Marco Altini
  • Home
  • Research & Publications
  • Apps & Projects
  • Blog

A look at a few months of HR and HRV measurements

21/5/2014

3 Comments

 
I've got only a few months per year where I'm relatively happy with my trainings. That's more or less between december and april, when temperatures in Holland are low (I suffer the heat too much). This year I measured my HR and HRV every morning while preparing for a half marathon, and I finally collected enough data to explore the two main aspects I'm interested in tracking while training:
  • short term physiological changes -> recovery and training load
  • long term physiological changes -> fitness level (VO2max)

As explained in another post, both changes are somehow related to HR and HRV. So let's have a look at what I've got in about three months of measurements.

Hardware & Software

Hardware:
All measurements were taken using either Under Armour's Armour39 or Polar's H7, given the high reliability. I took the measurements right after waking up, while still in bed.

Software:
I used HRV4Training, with the following settings:
  • 1 minute test duration
  • 8 breaths per minute
  • Single test (lying down)
In my experience paced breathing is very important to ensure reliability and repeatability of measurements. While some researchers agree [1], others claim the opposite [2]. Here you can see differences in RR intervals and rMSSD due to paced breathing. The sequence is about 2 minutes of normal breathing, followed by 4 minutes of deep breathing, and again 2 minutes of normal breathing. There's a 2x difference due to respiratory sinus arrhythmia (i.e. HRV due to respiration, clearly shown by the sinusoidal pattern in the middle of the second plot). That's why I stick to paced breathing, trying to keep this component constant.
Picture
Picture
HR & HRV
The only HRV feature I will consider in this analysis is rMSSD, together with average heart rate. There are a lot of reasons why rMSSD should be used instead of other features. Most importantly, its reliability for short duration measurements and the high correlation with training load shown in past research. For more information have a look at Andrew Flatt's and Simon Wegerif's blogs. They are both amazing resources if you are interested in HRV research with focus on training. 

Getting some perspective on my values

Any physiological parameter is very personal and should always be looked at in relation to our own baseline. While your heart rate can be normalized with respect to your age-predicted maximal, the situation is a bit more complicated for HRV, where there is no predefined range or zones. However I did want to get some perspective on my values, compared to what is out there. 

I plotted simulations from my data together with data simulated according to what was reported in literature about rMSSD  values [3,4] (this is another advantage of using this feature, since frequency domain features are computed differently by everyone, and even if HF power is also considered a good proxy to parasympathetic activity, it's almost impossible to compare results published in literature). 
Picture
The distributions are quite wide (published results come from very few subjects, typically in the order of 10). Hopefully soon enough I'll get enough data from HRV4Training to be able to provide better ranges for different populations (age and gender also play a factor here). Anyway for the moment this still shows some meaningful data, since I overlap with the "trained subjects" population, which falls between sedentary and athletes.

Read More
3 Comments

    Marco ALtini

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

    Archives

    December 2022
    August 2022
    June 2022
    April 2022
    March 2022
    February 2022
    January 2022
    December 2021
    November 2021
    October 2021
    September 2021
    July 2021
    June 2021
    May 2021
    April 2021
    March 2021
    February 2021
    January 2021
    December 2020
    November 2020
    October 2020
    September 2020
    August 2020
    July 2020
    June 2020
    May 2020
    April 2020
    March 2020
    February 2020
    January 2020
    November 2019
    October 2019
    May 2019
    April 2019
    March 2019
    November 2018
    October 2018
    April 2018
    March 2018
    June 2017
    December 2016
    July 2016
    March 2016
    September 2015
    August 2015
    May 2015
    March 2015
    February 2015
    January 2015
    December 2014
    May 2014
    April 2014
    January 2014
    December 2013

    RSS Feed

  • Home
  • Research & Publications
  • Apps & Projects
  • Blog