Interviewing project grant awardee Kevin Arvai

Today we’re interviewing Kevin Arvai. Kevin is a bioinformatician with an interest in personal genetic data and he was awarded a project grant to implement a project that will bring genotype imputation to the Open Humans community.

Kevin, please give our blog readers a quick introduction about who you are!

I am a data scientist at a clinical genetics company in Maryland. My background and formal education is in biology, however I completed a master’s degree in computational biology and bioinformatics. Like many, I’m riding the wave of data that our generation has found itself immersed in by competing in data science competitions and contributing to “open-” (source, science, data) projects. I’m particularly interested in machine learning and human genetics but looking forward to learning new skills by building Imputer.

When and how did you come to Open Humans?

I came to Open Humans in February 2018 after working on a project with the Director of Research, Bastian, at a hackathon hosted by NCBI.

Have you been involved in any projects on Open Humans so far, either as a participant or even running your own

Not only is this my first project working with Open Humans, this is my first project as part of a open source community. Open Humans was a welcoming and collaborative group of people that encouraged my ideas, so it seemed like a perfect fit to start contributing.

Your project Imputer was awarded one of the Open Humans project grants. Can you explain us what the project is about?

The goal of Imputer is to provide users with a more comprehensive picture of their genome. Direct to consumer genetics companies, like 23andMe, only genotype a small fraction of the genome. Researchers are finding new genetic locations associated with traits and diseases at a rapid pace. Users might be interested in knowing their genotype status for these new associations, but the locations may be in regions that direct to consumer tests are not genotyping. Imputer leverages the vast amount of genotype data made available by 1000 genomes project and by the Haplotype Research Consortium to provide Open Humans users with genotype estimates at additional locations in their genome.

How did you come up with the idea behind Imputer?

The genesis of Imputer was spawned from long conversation over lunch with Bastian.

Is there anything important that we didn’t cover so far that you’d like to add?

I’d like to encourage others who are “interested in, but anxious about” contributing to open source projects to take the leap! If you’ve found this post, Open Humans is a great place to start!

Kevin’s encouragement motivated you to take action? The Open Humans project grants are ongoing and you can apply for one too!

Open Humans, what’s next?

President Bartlet of The West Wing is calling his famous “What’s next” to his secretary after managing a task.

I just defended my PhD last week, and one question from virtually every person who attended and stayed for the after-party: What’s Next? Which initially felt a bit weird. After all, I already took my next step three months ago when I joined Open Humans as the Director of Research. But then I realized that this is a nice opportunity to reflect a bit on my first months and think about what my next goals for Open Humans are.

Where is Open Humans so far?

So far I spent good parts on learning the ropes. First of all, I had to find my way into the technical infrastructure of Open Humans. Learning the code base, the APIs, server setups and so on. And what better way to do this but starting my own projects? I thus integrated two new projects on Open Humans: First I connected my long-standing project openSNP with Open Humans – allowing users of both platforms to re-use their genetic data more easily. Then I started TwArχiv, which not only brings a new data source but also some data-visualization to Open Humans. This integration of Twitter data will hopefully also be a first step towards a more holistic view of personal data that includes non-medical data.

Hand in hand with the technical side of things I also found my way into the community around Open Humans. Learning which projects there are, how to best support them and also how to grow the Open Humans community even more. I not only got to know many of the brilliant individuals inside the Open Humans community, but I also helped them to achieve their goals – be it through bug fixes, relevant connections or finding out how to optimize our website to make it work for their needs. First steps towards a further community growth were also taken: We could announce the first three successful grant applications, all bringing new data sources to Open Humans. And a fourth grant announcement – enhancing existing data sets – will be out soon!

The Open Humans community grows nicely and is becoming more and more engaged. So things are on track. But where should we go from here? And what is the larger vision? Traditional academic research – as well as corporate data silos – put themselves into the center of all data collection. In contrast, Open Humans is very different to this. As Steph laid out in her blog post: Open Humans is a technological platform; a vibrant community; and a paradigm shift to how research is done at the same time. In addition to all these things there is one thing that I always mention when people ask me what Open Humans is: It is empowerment. Putting individuals in control of their own data and of research at large. And to me, this means more than ‘just’ giving people the choice of when and where to share their data.

