Tag Archives: qstop

Meetups This Week

This week, there will be three wonderful Quantified Self meetups in three countries on two continents. A couple of the groups will try a different format from the classic Show&Tell program. The Indianapolis group will get lunch and watch Show&Tell videos. Meanwhile, the Oxford group will have a working session where the attendees will explore a person’s data set. I love to see this experimentation, as organizers figure out what kind of format works for their group.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Monday (October 6)

Oxford, England

Thursday (October 9)

Oslo, Norway

Saturday (October 11)

Indianapolis, Indiana

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What We Are Reading

We’ve put together an nice list of articles for you to enjoy this weekend. As always, please get in touch if you have something you’d like us to share!

Articles
Finding Patterns in Personal Data by Kitty Ireland. Another great post from Kitty about using personal data to uncover interesting, and sometimes surprising, patterns. Some great examples in this post!

The Tale of a Fitness-Tracking Addict’s Struggles With Strava by Jeff Foss. Just because you can track, and you can get something out of it, might not mean you should. (I had a similar experience on a recent trip to Yosemite so this article was quite timely.)

Algorithmic skin: health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education by Ben Williamson. Quantified Self and self-tracking tools are not limited to only being used by conscious and willing adults. They’re also being developed for and used by a growing number of children and adolescents. What does this mean of health and fitness education, and how should we think about algorithms in the classroom and gym?

Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves by Jill Walker Rettberg. I just started this book and it appears offer some interesting perspectives on the current cultural shift toward technically mediated representation. The book includes a chapter on Quantified Self and is available for download in PDF and EPUB under a CC BY license.

Show&Tell
Why Log Your Food by Amit Jakhu. Amit started tracking his food in March (2014) and has since learned a few things about his preconceived notions about his diet, food, and what it takes to keep track of it all.

Even When I’m active, I’m sedentary by Gary Wolf. Gary and I used our recently released QS Access app to download his historical step data. Using some simple charting in Excel we found some interesting patterns related to his daily movement.

Visualizations
SleepJewel
When Do I Sleep Best by Jewel Loree. Jewel presented her sleep tracking project at a recent Seattle QS Meetup. The image above is just a small piece of a great set of visualizations of her data gathered with SleepCycle and Reporter apps.

20min
Lightbeam Visualization by Simone Roth. Interesting tool described here to track how your data and web activity is being tracked. You can check out the Firefox extension here.

Minard It’s About Time by Hunter Whitney. A nice post here about the different methods of visualizing temporal data.

Respiration Machine 0.3 (bellows) by Willem Besselink. A neat physical visualization and art project that represents breath using a Hoberman sphere.

From the Forum

There has been a lot of great discussion on the forum lately. Check out some of the newest and most interesting topics below.

QS Access App
Hypoxic – An App for Breathing Exercises with HRV Tracking
Sleep Tracking & Hacking Google Hangout
Personal Analytics Service for Software Developers
Using Facial Images to Determine BMI
The Right Tool? (tracking and plotting sleep)

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QS Access App: See your HealthKit Data in a Table

On Wednesday this week we learned that the QS Access app we submitted to the Apple store was approved. This means you can download the QS Access app on iTunes. We hope you’ll find it useful. Our app is a very simple tool for accessing HealthKit data in a table so that you can explore it using Numbers, Excel, R, or any other CSV compatible tool.

It is still early days for HealthKit, but my conversations with toolmakers at Quantified Self events convinces me that there will be many device and software makers that integrate with Apple’s platform for collecting and analyzing personal data. I hope this will allow more people to learn from their own data by reflecting on changes over time and by combining multiple data streams – such as activity, sleep, and nutrition – into a single visualization for comparison.

To give you your HealthKit data in tabular format, we’ve had to simplify it. QS Access shows your data in either “hourly” or “daily” chunks.  These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and daily values. This is just a starting point, and we’re looking forward to making it do more based on your feedback.

We very much hope that if you learn something from your data using QS Access, you’ll share your project by participating in a Quantified Self Show&Tell meetup and by joining us at QS15 Conference and Exposition next year in San Francisco. Suggestions about the app itself and interesting examples of usage can be shared with us directly by emailing us: labs@quantifiedself.com,

Read a short example of using QS Access to look at my activity data.
Find Support for QS Access in the QS Forum.

