldwide!
Are you passionate about sport and technology and have been using HRV4Training daily to better manage stress and improve your performance? We are looking for you!
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.
What do we expect from ambassadors?
What do we offer to ambassadors?
How do you Apply?
Deadline is October 31st!
In this post, I’d like to show how we can monitor progress (or lack thereof) in endurance sports using tools such as aerobic efficiency and cardiac decoupling analysis in HRV4Training Pro.
I will also show how training adaptations resulting from different training stimuli can be captured by these tools better than using standard training load analysis metrics such as chronic training load.
I hope you'll find it helpful.
Blog post by Marco Altini
Just a quick announcement that all new iPhones are already compatible with HRV4Training, you can see a short video on an iPhone 11 here on our instagram.
As you know the new iPhone has 3 cameras, hence some changes were required. In particular, you will need to use the camera in the corner, as shown here:
As usual, please double check our camera based measurement best practices, to make sure you'll collect high quality data on which meaningful analytics can be derived.
Below you can also see two minutes of RR intervals data collected with the phone and a Polar chest strap, showing the usual agreement between the two, as reported in earlier validation studies.
In this post we highlight a small update we implemented to make it easier for coaches and teams to monitor HRV trends.
In particular, we have seen from published literature how 3 to 5 measurements per week can be sufficient to monitor an athlete baseline, and similarly we have seen from practical experience that many teams prefer to measure in the 3-4 days in the middle of the week, far from games (for a more comprehensive overview of these aspects, check out this blog post).
Yet, lack of daily measurements makes it more difficult for coaches to get a quick overview of the team status in HRV4Training Pro, as some data might be missing. See for example the panel below where we have missing data for one athlete:
Some of the professional teams we work with do their research, and they know well that the important bit is to look at baseline changes with respect to normal values, as covered here and also in state of the art interventions showing improved performance when following HRV-based advice (see an overview here).
Hence, lack of a daily measurement should not be a problem, we could for example use the athlete's past week of data, compare to her or his normal values, and then show feedback based on the comparison between HRV baseline and normal values.
This is exactly what you can do by enabling the "Force HRV advice" in your Coach Panel:
At this point you will be able to get visual feedback on the missing athlete's HRV trend:
Which can of course be confirmed by opening the Overview page and looking at the data, in case you'd like to dig deeper:
We hope you will find this addition useful to keep track of your team physiological data efficiently and effectively.
Blog post by Marco Altini
We have released a new feature in HRV4Training Pro: Training Monotony.
In case you want to jump right in, and check out our latest feature, simply login at HRV4T.com with your HRV4Training credentials, then navigate to Insights / Training Load Analysis
As a coach, you can access these estimates for all your athletes from the Coach Panel.
Overview of the Training Load Analysis in HRV4Training Pro.
What is it?
Training monotony refers to the similarity of daily training. In practical terms, this is a statistical representation of how much your training stimulus is varying over time.
As in all other analysis included under our training load analysis, the first thing to do is to pick a training load metric, or training impulse. This can be relative effort, TSS, RPE, RPE x Duration or any other parameter that is relevant in your sport.
Once you have picked this parameter, HRV4Training Pro will analyze on a weekly basis, to determine training monotony.
Freshness, injury risk and monotony are computed from training impulse and can be representative of different processes. Freshness is about recovery and being race ready, injury risk compares your recent and habitual load to determine if you have increased load too much with respect to what you are used to, and therefore increased injury risk. Monotony concerns variation in training, with the idea that optimal performance is associated to higher variation.
How do you use it?
In general, low monotony (a value below 1.5 for example) is preferable so that different training adaptations can be triggered, while allowing for sufficient recovery to the body. Low monotony is normally associated to a polarized training and other periodization methods alternating high and low intensity workouts.
On the other hand, a high value for training monotony indicates that the training program might be ineffective and lead to stagnation, or lack of improvement. Hence, if your score tends to be higher, it might be time to try something different.
Here is for example a month of workouts of varying intensity and duration, resulting in very different relative efforts (the training impulse metric available in Strava and used for this example):
You can see how training monotony is indeed very low:
On the other hand here we have a week with very similar workouts (second row in particular):
Which corresponds to the yellow spike in monotony below, an indication that the stimulus recently has been always quite similar on a day to day basis, which might be unproductive for performance:
Needless to say, this is an oversimplification of the many processes affecting human performance. However, several authors have found that lower monotony is linked to higher performance, and therefore we hope this extra data point that can be informative and help you critically analyze your progress.
