Topic Archives: QS Access

Quantified Self and Apple’s ResearchKit

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Apple’s announcement of ResearchKit is strong evidence that Quantified Self practices are emerging as a major influence on medical research and other forms of knowledge making.

Apple talked about how their new effort focused on opening up health research is designed to combat five main current issues:

  • Limited Participation
  • Small sample sizes limit our understanding of diseases
  • Reliance on subjective data
  • Infrequent data provide only snapshots through time
  • One-way communication from researcher to participant (and only at the end of the study, if at all)

Furthermore, the design of ResearchKit allows the  participant to decide how data is shared. Apple will not see the data. Participants are allowed to be involved in the data collection in real-time, using the data they’re collecting to understand and inform their own health improvement plans.

In light of today’s announcement we wanted to highlight some of our favorite and most powerful examples of taking the research process into one’s own hands, making their own knowledge through thoughtful data collection and reflection. We invite you watch what’s possible now, and imagine with us what could be accomplished tomorrow.

Mark Drangsholt: Deciphering My Brain Fog

Lindsay Meyer on Tracking Hearing Loss

Thomas Christiansen on Learning from 60,000 Observations

Nan Shellabarger: 26 Years of Weight Tracking

Rob Rothfarb on Tracking My Blood

Last year we gather a fantastic group of researchers, toolmakers, and science leadership at the 2014 Quantified Self Public Health Symposium to discuss how personal data can impact personal and public health. That meeting culminated in a great report that touches on many of the aspects discussed today regarding ResearchKit. We invite you to download, read, and share that report. For a more nuanced look into how ResearchKit may impact the research community, we’re highlighting four great talks from the the meeting.

Susannah Fox shares research from the Pew Internet and Life Project and describes the challenges ahead for promoting self-tracking.

Margaret McKenna explores the issues, challenges, and ideas large scale self-tracking applications have in mind when they consider working with the research community.

Jason Bobe talks about the lessons learned from involving research participants in the data ownership and discovery process.

Doug Kanter describes what he’s learned from tracking and visualizing his diabetes data.

If you’re interested in how ResearchKit will be affecting self-tracking, personal data, and access to information, research and knowledge making, then stay tuned to our Access Channel here on QuantifiedSelf.com and on Medium.

We are sure to have many great talks and sessions that focus on ResearchKit at our QS15 Conference and Actrivate Exposition. We invite you to join us.


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

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What is in My Gut?

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This past fall we learned about a unique study, conducted at Stanford University, designed to contribute to the understanding of the human microbiome. This study also has a component not common to academic research — data is being returned to the participants. Intrigued, I contacted the principle investigator, Les Dethlefson, to learn more.

Ernesto: Tell me about the Dynamics of Human Microbiota study.

les_dethlefsen_at_the_benchLes Dethlefsen: Since I joined the Relman Lab at Stanford, I’ve been looking at the human gut microbiota, focusing on what affects it and how it changes over time. In our study, we are looking at three different perturbations, deliberate changes to the gut ecology, to see how the microbiota population is affected.

We are very interested in the patterns that emerge. In people who have very stable gut microbiota, does their microbiota remain that way when they undergo diet shifts, a colon cleanout, or an antibiotic? Or maybe people who have a stable gut microbiota most of the time are the ones who are most affected by something unusual such as taking antibiotics. We just don’t know enough to understand these patterns right now. So, we’re really looking for basic ecological information.

Ernesto: If you look at the popular press, it seems the microbiome is the new golden child of biological life sciences. We’re even seeing companies in Silicon Valley get involved with this kind of work.

Les: It is broader than that. It really is a worldwide interest on the parts of both the scientific community and the public. And unfortunately, we are probably going to see some overhype, just as we did with the Human Genome Project. But I do believe this is a very important area. I think there will be a lot of payoffs and health impacts from this research, although it’s not going to be everything.

The shift that, I think, would be good for us to make intellectually is to get rid of the “us vs. them” thinking, because we are symbiotic organisms.

We have evolved with a native gut microbiota, and native microbiota is pretty much everywhere. We have evolved together, so it’s fallacious — an artifact of our past ignorance — that we don’t think of our microbes as part of our physiology.

