Blog post by Marco Altini
This is a short blog post covering the latest update in HRV4Training Pro. As recently we have talked a lot about normal values and the big picture (here), how to use deviations from normal values to guide training (here) and the importance of looking not only at baseline HRV with respect to normal values, but also the coefficient of variation (a multiparameter approach, here), we have made a small change in HRV4Training Pro to provide an even better overview of your physiological data.
In particular, as we have thoroughly described here, HRV4Training can automatically determine how you are coping with your training load by combining heart rate variability, heart rate, coefficient of variation and training information. The detected trend is one of the following: coping well with training, being in a stable condition, risk of maladaptation or accumulated fatigue.
This analysis is present in the HRV4Training app under Insights / HRV Trends and also on HRV4Training Pro under Insights / Resting physiology.
However, with the latest update you can overlay the results of this analysis, so the detected trend, with your normal values, so that you can get a better overview of not only your physiological data with respect to normal values, but also the detected trend based on a combination of baseline HRV, heart rate and coefficient of variation, contextualized by training load.
Use code BIGPICTURE to get 15% off HRV4Training Pro.
As part of our "big picture" posts, today we cover the relationship between HRV and training load. In particular, this post is inspired by a recent study by Flatt and Howells, that you can find on Andrew's blog here.
This is an important topic as it can be a little confusing to the ones approaching HRV measurements and trying to better understand physiological stress in response to training and lifestyle.
The view that training should cause a dip in HRV is somewhat simplistic in my opinion. HRV is a measure of physiological stress, and periods of higher cumulative stress are typically highlighted by a reduction in HRV, but acute (day to day) changes are hardly simply linked to TSS or training intensity, let alone the fact that in a situation of positive adaptation to training, your HRV should increase (or at least be stable) when consistently increasing training load (more on this later).
So what is the relation between HRV and training load? Do we expect HRV to decrease after hard efforts? How does this relationship change when we look at elite athletes, with respect to the rest of us?
The authors started with the following hypothesis, as Andrew wrote in his blog: "Based on previous studies, we’d expect the weekly LnRMSSD mean (LnRMSSDm) to decrease and the coefficient of variation (LnRMSSDcv) to increase relative to baseline. We’ve observed this in collegiate soccer players and sprint-swimmers." - however, this is not what they found.
In untrained and recreational athletes, day to day changes in HRV reflect training load quite well, on average, as we have described and published in the past.
However, as we become more fit and more used to training stressors, our ability to cope with them changes, and our physiological responses change accordingly. This means we expect less acute drops. The extreme case here is elite athletes, which are used to high levels of volume and intensity, most likely already for years, and therefore physiological responses at the acute level are often less interesting, which is why baseline changes with respect to normal values should be analyzed instead, so that periods of higher stress or good adaptation can be captured, and changes can be made when necessary.
This is also what happened in the study mentioned above, as the players showed no change in HRV throughout two weeks of intensified training load. Even the CV, a measure of how well we are adapting to training, with lower values typically meaning better adaptation (HRV is not jumping around as much on a day to day basis), increased during the first few days, but then went back down while load kept increasing. These are changes normally associated with reduced load, so what happened here?
As Andrew states "The discrepancy here appears to be related to how players are tolerating and adapting to the training load. We often assume that increased loads will result in fatigue accumulation and temporary negative responses. However, these elite players demonstrated no reductions in subjective indicators of recovery status during the weeks of increased load. Additionally, there was no significant decrement in running performance (maximum aerobic speed) mid-way through the intensified microcycles. Thus, the preservation of autonomic activity (no change in LnRMSSDm) and less fluctuations (reduced LnRMSSDcv) seem to reflect a postive coping response to the training. In fact, individuals who demonstrated the lowest LnRMSSDcv during week 1 of increased load showed the most favorable changes in running performance (r = -0.74)."
CV and detected trend in HRV4Training Pro.
How are you coping with your training load?
What we can derive from this study, similarly also to what was shown in the recent study by Javaloyes and co-authors (covered here), is that a reduced CV is often associated with coping well with training, and the relation we expect to see is not between training load and HRV, but between HRV and adaptation, which is why these metrics are so important.
After all if all HRV was telling us was the intensity of our workout, there would be no need to measure it, but what we are measuring is how we are responding to that workout or block of workouts, with certain trends (stable or upwards HRV, reduced CV) being indicative of good adaptation, even in periods of very high load. This means also that the opposite trends, reductions in HRV or larger fluctuations in CV are signs of poor adaptation and should trigger changes in training.