Alright, that's all for this update. Enjoy.
Improved FTP estimation (for cycling), lactate threshold and running time estimation (for running) in HRV4Training Pro
Blog post by Marco Altini
We have improved our FTP, lactate threshold and running time estimates for HRV4Trainign Pro users using Strava. The latest estimates have already been released in HRV4Training Pro, with the goal to help you keeping track of your training progress.
In case you want to jump right in, and check out our latest feature, simply login at HRV4T.com with your HRV4Training credentials. As a coach, you can access these estimates for all your athletes from the Coach panel.
This work is an extension of our previously published analysis (see "Estimating running performance combining non-invasive physiological measurements and training patterns in free-living” which was accepted for publication at the 40th International Engineering in Medicine and Biology Conference).
The FTP, lactate threshold, half and full marathon estimates do not simply rely on your VO2max or on previous running times or short tests as you can find in other products or online calculators, but relies on a statistical model combining the following:
Blog post by Marco Altini
As previously reported we have added support for Samsung Galaxy's dedicated sensor.
The dedicated sensor is typically found next to the camera on the back side of phones such as the S7-S10, and is a sensor you can use rather than a cheststrap or the actual camera.
The advantage is that this sensor is designed to measure PPG, and therefore should allow you to obtain high quality data with high reliability, provided that PPG data is processed with accurate algorithms able to filter the data, clean it from artefacts, determine the location of peaks in the PPG signal, and then compute HRV from these peak to peak differences. The procedure we employ in HRV4Training has already been validated and is detailed in this blog post and also covered in this paper.
In this post, we'll show a few minutes of data collected under different conditions, highlighting how the dedicated sensor combined with our algorithms is a very accurate way to detect RR intervals and compute HRV (rMSSD in this analysis).
We have new designs for our running singlets, you can find them at this link.
We have released a new feature in HRV4Training Pro: Cardiac decoupling. You can find it under Insights / Aerobic endurance, together with our Aerobic Efficiency analysis.
Note that to use this feature you need to use HRV4Training linked to Strava, so that your workout summaries and laps can be analyzed.
What's cardiac decoupling?
Cardiac decoupling relates to your cardiac drift during an aerobic effort. What’s your cardiac drift? Basically, your heart rate increasing as a result of your body getting fatigued, during the second part of a workout.
To determine your cardiac decoupling, we compute the relation between output (pace or power) and input (heart rate) during the first and second half of a workout.
Intuitively, if heart rate increases at the same pace during the second part of a workout, or if your pace reduces in an attempt to keep your heart rate below a certain value, it means that your aerobic endurance for the distance is not well developed. Similarly, a ratio close to one or below 1.03–1.05 shows that your heart rate does not drift much during the second part of the workout, which is a sign of good aerobic endurance.
Here is 6 months of data, which include about 4–5 months of very good training in between two injuries:
In this post, we provide an overview of the main findings deriving from the past years of applied research in team settings. We’ve talked often about data analysis, looking at the big picture and the coefficient of variation as effective ways to monitor individual responses to training and lifestyle stressors, as highlighted for example in this case study. But what about team sports? In a team settings, a few questions pop up: can we derive anything meaningful from absolute values (for example assessments every few months instead of longitudinal monitoring)? How often should our team players monitor? When are the best days to do so? And most importantly: what are the main signs of positive and negative adaptation to training in different phases of the season? We all know each athlete responds differently, so how can we use measures of internal load such as HRV to adapt training based on individual responses?
We’ll go over the main studies answering these questions and provide guidelines for you so that you can make the most of HRV4Training Pro with your team. I’ve broken up this post in two main sections, one covering the most important points to remember and a longer one covering quite a few studies for the ones that are more interested in the details.
Studies selection criteria
In the past 50 years an enormous amount of research has been published looking at physiological responses to training. In this (far from comprehensive) blog post, we limited our analysis to works that we believe are of practical interest for coaches and athletes working in teams settings. Considering the huge technological developments of the past few years, which brought a significant shift in the way data can be collected (no need for a lab, less costs, more measurements), we will focus on studies that use methods similar to the ones you might be using today.
