Blog post by Marco Altini.
In a previous post I've covered acute HRV changes, i.e. changes in HRV following training. I've shown how we can use differences in HRV measurements between training day and the day following training to determine the impact of the training session on our physiology. What we typically expect in this situation is a reduced HRV on days following intense aerobic trainings. This method is one of the most common and reliable ways to use HRV, and is the principle behind the daily HRV advice provided by apps like HRV4Training.
In my previous post on acute HRV changes I showed as an example my data over a period of 2 months and highlighted how this type of analysis was included in HRV4Training to allow you to better understand how your trainings are affecting your physiology and recovery.
In this post, I will take the previous considerations a step further, and analyze data from about 400 HRV4Training users to determine if at the population level we are seeing the expected outcomes (i.e. HRV reductions on days following training).
For this analysis, I used the last 2 months (September and October 2015) of measurements collected from HRV4Training users. I filtered out all users with less than 20 measurements and 10 trainings annotated during the selected time period, to make sure I had enough data to make meaningful considerations. This procedure left me with data from 375 users, summing up to 19249 HRV measurements. As we can see from the plots below, the great majority of users measured using the camera, 1 minute recordings and 8 breaths per minute (this is the same setup I also use):
Here are some histograms on the main characteristics of the included users:
From the plots above we can see how HRV4Training users tend to be people that work out a lot (3-4 trainings per week or daily), are mainly male with relatively low BMI, and cover a very broad age range.
Acute HRV changes
Acute HRV changes were computed as follows; I first determined day to day differences in HRV, and then contextualized the HRV change with respect to training. Then, for each user I averaged HRV changes on days following training, and on days not following training, to determine if there was any difference.
In other terms, the questions we try to answer are the following; can we spot reductions in HRV on days following training? If so, can we spot greater reductions in HRV on days following more intense trainings?
Here is an example of the data used as input for this analysis. I randomly selected 20 users and plotted their rMSSD values and manually annotated trainings and training intensities:
This plot was just to give an idea of the data at hand. Now let's finally look at the results.
Similarly to what I was showing in the previous post on acute HRV changes, I computed HRV changes for different conditions:
We can see how rest days typically trigger a positive change in HRV, i.e. HRV is higher on the day following a day of rest. We can also see how easy trainings (plot on the right, second bar) are also resulting in an increase in HRV the following day. Easy trainings (e.g. a recovery run) can have a stimulatory effect on parasympathetic activity, therefore explaining the higher rMSSD value. However, for both average and intense trainings we have clear reductions, i.e. negative HRV changes. The reduction is HRV is clearly more marked for intense trainings, highlighting how we can use reductions in HRV as a marker of training load.
In this post I analyzed the relation between acute changes in HRV (rMSSD) and training intensity, on almost 400 HRV4Training users and 20 000 measurements. By looking at acute changes longitudinally over 2 months, we can get some better understanding of how we cope with training.
The data clearly shows that trainings of higher intensity result in a reduction in HRV the following day, highlighting once again how HRV can be used as a valuable tool to measure training load and recovery. The analysis shown above is also available inside the HRV4Training app, under Menu - Insights - Acute HRV Changes.
While this kind of relationship between training load and rMSSD was already shown multiple times in literature, I believe it is very interesting to be able to capture it in totally uncontrolled settings, outside of the laboratory environment. The sample of users used for this analysis almost completely relied on 60 seconds PPG measurements. The fact that we can easily spot the reductions in HRV following trainings of different intensities highlights the validity of 60 seconds PPG measurements and HRV4Training (however, don't forget to follow the best practices, otherwise making sense of the data will be much harder).
Validating known relationships is a first important step in trying to better understand complex relations between not only training, but also lifestyle and other stressors affecting our physiology, recovery and performance. The more data, the more tags/annotations, the more we will be able to build on clinical studies by outsourcing data collection to a broader population, using tools like HRV4Training.
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1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. The big picture
5. HRV and training load
6. HRV, strength & power
7. Overview in HRV4Training Pro
8. HRV in team sports
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
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
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
11. Samsung Galaxy
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
17. Intervals Analysis
18. Training Planning
19. Integration with Oura
20. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV by sport
3. HRV normalization by HR
4. HRV 101