To learn more about these aspects, check out two of our recent blog posts, one on the big picture, and one on how to use HRV to guide training. This is in line also with what Dan Plews argues in his analysis of HRV data pre-ironman, that you can find here. If you are into podcasts, we discuss some of these aspects at the Scientific Triathlon podcast.
While acute changes are informative and can help us make small adjustments following a bad night of sleep, a particularly hard workout, or early sign of sickness, what you should be focusing on is certainly the big picture, aiming for your HRV to be within normal range, and making adjustment aiming at prioritizing recovery should your HRV baseline go below normal values, so that performance can be optimized in the long term, as shown in multiple studies.
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.
What’s the study about?
The purpose of this study was to examine the effect of training prescription based on HRV in road cycling performance. After 4 weeks baseline measurements, 17 well-trained cyclists were split into two groups, HRV-guided and traditional periodisation group. The training program lasted another 8 weeks, and performance measures were taken before and after the 8 weeks in both groups.
HRV measurements were performed at home and without direct supervision (finally, like the rest of us do!). Now to the interesting bit: how was HRV used to guide training? First, the authors computed the Smallest Worthwhile Change (SWC). What’s the SWC again? 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. This is what we call your “normal values” in HRV4Training and HRV4Training Pro, see image below. In simple terms, the SWC is a statistical representation of your historical data, the green-ish band in our screenshot below.
Then, the authors computed a 7 days moving average of ln rMSSD, this is nothing different than your Recovery Points baseline in HRV4Training. When the 7 days moving average was outside of the SWC (baseline outside of your normal values), the prescribed training intensity was reduced, so from high or moderate it would go to easy or rest. In particular, getting a little technical, the SWC was built as half a standard deviation from the mean of the rMSSD and updated continuously throughout the study, so the normal values would stay up to date, exactly like we do in HRV4Training (we use 0.75 standard deviations in Pro, hence our normal values are a little wider).
Se an example of how you could implement the same protocol using HRV4Training Pro, which means holding back when your baseline (blue line) goes below your normal values (green-ish band).
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 last post of 2018, I'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.
What are your normal values and how should you use them?
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.
Normal values in HRV4Training
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:
An example of daily score (Today), and relation between the daily score and a person's historical data (bottom image, showing the desirable range, or normal values, basically where we expect the score to be unless there are significant changes due to higher stress, positive adaptation or other). The third figure shows a summary of the daily advice, which is normally color-coded in the homescreen, as reported in the History page, above the measurement bars.
Taking it to the next level in HRV4Training Pro
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.
An example of normal ranges (greenish bar) and baseline (blue bar) changes over time. Periods of significantly higher stress can be spotted easily as they end up below the lower bound of the normal ranges, while variations within the green band are most likely just due to normal variability in physiology on a day to day basis.
In particular, this is my data and it can be seen quite obviously how very high stress that was not training related caused a significant reduction in HRV, for several days, until my baseline ended up way below my normal values. As this period finally passed (shown in the third plot where I subjectively reported my "life stress"), I was also increasing consistently my training load. In the second part of the plots we can see how my body was showing the typical positive adaptations to high training load described by Dan Plews in his blog post linked above. Finally, things settled back within my normal range.
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.
The color-coded daily advice can also be highlighted in HRV4Training Pro, so that you can see what was the app advice with respect to your normal values, baseline and daily scores. As expected during higher stress I got quite a few yellow lights (caution), while positive adaptation to the higher training load was consistently in the green (all good).
Haven't tried HRV4Training Pro yet?
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 Endurance 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.
In case you want to jump right in, and check out our latest feature, simply login at HRV4T.com with your HRV4Training credentials. We have a promotion and until December 9th you can get 15% off using code STAYAEROBIC at checkout.
What's aerobic endurance?
Aerobic endurance (or aerobic efficiency) relates to your ability to sustain a given workload. 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 endurance 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 endurance.
Similarly, a higher power or faster pace at the same heart rate, is linked to improved aerobic endurance. By analyzing the relationship between input and output for running or cycling activities, you can easily track aerobic endurance changes over time, as you progress with your training.
What's the difference with VO2max estimation?
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 endurance, the estimate depends also on parameters that have very little to do with actual aerobic endurance and performance, for example body weight. Losing weight will increase your VO2max without necessarily improving your aerobic endurance 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 endurance. In particular, via the panel below you can filter workouts and environmental factors so that the resulting data is more representative of your aerobic endurance. 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.