One of the great advantages of having a large amount of research on the topic is that more and more groups have covered the same aspects, replicating protocols, studies and results, and therefore we are more confident about metrics, procedures and outcome than we would be if this was a younger field. In this post our goal is to consolidate the results in an easy to understand overview showing the main findings and how you can use them in your own team relying on HRV4Training Pro.
We tried to include studies that have been replicated or showed very similar outcomes across sports, technologies, measurement protocols, etc. - which normally is a good sign, meaning that we are reporting physiological responses that can be expected when going through certain training protocols (e.g. changes in HRV during pre-season).
The short(er) read
Published literature on the use of HRV measurements in team settings repeatedly highlighted the following aspects for what concerns measurement protocol, metrics used, data analysis and practical actionability:
Quick announcement that we have integrated Samsung's dedicated PPG sensor in HRV4Training. The sensor should be available in the Galaxy S6, S7, S8, S9 and S10 (in the images below you can see the S7). In this case we use the infrared light, and therefore the flash will not be used.
In this post, we cover a few insights derived from recent research on HRV and the menstrual cycle, including a journal paper just published by Patricia Doyle-Baker and her group at the human performance lab, University of Calgary, using HRV4Training.
This is the first study ever to monitor HRV daily during the menstrual cycle to understand the impact of different phases of the cycle on autonomic activity (which is crazy if you ask me, considering that it's 2019). Anyhow, we are glad HRV4Training made it possible to finally collect real life data with high compliance and I hope this write up will be useful to better analyze your own data or your athlete's data, so that you can include an additional piece of information in the decision making process.
In literature, the relation between menstrual cycle and HRV is investigated to understand if the menstrual cycle can act as a confounding factor when analyzing HRV data, for example because of changes during the different phases of the cycle that would require to interpret the data differently. The first potential confounding effect of the menstrual cycle brought up in literature is at the population level, so for analysis that look at sex differences in HRV features. This is not really something too relevant in our case, as we always stress that data should be analyzed at the individual level, with respect to your historical data, and not compared to others (Aubert et al, heart rate variability in athletes). Many studies in literature have shown that regulation of the ANS is modified during the menstrual cycle, hence the need to further investigate the relationship with our marker of parasympathetic activity, rMSSD.
A second and more relevant aspect, tightly coupled with what just discussed, is that if different phases of the cycle have an influence on autonomic activity, then even at the individual level HRV data might be affected by the cycle phase, which should be accounted for when we look at our data. HRV analysis in women may be inconsistent if HRV cannot be considered stable across the menstrual cycle or if the expected differences are not accounted for. This can be an issue as interpretation may lead to inappropriate conclusions.
In this post, I'd like to show some data to highlight a few important aspects when analyzing your heart rate variability (HRV) data. In particular, I'd like to cover some misconceptions about the relationship between training and HRV as well as the importance of lifestyle and psychological aspects (context!).
We'll use my own data collected between January and April 2018, so 3 months in which I went from best shape of my life to injured and then back to training regularly post-injury, but in poor shape (detrained). We'll look at:
I hope this case study can be a good starting point to identify useful ways to look at your data using HRV4Training Pro.
Blog post by Marco Altini
In this post we’ll show two methods we have implemented in HRV4Training Pro to let you easily track changes in aerobic endurance while preparing a running or cycling event, so that you can analyze your progress:
Using these two methods and analyzing changes systematically over time with respect to your historical data, it should be easy to track improvements (or lack thereof) over time and make meaningful adjustments to your training plan.
Learn more at this link.
How to use HRV4Training to monitor adaptation to training and adjust things on the go: a case study.
Blog post by Marco Altini
In this post, we go over the 12 weeks leading to Serena's first marathon.
We'll see how HRV data can be used to analyze positive adaptations (increasing or stable HRV baseline) and to determine when to hold back if necessary (HRV baseline below normal values, or maladaptation detected).
We'll also see how to analyze training intensity distribution and how to determine race pacing strategy using HRV4Training Pro.