Ernesto: It seems like exploring the deep sea, an unknown world that we’re just starting to peek into.

Les: It’s along those lines. You’re not wrong about that. But unlike, let’s say, the deep waters surrounding an undersea hydrothermal vent, we already know a lot about human physiology. There are a lot of molecular details and genetic pathways that we already have worked out. The context is somewhat understood.

And now, we have a reasonable start on the initial research: What microbes are present, and where? What’s the range of what we think is the normal distribution? We certainly don’t know enough, because we only know about people in the developed world. However, this may not represent all of human diversity or a very natural state of the gut microbiota.

Candida albicans

Candida albicans

Ernesto: Let’s get back to your study. You are asking participants to send microbiome data in the form of fecal matter and urine to your lab. What are you doing with those samples?

Les: We ask participants to provide both stool and urine samples. With the stool sample, we apply four different methodologies to turn it into data. One is the very common 16S ribosomal RNA (16S rRNA) gene sequencing approach. It’s relatively standard and inexpensive. It acts like an ID card for microbial taxa — telling us approximately what strains are present and in what relative abundance. We have a lot of data like that already for comparison.

sequencerThe second approach we will be applying is metagenomic sequencing, wherein we will be sequencing a random selection of all the genomes of the microbial types that are present. We can’t take this to completion, even with the dropping cost of sequencing, especially because there are some very, very rare microbes that we barely even have the chance to see at all. But we can get a pretty good swathe of genetic sequence data from all the microbes.

The third approach is even more ambitious. It’s called metatranscriptomics. Genes can be carried by any critter, you and I included, but not expressed. Knowing which genes are turned on, and to what extent they’re turned on is a better measure of the biological activity that is actually happening. The metagenome is a measure of potential activities, what the bugs can do. The metatranscriptome shows what the microbes are actually doing. Metatranscriptomics is even more challenging than metagenomics partly because of the nature of messenger RNA (mRNA). It’s a highly unstable molecule. There are technical challenges, but we’re ambitious enough to try to collect information on gene expression.

The fourth approach is not based on gene sequences, but on chemical composition. Metabolomics is the name given to a number of these approaches that are not directed to a specific chemical. These are techniques that try to measure a broad swathe of chemicals present in the environment and their relative abundance. This is a technology that we, in the Relman Lab, know very little about. We’re collaborating with the Nicholson Lab in Imperial College in London, and they will be doing the metabolomic analyses on the stool samples. That may be even closer to where the rubber meets the road — knowing not just the gene expression but also the resulting chemical changes that are happening in the environment.

MicrobeHeatMapMetabolomics takes us to the other type of sample we’re collecting: the urine samples. We aren’t doing this because we have an interest in the urinary microbiome itself, but because, as the Nicholson Lab suggested, the urine provides a more complete, integrated picture of the co-metabolism between the human host and most of the gut microbiota. So while metabolomics for the stool samples would primarily measure the gut microbial activity and what they contribute to the host’s physiology, the urine provides a more integrated picture about how the host metabolism works in concert with the gut microbiota.

Ernesto: If a participant is going to be contributing all of that data, will they have access to it?

Les: As someone with similar interests, I certainly knew that a huge motivation for people to join the study would be the access to their own data. We offer monetary compensation, but for the amount of time that will be spent in contributing samples, it is probably trivial. We knew we would attract the curious, scientifically inclined, and practising scientists. Of course, they would want to see their data.

The Institutional Review Board (IRB) was quite open to us sharing information with the participants about their own microbiota. It probably helped that there’s publicity  about ways people can get this information. There is the American Gut project, offering an assessment of your microbiota for a donation, and uBiome, a private company offering the same kind of service.

I, or another staff member of the study, are going to share this microbiota data with each participant in a conference call. So in effect, I’m going to be a microbiota counselor. It’s nowhere near as high-stakes as sharing genome information. We don’t know enough to say, for example, that this microbiome is definitively healthy, or that it’s unhealthy, or what the exact risks of diseases are due to this particular composition. So we will be putting this information in context, and we will be available as interpreters of the scientific literature. We may be able to say that there is a statistical association between a particular microbial group that someone may have in their gut and some health-related outcome.

