Blog post by Marco Altini
In this video with Rebecca Caroe at the Rowing Chat, we discuss:
Thank you for having us!
Blog post by Marco Altini
One of my favorite guest posts on our blog was written a few years back by Andrew Flatt. Andrew is a brilliant scientist and coach and has dedicated much of his time to investigating the relationships between HRV, training load and other stressors, in a variety of sports and athletes.
The full article focuses mostly on strength and power athletes, and can be found here. However, I believe some aspects are really applicable to any sport, and I'd like to report them here and show a little example using my own data collected with HRV4Training and the Pro platform.
"Highly trained individuals are more likely to see a pronounced decrease in HRV the following morning in response to a training session if:
1. The training stimulus is considerably greater than the individual typically experiences (an abrupt increase in load
2. The training stimulus is novel or different from what the individual is accustomed
3. Training is otherwise normal, but non-training related stressors are affecting recovery"
Point 3 is the one I'd really like to focus on in this post. Non-training related stressors are key. There's a whole body of research looking for example and how injury risk can increase due to high stress regardless of any changes in training, see psychophysiological models of injury for example. Needless to say, our capacity to handle stress is limited, and this exactly why HRV measurements are useful: they provide an overall marker of stress
In my view, non-training related stressors are an often overlooked great reason to use HRV monitoring, despite the fact we all understand well that if something is bothering us (issues at work, at home, financial concerns, etc) we can hardly focus and perform optimally.
As a recreational runner, I love to try to make a bit of progress, pushing myself to higher loads from time to time. When I manage to do so gradually and consistently, over longer periods of time ( > than a year for example), I do not expect my HRV to drop during an acute high-load block, as my body is well conditioned, and should assimilate the stimulus properly.
When we add other stressors though, the situation can easily change, and this is exactly why it is important to objectively monitor physiological stress and individual responses to stress (in a generic way), so that we can get the full picture. It would be really naive to think that the only thing that matters is training, and all we do in the remaining 22-23 hours of our day is irrelevant.
Lets look at some data:
Above we can see three plots, with daily measurements collected over 3 months, first thing in the morning using the HRV4Training app:
We can also look at the correlation between physiological data and subjective metrics, in the Explore Correlations page that you can find under Insights in HRV4Training Pro:
In the plot above we can see again the strong baseline correlation between heart rate variability measured first thing in the morning, and lifestyle stress. As you can see "work stress" for me is typically the largest factor behind drops in HRV, especially when looking at the big picture (baseline more than day to day correlations).
Alright, hopefully this post gives you a more practical view of what to expect and how to interpret the data, always remember that multiple stressors play a role at the same time, and it is therefore beneficial to look at the data over longer periods of time, including normal values and baseline changes, and contextualizing physiological data (HRV or HR) with respect to your subjective annotations and training load. This is all computed for you in HRV4Training Pro.
We are starting our first partnership at HRV4Training in the Bundesliga today, with Hannover 96
Wishing the best for the upcoming season to the club. Thank you Tobi for your support!
🔬📲 ⚽️ 🇩🇪
Blog post by Marco Altini
We put together a deck with practical information for players of organizations relying on our platform or simply getting started with measurements of resting physiology. Learn more below.
In this video with doctor Ram Yogendra, we discuss:
After a short break football season is about to start again in Italy.
We are proud and excited to be working for a third year with Bologna Football Club, helping the team to monitor physiological responses to training and lifestyle stressors.
We hope HRV4Training will make it easier to keep an eye on the athletes' health as well during these exceptional times.
Grazie Nicolò, e forza ragazzi!
Heart Rate Variability (HRV) Biofeedback and Athletic Performance: Expected physiological, psychological and performance outcomes
Blog post by Marco Altini
In this last part of our introductory series on HRV Biofeedback, we provide an overview of the main outcomes of HRV Biofeedback interventions, in terms of performance changes as well as physiological or psychological outcomes
We cover early explorations, then move towards higher-quality studies and finally recent attempts to investigate HRV Biofeedback interventions in more applied and practical settings
In other words, in this post, we aim at answering the following questions: what is the effect of HRV Biofeedback on athletic performance? How do physiological and psychological measures change following an intervention in athletes?
