Tag Archives: FitBit

Fitbit vs. Moves: An Exploration of Phone and Wearable Data

Like many people paying attention to the press around Quantified Self, self-tracking, and wearable technology I was intrigued by the many articles that focused on a newly published research letter in the Journal of the American Medical Association. The letter, Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data, authored by Meredith A. Case et al., described a laboratory study that examined a few different smartphone applications and self-tracking devices. Specifically, they tested the accuracy of steps reported by the three different apps: Moves (Galaxy S4 and iPhone 5s),  Withings Health Mate (iPhone 5s), and the Fitbit app (iPhone 5s), three wrist-worn devices: Nike Fuelband, Fitbit Flex, and the Jawbone UP24, and three waist-worn devices: Fitbit One, Fitbit Zip, and the Digi-Walker SW-200. Participants walked on a treadmill at 3.0 MPH for trials of 500 steps and 1500 steps while a research assistant manually counted the actual steps taken. Here’s what they found:

JAMA_PhoneWearables

As the data from this research isn’t available we’re left to rely on the authors description of the data. They state that differences in observed vs device recorded steps counts “ranged from−0.3% to 1.0% for the pedometer and accelerometers [waist], −22.7%to −1.5% for the wearable devices [wrist], and −6.7% to 6.2% for smartphone applications [phone apps].” Overall the authors concluded that devices and smartphone apps were generally accurate for measuring steps. However, much of the press around this study dipped into the realm of sensationalism or attention grabbing headlines, for instance: Science Says FitBit Is a Joke.

Part of our work here at Quantified Self Labs is to encourage and help individuals make sense of their own data. After reading this research letter, or one of the many articles which covered it, you might be asking yourself, “I wonder if my device is accurate?” or “Should I be using a step tracking device or just my phone?” In the interest of helping people make sense of their data so that they can come to their own conclusions I decided to do a quick analysis of my own personal data.

For this analysis I examined the step data derived from my Fibit One and the Moves app I have installed on my iPhone 5. (Important note: the iPhone 5 does not have the M7 or M8 chip present on the 5s and 6/6+, respectively, which natively tracks steps.) I had a sneaking suspicion that my data experience differed from the findings of Case and her colleagues. Specifically, I had a hypothesis that the data from every day tracking via the Moves app would be significantly different than data from my Fitbit One.

Methods

First, I downloaded and exported my daily aggregate Fitbit data for 2014 using our Google Spreadsheets Fitbit script. I then exported my complete Moves app data via their online web portal. To create a daily aggregate step value from my Moves data I collapsed all activities in the summary_2014.csv file for each day. (Side note: We’ll be publishing a series of how-to’s for doing simple data transformations like this soon). This allowed me to create a file with daily aggregate step data from both Moves and my Fitbit for each day of 2014. Unfortunately I did not have my Fitbit for the first few weeks of 2014 so the data represents steps counts for 342 days (1/24/14 to 12/31/14).

Results

I found that my Fitbit One consistently reports a higher number of total steps per day than my Moves app. Overall, for the 342 days I had 689,192 more steps reported by Fitbit than by the Moves app. The descriptive information is included in the table below:

FitbitMoves2

Another way to look at this is by visualizing both data sets across the full time-frame:

Click for interactive version in Google docs.

Click for interactive version in Google docs.

There a few interesting things to point out in this dataset. On two days I have 0 steps reported from my Moves app. One day, Moves was unable to connect with their online service due to me being in an area with little to no cell signal. On the other day my phone was off, probably due to an iOS 8 release and having to reboot my phone a few times.

It is also clear to me that differences in data are related to how I wear my Fitbit and use my phone. For my Fitbit, it is basically on my hip from the time I wake up until the time I go to bed each night. However, my phone isn’t always “on my body” throughout the day. I think this is probably the case for more people.

Since I wear my Fitbit at all times some of the data it captures erroneously is included in the total step count. For instance, for the last few months in this data set I was commuting about 10 miles per day during the week by bike. This data is accurately captured as cycling by Moves, but captured as steps by my Fitbit. Therefore some over-reporting by Fitbit is present in the data.