The QS Access App was authored by our long time QS Labs friend and collaborator Robin Barooah.

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Christel De Maeyer: My Journey With Sleep Monitoring

Like many people, Christel de Maeyer felt that her sleep could be better. Presenting at our 2013 conference in Europe, Christel shares what she learned from collecting over three years of sleep data.


What did Christel do?
Christel tracked her sleep for 2 years with various devices. She tested the effects of different variables on her sleep quality, including consumption of alcohol, keeping a consistent wake time and changing her mattress.

How did she do it?
She used the Zeo to track sleep for two years, before switching over to a BodyMedia device. While making changes she monitored how her sleep data changed, as well as how she felt.

What did she learn?
Before self-tracking, Christel felt that she woke up frequently during the night, and the Zeo confirmed this. On average she woke up around 8 to 9 times. She suspected the mattress could be part of the problem. After considerable research, she replaced her mattress (to one that had a foam top), successfully reducing her wake-ups to 4 or 5.

Christel discovered that her sleep patterns looked significantly different after just two glasses of alcohol. Her REM diminishes to nearly 0% (though deep sleep seems unaffected).

Christel also found that total sleep time was less important for how she felt the next day than the combination of REM and deep sleep. Even if she only sleeps for six hours, as long as she gets at least 2 hours of combined REM/deep sleep, she feels good.

In addition to these findings and others she explores in the video above, Christel has taken her lessons and now helps others with sleeping issues. You can find more at her website.

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Meetups This Week

This week there will be three meetups in three countries on three continents. The theme for the Bogotá group will be the quantification of emotions. The Brussels meetup will take a look at AmWell’s tools for monitoring stress, sleep and activity.  And in Pittsburgh, they will feature casual hands-on demos before their talks.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Wednesday (October 1)
Pittsburgh, Pennsylvania
Brussels, Belgium
Bogotá, Colombia

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What We Are Reading

We hope you enjoy this weeks list. Feel free to submit articles, show&tell self-tracking stories, and QS data visualizations. Just email me!

Articles
Why can’t you track periods in Apple’s Health app? by Nat Buckley. With the recent re-release of Apple’s HealthKit enabled self-tracking and personal data system it no wonder that people are taking a long hard look at what data is being excluded. With the popularity of menstruation tracking apps (this app has nearly 30,000 ratings) it’s surprising this was overlooked. This excellent post is a must read on the topic.

Now That Cars Have Black Boxes, Am I Being Tracked? by Popular Science Editors. Questions and concerns about surveillance are becoming more commonplace. As someone who is looking to purchase a car in the next year or so I was happy to see this post come across my stream.

The Quantified Self community, lifelogging and the making of “smart” publics by Aristea Fotopoulou. I love it when people take a thoughtful look at the Quantified Self community and write about their experiences:

For me, the potential of QS for public participation lies in the show and tell meet-ups that constitute a central feature of this community. Meet-ups enable the exchange of stories about the success or failure of lifelogging practices; they allow people to connect and form synergies around common interests, and to explore wider questions such as personal data management and ownership. [...] members touch upon key political issues and create temporary spaces of dialogue: what happens to personal data, who has access to these data (is it private individuals, governments or corporations)? For what purposes (medical research)? And how can these data be interpreted (by algorithms, visualisations) and used to tell stories about people?

Stepping Down: Rethinking the Fitness Tracker by Sara M. Watson. Sara uses her personal journey of recovery from hip surgery to frame an interesting question: Should we trust our fitness trackers to prescribe movement goals?

Show&Tell
Practical Statistical Modeling: The Dreaded After-School Carpool Pickup by Jamie Todd Rubin. Jamie wanted to understand if there was a way he could reduce how much time he spent waiting in line to pick up his son from school. Why not track it and model it!

Bulletproof Diet and Intermittent Fasting: 1.5 Year Results by Bob Troia. Bob takes a deep dive into his data to see if this particular diet is having beneficial health effects. Click for the great data, stay for the wonderful discussion and very, very thorough write-up.

