Jaime Williams: Exploring my Data

JaimeWilliams_FullFitbitData

Jaimie 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 Jaime 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. Loius organizer, William Dahl and Jaime for the original posting of this talk!

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Sue Lueder: Solving a Food Allergy Mystery

SueLueder_Allergy

 

Sue Lueder had a mystery stomach ailment that started after a vacation to Spain in 2011. When she returned from her trip she was beset by consistent and frequent burping attacks. After visiting her physician and receiving a diagnosis for heart burn, which she didn’t trust. she began to track her attacks and her diet. In this talk, presented at our 2013 Global Conference, Sue how she tracked he symptoms and used the data to make sense of this mystery food allergy.

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What Did She Do?
Sue tracked her diet and the frequency and severity of her attacks.

How Did She Do It?
Sue was able to explore the data she was entering in to her self-designed spreadsheet tracking system. She used a few of the analytical tools and visualizations built into Excel to explore her data.

What Did She Learn?
Her analysis was able to pinpoint that dairy was probably the main culprit responsible for her attacks. Sue found out that she was able to improve her “good” days from 32% to 51% of the days she was tracking when she reduce dairy in her diet. When she experimented with adding dairy her findings were confirmed.

Tools
Excel

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Meetups This Week

This week we have four different meetups in three countries! The great community in Toronto will be meeting for the 26th time to share personal stories of self-tracking. In Estonia they’ll be discussing the future of wearables and biome tracking. Over in Minneapolis members will be talking about making sense of data from “old school” scales.

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!

Tuesday (October 28)

Minneapolis, Minnesota

Thursday (October 30)

Toronto, Ontario
Tallinn, Estonia

Saturday (November 1)

Dallas/Fort Worth, Texas

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Tim Ngwena: My Music Listening Habits

TimN_Music

In 2009 Tim Ngwena switched on Last.fm and he’s been running in across all his devices ever since. Earlier this year he decided to take a deep dive into his listening data to see what he could learn.

I realized that I was listening to the same old thing and I began to think about changing what I was listening to. But how can I change? Where can I start? I also wanted to learn something about my music, what I was listening to and who was behind the sounds. I decided to focus on music because it was doable.

In this talk, presented at the London QS meetup group, Tim explains how he was able to make sense of almost five years of data and learn more about himself and his listening habits.

What Did Tim Do?
Tim explored his music data along side additional information such as location data from Moves to learn about his musical tastes, listening habits, and explore new visualization and data analysis techniques.

How Did He Do It?
Tim exported his data, used the Last.fm API and some data cleaning and organizational tools to create a simplified and extensive database of his music listening history and associated data. He then visualized that data using Tableau.

What Did He Learn?
Tim learned a lot about himself and what the music he listens to says about him. He describes a few of the most interesting below,

Basically 80% of my listening comes form 10% of the artists that I have in my library.

I’ve listened to Erykah Badu for over a week (7.2 days). It led me to ask what is she saying to me?

Monday is my jam time. I’m listening from the morning into the evening.

I listen to music mostly when I’m walking.

Tim also learned a lot through the process of designing and creating his data visualization. The visualization, which you can explore here, made him think about being able to see the big picture when he has so much linked data.

I think context is important and you need to see all that information in one place and the tools I’m using allows me to do this.

Tools
Last.FM
Moves
Alteryx
Tableau

His slides are also available for you to explore. Tim has also written up an excellent blog post describing his process.

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QS Access App Visualization Showcase

Two weeks ago we announced the release of the QS Access App so you could access your HealthKit data in tabular format for personal exploration, visualization, and analysis. In that short period of time, we’ve seen a good number of downloads and positive feedback.



We know from our experiences hosting in-person and online communication about personal data that seeing real-world examples of what is possible is what inspires people to engage and ask questions of their own data. With that in mind we’re excited to announce our QS Access Visualization Showcase.

We are looking to you, our amazing community of trackers, designers, and visualizers, to show use what you can do with data gathered from using the QS Access App. Make heatmaps in D3, complete analyses and visualizations in Wizard, or just make meaningful charts in Excel. If you’re visualizing your QS Access data we want to see it.