Conclusion

For my own data I found that the Fitbit reports higher steps on most, if not all days, than the Moves app on my iPhone 5. There are a few caveats with this data and analysis that are worth mentioning. First, this exploration was intended to begin a conversation around the real-world use of activity monitoring apps and devices, and the data they collect. It was not intended as a statement on truth or validity (however I would welcome the help of a volunteer to follow me around with a manual clicker counting all my steps). Second, this analysis was undertaken in part to help you understand that scientists of all types, be it citizen or academic, have the ability to work with their own data in order to come to their own conclusions about what works or doesn’t work for them. Lastly, this analysis was completed very quickly and I am sure that other individuals may have different ideas about how to explore and analyze the data. For this reason I’m posting the daily aggregate values in a open Google Spreadsheet here.

If you’re inspired to analyze your own data in this way we’d love to hear from you. Reach out on twitter or send us an email. We’re listening.

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What We Are Reading

Below you’ll find this week’s selection of interesting bits and pieces from around the web. Enjoy!

Articles
Open Books: The E-Reader Reads You by Rob Horning. A fantastic essay about the nature of delight and discovery, and how that may (is) changing due to data collected from e-readers. For those interested in books and data this article By Buzzfeed’s Joseph Bernstein is also an interesting read.

Flashing lights in the quantified self-city-nation by Matthew W. Wilson. Quantified Self, smart cities, and Kanye West quotes – this commentary in the Regional Studies, Regional Science journal has it all. Read closely, especially the final paragraph, which gives space to think about the role the institutions and companies that provide cities with the means to “be smart” have in our in social and urban spaces.

Most Wearable Technology Has Been a Commercial Failure, Says Historian by Madeleine Monson-Rosen. This is a interesting book review for Susan Elizabeth Ryan’s Garments of Paradise which had me thinking about the nature of wearables, customization, and expression.

‘The Cloud’ and Other Dangerous Metaphors by Tim Hwang and Karen Levy. This was mentioned so many times over the last few days by so many smart friends and colleagues that I had to set aside time to read it. It was time well spent. The authors make the case that how we talk about data (personal, public, mechanical, and bioligical) is tied to the metaphors we use, and how those metaphors can either help or hinder the broader ethical and cultural questions we find ourselves grappling with.

Why the Internet Should Be a Public Resource by Philip N. Howard. This isn’t the first, nor will it be the last, argument for changing the way we think about and regulate the Internet. Worth reading the whole things, but in case you don’t consider this point:

And then we might even imagine an internet of things as a public resource that donates data flows, processing time, and bandwidth to non-profits, churches, civic groups, public health experts, academics, and communities in need.

Computers Are Learning How To Treat Illnesses By Playing Poker And Atari by Oliver Roeder. How does research into algorithms and AI intended for winning poker games morph into something that can optimize insulin treatment? An interesting exploration on the background and future implications of computers that can learn how to play games.

Data Stories #45 With Nicholas Felton. by Enrico Bertini and Moritz Stefaner. In this episode of the great Data Stories podcast Nicholas Felton talks about his background, his interest in typography, and what led him to start producing personal annual reports. Super fun to listen to them geek out about the tools Nicholas uses to track himself.

Increasingly, people are tracking their every move by Mark Mann. A great peak into some of our QS Toronto community members and how they use self-tracking.

Quantified Existentialism by Ernesto Ramirez. I’m putting this last here because it feels a bit self-congratulatory. Earlier this week I took some time to examine how common it is for people to express their relationship with what counts when they use self-tracking tools. It was a fun exercise.

Show&Tell
Insights From User Generated Heart Rate Variability Data by Marco Altini. While not a personal show&tell (however, I’m sure his data is in there somewhere), this great post details what Marco was able to learn about HRV based on 230 users and 13,758 recordings of HRV.

Quantify This Thursday: No Coding Required by Kerri MacKay. A bit different post here, more of a how-to, but I found it really compelling the lengths Kerri went to get get her Fitbit data to show up on he Pebble watch. I was especially drawn to her explanation of why this method is important to her:

The reality is, getting nudges every time I look at the clock or dismiss a text notification on my Pebble (via my step count) is yet another way to make the wearing-a-wearable less passive and the data meaningful.

Correlating Weight with Blood Pressure by Sam. A short and simple post detailing how Sam used Zenobase and his iHealth devices to see how weight loss was associated with his blood pressure.

