Over at Kevin Kelly and Gary Wolf's Quantified Self blog ("Tools for knowing your own mind and body") guest blogger Alexandra Carmichael explains how she keeps a record of 40 different things in her life every day, and what she's learned about herself from studying the data.
I track these things about my health and personal patterns every day:
- sleep (bed time, wake time, sleep quality, naps)
- morning weight
- daily caloric intake (each meal, total calculated at end of day)
- mood (average of 3 positive and 3 negative factors on 0-5 scale)
- day of menstrual cycle
- sex (quantity, quality)
- exercise (duration, type)
- supplements I take (time, dosage)
- treatments for vulvodynia (a chronic pain condition)
- pain of administering the vulvodynia treatment I take (0-5)
- vulvodynia-related pain (0-5)
- headache,nausea (0-5)
- time spent working, time with kids
- number of nursings and night wakings (I'm a mom)
- unusual events (text)
The mood factors I measure every day are:
5. Feeling beautiful / self-love
6. Feeling fat / ate too much
She's come to the realization that her mood is much better on days she exercises, and on days when her mood rating is low, she overeats.
I hadn't expected my tracking to unearth such deep, emotionally charged issues. I did expect the optimization which often accompanies tracking, but when striving for an optimized ideal, the question becomes how to decide what "ideal" means. I just don't have an intuitive sense of what the data "should" look like. Are such wild swings in caloric intake normal? What do other people's patterns of mood, sleep, and exercise look like? I'd love to see some kind of comparable, to get some sense of where my patterns fit on the distribution curve. Part of my motivation in sharing my data is to encourage others to do the same. Let's learn from each other!
It's fascinating stuff, and it will be even more fascinating when people start sharing this data and analyzing it in various ways.