As always, while this post is about data, there is no use in data without common sense. Data is not here to replace our brain. Data is here to help us improve our understanding of our body and perception of stress and effort - something we are really bad at, especially as recreational athletes.
Hopefully, the tools we have developed as well as this case study will help you to learn more about how you respond to stress and to manage things better.
Thank you again Serena for working with me in these three months and congratulations again on your sub-4 marathon.
Train smart, run faster
In this post we highlight a recent feature we have helped to help you keep track of your improvements during speed sessions or specific workouts.
In particular, as runners or cyclists, there are a few workouts that we might tend to do over and over again during different phases of our training plan, which help developing certain skills (e.g. neuromuscular fitness and speed as well as VO2max).
Countless times we had to go back an forth in our log to see how much progress we had made, browsing months of data and trying to do the math on our average splits and recoveries.
To make the process easier, we built the Intervals Analysis feature in HRV4Training Pro, which lets you pick the following:
Then the analysis will show you number of reps, average duration, speed or pace and heart rate or power data. This way you can easily track improvements (or lack thereof) over time.
What do I need to for this analysis to work?
Note that you need to use Strava and to track Laps in your workouts, otherwise this analysis won't work. If you have your Laps in Strava correctly set matching your intervals, then we will be able to analyze the data as shown below.
Can't see your workout data?
If you have linked your Strava to HRV4Training and still can't see your intervals, make sure recorded laps match what you are searching for. You can see your laps in Strava from the Laps screen shown below. Note that your power and heart rate data musth also be recorded correctly. Feel free to contact us if you think everything is configured correctly but you still are unable to see your intervals.
You can try the new feature by logging in at HRV4t.com
We have released a new integration in HRV4Training, which allows you to read sleep data and whole night HR and HRV from your Oura ring (or more specifically, from Oura Cloud).
How does it work?
To setup the integration, go to Menu / Settings in your iPhone or Android device, and scroll down until you see the Link to Oura entry.
After you have authorized Oura, we will set up the connection to automatically read wakeup time, bedtime and sleep quality. Additionally, you will be able to also read resting heart rate and HRV using the ring's data instead of the morning measurement (more on this later).
No data found
Once you have linked Oura and set the parameters you'd like to read, we will read the data when you take the measurement or in case you read also HRV from Oura, when you tap the Read from Oura button (which will replace the 'Measure HRV button").
Make sure to have your data in Oura Cloud, before using HRV4Training. Note that you might have synched your ring and app, and have the data in the app, but that is not sufficient as we read from Oura Cloud, hence you need to make sure to have the data there, or we won't be able to access it.
Here is how to get your data to Oura Cloud from your Oura app: how to.
Reading HRV from Oura
In case you prefer to use your night data instead of taking the morning measurement, you can do so by enabling the Heart rate and HRV check box in the Oura Settings in HRV4Training.
In this case you can also read data later on during the day instead of right when you wake up, as we will be using your night's average heart rate and HRV to determine Recovery Points and other metrics in our app, but there are some caveats to consider (see next section).
Morning measurements vs night measurements
Morning measurements have been used for a long time in the context of tracking chronic physiological stress in response to training and lifestyle stressors.
On the other hand, mainly because of the difficulties in acquiring such data, night data has been used a little less. This being said, as scientists have been active in this field for decades, you can find several papers looking at the relation between nocturnal HRV and training load, for example here, or here, similarly to what we have shown for morning measurements here. In our recent overview of HRV in team sports we covered studies that collected data both in the morning and in the night, you can read it here.
In our opinion, there is little doubt that night HRV is reflective of physiological stress, similarly to morning measurements, and therefore we believe both approaches are valid in terms of acquiring data representative of chronic stress and helping you making sense of the data over time. It is of course key that the sensor used to measure night data is reliable, and this is the case for Oura, which shows extremely good agreement with ECG in this validation where rMSSD was computed from night recordings.
However, while both methods are able to capture changes in physiology relative to your baseline and normal values over time, the absolute values will most likely differ. What does this mean? Simply put, that you cannot interchangebly use one method or the other, but you have to stick to one, either morning measurements or night measurements, and then use always the same method so that data can be analyzed meaningfully over time.
