Tag Archives: Health

What We Are Reading

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Articles

The validity of consumer-level, activity monitors in healthy adults worn in free- living conditions: a cross-sectional study by Ty Ferguson, Alex Rowland, Tim Olds, and Carol Maher. A very interesting research study examining the accuracy of different consumer activity trackers when compared to “research-grade devices.” Free living only lasted a few days, but it’s a great start to what I hope to see more of in the research – actual use out in the wild.

The Healing Power of Your Own Medical Records by Steve Lohr. Steven Keating has a brain tumor. He also has over 70GB of his medical data, much of which is open and available for anyone to peruse. Is he showing us our future? One can hope.

Mr. Keating has no doubts. “Data can heal,” he said. “There is a huge healing power to patients understanding and seeing the effects of treatments and medications.”

Why the DIY part of OpenAPS is important by Dana Lewis. Always great to read Dana’s thoughts on the ever evolving ecosystem of data and data-systems for people living with diabetes.

Why I Don’t Worry About a Super AI by Kevin Kelly. I, for one, am super excited for advancements in artificial intelligence. There are some that aren’t that excited. In this short post our QS co-founder, Kevin Kelly, lays out four reasons why he, and maybe why all of us, shouldn’t be fearful of AI now or into the future.

Responding to Mark Cuban: More is not always better by Aaron Carroll. Earlier this week Mark Cuban started a bit of an kerfuffle by tweeting out, “1) If you can afford to have your blood tested for everything available, do it quarterly so you have a baseline of your own personal health.” What followed, and is still ongoing, is a great discussion about the usefulness of longitudinal medical testing. I’m not sure I agree with the argument made here in this piece, but interesting nonetheless.

Show&Tell

My Quantified Email Self Experiment: A failure by Paul Ford. Paul takes a look at his over 450,000 email messages dating back 18 years. He find out a lot, but states that he doesn’t learn anything. I disagree, but then again, I’m not Paul. Still fascinating regardless of the outcome.

Filling up your productivity graph by Belle Beth Cooper. Want to understand your productivity, but not sure where to start? This is a great post by Belle about how she uses Exist and RescueTime to track and understand her productive time.

Visualizations

2014: An Interactive Year In New Music by Eric Boam. We’ve featured some of Eric’s visualization work here before, but this one just blew me away. So interesting to see visualization of personal data, in this case music listening information, turned into something touchable and engaging.

TitatnicData
“Women and Children First” by Alice Corona. A fascinating deep data dive into the Titanic disaster. Was the common refrain, “Women and children first!” followed? Read on to find out.

Access Links

HHS Expands Its Approach to Making Research Results Freely Available For the Public
European Food Safety Authority (EFSA) Grants Public Access to Data through Scientific “Data Warehouse”
FDA ‘Taking a Very Light Touch’ on Regulating the Apple Watch
Selling your right of privacy at $5 a pop

From the Forum

Survey on Self-tracking for weight-related purposes
Aging Biomarker Test

 

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Alice Pilgram: My Journey with Diabetes

In 2008 Alice Pilgram was diagnosed with type 2 diabetes. Faced with numerous life changes and having to now track multiple pieces of data, she started to feel overburdened. In this talk, presented at the Bay Area QS meetup group, she explains how a new simple tracking system helped her see the bigger picture.

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Better by Default: An Access Conversation with John Wilbanks

Photo by Joi Ito

Photo by Joi Ito

John Wilbanks is the Chief Commons Officer for Sage Bionetworks. You may not recognize John’s name or the name of his organization, but after today, you may want to commit it to memory. On March 9, 2014 Tim Cook introduced us to the idea of ResearchKit and then turned it over to Jeff Williams who described the new initiative and the five applications that were launched in collaboration with leading academic labs, advocacy groups, and research organizations. Sage Bionetworks is responsible for two of those apps, and John is responsible for leading design and development of many of the novel research tools and methods used by Sage. The following is an edited transcript of a conversation we had with John after the announcement.


