Blog post by Marco Altini 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. 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. Do normal values matter?In the context of HRV analysis, we always stress the importance of looking at your own baseline and monitor deviations from the baseline. 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). Hence, we highly recommend to focus only on relative changes, which is the most powerful way to make sense of your data, as shown in these articles: An example of how to use HRV data and analyze relative changes over time in response to training and lifestyle stressors. The dataDespite the fact that we highly recommend focusing on relative changes over time, in this post we provide an overview of normal values, which we advise using only to satisfy your curiosity. In particular, we will be looking at HR and rMSSD data. Most of the data was acquired using 60 seconds measurements in the morning. 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. HR, HRV and ageAge 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 agerMSSD reduction with age. Entire dataset on the left, clustered by age group on the right. HR and ageHR & age, no clear link. Entire dataset on the left, clustered by age group on the right. HR, HRV and genderThe 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 genderrMSSD & gender, no clear link. Entire dataset on the left, clustered by age group on the right. HR, age and genderHR & gender. Entire dataset on the left, clustered by age group on the right. SummaryIn this post we've explored the relation between HR, HRV, age and gender. We limited our analysis to these two basic stratifications as we easily end up with a small sample when we start stratifying over 3 or 4 parameters. 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. Below you can see some of the relations explored in this post as they appear in the HRV4Training app. rMSSD reduction with ageIn 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. HR constant across age groupsFor HR data, we don't see any shift, as baseline HR data does not seem to be linked to age.
1 Comment
5/6/2016 08:11:41 pm
Marco, really excellent post here and your update to the app. Very cool to see the actual statistical comparisons to other HRV4T users. Your app surpassing the 3000+ figure for your sample base is also quite impressive. Congrats! Since you had to omit some portion of the users, the actual number of HRV4T users is probably significantly higher.
Reply
Your comment will be posted after it is approved.
Leave a Reply. |
Register to the mailing list
and try the HRV4Training app! This blog is curated by
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 |