Tag Archives: diabetes
For people who take insulin, self-measurement is a matter of life and death. No wonder, then, that people with diabetes who track their blood glucose have been so important in advancing techniques of visualization,and understanding data. At the Quantified Self Europe conference in Amsterdam this year, we were honored to host a panel discussion on Data Visualization and Meaning with Joel Goldsmith (Abbott Diabetes Care), Jana Beck (Tidepool), Doug Kanter (Databetes), and Stefanie Rondags (diabetes coach and blogger).
This discussion strikes me as widely important for self-trackers whether or not we have diabetes. Many of us will be tracking blood glucose in the near future. And the issues of data access, understanding, and clinical relevance that people with diabetes are working on resemble challenges commonly faced by anybody who is tracking for health.
For instance, Jana Beck was asked during the Q&A about her health care providers. How receptive are they to the important experiments she’s done to improve her health based on the data she’s collected? ”None of my endocrinologists have been very receptive to this approach,” she answered. “My A1C tends to fall within the range of what’s considered the gold range for people with Type 1. But I’m interested in optimizing that further. Often, I don’t even see them more than twice a year.”
Jana, Stefanie, and Doug all showed their own data in the context of discussing experiments and decisions that have had a major impact on their wellbeing. All were clear that the domain of these experiments and decisions is not healthcare as traditionally understood; but nor is it a matter of general fitness or lifestyle. The domain of these experiments is different and perhaps still unnamed. Self-collected data can and should essential health decisions, but the most advanced techniques of understanding this data are still being developed in an ad-hoc, grassroots way, by knowledgeable and open minded individuals who have a strong interest in learning for themselves.
At the end of the session I asked Joel Goldsmith, of Abbott Diabetes care, about the future prospects of the Freestyle Libre, a minimally invasive wearable blood glucose monitor that is not yet available in the US. (Disclosure: Abbott Diabetes Care was one of the sponsors of the QS Europe Conference.) The Freestyle Libre has a sensor in the form of a patch worn on the arm, and a touchscreen reader device that you lift close to the sensor to get a reading. There is no finger prick involved. While this and competing minimally invasive or non-invasive glucose monitors will almost certainly continue to be regulated as medical devices and understood as part of the health care system, many other people will also use them, and the flood of data and the questions that go with it will challenge our understanding of where this type of information should live.
The video above contains the full session, including the Q&A.
Howard Look is the Founder and CEO of Tidepool, a non-profit, open source development organization dedicated to reducing the burden of type 1 diabetes by building a platform that can integrate all diabetes device data in a single location. Most importantly, he is the father of a daughter with type 1 diabetes. Howard understands the challenges of effectively managing diabetes, and the essential role data plays in the minute-by-minute management this disease requires. The challenge, however, is finding a way to easily access and understand all of the data generated by the various devices a person with diabetes has at their disposal. What follows is an edited transcript of our conversation with Howard about how Tidepool has been addressing access to diabetes device data for the diabetes community.
QS: You’ve been busy over the last year – can you catch us up on the latest from Tidepool?
Howard: We made significant strides at Tidepool in 2014. Most notably, we announced partnerships with Asante, Dexcom, Insulet, Tandem, and Abbott Diabetes Care, all of whom gave us their data protocols. No money changed hands with these agreements, they simply understand that liberating diabetes device data is a good thing. You will notice the absence of the two largest diabetes device manufacturers from that list: Medtronic, and Johnson & Johnson, who make Animas and LifeScan products. We’re still having active conversations with them both and are hopeful that they will also enter into data protocol agreements with us.
Additionally, we’re just starting to realize the potential for our platform in the research community when you have all of the data in one place. We’re supporting a study with the Jaeb Center for Health Research and T1D Exchange, the largest coordinating organizations for type 1 diabetes studies, by putting our software in 15 of the top diabetes clinics in the United States. Our software enables researchers to access the full range of diabetes devices in one place. They are no longer restricted to only certain insulin pumps and blood glucose meters because those were the only devices whose data they had the ability to access. Now, all diabetes data is on the table and we’re showing the true value of integrating data from multiple places.
QS: Can you speak a bit more on the idea of access and how integral that idea is to the work you’re doing?
Howard: At Tidepool, the idea of access speaks to the core of what we’re trying to accomplish and promote throughout the world of health care.
