Quantifying Myself: Self-Tracking Failures
These tools have become easier to use, thanks to extensive databases listing the nutritional content of food, as well as smart-phone apps that can scan a product’s bar code to automatically get that information. But I used this for about three days before giving up; manually calculating the caloric content of everything I ate and then entering it into the site was just too time-consuming. For someone like me, who makes most of what I eat from scratch, bar codes and databases don’t make calorie tracking much easier.
Apps like Mealsnap purport to give a rough estimate of calorie content from a picture of what you’re going to eat, but I highly doubt their accuracy. But I might try taking pictures of what I eat. According to research discussed at the Quantified Self conference in May, this can help you eat less, even if you don’t calculate how much you ate. A biotech incubator in San Diego has promised the holy grail of calorie counting—a device that would automatically track calorie intake, as the Fitbit does for activity—but has yet to explain how it works or how accurate it is.
In addition to new wireless devices for self-tracking, a growing number of smart-phone apps are available to monitor more subjective states, such as mood, migraines, and pain. I tried a popular mood-tracking site called Moodscope, which administers a daily questionnaire to assess your mood. But, just as with calorie counting, I only stuck with it for a few days.
I found that I have a hard time assessing subjective states. (Perhaps I need some mindfulness training, which I could remedy with Equanimity, a meditation tracking app.) But others have found programs like Moodscope very helpful. Alexandra Carmichael, founder of a patient networking site called CureTogether, describes her experience here.
Perhaps the biggest limitation I found for many self-tracking devices is the lack of tools to help make sense of all the new information at my fingertips. While the individual devices incorporate software to analyze the data they collect, it’s difficult to analyze all the data en masse. For example, does the number of steps or level of activity during the day influence stress level in the evening or sleep quality at night? Do the active periods of sleep recorded by the Fitbit coincide with awakenings or transitions between sleep states recorded by the Zeo? (I would also like to attach an accelerometer to my cat and correlate her data with my sleep patterns.)
There are no existing apps for a non-programmer like me to do that kind of analysis, and I’m unlikely to try to do it by hand. And while the Zeo provides an easy way to export your raw data, the Fitbit does not. That looks like it’s beginning to change, however. A number of devices have released APIs so that software developers can create new apps to manage your data. And Runkeeper, a smart-phone app initially developed to track runs via the phone’s GPS, is creating a “powerful correlation engine” called Health Graph that should enable this kind of analysis. (Stay tuned for a guest blog from Jason Jacobs, Runkeeper’s founder.)
For the most part, individual self-tracking is limited to simple experiments that examine the effect of one variable on a single output. But if self-tracking tools are supposed to be able to help us understand and change our behavior in the real world, they need to be more sophisticated.
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