Tag Archives: participation

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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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?

LesDethlefsenPostTop2

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.

sequence_data3

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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