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! Comments are closed.
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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 |