Search Results for: weight
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!
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
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.
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!
Thursday (September 25)
We hope you enjoy this week’s list!
Big Data in the 1800s in surgical science: A social history of early large data set development in urologic surgery in Paris and Glasgow by Dennis J Mazur. An amazing and profoundly interesting research paper tracing the use of “large numbers” in medical science. Who knew that is all began with bladder stones!
Civil Rights, Big Data, and our Algorithmic Future by Aaron Rieke, David Robinson and Harlan Yu. A very thorough and thoughtful report on the role of data in civil and social rights issues. The report focuses on four areas: Financial Inclusion, Jobs, Criminal Justice, and Government Data Collection and Use.
Caution in the Age of the Quantified Self by J. Travis Smith. If you’ve been following the story of self-tracking, data privacy, and data sharing this article won’t be all that surprising. Still, I can’t help but read with fascination the reiteration of tracking fears, primarily a fear of higher insurance premiums.
Patient Access And Control: The Future Of Chronic Disease Management? by Dr. Kaveh Safavi. This article is focused on providing and improving access and control of medical records for patients, but it’s only a small mental leap to take the arguments here and apply them all our personal data. (Editors note: If you haven’t already, we invite you to take some time and read our report: Access Matters.)
Perspectives of Patients with Type 1 or Insulin-Treated Type 2 Diabetes on Self-Monitoring of Blood Glucose: A Qualitative Study by Johanna Hortensius, Marijke Kars, and Willem Wierenga, et al. Whether or not you have experience with diabetes you should spend some time reading about first hand experiences with self-monitoring. Enlightening and powerful insights within.
Building a Sleep Tracker for Your Dog Using Tessel and Twilio by Ricky Robinett. Okay, maybe not strictly a show&tell here, but this was too fun not to share. Please, if you try this report back to us!
Digging Into my Diet and Fitness Data with JMP by Shannon Conners, PhD. Shannon is a software development manager at JMP, a statical software company. In this post she describes her struggle with her weight and her experience with using a BodyMedia Fit to track her activity and diet for four years. Make sure to take some time to check out her amazing poster linked below!
The following two visualizations are part of Shannon Conners’ excellent poster detailing her analysis of data derived from almost four years of tracking (December 2010 through July 2014). The poster is just excellent and these two visualizations do not do it justice. Take some time to explore it in detail!
Tracking Energy use at home by reddit user mackstann.
“The colors on the calendar represent the weather, and the circles represent how much power was used that day. The three upper charts are real-time power usage charts, over three different time spans. I use a Raspberry Pi and an infrared sensor that is taped onto my electric meter. The code is on github but it’s not quite up to date (I work on it in bits and pieces as time permits I have kids).”
At our 2013 Quantified Self Global Conference we were excited to share a variety of beautiful and insightful data visualizations from our community. In the months leading up to the conference we asked attendees to send in their own personal data visualizations along with a short description. In our 6 years of hosting Quantified Self meetups and events, as well as running this website, our forum, and social channels, we’ve seen the power of data visualization as a story telling medium. We exist in part to help people tell their stories – about the data they collect, the changes they create, and the insights and new knowledge they’re excited to share.
Today we’re sharing a few of our favorite visualizations from past conferences. The images and descriptions below represent a wide a variety of tracking experiences and techniques, and we hope to showcase eve more unique personal data projects at our upcoming QS15 Conference & Exposition.
Tracking Sleep by Anita Lillie
This is concatenation of screenshots from my sleep app. Most sleep apps don’t let you zoom out like this and still see daily/nightly detail, so I just made it myself. I like that it shows how almost-consistent I am with my sleep, and made me ask new questions about the “shape” of a night of sleep for me.
2.5 Years of My Weight by Mette Dyhrberg
I gained a lot of insights from this heat map. The most obvious weight gain was no surprise — that’s when I periodically don’t track. In any case, the big picture patterns are easily identified with a heat map. Realized looking at this heat map that the point of no return was mid-April 2012 — my data shows that was when I switched protein shakes with an egg based breakfast. I have since experimented and seen that protein shake in the morning seems to keep my blood sugar more stable and as a result my weight under control!
One Month of Blood Sugar by Doug Kanter
This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).
Tracking Productivity by Nick Winter
My percentile feedback graph of my development productivity helps my motivation.
Six Months of My Life by David El Achkar
This is my life during the past six months. Each square = 15 minutes. Each column = 1 day. This picture represents 138 days or 3,000+ activities.
My Thesis Self Portrait by Sara M. Watson
Here’s a period of a few days of webcam images taken using Stan James’ LifeSlice during the final days of editing my thesis on Quantified Self uses of personal data. Serious business!
Sleep and Meaningful Work by Robby Macdonell
In an average work day, I don’t consider communication (email, instant message, etc) to be terribly meaningful work. I’d much rather be working on building software. Getting more sleep the night before increases the amount of meaningful work I’m likely to do in a day.
70 Days of Pulse by Laurie Frick
Pulse rate over 24 hours for 70 days from my Basis watch. Grey=null, blues=85
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.
There are a stellar collection of meetups going on 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!
Tuesday (September 2)
The Geneva group will feature a show&tell about tracking weight-loss alongside Basis Band data.
Wednesday (September 3)
The great Oslo group will be having their fourth meeting.
Thursday (September 4)
The Berlin group is adopting a workshop format this week to talk about habit formation.
The Cologne group has a packed program with talks on happiness, healthcare, and cryonics.
Sunday (September 7)
The Lincoln group has a great program that will cover measuring ketosis, the effect of antioxidants on migraines, tracking sleep, and using DNA to inform fitness plans.
Enjoy this week’s list!
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.
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…
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!
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.
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.
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!