Visualizations
WithingHolidays
The Effect of End of Year Festivities on Health Habits by Withings. The above is just one of four great visualizations from Withings exploring how the holidays affect how users sleep, move, and weight themselves. Unsurprisingly people are less likely to weight themselves on Christmas day (I looked at my data, I am among those non-weighers).

SimonData
Simon Buechi: In Pure Data by Simon Buechi. A simple, elegant dashboard intended to represent himself to the world.

MatYancy_Coding
Grad School Coding Analysis by Matt Yancey. The above is just a preview of two fantastic visualizations that summarize the coding Matt did while enrolled in the Northewestern Masters of Analytics program.

Fitbit_NewYears_Steps
News Year’s Eve Celebration in Steps by Lenna K./Fitbit. A fun visualization describing differences in how people in different age groups moved while celebrating the new year.

From The Forum
How do I visualize information quickly? (mobile app)
Monitoring Daily Emotions
Best Heartrate Monitor that syncs with Withings Ecosystem
Is the BodyMedia Fit still alive?
Capture Online Activities (and More) into Day One Journal Software (Mac/iOS)

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What We Are Reading

Have a great time exploring these links, posts, and visualizations!

Articles
At Quantified Self, I forget I have Parkinson’s by Sara Riggare. Sara is a longtime member of our worldwide QS community and this heartfelt post about her experience at our conferences was wonderful to read. Experience the conference yourself and meet Sara at our QS15 Global Conference and Exposition. Register here 

Standards for Scientific Graphic Presentation by Jure Triglav. Jure is a doctor, developer, and researcher interested in how data is presented in the sciences. In this post he goes back in time to look at previous standards for presenting data that have largely been forgotten.

Painting with Data: A Conversation with Lev Manovich by Randall Packer. In this great interview, researcher, artist, and visualization expert, Lev Manovich, explains his latest work on exposing a window onto the world through photos posted to popular social apps.

Big Data, LIke Soylent Green is Made of People by Karen Gregory. A thoughtful essay here on automation, algorithmic living, and the change in value of human experience.

“In the production of these massive data sets, upon which the promise of “progress” is predicated, we are actually sharing not only our data, but the very rhythms, circulations, palpitations, and mutations of our bodies so that the data sets can be “populated” with the very inhabitants that animate us.”

When Fitbit Is the Expert Witness by Kate Crawford. I almost didn’t include this article in this week’s list. The story has been circulated so many times around the web this week, mostly without any real thought or examination. However, I found that Kate Crawford did a good job putting this news in context without resorting to sensationalism.

How California’s Crappy Vaccination Policy Puts Kids At Risk by Renee DiResta. A bit of a sensational title, but a great post that uses a variety of open data sources to showcase a growing concern about childhood vaccination policies in California.

Show&Tell
How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015 by Jamie Todd Rubin. I’ve been a big fan of Jamie’s writing since I found it earlier this year. He’s voracious self-tracker, mostly related to his tracking and understanding his writing, and this post doesn’t disappoint.

Sleeping My Way to Success with Data by Pamela Pavliscak. A great post by Pamela here about her experience starting tracking her sleep with the Sleep Cycle app. A great combination of actual data experience and higher-level thoughts on what it means to interface with personal data. I especially love this quote referencing her experience interacting with other sleep trackers,

“And they are doing the same thing that I’m doing — creating data about themselves, for themselves.”

Visualizations
IntoTheOkavango
Into the Okavango by The Office for Creative Research. A really neat interactive project by researchers, scientist, and the local community to document an expedition into the Okavango Delta in Botswana.

Strava
A Day in the Bike Commuting Life by Strava. The data science team at Strava put together a neat animation comprised of one-day of cycling commutes in San Francisco. Unsurprisingly, the Golden Gate Bridge is quite popular among cyclists.

From the Forum
Sleep tracking for new parents
Different Approach to ZEO Headband
Hello Everybody!
MyStress
New Self-Quantifier

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What We Are Reading

We had a lot of fun putting together this week’s list. Enjoy!

