Apple Watch update: improved HRV analysis using iOS13, Watch OS6 and RR intervals available in Health
Blog post by Marco Altini
In previous posts we have shown how you can use HRV4Training to read HRV data from the Health app, convert that data (SDNN) to Recovery Points (a more readable metric), and analyze your physiology similarly to what we normally do when you measure using the phone camera or an external Bluetooth sensor.
With the release of iOS13 and Watch OS6, Apple provides RR intervals directly in the Health app, which we can use to compute rMSSD, Recovery Points and signal quality, just like we do with the validated camera based measurement or using external sensors. In this post, we'll look at the quality of the data as well as provide instructions for you to use the Apple Watch with our app.
Let's start with the practical aspects and then move to data quality.
How to use the Apple Watch with HRV4Training
Due to the fact that RR intervals can only be accessed by apps via the Health app, you need to follow these steps in order to gather meaningful data:
If you do not get your data in Health right after using the Breathe app, try to synch your Apple Watch and it will show up a few seconds afterwards.
Always remember that context is key, so while the Apple Watch writes somewhat random HRV numbers also during the day or night, that data could be affected by artifacts, and it is always decontextualized.
To properly interpret physiology, data must be acquired under standard, reproducible conditions, and the best way to do so is with a measurement as soon as you wake up, or with a night long measurement (not just a minute or two over a night). Only in this way, you'll be able to determine how you are responding and adapting to training and lifestyle stressors, as shown in this post and in this case study.
If you have used already HRV4Training with your Apple Watch, then you do not have to do anything different, but we will be able to provide you with a better analysis of your parasympathetic activity, as we can now compute directly the rMSSD feature and Recovery Points, instead of estimating it from SDNN.
Comparison with chest straps
Data was acquired using the Apple Watch and a Polar H7 (previously validated with respect to ECG here) connected to a different device running the HRV Logger app, which is an app that simply records everything coming from the sensor plus additional features.
During data acquisition, we collected data a few minutes while breathing freely, and a few minutes while deep breathing, to elicitate higher HRV due to RSA. You will see in the plots below visually the effect of deep breathing as we get greater swings in RR intervals. Then, we built a simple app to read the Apple Watch RR intervals from Health, so that we could compare them to what we collected with the Polar chest strap.
A final note on data synchronization: data cannot be perfectly synchronized because it is not timestamped by the sensors. What we can do is either to log real time and then to split data in windows based on when data was collected, then compute HRV features on these windows or to sum up RR intervals over time. For this analysis we went with the second option and also tried to visually align the data streams.
What can we derive from these data? You can see clearly almost perfect correlation between Polar H7 and Apple Watch for all conditions (relaxed vs paced breathing as highlighted by bigger oscillations in RR intervals or instantaneous heart rate), meaning that the sensor works really well in this modality.
Heart rate variability: rMSSD
As features, we will look only at rMSSD, the only feature we really care about. rMSSD is a clear marker of parasympathetic activity and the main feature we use for our analysis in HRV4Training, similarly to what other apps do as well. Additionally, the sports science community seems to have settled on this feature for several reasons (apart from the clear physiological link, as mathematically it captures fast changes that are due to how the vagus nerve modulates heart rhythm, there are also practical implications, as it is easy to acquire, easy to compute and reliable over short time windows and less controlled conditions), and therefore we'll stick to it.
What we expect given the data above is to see extremely close values between the Polar H7 chest strap and Apple Watch data.
For the plot below, I computed rMSSD for each time window:
Results are very good considering normal variation in physiology and limitations in data synchronization.
What are Recovery Points? A more human friendly HRV score, based on rMSSD. For more information, read this.
How accurate is the Apple Watch in measuring HRV? Very accurate, provided you stay completely still and use the Breathe app to take a measurement.
When should I use the Breathe app to take a measurement? First thing in the morning.
How much time do I have after measuring with the Breathe app, to fill in my tags in HRV4Training? You have three hours. When you tap 'read from Health' we always check only the last three hours, and see if we can find any HRV scores in the Health app, then take the last one. For this reason, we highly recommend reading data right after you have measured.
Should I use the Watch or the camera? Up to you. We consider both methods equivalent, and it is entirely based on your preference that you should make the call. What matters the most is that you are consistent over time, hence simply use what you consider the easiest and most practical method for you.
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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
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