Due to Nike’s generosity, nearly everybody in our class wore a Nike Fuelband between mid-January and the date of this posting (March 3). While Nike offers a public API for viewing the data, it’s not that comprehensive. Using some basic packet sniffing, and borrowing individuals’ fuelbands for < 30 seconds, connecting it to my laptop, and allowing the Fuelband to attempt to connect to api.nike.com website, I managed to collect the each individual’s Access Token, which I then passed to api.nike.com alongside some other parameters to receive a wide array of data, including daily minute by minute fuel data, calories, steps, and distance. 

Here I’ve compared male and female activity for the class on an average Tuesday:

You can find on visual inspection schedule similarities between individuals as shown in the next two examples:

Here you can see activity for both individuals fits between scheduled points in time

Here I’ve compared my data to the average male in the class on Fridays.