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Blog post by Marco Altini We have just released a new version of HRV4Training for iPhone and Android, which deploys our latest VO2max model. In particular, this model better accounts for hilly terrain and provides more accurate VO2max estimates for runners that train on hills. Normally, the relationship between running speed and heart rate is used to estimate VO2max, as we cover in more detail here. Intuitively, a lower heart rate at the same speed, means that you are getting fitter. However, this relationship falls apart when we run on trails, or include much elevation gain in our runs. Below is an example of my own data, where you can see a few things:
 In the last part of the graph you can see how my interest shifted toward trail and ultrarunning (very few flat runs). As a result, VO2max estimates show a large decrease. In fact, little change is present in speed over heart rate when looking only at these flat runs (yellow line). This makes it hard to track progress in aerobic efficiency or VO2max (or whatever you want to call how your heart rate changes at a given speed). With the newly released model, we are able to better account for these changes, and provide a more accurate estimate. To build this, we modeled the difference in estimated VO2max for the same person when running flat vs hilly, in relation to the average grade of the runs (N = 10 000). There are of course still limitations, for example no knowledge of how technical is the terrain, but it should be an improvement. Below are results showing the original estimate (which was a match with testing in the lab), then the poor performance of the model on hilly runs, and the updated results today, for me (right) and Alessandra (left). We both had an accurate estimate when living in The Netherlands, then got a reduction in VO2max simply because of the different efficiency (relationship between pace and heart rate) when running on hills in Italy, and then finally you can see the prediction of the new model, which is close to the original one.  Enjoy!
Blog post by Marco Altini We have made some changes to the Heart Rate Variability Logger app on iPhone (http://hrv.tools) to make it easier to compare pre and post-exercise data. The goal of these measurements is to assess the impact of intensity (or other stressors, such as the heat for example). Measuring heart rate and HRV before and after a workout, we isolate the training stressor in a way that allows us to assess and compare autonomic control. I have discussed these aspects in greater detail in my blog here. This approach, is based on Stephen Seiler's research and could be a practical way to determine if training was executed according to prescription or if the intensity or the addition of other stressors, caused a greater autonomic disruption (and therefore a need for more recovery)  With the HRV Logger you can take measurements before and after exercise, and run the same comparisons shown below, directly in the app (the Compare tab is available only on iPhone). Make sure to configure the app as below:
I would like to add a note about artifacts here and how the Compare view allows you to filter them out even when the RR intervals timeseries is impacted. Post-exercise, these days I have many ectopic beats. You can see below that some of them remain even after artifact correction (the spikes in the second recording, right end side).  When we analyze the data, we can get rid of features that have been computed using the Outlier removal button, in the Compare tab. First, select the recordings to compare. Then, you will see histograms showing the distribution of the data, for the selected feature (typically I would look at heart rate and rMSSD). You can also see the averages (e.g. rMSSD = 68.1 pre-run and 10.8 post-run):  As mentioned earlier, there were some artifacts in the RR intervals, that might impact rMSSD. If you toggle the Outlier removal button, you will get a cleaner picture without the need to export and re-process the data. Here for example rMSSD post-exercise becomes 8 ms.
