[mg]

I do something similar, using a text file instead of a spreadsheet or app:

https://www.gibney.org/a_syntax_for_self-tracking

To make this fast and easy, I developed the syntax described in this essay. It turned out to work pretty nicely. I have logged over 14,000 entries over the last 2 years.

[medstrom]

This looks similar to how I believe Nomie logs data behind the scenes. But it looks for keywords on the line and transforms them into data e.g. "Today I #walked(4)" gets coded in JSON as { walked: 4}.

https://github.com/open-nomie/nomie5

[yardstick]

What conclusions were you able to come to from this data? What was the cause of your headaches and sore eyes etc?

[mg]

Well, as they say: The issue with statistics is that you can never be sure :)

As I am not aware of any existing software to analyze causalities in such an irregular log, I started writing my own this year.

It is an interesting challenge. The approach I currently take is a visual one: I draw a chart on which every notch on the x-axis is a 24-hour interval. And two lines on it. One is the line where I did not do something (say take Vitamin D3) and the other line is where I did do it.

The y-axis is the probability of an effect (say having a headache) in that time interval.

The center of the graph is the 24-hour interval after the treatment. Then the 24-48 hour interval, then the 48-72 hour interval etc. And left to the center is the 24 hour interval before the treatment. Then the 24-48 hour interval etc.

So it would be a "perfect" graph if the two lines look very much the same before the treatment and very much different after the treatment.

Some treatment/effect pairs look promising, but so far, I don't have a result clear enough that I feel confident enough to publish it.