Daniel Plews, age group ironman world champion and course record holder. First HRV4Training ambassador.
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?
What do we offer to the ambassador?
Deadline: November 25th.
To apply please send a message to email@example.com
Full article at this link.
Blog post by Marco Altini
As previously reported we have added support for the CorSense sensor by Elite HRV.
CorSense is a sensor you can use rather than a cheststrap, and it is compatible with most Apple iOS and Android OS devices.
In this post, we'll show a few minutes of data collected under different conditions, highlighting how the sensor is very accurate in detecting RR intervals and can therefore be used reliably for HRV analysis.
Data was acquired using the CorSense sensor and a Polar H7 (previously validated with respect to ECG here), both connected to a different device running the HRV Logger app, which is an app that simply records everything coming from the sensor plus additional features.
During data acquisition, we collected data a few minutes while breathing freely, and a few minutes while deep breathing, to elicitate higher HRV due to RSA. You will see in the plots below visually the effect of deep breathing as we get greater swings in RR intervals.
A final note on data synchronization: data cannot be perfectly synchronized because it is not timestamped by the sensors. What we can do is either to log real time and then to split data in windows based on when data was collected, then compute HRV features on these windows or to sum up RR intervals over time. For this analysis we went with the second option and also tried to visually align the data streams.
Blog post by Marco Altini
We have released a new feature in HRV4Training Pro, Functional Threshold Power (FTP) estimation for cyclists. In this post, we go over the background, cover how you can use this feature to plan and track your training and progress and also provide additional details about the underlying algorithms that we developed to estimate FTP and their accuracy, as well as some of the current limitations.
In case you want to jump right in, and check out our latest feature, simply login at HRV4T.com with your HRV4Training credentials.
HRV4Training can now be used to read HRV data from the Health app, convert that data (SDNN) to Recovery Points (a more readable metric), and analyze your physiology similarly to what we normally do when you measure using the phone camera or an external Bluetooth sensor. You can learn more about this feature in this blog post, while below we go more practical on how to use your Watch with our app.
Due to some limitations in the way apps can communicate with the Apple Watch, you need to follow the following steps in order to gather meaningful data:
If you do not get your data in Health right after using the Breathe app, try to synch your Apple Watch and it will show up a few seconds afterwards.
Always remember that context is key, so while the Apple Watch writes somewhat random HRV numbers also during the day or night, that data could be affected by artifacts, and it is always decontextualized.
To properly interpret physiology, data must be acquired under standard, reproducible conditions, and the best way to do so is with a measurement as soon as you wake up, or with a night long measurement (not just a minute or two over a night).
What are Recovery Points? A more human friendly HRV score. For more information, read this.
How can you use SDNN instead of rMSSD to generate Recovery Points? SDNN captures physiological stress similarly to rMSSD hence it can be used in a similar manner. For more information, read this.
How accurate is the Apple Watch in measuring HRV? Very accurate, provided you stay completely still and use the Breathe app to take a measurement. For more information, read this.
When should I use the Breathe app to take a measurement? First thing in the morning.
How much time do I have after measuring with the Breathe app, to fill in my tags in HRV4Training? You have one hour. When you tap 'read from Health' we always check only the last hour, and see if we can find any HRV scores in the Health app, then take the last one. For this reason, we highly recommend reading data right after you have measured.
Why can I see only Recovery Points and Heart rate instead of all HRV features when using the Apple Watch to measure? The Apple Watch does not provide us with RR intervals that could be used to compute different features, but only with SDNN. Hence, other features cannot be computed, apart from the Recovery Points that we show in the app.
Should I use the Watch or the camera? While the watch provides accurate data under ideal conditions, the metrics reported are limited with respect to the ones we can compute directly using the camera or a bluetooth sensor. Similarly, having no access to RR intervals or raw data, we need to trust Apple to correctly identify and remove potential artifacts. Hence the camera still remains our preferable method, unless you find it uncomfortable or are experiencing any issues.
Heart Rate Variability (HRV) features: can we use SDNN instead of rMSSD? A data-driven perspective on short term variability analysis
One really good thing about the sports science community, is that we have settled on what feature to use when we talk about heart rate variability (HRV).