Parkinson mPower App

This is Big

Think about what we’re doing in the context of it being open source. ResearchKit is going to be great for the QS community. They are perfectly suited to take advantage of ResearchKit. Let’s look at the five apps that were released today. They cover complex, but ubiquitious diseases. Plus the diseases are a great fit for the technology and sensor capabilities. But what is going to be amazing is when ResearchKit is released as open source. Anyone will have the ability to reskin these existing five apps to make novel research tools. Our Parkinson’s app might become a Huntington’s app. Our breast cancer app measures cognition really well, maybe it gets reskinned into a focused cognition tool. The app that focuses on diabetes could become a diet research app.

Large research institutions are going to jump all over this, but I’m more excited about the idea that small groups of people who have a bit of technical skill can work together to reskin the apps and turn them into longitudinal studies of 20 or 50 people. There may be five of those small groups, maybe 100. We can then work to stich those groups together and learn even more.

So the rare and chronic disease folks and the tech and QS community are involved. Now we come to everyone else. Everyone who isn’t suffering or presenting with a medical issue. We have to figure out ways to get them to participate.

We have a long tradition of creating incentives to participate. There is no reason we can’t create novel incentive methods to bring people into research. Maybe you receive a federal income tax credit. Maybe you’re allowed to enroll in long term care insurance at a reduced rate. There are a lot of ways to bring people in and that’s not touching the innate altruism of people and their curiosity. We’re already seeing this with the Precision Medicine Initiative. People want to take part, they want to engage.

Once we start getting that engagement, and we begin to see a diverse ecology of applications built on top of ResearchKit, then we’ll start to see success. It may take a bit of time. It won’t happen with this iteration. Maybe not even the second, but when we get enough devices, apps, participants, and improved interoperability between them all we’ll start to see the power of network effects.

You have to remember, there are no “killer apps” without network effects. Email wasn’t a big deal in the late 80′s because you couldn’t reach anyone outside your system. But then the web came, we connected the dots, the nodes, and then there it was, the power of the newtork.

I see QS and our current state of devices, apps, and tools being very similar to all those nerds typing away in the 80′s. They were okay with what they had because they could work with it. Then the net came and you have more control and more interefaces. That leads to the killer apps. That’s why we’re building this, for the third or fourth wave.


Share the Journey app

Everything we’re doing, the whole stack working together, is new. Consenting participants using well-designed and open-source Participant-Centered Consent toolkits. Giving participants direct access to their data. Securely hosting automatically de-identified data in the cloud using our Bridge Server. None of these have been done before at the same time.

We’re a non-profit, so we can be this icebreaker. We can take these risks and experiment and iterate and learn. No one asks us how we’re going to make money. We have a different bottom line — what is best for everyone involved? That outlook gives us the freedom to do this work.

I’m honored to have the opportunity to create a product that pushed my beliefs: participants-centric principles. If you’re a participant, then you decide. You’re in control. You can do cool stuff with the data. You can stop answering questions whenever you want. You can delete the app. We live in a world in which the politics of technology are dicteated by code and they often don’t share those ideas. To get this opportunity is amazing.

These moments don’t happen often. This is IBM going open source in the late 90s. It’s that big. I wasn’t sure I was going to be a piece of something that big. We’re trying to change culture.

In 1998 Lawrence Lessig proposed the pathetic dot theory in his book, Code and Other Laws of CyberspaceHe theorized that four forces control what we do: Law, Architecture, Social Norms, and the Market. He went futher and differentiated west coast law, which was quickly becoming dominated by software code and east coast law, what we normally think of when we think of laws.

I think up until now we’ve failed to properly take up that lever, to use software code, west coast law, to express something better. People often forget that building software means expressing an opinion. We created our apps, our tools, our systems to reflect our opinion, that participants should be at the center of research. And then we’re giving it away. That’s our position, and it’s better by default.

But, we’ve just started. The fun now is that we get to test it. I’ve always said that we can’t screw it up any worse than it already is. This isn’t the end. It’s not finished. We’re going to keep changing and learning.


John can be found online at del-fi.org and @wilbanks.

We invite you to share your data access stories, and this article with the #qsaccess hashtag and follow along on quantifiedself.com@quantifiedself and our Access Matters Medium publication.