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Microbiome sequence data

Ernesto: Will participants be getting a copy of their data as well?

Les: Yes, we will provide that. I have an open source mentality. Added to that is the fact that there are many practicing scientists signing up for the study and saying they want data, not just a PDF summary. I am happy to provide the data in as raw a format as people want. They can get the raw sequence information, a low-level summary (which is the result of the first pass of data processing), or the final summary. I have permission and full intention to share all the data derived from a person’s samples with that person.

Ernesto: Do you think we will see this happening more in the future?

Les: I think we will probably see more of it in the future. We’re moving in the direction of access to information. The open source movement has reached the health and medical realm from its origins in tech and computing. I think the participatory nature of access to data and scientific information is a good thing. It has started, and I don’t see any way of reversing the trend. I would hope that it becomes the norm that there is some appropriate level of sharing, that research participants have access to their data if they wish, and in a way that lets them interpret that data appropriately.

I believe that people have a right to that level of knowledge about their bodies, and if we, scientists, are generating that knowledge, there’s no reason not to share it with the individuals.


The Dynamics of Human Microbiota study is currenlty recruiting participants. If you’re interested in learning more about the ecosystem within read more about the study and check to see if you’re eligible to participate here.

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

 

 

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QS Access: Data Donation Part 2

In our Access Channel we’re trying to expose ideas, efforts, and insights about personal data access and it’s role in both generating personal and public insights. The last time we wrote about data donation we mentioned a few different projects that allowed you to collect and/or publish your self-tracking data for others to view and access. Today we’re going to showcase a few research-focused projects that collect personal data, but also allow participants to access the data they contribute. This seemingly minor addition, participant access to data, is actually a process not commonly employed by research studies. We’re very interested in new participatory models of research that respect participant’s rights to fully understand and access the data they contribute. If you know of others please get in touch and we’ll add them to the list.

Personal Genome Project: Harvard
Probably the most well-known of these research projects is the ongoing Personal Genomes Project based at Harvard University (PGP). Led by George Church and an outstanding team, the PGP is an ongoing research project recruiting participants to “share their genetic, health, and trait data in a public and non-anonymous manner. Participation is free.

American Gut
Much like the project above, the American Gut project is an open call for participant to collect and share their data. In this case it is human microbiome data. Although enrollment is not free (they request donations starting at $99 to participate) data is returned to participants. (If you’re interested in participating in microbiome research, but live in Europe see the British Gut project)

Dynamics of the Human Microbiota
This new project, based out of Stanford, is also exploring the human microbiome. This study includes a variety of different perturbations and longitudinal data collection. Participants are compensated for their participation, their data is made accessible to them, and they have the opportunity to discuss their results with the study staff.

For those of you interested in research methods and ethics we recommend reading this brief article by Jeantine E. Lunshof, George M. Church, and Barbara Prainsack: Raw Personal Data: Providing Access

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Do It Yourself Diabetes

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Dana Lewis and Scott Leibrand are the creators of the amazing “Do-It-Yourself-Pancreas-System,” also known by #DIYPS. We had a few question for them.

Ernesto: Why build your own pancreas?

IMG_3561Dana: I’ve had Type 1 diabetes for about 12 years. I use an insulin pump and a continuous glucose monitor (CGM), but the devices are separate. They don’t talk to each other. I have to look at the data from the CGM and then make decisions about my insulin. I have to make about 300 decisions per day on average. It’s really fatiguing. So we created some algorithms that took my blood glucose data, the amount of insulin that I’ve given myself, and the amount of carbohydrates that I’ve decided that I’ve eaten, and ran them over and over again to give me a prediction of what my blood sugar was going to be and whether I need to take any action. Instead of having to constantly do the math myself, our system will push an alert to my phone or watch.

Ernesto: Does it dose you automatically?

Dana: Originally no, but more recently we’ve built a full closed loop version of #DIYPS, that is essentially an artificial pancreas, that talks to my pump and adjusts to give me a little more or a little less insulin.

