Patients self-tracking symptoms, environmental stimuli and behaviors, to get information that allows them to take control of their own health. It seems like a no-brainer. Even more so if this is done in the context of a well-designed and monitored personal experiment with or without the support of a Quant Coach or Quant-Friendly Doctor.
Great. Now how to get people to actually do the tracking?
On a technical level, the process of self-tracking is currently still quite cumbersome. Just as PCs were clunky and mostly used only by highly-motivated geeks in the mid-to-late 1970s, self-tracking technologies are in their infancy and currently require a lot of time, effort and expertise to implement in an effective way.
For example, consider the a modestly-complex project of monitoring physical activity, sleep, weight, mood, and pain level, and analyzing this data to discover the relationship between these variables. Perhaps to discover how physical activity and body composition affect sleep or pain. One could acquire a BodyMedia armband activity monitor, a Zeo sleep monitor, a Withings wireless weight scale, and perhaps use the Mymee app to actively track mood and pain. Each of these devices requires its own setup process, and configuration of a separate account on each company’s central server. Accessing the data requires separately downloading or extracting data from four separate technology platforms, and manually combining it in Excel or another data manipulation program.
One could try to use a data aggregation platform to facilitate the data visualization. RunKeeper can pull data from Zeo and Withings. Fluxtream can pull data from Withings and Zeo as well as Google Latitude, Twitter and other sources. Bodytrack can pull data from BodyMedia, Zeo, Mymee, and soon all the devices supported by Fluxtream. But setting up these platforms and their various connectors takes some tech savvy, and tools to automatically analyze the data are still very limited.
So what’s next?
The short-term plan is to develop ways to support people/patients to implement and analyze self-tracking experiments seamlessly. This may initially involve “mechanical turk” solutions using people to connect the current gaps in technology. But ultimately improved software will allow even non-geeks to get prompt and beautifully-presented self-tracking feedback, to make “N of 1″ personal experiments accessible to all who are interested.
Importantly, the unifying software tools need to be open source and not controlled by a device manufacturer or other entity that might restrict a user’s ability to control their own data, or aim to profit by secretly mining the data on the back end.
Beta testing is in process. Stay tuned!
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