by Stuart Lynne
This is a quick overview or HRV Readiness with some pointers to how it is computed by various applications and commentary on various approaches. I put these summaries together so that I can add appropriate functionality to my Fitness applications.
HRV or Heart Rate Variability is used by various applications as a way to use heart rate measurements to determine health, fitness and ability to perform exercise.
One of the Best of Class applications is HRV4Training by Marco Altini, especially given the extensive documentation and blogging done by the author.
An excellent starting point is his four part Ultimate Guide to Heart Rate Variability. This covers:
Generally speaking HRV refers to measuring the change in time between heart beats. The time between each heart beat is referred to as the RR time. When looking at a set period of time it is possible to look at the variance between each. Even though on average the time is the same, there is a variance between each, and HRV looks at the size and frequency of the the variance.
The first statistic that is considered is the RMSSD, or the root mean square of the successive differences.
A second statistic is the SDNN, or the standard deviation of the of the normal-to-normal R-R intervals.
Once the RR data has been collected calculating eithr RMSSD or SDNN for a period is a simple task.
Various apps manipulate these to provide a user friendly HRV score, typically between 1 and 10 or 1 and 100.
HRV4Trainings HRV score transforms the RMSSD statistic to get Recovery Points:
This provides a score that is usually between 1 and 10.
The age adjustment is done to allow users to compare their numbers to larger populations ignoring age. The actual adjustment for age is not currently documented.
The Elite HRV 1-10 Relative Balance Score transforms the RMSSD with a natural log.
From the above citation:
A natural log (ln) is applied to RMSSD. RMSSD does not chart in a linear fashion, so it can be difficult to conceptualize the magnitude of changes as it rises and falls. Therefore, it is common practice in the application of RMSSD to apply a natural log to produce a number that behaves in a more linearly distributed fashion.
The ln(RMSSD) is expanded to generate a useful 0 to 100 score. The ln(RMSSD) value typically ranges from 0 to 6.5. Using over 6,000,000 readings from our database, we have been able to sift out anomalous readings and create a much more accurate scale where everyone fits in a 0 to 100 range – even Olympians and elite endurance athletes.
HRVTraining HRV from ithlete and BioForce
Comments from Andrew Flat
… ithlete, decided to modify the RMSSD value collected by ithlete to make for a more intuitive and easily interpretable figure for non-expert users. The value you see from the app is the natural log transformed RMSSD multiplied by 20 (lnRMSSDx20). This modification essentially provides a figure on a 100 point scale (though ithlete scores above 100 are possible in highly fit individuals, though not common).
Note: lnRMSSDx20 is a patented formula and therefore those interested in using this commercially must acquire a licence.
And from ithlete
The following material is based on extracts from US patent 8666482 and links to relevant supporting research, and explains how ithlete measures HRV and creates training indications for the user.
… Although just 30 seconds is sufficient for RMSSD from a signal processing perspective, the 1 minute measure of LnRMSSD has now been validated in a peer reviewed journal paper, which reports an intraclass correlation of 0.98 (0.93, 0.99) and 0.0 bias (LoA 0.22) for the 1 minute measure compared to the criterion measure of 5 minutes.
Since the first prototypes of ithlete, a 7 day weekly moving HRV average has been used as an individual baseline from which to identify significant changes. This has also been validated as best practice in this review by Plews (2013).
Apple supports some heart rate analysis with recent Apple Watches. Specifically they provide a Heart Rate Variability chart which shows a (typically) daily score. This is based on SDNN.
Commentary by Marco Altini: HRV4Training Blog
The quick summary is that SDNN and RMSSD are similar and respond to the same stressors so are somewhat but not exactly equivalent. Possibly the bigger issue with the Apple Watch is the randomness of the data collection overnight.
This is an excellent commentary on when (not) to compare HRV Readiness scores from different applications.