As many of you know, HRV can be computed in many different ways, starting from our basic unit of information, the RR intervals (beat to beat differences in instantaneous heart rate). The sports science community through the work of many, including Martin Buchheit, Daniel Plews, Paul Laursen, Andrew Flatt, Martin Esco, Fabio Nakamura and a few others, in the past 10-15 years, settled on rMSSD as the most meaningful and practical feature to use in applied research and real life, when working with athletes.
Why rMSSD? Well, first of all, most sports scientists are physiologists, they know what they are talking about when considering physiological processes in the human body, and it turns out what also came up from all these studies, is that there is mainly one thing that can be measured using short term HRV features: parasympathetic activity. Without going into another primer on HRV (see this post if you are looking for one), parasympathetic activity represents our body's rest & recovery system, and can be captured in terms of HRV: a stressor might for example induce a physiological response in terms of reduced parasympathetic activity, which translates into lower HRV as the nervous system modulates heart rhythm in response to such stressor. Parasympathetic activity acts quite fast, in the matter of seconds. How do we capture these fast changes? rMSSD, due to how it is computed (just math), captures fast changes in instantaneous heart rate, hence it reflects very well parasympathetic activity. It's also easy to compute and standardized, hence we can be certain we all talk about the same thing, which is a good starting point.
Wonderful, we have a feature that everybody agrees on, and has also a clear link to how physiology works. All problems are solved and we can use HRV4Training or our favorite HRV app to gather data, compare results, and learn a bit more about how our body responds to training and life stress.
Well, not so fast.
Blog post by Marco Altini
I have helped Strava developing their current Relative Effort, a metric used to quantify training effort, combining intensity and duration. You can read Strava's official launch blog post here as well as an interview I gave here if you are interested in learning a bit more about how this metric was developed and how you can use it effectively (the website hosting the interview is actually not available anymore, hence I am linking below only the official Strava blog mentioning this work).
Blog post by Marco Altini
Sharing a podcast I've recorded recently. Thank you Mikael for inviting me to discuss HRV4Training, physiology and human performance on the Scientific Triathlon podcast: https://scientifictriathlon.com/tts144/
In this podcast, we skip the basics of HRV (that hopefully are clear by now!) and go directly into how you can use the data, in particular covering:
Will wrap that up in a blog post soon, but you can see some screenshots here should you be interested: https://www.strava.com/activities/1798124065/overview
Since launching HRV4Training, the easiest and most cost-effective solution to acquire high quality physiological data, in particular HR and HRV at rest, we published a fair amount of work. From the validation of the camera-based measurement, to acute day to day changes in physiology (heart rate and HRV) in response to training, to methods to estimate VO2max from workout data, methods to estimate running performance and the relation between HRV, training load and injury in Crossfit. Transparency and solid scientific grounds are what we believe in, which is why we started documenting to the public and validating our work since day zero.
Most importantly, HRV4Training gave the opportunity to universities not associated with us, to collect physiological data easily in the wild, potentially leading to additional insights. More and more universities have started publishing papers based on data collected using the app and web platform in the past few years, and we'd like to cover the last two that came to my attention recently. Both papers are authored by Sara Sherman, who was a master's student at the University of Alabama, under the supervision of Michael Esco, who needs no introductions.
Sara is currently pursuing a PhD at the University of Illinois-Chicago and will be providing her input alongside mine in this overview of her work.
The two studies authored by Sara cover two different topics, both investigated in a population of thirty-one NCAA Division I female rowers at the University of Alabama, Tuscaloosa.
Let's break down this section into measurement time and menstrual cycle, and discuss the two studies separately.
Just a quick announcement that all new iPhones are already compatible with HRV4Training, short video on an iPhone XS below.
As usual, please double check our camera based measurement best practices, to make sure you'll collect high quality data on which meaningful analytics can be derived.
We have just added support for the CorSense. CorSense is a sensor you can use rather than a cheststrap, and it is compatible with most Apple iOS and Android OS devices.
Now you can use it with HRV4Training, too.
Blog post by Marco Altini
We have just released our latest feature in HRV4Training Pro: half marathon and full marathon running time estimates. In this post we go over how these prediction models work.
In particular, this work is an extension of our previously published analysis (see "Estimating running performance combining non-invasive physiological measurements and training patterns in free-living” which was accepted for publication at the 40th International Engineering in Medicine and Biology Conference, full text here, while another blog post explaining the paper can be found here).