As per Stephen's research, easy training should show almost no change in rMSSD post-exercise with respect to pre-exercise. This can be a useful test to assess if your sessions are truly easy (below aerobic threshold). You want those bars to be really close or overlapping. Additionally, you can assess the impact of other stressors, such as the heat. Despite running very slowly and trying to keep intensity low when I recorded the data above, it is clear from the data change in autonomic activity that the heat for me is a large stressor, apparently as large (or larger) than high-intensity training. Enjoy. Blog post by Marco Altini I had a nice chat with Bevan discussing how to collect high quality and meaningful HRV data, how to interpret that data, our latest research with HRV4Training, and more You can find it at this link, thank you for listening!  We have just released the latest version of HRV4Training, which brings a complete re-design of the app. It should be easier now to better understand if your resting physiology is within your normal range. That's what matters the most. You can find a quick start guide, here. You can use HRV4Training either with your phone camera (a method that has been validated and independently validated), or with an Apple Watch, Oura ring, Scosche Rhythm24 or Bluetooth strap. All sensing modalities are valid as long as you follow a few simple best practices. Thank you for your support. Enjoy!  Blog post by Marco Altini A common misconception is that HRV should dictate how you can perform. However, this is not the case. For example, delayed onset muscle soreness (DOMS) and nervous system recovery are on a different schedule. In this blog, I would like to discuss what it means when your HRV is still within normal after a hard workout, and what you should expect after such sessions. This can be confusing at times, but there is nothing better than a good (within normal range) HRV after a hard session. Why would this be the case? A good HRV after a hard session shows that you were able to quickly bounce back. This is a sign of good fitness and an highlights how an adequate training stimulus was applied. Most importantly, you should not expect your HRV to sink after a hard workout. If that is the case, it does not mean that you did a "good workout" (other common misconception), but it means that you dealt poorly with the workout and could not bounce back within a reasonable time. Elite athletes hardly ever see dips in their HRV post hard workouts. Are they not training hard or not training enough? unlikely. However, their autonomic nervous system recovers much faster than in other trained individuals, as we can see from the figure below (full paper here).  If your HRV stays suppressed after 24 hours since your workout, most likely:
These are key points highlighted by Andrew Flatt in his research. When it comes to recovery, HRV is one piece of the puzzle. Having recovered quickly from a nervous system point of view means you have the capacity to assimilate the stimulus. Additionally, a stable HRV highlights how you are also dealing well with non-training related stressors (even more important). Yet, there should be no surprise if your daily HRV does not correlate with muscle soreness or feel (whatever that means) or performance. HRV is your response: keep it stable. If today you are resting and your HRV looks good, you are in an ideal situation. For any feedback on this blog, feel free to reach me here. We've updated our Ultimate Guide to Heart Rate Variability (HRV), part 1:
You can find it here. We hope you will find it useful, thank you for reading!  Blog post by Marco Altini It is a common misconception that HRV should track training load, reducing when training load is higher. In particular, studies looking at the relationship between HRV (and other metrics) with training load over time, look at how these metrics correlate. However, 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. What is the point of having another metric that gives you the exact same information? By definition, if a metric is perfectly correlated to training load (positively or negatively), then it is a useless metric. If HRV had a perfectly negative correlation with training load, It would not add any information to the training and recovery equation. Ironically, these studies would interpret the metric with the highest negative correlation with training load as the best metric (!). On top of this, HRV is all about individual responses. A non-relationship at the group level does not tell us anything at the individual level. Maybe a few people responded very well and had increased HRV. Other people had a suppression in response to the same load. This is exactly why we monitor. It makes sense to analyze group-level data in response to acute stressors (see for example our paper here where we look at training, sickness, alcohol intake and the menstrual cycle) However, in the long run, acute and chronic responses differ. As such, a group level analysis does not tell us anything about the individual response. The notion that increased load should trigger a reduction in HRV is very simplistic. We can have stable or increased HRV when increasing load (a sign of positive adaptation) and decreased HRV with reduced load because of other stressors (travel, work). Check out this blog for more information on HRV trends. How should we use HRV and training load information then? If our training load is increasing and our HRV stays within normal or increases, that’s great, it means we are responding well to stress. This is confirmation that we can take the load, maybe even increase it a little more. In general, HRV should not negatively correlate with load. By measuring your resting physiology first thing in the morning, you can understand how you are responding to training (and other stressors), and use that information as part of your decision-making process. If you are coping well with stress, HRV will not be decreased.  For more misconceptions about HRV tracking, check out Part 4 of my Ultimate Guide To Heart Rate Variability.
