Marco Altini
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Insights from user generated heart rate variability data

15/1/2015

 
In this post I'll explore the dataset I acquired over the last year (Jan 2014 to Dec 2014) from HRV4Training's users. HRV4Training is an app I made to quantify physiological recovery using heart rate variability (HRV), providing advice to athletes and sport enthusiasts on their daily trainings (see here for details on how the app works). 

I released the app for iOS around a year and a half ago. The first stable version was released around Jan 2014, and a major update followed last year before summer, which improved UI, reliability of the camera data acquisition and also connected the app to a backend (on Parse). At that point, I had already blogged about insights from my own data (see this post) and I wanted to see if I could find similar effects of training on HRV on a wider user population, therefore further validating the effectiveness of HRV measurements as a marker of physiological recovery. 
Picture
I'll cover three aspects:
  1. dataset: number of users, users data, settings, etc.
  2. HRV recordings: values, differences by age, etc.
  3. HR, HRV and training: effect of training intensity on heart rate (HR) and HRV

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    Marco ALtini

    Founder of HRV4Training, Advisor @Oura , Guest Lecturer @VUamsterdam , Editor @ieeepervasive. PhD Data Science, 2x MSc: Sport Science, Computer Science Engineering. Runner

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