Ernesto: Who writes the code?

Scott: I’m doing all the coding. I’m sure Dana could, but she has a lot going on and designs the algorithms. My title is Chief Spaghetti-Coder. This is the bleeding edge. It doesn’t need to be elegant code.

Ernesto: What have you learned from building your own pancreas?

Dana: The beauty of a CGM is that it gives you a data point every five minutes. Over the past year I’ve produced more than 130,000 data points of blood sugar levels alone. That gives me an incredible picture of what’s happening. With a traditional meter, it’s rare to find somebody who tests up to even 10 times a day. And the standard use for an insulin pump is very much “set it and forget it.” The #DIYPS allows me to customize without having to constantly adjust my insulin pump manually, and that frees me up to live my life, work, and do whatever it is that I want to do.

A visualization of Dana’s Data over the first year of the #DIYPS system.

A visualization of Dana’s Data over the first year of the #DIYPS system.

Ernesto: How did this project start?

Dana: We first started building the system just to make the alarms on the device louder, to wake me up because I would sleep through them. The device manufacturers didn’t seem to have a solution. Then we started looking at getting the data onto a computer so Scott would be able to view it. At the time, we had recently started dating, and he lives 20 miles away. I wanted him to be able to see what my blood glucose level was, so if it was low, he could text me; and if I didn’t respond, he could call 911. But we didn’t have a way to get the data off of the device.

Scott: The key moment was when we saw a tweet from John Costik, who was working on the Nightscout Project. Nightscout is open source code that helps people transmit their CGM data to other devices. I tweeted John right away: “Hey it would be awesome if we could get access to this code.” That’s really where it started. And along the way the whole process has been extremely public. We’ve been tweeting, blogging, and making everything we’ve been doing completely visible.

Ernesto: I’ve seen you tweet using the hashtag #wearenotwaiting. What does that mean?

Dana: #WeAreNotWaiting is a hashtag that was coined at a conference hosted by an online diabetes advocacy and information sharing community called DiabetesMine.com. For me it means that we’re not waiting for traditional device manufacturers to come out with the product. In three to ten years there’ll be devices like our artificial pancreas systems out in the market, being sold by companies approved by the FDA. I need to be alive when that system gets out in the market in, perhaps, five years.

I need to be alive when a cure becomes available.

Scott: Right about the time that we started working on #DIYPS, the Nightscout Project started to grow really quickly. There are now over 10,000 people in the CGM in the Cloud group. Over 2,000 people are using Nightscout to view their own or their loved ones’ blood sugar levels remotely on phones, watches, and other devices. This is real stuff that’s making a real difference in the world. And that’s only going to accelerate as more people do more interesting things like this closed loop that we’ve just done.

Ernesto: You’ve written about “data as free speech.” What do you mean? How can data be speech?

Dana: People often don’t understand why its legal for us to ‘hack’ a CGM and an insulin pump. (Note that hacking isn’t a negative thing; we’re just sharing the data across devices!) They assume that because all my DIY gadgets are not FDA-approved to use them the way I’m using them is somehow against the rules. But I can treat my own body, my own diabetes, the way I want to. And if I share my data, that’s obviously a kind of speech. But if we decide to share our code? I think the FDA sees this as a gray area. We very much want to continue our conversations with regulators.

Ernesto: Where do you see your project going?

Dana: I feel that every time I answer this question my answer changes, because my understanding of its potential is constantly changing. I never would have thought that any of what we’ve done was possible. Right now one of our goals is to make sure that the knowledge we gained about diabetes through our work with #DIYPS is adopted by clinicians, and that patients have access to this new information for treating diabetes. We’re also taking #DIYPS to a new level with #OpenAPS, an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available to more quickly improve and save as many lives as possible and reduce the burden of Type 1 diabetes.

Dana with the #OpenAPS system.

Dana with the #OpenAPS system.

Scott: A few of months ago, at a conference convened by the advocacy group DiabetesMine, we got up and talked about our project, and I said: “I’m putting a stake in the ground that we’re going to make a closed loop artificial pancreas by August 1st, which is the date we’re getting married.” Everybody applauded and thought that was awesome. Then we went home. And we had it done in two weeks.

