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.
Facebook (my own images)
Python / OpenCV
Slides from Shawn’s talk are available here.
It’s link-apolooza time! Enjoy these great news pieces, blog posts, personal data stories, and visualizations.
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.
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.
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.
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.
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
Today’s Number is 35: The age of the spreadsheet!
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 2001 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.
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.
This is Adam Johnson’s third QS talk. Previously he’s discussed the lifelogging tool he developed and uses and how he re-learned how to type in order to combat RSI. In this talk, Adam gives an update to his self-tracking focused on three areas: tracking an long-distance cycling trip, his streamlined lifelogging process, and how he’s using the Lift app to track his habits.
What Did Adam Do?
In general, Adam is dedicated lifelogger who’s been tracking what he’s doing for over a year. Adam cycled 990 miles from Lands End to John O’Groats with his father and brother over 14 days and tracked it along the way. Because he wasn’t able to “lug around his Mac” to complete his regular lifelogging he decided to update his custom system to accept photos and notes. Lastly, he added habit tracking to his daily lifelogging experience by using the Lift app.
How Did He Do It?
Adam tracked his long distance cycling journey by using Google location history and a Garmin GPS unit. He was able to export data from both services in order to get a clear picture of his route as well as interesting data about the trip.
He also updated his lifelogging software so that it could accept photos and notes he hand enters on his phone. The software, available on GitHub, gives him an easy way to track multiple event such as how often he drinks alcohol and how much he has to use his asthma inhaler.
Lastly, Adam tracked the daily habits he wanted to accomplish such as meditating, reading, making three positive observations, and diet, using Lift.
What Did He Learn?
Everything Adam learned is based on his ability to access and export his data for further analysis. From his cycling trip he was able to make a simple map to showcase how far he traveled based on Google location history (which did have some issues with accuracy). He also was able to see that he traveled 1,004 miles, cycled for 90 hours, burned 52,000 calories, but didn’t lose any weight.
Using his updated lifelogging system, he was able to explore his inhaler use and after a visit to the doctor was able to “find out a boring correlation” that a preventative inhaler works and his exercise induced inhaler usage went to almost zero.
Finally, because Lift supports a robust data export, Adam was able to analyze his habit data and began answering questions he was interested in, but aren’t available in the native app experience. He found that seeing a visualization of his streaks as a cumulative graph was inspiring and motivating. He also explored his failures and found that Saturdays, Sundays, and Mondays were the days he was most likely to fail at completing at least one of his habits.
Slides of this talk are available on Adam’s GitHub page here.
Google Location History, Garmin GPS, Lifelogger, Lift, Photos, Notes
A long one this time. Enjoy the words, numbers, and images herein.
New biometric tests invade the NBA by Pablo S. Torre and Tom Haberstroh. Data and statistics are nothing new in professional sports. They’ve even made Academy Award nominated movies based the idea that data can help a team win. Until now data on players and teams has come from analysis of practices and gameplay. This great piece opens another discussion about collecting even more personal data about how players in the NBA live their lives off the court. Recall that athletes, coaches, and owners have been talking about out of game data tracking since 2012.
Misleading With Statistics by Eric Portelance. We’ve featured these type of articles before, but the example used here by Eric is not to be missed. So many times the data visualization trumps the actual data when a designer makes editorial choices. After reading this piece you’ll think critically the next time you see a simple line chart.
Handy Tools & Apps by Ray Maker. A great resource for athletes and exercisers who use a variety of tools to capture, export, and work with the activity and workout data we’re collecting.
Happiness Logging: One Year In by Jeff Kaufman. A great post here about what Jeff has learned about himself, what is means to log something like “happiness”, and the power of tagging data. After looking at his data, and a commenter’s from the r/quantifiedself subreddit, I’m wondering about the validity of 10-point scales for this type of self-tracking.
Redshit/f.lux Sleep Experiment by Gwern. Our esteemed friend and amazing experimenter is back with another analysis of his sleep data. This time he explains his findings from using a program that shifts the color temperature on his computer away from blue and towards red.
