A month ago I got a Fitbit Ultra. It’s a small gadget that you carry around with you all the time to monitor your activity. How many steps you take, how many flights of stairs you go up, how far you walk, how much sleep you get and how restful it is, and much more. It also comes with apps and tools for monitoring stuff like diet and weight. And it makes all of this information available to you, both through a website and through an API.
This isn’t really a review – there are plenty of those about already if you’re interested (Of all the reviews I’ve read, The Verge’s review is the closest to what I would write if I was gonna write one). Instead, I want to talk about the fitbit from the perspective of a data-geek.
Before I start, it’s worth putting this in context. I am loving the fitbit, but I don’t pretend that it’s necessarily something you have to get. Put it this way – I used CurrentCosts to monitor my home energy usage on the web and on my mobile, I wrote code to find out which keyboard keys I press most often, I made a whole website to visualise patterns in what I watch on TV, I wrote code to make map visualisations of where I go with my mobile, I wrote code to use a webcam and face recognition software to measure how my mood changes as I watch different TV programmes or play different console games… I could go on (no, really), but you probably get the point.
I find this sort of personal data stuff fascinating. I’m not the only geek in the world like this – Stephen Wolfram wrote a great blog post last month about some of the stuff that he collects that really puts me to shame.
But when I say that I think the fitbit is awesome, just bear in mind where I’m coming from, okay?
In this post, I want to give examples of the data that it makes available, and what sort of things you can do with it.