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What We're Reading

Enjoy this week’s list of articles, links, show&tells, and visualizations.

Articles
Personal Health Data: Five Key Lessons for Better Health by Patti Brennan and Stephen J. Downs. A fantastic post by two great thinkers in the world of personal health and data. They outline five key challenges that must be addressed in order to have meaningful use of personal health data.

It’s Time for Open Data on Open Data by Luke Fretwell. A short but meaningful post here. With all the clamor for more government open data portals it’s time to start exploring how they’re actually being used and what can be done to improve them.

The NFL Gets Quantified Intelligence, Courtesy Of Shoulder Pad-Mounted Motion Trackers by Darrell Etherington. As a sports fan and spouse of someone who works in sports media production I am fascinated by how the world of personal data is quickly colliding with professional athletics. We’ve long looked towards athletes for inspiration and examples of how data can be used to understand and improve and I’m very interested to see how the NFL will make use of this data. Maybe we’ll see more sabermetric-like player and team analysis?

Show&Tell
Heart Rate Variability While Giving a Public Speech by Pau LaFontaine. Paul gave a show&tell talk at a recent Bay Area QS meetup and tracked his heart rate variability. This post explains his data, and what he learned about the stress involved with public speaking. Be on the lookout soon for his show&tell talk video.

Chronic Diease and Self-Tracking – Part 1 by Sara Riggare. Sara is a longtime contributor in the Quantified Self community, having spoken at each of our three QS Europe Conferences. In this post she explains her new exploration of her resting heart rate and poses some interesting questions. We’d love to have you help her out!

Raspberry Pi Sleep Lab How-To by Nick Alexander. Nick was bothered by a common nightly occurrence, kicking off his covers in the middle of the night. Like any enterprising technologist, he enlisted his technical expertise to help examine this problem. This post is an amazingly detailed “How To” for building and setting up your own personal sleep monitoring tool complete with video, environmental information, sound, and sleep data.

Visualizations
This week I’ve been exploring how people are making using physical data visualizations. During some research I found a great resource, the List of Physical Visualizations. A few images below are from that great list, be sure to spend some time exploring the many different examples and then reading the excellent research paper linked below.

lego_timetrack_workweek

cyl3

GraphConfB

keyboard351-597x360

Evaluating the Efficiency of Physical Visualizations by Yvonne Jansen, Pierre Dragicevic, and Jean-Daneil Fekete. The first empirical study of the effectiveness of physical visualizations for conveying information. Using 3D bar charts as a primary example, the authors were abel to show that physical visualizations are more effective than their digital on-screen counterparts for some information retrieval tasks.

From the Forum

Data Aggregation
Idea for a Life Tracker Application
How can I log my teeth?
Home Potassium Testing

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Conference Preview: Discussing Families & Self-Tracking

Last June, the Pew Internet Research Project released a report entitled, Family Caregivers are Wired for Health. The authors - Susannah Fox, Maeve Duggan and Kristen Purcell - found that 40% of Americans are caring for an adult or child with significant health issues. Of special interest to us: “When controlling for age, income, education, ethnicity, and good overall health, caregivers are more likely than other adults to… track their own weight, diet, exercise routine, or other health indicator.” (Emphasis added.)

Our Bay Area co-organizer Rajiv Mehta was a community peer reviewer of the survey. At the upcoming 2014 Quantified Self Europe Conference, Rajiv will co-lead a breakout with Dawn Nafus of Intel Labs on the role of families in self-tracking practice. If you are involved in or curious about family caregiving, you’re invited to come and take part in what will be a great discussion.

The QS Europe Conference is just a few weeks away; come if you can!

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Fit Fifties, Sound Sixties: Maria Benet on Active Aging

Maria Benet began tracking her activity a few years ago as a way to lose weight and take control of her health. What started with a simple pedometer and a few custom Access databases has morphed into a multi-year tracking project that includes news apps and tools. Her progress and data has even spurred her on to new experiences and athletic endeavors. Watch her talk, filmed at the Bay Area QS meetup group, and read the transcript below.

