We have just released an improvement to the correlations analysis in HRV4Training Pro, which now adds color coding to make it easier to spot stronger relationships between your annotated tags and your physiological data.

​You can try the new feature on HRV4Training Pro for free at this link, or use code SCIENCE for 15% off any package.

The correlation analysis in HRV4Training Pro lets you pick any timeframe between 30 days and 2 years. However, in general, we think that using a time frame between 60 and 90 days is ideal. 

Why  is that? Most likely the stressors you face will change over time, and similarly your response to certain stressors will change, therefore we believe it can be more helpful to look at these relationships in the relatively short time frame (e.g. 60-90 days), to get a better idea of what factors are influencing your physiology the most. Shorter windows (e.g. 30 days) might not have enough data, unless some really large stressor was present (for example if you go from sea level to 2000m / 6000ft of altitude, then you will certainly see a strong correlation between resting heart rate and altitude), otherwise it might be better to extend the window. On the other hand, longer windows (e.g. a whole year) might fail to capture more complex, multidimensional relationships between various training and lifestyle aspects, and your physiology.  

Finally, we would recommend to look at baseline correlations, more than day-to-day correlations. Baseline correlations are computed on the 7 days moving average of each variable, and therefore provide a more stable trend of the data. Typically, this is more insightful than to look at the individual data points, especially in the longer term.

Below you can seen an example:

• Negative correlations are shown in yellow. Remember that negative means that for example your HRV goes up while the other parameter goes down. It does not mean that this is a bad thing! If you have a negative relationship between life stress and HRV it simply means that your HRV goes up when  your life stress goes down, as show here for my own data.
• Positive correlations are shown in blue, in this case a higher HRV is associated with a higher value for a given parameter, for example mental energy in my case

and here is the actual data for the strongest correlation in my data, which is 'lifestyle stress', a tag I use to represent work-related stress:

The important part after you start looking at these correlations, is not to jump to conclusions. For example, it could be that the relation you are seeing is actually caused by another variable excluded by the analysis. However, this can be a useful starting point to explore your data, and we hope the new color-coding will make it a bit easier.

Enjoy

We are excited to start a new partnership for the upcoming season, providing direct integration between HRV4Training and The Australian Institute of Sport (AIS) athlete management system

> The AIS is Australia's strategic high-performance sport-agency, which leads and enables a united and collaborative high-performance sport system that supports Australian athletes to achieve international podium success

We wish the best to all athletes at the AIS and hope HRV4Training will make it easier to capture the athletes' response to training and lifestyle stressors, therefore enabling the coaching staff to further individualize training

Aerobic efficiency relates to your ability to sustain a given workload. Good endurance athletes tend to have high aerobic endurance, 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.

You can do so in HRV4Training Pro, under Insights / Aerobic  endurance.

The  aerobic efficiency feature in HRV4Training Pro can help you better understand if your training is progressing well, without the need for specific testing. As you go for example from the off season to your base (or other) training phase, you should see your aerobic efficiency improve as your pace gets a bit quicker  at a given heart rate for example. 

Similarly, you could use aerobic efficiency to capture your response to environmental factors such as a training camp at altitude. During my research I have used exactly this principle for example to determine which athletes have or have not adapted to a training camp at altitude, based on their aerobic efficiency getting back to pre-camp values, or failing to do so  (you can learn more here).

Football season is starting again in Italy. Busy weeks with a packed schedule post-quarantine, while the players work on getting back in top shape

A pleasure to start a new collaboration with Venezia FC during these times. We hope HRV4Training will be helpful to monitor internal load and individual responses to stress

⚽️ 🔬📲

Riparte il campionato post-quarantena, impegni frequenti e condizione da recuperare. Un piacere collaborare con il Venezia calcio durante questa ripresa

Speriamo che HRV4Training via sia utile per monitorare al meglio i carichi in questo finale di stagione

Grazie Fabio e Davide. Forza Venezia!

Blog post by Marco Altini ​

Alejandro Javaloyes and co-authors have been working for quite some time already on HRV-guided training, which we have discussed earlier with Alejandro himself. 
​
In this study, the authors put to the test standard block periodization vs HRV-guided training, showing better results for the HRV-guided approach.

​Let's look at the details of this study, before we cover how you can use the same approach in your own training.