Articles
A Spreadsheet Way of Knowledge by Steven Levy. A few weeks ago we noted that it was the 35th anniversary of the digital spreadsheet. Steven Levy noticed too and dug up this piece he wrote for Harpers in 1984. If you read nothing else today, read this. First, because we should know where our tools come from, their history and inventors. And second, but not last nor least, because it has wonderful quotes like this:

“The spreadsheet is a tool, and it is also a world view — reality by the numbers.”

The Ethics of Experimenting on Yourself by Amy Dockser Markus. With new companies cropping up to help individuals collect and share their personal data there has been an increased interest in citizen science. A short piece here at the Wall Street Journal lays the groundwork for what may become a contentious debate between the old vanguards of the scientific institution and the companies and citizens pushing the envelope. (The article is behind a paywall, but we’ve archived it here.)

Better All The Time by James Surowiecki. I started reading this thinking it would be another good piece about the digitization of sport performance and training, and it was, but only partly. What begins with sports turns into a fascinating look at how we are succeeding, and in some cases failing, to improve.

Article 29 Data Protection Working Party: Opinion 8/2014 on the Recent Developments on the Internet of Things. Do not let the obscure boring title fool you, this is an important document, especially if you’re interested in personal data, data privacy, and data protection rights. Most interesting to me was the summary of six challenges facing IoT data privacy and protection. I’m also left wondering if other countries may follow the precedents possibly set by this EU Working Party.

30 Little-Known Features of the Health and Fitness Apps You Use Every Day by Ash Read / AddApp. Our friends at AddApp.io put together a great list of neat things you may or may not know you can do with various health and fitness apps.

Man Uses Twitter to Augment his Damaged Memory by John Paul Tiltow. Wonderful piece here about Thomas Dixon, who uses Twitter to help document his life after suffering a traumatic brain injury that severely diminished his episodic memory. What makes it more interesting is that it’s not just a journal, but also a source of inspiration for personal data analysis:

”Sometimes if I have like an hour, I’ll be like ‘How’s the last week been?’“ Dixon says. ”I’ll look at the past week and I’ll go, ‘Oh, okay. I really do want to get a run in.’ So I will use it to influence certain decisions.”

Patients and Data – Changing roles and relationships by David Gilbert and Mark Doughty. Another nice article about the ever-changing landscape that is the patient/provide/insurer ecosystem.

Show&Tell
The Quantified Anatomy of a Paper by Mohammed AlQuaraishi. Mohammed is a Systems Biology Fellow at Harvard Medical School, and he’s an avid self-tracker. In this post he lays out what he’s learned through tracking the life of a successful project, a journal publication (read it here), and how he’s applying what he learned to another project.

Calories In, Calories Out by (author unknown). A fascinating post about modeling weight reduction over time and testing to see if said model actually matches up with recorded weight. Not all math and formulas here though,

“I learned several interesting things from this experiment.  I learned that it is really hard to accurately measure calories consumed, even if you are trying.  (Look at the box and think about this the next time you pour a bowl of cereal, for example.)  I learned that a chicken thigh loses over 40% of its weight from grilling.  And I learned that, somewhat sadly, mathematical curiosity can be an even greater motivation than self-interest in personal health.”

Fitness Tracker on a Cat – Java’s Story by Pearce H. Delphin. A delightful post here about tracking and learning about a cat’s behavior by making it wear at Fitbit. Who said QS has to be serious all the time?!

Visualizations
MattYancey_Fitbit
100 Days of Quantified Self by Matt Yancey. Matt downloaded his Fitbit Flex data using our data export how-to then set out analyzing and visualizing the data. Make sure to click through for the full visualization.

IAMI_jpg
IAMI by Ligoranoreese. If you’re in San Francisco consider stoping by the Catherine Clark Gallery for this interesting exhibit. The duo, Ligoranoreese, created woven fiber optic artwork based on Fitbit data.

List of Physical Visualizations. I can’t say it any better than Mortiz Stefaner: “Remember that epic list of data sculptures and physical data visualizations? Well, it became more epic.

From the Forum
Anyone have a good way to aggregate and visualize data?
Questions about personal health tracking
Hello QS
Call for Papers: special issue of JBHI on Sensor Informatics
Sleep Tracking Device – BodyEcho

 

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Jamie Williams: Exploring my Data

JaimeWilliams_FullFitbitData

Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.