Here is an example of our data showing for example a big dip in HRV the day following a half marathon race:
We can see:
Similarly in HRV4Training:
Here we can also see a relatively stable period in terms of HRV leading up to the race (tapering), a big post race dip (March 18th) and a few days with lower values following the race.
Here we also have the added benefit of HRV4Training's trend analysis, which looks at HRV, HR, coefficient of variation and training load to determine how you are responding to your current training block, and indeed highlights a bit of post-race struggle in this specific case.
Given what is discussed above, we recommend taking the measurement in the morning as you normally do, and use the ring mainly to track sleep.
If you decide to use the ring also for your HRV data, just keep in mind that you might need to acquire a new baseline and new normal values, which can take up to 2 months. In this case it might be simpler to start over by creating a new account.
In this post we highlighted our latest integration. In this case more than ever, we decided to move forward due to the overwhelming feedback received by our community.
Thank you everyone for taking the time to provide your input and appreciation for how we analyze and interpret the data in HRV4Training. It is our belief that helping you making sense of the data is what we do best here, and therefore we are happy to expand the set of compatible devices for the ones that prefer to collect data passively in the night.
Look at the big picture by easily analyzing your recent trend with respect to your hsitorical data. Try HRV4Training Pro at HRV4T.com
Blog post by Marco Altini
Recently we have talked a lot about normal values and the big picture (here), how to use deviations from normal values to guide training (here) and the importance of looking not only at baseline HRV with respect to normal values, but also the coefficient of variation (here).
In this post, we show how you can easily analyze your data and look at the big picture, using the Overview page in HRV4Training Pro. In particular, we'll look at:
You can try HRV4Training Pro at HRV4T.com and get 15% off using promo code BIGPICTURE
Baseline and normal values
When you login in Pro, tap the Overview button in the top bar, and you'll get this view:
In this page you can analyze your physiological data focusing on the big picture, instead of day to day variability. In particular, the top plots shows your HRV baseline, depicted in light blue, with respect to your normal values.
Periods of higher or lower physiological stress can be easily highlighted when the baseline moves below or above your normal values. Normal values are computed based on your historical data, in particular as one standard deviation from your mean HRV, using always the previous 60 days of data:
Context: training load and subjective data
The second and third plot provide more context, allowing you to display data related to training load and other tags you might be annotating in the morning after the measurement.
Here is a training load example, showing positive adaptation to an increased load (baseline stays within normal values and even increases a bit):
Blog post by Marco Altini
In this blog post we cover our latest update in HRV4Training Pro, which makes it easier to analyze the two most important parameters when it comes to interpreting your physiological response to training and lifestyle stressors:
What's the CV?
The amount of day to day variability in your HRV scores is the Coefficient of Variation (CV or CV HRV). This is different from your baseline, which is simply the average of your score over the past week
Let's look at an example to make things more clear. If your baseline this week is 8, it could be that you had a few days with very similar scores, say 7.9, 8.1, 8, etc. or it could be that your scores were jumping up and down quite a bit more, say 6, 10, 7, 9, etc. - in both cases averaging at 8, your baseline.
Got it? The amount of variability is the CV, so in the first case with similar values, the CV is small (there is little day to day variability), while in the second case, the CV is large (more day to day variability).
Note that as always, small and large are both determined based on your historical data, everything is relative. Let's look at an example for a person's data over time:
The top plots show daily HRV score and the baseline, 7 days moving average (in blue). The bottom plots show the CV. You can see how the CV increases when the daily scores are jumping around more, while decreases when we have more stable values, regardless of the fact that the baseline is fairly similar in the two conditions. We'll learn in the next section why this matters. These plots are available in HRV4Training Pro under Insights / Resting Physiology.
Why do we care?
We've been covering a few times in the past weeks the fact that the most important aspect to analyze is how your baseline is going with respect to your normal values, as a baseline within normal values shows a stable physiological condition and good adaptation to training, while a baseline below normal values, shows significantly higher stress and the need to hold back. To learn more about this, check out our post on the big picture and training prescription using HRV.
However, as anticipated in the intro of this post, there is another parameter which is very important, and that's the CV. In particular, the amount of day to day variability (the CV), combined with baseline changes with respect to normal values, can provide additional insights on adaptation to training and other stressors.