Dana: For anybody who wants to get involved in this, we would love to talk to you. There are so many people with diabetes and there is so much data that drives the management of this disease.

But there’s not a lot of awareness of how many diseases, including diabetes, could have their care revolutionized just by having better access to data.

That’s the thread of Quantified Self that I’m most interested in. The diabetes community happens to be one of the first to take advantage of what’s possible.

Dana tweeted her blood glucose data during this interview.

We invite you to share your data access stories, and this Access Conversation with the #qsaccess hashtag and follow along here in our Access Channel quantifiedself.com and @quantifiedself.

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QS Access: Personal Data Freedom

We are happy to welcome this guest post by Madeleine Ball. Madeleine is the Senior Research Scientist at PersonalGenomes.org, co-founder of the upcoming Open Humans project, and the Director of Research at the Harvard Personal Genome Project. She can be found online @madprime.

MBallThe digital trails we create are becoming thick and personal. Increasingly, people choose to collect meaningful data about themselves. Activity tracking to understand health and fitness. Genetic testing to understand ancestry and inheritance. Incidental data also expands: smartphones quietly observe our location through the day. Who gets to see our data? Can we see our own data? Beyond “privacy policy” documents, people are starting to call for something stronger: for personal data ownership.

Unpacking “data ownership”

It’s worth unpacking this phrase. What do we mean by “data ownership”? If we want to see changes, we need to start with a little more clarity.

Legally, data is not property. There is no copyright ownership of facts, as they are not “creative work”: the United States Supreme Court famously established this in the landmark case Feist vs. Rural. They are not patents, there is no invention. They are not trademarks. There is no “intellectual property” framework for data.

 Yes, data is controlled: through security measures, access control, and data use agreements that legally restrict its usage. But it’s not owned. So let’s set aside the word “ownership” and talk about what we really want.

Control over what others do

One thing we might want is: “to control what others do with our data”. Whom they share it with, what they use it for. Practically this can be difficult to enforce, but the legal instruments exist.

 If a company is generating data about you, then the “control” you have is spelled out in their contractual agreement with you. Check the policies: “Privacy policy” or “Data use policy” documents are a standard feature.

 Think about what you really want. Are you opposed to commercial use of your data? Look for words like “sell”, “lease”, and “commercial”. Are you concerned about privacy? Look for words like “share”, “third-parties”, and “aggregate” – and if individual data is shared, find out what that data is.

Companies won’t change if nobody is paying attention and nobody knows what they want. We can encourage change by getting specific, and by paying more attention to current policies. Raise awareness, criticize the bad actors, and praise the good ones.

Personal data access and freedom

The flip side of “data control” is our own rights: what can we do with our own data? We want access to our personal data, and the right to use it.

 This idea is newer, and it has a lot of potential. This was what Tim Berners-Lee called for, when he called for data ownership last fall.

“That data that [firms] have about you isn’t valuable to them as it is to you.”

I think it’s worth listening, when the inventor of the world wide web thinks we should have a right to our data.

So let’s spell it out. Let’s turn this into a list of freedoms we demand. We should be inspired by the free software and free culture movements, which advocate for other acts of sharing with users and consumers. In particular, inspired by Richard Stallman’s “Four Freedoms” for free software, I have a suggested list.

Three Freedoms of Personal Data Rights

Raw data access – Access to digital files in standard, non-proprietary file formats.

Without raw data, we are captive to the “interface” to data that a data holder provides. Raw data is the “source code” underlying this experience. Access to raw data is fundamental to giving us the freedom to use our data in other ways.

Freedom to share - No restriction on how we share our data with others.

Typically, when data holders provide access to data, their data use agreements limit how this data may be shared. These agreements are vital to protecting user privacy rights when third parties have access, but we have the right make our own sharing decisions about our own data.

Unrestricted use – Freedom to modify and use our data for any purpose.

 Data use agreements can also impose other limitations on what individuals can do with data. Any restriction imposed on our use of our data impinges on our personal data rights. Freedom for personal data means having the right to do anything we wish with data that came from us.

