| Since I'm chomping at the bit waiting to look at my 23andMe results (available as of today) while listening to folks explaining personal self-tracking preferences at the Quantified Self meetup, I'll try to explain my fascination with the quantification of self (or numerical translation of personal health and life narratives) in genomics terms. Hang with me here - this will be a broad and simplistic metaphorical construct that probably won't withstand rigorous study...
In computational genomics, we have two broad areas of data that we examine for interactions (patterns): 1. expression 2. sequence info and we essentially 'clock' or 'map' this data to 'read' results.
The idea of establishing a 'Me-ome' or a 'self-ome' that we're exploring here is nothing new - since we've been drawing family/hunting groups on cave walls we've been mapping the orbital relationships of 3 essential constructs:
1. Me 2. You 3. Everything else
The challenge with #quantifiedself data collection isn't the hunting, and it isn't the gathering. I've got data about me, and you're more than likely, as social human animal, to share data about you (sometimes with more or less prompting required based on your personality, etc).
It usually isn't even the drawing (or visualization) of that data collection - it's the translation. How do we code data so others can participate in the conversation of our lives? In our personal narratives? (My short answer, in less than 140 characters = Twitter).
Superficially, methodologies and metrics are what we're all exploring here, but it's also an area of study I'll be pursuing, n=1 style, as a Fellow at the Health Srategy Innovation Cell at Massey College (University of Toronto).
If you're digging into the role (theoretical and applied) of self-expression, microchoice and microrelevance in healthcare, get in touch.
Building tools that allow self-expression to go from additive to exponential requires a sample size greater than one. Drop your digits or coding in the comment box.
It's transcription time... | 
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