HRV helps us to quantify individual responses to stress (you can find plenty of case studies here). The hypothesis for HRV-guided training is therefore that by providing the most appropriate training stimuli in a timely manner, when your body is ready to take it, positive adaptations will occur and you will be able to improve performance. Normally a timely manner means when your daily HRV or baseline HRV is not suppressed, with respect to your historical data.

In their previous paper Alejandro and co-authors already showed how HRV-guided training could lead to better performance, and reported: "hypothesis for this greater adaptation to training for the HRV guided group is in line with the idea of performing high intensity training when the athlete is in optimal conditions to perform it. Therefore, these differences in PPO changes may be due to a better timing in the programming of high intensity training”.

Needless to say, our capacity to handle stress is limited, and while periodization is possibly an important starting point, we need to be able to add flexibility and provide the right stimulus at the right time, which is  what HRV allows us to do.

For this study, the authors used the HRV4Training app to collect data first thing in the morning. 20 well trained cyclists were split into two groups, an HRV-guided group and a control group. The HRV-guided group used our app to collect rMSSD data, which was then transformed (taking the logarithm, this is the same transformation we have when we report you Recovery Points, with the only difference that we also multiply it by 2 to make it a bit more user friendly, as it ends up in a 6 to 10 range for most people). 

The data was used as follows: when the HRV baseline fell outside the normal values for an athlete, training intensity changed from high intensity training to low-intensity training or rest. 

There's a lot in this paper that is worth mentioning, in terms of two aspects

• Performance outcomes
• Physiological changes

While it is obvious that the main goal here is to improve performance, looking at physiological changes between the baseline and load periods paints a clear picture that we have seen over and over also in team sports: athletes that cope better with the load, or struggle less to adapt, have a physiological profile that typically results in stable baseline HRV and reduced coefficient of variation (CV) . The interesting bit here is that by using HRV-guided training, we are able to avoid those periods in which the athlete struggles more (and indeed the data in the paper shows reduced CV and stable HRV only for the HRV-guided group, since high intensity training was not performed when things were not looking great). As a result, we'll see below that performance benefits too.

This is something that came up also recently in my research on elite triathletes going to an altitude training camp, where the athletes that adapted better to the new stressor (in my case altitude), did not experience an abrupt change in the CV, while athletes that struggled to adapt, had a much higher CV at the beginning of the camp (hopefully I'll be able to share more on this later on).

Back to the study, in terms of performance measures: cyclists performed: (a) a graded exercise test to assess VO2max, peak power output (PPO), and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and (b) a 40-minute simulated time-trial (40 TT). The HRV-guided group improved VO2max, PPO, WVT2, WVT1 and 40 TT, while the regular block periodization group improved only WVT2.

As mentioned by the authors in their discussion: "Despite the beneficial effects reported by block periodization models, concentrated workloads without valid and reliable measures of the response to training could lead to an overreached state that limits training adaptation. In this study, HRV was used to determine whether athletes were able to perform high-intensity training" and concluding: "The evidence from this study gives further supports to the notion that HRV is a valid and reliable tool to detect the daily recovery/fatigue and subsequently prescribed training in well trained cyclists. Thus, the implementation of daily HRV measurements and practical methodologies to change the training prescription on a daily basis could lead to better timing in prescription, thereby giving greater insight into the programming puzzle and optimizing training regimes to enhance both fitness and performance"

This approach has shown to be effective in a few studies already:

• Vesterinen et al. in runners
• Javaloyes et al. in cyclists
• ​The current study

The good news is that it can be easily implemented for you and your athletes, as all you have to do is to scale down the planned intensity when your HRV is suppressed. For example in the figure below, you would reduce intensity starting around the days where you see the baseline going below the athlete's normal values, as shown in HRV4Training Pro:

This method makes intuitive sense, as our capacity to handle stress is limited, you do not what to go really hard when your body is already struggling to manage stress (and remember, stress means both training and non-training related stressors, which is why HRV data is particularly relevant, and monitoring training load alone is insufficient - ​check out this case study for a clear case of psychological stress).

Personally, I believe this paper is what I've been working for in the past decade. It doesn't take much to understand that our capacity to handle stress is limited, and that stressors (training as well as lifestyle) can be better balanced by most of us.

It is however far from straightforward to develop valid, reliable and easy to use instruments that can enable this process, to the point that new research and practical insights can be derived consistently by the use of such an instrument. 