What should Open Humans be?

Empowerment means giving people the opportunity and chance to explore and understand their own data. Be it on their own – or in collaboration as a community outside the traditional academic research setting. The growth of the independent Open Artificial Pancreas community – which aggregates their own data through Open Humans – is a stellar example for this empowerment. As stewards of the Open Humans ecosystem it is our responsibility to support people to run projects like these. It is up to us to make it easier to create and run projects on Open Humans – empowering more people including those who are not highly programming savvy. Open Humans offers the unique chance to democratize science, enabling people outside academia to do new research that has never existed before. To pull this off we have to become more inclusive in our approach. This means getting everybody on board who has great ideas for research.

First steps towards this direction have been made already: We now have a first data uploader template that allows everyone to create their own, data-collecting Open Humans project while requiring zero programming knowledge. Instead a web browser is enough to do the complete setup. A similar idea for the administration of projects should become a reality in the near future. Furthermore, we are on the way to create shareable analyses notebooks. These can be written and run by everyone – facilitating community-driven data analysis. By increasing our inclusivity more we will not only see more projects on Open Humans, we will also see a much wider diversity in how these projects will use data. I can’t wait to interact with all of them.

I see this diversity reflected in the kinds of data that will be on Open Humans and the kinds of research that will be done with it. Traditionally many of the projects on Open Humans have and had a focus on health. But I don’t see why this should be the sole kind of research that profits by being run with and by highly involved participants. After all, while much of the Quantified Self revolves around health, it is far from the only topic: People are interested in their personal finance data, phone usage, emails and more. And so are social scientists, economists and other academic disciplines. My goal is to get these people on board for Open Humans too, showing them the huge benefit that an engaged study population offers.

Let’s just think of a simple example: Everyone can pay Twitter to get access to their firehose of data or just scrape tweets for keywords from the web. But who but Open Humans can offer potential access to 200 or more full Twitter archives that are available right now? And more importantly, who offers the possibility to get in touch with these people and as such a way to get additional metadata and consent them? The same is true for virtually all kinds of social media data and many other data types. Humans are more than their bodies, and Open Humans should reflect this.

So this is what’s next for Open Humans: Creating an ecosystem that enables the largest possible number of people to do research; that collects and enables the re-use of the most diverse set of data; and that brings together participants and researchers from all disciplines and walks of life – informing each other and creating the most interesting research.

An interview with project grant awardee Anh Nguyet Vu

Today we’re interviewing Anh Nguyet Vu. She is the recipient of one of our Project Grants. With MyFitnessPal Miner she not only brings a new data source to Open Humans, she is also working on visualizing these data and connecting them to genetic data. 

Hey Anh Nguyet, please give our blog readers a quick introduction about who you are! How did you come up with the idea behind MyFitnessPal Miner?

Generally I wouldn’t want to introduce myself by talking about my problems, but in this case it does give you the story behind the project. So when I was a freshman in undergrad, I faced a problem that would eventually lead to the development of MyFitnessPal Miner. This problem, no doubt a familiar one for many others, was weight gain. Since I was (and am) the kind of person who believes that “what cannot be measured cannot be managed”, I started tracking dietary intake. Because I was already tracking what I was eating, I became interested in the quantified self movement, and it wasn’t long before I was convinced that collecting other types of data would be valuable. I experimented with many food logging tools, including MyFitnessPal (which was never my primary app, but it happens to be the most popular one today). I also tried a variety of activity and exercise trackers before Fitbit hit mainstream. Probably my most earnest project was tracking how much time was spent on different activities, down to a minute’s resolution, over a span of three months.