We also know that data visualization design and creation is not trivial work. To support the community and help expose the visualization work we’ll be awarding free tickets to our QS15 Global Conference & Exposition to individuals who use QS Access to create unique and interesting visualizations. We’ve earmarked two tickets (a $700 value) for outstanding work.  If you’re selected, we’ll also work with you to showcase your work at the QS15 Conference and Exposition so other community members and attendees can explore and learn from their own data.

Submit your visualizations here.

 

If you’re in the Bay Area come to our QS Meetup on November 11th at the Berkeley Skydeck. You can showcase your visualization and tell our community what you’ve learned from accessing and visualizing your data.

Example Visualizations

HealthKit is still new and the number of apps that integrate with it is growing by the day. At QS Labs we’ve done a bit of work making simple visualizations that are meaningful to us.

Steps and Sedentary Activity

Gary3334

Gary19541

Gary has an iPhone 5s which has native step tracking. We used the QS Access app to export his hourly step totals and made these simple line graphs in Excel. You can read more about what he learned from these simple data visualizations here.

How Much Do I Run?

Ernesto is an avid runner and enjoys running along the quiet trails in Los Angeles. He was interested to see how often he actually runs and if there’s any pattern to his running. Using a well-designed D3 template he was able to make a calendar heatmatp of his running distance.

 

Screen Shot 2014-10-17 at 12.39.49 PM

Example Data

If you don’t have any HealthKit data to work with, or just want to play with some example data we’ve created a few files that you can use as examples. Download the files below from our GitHub account and make sure to read the documentation to understand where the data is coming from.  Descriptions of the data files and sources are available in our QS Access Data Examples repo on Github.

Example Data:
ER_HealthKit_DailyData
ER_HealthKit_HourlyData

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Shawn Dimantha: My Face, My Health

ny-quantified-self-showtell-26-using-facebook-for-health-insights-14-638

Shawn Dimantha is always looking for easier ways to track his health. He uses a variety of self-tracking tools, but a few months ago he became interested in exploring what he could do given his engineering and health IT background. He was inspired by immersion, an MIT-developed email analysis tool, which helped him understand who he was communicating with, and by Wolfram Alpha’s Facebook analysis tool. Focusing on Facebook and the wealth of image-based data in his profile he asked himself if images could be a window into his health. After reading a research paper on the use of images to predict body mass index he decided to see what he could learn my implementing a similar procedure on his own images.

What Did Shawn Do?

I used photos from my Facebook account to track my health, the reason I did this because I wanted to see how a simple heuristic I used for tracking my health daily could be implemented in the online world given the huge amount of photos that are and have been shared on a daily basis. I notice when I gain or lose weight, am stressed or relaxed from my seeing my face in my mirror. I was partly inspired by the self-photo collages presented on YouTube.

How Did He Do it?

I selected photos of my face from my Facebook account, cropped out my face and used some software and manual tagging to measure the ratio of different fiducial points on my face (eye-eye length, and cheek to cheek length) over time to help serve as a proxy for my health.

What Did He Learn

Facial image data needs to be cleaned and carefully selected. Face shapes are unique and need to be treated as such. Data that is not present is often more telling than what is present. Life events effect my weight and should be put into context; however causation is harder to determine than correlation. By being more conscious of my score and I can change my behavior before things get off track.

Right now I’m turning this into a product at Enfluence.io where I’m focused on using it to help with preventive health.

Tools

Facebook (my own images)
Python / OpenCV

Slides from Shawn’s talk are available here.

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Meetups This Week

There will be eight meetups this week in four countries on three continents. The group in St. Louis will be talking all things activity trackers, including how to get your data out. In Stockholm, they are teaming up with a local biohacking group to talk about biofeedback. The Philadelphia group will feature a show&tell by a neuroscientist who used self-tracking to “eliminate negative influences.” The Berlin group will have three show&tells on emotion tracking, happiness hacking, and quantified healing. We also expect some excellent videos and pictures to come out of the Boston, London, Shanghai, and Miami groups meeting this week.