In the published work we built models able to estimate running performance (10 km time) using 2 years of real world data from more than 2000 individuals, including morning physiological measurements obtained using HRV4Training, workouts acquired from Strava and TrainingPeaks, anthropometrics and training patterns.
Next week it's HRV4Training 5 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 here: https://www.hrv4training.com/pro--teams.html), or a singlet (see pic below).
To participate, simply write a post or blog on HRV4Training (anything you like or find useful for your training or lifestyle in general), and tag us (or email it to us) either on Instagram or Facebook (or your blog!) - you have until next week (August 18th).
Just make sure we know you wrote it :)
Marco & Ale
Just a quick update that we have just added support for several Android phones that struggled with HRV4Training, in particular the ones with multiple cameras such as the Huawei P20 Pro.
Please make sure to double check this list of practical tips for your measurement. and in particular making sure your phone is not doing many other operations during the measurement, maybe give it a try even in airplane mode to ensure there is not much background network activity, in case you are experiencing problems.
Blog post by Marco Altini
In the past few months we’ve been busy building HRV4Training Pro, a web platform for individuals and teams aiming at better understanding how different stressors affect their body, so that adjustments towards better health and performance can be made.
In this post, I’d like to cover the main approach behind our new platoform, deriving from the past 5 years of learnings. Since we launched the first and only validated camera-based Heart Rate Variability (HRV) measurementa couple of years back, we had the opportunity to learn a lot through continious iterations and feedback from our community as well as from top scientists in the field.
From the average guy just like myself, to elite triathletes that I occasionally enjoy slowing down, HRV4Training made it extremely easy for everyone to gather meaningful data points linked to physiological stress.
So, what’s HRV4Training Pro about?
Blog post by Marco Altini
Scosche recently announced their latest sensor, the Rhythm24. The new sensor builds on the previous Rhythm+, and adds a lot of new functionalities, including an heart rate variability (HRV) mode. For a detailed overview of the many functionalities of the sensor, check out DC rainmaker's review here.
In this post, we will focus on only one of the many new features this sensor brings, which is the ability to send accurate RR intervals, which can be used for heart rate variability analysis once connected to an app such as HRV4Training. We've been very happy with a few preliminary tests, and would recommend using this sensor to everyone that had trouble with the camera based measurement (for example on some of the non-compatible Android phones) or simply prefers to rely on an external sensor.
You can get the sensor at this link or clicking below:
If you've been following some of our previous validations, you know that most wristbands, while accurate for heart rate analysis, cannot be used for HRV, as they heavily filter the signal (a procedure typically needed to acquire a more stable signal, less affected by motion artifacts, and therefore able to guarantee a more reliable heart rate during exercise, which is the main target application of such sensors).
Let's look at some data.
Publication: "Estimating running performance combining non-invasive physiological measurements and training patterns in free-living"
Our latest work, titled "Estimating running performance combining non-invasive physiological measurements and training patterns in free-living" was accepted for publication at the 40th International Engineering in Medicine and Biology Conference.
In this work we build models able to estimate running performance using 2 years of real world data from more than 2000 individuals, including morning physiological measurements obtained using HRV4Training, workouts acquired from Strava and TrainingPeaks, anthropometrics and training patterns.
In particular, we provide insights on the relationship between training and performance, including further evidence of the importance of training volume and a polarized training approach to improve performance.
More details, at this link.
This post is mainly motivated by countless emails (and a few negative reviews) that we have received regarding the possibility to integrate the Apple Watch in HRV4Training, which clearly highlight some misinformation around the topic of both HRV and the Apple Watch itself.
Simply put, at this stage, the Apple Watch cannot be used for reliable and meaningful HRV analysis using third party apps.
Interested in learning why? Read on.
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1. Intro to HRV
2. HRV 101
3. How to use HRV, the basics
4. HRV guided training
5. The big picture
6. HRV and training load
7. HRV, strength & power
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. rMSSD vs SDNN
7. Normal values and historical data
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Zoom HRV vs Polar
7. Apple Watch and HRV
8. Scosche Rhythm24
9. Apple Watch
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. HRV4T Coach advanced view
8. Acute HRV changes by sport
9. Remote tags in HRV4T Coach
10. VO2max Estimation
11. Acute stressors analysis
12. Training Polarization
13. Custom desirable range / SWC
14. Lactate Threshold Estimation
15. Functional Threshold Power(FTP) Estimation for cyclists
16. Aerobic Endurance analysis
1. HRV normal values
2. HRV by sport
3. HRV normalization by HR