Blog post by Marco Altini

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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.
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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.
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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 two dips due to a marathon and a few days slacking off around new year's, plus getting sick:

We can see:

• a few low days at the beginning of the recording (around the marathon, and post marathon, some data here is missing)
• a stable or increasing HRV in the middle part of the plot, with a dip for a single day
• a few days with lower values again around new year's eve (towards the end of the plot)

Similarly, in HRV4Training (data, in this case, was collected first thing in the morning using the phone camera) we can clearly see the two dips in HRV below normal values after the marathon — November 3rd and around new year's, as well as a stable HRV between the two events):

In HRV4Training we have a few added benefits that make it easier to analyze the data:

• The normal values range (horizontal band), shows what changes in physiology are significant and what changes are just normal day to day variations, hence here it is easier to identify the two big dips happening with the marathon and around new year's eve. Learn more about normal values here.
• The possibility to highlight different aspects that you can log (menstrual cycle, sick days, traveling - which can be automatically tracked - etc.), as shown by the smaller color-coded figures above.
• ​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 maladaptation and accumulated fatigue, in the second case even before HRV ends up below normal values.

Hopefully the figures above make it quite clear that physiology needs to be contextualized (both in terms of external factors, such as training, traveling etc. - and in terms of what changes are significant, as shown by the normal values band), otherwise it is hard to derive meaningful insights.

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

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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.
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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. ​

Blog post by Marco Altini and Alejandro Javaloyes. You can reach Alejandro via email here, and also follow him on twitter.

In other blog posts, we’ve talked about how to use HRV data on a day to day basis, and how to look at the big picture, meaning at medium and long term trends in HRV baseline, with respect to your historical data, as clearly displayed in HRV4Training Pro. 

The idea, is always to use the data in the best way possible, so that you can understand how your body is responding to your training plan, and make adjustments (for example by providing the most appropriate training stimuli in a timely manner, when your body is ready to take it, so that positive adaptation will occur and you will be able to improve performance). HRV allows us to capture such body response to the input we provide (training), but the challenge remains to decide how to act on this information. 

As more studies investigate different protocols to prescribe training based on your own individual physiological responses (read: HRV), a clearer picture is emerging. In this post, we cover the latest study by Alejandro Javaloyes and co-authors, titled Training Prescription Guided by Heart Rate Variability in Cycling and published in the International Journal of Sports Physiology and Performance in which the authors prescribed training based on HRV in a group of cyclists. Alejandro has been kind enough to provide us with a comment on his current and future work, which is reported below. ​

In this post you will also learn how to apply the same strategy to your own training plan, using HRV4Training Pro, which you can try for free at this link or get 15% off any subscription using code SCIENCE until end of the week. ​​

Blog post by Marco Altini

This post is about our latest feature in HRV4Training Pro: Training Planning. Training Planning is currently being beta tested, and will be available for everyone on HRV4Training Pro around May 2019.

Training Planning is a Beta feature for advanced users and coaches. With Planning by HRV4Training, you can create a structure for your next main event either for running or cycling.

You will be able to choose between different types of periodization, high intensity sessions per week, long workouts, and more. Note that Planning will not create a detailed schedule for you (similarly to pre-compiled plans in training books), but only a structure based on the input you provide. Individual workouts will have to be specified by you or your coach.

When it comes to planning, flexibility is key. Training Planning (Beta) by HRV4Training will seamlessly integrate with your HRV4Training data, providing tips on how to make small changes on a day to day basis (e.g. push a hard session when not physiologically ready). You will also be able to swap weeks and change individual workouts whenever needed. 

In this post, we'd like to talk about the big picture. In particular, on how to interpret your HRV data with respect to your historical data, so that you do not get lost in small irrelevant changes that naturally happen as your physiology is never in the same exact state, and instead you can focus on meaningful, significant changes that might require more attention or simply truly represent positive adaptation to training and other stressors.

