Data interpretation issues in wearables
More and more wearables have started capturing Heart Rate Variability (HRV) data overnight and combining it with other parameters (e.g. heart rate, sleep data, physical activity) to provide readiness or recovery advice to the user.
Despite some inconsistencies over the past years, as of the end of 2022, Oura, Whoop, and Garmin all work in a very similar way when it comes to HRV measurement. However, the way the data is used in all of these tools when building readiness or recovery scores or when providing advice to the user is often problematic and inconsistent.
Naive interpretations (higher is better), lack of a normal range (what’s a meaningful change?), and confounding your physiological response with your behavior, are common issues that limit the utility of data collected with wearables.
In my latest blog, I cover data analysis and interpretation to provide you with some useful tips and tools that should allow you to make the most of the collected data and ignore inaccurate interpretations provided by most tools out there.
Thank you for reading.
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Founder of HRV4Training, Advisor @Oura , Guest Lecturer @VUamsterdam , Editor @ieeepervasive. PhD Data Science, 2x MSc: Sport Science, Computer Science Engineering. Runner