In the latest study using HRV4Training to look at the relationship between acute changes in HRV (your daily value, with respect to your normal range) and performance, Justin DeBlauw and co-authors have found that "Daily HRV monitoring provides valuable insight that an individual’s peak power and speed may be compromised during cycling performance" In particular, performance metrics such as peak power and speed, were lower when performing time trials on a day in which HRV was outside of the participant's normal range Check out the full study, titled "Association of Heart Rate Variability and Simulated Cycling Time Trial Performance", at this link  Blog post by Marco Altini I've enjoyed being back on the Scientific Triathlon podcast after a few years. In this episode, we discuss: • HRV basics • our latest research • measurement devices • morning vs night • HRV vs heart rate vs readiness or recovery scores • how to use the data (HRV-guided training, practical tips) and more episode link here, thank you for listening!  Blog post by Marco Altini I often talk about your "normal values" as the only meaningful way to make use of Heart Rate Variability (HRV) data. But what are these normal values? Why are they important? Simply put, your normal values are a representation of your historical data. They allow you to understand if acute (daily) and chronic (weekly) HRV responses are showing meaningful changes or just small variations that you should not worry about. Normal values make the data actionable. Why do we need normal values? This has to do with the high day to day variability in HRV data. It might be easier to show this with an example. Let's look at the data below. This is typical HRV data, there is much variability between consecutive days. Should the highlighted reduction trigger concerns and adjustments in our plans or not?  Technology for Heart Rate Variability (HRV) measurement at rest is getting better every day. However, using a sensor able to measure accurately, is only the first step. In order to make use of HRV data, we need to make sure that the data is not only measured accurately, but measured at a meaningful time. Here is where many sensors that measure automatically fail. Finally, once we have collected accurate data at the right time, we need to be able to interpret that data, accounting for the high day-to-day variability in HRV. This last step is key to actionability. 1. Collect accurate data 2. Collect meaningful data 3. Interpret the data Check out our latest blog to learn more.  Blog post by Marco Altini One of the most interesting ways to analyze heart rate variability (HRV) data is to look at the amount of day-to-day variability in your HRV scores. That's what we call the Coefficient of Variation (CV HRV) The CV HRV is different from your baseline, which is simply the average of your scores over a week. In simple terms, the CV HRV reflects how much your scores jump around on a day to day basis.
 Why do we care?Normally, the most important aspect to analyze is how your baseline is going with respect to your normal values. A baseline within normal values shows a stable physiological condition and good adaptation (check out this blog for more information about the normal range)  However, the amount of day to day variability (the CV HRV), combined with baseline changes with respect to normal values, can provide additional insights on adaptation to training and other stressors. The CV HRV can flag issues in response to stress, before a baseline reduction. Quoting Andrew Flatt ”the preservation of autonomic activity and less fluctuations (reduced CV HRV) seem to reflect a positive coping response ... In fact, individuals who demonstrated the lowest CV HRV during increased load showed the most favorable changes in performance" A reduced CV HRV is often associated with coping well with training. This means also that larger fluctuations in CV HRV are signs of poor adaptation and might reflect issues in maintaining homeostatic control. How do you use the CV HRV?Let's look at one example. I've discussed before how "work stress" is what drives changes in HRV for me (that's where I need to "perform"). Below you can see two similarly stable HRV trends (in the boxes), as well as my increasing subjective stress. What about the CV HRV? Was it capturing anything differently?  Let's look at the data. We can see how the HRV response to increased stress was still within my normal range but included a lot of jumping around (high CV), which represents a poor response, eventually leading to suppressed HRV. Only reducing the stressor finally caused a rebound to normal.  The CV HRV had captured very well the poor response. Learning what drives big changes in stress is probably the first step to do something about it, whenever it's possible. Short recapIdeally, in the medium term, these are good trends we should hope to see if we are responding well to the various stressors in our lives:
Pay attention to deviations from these trends to spot potential issues in advance, which is easy to do in the HRV4Training app. You can learn more about trends in resting physiology, in this blog post. I hope you have found this blog informative! We've made a few changes in HRV4Training Pro You can now select rMSSD on the Overview page. As the name implies, the Overview page gives you an overview of your physiology, training load, and subjective data (see here for some examples) You can try Pro here: https://hrv4t.com  If you need a refresher on rMSSD and the different HRV features, I put together this blog post where I cover all of the most important ones Our recommendation would be to keep it simple, and use only what we call HRV in the app (which is simply a transformation of rMSSD), or rMSSD, a clear marker of parasympathetic activity (i.e. a lower value with respect to your historical data is associated with higher stress) If you use HRV4Training Pro with your athletes, clients or team, we have made some changes to the Coach Panel too, so that you can select rMSSD and quickly see the daily HRV with respect to each athlete's normal range, as shown below This should make it easier to use the data to identify periods of higher stress, e.g. a daily score below an athlete's normal range. Pro allows you also to add some context, for example looking at annotations such as the one identifying menstruation days in the example below  We hope you'll enjoy the latest update and wish you a happy new year
Welcome to the HRV4Training team to all our new ambassadors Full list at this link Looking forward to working together in the next months Thank you for your support and all the best for the new year!  Blog post by Marco Altini In this podcast, I chat with Matt and Hanna at 80/20 endurance. I’ve learned a lot over the years from Matt’s books, and it was a real pleasure to spend some time talking about HRV and training. Thank you & enjoy  We have just released the latest HRV4Training update, including our new trends detection, which combines:
if you are a HRV4Training Pro user, with the latest update you will also be able to see the detected trend in the baseline page, together with your normal range. You can try Pro for free by logging in at HRV4T.com, here. Enjoy the update and see you next year for more.  Blog post by Marco Altini Last week I had the opportunity to chat with Jason Koop and Corrine Malcom about heart rate variability (HRV), "readiness" scores, resting physiology, and wearables, in the context of training If you are interested in understanding the nuances of these aspects, including their strengths and limitations, this episode is a good starting point Don't fall for cults or for those who trivialize everything that is human physiology. Instead, strive to understand these aspects, and you'll be a better athlete or coach Thanks again for having me!  Blog post by Marco Altini In the second part of our latest paper, we analyzed individual stress responses to:
Using 1 year of data per person, for 28 000 people. This is in my view the most interesting part of the paper Why?  This type of analysis allows us to answer important questions: Can a morning measurement capture individual stress responses effectively? Is it worth the trouble to look at HRV, or is HR enough? What is the difference between the two, when it comes to stress responses? Data collectionMeasurements and annotations (training intensities, sickness, etc.) were collected using HRV4Training, first thing in the morning Most measurements were taken with the phone camera (validation here). Let's quickly look at our analysis framework first. How do we analyze individual stress responses? For each person, any given day there will be many stressors. However, if we take hundreds of days of data per person, and look at one stressor at a time, we can isolate the stressor and better understand its impact on resting physiology.  What did we learn?Training intensityBelow are the results for training intensity (low vs high-intensity days). The change in HRV is 4.6% while for heart rate is 1.3% (with respect to a person's average). HRV is therefore more sensitive to this stressor. The change in HRV does not reduce across age groups, indicating how HRV captures training stress equally well for older individuals, while the change in HR reduces. Additionally, women tend to have a less marked response (more about this later)  We also split the annotated intensity into four categories, as shown below. Once again we can see how HRV is more sensitive to changes in training intensity, but also how these measurements capture very well self-reported training intensities:  Menstrual cycleThe change in heart rate was 1.6% between the follicular and the luteal phases, while the change in HRV was 3.2%. Once again, HRV is more sensitive. These differences might also be the reason why other stressors show somewhat less marked responses in women.  Alcohol intakeChanges in alcohol intake are 3-4 times larger than changes due to training or the menstrual cycle (6% change in heart rate and 12% change in HRV).  SicknessNot surprisingly, sickness is also a very strong stressor, similarly to alcohol intake (6% change in heart rate and 10% change in HRV).  What are the implications?When using HRV for training guidance, lifestyle is key, and poor lifestyle or health issues will take over. A holistic approach to health and performance is needed. Strength of the stressorTo recap, changes due to training intensity and the menstrual cycle are typically 3-4 times smaller than changes due to sickness or alcohol intake. Changes in HRV are 2-4 times larger than changes in heart rate in response to the same stressors. InterpretabilityWhen we contextualize the percentage changes reported in this paper with what we know from literature, e.g. that the smallest practical or meaningful change in heart rate is 2% and in HRV is 3%, we can see how changes in heart rate are below this threshold, and therefore smaller than normal day to day variability.