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 20)
Boston, Massachusetts

Tuesday (October 21)
London, England
Miami, Florida
St. Louis, Missouri

Wednesday (October 22)
Philadelphia, Pennsylvania

Thursday (October 23)
Berlin, Germany
Stockholm, Sweden

Sunday (October 26)
Shanghai, China

Also, here are some photos from last week’s meetup in Zürich:
Zurich-10-14-14ZurichOcthighres_421558252

 

[Edit 10/20/14: Added Stockholm and Shanghai]

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What We Are Reading

It’s link-apolooza time! Enjoy these great news pieces, blog posts, personal data stories, and visualizations.

Articles

Robert Wood Johnson Foundation Launches Initiative to Assess How Data Can Be Used to Improve Health by RWJF Staff. Some exciting news coming out of RWJF this week about their new program to explore how individuals and communities are using health data and information. Don’t forget to read the accompanying blog post to learn more.

“For These Times”: Dickens on Big Data by Irina Raicu. Who knew the philosophical debate on a life governed by measurable facts had such a pedigree!

How and Why We Are Working with the FDA: Background and a Brief Summary of the Recent Meeting with the FDA about the Nightscout Project by Scott Leibrand. We’re big fans of the Nightscout project here at QS Labs. It’s great to seem them moving forward with a productive dialogue with the FDA.

Sir Tim Berners-Lee Speaks Out on Data Ownership by Alex Hern.

The data we create about ourselves should be owned by each of us, not by the large companies that harvest it, Tim Berners-Lee, the inventor of the world wide web, said today.

Sensors and Sensibility by Andrew Leonard. One day we might look back at our fears of insurers nefariously using our data to adjust premiums. Until then, that fear is alive and real. Thorough reporting here from the new Backchannel.

One Quantified Self App To Rule Them All by Chris Roth. As Chris explored the growing QS space and worked on his own open-source logging app he noticed a few things. Read on to see his take on where the space should be evolving.

Quantified Health and Software Apps by Sara K. Moir. What started as a Tweetstorm about her experience with MyFitnessPal expanded into a great exploration about what it means to be a user (and designer) of health behavior tracking tools.

Show&Tell

AZ_timetext2
How Text Messages Change from Dating to Marriage by Alice Zhao. Only a data scientist would celebrate a six-year anniversary with a thoughtful and thorough analysis of their communication. Alice did a great job here showing what’s changed over the years as her and her husband have moved from courtship to marriage.

ZT_Weight_small
Losing 58.3 Lbs For Science by Zachary Townsend. Zachary just finished up his participation in the One Diet Does Not Fit All: Weight Loss study. Over the last year he’s lost nearly 60lbs and learned a lot about himself and his diet.

Using JSL to import BodyMedia Fit Activity monitor data into JMP by Shannon Conners. We featured Shannon’s amazing visualization work in our September 20th edition of What We’re Reading. She returns here with a thorough how-to on how to explore BodyMedia and MyFitnessPal data in JMP. Even as a non-JMP user I was delighted to find out about the MyFitnessPal Data Downloader Chrome Extension she used to download her meal data.

Visualizations
AG-weekly-skyline-graph
My Up Skyline for the Week by Abe Gong. Abe is a data scientist at Jawbone was taking a look at his own activity data and decided to use the then new Jawbone API to download his data and make some interesting visualizations.

ER_SolarEclipses
Your Life on Earth by the BBC. Not a typical QS visualization, but unique and interesting to see what’s happened in and around the world over the course of your life.

I’ve been exploring upgrading my data visualization skills by learning D3. If you’re in the same boat or want know someone who is then you can point them towards this great intro from the engineers at Square.

From the Forum

Decode Your Chronic Illness
The Ethics of QS
Tasks & Measures
Lifelogging to Prevent Depression
Wearable Body Temperature Tool
Sleep Apnea Treatments
Looking for Android Time Tracking App

Today’s Number is 35: The age of the spreadsheet!