(Editors Note: We’re excited to have Maria attending the 2014 Quantified Self Europe Conference where we hope to hear an updated version of this wonderful talk.)

What did I do?

Hi, my name is Maria Benet and I am happy to tell you that only about two-thirds of me is here to talk about my tracking project. I mean that literarily, because in the 10 years since I’ve been self-tracking I lost over 50 pounds while getting fitter.

In my early 50s, I was overweight, out of shape, with bad knees, and when not cranky, depressed. I was already on meds for high blood pressure and was looking at the prospect of more prescriptions down the road.

So, what did I do to change my situation? I set about tracking my activity levels, my weight and my food intake with the help of apps, wearable devices – plus — in databases and Excel spreadsheets that I designed. Until late 2011, I tracked inconsistently, but once I discovered mobile apps and wearable devices — I became more systematic and consistent about tracking weight, food intake, and fitness data.

How did I do it? 

When I first started — losing 50 pounds seemed daunting, but going for a walk at least 5 days a week seemed less formidable. To track walks I was going to take in the hilly neighborhood where I live, I created a simple Access database.

I bought a pedometer, hiking shoes, and off I went. After walking, I recorded the duration, the number of steps, and calculated the distances I covered. I also charted my routes by naming the streets, and made notes about the weather and my mood during the walk.

Recording the data turned out to be a form of reward in itself. At the start of this tracking project, I enjoyed seeing the database grow a little more than I enjoyed the actual walks themselves.

Over time, the walks got longer, steeper, and eventually included actual hikes. I also took up the practice of Yoga regularly, and then added Pilates to my exercise repertoire.

Along the way, I also started to lose weight. Though I didn’t weigh myself every day, I began to pay attention to the kinds of foods I ate and tried to wean myself off processed foods in general.

They say you get fit in the gym, but lose weight in the kitchen. In September 2011, when I discovered LoseIt, it became my virtual kitchen: LoseIt helped me see what foods I ate regularly, which of these spiked my weight, even if my calorie intake stayed the same. I noticed these relationships anecdotally, rather than by finding statistical correlations between them.

Tracking in LoseIt helped me realize that as much as I love bread and beer, they are not my friends. Two years ago, an allergist confirmed my wheat sensitivity through blood tests and an elimination diet.

I added Endomondo to my tool box a few months later, since I liked having the maps and stats it offered, in addition to the other data it showed. By December I also added a Fitbit, as with it I could track more accurately how many steps I took and approximate better the number of calories I burned. The Fitbit was like going back to the pedometer, but to one on steroids.

With the Fitibit, I focus mostly on the Very Active Minutes it claims to measure. Increasing that number over time became a game. In 2012, I was averaging about 57 minutes a day, which put me in the 98th percentile. Increasing to 69 minutes only brought me to the 99th percentile, as the Fitbit population also has increased over time.

The Fitbit turned out to be a catalytic tool, because it spurred me on to push the perceived limits of my fitness abilities and possibilities further. It ended up putting wheels under my dreams.

In the spring of 2012,I took up cycling to increase my active minutes and challenge a mental habit of opting out of things because of a fear of failure or thinking of them as not age appropriate. Biking, in turn, added to my collection of gadgets and apps for tracking the metrics involved.

By 2012 then, in addition to LoseIt and Fitbit, I was tracking workouts with a Garmin GPS watch with a HR monitor and my bike rides with a Garmin Edge computer, uploading the data to the Garmin site, to Endomondo and Strava, as each had strengths the other lacked, from my perspective.

To complicate data gathering, back in January 2012, I started a basic Excel spreadsheet that tracks highlights from each of these apps in an application-independent reference for me. In Excel I track the type of activity, duration, distance, if applicable, average and maximum heart rate, Strava suffer points, (a measure of exertion), the hours I slept and how that sleep seemed to me, and additional notes about the day I might think relevant.

The plethora of my gadgets and apps might earn me an entry into the next edition of The Diagnostic and Statistical Manual of Mental Disorders. But exploring these tools was, and still is, my way of looking for a comprehensive and personalized way to track the quantities in my habits and activities that make for a qualitative difference in my life … which brings me to what I learned so far:

What did I learn?

