thanks!
My take is the following: many HRV features are simply statistical ways to look at the same thing (variability between heart beats, or changes in heart rhythm), hence they tend to be highly correlated.
Historically, a few features had been used more than others, for different reasons:
- SDNN: this was used in the context of 24 hours measurements, so that we would get an understanding of cardiac variability changes throughout the day, as a response to circadian rhythm and acute stressors. It was mainly about distinguishing no variability at all (the inability of the system to react to any stressor, as it can happen in case of severe chronic conditions / disease) vs a healthy cardiovascular system — as SDNN mathematically computes the amount of variability in our 24 hours of RR intervals (beat to beat differences). This method allows to quantify macro-differences in physiology between specific medical conditions and healthy controls (between-individual studies). This method is also highly dependent on physical activity and other confounding factors that affect physiology during the day. Personally, I would speculate that most differences between groups detectable by SDNN over 24 hours are also captured by morning or night measurements (well contextualized resting physiology) in terms of clear markers of parasympathetic activity such as rMSSD or HF. When the Apple Watch started reporting SDNN a few years back, I looked at it a bit more in detail, and wrote this: Heart Rate Variability (HRV) features: can we use SDNN instead of rMSSD? A data-driven perspective on short term variability analysis - while again all metrics tend to capture variability, SDNN is not ideal as it captures mathematically deviations from the mean, not high frequency beat to beat changes due to vagal activity (see next points)
- Frequency domain features: probably the most misunderstood features. There is not such a thing as measuring autonomic balance or sympathetic activity via these measurements. In my opinion, the only thing we can do well is to measure parasympathetic activity (or vagal activity), which I would argue is what matters (at rest). While we finally moved away from LF as a marker of sympathetic activity, HF is still considered a good marker of parasympathetic activity. This is correct as vagal activity happens real fast (a matter of milliseconds), and therefore can be captured by high frequency changes in heart rhythm. The main challenge with this feature (and all frequency domain features) is that they highly depend on breathing rate (and are computed differently by everyone as there are various choices to be made from a mathematical point of view on interpolation, windowing, FFT, etc.). Note also that these thresholds (like all thresholds) are just estimates and fail frequently. My favorite example is the following: say you are doing a deep breathing exercise to strengthen the parasympathetic system (biofeedback / meditation, etc.). In this case you are “as parasympathetic as you can be” and yet your deep breathing will move the dominant frequency to the LF frequency band (if you breathe around 6 breaths per minute for example, as recommended for these practices), and therefore your LF will increase, HF decrease, and none of these metrics will make sense. On the contrary, rMSSD will reflect the increase in parasympathetic activity.
- rMSSD: finally, in the last decade the scientific community (across many fields, not only sports science) started using a lot more rMSSD, as mathematically it also captures these beat to beat variations we are interested in (siimlarly to HF), but is not as dependent on breathing frequency, hence we can consider it the best marker we have of parasympathetic activity.
Long story, but in my view, rMSSD (or recovery points) are the only metric we should look at, in terms of HRV features for short morning measurements taken at rest, as our goal is to quantify baseline physiological stress in response to acute and chronic stressors (both training and lifestyle related), which is reflected very well in parasympathetic activity. If our goal is to look at things like deep breathing, then by all means LF is the best candidate, but this is a different application (make sure in this case to use at least 2 minutes of data, as otherwise LF cannot be computed correctly).
The way I use the data is normally the following:
- rMSSD: more subtle changes in “stress”, in my case as I am not an athlete, these are often tightly coupled to work stress (example here: There's more in life than training: non-training related stressors and recovery). I consider HRV useful for day to day adjustments and continuous feedback, not much as “fitness marker” or something to optimize in the long term (if you are healthy and have a healthy lifestyle, your baseline is unlikely to change)
- resting heart rate: informative for huge stressors (e.g. getting sick), as well as seasonal changes and changes in fitness over periods of many weeks / months
- add training load and subjective feeling for context, and you tend to have a decent overview of what is going on and when it is a good idea to slow down a little to prevent larger setbacks
Hope this helps a little!