Tag Archives: QS Access
Enjoy this week’s list!
Big Data for the Spirit by Casey N. Cep. Interesting piece here on SoulPulse, a study using text messages to examine spirituality. Can faith be measured and quantified? These researchers are trying to find out.
Big Data Not Doping: How The U.S. Olympic Women’s Cycling Team Competes On Analytics by Bernard Marr. Nice short article on Sky Christopherson and the personal data-driven training program that resulted in a silver medal at the 2012 Olympics for the Women’s track cycling team.
The Quantified Cow: Wearables Will Monitor Animals As Closely As Humans by Ben Schiller. First we put sensors on ourselves. Then we started putting them on our pets. Now, we’re working on putting them on our cattle. What’s next?
Quantified Self: Step Counting by Chad Lagore. Chad wrote up a great analysis of what he learned from analyzing step data natively tracked through his iPhone. Of course, special kudos to him for using our QS Access app to download his data.
Where Are the Jobs? by Robert Manduca. Robert took data from the Census Bureau’s Longitudinal Employer-Household Dynamics dataset and visualized each job as a dot on the map. Fascinating to see where different industries cluster around the United States.
Hippo Attack! by Jer Thorp. Ever wonder what happens when you’re attacked by a hippopotamus? Above is the plot of Dr. Steve Boyes’ heart rate during the attack. Make sure to click through for an amazing account of the event.
From the Forum
This Week on QuantifiedSelf.com
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.
Today, we are participating in the “Data and Innovation at the Climate-Health Nexus” panel hosted by the White House Office of Science and Technology Policy. When we’ve spoken to people about this meeting the reaction we tend to receive is, “What does Quantified Self have to do with climate change?” It’s a valid question, and one we hope to answer during the panel. Today we wanted to take some time here to talk about why we’re a part of this important conversation.
It’s no surprise that data and data collection is becoming a part of the normal course of our everyday lives, from the data we choose to collect about our health and wellness to the so-called “data exhaust” we’re creating as we use different technological systems. The practice of self-tracking, collected data about yourself to answer interesting questions or help change behavior, has often been linked to narcissism or navel gazing. We know from our experience interacting with a worldwide community of self-trackers that this isn’t the case. Individuals who track, analyze, visualize, and learn from their own data also tend to do something else: share it. You just have to take a peek at our over 750 show&tell videos to see that sharing experiences, techniques, and outcomes is a core component of our work and our community. It’s the reason we hold conferences, support over 100 meetups around the world, and share on this website.
We also know that data is powerful. It can help us understand ourselves, but also the world around us. We’ve been watching closely as new citizen science, one-off projects, and commercial toolmakers have started to incorporate ways to sense and measure the personal and local environment. From air quality sensors integrated into in-home video monitors to crowdsourced DIY environmental sensing devices – we’re beginning to see the power of data for understanding the environment around us, and perhaps more importantly, how the environment plays a role in the health and wellness of our communities. A great example of this comes from our friends at Propeller Health. Recently they announced the launch of AIR Lousiville, a “first-of-its-kind data-driven collaboration among public, private and philanthropic organizations to use digital health technology to improve asthma.” By combining air quality data with geolocated asthma inhaler use data they hope to better understand and positively impact their local environment and reduce the burden of asthma in the Louisville community.
This is just one example of individuals coming together as a community to generate and contribute data about themselves, their environment, and their health to drive a much needed conversation. A conversation about the complex, and important, relationship between the environment and health. We’re hoping to see more and, to that extent, we’re excited to announce that starting at our 2015 Quantified Self Public Health Symposium we’ll be officially launching, in collaboration with with the U.S. Environmental Protection Agency, Personal & Community Environmental Data Challenges, calling on researchers and companies making wearables, sensing, data-visualization, and digital health-tools to join a national conversation about the importance of gaining a more detailed view of environmental impacts on health. This challenge is just one in a great list of commitments from leading companies and institutions designed to advance the Obama Administration’s Climate Data Initiative.
We invite you to learn more about our challenge announcement and our participation in the symposium on Data and Innovation at the Climate-Health Nexus by reading our brief press release here.
You can also learn more about national initiatives, programs, and newly released climate data from the following Fact Sheet: Administration Announces Actions To Protect Communities From The Impacts Of Climate Change
Update: The video from the panel is up and can be found here. The panel actually starts an hour and 19 minutes in to the video.