This means that heart rate is not sensitive enough unless we have very strong stressors (e.g. alcohol intake or sickness). This also means that while HRV is more sensitive, it is also less specific, as shown by the typically smaller effect sizes. In other words: changes in heart rate are often of no practical utility (smaller than daily variability). On the other hand, higher stress will be reflected on HRV data no matter where it comes from and it might be difficult to get to the source (context is key). We speculate that these findings might lead to new forms of HRV-guided training, where rest days are prescribed based on large changes in HR (as these capture only very strong stressors), while training intensity is modulated based on more subtle HRV responses. You can find the full text of the paper, here. Thank you for reading Blog post by Marco Altini In our recent publication, we analyzed the relationship between heart rate and HRV with respect to individual characteristics such as:
In a large sample of 28 000 individuals. What did we learn? Some of the findings are larger-scale replications of what we knew already Consistent results with published literature that used different data collection procedures is a good first step It gives confidence in the quality of the data when we start digging a bit deeper Sex Women have higher resting heart rate than men, but very similar HRV In fact, at a younger age, women have a slightly higher HRV. This is of interest as a higher heart rate would normally be associated with lower HRV. The discrepancy might be due to hormonal differences.  BMI Both underweight and overweight/obese categories show what we have called in the paper a suboptimal physiological profile, meaning that resting heart rate increases and HRV reduces when deviating from the normal range. The strongest deviation is for the obese category.  Age There was no correlation between resting heart rate and age, and a moderate correlation between HRV and age. This is one of the most interesting relationships, as heart rate and HRV clearly decouple and are representative of different processes (more on this later).  Physical activity level The association between physical activity level and resting physiology is stronger for heart rate (r = 0.30, moderate effect size) than for HRV (r = 0.21, small effect size). When we break this down by age group, things get even more interesting. The correlation between physical activity level and HRV reduces by age, getting to r = 0.13 for older individuals. Only for very young individuals (20-30 age group) there is a decent association between fitness and HRV.  Finally, we built models to determine how much variance age, sex, BMI and physical activity level could explain. Are they sufficient to get a good understanding of inter-individual differences? Not really, as they explain 19% of the variance in heart rate and only 15% in HRV.
What are the implications of these findings? A low HRV in aging individuals might be associated with a deterioration of regulatory mechanisms. The weak link between physical activity and HRV as we age might similarly be associated with reduced baroreceptor sensitivity. On the contrary, increased stroke volume due to high levels of physical activity maintain resting heart rate low even for older age groups. In terms of explained variance, it is clear that genetic factors are key in explaining differences in heart rhythm between people. An important implication here is that in our opinion, targeting improvements in HRV as intervention goals might not be realistic, given the strong heritability coupled with reductions with age and low explained variance associated with lifestyle factors such as physical activity level. But there's an important caveat. In this work, we had a large sample. However, this sample is not representative of the whole population, but only of relatively healthy or health-conscious individuals. There might be more to gain for e.g. who never exercises, is overweight, etc. This is why HRV as an absolute value is of little interest (in our opinion). On the other hand, HRV was able to capture day-to-day stressors within individuals with high sensitivity, as I will cover in a future blog. You can find the full text of the paper, here. Thank you for reading! Blog post by Marco Altini Professor Maria Carrasco-Poyatos and co-authors at the University of Almería just published the latest paper on HRV-guided training, titled "Heart rate variability-guided training in professional runners: effects on performance and vagal modulation" Thank you Maria and co-authors for using HRV4Training and for involving me in this work. You can find the paper, here, including the main highlights:
 HRV4Training is looking for brand ambassadors worldwide We believe in empowering individuals with the ability to measure and interpret physiological data so that training and lifestyle stressors can be better balanced, resulting in improved health and performance Learning from athletes and coaches is an invaluable part of the journey, and we are looking forward to getting to know you and your experience Are you passionate about sport and technology and have been using HRV4Training daily at least for 6 months to better balance stress? We are looking for you! What do we offer to the ambassador and mentors?
What do we expect from the ambassadors and mentors?