From a policy perspective, we believe that the therapy data generated by these devices does not belong to the company – it belongs to the patient. It’s their personal health data. When you consider insulin delivery, blood glucose values, basal rates, carbohydrates ingested, all of these data points related to diabetes management is patient-owned data. Simply put, if you show a number on the screen for the purpose of delivering therapy, that data belongs to the patient. This is the fundamental approach we’ve taken to conversations with device manufacturers. For other data, like internal device diagnostic data, it’s fair for a device maker to consider that proprietary and we will respect that.
This approach falls perfectly in line with one of the core tenants of HIPAA: Portability of Data. HIPAA guarantees people with diabetes the right to access to their data, which makes encounters with responses along the lines of “HIPAA regulations prevent us from giving you access to this information” particularly frustrating. There is no ambiguity in my mind that it is irresponsible to not give people with diabetes access to their data. Lack of access means you are forcing people to use terrible tools to compute their own insulin doses which has the potential for horrible outcomes, which makes things worse for everyone in the health care conversation.
By liberating data, we can create an ecosystem of software and application and devices that make managing diabetes easier.
QS: What are some of the biggest challenges you face in bringing your platform to market? Are they more technical or regulatory?
Howard: When it comes to addressing a challenge as complicated as integrating all diabetes device data in a single location, there are many technological and political barriers that put up resistance along our way. But let me be clear, regulatory matters are not one of them. Believe it or not, working on FDA documentation is not difficult, and everyone I’ve spoken with at the FDA completely understands the value in data standards and liberating data from devices.
The FDA is not the problem.
For us, the biggest barriers we face are getting access to device data protocols and funding. That’s really it. When Medtronic and Johnson & Johnson formally make their data protocols available to us, we’ll consider that a job well done, but we’re not there yet. And funding as a non-profit is a constant battle that will not surprise anyone in the Silicon Valley. We’ve done a good job addressing both of these barriers, but there’s still work to be done.
Having the support of the two biggest philanthropic organizations in the type 1 diabetes world, Juvenile Diabetes Research Foundation (JDRF) and the Helmsley Charitable Trust, has made a huge difference for us. I cannot overstate how important the support of these two groups has been to our mission. Their grants send a loud and clear message that they believe in our mission and they want this to happen. Saying things like “this is what patients want” is one thing, but when we have the backing of the JDRF and Helmsley, our mission is given much more credibility.
QS: There are a number of moving parts in the Tidepool equation – the patient community, regulatory agencies, the diabetes device manufacturers, and the researchers looking to Tidepool to make data collection easier for their work – what message do you have for these groups?
Howard: At the end of the day, my message to the patient community is keep demanding your data. It’s yours. Simple as that. Engaged patients who understand and want to be involved have better outcomes, but in order to do that you need access to data and software and tools to take care of yourself.
My message to regulators: keep doing what you’re doing. I know you understand the value in liberating data, of device data standards, of agile software development, and that access regulation gets in the way of progress and innovation, and is harmful to patients. Keep this conversation going and together we can usher in a new era of access.
To the device makers: publish your device data protocols for existing devices, adopt standards like Bluetooth Smart and IEEE 11073, adopt cloud service protocols like OAuth2 and Rest API’s to enable access to their data. I believe a rising tide raises all boats, and enabling an ecosystem of access will increase adoption of your insulin pumps and continuous glucose monitors.
And to the science community, this is the world of big data and open data, we’re going to do our part by asking our users if they will donate their data to an anonymized research database and if they want to donate their identified data to the T1D Exchange. We should be getting as much data into the research world as possible, while respecting privacy issues, and you should be part of that collective.
QS: Do you have any final thoughts for the Quantified Self community?
Howard: Ten years ago, you had to be a multi-million dollar device company in order to do any of the things the QS community is capable of today. Now? You can buy some parts off of sites like SparkFun and build your own medical devices now. Now it’s about acknowledging that the more we enable that interconnectivity we encourage people to tinker, even with life and death things like insulin delivery, the collective intelligence of the community is going to cause great solutions to come out of all this.
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.
Ernesto is in sunny Austin for SXSW, so I’m filling in to gather this week’s articles and links for your reading pleasure.
Apple ResearchKit concerns, potential, analysis by MobiHealthNews. ResearchKit was a big surprise coming out of Apple’s Special Event this week. It was quite difficult to select just one representative article about the ensuing conversation, so this round-up serves nicely.
