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):

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:

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:

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.

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.

Enjoy!

Collecting high quality data is always the first step, but of course what we want to do is to make meaningful adjustments using that data. How do you do that? Here are some links that can help understanding the relation between physiology, training and lifestyle stressors:

• Case study
• The big picture

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. 

​Enjoy

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.

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:

​We have new designs for our running singlets, you can find them at this link.

Enjoy!

​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. ​

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:

1. The basics: acute response to high-intensity training
2. The more interesting stuff: positive long term adaptation to high intensity and high volume training
3. Context: negative response to poor lifestyle choices following an injury
4. Feedback loop vs fitness marker: assessing full (physiological) recovery
5. Putting it all together

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:

• Aerobic efficiency
• Cardiac decoupling (currently in Beta)

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.

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:

• Timeframe on which to perform the analysis
• Sport to analyze (at this stage only running or biking)
• Main metric to use for the analysis of the hard part (distance, e.g. if you run 400m reps, or time, if you run or ride for 2' reps)
• Main metric to use for the analysis of the easy part.

​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.

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.

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

​Enjoy!

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). 

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).

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 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:

• a relatively stable period in terms of HRV leading up to the race (tapering)
• a big post race dip (March 18th, race day is what we have highlighted above, March 17th)
• a few days with lower values following the race

​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.

Enjoy.

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:

• baseline and normal values
• context: training load and subjective data
• coefficient of variation
• automatically detected trend

​
You can try HRV4Training Pro at HRV4T.com and get 15% off using promo code BIGPICTURE

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:

• Your current baseline with respect to your normal values.
• The Coefficient of Variation (CV) of your HRV.

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:

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. 

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:

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. 

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.

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)."

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. ​