We are going to select up to 30 brand ambassadors, we accept applications in English, Italian and Spanish. Would you like to spread the word about HRV4Training in another language? Send us a message with your proposal for our consideration! To participate in the selection process, fill in this google form. Deadline is November 30th, 2021  Blog post by Marco Altini Excited to share our latest paper on heart rate and heart rate variability (HRV):
This one has been in the making for a long time, and brings it all together: developing an accurate and affordable method to measure resting physiology, analyzing data at a scale that goes far beyond what we can do in the lab, and as a result, new, valuable insights Give it a read, here  Blog post by Marco Altini You’ve been preparing your race for several months and it’s finally time to taper. Training went really well and you are doing everything right And yet, your heart rate variability (HRV) reduces Learn more about HRV during taper, in my latest blog  Blog post by Marco Altini Heart rate variability (HRV) trends over long periods of time (e.g. from weeks to months) are one of the most interesting and complex aspects to analyze when it comes to resting physiology While day-to-day (or acute) changes reflect well stressors such as training intensity, the menstrual cycle, sickness, alcohol intake, or travel in the day(s) before the measurement, in the long term things are quite different In this post, I will cover our approach to trends analysis in HRV4Training, and cover some of the features in the app that should help you make sense of the data in the longer term Learn more, here  Blog post by Marco Altini  Daily measurements and 7-days moving average for lying down (left) and sitting (right) measurements A few months ago after an interesting chat with an elite team and Andrew Flatt, I started measuring my HRV in the morning while both 1) lying down and 2) sitting. Normally, I have always been measuring while lying down in bed, but Andrew brought up some interesting points which I thought were worth investigating further, at least in my own data. In particular, the reason for doing so, is the following: adding a little stress (e.g. sitting or standing instead of lying down), might better capture your physiological response (or capacity to deal with stress for the day). It's a physiological challenge (more on this later). In terms of the protocol, i would up and measure while lying down in bed, using HRV4Training (phone camera for one minute). At that point, I would sit, and after a few seconds, I'd measure also while sitting (another minute with the camera). Between measurement, the time would be rather short, something like 30 seconds. Let's look at the data. First of all, I'd like to stress how simple measures of agreement might be inadequate, e.g. correlation (often reported) does not tell us anything about deviations from normality.  Correlation between daily measurements (left) and 7-days moving averages (right) for lying down and sitting data While we can have a look at day-to-day correlation and baseline correlations, as shown above, a better way to understand if we are capturing the same information is to use the data the way it should be used for decision making: monitoring deviations from our historical data and normal values (acute drops, etc.). How do we do that? We can use part of the data to compute the normal values (40-60 days), then look at baseline and acute drops. Here both measures show very similar responses: highly suppressed HRV on the same days, baseline reaching the bottom of the normal range the same period, etc. - it is quite clear that both lying down and sitting are capturing the same trends (which would result in the same advice, especially for acute drops and baseline changes, the two key factors in HRV-guided training):  To sum up: in my data, both measures are very similar, especially in the longer term (baseline correlation) as well as in terms of acute drops (single days below the normal range, highlighted in blue in the previous image). What differed? Lying down seems like a squeezed version of sitting. Paraphrasing Andrew, applying a little stress (e.g. sitting) might better uncover the physiological response response, and this in turn, might explain the higher day to day variability when sitting. Note that this doesn't mean HRV will be lower (quite the contrary). Anecdotally I've heard from quite a few people that "when switching to night data, my physiology never changes" (unless sick, or drinking too much, e.g. very large stressors) Do measurements while lying down, and in particular night measurements simply lead to less day-to-day variability or is this associated to the measurement being less responsive to stressors, due to lack of a physiological challenge? In my opinion, both positions are valid and rather similar, as shown by the data here. While some have quite a dogmatic approach ("never measure in this or that position"), it is clear that the same processes are captured. However, there are also differences, and it is important to think it through and consider potential mechanisms and what could be best in an individual case. Most likely, the level of the athlete, baseline HRV (e.g. on the low vs high side of population values), type of sport practiced by the athlete (e.g. endurance or not), as well as other factors such as the application of interest (daily guidance or monitoring long term trends, performance or health oriented) and practical considerations (what modality makes it easier for the athlete to collect daily measurements consistently) should play a role in this decision. One minute of your time might be worth the insights after all. I hope you have found this blog useful, feel free to follow up here on Twitter.  Lying down (left) and sitting (right), including HRV4Training's detected trend
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Marco Altini, founder of HRV4Training Blog Index The Ultimate Guide to HRV 1: Measurement setup 2: Interpreting your data 3: Case studies and practical examples How To 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 HRV Measurements Best Practices 1. Context & Time of the Day 2. Duration 3. Paced breathing 4. Orthostatic Test 5. Slides HRV overview 6. Normal values and historical data 7. HRV features Data Analysis 1a. Acute Changes in HRV (individual level) 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 8. CorSense 9. Samsung Galaxy App Features 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 Other 1. HRV normal values 2. HRV normalization by HR 3. HRV 101 |
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