Learn more at this link
Next week is HRV4Training's 7 years birthday, and we are doing a small giveaway
You can win either free access to HRV4Training Pro for one year (more info on the web platform can be found at HRV4T.com) or a t-shirt (either the running singlet or regular t-shirt)
To participate in the raffle, simply write a post or blog about HRV4Training either on Instagram or Facebook (or your blog!) and tag us (if you have a private profile, please email us a screenshot at hello@HRV4Training.com otherwise we cannot see it) - Just make sure we know you wrote it 👩🏻💻
We are looking forward to learning more about what you like or find useful and how you have been using the platform so far
Deadline is next week!
Blog post by Marco Altini
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.
What are correlations about?
Citing Wikipedia: "Correlation refers to any of a broad class of statistical relationships involving dependence. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice."
In other words, looking at correlations can help us to pinpoint which parameters have a stronger impact on our physiology, and potentially make adjustments (e.g. if there is a strong negative correlation between work stress and HRV, maybe we should try to reduce work stress).
How should I configure this analysis?
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:
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.
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
Blog post by Marco Altini
What's aerobic efficiency again?
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.
How can you use this feature?
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!
Heart Rate Variability (HRV) guided training to improve performance: latest publication using HRV4Training
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.
What's HRV-guided training about?
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
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"
How can you use the same approach?
This approach has shown to be effective in a few studies already:
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).
Monitoring individual responses
The HRV baseline is the first parameter that we should be looking at, as it clearly captures overall stress on the body. However, as we have discussed above, the coefficient of variation (or CV) is a very useful parameter to detect potential issues. In particular, an increase in the CV is typically representative of poor adaptation or difficulties in coping with training (and other stressors).
Using Pro, you can also easily check the coefficient of variation, which again should not increase much in periods in which we are responding well to the load. Additionally, the coefficient of variation can show us early warnings of poor adaptation even when the baseline is still within normal values, as this is a sign of disrupted homeostasis and difficulty in coping with the stressors present in a given moment, as shown below:
Closing the loop
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.
Other useful resources
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:
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
Blog post by Marco Altini
Life can be demanding, from both a physical and psychological point of view. Our health and performance can be affected by how we are able to effectively cope with stressful situations and deal with anxiety, or in broader terms, our ability to emotionally self-regulate is key
Heart Rate Variability (HRV) Biofeedback can directly affect physiological and psychological factors through deep breathing exercises and is an ideal strategy to help us self-regulate and better cope with stressful situations
We are working on something new. Learn more about biofeedback at HRV4Biofeedback.com
Practically speaking, HRV Biofeedback consists of providing an individual with real-time feedback on instantaneous heart rate and respiration changes while being instructed to breathe at low frequencies (Lehrer and Gevirtz, 2014)
From a physiological point of view, we can consider homeostasis as a starting point to understand the rationale behind using HRV Biofeedback. As the body via the autonomic nervous system (ANS) responds to stressful stimuli in an attempt to maintain a state of balance, we can determine how effective this physiological self-regulation process is, by measuring the ANS
During HRV Biofeedback, an individual is instructed to breathe at low frequencies. Breathing at low frequencies (or deep breathing) causes large oscillations in the instantaneous heart rate, which synchronize with breathing rate. The influence of breathing on heart rate is called Respiratory Sinus Arrhythmia (RSA) and is mostly modulated by the parasympathetic branch of the ANS (Lehrer and Gevirtz, 2014). Hence, deep breathing results in training of the parasympathetic system, which might explain at least part of the positive effects of HRV Biofeedback reported in the literature in the context of reducing stress and anxiety (Goessl, Curtiss, and Hofmann, 2017)
Strengthening the parasympathetic nervous system could also motivate using HRV Biofeedback in athletes, with the potential of improving emotional self-regulation, coping mechanisms, and performance (Khazan, 2016; Pusenjak et al., 2015)
Check out these resources to learn more about the physiological underpinnings of HRV Biofeedback:
What's the difference between morning HRV measurements and biofeedback?
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
What about HRV biofeedback then?
HRV Biofeedback is a technique that we use to improve self-regulation, and also strengthen the parasympathetic system. While our morning measurements should be done while resting and breathing naturally, during biofeedback we use deep breathing to elicit higher parasympathetic activity
You can see your biofeedback session the same way you see your other training sessions, this is something you do so that in the longer term, there can be beneficial changes in health and performance. Biofeedback is just a positive stressor
Where do regular baseline HRV measurements and biofeedback meet?
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
We are building something new
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:
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're glad you've enjoyed the new History page and thought to bring a bit more color to the Baseline page too.
Similarly to what we have discussed in the previous article, the goal of the color-coding is to make it easier to understand when there is significantly more stress on your body, or when things are trending well.