I ran a randomized experiment with a free program (Redshift) which reddens screens at night to avoid tampering with melatonin secretion and sleep from 2012-2013, measuring sleep changes with my Zeo. With 533 days of data, the main result is that Redshift causes me to go to sleep half an hour earlier but otherwise does not improve sleep quality.
Make sure to join the discussion on the forum!
Schedule Abstracted by Mike McDearmon.
Even a hectic schedule can have a sense of serenity with all text, labels, and interface elements removed.
Location History Visualizer by Theo Platt. The data above is actually my full Location History from Google Takeout. Theo made this simple and fast mapping visualization tool. Try is out yourself!
Lifelogging Lab. No visualizations here, but if you’re a designer, visualizer, or just have some neat data then you should submit it to this sure to amazing curated exhibition.
From the Forum
The ethics of QS
Call For Papers: HCI International 2015 Los Angeles
Pebble for Fitness Tracking
QS Business Models
QS, Light, Sleep, Reaction Timing, and the Quantified Us
Are you using your data to write a reference book or tell a story?
Cliff Atkinson is a consultant who helps people tell their stories and showcase their data in clear and understandable ways. It’s no surprise that when he became interested in understanding himself he turned to his experiences with visual storytelling. In 2012, at a New York QS meetup, Cliff spoke about how he’s embarked on a project to “quantify the “unconscious.”
What Did He Do?
Cliff began this project because he was noticed that there were “recurring patterns of procrastination and motivation” going on in his life. He began trying to understand them by turning to the large body of literature on human psychology. Then he asked himself, “Would it be possible to use some quantitative methods to track what was happening.” Using what he’d learned in his research and his experiences he decided to track his body, emotions, and mind.
How Did He Do It?
Cliff used his expertise and knowledge around visual storytelling to create an interesting system of visual diaries with which he could record information in his three areas of interest: the body, emotions, and the mind. Using Penultimate, and iPad app for sketching and notation, along with some clip art, he tracked physical, emotional, and cognitive events.
What Did He Learn?
The process of creating a space to reflect and record how he’s feeling across these three chosen domains has created a space for Cliff to better understand himself and how his mind works. This is still a work in progress and it sounds like Cliff is still exploring how to better understand the data he’s capturing over a longer period of time and even correlating it with other information such as his work and speaking engagements.
“One of the models for therapy is that somebody else helps you. I think with the quantified self and the things we’re doing we can take some of that power into our own hand and start to come to some personal understanding of what’s going on in our own lives.”
Steve Zadig is the COO of Vital Connect, but when he’s not busy with his job he’s out racing high performance vehicles. In this talk, presented at our 2013 Global Conference, Steve explains how he uses data to help him achieve his racing goals.
What did Steve do?
Steve wanted to get more information about how his body was reacting during racing. Frustrated that he was getting a lot of diagnostic data from his car and not any from himself he sought to track different biometrics to see what he could learn about what happens while he’s behind the wheel.
How did he do it?
Steve wore a Vital Connect patch to record and transmit his respiration rate, heart rate, and stress levels while he was was racing.
What did he learn?
After the race Steve was able to match the data with specific points and events during the race. He learned how something major, like spinning out of control, caused a large spike in stress, and how when he’s feeling in the zone his body responds with a lower heart and breathing rate.
“It’s about knowing. It’s about the knowledge of what’s happening with your body and how to deal with that.”
The QS15 Conference and Exposition is fast approaching. We invite you attend and give show&tell talks just like this one about your tracking and personal data experiences.
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!
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.
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.
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.
It’s About Time by Hunter Whitney. A nice post here about the different methods of visualizing temporal data.
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)
We hope you enjoy this weeks list. Feel free to submit articles, show&tell self-tracking stories, and QS data visualizations. Just email me!
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?
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.
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.
Visualizing 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.
It’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.
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.
- 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.”
- Request Access to the Partner API
- Email the API team at Fitbit
- They should email you back within a day or two with response
- 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.
- 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.
- 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!