In the short term, access to raw data can seem obscure and irrelevant: most users cannot explore this data. But like the source code to software, access to this data has great potential: a few will be able to use it, and they can share their methods and software to create new tools.

Raw data access is also an opportunity for us to share for the greater good, on our own terms. We could share this data with research studies, to advance knowledge and technology. We could share data with developers, to develop software around it. We could share it with educators, with artists, with citizen scientists. We could even cut the red tape: dedicate our data public domain and make it a public good.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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My Device, My Body, My Data

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In 2007, after collapsing while rushing to board a train, Hugo Campos was diagnosed with hypertrophic cardiomyopathy, and an ICD (implantable cardioverter defibrillator) was implanted in his chest to track and regulate his heart rhythm. To his great surprise, he discovered that it was very difficult to gain access to the data being generated inside his own body. Today we’re inaugurating what we hope will be a regular series of “QS Conversations” about data access with an interview with Hugo about his long battle for the right to see what’s happening inside himself.

Ernesto: Why does access to your ICD data seem so important to you?

campos_hugo_medx2014 copyHugo: I have a computer with firmware, processor and memory regulating my every heartbeat, wired into my heart, and buried inside my body. I can’t even see it. A corporation in the cloud, located out of state, has a wireless, transparent access to a device that’s implanted in my body, but the only control I have is to unplug the remote monitoring unit in my house to prevent them from getting the data. This creates a very unsettling feeling of not having autonomy. I’m paying thirty thousand dollars for a device, having it implanted inside my body, and then being locked out of it.

Ernesto: Was there something that happened that set you on this path?

Hugo: Yes. For a long time I’d been on my spouse’s health care plan, but when he decided to freelance and quit his job, I couldn’t get health insurance. This was before the Patient Protection and Affordable Care Act. Kaiser denied me because I had a heart condition. Anthem Blue Cross denied me as well. Now put yourself in my shoes. Here I am, being denied access to the device because the system “knows better” and I could harm myself, but now they can’t give me service at all.

Ernesto: You can only get access to your ICD through the medical system, but the medical system won’t take you because you have an ICD?

IMG_1542 copyHugo: Right, so I had to figure out a way to protect myself. I looked at it as kind of an extension of my Second Amendment rights. I’m not particularly pro-gun, but I look at it as the ability to defend myself. If the system was really unavailable, I have to at least be able to interrogate my ICD. So, I went on eBay and bought a pacemaker programmer that gave me full, unrestricted access to my implanted device. I can change its programming, shut it off, deliver therapy, and do as I wish. In fact, it’s the same machine that clinics use. I also went to Greenville, South Carolina, and took a class on how to program ICDs and pacemakers. I thought, “Okay, I may not become a cardiac electrophysiologist by any stretch of imagination, but”–to use the firearm metaphor again–”at least I have a basic understanding of gun safety so I don’t shoot myself.”

Continue reading

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QS Access: Backing up HealthKit Data

As you may know, we’re very interested in how HealthKit is shaping and extending the reach of personal self-tracking data. Last week, during Apple’s quarterly earnings call, Tim Cook mentioned that “There’s also been incredible interest in HealthKit, with over 600 developers now integrating it into their apps.” (emphasis mine).

This morning, we were alerted by Sam Rijver and Daniel Yates that special attention to how you backup up your phone is required in order to backup and have access to your HealthKit data:

 

For those of you that are unfamiliar with backup options for your iOS device. Here’s a quick gif to walk you through the process of encrypting your iOS backup so that you can restore your HealthKit data if anything happens to your device:


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QS Access: Precision, Patients, and Participation

This morning President Barack Obama announced a new Precision Medicine Initiative, a key $215 million piece of the proposed 2016 budget. Much has been written since last week’s State of the Union, when this initiative was first mentioned by President Obama. In brief, the initiative is an investment in new programs and funding initiatives at major government bodies that influence the current and future health of all Americans, including the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC). These programs will focus on developing “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.”