What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.

How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.

What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.

Tools
Fitbit
Withing Blood Pressure Cuff
AskMeEvery.com
Automatic
Mathematica
D3.js
Python

Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!

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How to Download Minute-by-Minute Fitbit Data

IntradayDataChart

Earlier this week we posted an update to our How To instructions for downloading your Fitbit data to Google Spreadsheets. This has been one of our most popular posts over the past few years. One of the most common requests we’ve received is to publish a guide to help people download and store their minute-by-minute level step and activity data. Today we’re happy to finally get that up.

The ability to access and download the minute-by-minute level (what Fitbit calls “intraday”) data requires one more step than what we’ve covered previously for downloading your daily aggregate data. Access to the intraday data is restricted to individuals and developers with access to the “Partner API.” In order to use the Partner API you must email the API team at Fitbit to request access and let them know what you intend to do with that data. Please note that they appear to encourage and welcome these type of requests. From their developer documentation:

Fitbit is very supportive of non-profit research and personal projects. Commercial applications require additional review and are subject to additional requirements. To request access, email api at fitbit.com.

In the video and instructions below I’ll walk you through setting up and using the Intraday Script to access and download your minute-by-minute Fitbit Data.

  1. Set up your FitBit Developer account and register an app.
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on the “Register an App” at the top right corner of the page.
    • Fill in your application information. You can call it whatever you want.
    • Make sure to click “Browser” for the Application Type and “Read Only” for the Default Access type fields.
    • Read the terms of service and if you agree check the box and click “Register.”
  2. Request Access to the Partner API
    • Email the API team at Fitbit
    • They should email you back within a day or two with  response
  3. Copy the API keys for the app you registered in Step 1
    • Go to dev.fitbit.com and sign in using your FitBit credentials.
    • Click on “Manage My Apps” at the top right corner of the page
    • Click on the app you created in Step 1
    • Copy the Consumer Key.
    • Copy the Consumer Secret.
    • You can save these to a text file, but they are also available anytime you return to dev.fitbit.com by clicking on the “Manage my Apps” tab.
  4. Set up your Google spreadsheet and script
    • Open your Google Drive
    • Create a new google spreadsheet.
    • Go to Tools->Script editor
    • Download this script, copy it’s contents, and paste into the script editor window. Make sure to delete all text in the editor before pasting. You can then follow along with the instructions below.
    • Select “renderConfigurationDialog” in the Run drop down menu. Click run (the right facing triangle).
    • Authorize the script to interact with your spreadsheet.
    • Navigate to the spreadsheet. You will see an open a dialog box in your spreadsheet.
    • In that dialog paste the Consumer Key and Consumer Secret that you copied from your application on dev.fitbit.com. Click “Save”
    • Navigate back to the scrip editor window.
    • Select “authorize” in the Run drop down menu. Click run (the right facing triangle).
    • Select “authorize” in the Run drop down menu. This will open a dialog box in your spreadsheet. Click yes.
    • A new browser window will open and ask you to authorize the application to look at your Fitbit data. Click allow to authorize the spreadsheet script.
  5. Download your Fitbit Data
    • Go back to your script editor window.
    • Edit the DateBegin and DateEnd variables with the date period you’d like to download. Remember, this script will only allow 3 to 4 days to be downloaded at a time. 
    • Select “refreshTimeSeries” in the Run drop down menu. Click run (the right facing triangle).
    • Your data should be populating the spreadsheet!

If you’re a developer or have scripting skills we welcome your help improving this intraday data script. Feel free to check out the repo on Github!

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Downloading Fitbit Data: Update

We’re posting a quick note today to let you know that we’ve updated our “How To Download Your Fitbit Data” post. It now included separate instructions for both the old and new versions of Google Spreadsheets. This is just the first in a series of planned updates. We hope to post additional updates to allow you to have deeper access to your Fitbit data including, heart rate, blood pressure, and daily goal data.

If you’re using this how-to we’d love to hear from you! Are you learning something new? Making interesting data visualizations? Discussing the data with your health care team? Let us know. You can email us or post here in the comments.

ERFitbit_092214

Click to view the interactive version.

