Blog post by Marco Altini.
This post is the first of a series on best practices for short heart rate variability (HRV) measurements typically used to monitor physiological stress by apps like HRV4Training. In future posts I will cover a few aspects: context & measurement time of the day, measurement duration, measurement type (lying, standing or orthostatic), paced breathing, and measurement frequency (how often?). Let's start with the measurement's time of the day.
Context & time of the day
HRV is affected by many factors beyond the ones we are trying to measure. HRV4Training users are interested in the relation between training intensity and recovery, which can be captured as a reduction in parasympathetic activity after intense trainings. This relation between training intensity and recovery can therefore be determined by looking at drops in HRV features representative of parasympathetic activity, such as rMSSD or HF.
However, many other factors also impact HRV. For example, physical activity performed before (or during) the measurement tends to increase heart rate (HR), since the heart needs to pump more oxygen to the body's muscles, and together with increased HR, comes reduced HRV. Another example is mental stress, when under stress our body triggers the fight or flight response, which results again in higher HR and reduced HRV (a good discussion on this can be found on the mybasis blog, here).
Let's look at some data. The first obvious situation we can look at, is physical exercise. Here we can see a plot of about 30 minutes including a few minutes before training, a run and a cool down phase. Clearly, while running HRV (regardless of what feature we look at) is reduced, as we can see from the almost constant RR intervals in the central phase of the recording:
However, consistent reductions in HRV can be seen even when physical exercise is almost completely absent. For example in the next figure I plotted data recorded while giving a presentation at a conference (simply standing), showing a similar pattern to the one we had before. Again, we have an initial phase (before going on stage), a central phase with reduced HRV (during the talk) and a "recovery" phase:
And finally another condition in which we have no physical activity, but where we have increased HRV / parasympathetic activity, meditation:
All of these measurements were taken using a Polar H7, the HRV Logger app, and on the same person. In the next figure we can see how the three different conditions compare across all time and frequency domain HRV features:
As expected, rMSSD and HF are very low during training or public speaking. The conditions just analyzed are rather "extreme" and therefore serve the purpose well, when it comes to understanding the importance of context, for both mental and physical stressors and their impact on HRV.
However, it should be noted that even much more common conditions such as eating (and digestion) or drinking, as well as mental stress (even at "lower levels" compared to public speaking) can cause changes in HRV that can make interpretation of our measurements more complex, if we are interested in monitoring recovery.
See for example this simple test I've done using Camera HRV, measuring HR and HRV before and after drinking coffee. As expected, HR increases while HRV (rMSSD in this case) decreases:
Even when we limit external stressors and physical activity, changes in HR and HRV naturally occur during the day due to the circadian rhythm, our "natural clock".
So how do we get to measurements that we can interpret, even though HRV can be influenced by so many different factors?
The morning measurement
We need to isolate context, providing stable and repeatable conditions where the influence of all external factors is kept to the minimum. In the medical world, where many of these measurements originated, the influence of external factors is limited by performing standard protocols, typically in the morning.
This is the case in many situations, for example measuring your weight before breakfast, measuring blood pressure in standard conditions (sitting, arm position, etc), and similarly, measuring HRV to determine the impact of trainings on recovery and physiological stress.
In many studies, the protocol for HRV measurement consisted in morning measurements while lying down, sometimes after an initial "adaptation phase" that is afterwards discarded.
For these reasons the best moment to take your HRV measurement should be right after waking up, possibly while still in bed, and before reading your email or start thinking about work or other aspects of your life that might cause additional stress or anxiety.
Alternatively, a routine that involves a few more steps, e.g. go to the bathroom, empty the bladder, stand or lie down again and take the measurement, also works, as long as it is as consistent as possible during different days. Another option are night long measurements, which are however still rather unpractical, but follow a similar principle, i.e. isolate context and measure under the same, stable, repeatable conditions on a daily basis.
A few more tips on the ideal morning routine and measurement procedure can be found in two podcasts recently published on thequantifiedbody.net by Andrew Flatt.
Register to the mailing list
and try the HRV4Training app!
This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
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
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
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
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
9. 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. 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
1. HRV normal values
2. HRV normalization by HR
3. HRV 101