A heuristic for feeling fine

This is an on-going self-research project about using a number of physiological parameters to create a personal heuristic for whether I'm feeling fine or under the weather. It so far is mainly focused on using data from an Oura Ring and makes use of around 4 years of historic data (from November 2018 to December 2022).

Background

Over the years of using my Oura Ring I have settled into the habit of opening up the app in the morning to check my "readiness" metrics, which are mainly calculated based on the resting heart rate, heart rate variability (HRV), body temperature and respiratory rate. Despite all of these metrics being influenced by how "well" one sleeps, I have found these to more interesting than the predicted sleep stages, sleep timings etc. A big reason for finding those metrics more interesting is that I felt that they can be quite a good predictor of how well or crummy I will feel for the upcoming day or days by looking at whether they deviate from my expected baseline or not, similar to how Quantified Flu allows one to look at outliers.

Generally having my body temperature and resting heart rate going up and my HRV and respiratory rate going down seems to signal some kind of disturbance. Disturbances can be caused by a number of things:

• Coming down with an infection
• Being jet lagged
• Drinking alcohol in the evening
• Acute stress

As I know my own context (i.e. what I did the day before) many times I can rule out many of the factors (e.g. being on vacation, without jet lag and no drinks? might be an infection coming). Over the years I feel like I've created some mental heuristics of what the "normal" range of values is I would expect and at which deviations I should at least think a bit about whether there might be something going on. For example, a rise in body temperature by 0.4 ºC or a nightly resting heart rate of over 60 bpm are borderline and to me feels like something is going on.

Questions & Methods

While those mental heuristics are nice for having an intuition, they are a bit limited as it's hard to update my own expectations of what is normal and it's nearly impossible to make a prediction that's not based on looking at individual metrics. That's why I wondered: Can I come up with a rule of thumb that can be put into code for making a better heuristic?

Given that my own mental approach was just looking at "abnormal deviations from my expectation" I decided to just go with the more statistical concept of looking at standard deviation (σ), which measures the degree to which individuals within the sample differ from the sample mean. By looking at the standard deviation for those values, I can establish what the "normal" range for each of the four metrics of interest (resting heart rate, heart rate variability, body temperature) would be.

Calculated from 1,484 days of data

Metric	Mean	σ	2σ
Resting heart rate	56 bpm	51 – 60 bpm	47 – 64 bpm
Heart rate variability	40 ms	30 – 49 ms	21 – 59 ms
Body temperature deviation	0.0 ºC	-0.22 – 0.22 ºC	-0.44 – 0.44 ºC
Respiratory rate	14.4 /min	13.6 – 15.1 /min	12.7 – 16.1 /min

With this we can now establish