Visualizations


Quotidian Record by Brian House. I’ve been a fan of Brian House since his early days visualizing Fitbit data. I was reminded of this work during a conversation about geolocation data and thought it would be a nice addition to our visualization list.

KMcCurdy_SMVisualizing My Daily Self-Management by Katie McCurdy.

What does my daily medication and self-management look like? How could I visualize this regimen? How can I communicate the ‘burden’ and work of caring for myself?

I decided to draw pictures of the things that I need to do on a daily basis; that way I could show the workshop attendees what my day was like instead of just telling them.

JawboneTimetoEatIt’s Time to Eat by Karl Krehbiel. Karl, a data science intern at Jawbone used the data from their global community of users the determine the likelihood of food and drink consumption during the day. Really fun and interesting visualizations here.

From the Forum
Seeking opinions of diabetic self-trackers for non-profit project
Five Years of Weight Tracking
What Disclaimer should I use when making my personal #quantifiedself data public?

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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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Paul LaFontaine: Upset Every Other Minute

How many times during the course of the day do you find your mental state drifting into negativity, feeling like you’re lost, or just plain stressed? How could you even keep track of this, and why would you want to?

PaulLF_upsets

What Did Paul Do?
Paul LaFontaine has been tracking what he calls “upsets” to better understand himself, the way he works, and to see if he can improve his mental and physiological response and recovery.

Upsets are something physiological that were happening beneath the surface, and they’re trackable. It didn’t have to be emotional, but there had to be a signal. This project is part of an longer ongoing study. Before this current iteration I manually logged over 3,000 upsets and what I found is that most of my upsets were self-induced. I’d be in a calm environment, but then become upset about something. I wanted to use technology because I was afraid of bias and I know I was missing some upsets.

How Did He Do It?

I used the HeartMath EMWave2 that measures heart rate variability and indicates when you’re in and out of coherence. When I was out of coherence I captured that as an upset. I would stop what I was doing and use an audio recorder to keep track of the time, how long I was upset, the reason, and what method I used to recover. I tracked 71 sessions (each session was 25-45 minutes) totaling 42 hours of tracking time. I logged 1292 upsets during this period.

What Did He Learn?
Paul analyzed his data and found some very interesting insights about his upsets, his reasons for being upset, and the effectiveness of his recovery techniques.

I found that I was triggering an upset every 2 minutes. My wife said something must be wrong with me, but this stayed relatively constant through the tracking period. I started to think of it like skiing a mogul course. The moguls didn’t move, it was about how effective I could move through them. And, dealing with upsets is like playing whack-a-mole. They come fast and furious and every second counts.

For recovery I was able to find that my most effective technique was breathing. By returning to six breaths per minute routine I was able to improve recovery time from 33 seconds to 17.8 seconds. It was the primary way I could remove myself from being upset and make myself calmer.

We want to thank Paul for presenting this great QS project at the Bay Area QS Meetup group. Make sure to watch the full talk below to learn more about Paul’s methods and findings, then hop over to his website where you can read about how he tracked his stress during this talk.

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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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Meetups This Week

This week will see eight meetups in three countries on three continents and will show the different forms that Quantified Self meetups take. There will be longstanding groups like New York, who are putting on their 26th meetup, while Miami will be hosting their very first. London will be convening over 100 people with formal presentations, while Ashland will have an engaging discussion within a small group.

Show&Tell presentations will include Vienna’s Leonard Tulipan on how he applied his startup’s classification of “vanity metrics” vs. “actionable metrics” to his own medical data. Oxford member John Courouble will talk about the data around his weight loss plateau.

In addition to the standard show&tell format, Sydney will have breakout groups, while the Minnesota group will discuss the tools that their community is using. The Vienna and Portland groups will have guest speakers, including Vestigen, a company that tracks blood biomarkers for disease and Zenobase, which helps people aggregate and visualize their data, respectively.

If you are near any of these special events, you will definitely want to attend. To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Monday (September 22)
London, England
Sydney, Australia

Tuesday
(September 23)
Ashland, Oregon
Miami, Florida
Minneapolis, Minnesota
Vienna, Austria

Wednesday (September 24)
New York City, New York
Oxford, England

Thursday (September 25)
Portland, Oregon

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