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I do something similar, using a text file instead of a spreadsheet or app: https://www.gibney.org/a_syntax_for_self-tracking https://www.gibney.org/a_syntax_fo

by mg 5y ago

I do something similar, using a text file instead of a spreadsheet or app: https://www.gibney.org/a_syntax_for_self-tracking 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.

yardstick 5y ago
What conclusions were you able to come to from this data? What was the cause of your headaches and sore eyes etc?
- mg 5y ago
  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.
deleted 5y ago
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medstrom 5y ago
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 https://github.com/open-nomie/nomie5

by mg 5y ago

yardstick 5y ago

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

mg 5y ago
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.

deleted 5y ago

[deleted]

medstrom 5y ago

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