For years, the belief was that it was impossible to measure HRV accurately using a phone camera, which is something now pretty much everyone takes for granted. Similarly, there was a lot of confusion on metrics and how to interpret them, and hopefully visualizations such as the ones we have provided showing daily scores, baseline and normal values, as well as our guides and case studies, are finally making this process much clearer too. These methods, are the same adopted in the study above. 

I hope our tools and these studies will help you making the most of something as simple and as powerful as measurements of resting physiology.

We recently put together a series of posts that cover many aspects of HRV measurement, data interpretation as well as plenty of examples that you can look at to better understand how to make use of the data, and how HRV relates to training and lifestyle stressors.

​Check them out at these links:

• Part 1: Measurement setup, best practices and metrics
• Part 2: Interpreting your data
• Part 3: Case studies and practical examples
• Part 4: Common misconceptions

We have modified the Coach Panel in HRV4Training Pro to allow you to display the Overview page directly in the panel, without the need to navigate to each athlete's individual pages.

The Overview page (more info here) is probably the most important single page in the platform, as it shows daily scores, normal values and baseline, making it really easy to quickly understand how things are trending, and how an athlete is responding to training and lifestyle stressors.

As you  can see from the images below, tapping "Show Overview" you will be able to see the plots directly in the Coach Panel, and save a little time during your daily check-ins. We hope you'll enjoy the new feature, which you can try at HRV4T.com
​

If you are familiar with our work or the way we normally talk about HRV, that's probably the first question that comes to mind

HRV analysis can be used for various applications. What we do at HRV4Training is to quantify baseline physiological stress (what we could call "chronic" stress), and how this changes in response to training and lifestyle over periods of weeks or longer. To quantify baseline physiological stress, our measurements need to be taken in a very precise moment, which is first thing in the morning, so that we can avoid the effect of confounding factors. You can find a few examples here

By capturing changes in resting physiology, we can provide useful feedback that helps individuals to make meaningful adjustments to better balance training and lifestyle. This is particularly relevant as we all respond differently even to the same stressors depending on various aspects (how novel is the stressor, how much of that stressor we are used to take, what other stressors are present), hence only by measuring our individual response we can figure out if it's all proceeding according to our plans or not

Normally we would recommend doing biofeedback exercises as an add on the regular morning measurement done with HRV4Training. Combining biofeedback with morning measurements taken with HRV4Training, you could see also potential changes in baseline chronic physiological stress as measured in a known context (first thing in the morning), as a result of your biofeedback sessions

In the next weeks, we'll start beta testing our new product, specifically designed for biofeedback. Visit HRV4Biofeedback.com to learn more

We've talked many times about how HRV is a particularly useful marker as it captures stress regardless of the source. In the context of training, this is helpful because the way we respond to a stressor (for example a workout) can depend not only on the stressor itself (novelty, volume, intensity) but also on what other stressors are present (poor sleep, traveling, work-related stress, family worries, etc.) - and needless to say, our capacity to handle stress is limited. Measuring  HRV allows us  to capture our individual  response to everything that is going on from training to lifestyle, and make adjustments when necessary

The recent events with the global pandemic put most of us in a position that caused additional stress. Here is another clear example of how psychological stress can have a large influence on our physiology, even in the absence of training. Raul is a marathon runner from Spain, one of the countries hit the hardest by the global pandemic, and with the most strict contentment measures in terms of the enforced lockdown

We can see in the data below how after the first few days of the lockdown, the baseline starts to lower. Within a week or two, the baseline is below Raul’s normal values, a clear sign of high stress

This data once again speaks to how naive it is to think that HRV should only be associated to training, as we have discussed in our blog post on common misconceptions. As things slowly improve out  there, Raul's physiology is getting back where it was. Thank you Raul for sharing this data, and all the best for your training and health

Special thanks to Rob Wallace for inviting Marco to speak to triathlon coaches in Australia about HRV and our work with HRV4Training. In the one hour webinar, we cover:

• Theory about heart rate variability
• How to collect data (available technologies)
• Guidelines (measurements, confounding factors)
• Data analysis and interpretation (normal values, case studies)
• HRV4Training Pro & teams 

​
The webinar was recorded and is available at the link below. Enjoy.