It was inevitable that I would want to incorporate genetic data. To gather all the other kinds of data without considering your personal genetics is to miss out on a crucial part — especially if you wanted to optimize health, as I was a little obsessive about. I had a self-defined area of concentration called “personalized medicine” (also known as “precision medicine”) for my undergraduate major at Stanford. I think more people understand personalized medicine as tailoring drug treatments for an individual’s genetic makeup, but if you believe in “food as medicine”, then it should encompass nutrition as well.

Your project MyFitnessPal Miner was awarded one of the Open Humans project grants. Can you explain us what the project is about?

MyFitnessPal Miner exists with three goals. The first is about making the data more accessible, allowing you to get your own data in a format useful for other projects, including ones on Open Humans. The app ports your data to standard .csv files and does some additional parsing to create potentially useful tags. For example, it tries to recognize instances of fast food by matching records containing restaurant chain names.

The second goal is integrating that data with current genetic resources. There are some really interesting studies on how your genetics influences and interacts with your diet, such as your preferences for salty/sweet/bitter foods, risk for specific food intolerances, and how you’d react to a low-carb versus a high-carb diet. When curating these kinds of studies, I think that many people must also be curious about how the findings apply to them. So if you have 23andMe data and MyFitnessPal data, the app gives you a kind of integrated dashboard of genetics and nutritional behavior. You might, for instance, be able to see that your fast food consumption is greater than average, and that this seems congruent with what a published study has found given your genetic variants. Or next to the summary of your actual sodium intake, you might notice the relevant finding that your genetics predict that your blood pressure is fairly sensitive to how much salt you’re eating. However, because MyFitnessPal doesn’t contain explicit data for vitamins and minerals, not every related published finding can be connected with your real-life dietary data, unless the app can be made to intelligently infer vitamin and mineral intake from the food records.

Beyond comparing existing information, through the app it should be possible to use your real-life dietary data along with your genetic data to suggest something new. This third goal is kind of a reach goal given the limited time frame I have, but it’s the essence of an Open Humans project. I’d still have to think about the questions that are feasible and the methodology for them. Hopefully it won’t be just me, and there will be people in the Open Humans community who’d want to build upon MyFitnessPal Miner.

I do also hope that there will be interest outside of Open Humans. You can recall that all of this started not with my interest in genetics, but with food tracking. Well, it’s the start of a new year, and there will be a lot of people doing that as they pursue a healthier lifestyle. Some will seek understanding of their calorie and macronutrient patterns and then be hungry for additional value from their collected data. Being shown where the genetics tie in to create that additional value can perhaps entice people to bring their genetic data to the project, and therefore to Open Humans.  

When and how did you come to Open Humans?

I consider myself a relatively new member of Open Humans, since I joined in the fall of 2017. Around that time I was doing research for a start-up, where a colleague mentioned Open Humans and said making his genetic data public was something he wouldn’t do. I, on the other hand, was someone who was already quite open, having been to quantified self meetings to hear others share their data and insights and to share mine.

Have you been involved in any projects on Open Humans so far, either as a participant or even running your own?

When I joined Open Humans, I made my data accessible to all studies. I would think more about running my own study after finishing the development of MyFitnessPal Miner.

Genevieve now analyzes private data!

Genevieve Genome Report is a tool that takes genome, exome, or 23andMe data and produces a report comparing your genome to the “ClinVar” database – a public compilation of publish reports and databases.

This might uncover reports about to rare variants with potentially dramatic effects: people typically carry several “recessive diseases”, and this report might uncover some of yours. But it might also uncover mistakes in the literature! Research is messy, and so is this. To help everyone sort through the evidence, Genevieve also invites users to edit collaborative notes regarding reported effects.

As such, the tool is not a clinical tool, no more than Wikipedia is! It’s open source, freely shared, and intended for collaborative learning. It’s my own personal project – and I’ve extended it to enable private data analysis, and empower more folks to explore their data.

Meet the TwArχiv

This is a cross-post from my personal blog, where I also introduced the new Open Humans project I created.