I learned that small quantitative changes in particular daily habits add up to a big difference in quality of life in general.

The incremental additions in my tracking methods and number of gadgets I added produced a lot of data, which I haven’t analyzed closely, because I was already getting a lot of return from them in the form of new experiences in my life.

The most memorable of these experiences is my having completed the metric century ride on the Tour de Fuzz in Sonoma last September. In the space of a little over a year I went from covering barely 8 miles in an hour on my first rides to completing 63 miles in 5 and ½ hours and feeling ready to ride a lot more.

It has been said that motivation is what gets us up and going, but it’s habit that keeps us going. So it is with my tracking: though the motivation was to lose weight, the habit of tracking and keeping an eye on the numbers are what allowed me to go from daily small changes to a much bigger transformation from the overweight, depressed, and achy person I was 10 years ago to who I am now: someone interested in health and fitness and setting goals I can meet.

I learned that for me the act of tracking is the project itself. Although the data I generate can be charted and described in numerical relationships the number that brings me the information that makes a difference in my life, is a simple 1 – or tracking one day at a time.

I love to see the numbers my Garmin and Fitbit generate, but in the end, the quantified self for me is not so much about the measured life as it is about keeping those numbers coming through a well-lived and, more importantly, well-enjoyed life as I go from my fitter fifties into what I hope will be my sounder sixties.

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Thomas Christiansen on Learning from 60,000 Observations

It’s an iterative process. I’m peeling an onion, and I can continue peeling that onion for the probably the rest of my life.

How many times have you sneezed today? This month? Over the last 3 years? Thomas Christiansen knows his sneeze count because he’s been tracking them since 2011. We’ve actually heard from Thomas before, but we were happy to have him give an update on his unique self-tracking project at the 2013 Quantified Self Global Conference.

To better understand his allergies and his overall health, Thomas began tracking a discrete phenomena, his sneezes. By plotting them over time and then exposing himself to other data like sleep, travel, and diet he’s been able to start to understand himself better. Watch his talk below to see what Thomas learned, and how he thinks about his process of continuous learning.

This video is from our 2013 Global Conference, a unique gathering of toolmakers, users, inventors, and entrepreneurs. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

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Nancy Dougherty on Quantified/Unquantified

Nancy Dougherty has talked to us in the past about her experiences with exploring self-tracking and how mindfulness interacts with the technological processes of gathering and understanding personal data. In this short Ignite talk, given at the 2013 Quantified Self Global Conference, Nancy digs a bit deeper into her personal experiences when she gave up tracking while maintaining what she calls, “the QS mindset.”

This video is from our 2013 Global Conference, a unique gathering of toolmakers, users, inventors, and entrepreneurs. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

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Pew Internet Research: 21% Self-Track with Technology

Today the Pew Research Center’s Internet & American Life Project released their latest findings in their ongoing research on the role of the Internet and technology in health and wellness. This latest report, Tracking for Health, is of particular interest to the Quantified Self community because it focuses on self-tracking. Thanks to Pew Associate Director, Susannah Fox, who gave us an advanced look at the results, we are able to bring you some reflections on this initial foray into measuring the impact of self-tracking.

Before we get to our discussion with Susannah it’s probably best to help set the stage with some of the most interesting findings.

Overview of Tracking

  • 69% of adults track a health indicator for themselves or others.
  • 34% of individuals who track use non-technological methods such as notebooks or journals.
  • 21% of individuals who track use at least one form of technology such as apps or devices.

Continue reading

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Stuart Calimport on The Memome Project

Stuart Calimport is on a quest to find the most useful memes for health and well-being. He started the Human Memome Project, and spent a year and a half collecting all his ideas about health. He classified 5137 of these ideas as healthy/ethical/optimal and 6581 of them as unhealth/unethical/sub-optimal. In the video below, Stuart shares some examples of his memes, as well as his process for optimizing meme rate generation, and what he has learned about himself on this adventure. (Filmed by the London QS Show&Tell meetup group.)

The Memome Project from Ken Snyder on Vimeo.

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