Check out the latest episode on the Oxygen Addict triathlon podcast, featuring our work and a lot of practical tips on how to use HRV4Training and heart rate variability to make adjustments to your training 

Dr. Marco Altini is the creator of the HRV4Training App. In this episode, we dive deep into heart rate variability, and why it might well be the most important training tool to getting the most out of your limited training time! ​

​Thank you Rob for having us!

We have partnered with the Biomedical department of the University of Milan to provide free Heart Rate Variability apps in the context of a new study investigating the effect of home isolation on the cardiac autonomous nervous system.

Prolonged isolation studies during quarantines have focused mainly on the clinical effects of isolation, reporting emotional disorders, irritability, insomnia, poor concentration, deterioration of working capacity, stress symptoms and decision-making skills. The reduction in physical activity seems to contribute to the establishment of these dynamics.

The purpose of this project is instead to monitor the psycho-physiological impact of home isolation that is occurring in Italy as a consequence of the general lockdown aimed at containing the pandemic spread of covid-19.

Psychological monitoring will focus, through the remote administration of validated questionnaires, on sleep disorders (Pittsburg Sleep Quality, PSQI), affective and emotional states (UCLA Loneliness scale, Profile of Mood States, POMS), on trait and state anxiety (State-Trait anxiety Inventory, STAI) and on the level of physical activity (International Physical Activity Questionaire, IPAQ).

Physiological monitoring will evaluate the modulation of heart rate by the autonomic nervous system (HRV), as measured using the Camera HRV app, so that questionnaires data and objective physiological stress levels can both be analyzed.

We would like to thank professor Giampiero Merati of the University of Milan for involving us in this project, and we hope our tools we'll be helpful to gather objective data on ANS activity.

In the meantime, some N = 1 (well, 2) data of our own poor response to 6 weeks of self-isolation, showing how the baseline is now either below normal values or close to the lower end, a clear sign of significant stress:

Take care and stay safe

HRV data has an inherently high day to day variability. This means that there can be large fluctuations between consecutive days, which is different from parameters that you might be more familiar with (for example your heart rate or your body weight). These fluctuations differ also a lot between people, which is why you should always be looking only at your own data, and there is little use in comparing with others.

What are the implications? To make effective use of the data, we need to be able to determine what changes are trivial in your specific case, or just part of your normal day to day fluctuations, and what changes do matter and might require more attention or simply truly represent a positive (or negative) adaptation to training and other stressors.

This is what we do in the app, and what is shown in the color-coded bars and home screen message. Typically, a green bar means that your daily score is within your normal range, which is determined using the past two months of your data.

​With the new color coding, you should be able to quickly see how things have been going in the recent week and what day to day changes are outside of your normal values, so that you can make adjustments when needed (e.g. trying to reduce stress or training intensity when your daily score is below your normal values).

Blog post by Marco Altini​

​Chances are that you if are reading this blog or have been using HRV4Training, you are well aware of the importance of monitoring your physiology over time in response to various stressors, and interpreting your data only with respect to your own historical data. 

With the ongoing global pandemic, the medical community has been looking into alternatives that can help to identify and managing individuals that might require additional care, which brought a renewed interest into wearables or in general in tools that can provide physiological measurements. Heart rate, HRV, temperature, are all great candidates as these parameters have shown a consistent response to infections [1, 2].

In the past few weeks we have started a few collaborations with research institutions in Italy and Germany, providing the technology required to further investigate the relationship between (among other parameters), resting physiology and covid-19 infections.

However, as a user of our system, you might want to know what to look at as a potential indicator of infection or sickness based on our current knowledge and published research.

​Thus, in this post, I cover the basics of heart rate in the context of infections, and show a new feature we have built in HRV4Training Pro to help you identify more easily periods in which heart rate is abnormal, and therefore might require more attention.
​
Please remember that heart rate (as well as HRV, temperature, or any other parameter) is just an indicator, and not a diagnostic tool. 
​

Something as simple as measuring resting heart rate, is a very useful tool to capture changes in physiology due to our body fighting various forms of stress, including infections [1]. In particular, infections tend to show up in the data as increased heart rate (and reductions in HRV). You have probably noticed it in the past if you were ever sick while also collecting data longitudinally. This can happen a short time before we actually realize that something is going on (say a day or two in my experience). 

​Already years ago, Michael Snyder at Stanford University ran studies in free living using commercially available sensors and found quite striking relationships between heart rate and infection (if you are into papers, I highly recommend this one [2]). We have also shown reductions in HRV and increases in HR with respect to self annotated sick days, in this blog post.