​HRV analysis requires a mindset shift. First of all, we need to understand the nature of the data and the constant re-adjusting of the autonomous nervous system, and therefore take all the necessary steps to acquire a reliable measurement. This is typically addressed by the morning routine: the importance of context, limiting external factors, measuring as soon as you wake up and in the same body position every day. 

Secondly, we need to shift from a “higher is better” to a “normal is better” mentality, as physiologically speaking, being in a stable condition is typically a good sign. 

The inherent variability of HRV measurements is something that your app or software of choice, needs to deal with. This is something we have spent a lot of time researching and designing in HRV4Training, starting with the way the daily advice is built.

A software that interprets any HRV increase as a good sign, or any HRV decrease as a bad sign, is failing to correctly represent the fact that there are normal variations in physiology, and that only variations outside of this normal range, should trigger concern or more attention or simply be interpreted as actual changes. 

Scientists typically talk about the Smallest Worthwhile Change (SWC). What’s the SWC? We  talk about the SWC when we want to identify changes in a metric that are not only due to chance or some confounding factor, but are a true representation of an underlying change in performance or adaptation in your physiology.

If your HRV differs one day from the other, or even in terms of repeated measurements within a few minutes (especially in this case), it could be that such change is simply due to normal variations in physiology, and a small decrease or increase, is completely irrelevant. What the measurement is telling you is that it’s all normal.

This is why in HRV4Training we call the SWC “normal values”, as this is a range determined using your historical data and highlighting what changes in HRV are simply normal due to the nature of a parameter that is always changing a bit, and what changes in HRV are significant.

​For example, a score that is below your SWC or normal values, clearly highlights higher stress and the need for recovery. A number that is on the other hand just a little lower than your previous score, means absolutely nothing, and you should not be overthinking it.

The HRV4Training app does the math for you. We deal with day to day variations and the fact that physiology is often changing by learning what variations are normal in your specific case, and building a model relying on the past 60 days of measurements so that only significant changes will be interpreted as such, when providing daily advice for your workouts.

In the app homescreen, we always provide a message and a visualization telling you where your daily score stands, with respect to your normal values. The daily advice, which combines this information with your subjective scores, is also reported in the small dots in the History page:

As explained elsewhere, focusing on your historical data and normal values, so that we can go beyond day to day variability, was one of the principles behind the development of HRV4Training Pro. ​

HRV4Training Pro builds on our previous work on physiological trends to easily highlight how your baseline is changing with respect to your historical data and allow you to understand if variations are just normal or are consistently outside of your normal ranges, at a glance. In this case, we want to shift even more from daily scores to medium and long term trends, hence the normal values are a slightly narrower range, and what we look at, is where the baseline - instead of the daily score - stands with respect to your normal values.

​A baseline drifting towards the bottom or outside the normal values, highlights periods of significantly higher stress where recovery should be prioritized. On the contrary, a baseline going above the normal range on a period of higher training load, is typically a sign of positive adaptation to training.  

The main point that I have been trying to make is that context and your historical data are key for data analysis and interpretation. The software you decide to use needs to be able to contextualize your measurement with respect to your historical data, so that you can easily determine if a score is within your SWC or normal values, or if it is not and you should pay a little more attention to it, potentially implementing changes in your planned training. ​HRV4Training and HRV4Training Pro provide very intuitive visualizations of your historical data, that we hope can make it easier to correctly interpret physiological changes for you and your team. 

Note that all that has been discussed is completely independent from your interest in training, regardless of the application of interest, HRV data must always be interpreted and analyzed with respect to a person's historical data and normal values, otherwise it is hardly possible to understand if a change is significant or it is simply a normal variation in physiology. 

We hope you'll find this post and our visualizations useful to better understand physiological adaptations to training and lifestyle. 