From Flatt et al. ”Thus, the preservation of autonomic activity (no change in LnRMSSDm) and less fluctuations (reduced LnRMSSDcv) seem to reflect a positive coping response to the training. In fact, individuals who demonstrated the lowest LnRMSSDcv during week 1 of increased load showed the most favorable changes in running performance (r = -0.74)." - as discussed in our blog on training load.
A reduced CV is often associated with coping well with training. What we are measuring is how we are responding to a workout or block of workouts, with certain trends (stable or upwards HRV, reduced CV) being indicative of good adaptation, even in periods of very high load. This means also that the opposite trends, reductions in HRV or larger fluctuations in CV are signs of poor adaptation and should trigger changes in training.
Coefficient of Variation in HRV4Training Pro
In our latest HRV4Training Pro update, we have added the possibility to highlight the current trend in your CV, directly in the Overview page, so that you can see at the same time your baseline with respect to normal values, as well as how the CV is trending (stable, increase or decrease). See an example below:
Using this visualization you can more effectively analyze the CV, as only significant changes are color coded (stable, or the gray scores, simply mean that changes are trivial during a given period), hence you can see when for example your scores seem to be jumping around a bit too much (yellow bars), even if the baseline is still within normal range.
Putting it all together: automatically detected physiological trend
As we have thoroughly described here, HRV4Training can automatically determine how you are coping with your training load by combining heart rate variability, heart rate, Coefficient of Variation and training information. The detected trend is one of the following: coping well with training, being in a stable condition, risk of maladaptation or accumulated fatigue.
While we have added the CV trend in the view just described, you do not really need to do the math yourself, as this analysis is present in the HRV4Training app under Insights / HRV Trends and also on HRV4Training Pro under Insights / Resting physiology.
You can overlay the trend detection analysis with your normal values as well, so that you can get a better overview of not only your physiological data with respect to normal values, but also the detected trend based on a combination of baseline HRV, heart rate and coefficient of variation, contextualized by training load.
This was a short blog post covering the latest update in HRV4Training Pro. As recently we have talked a lot about normal values and the big picture (here), how to use deviations from normal values to guide training (here) and the importance of looking not only at baseline HRV with respect to normal values, but also the coefficient of variation (a multiparameter approach, here), we have made a few changes in HRV4Training Pro to provide an even better overview of your physiological data. We hope you will find these updates useful to better understand how you are responding to training and lifestyle stressors.
Take it easy.
Blog post by Marco Altini
This is a short blog post covering the latest update in HRV4Training Pro. As recently we have talked a lot about normal values and the big picture (here), how to use deviations from normal values to guide training (here) and the importance of looking not only at baseline HRV with respect to normal values, but also the coefficient of variation (a multiparameter approach, here), we have made a small change in HRV4Training Pro to provide an even better overview of your physiological data.
In particular, as we have thoroughly described here, HRV4Training can automatically determine how you are coping with your training load by combining heart rate variability, heart rate, coefficient of variation and training information. The detected trend is one of the following: coping well with training, being in a stable condition, risk of maladaptation or accumulated fatigue.
This analysis is present in the HRV4Training app under Insights / HRV Trends and also on HRV4Training Pro under Insights / Resting physiology.
However, with the latest update you can overlay the results of this analysis, so the detected trend, with your normal values, so that you can get a better overview of not only your physiological data with respect to normal values, but also the detected trend based on a combination of baseline HRV, heart rate and coefficient of variation, contextualized by training load.
Use code BIGPICTURE to get 15% off HRV4Training Pro.
As part of our "big picture" posts, today we cover the relationship between HRV and training load. In particular, this post is inspired by a recent study by Flatt and Howells, that you can find on Andrew's blog here.
This is an important topic as it can be a little confusing to the ones approaching HRV measurements and trying to better understand physiological stress in response to training and lifestyle.
The view that training should cause a dip in HRV is somewhat simplistic in my opinion. HRV is a measure of physiological stress, and periods of higher cumulative stress are typically highlighted by a reduction in HRV, but acute (day to day) changes are hardly simply linked to TSS or training intensity, let alone the fact that in a situation of positive adaptation to training, your HRV should increase (or at least be stable) when consistently increasing training load (more on this later).