Infections or getting sick in general, are one of the few conditions in which I would almost consider heart rate superior to HRV (or at least equal). Why is that? Heart rate does not change much on a day to day basis, and is less sensitive than HRV to changes in physiological stress due to for example training, lifestyle stress, etc. (we have previously quantified these differences, with heart rate changing only 0.5-1% in response to hard training sessions for example, and HRV changing on average 5-8%, full paper here [3]). On the other hand, heart rate changes to a much greater extent when facing an infection (I'll show a clear example later).

Hence heart rate can be a simpler marker to look at, less affected by other stressors, and we could look at changes in heart rate to make sure things are staying within a normal range (based on your historical data). Which brings me to our next point.

That's all.

As mentioned above we have just extended HRV4Training Pro to visualize normal values also for your heart rate data. You can find this new feature under the Overview page in HRV4Training Pro, and get 20% off using referral code STAYSAFE

In previous posts, we’ve shown a few examples of what to expect in terms of the relation between HRV and acute stressors (for example traveling, alcohol intake, a hard workout) and longer-term stressors (positive adaptation to training, work stress, poor lifestyle choices, etc.).

This part is all about examples and case studies that highlight many of the aspects previously discussed so that you can intuitively see how morning HRV measurements are an effective way to capture changes in stress in response to training and lifestyle stressors.

Enjoy!

This is the second part of our series of educational posts on heart rate variability (HRV).

In particular, this post is all about how to use the data. We cover:

• Fluctuations between consecutive days (normal values and how to identify periods of higher stress)
• Acute changes: what happens after a strong stressor?
• Long term changes: how are you responding and adapting to training and lifestyle stressors? (positive and negative responses, the importance of context)
• How to make adjustments (starting with a training plan, research on HRV-guided training)

You can find the full article at this link.

This misconception is mostly deriving from a paper that a few years back stated that subjective metrics are better than objective ones in monitoring athlete training response. 

But let’s look at what was actually analyzed in the paper.

The authors looked at how training load related to both subjective and objective metrics, hence according to the paper, the reference to determine if a metric is a valid metric, is how it correlates to training load. 
​
In my opinion, the whole assumption that you should find the metric that “correlates the most” with training load, makes very little sense. Why? Because you are already measuring training load, so what is the point of having another metric that gives you the exact same information? Well, none. By definition, if a metric is perfectly correlated to training load, then it is a useless metric, as it does not add any information to the training and recovery equation (but ironically, it would have been interpreted by the study as the best metric).

I’ve already discussed before how the notion that increased load should trigger a reduction in HRV is very simplistic. As a matter of fact, we have seen we can have stable or increased HRV when increasing load (a sign of positive adaptation) as well as reduced HRV with low load because of other stressors (travel, work, etc.). HRV tells you how you are responding and coping with stress, and you can use that information as part of your decision-making process (you can find many case studies here). 

Finally, don’t get me wrong, it is fairly obvious that subjective metrics are also extremely important. This is why we include a questionnaire after the measurement so that you can take a minute to pause, and self-assess how you are feeling subjectively, a key part of the process.

A smart coach, educator or athlete, understands that training load, HRV, and subjective metrics all provide important information that needs to be integrated daily, to decide the better course of action. 

There is no winner between objective and subjective metrics, they all serve a purpose. ​Isn't that obvious?

Blog post by Marco Altini

In this series of posts, I’ll provide an overview of best practices for your Heart Rate Variability (HRV) measurements (part 1), and tips on how to analyze and interpret your data over the short and long term (response to acute stressors, longer-term trends, etc. — in part 2). I’ll include quite a few case studies so that you can clearly see how you can too make use of the data (in part 3). Finally, the last post will cover a few misconceptions (utility with respect to subjective scores, non-training related use, strength training, etc. — part 4).

HRV is nothing new, and fairly simple to use effectively, but poor standardization and methodological inconsistencies make it difficult sometimes for people to make good use of the technology or understand what is reported in the scientific literature. Hopefully, these posts will help, but please feel free to ask questions should you have any doubts.

You can find part 1 at this link.

LAFC performance director Gavin Benjafield talks about how apps like HRV4Training are giving the team a leg up in its push for MLS Cup glory, check out the article at this link.

​Huge thank you to Gavin for his words and support of our work, and congratulations on a great season last year