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Kouris Kalligas: Analyzing My Weight and Sleep

Like anyone who has ever been bombarded with magazine headlines in a grocery store checkout line, Kouris Kalligas had a few assumptions about how to reduce his weight and improve his sleep. Instead of taking someone’s word for it, he looked to his own data to see if these assumptions were true. After building up months of data from his wireless scale, diet tracking application, activity tracking devices, and sleep app he spent time inputing that data into Excel to find out if there were any significant correlations. What he found out was surprising and eye-opening.

This video is a great example of our user-driven program at our Quantified Self Conferences. If you’re interest in tell your own self-tracking story, or want to hear real examples of how people use data in their lives we invite you to register for the QS15 Conference & Exposition.

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What We Are Reading

Enjoy this week’s list!

Articles
Effect of Self-monitoring and Medication Self-titration on Systolic Blood Pressure in Hypertensive Patients at High Risk of Cardiovascular Disease by Richard McManus et al. An interesting research paper here about using self-monitoring to reduce blood pressure. The paper is behind a paywall, but since you’re nice we’ve put a copy here.

Apple Prohibits HealthKit App Developers From Selling Health Data by Mark Sullivan. Some interesting news here from Apple in advance of their new phone and possible device release in a few weeks. I applaud the move, but would like to see more information about data portability in the next release.

Science Advisor, Larry Smarr by 23andMe. Great to hear our friends 23andMe and Larry Smarr are getting together to help work on understanding Inflammatory Bowel Disease. If you’ve been diagnosed with Crohn’s disease or ulcerative colitis consider joining the study.

Personal Health Data: It’s Amazing Potential and Privacy Perils by Beth Kanter. A lot of people have been talking recently about the privacy implications of using different tracking tools and technologies. In this short post Beth opens up some interesting questions about why we might or might not open up our personal data to others. Make sure to read through for some insightful comments as well.

Show&Tell
Let’s Talk About 3 Months of Self-Quantifying by Frank Rousseau. Frank is one of the founders of Cozy Cloud, a personal could service. He’s also designed Kyou a custom tracker system built on top of Cozy. He’s also been using the services to track his life. In this post he explain how tracking his activity, sleep, weight, and other habits led to some interesting insights about his behavior.

The iPhone 5S’ M7 Predictor as a Predictor of Fitbit Steps by Zach Jones. A great post here by Zach as he explores the data taken from his iPhone 5S vs. his Fitbit.

Using Open Data to Predict When You Might Get Your Next Parking Ticket by Ben Wellington. Not strictly a personal data show&tell here, but as someone who suffers from street sweeping parking tickets somewhat frequently I found this post fascinating. Now to see if Los Angeles has open data…

Visualizations
RWTime
What Time of Day Do People Run? by Robert James Reese, Dan Fuehrer, and Christine Fennessay. Runners World and Runkeeper partnered to understand the running habits of runners around the world. Some interesting insights here!

FitbitMin
What Happens When You Graduate and Get a Real Job by Reddit user matei1987. A really neat visualization of min-by-min level Fitbit step data.

DataDesign
Data + Design by Infoactive and the Donald W. Reynolds Institute. A really interesting and unique take on a data visualization book. This CC-licensed, open source, and collaborative project represents the work of many volunteers. I’ve only read through a few chapters, but it seems to be a wonderful resource for anyone working in data visualization.

From the Forum
Good Morning World!
Quantified Chess
New Activity Tracker to Replace BodyMedia?
Indirect Mood Measures
OPI TrueSense for Sleep Tracking

Want to receive the weekly What We Are Reading posts in your inbox? We’ve set up a simple newsletter just for you. Click here to subscribe.  Do you have a self-tracking story, visualization, or interesting link you want to share? Submit it now!

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Jan-Geert Munneke on Tracking Snoring and Sleep

Jan-Geert Munneke has had an issue with snoring for quite a while. He started off his self-tracking journey by tracking his snoring with the Snore Lab app. Having this data led him to think about how he could understand what was going on while he was sleeping. So, he decided to incorporate more sensors to better track his sleep. In this talk, from our 2013 Quantified Self Europe Conference, Jan-Geert describes what he found from combining data from different devices and how it’s inspired him to think about how he could track other aspects of his sleep.

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