So what is the relation between HRV and training load? Do we expect HRV to decrease after hard efforts? How does this relationship change when we look at elite athletes, with respect to the rest of us?
The authors started with the following hypothesis, as Andrew wrote in his blog: "Based on previous studies, we’d expect the weekly LnRMSSD mean (LnRMSSDm) to decrease and the coefficient of variation (LnRMSSDcv) to increase relative to baseline. We’ve observed this in collegiate soccer players and sprint-swimmers." - however, this is not what they found.
In untrained and recreational athletes, day to day changes in HRV reflect training load quite well, on average, as we have described and published in the past.
However, as we become more fit and more used to training stressors, our ability to cope with them changes, and our physiological responses change accordingly. This means we expect less acute drops. The extreme case here is elite athletes, which are used to high levels of volume and intensity, most likely already for years, and therefore physiological responses at the acute level are often less interesting, which is why baseline changes with respect to normal values should be analyzed instead, so that periods of higher stress or good adaptation can be captured, and changes can be made when necessary.
This is also what happened in the study mentioned above, as the players showed no change in HRV throughout two weeks of intensified training load. Even the CV, a measure of how well we are adapting to training, with lower values typically meaning better adaptation (HRV is not jumping around as much on a day to day basis), increased during the first few days, but then went back down while load kept increasing. These are changes normally associated with reduced load, so what happened here?
As Andrew states "The discrepancy here appears to be related to how players are tolerating and adapting to the training load. We often assume that increased loads will result in fatigue accumulation and temporary negative responses. However, these elite players demonstrated no reductions in subjective indicators of recovery status during the weeks of increased load. Additionally, there was no significant decrement in running performance (maximum aerobic speed) mid-way through the intensified microcycles. Thus, the preservation of autonomic activity (no change in LnRMSSDm) and less fluctuations (reduced LnRMSSDcv) seem to reflect a postive coping response to the training. In fact, individuals who demonstrated the lowest LnRMSSDcv during week 1 of increased load showed the most favorable changes in running performance (r = -0.74)."
CV and detected trend in HRV4Training Pro.
How are you coping with your training load?
What we can derive from this study, similarly also to what was shown in the recent study by Javaloyes and co-authors (covered here), is that a reduced CV is often associated with coping well with training, and the relation we expect to see is not between training load and HRV, but between HRV and adaptation, which is why these metrics are so important.
After all if all HRV was telling us was the intensity of our workout, there would be no need to measure it, but what we are measuring is how we are responding to that workout or block of workouts, with certain trends (stable or upwards HRV, reduced CV) being indicative of good adaptation, even in periods of very high load. This means also that the opposite trends, reductions in HRV or larger fluctuations in CV are signs of poor adaptation and should trigger changes in training.
To learn more about these aspects, check out two of our recent blog posts, one on the big picture, and one on how to use HRV to guide training. This is in line also with what Dan Plews argues in his analysis of HRV data pre-ironman, that you can find here. If you are into podcasts, we discuss some of these aspects at the Scientific Triathlon podcast.
While acute changes are informative and can help us make small adjustments following a bad night of sleep, a particularly hard workout, or early sign of sickness, what you should be focusing on is certainly the big picture, aiming for your HRV to be within normal range, and making adjustment aiming at prioritizing recovery should your HRV baseline go below normal values, so that performance can be optimized in the long term, as shown in multiple studies.
Register to the mailing list
and try the HRV4Training app!
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. The big picture
5. HRV and training load
6. HRV, strength & power
7. Overview in HRV4Training Pro
8. HRV in team sports
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. rMSSD vs SDNN
7. Normal values and historical data
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. Zoom HRV vs Polar
7. Apple Watch and HRV
8. Scosche Rhythm24
9. Apple Watch
11. 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. HRV4T Coach advanced view
8. Acute HRV changes by sport
9. Remote tags in HRV4T Coach
10. VO2max Estimation
11. Acute stressors analysis
12. Training Polarization
13. Custom desirable range / SWC
14. Lactate Threshold Estimation
15. Functional Threshold Power(FTP) Estimation for cyclists
16. Aerobic Endurance analysis
17. Intervals Analysis
18. Training Planning
19. Integration with Oura
20. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV by sport
3. HRV normalization by HR
4. HRV 101