Philosophy, bicycles and brains, opinions on tracking sleep, learning from actually tracking sleep, and visualizing work through vigilant self-report – all these and more in our reading list below. Enjoy!
Sleep apps and the quantified self: blessing or curse? by Jan Van den Bulck. Here at QS Labs, we’re very interested in how the academic and research world is colliding with those of us using tools of measurement previously restricted to science. In this Letter to the Editor, published in the Journal of Sleep Research, the author lays out an interesting set of opinions about the increasing availability and use of commercial sleep tracking devices. (You can access the full pdf here.)
Measuring Brainwaves to Make a New Kind of Bike Map for NYC by Alex Davies. Readers of the QS website may remember a great show&tell talk we featured back in May of 2014. In that talk, Arlene Ducao discussed her MindRider Project, an EEG tracking bicycle helmet. In this short piece, we learn that Arlene has continued this awesome work and has produced MindRider Maps Manhattan, exposing the brain data of 10 cyclists as they transversed New York City.
Big Data and Human Rights, a New and Sometimes Awkward Relationship by Kathy Wren. Earlier this year the AAAS Science and Human Rights Coalition held a meeting to discuss the intersection of personal data collection and human rights. This short article describing some of the key discussion points is a great place to start if you’re exploring what “big” and personal data means to you and your use of the tools and services that collect it. (Videos of the meeting are also available.)
How Theory Matters: Benjamin, Foucault, and Quantified Self—Oh My! by Jamie Sherman. A very interesting and thought-provoking essay here on the nature of self-tracking and data collection framed against the works of Michel Foucault and Walter Benjamin. We count ourselves lucky to have Jamie as an active member and observer of our QS community.
But taken together, Foucault and Benjamin suggest that the penetration of data into daily life is part of a larger shift underway, and that changes we can already see in social life, politics, and labor are not unrelated, but rather intimately linked.
Compulsory Quantified Self by Gwyneth Olwyn. I think it’s good practice to try and expose ourselves to all sides of the conversation around self-tracking, the positive and the negative. In this blog post Gwyneth describes a few ideas about the purpose and outcomes of self-tracking, especially when the self is superseded by the demands of others (such as in a workplace wellness program).
Sleep Data Analysis with R by Ryan Quan. Ryan has been tracking his sleep with the Sleep Cycle app for the last two years. In this excellent post he explores and plots his data (yay export!) to see when he goes to sleep, how long he sleeps, and what really makes up “quality sleep.” Love the fact that he included his R code and sample data. Go Ryan!
Quantifying Goals Using Key Performance Indicators (KPIs) by Bob Troia. No data in this post, but I found it particularly inspiring to see how Bob was planning on keeping track of his goals for this year. If you’re looking for ideas for tracking your 2015 goals and Key Performance Indicators this is a great place to start.
The Resume Of The Future by Eric Boam. The above is one of the two beautiful visualizations created by Eric to explore his daily work activity and interactions. This visualization shows what he was actually spending his time on. How did he collect the data? Well, he used the Reporter App to ask himself three questions: “where are you, what are you doing, and who are you with?” Make sure to read his post, he developed very interesting insights through collecting this data.
Weight Loss: What Really Works? by Emi Nomura and Laura Borel. Another fascinating data analysis project here by the Jawbone data science team. They examined the behaviors of a group of users who lost at least 10% of their starting weight vs users with no weight loss and found that the biggest difference in behavior was tracking meals.
Mapping my Last Two Years of Runs and Rides
While browsing the r/dataisbeautiful subreddit I stumbled upon this interesting tool/company that visualizes the maps of your runs and bike rides by connecting to your Runkeeper or Strava account. Above I’ve included my 2013 and 2014 maps. Clearly I need to find some new running routes in my neighborhood. (click through to enlarge)
QS Access Links
As part of our new work highlighting stories, issues, and innovations related to personal data access we’re going to start publishing a short collections links in this space. As this works grows be on the lookout for a new Access Newsletter from QS Labs.
Who Should Have Access to Your DNA?
What FDA developments in Diabetes mean for FDA approval in Digital Health
Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation?
WTF! It Should Not Be Illegal to Hack Your Own Car’s Computer
Unique in the shopping mall: On the reidentifiability of credit card metadata
Majority of Consumers Want to Own the Personal Data Collected from their Smart Devices
Who Owns Patient Data
Los Angeles County Supervisors OK Creation of Open-Data Website
Two weeks ago we announced the release of the QS Access App so you could access your HealthKit data in tabular format for personal exploration, visualization, and analysis. In that short period of time, we’ve seen a good number of downloads and positive feedback.