There is a lot of information being circulated about this new initiative, and we’ve collected some links below, but we’d like to highlight something directly related to our interests in self-tracking data, personal data access, and new models of participatory research. In this morning’s announcement President Obama mentioned a long-term goal of creating a participatory research cohort comprised of 1 million volunteers who will be called upon to share personal medical record data, genetic samples, biological samples, and diet and lifestyle information. This is truly an ambitious goal and we are happy to see the President take care to mention the importance of including patients and the individuals who collect this data in the decision making and research process. For example, here is the description of this specific program from the NIH Precision Medicine Infographic

NIH_PM_Participation

Here at QS Labs, we’re dedicated to helping create and grow a culture that enables everyone to generate personal meaning from their personal data. Sharing, participation, and exploring new models of discovery are a core themes we’re exploring as part of our QS Access work. We’ll be following this initiative as it moves from today’s announcement to tomorrow’s reality. Be sure to stay tuned to our QS Access Channel for more updates as we learn more.

Learn more about the Precision Medicine Initiative
NIH mini site describing the initiative
White House Blog: The Precision Medicine Initiative: Data-Driven Treatments as Unique as Your Own Body
FACT SHEET: President Obama’s Precision Medicine Initiative
A New Initiative on Precision Medicine by Francis Collins and Harold Varmus (New England Journal of Medicine).

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QS Access: Data Donation Part 1

New sensors are peeking into previously invisible or hard to understand human behaviors and information. This has led to many researchers and organizations developing an interest in exploring and learning from the increasing amount of personal self-tracking data being produced by self-trackers. Even though individuals are producing more and more personal data that could possibly provide insights into health and wellness, access to that data remains a hurdle. Over the last few years a few different projects, companies, and research studies have launched to tackle this data access issue. As an introduction to this area, we’ve put together a short list of three interesting projects that involve donating personal data for broader use.

DataDonors.org
Developed and administed by the WikiLife foundation, the DataDonors platform allows individuals to upload and donate various forms of self-report and Quantified Self data. Data is currently available to the public at no cost in an aggregated format (JSON/CSV). Data types includes physical activity, diet, sleep, mood, and many others.

OpenSNP.org
OpenSNP is an online community of over 1600 individuals who’ve chosen to upload and publicly share their direct-to-consumer genetic testing results ( 23andMe, deCODEme or FamilyTreeDNA) . Genotype and phenotype data is freely available to the public.

Open Paths
Open Paths is an Android and iOS geolocation data collection tool developed by the New York Times R&D Lab. It periodically collects, transmits, and stores your geolocation in a secure database. The data is available to users via an API and data export functions. Additionally, users can grant access to their data to researchers who have submitted projects.

We’ll be expanding this list in the coming weeks with additional companies, projects, and research studies that involve personal self-tracking data donation. If you have one to share comment here or get in touch.

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QS Access: Self-Report & Quantified Self in Health Research

As part of our new Access channel we’re going to highlight interesting stories, ideas, and research related to self-tracking data and data access issues and the role they take in personal and public health. We recently found this expert report, published in the International Journal of Obesity, that tackles issues with the data researchers rely on for understanding diet and physical activity behaviors, and ultimately concludes that the data is fundamentally flawed.

Researchers has known for a long time that relying on individuals to understand, recall, and accurately report what they eat and how much they exercise isn’t the best way to understand the realities of everyday life. Unfortunately for many years, this was the only way to track this information – interviews, surveys, and research measures. Only recently have tools, devices, and methods matured to a point where objective information can be captured and analyzed.

The authors of this article make the case that obesity and weight management fundamentally relies on getting these numbers right, and unfortunately most research hasn’t. Reading the background on self-report data and the call to action the authors make for developing and using more objective measures we can’t help but wonder about the role of commercial personal self-tracking tools. How can we, as a community of users, toolmakers, and researchers work together to open up access pathways so that the millions of people tacking pictures of their meals and uploading their step data can have a positive impact on personal and public health? This is an open question, one that we’re excited to be working on.

If you’re interested in these type of questions, or working on projects related to data access we invite you to get in touch and keep following along here with us.

 

 

 

 

 

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