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:

1. Select Health as data source under Menu / Settings in the HRV4Training app, then authorize HRV4Training to read HRV data and RR intervals from Health, when automatically prompted
2. When you wake up, take a measurement using the Breathe app on your Apple Watch
3. Right after, open the HRV4Training app on your phone, tap 'Read from Health' from the main screen, and that's it. We'll be doing the math and prompting you with the usual Tags to fill in, so that you can add context around your measurements.

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.
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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.

FAQ

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.

Useful resources

Collecting high quality data is always the first step, but of course what we want to do is to make meaningful adjustments using that data. How do you do that? Here are some links that can help understanding the relation between physiology, training and lifestyle stressors:

• Case study
• The big picture