#WhatIfResearchKit: What if Research Kit actually, truly, worked… by Christopher Snider. Okay, I failed to keep to one article on ResearchKit. This post chronicles a series of Twitter conversations on the question: if ResearchKit does work, what are the possibilities?
The Electric Mood-Control Acid Test by Kevin Bullis. Thync is a sort of evolved version of a transcranial direct current stimulation (TDCS) device. A technology with a lot of potential and controversy, this article explores why the brain-enhancing effects of the TDCS only work for some people. By the way, if you are a fan of Philip K. Dick, Thync may remind you of the mood organ that was in Do Androids Dream of Electric Sheep?
Automated Learning by Nichole Dobo. Some school classrooms are experimenting with ”Blended learning”, a method of combining classroom teachers and computer-assisted lessons. A detail that stuck with me is the description of three large displays that show where each student is supposed to go that day, based on the results of the previous day’s lesson.
The Mouse Trap: Can One Lab Animal Cure Every Disease? by Daniel Engber. An in-depth how science’s predominant use of lab mice could be limiting our knowledge of disease. Of relevance to self-trackers because many models of optimal health are in part based on mouse studies.
Analyzing a Year of My Sleep Tracking Data by Bob Troia. This is a superb exploration of Bob’s sleep data from 2014 as collected by his Basis watch.
Notes on 416 Days of Treadmill Desk Usage by Neal Stephenson. The author of Snow Crash and The Cryptonomicon is a long time user of a treadmill desk, but when he began having pain in his left leg, he had to reevaluate how he used his favored tool.
Qualities of #QuantifiedSelf by Christina Lidwin. A fascinating analysis of the #quantifiedself hashtag.
First medical apps built with Apple’s ResearchKit won’t share data for commercial gain by Fred O’Connor
Talking Next-Gen Diabetes Tools with Dexcom Leaders by Mike Hoskins
From the Forum
Mood Tracking Methods?
Howto track laptop uptime
CCD or CCR conversion tools?
What gets measured, gets managed – Quantified Self in the workplace
Best ECG/EKG Tool for Exercise
Best iOS app to track water/coffee/alcohol intake?
This Week on QuantifiedSelf.com
QS15 Sponsor Highlight: RescueTime
Quantified Self and Apple’s ResearchKit
Better by Default: An Access Conversation with John Wilbanks
QS15 Conference Preview: Jamie Williams on Tracking My Days
Quantified Self Styles
Lastly, I’ll leave you with a lovely little comic with a message that many self-trackers can relate to.
The Secret by Grant Snider
We hope you enjoy this week’s selection of links, show&tell posts, and visualizations!
Hacking Your Brain by The Economist. Increasing performance and cognitive functioning, reducing depression, improving memory – if you could use a simple tool to get all these done, would you? What if that device was delivering electrical current to your brain? That’s the promise of transcranial direct current stimulation.
Talking Next-Gen Diabetes Tools with Dexcom Leaders by Mike Hoskins. Wonderful interview here with Terry Gregg (chairman) and Kevin Sayer (CEO) of Dexcom. Particular focus is given to their reaction and ideas regarding the open source Nightscout project.
Scientists threatened by demands to share data by Victoria Schelsinger. An older article (2013) about the shift towards open data and data sharing in academic science and it’s potential impact and possible pitfalls.
”’I think the public thinks that we’re all learning from everyone else’s work. That’s not true, and furthermore, it’s not true in ways that are even worse than you might think.’” – Heather Piwowar
Changing Representation of Self-Tracking by Deborah Lupton. It’s always great to hear that Deborah has released new writing. Her thoughtful analysis about self-tracking, data as culture, and data as object is consistently fantastic. Great addition to her growing body of work here.
Why Pets Are the Future of Fitness Wearables by Annie Lowrey. An interesting take on how the rise of tracking tools for pets may impact pet owners. Reminds me of research conducted by my old colleagues at San Diego State University: Physical activity, weight status, and neighborhood characteristics of dog walkers (Spoiler: Having a dog is associated with being more physically avtive.)
This guy is the Mark Zuckerberg of open-source genetics by Daniela Hernandez. A few weeks ago we highlighted an article by Daniela that focused on the fantastic openSNP project. She’s back with a profile of one of the founders, Bastian Greshake. (Full disclosure: I am openSNP member #610.)
Personal Sleep Monitors: Do They Work? by Christopher Winter. Superb experiment here to try and understand the accuracy of different sleep trackers.
What I’ve learned after 10 years of quantifying myself by Maxim Kotin. The title says is all.
