I’m not sure. I manually input each of rHR, rMSSD and SDNN each day
i dont have to, they are there.
If you are using HRV4Training go to export and export it on dropbox
Then on intervals.icu go to settings and on dropbox settings
In a 3-zone system:
-
LIT would be Z1
-
HIT would be Z3
-
As Normal is as per your plan, meaning you could do LIT, HIT or Rest (whatever is planned).
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Rest would be no workouts, but walking is okay.
Good thing i can walk on those rest days, imagine staying on bed all day…errr i cant go to work today! My app says rest lol
So strength training is a no go as well on Rest days?
PS: actually, both types of rest days:
- the ones HRV Status suggests
- the ones your training plan want’s you to do
It depends on the load created during a strength workout. The body would see it as stress, and can’t differentiate between one type and another.
As a cyclist, doing upper body work adds stress but not fatigue to the legs (as an example).
Hi guys,
I know it’s a little bit off topic but I found it could be a good start point and we can separate it later on.
Does anybody else use the iPhone camera to take HRV morning measures with HRV4Training?
What are your thoughts?
I do have a H10 but the camera is so much more convenient to use…
But I have my concerns with finger positioning and pressure.
I know Marco Altini has a paper showing no difference between the camera and the H10 results but I would like to hear from regular normal people like us on real world and not in a controled environment.
Thank you.
I use an iphone camera with the HRV4Training app.
The values seem to match my physiological feelings and is a good match with the data I was recording using my Garmin FR55 using a similar procedure (first thing in the morning while in the bathroom).
Measuring optically in “HRV4Training”, the index finger of the right hand, I see the rule that this finger must touch the camera more vertically, with the fingertip, rather than flat (with a pillow).
When I touch the pillow (flat) then the data quality is described as “GOOD”. In contrast, the vertical (tip) method is always “OPTIMAL”.
Perhaps it depends on the skin and its thickness?
I do not know, but this is my experience with the camera measurement in “OnePlus 8Pro” and “HTC U12 +” (for comparison).
However, when actually measuring with a camera, the results are “repeatable” (for the test, measurements are made 0.5-2 minutes apart). With the sensor, the results could be less “run over”.\
regards
Artur
Is SDNN a calculated value? I’ve connected intervals.icu with my Oura, but this value is not being pulled from it. I have HRV4Training app installed as well but it has happened many times that when I tried to sync it in the morning, it couldn’t pull Oura’s data (even though the data was uploaded to server) and the whole necessity of manually syncing the app with another app that it’s connected to kinda makes me not want to use it 
Also one question - if I don’t have SDNN, does it make the recommendation less precise?
Good Question. I was also not sure. this is what a Github project had written down. I’m currently looking at HRV w/ interest as well.
SDNN absolutely has value as an extra parameter for HRV. But there is less information available. Where rMSSD is the root mean square of the successive differences, SDNN is the standard deviation of the successive Normal to normal beats.
One situation is when rMSSD is slightly lower then normal combined with a low SDNN. Based on rMSSD alone, advice would be to train as planned. But with the low SDNN, training should be limited to low intensity. Most of the time rMSSD and SDNN ‘agree’.
As research moves on, I will at some point add SDNN to the Training Advice. But it won’t be before things are clear.
Here’s a comparison:
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I also use Kubios. I’ve written to their support asking for a way to export the data. All they could tell me is that at some point, they will provide a way to export the data. How? When?
It’s important to always use the morning values for HRV.
It makes no sense to use a daily avg because on rest days that will be much lower then on a a day you’re out for a long event.
The Oura API provides raw R-R data for sleep periods (at least it looks like it in the documentation. I’m still wearing the ring sizer 
so it’ll be a week or two until I get real data)
I plan on doing a manual morning reading (3min session - get rMSSD from API for that session - no R-R data though). If there is any way to calculate the SDDN from those R-R from sleep that would be easy enough to program in python/rust (I’m running custom scripts that feed into the intervals.icu api anyways, so one function more would be okay)
The question would be: how to calculate SDDN from R-R (and if that data is actually what I think it is)
var sdnn: Double {
get {
let average = avnn // ave of RR intervals
var d = 0.0
for beat in beats {
let v = beat.interval - average
d += (v * v)
}
let size = beats.count
return sqrt(d / Double(size - 1))
}
}
Does Kubios / HRV4Training etc all just uses these same calculations from the RR intervals (and filtering for either ectopic beats / outliers) and then that’s where the SDNN/rMSSSd is obtained?
I’m getting interested to see if I can implement this and then have the BreakAway app send the data direct to intervals.icu
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Also when rMSSD is normal and rHR normal, a low SDNN can mean the body will respond very well to an aerobic block
Yes, Oura uses the night time HRV measurement. This is what the HRV4Training help states about the difference between morning and night HRV.
In our opinion, there is little doubt that night HRV is reflective of physiological stress, similarly to morning measurements, and therefore we believe both approaches are valid in terms of acquiring data representative of chronic stress and helping you making sense of the data over time . It is of course key that the sensor used to measure night data is reliable, and this is the case for Oura, which shows extremely good agreement with ECG in this validation where rMSSD was computed from night recordings.
However, while both methods are able to capture changes in physiology relative to your baseline and normal values over time, the absolute values will most likely differ. What does this mean? Simply put, that you cannot interchangebly use one method or the other, but you have to stick to one, either morning measurements or night measurements, and then use always the same method so that data can be analyzed meaningfully over time.
Full article: Oura ring integration: read sleep data, whole night heart rate and HRV in HRV4Training
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No. Kubios calculates SDNN differently from EliteHRV and HRV4T. I have an email from Kubios somewhere explaining the difference… I’ll dig it out and post it here.
Edit
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As fa as I can tell the Oura API (v1 and v2) do not return SDNN data only rMSSD.
Calculating SDNN and/or rMSSD is not difficult. It’s simple math.
But the difficulty lies in filtering out the ‘abnormal’ RR data. There can be differences in the way the artifacts are filtered out but the Kubios way is considered the Gold Standard.
This is extremely important because one ‘wrong’ beat interval can screw up the entire result.
If you want to experiment with a good filter/calculation for rMSSD, check out FatMaxxer on Github. It is a Javascript considered to be 99.9% equal to the Kubios method. Once you got the filtering correct, SDNN is a fairly simple math function.