You can try HRV4Training Pro by logging in with your credentials here and get 20% off until end of the year using referral code bigpicture 

Overwhelmed by the response to our 2019 HRV4Training Ambassador program, we'd like to thank all the athletes and coaches that reached out to support our work.

HRV4Training ambassadors help us build our community. We have strong values that we believe are reflected in our work, as we put science and knowledge discovery before anything else.

As we try to empower more individuals with the ability to measure and interpret physiological data, learning from athletes and coaches that have been gathering and analyzing data for a long time, is an invaluable part of the journey.
​
Our ambassadors embrace these values and we cannot thank them enough for their support.
At this link you can learn more about who they are or apply to become one.

​We have released a new feature in HRV4Training Pro, Aerobic Efficiency Analysis for runners and cyclists. In this post, we go over the background, cover how you can use this feature to track changes in aerobic endurance as you progress in your training and also provide additional details about how this differs from other estimates we provide, and how you can benefit the most from this feature.

A follow up of this work can be found in this blog post, where we cover cardiac decoupling.

In case you want to jump right in, and check out our latest feature, simply login at HRV4T.com with your HRV4Training credentials.
​

Aerobic efficiency relates to your ability to sustain a given workload. Endurance athletes tend to have high aerobic efficiency, meaning that they can sustain a relatively high workload (for example pace or power), at a relatively low effort (typically measured in terms of heart rate). 

To determine your aerobic efficiency we compute the relation between output (pace or power) and input (heart rate). Intuitively, a lower heart rate for the same output (pace or power), when consistently shown over periods of weeks, translates into better aerobic efficiency. 

Similarly, a higher power or faster pace at the same heart rate, is linked to improved aerobic efficiency. By analyzing the relationship between input and output for running or cycling activities, you can easily track aerobic efficiency changes over time, as you progress with your training.

If you are familiar with our work on VO2max estimation, you'll know that the same principle just explained, is also the principle behind VO2max estimates. In particular, the ratio between heart rate and pace or power  is used as one of the predictors in the VO2max estimation model. You can learn more about VO2max estimation here. 

What's the difference then? While VO2max is a good marker of cardiorespiratory fitness and aerobic efficiency, the estimate depends also on parameters that have very little to do with actual aerobic efficiency and performance, for example body weight. Losing weight will increase your VO2max without necessarily improving your aerobic efficiency or performance. 

Additionally, there are factors that can only be partially accounted for when estimating VO2max. A few examples are: running on trails or difficult terrains, which reduces pace and makes your data not really representative of your fitness, very short workouts where heart rate does not reach steady state, very long workouts where heart rate drifts, environmental factors such as hot days or training at altitude, etc. - the list goes on. 

While many of these parameters are simply impossible to account for, what we can do is give you more control over what data is used to track changes in aerobic efficiency. In particular, via the panel below you can filter workouts and environmental factors so that the resulting data is more representative of your aerobic efficiency. You can also select how much data you'd like to use for each data point, for example selecting light smoothing, only this week of data will be used, while using average smoothing, which I recommend, uses 3 weeks of data.

The filtered workouts are also listed at the bottom of the page:

Blog post by Alessandra

​HRV4Training is looking for brand ambassadors worldwide!

Are you passionate about sport and technology and have been using HRV4Training daily to improve your performance? We are looking for you!

​We are going to select up to 10 brand ambassadors, and we accept applications in English, Italian and Spanish. Please read below for instructions.

How can you apply?
To participate in the selection process, send us a couple of paragraphs about yourself, why you chose our app, what you love most about it and what do you expect from this collaboration (250 words max).

Don’t forget to add a picture of yourself in action and your social media profile/s.

What do we expect from the ambassador?

• One post per quarter (or more frequently), to schedule together

What do we offer to the ambassador?

• Free HRV4Training singlet
• Free access to HRV4Training Pro (our web platform) for personal use for 1 year
• Social media reposting 

Deadline: November 25th. 

To apply please send a message to hello@hrv4training.com

Full article at this link.