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. On the other hand, a yellow score means that your daily score is below your normal values, or your subjective data is trending negatively (poor sleep, lack of motivation, high soreness and poor perceived performance make up the subjective score). A few yellow bars in a row pinpoint higher stress and the need to prioritize recovery strategies.
With the new color coding, you should be able to quickly see how things have been going in the recent weeks 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).
See below an example of our data, showing a less than ideal two weeks with cumulating stress due to a combination of factors, starting with the lockdown and resulting self-isolation.
Take care and stay safe
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
Quick announcement, the latest HRV4Training update includes a color-coded history view showing the daily advice for a given day.
What does this tell me?
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).
You can learn more about normal values in our Ultimate Guide To Heart Rate Variability: Part 2, which is all about data analysis and interpretation or see some case studies in Part 3.
We hope you'll find this improvement useful. Enjoy!
Better tools to help you keeping track of physiological changes in response to infections and sickness
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.
Why heart rate?
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 . 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 ). 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 ). 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.
What's a "significant" change?
While it is quite clear from published literature, analysis of user generated data, and anecdotal evidence, that there is a strong link between heart rate and infections, the question remains of being able to identify when a change in heart rate is "significant", and therefore might be representative of some underlying abnormality, and when it is not. This is what we aim to do with the latest update in HRV4Training Pro.
We have discussed extensively in the past the importance of being able to determine when your physiological data (heart rate or HRV) is "significantly" outside of your normal range, and how the inherent variability in these metrics requires a different approach from the typical (higher - or lower - is better). We need first to understand that there will always be fluctuations between days, and increases or decreases are not necessarily good or bad signs, depending on how large those changes are. This is why HRV4Training always looks at your data only with respect to your historical data (up to 60 days of data) and what we call your normal values. In other words, we check if your daily score is within the range where it should most likely be given your past recordings, provided that nothing odd is going on. If your data is outside of this range, then an important stressor is most likely acting.
This is the process we use to help you interpret your HRV data, and 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.
Case study: Laurens
Laurens is a triathlete and HRV4Training ambassador who shared his data with us after getting sick. While Laurens never got tested for covid-19, he had the common symptoms and felt he most likely had the virus during this period.
You can see how his heart rate (typically around 40bpm) jumps really high in response to the infection. You can also see how there are sometimes higher heart rates also in previous data (maybe a glass of wine), but in his data, only the infection causes heart rate to be way higher than normal and remain elevated for days, causing an immediate spike in the baseline as well (7 days moving average), which goes outside of his normal values, highlighting an abnormality.
In Laurens' data we can also appreciate how long it takes for things to get back to normal, with a gradual decrease in heart rate, which is however still not where it used to be.
Quoting Laurens, "it still takes a few weeks for your body to fully recover and it is therefore very important to keep a close eye on this. I have now reduced my training again back to one time and my heart rate is slowly decreasing again".
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
 Buchan, C. A., Bravi, A., & Seely, A. J. (2012). Variability analysis and the diagnosis, management, and treatment of sepsis. Current infectious disease reports, 14(5), 512-521.
 Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Rose, S. M. S. F., ... & Sonecha, R. (2017). Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS biology, 15(1), e2001402.
 Altini, M., & Amft, O. (2016, August). Hrv4training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2610-2613). IEEE.
Special thanks to Pro triathlete Scott Bayvel for inviting Marco for a chat on his YouTube channel. In the video, we cover the following:
Link here. Enjoy
Special thanks to Michael and Andrew at the Endurance Innovation Podcast for inviting Marco for a chat. In this podcast, we cover:
Enjoy and stay safe
You can find the episode here.
Register to the mailing list
and try the HRV4Training app!
This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. HRV and training load
5. HRV, strength & power
6. Overview in HRV4Training Pro
7. HRV in team sports
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
16. Coefficient of Variation
17. More on CV and the big picture
18. Case study marathon training
19. Case study injury and lifestyle stress
20. HRV and menstrual cycle
21. Cardiac decoupling
22. FTP, lactate threshold, half and full marathon time estimates
23. Training Monotony
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Scosche Rhythm24
7. Apple Watch
9. Samsung Galaxy
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. Acute HRV changes by sport
8. Remote tags in HRV4T Coach
9. VO2max Estimation
10. Acute stressors analysis
11. Training Polarization
12. Lactate Threshold Estimation
13. Functional Threshold Power(FTP) Estimation for cyclists
14. Aerobic Endurance analysis
15. Intervals Analysis
16. Training Planning
17. Integration with Oura
18. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV normalization by HR
3. HRV 101