A History of Checkins: Facebook Checkin Stats by Octavian Logigan. Octavian breaks down three years of his location checkin history and describes what he learned through examining seasonal trends, category breakdowns, and travel patterns.
I love the sleep tracker, so I can quantify this kind of information! (I have a 2yo and a 5yo….) by reddit user EclecticBlue. Fun visualization here of Fitbit sleep data. Also, great comments in the thread.
Locals & Tourists by Mapbox & Eric Fischer. I could spend hours exploring this interactive map of tweet locations by “tourists” and “locals”. (Special thanks to Beau Gunderson for point out that Eric also did a similar project with geotagged Flickr photos)
The Impact of Weather on Human Activity by Paul Veugen. The team at Human “1.9M activities in Boston & NYC to see the impact of weather on Human activity.” Make sure to click through for the full visualization.
FCC & FDA moving connected health forward by establishing wireless medical test beds
Nike+ Running Expand Global Partnerships
Will Our Fitness Data Be Used Against Us?
As the “quantified self” industry explodes, who will control the data — us or them?
This Week on QuantifiedSelf.com
Gordon Bell: Every Beat of My Heart
QS15 Conference Preview: Stephen Cartwright on 17 Years of Location Tracking
What’s in My Gut
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).”
Stefan Hoevenaar’s father had Type 1 Diabetes. As a chemist, he was already quite meticulous about using data and those habits informed how he tracked and made sense of his blood sugar and insulin data. In this talk, presented at the 2014 Quantified Self Europe Conference, Stefan describes how his father kept notes and hand-drawn graphs in order to understand himself and his disease.
Last year Alex Collins was diagnosed with Type 1 diabetes. Prior to his diagnosis Alex was frequently engaged in different types of exercise and physical activity. After his diagnosis his doctor mentioned that he might have a hard time exercising and controlling his blood sugar to prevent hypoglycemia. In this talk, presented at the London QS meetup group, Alex described his process for tracking and understanding the data that affects his day-to-day life so that he could “live my life normally without a high risk of complications.” This process of collecting and analyzing data has even pushed him to continue to explore his athletic boundaries, resulting in a running a ultramarathon and setting the world record for the fastest marathon while running in an animal costume.
Slides are available here.
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). -Doug Kanter
Richard Bernstein, an engineer with diabetes, pioneered home blood glucose monitoring. What he learned about himself contradicted the medical doctrine of his day, but Bernstein went on to become an MD himself, and established a thriving practice completely devoted to helping others with diabetes. We think of Dr. Bernstein as a hero because he used self-measurement to support his own learning, and shared what he learned for general benefit.
Tracking personal metabolism is a necessity for diabetics, and it is also something that will become increasingly common for many people who want to understand and improve their metabolism. Diabetics are also leading the fight for personal access to personal data, and we’re looking forward to meeting inspiring activists and toolmakers today at the DiabetesMine D-Data Exchange meeting in San Francisco. In honor of this meeting, we’ve put together an anthology of sort of QS Show&Tell talks about diabetes and metabolism data.
Jana is a Type 1 diabetic and data visualization practitioner who has been working on creating new techniques for understanding that data from her Dexcom continuous blood glucose monitor. In this talk, she described some of her newest techniques and her ongoing work with Tidepool.org. You can also view her original QS show&tell talk here.
Doug has been featured here on the QS website many times. We first learned about Doug through his amazing visualizations of his own data (like the image above). At the 2013 QS Global Conference, Doug shared what he learned from tracking his diabetes, diet, activity, and other personal data and his ongoing work with the Databetes project.
We spoke with Doug about his experience with tracking, visualizing and understanding his diabetes data. You can listen to that below.
James is a graduate student, professional cyclist, and a Type 1 diabetic. In this talk at the QS San Diego meetup group he talked a bit about how he manages his diabetes along with his near super human exercise schedule and how he uses his experience to inspire others. (Check out this great article he wrote for Ride Magazine.)
Brooks, a Type 1 diabetic, was tracking his blood glucose manually for years before switching to a continuous blood glucose meter. In this talk he describes what he’s learned from his data and why he prefers a modal day view.
Bob tracked his fasting blood glucose, diet, and activity to find out what could help him lower his risk of developing type 2 diabetes.
Vivienne’s son was diagnosed with Type 1 Diabetes two years ago and she’s applied her scientific and data analysis background to understand her son’s life.