Tag Archives: data analysis

Eric Jain on Sleep and Moon Phases

Eric Jain stumbled upon a study published in 2013 that found the a full moon was associated with less sleep. Being an avid self-tracker and a toolmaker he decided to find out if that was true for him as well. Eric used his tool, Zenobase, to import, aggregate, filter, and then analyze his sleep data in a few unique ways. While he found some evidence that a full moon was associated with less total sleep he wasn’t able to make any statistically significant results. Watch his short video below, filmed at the Seattle QS meetup group, then take a look at his great screencast where he walks through all his steps to complete this analysis.

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Fuelband vs Fitbit: Do They Agree on Steps?

We are not the only ones curious about whether our activity level looks different when seen with different trackers. Bastian Greshake, co-founder of OpenSNP.org, has been comparing his FuelBand and his Fitbit for months. Here’s what he found.

bastian greshakeInspired by Ernesto’s post I wanted to take a look at how my data for the Fitbit and the FuelBand compare to each other. I started wearing the FuelBand in October of last year. Since then it has only left my wrist to recharge the battery. I was already carrying a Fitbit Ultra, which I’ve had since May 2012. I wear the FuelBand on my dominant arm. The Fitbit is usually clipped to the pocket of my jeans and I have it on my non-dominant arm while sleeping. From my day-to-day experience I have a sense that FuelBand steps are  usually a good way below the Fitbit steps. But I also thought that the difference was getting smaller, probably due to firmware updates on the FuelBand.

Using the Fitbit-API (and it’s integration into openSNP) it’s quite easy to get a file that contains all step counts measured with the Ultra. If you have an openSNP account you can download the complete file, also including sleep data and body measurements here. Unfortunately the Nike+ API  isn’t ready yet, so one needs to manually scrape the data. As this is boring work that can’t easily be automated I only got FuelBand step data back to 2013/11/16. Still, that should be enough to get a first insight on how both devices compare.

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Ian Clements on Analyzing his Cancer Data

Ian Clements has been self-tracking since 1974 – mostly exercise, weight, and general health indicators. But in 2007 he was diagnosed with terminal cancer. This set off a more comprehensive mission of self-tracking to figure out which lifestyle changes and supplements were helping him to live longer. In the video below, Ian walks through his fascinating and detailed journey in data analysis land and shares the lessons he has learned. (Filmed by the London QS Show&Tell meetup group.)

Data Analysis from Ken Snyder on Vimeo.

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Numbers From Around the Web: Round 9

Some people may be wondering how I find all the amazing people conducting neat self-tracking experiments and creating jaw-dropping personal data visualizations. Well, for the most part I just listen. I’m constantly paying attention to what’s being said on twitter about #QuantifiedSelf. When that doesn’t work I just use the power of Google to find people who are blogging about self-tracking, self-experimentation, or personal data. It’s great to look through the search results and see how many people are sharing their personal stories and insights. While doing some searching this morning I stumbled across a project that immediately brought a smile to my face. Hopefully you’re excited by this as much as I am.

Chris Volinsky is a statistician at AT&T Research and he’s no stranger to handling large data problems. Back in 2008 he was part of the team that won the $1 Million Netflix prize. He also has quite the impressive list of research papers that illustrate the many different uses of cellphone location data. But what is really interesting about Chris is his newest project: My Year of Data

Back in November of 2011 Chris started off a blog entry that with this:

My name is Chris. I am 40 years old. I am 5’9 1/2″ and weigh 174 pounds. I walked 9,048 steps and have consumed 1,406 calories today (so far).

Realizing that he’ld been gaining weight and wasn’t at his optimal health he decided to take a data-centric approach to improving his health. He is a statistician after all. So far, he’s found some interesting things. Take for instance his weight and dietary tracking.

As he explains in this post, Chris typically has a hard time tracking his diet consistently. This can be pretty frustrating when you hear about how important it is to eat this or not eat that to help with weight reduction. Rather than get frustrated Chris turned to the data to see what he could learn. When he stopped looking at the data he was entering and started looking at the missing data an interesting trend lept out. He found that fluctuations in his weight appeared to be correlated with whether or not he was logging food. Take for instance the plot below. It appears that there is a pretty clear association with periods of weight loss and periods of actively logging his food (pink zones). The opposite also appears to be true – no food logging = weight gain.

Weight chart with food tracking highlighted in pink

So this is where a typical NFATW post would stop. We have an interesting finding and a neat data visualization. But, Chris is doing something much more interesting than just talking about his weight data. He is on a long-term self-tracking and self-discovery journey and he is trying to enlist other interested parties to help him. Chris is going the extra step and posting all of his self-tracking data online for anyone to analyze, visualize, or just get inspired.

You can access all of his amazing data via a public dropbox folder that he’s set up. He even has a nice README file explaining the datasets and formats. So far he’s sharing the following:

  • Fitbit: sleep and activity data
  • FitLinxx: weight training data from gym activities
  • Livestrong: dietary tracking data
  • Runkeeper: running and other exercise activity data
  • RescueTime: productivity tracking (computer/internet use)

All the data is open and available for you to play with. This should be a really interesting project to keep “track” of in the future (pun definitely intended). To help inspire some action on your part I took some time today and looked at Chris’s most recent available data to see what I could find out. I downloaded his Fitbit data and decided to look for any interesting patterns. Turns out that when taking a look at his daily patterns of activity there seems to be something going on on Thursdays that reduces his step count and activity time . Also, Saturday is by far the best day with an average of 9,862.56 steps and a 5.3 hours spent being active (data available here).

Mean steps per day

Mean activity minutes per day

Make sure to reach out to Chris over at his blog and take a took at his data to see what interesting thing you can figure out!

Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

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FitBit + Google Spreadsheets = Awesome

This post and instructions are no longer up to date. For a current how-to please visit the updated post

On February 11th FitBit released their API into the wild and let developers get to work. Since then there have been some very neat integrations. One of the best uses of the API it the open source script that enables users to download their data into google spreadsheets. Developed by John McLaughlin, this script gives everyone the ability to get their historical data from FitBit and play with visualizations and analytics. Even someone without any programming experience can start creating very neat dynamic charts and graphs in under 30 minutes. For example I created the the following charts in just a few minutes (click images for interactive versions):

Daily Step Count:

If you already have a FitBit you might be wondering how to actually implement John’s script to grab your own data and start making fun charts and graphs. It takes about 15 minutes from start to finish to set up your FitBit developer account and then set up the script in Google Docs. The step-by-step process is outlined after the jump.

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