Blog post by Marco Altini.
In August 2015 we blogged about HRV normal values, and based our analysis on published literature as well as about 800 HRV4Training users. Our initial analysis was included in the HRV4Training app, and the ones currently using it can still find it under Menu / Population comparison.
As we've gathered more data in the past few months, this post is an update on our previous analysis. We've also redesigned the Population Comparison screen in the app, and will be introducing it here.
Let's get started with a recap of our previous post.
What are population values and what can we use them for?
For many parameters we are often dealing with, such as body weight, temperature, blood pressure, we typically have in mind some range reference values that are supposedly normal or indicative of a good health condition. For example, a healthy heart rate is somewhat between 40 and 80 beats per minute, systolic and diastolic blood pressure should be around 90-120 and 60-80 respectively, etc. Typically, we know these values because they were derived from some sample of the population in past epidemiological research.
Simply put, population (or normal) values are summary statistics from a big number of people (our sample). If we collect data properly, they will be representative of the values in our population of interest. However, a simple average HRV of the entire population might not be the most insightful summary to look at (as it isn't for blood pressure or heart rate either). What we can do to get a bit more insightful data, is to start stratifying and creating subsets of the population with different characteristics (e.g. age, gender, etc.) and try to understand what factors influence HRV, and what are population values for people similar to us.
In the context of HRV analysis, we always stress the importance of looking at your own baseline and monitor deviations from the baseline, however the baseline must also come from somewhere. Our baseline HRV is probably affected by some factors that we cannot easily measure (genetics, for example, as reported once again recently), other factors that change but we have no control on (e.g. age), and factors we can probably influence (lifestyle).
Unfortunately, when it comes to HRV and normal values, things get a bit difficult to extrapolate. This is due to many reasons, one being that there is no single HRV number, and therefore before we start talking about normal values we should be talking about what feature we want to consider and why. Secondly, the protocol used to acquire HRV data differs between studies, with supine tests, tilt tests, nocturnal HRV recordings, all frequently used. Finally, acquiring HRV data was not that easy until a couple of years ago, therefore making it difficult just to acquire representative data on a decent amount of people.
We will be looking at HR and rMSSD data, as collected by HRV4Training. Most of the data was acquired using 60 seconds measurements in the morning. I've included all users that took at least 1 week of measurements between October 2015 and March 2016, discarded all HR outside of the 30-110 bpm range, discarded all rMSSD values outside of the 5-280ms range and discarded all camera-based measurement that reported quality below optimal.
As mentioned above, this procedure leaves us with about 3000 users, which is a pretty good sample size. However, this is still a small number when we start stratifying by different characteristics, and therefore we will be looking only at macro differences (e.g. very big age groups), because otherwise we end up with a sample which is just too small to derive any meaningful conclusion. If you'd like more stratifications, get all your friends on HRV4Training and we'll have more data next time we do this analysis.
HR, HRV and age
Age is an interesting parameter as it is probably the single factor that can explain most of the variation in HRV data (among the ones we gather). On the contrary, there seem to be no strong link between heart rate and age. Our data confirms clearly what is know from literature, as shown below.
rMSSD and age
rMSSD reduction with age. Entire dataset on the left, clustered by age group on the right.
HR and age
HR & age, no clear link. Entire dataset on the left, clustered by age group on the right.
HR, HRV and gender
The relation between physiological parameters and gender seems to be less obvious. In the context of HRV, studies have been inconclusive and most likely other confounding factors played a role. In our data, similarly to what we reported in the past, there are no consistent differences between men and women for rMSSD data. However, we do have a difference in HR, which shows up consistently across age groups. As we gather more data it could be interesting to try to explore what other parameters might be explaining this difference. Here are the data for HR and rMSSD, first across all users and then by age group. Apologies for the stereotyped colors, they were the default in ggplot/R.
rMSSD, age and gender
rMSSD & gender, no clear link. Entire dataset on the left, clustered by age group on the right.
HR, age and gender
HR & gender. Entire dataset on the left, clustered by age group on the right.
In this post we've explored the relation between HR, HRV, age and gender in a sample of about 3000 HRV4Training users. We limited our analysis to these two basic stratifications as we easily end up with a small sample of users when we start stratifying over 3 or 4 parameters (it's never gonna be enough data, as you can always do one more stratification).
However, other factors would be interesting to explore, for example training load as a proxy to fitness level, something we recently introduced in the app, which is most likely strongly related to changes in baseline HR, and probably less on changes in baseline HRV.
We will keep revisiting this analysis as well as the Population Comparisons screen in the app, as we gather more data. Below you can see some of the relations explored in this post as they will appear in the HRV4Training app in about 2 weeks.
rMSSD reduction with age
In the plots below you can see how the filtered data, shown in darker blue, moves towards the left, i.e. towards lower rMSSD values, as we select groups of older age. You can also see how my own data fits better the 35 to 50 distribution than the 35 or younger one. Maybe I need to take a holiday.
HR constant across age groups
For HR data, we don't see any shift, as baseline HR data does not seem to be linked to age.
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1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
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
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
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
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
1. Intro to HRV
2. HRV normal values
3. HRV by sport
4. HRV, strength & power
5. AngelSensor & HRV
6. HRV 101: How to
7. Top 5 most read articles
8. HRV normalization by HR