There is an entire movement that’s focused on the quantified self, but I didn’t really become interested in it until I read a blog post by Stephen Wolfram, physicist and creator of Mathematica. His post took a look at his own personal analytics. Upon reading that article, I realized that I also captured (or could capture) much of the same data. What kind of data are we talking about? Strictly speaking, we’re I’m talking about information that you can collect without having to be aware you are collecting it. For instance: I use a FitBit Ultra device1. I clip it to my pants during the day, and wear it around my wrist at night. Otherwise, I forget about it. By doing this, I get a wealth of data: how many steps I’ve taken, how far I’ve walked, how many flights of stairs I’ve climbed. How many calories I’ve burned, how long it took me to fall asleep, how many times I woke up on the middle of the night, etc. I don’t have to do anything. I just wear the device and it collects data.
I have an iHealth wireless blood pressure wrist monitor. I can slip this device onto my wrist anytime, press a button to take my blood pressure (and pulse) and have the information sent automatically into Evernote.
My primary computers are an iMac in my home office, and a Windows laptop at my day job. On both of these computers, I’ve installed key loggers. These key loggers don’t record my keystrokes. Instead, they count them. Without me having to think about it.
Combine the FitBit data, the iHealth data, and the key logger data and you have a pretty good picture of my daily activity, without me having to do anything other than what I normally do. But how do you capture this information in Evernote?
There are two ways you can do it, depending on your level of skill with technology and determination. I’ll break these two methods down into “Simple” and “Advanced” and outline each.
Simple Methods for Capturing the Quantified Self in Evernote
Here is a simple method for capturing your FitBit summary data in Evernote:
- Be sure you are signed up for FitBit’s weekly summary email.
- Create a mail filter2 to identify the weekly summary email. The action you want to perform when this summary arrives in your inbox is to forward the message to your Evernote email account:
and the next step:
The resulting note in Evernote looks like this3:
For the iHealth wristband device, it is even easier. You can configure the device to send the results to Evernote through the website. There are two types of resulting notes. Almost at once you get a note that shows your blood pressure and other measurements (pulse, etc.):
Advanced Methods for Capturing Quantified Self Data in Evernote
The above methods are nice for the basics, but if you want details, it is sometimes better to go off on your own and grab them. Fortunately, many of these services provide APIs (application programming interfaces) for doing just that–if you know how to code; or if you know someone who knows how to code.
For instance, I like getting a little more detail about my FitBit activity than what is provided in the weekly summary. Indeed, in the spirit of “remember everything” I like to know my activity on a day-to-day basis. So, using the FitBit API, I wrote some code that extracts this information and records it in a Google Spreadsheet. It also sends a summary email to me and my Evernote account each evening. That summary email looks like this:
This provides a much more rich set of data. It includes not only my steps, but how many floors I climbed, calories I burned, and my sleep habits. (Of course, I haven’t slept yet today so there are no numbers.)
I’ve also used the iHealth API to capture some custom iHealth notes, to say nothing of some local scripts on my iMac and Windows laptop that capture my keystroke counts for the day.
Why is this information useful? Well, to some extent, it helps me “remember everything,” even those things that I am not entirely conscious of. The data that goes into Evernote is simply a summary of course, a way that helps complete my view of my day4. I also capture this data in ways that can be better analyzed. I can, for instance, look at what I did in a given day and plot it against my blood pressure to see what makes for a stressful day. I can use simple algorithms to parse out my morning workouts so that I can identify the days I worked out and how well I did, without having to explicitly record my workout in Evernote.
But the greatest value is in what I haven’t yet thought of. How will this information be useful in the future? When my doctor asks during a check up if I have been stressed lately, all I have to do is call up the last 30 days of my blood pressure and compute an average to provide an accurate answer. When I want to learn when the most optimal time for me to do my writing is, I can pull data from my keylogger to determine when my optimal writing times seem to be.
Mostly, for me, it is just fascinating to capture this data, along with everything else I capture in Evernote. It helps paint a more complete picture of my life and goes the extra mile in terms of “remembering everything.”
For those who missed it: I was a guest over at Lifehacker yesterday, answering question about paperless living. Head on over to Lifehacker to see the full Q&A. And as always, this post, and all of my Going Paperless posts are also available on Pinterest.
- FitBit no longer makes the Ultra. The new equivalent is their FitBit One device. ↩
- I use Gmail. ↩
- Yes, I picked the week that we took our kids to Disney World as a way of impressing everyone with just how active I can be. ↩
- I have a saved search that shows me all of the notes created today and another one for “yesterday.”) ↩