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Ralph Pethica: Improving My Fitness With Genetics

1_RalphPethica_QS Talk 2014 Powerpoint

One interesting aspect of personal data is how it can reveal what is unique about you. Nowhere is this more true than with genetic information coming from DNA testing kits. However, people are still at an early stage on how they apply that information to their lives. Ralph Pethica, who has a PhD in genetics, was interested in what his DNA could tell him about how to train more effectively. His findings were presented as an ignite talk at the 2014 QS Europe Conference.

What did Ralph do?
Ralph loves to surf. When it is the off-season, he trains so that his body will be in good condition for when the warm weather rolls back around. He used genetic research to inform how he designed his training plans.

How did Ralph do it?
Ralph used a 23andMe kit to find out his genetic profile. He researched those genes that have been found to have an impact on fitness to see his body should respond to exercise. For example, did he possess genes that gave him an advantage in building muscle with resistance training? He then modified his training routines to take advantage of this information and monitored his results (using the Polar watch and a Withings scale) to see whether his assumptions held up.

What did Ralph learn?
Ralph found out that he has genetic disadvantages when it came to strength training. This told him that progress in this area depended more on his lifestyle. In particular, he found that eating immediately after working out was important.

When it came to cardio exercise, he had a number of genetic advantages. The unexpected downside to this is that his body adapts quickly to any training regimen, resulting in a plateau. To get around this, he varied his training plan and monitored his results. On one day, he would cycle at a steady rate, while the next, he would use high-intensity intervals. His body seemed to respond to the varied training plan and he hit fewer plateaus. Without knowing which genes he possessed, and reading current research on those genes, it is unlikely that he would have discovered these effective customizations to his training plan.

Ralph has taken what he’s learned and built a tool called Genetrainer to help people use their genetic information to inform their fitness plains. You can check it out here.

Tools: Genetrainer, 23andMe, Polar RCX5, Withings Smart Body Analyzer

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Inclusion and Diversity at QS15

Today we’re happy to announce that we’re opening up a scholarship application for the QS15 Conference and Exposition. Since our first conference in 2011 our aim has been to foster an inclusive environment, and with the help and guidance from many attendees we’ve benefited greatly from exposing ourselves to the wide range of ideas about what it means to get “personal meaning from personal data.”

Last year, thanks to the leadership of our own longtime friend and collaborator, Amelia Greenhall, we published our first anti-harassment policy for the 2013 QS Global Conference. We leaned heavily on the great work of the Ada Initiative to make sure that our event attendees were supported and protected. We were further inspired by QS Boston and QSXX Boston organizer, Maggie Delano, to implement a code of conduct in order to make sure that our meetups are a welcoming place where community members can come together and safely share and learn from each other.

Opening up this scholarship application is an continuation of these ongoing efforts to support diversity and openness. We’re taking cues from other exemplary events such as Portland’s XOXO Conference & Festival and listening to thoughtful leaders in our community. QS15 is not your typical tech-focused event, our conferences never have been. They’re special because they’re attendee-driven. The community guides the program by sharing their self-tracking experiences and facilitating discussions on a wide range of topics. It makes sense to turn our beliefs on inclusion and diversity into action by welcoming and supporting those who have typically been underrepresented in our events and the broader techno-culture. These efforts also reflect our mission to support access. We’re currently in the early stages of a new effort to encourage and communicate about the importance of personal data access (see our QS Access App here). But access doesn’t have to stop at being able to download a CSV file. Access to our community of leaders, exemplary users, toolmakers, and researchers matters too.

If you identify with a group that has been underrepresented and would like to attend the QS15 Conference and Exposition we want to hear from you. We’ve made a simple application form for you to fill out so you can tell us a little about yourself. We’ll be reviewing applications as they come in. Because the conference is attendee-drive we place an emphasis on those would like to contribute to the program. We run our conferences on a shoestring, but this year we are going to do what we can to provide both registration and travel grants in this program.

Apply for a Diversity and Inclusion Scholarship

We invite you to help support this program. When you register for the conference you’ll see a additional registration field marked “Donation.” We are grateful for your support, at any amount.

We’re experimenting with moving our comments from the blog to our QS Forum. To discuss this post we invite you to join the forum thread here

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