We know from our experiences hosting in-person and online communication about personal data that seeing real-world examples of what is possible is what inspires people to engage and ask questions of their own data. With that in mind we’re excited to announce our QS Access Visualization Showcase.
We are looking to you, our amazing community of trackers, designers, and visualizers, to show use what you can do with data gathered from using the QS Access App. Make heatmaps in D3, complete analyses and visualizations in Wizard, or just make meaningful charts in Excel. If you’re visualizing your QS Access data we want to see it.
We also know that data visualization design and creation is not trivial work. To support the community and help expose the visualization work we’ll be awarding free tickets to our QS15 Global Conference & Exposition to individuals who use QS Access to create unique and interesting visualizations. We’ve earmarked two tickets (a $700 value) for outstanding work. If you’re selected, we’ll also work with you to showcase your work at the QS15 Conference and Exposition so other community members and attendees can explore and learn from their own data.
If you’re in the Bay Area come to our QS Meetup on November 11th at the Berkeley Skydeck. You can showcase your visualization and tell our community what you’ve learned from accessing and visualizing your data.
HealthKit is still new and the number of apps that integrate with it is growing by the day. At QS Labs we’ve done a bit of work making simple visualizations that are meaningful to us.
Steps and Sedentary Activity
Gary has an iPhone 5s which has native step tracking. We used the QS Access app to export his hourly step totals and made these simple line graphs in Excel. You can read more about what he learned from these simple data visualizations here.
How Much Do I Run?
Ernesto is an avid runner and enjoys running along the quiet trails in Los Angeles. He was interested to see how often he actually runs and if there’s any pattern to his running. Using a well-designed D3 template he was able to make a calendar heatmatp of his running distance.
If you don’t have any HealthKit data to work with, or just want to play with some example data we’ve created a few files that you can use as examples. Download the files below from our GitHub account and make sure to read the documentation to understand where the data is coming from. Descriptions of the data files and sources are available in our QS Access Data Examples repo on Github.
On Wednesday this week we learned that the QS Access app we submitted to the Apple store was approved. This means you can download the QS Access app on iTunes. We hope you’ll find it useful. Our app is a very simple tool for accessing HealthKit data in a table so that you can explore it using Numbers, Excel, R, or any other CSV compatible tool.
It is still early days for HealthKit, but my conversations with toolmakers at Quantified Self events convinces me that there will be many device and software makers that integrate with Apple’s platform for collecting and analyzing personal data. I hope this will allow more people to learn from their own data by reflecting on changes over time and by combining multiple data streams – such as activity, sleep, and nutrition – into a single visualization for comparison.
To give you your HealthKit data in tabular format, we’ve had to simplify it. QS Access shows your data in either “hourly” or “daily” chunks. These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and daily values. This is just a starting point, and we’re looking forward to making it do more based on your feedback.
We very much hope that if you learn something from your data using QS Access, you’ll share your project by participating in a Quantified Self Show&Tell meetup and by joining us at QS15 Conference and Exposition next year in San Francisco. Suggestions about the app itself and interesting examples of usage can be shared with us directly by emailing us: email@example.com,
The QS Access App was authored by our long time QS Labs friend and collaborator Robin Barooah.
We recently released our QS Access app, which allows you to see HealthKit data in tabular format. Not very many tools feed data into HealthKit yet, but Apple’s platform does pick up step data gathered by the iPhone itself. I have step data on HealthKit going back about two weeks. When Ernesto Ramirez and I were playing around with QS Access, loading the data into Excel and looking at some simple charts, I learned something: Even when I’m active, I’m sedentary.
My daily step totals ranged from a depressing 3334 steps on Thursday, September 18 to an inspiring 21,634 steps on Friday, September 25, but – as these charts clearly show – even on the extreme days my activity was concentrated into relatively short periods when I got up from my desk and went out to do something. Most hours, every day, were spent with hardly any movement at all. I’m sitting at my desk, and sitting at my desk some more, and sitting at my desk still more. That’s probably not good. No, not good at all.
Pulling my data out of HealthKit and seeing a few simple charts gave me a bit of insight that I hope will lead to a change in how much I sit. It was a great to be able to easily make some simple analysis of my data. I hope you’ll find QS Access useful also (you can learn more about it here). Please share what you learn in the QS Access thread in the QS Forum or by emailing us about your projects: firstname.lastname@example.org.