Quantified Self

Qualification before quantification

I have used Textmind for quantitative self-tracking in the past, but don’t currently. It generally isn’t necessary. If I need to answer a specific question, I can simply review the relevant time period, note the relevant information, view it in its condensed form, make a hypothesis, design the experiment, then execute. It’s easier to go back once one knows what one is looking for, than to try to predict what one will someday need.

Qualitative information includes a lot of rich context that is stripped from quantitative data, which can be very helpful in hinting at answers.

Before I’d start stripping out quantitative data from my logs, I’d want to do pairwise nested qualitative reviews. E.g. compare Monday and Tuesday. Then compare Wednesday and Thursday. Then compare Monday-Tuesday to Wednesday-Thursday. Etc.

I’ve done that before, and it gives one a pretty good idea of what’s going on. At that point quantitative info becomes valuable to insert some objectivity, to ensure one isn’t fooling oneself or missing subtle trends.

One good way to collect quantitative data is to treat the qualitative review as a data-cleansing opportunity, to extract some reliable numbers from the day’s record, for whatever you currently care about.

Another way is to search through the raw log text for standard keywords, if one is consistent.

Metadata such as keystrokes etc recorded by selfspy can be relevant, although fluctuations will occur due to e.g. travel.

It’s possible to construct metrics on time usage from the timestamped logs, although cleansing that data is best done during a qualitative review.

It’s helpful to have e.g. a consistent pain scale one uses to rate experiences, with a searchable keyword.

Some quantitative data is naturally isolated during my “proc sprinted” processing cycle. All financial transactions are packaged into headings and filed under “by-Time”, since time is money.

If one really wanted to capture streaming quantitative data, I’d recommend the following: Rate every time block on the desired metrics. Ratings should have defined meanings to reduce subjective drift. Put them in a special format after the timestamp, like so: [2019-12-06 Fri 15:56]{(energy, 3)(mood, 3)}

I’m not sure whether doing this is worthwhile or too distracting. My focus is entirely on releasing Textmind so that others can enjoy the cognitive benefits. After that’s done, I plan to setup Dbmind, which will be quantified self for the purpose of further improving my health. Db stands for “database”. I feel that’s the right medium for storing and manipulating quantitative data.

Because Dbmind is backed by Textmind, Dbmind can specialize on a supplementary role without worrying overmuch about intractable issues such as data gaps and hygiene.