Insights from user generated heart rate variability data

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. 

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

Read More