Would you like to analyze your Twitter history? Exactly 5 years ago Twitter started offering the option for users to download their full archive of personal tweets. The archive gives you a change to quickly browse through your personal history and find those funny cat pictures you once posted. But there is additional value in the archive, transcending the trips down to memory lane. For example, by looking into a full Twitter archive one can investigate longitudinal trends in interaction behaviour or geotag-based movement patterns. While Twitter archives come with their own user interface, they are not really designed for such deeper dives into the data. Which is why I have been working on a small tool called TwArχiv that tries to allow for such insights.

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The plot above is a static example of what TwArχiv does. It gives you an idea of how my personal reply behaviour has changed over time. Using a gender-guessing Python library it classifies the users I replied to based on their first names. You can quickly see that I did have an extreme male-bias starting in early 2009 that becomes less pronounced in more recent years. What happened in 2009? I became an active member of the overwhelmingly male German Pirate Party. Go figure.

The plots that the TwArχiv generates include further interesting data. You can see a full, interactive demo done on my personal Twitter archive. The TwArχiv also looks into how your tweet behaviour changes with respect to whether you use Twitter for replying, retweeting or making original posts on your own; at which times of the day you tweet; and even how you move across the globe while tweeting. The gif below gives you a small glimpse into parts of my 2016 movements based on tweets.

If you want to analyse your own Twitter archive please give TwArχiv a try. As it uses Open Humans for the archive storage you can optionally also choose to make that data publicly available. If you have an unprotected Twitter account this data is already public but harder to systematically access (at least for anyone but Twitter itself). So why not help out social media researchers everywhere by giving your data?

If you have ideas for some data analyses that are missing so far: The code of TwArχiv is open source and I’d love to get your contributions and suggestions. And if you have a larger feature/analysis idea that’s not in TwArχiv yet, Open Humans is giving out grants of up to $5,000 for projects that help to grow their eco-system. Improving on TwArχiv is a perfect match for this.

An interview with project grant awardee Benjamin Carr


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Today we’re interviewing Benjamin Carr. He is not only a long-term member of the Open Humans community, but also the recipient of two(!) of our Project Grants.

His two – closely related – projects are the integration of both Google Fit and Microsoft Healthvault into the Open Humans Network.

Benjamin, please give our blog readers a quick introduction about who you are!

I originally became aware of the Personal Genome Project, way way back at its dawn in 2005 while I was working at Woods Hole Oceanographic Institution in Physical Oceanography. The idea to a young scientist that PGP was basically providing seed money to make human DNA sequencing affordable was amazing, and I was quick to sign up, and encourage my whole family to join as well. For those that don’t know, becoming a PGP volunteer was a multi-year process requiring a multitude of questionnaires. I didn’t receive my tubes to spit into until 2012! I attended a couple of the GET conferences in Cambridge, MA when I was across the river working on my PhD in Biology at Boston University. My science and technology passions found an excellent intersection with Open Humans.

You have been a part of the Open Humans community for a long time. How did you initially come to Open Humans?

I’m not sure of the exact date, but it was shortly after launch in 2015 that I joined Open Humans. Most likely due to an email blast from Madeleine Ball to the PGP group. I quickly linked up as much data as I had available at the time, and made things like Wildlife of Our Homes public. Furthermore, I am one of just 26 People to admit that we owned a “Jawbone Fitness tracker,” an early step counter and Fitbit competitor.

Between trying to spur communications on the forum and Slack channel, submitting issues and pull requests for the website itself and individual projects, I’ve banged my head against most things Open Humans, save the legal bits.  

You volunteer for Open Humans, what are your responsibilities and how did you end up in that role?

Like all of us I volunteer data. But I also help to edit mailings and postings that go out in email blasts and on the blog. I report issues on Github and Slack. I try to help new users and developers when I can and do  a bit of coding for different parts of the system myself, and keeping an eye out for bugs and vulnerabilities. I also started managing the Facebook Page in March.

Open Humans is looking for more than just data, programmers, and scientists, the idea is to make lots of information “open”. Having had some experience and the resources to manage a social media system and strategy my offer to try to make the Facebook Community more vibrant was welcomed with open arms. Hopefully you’ve noticed the steady stream of what I hope are interesting links and stories on our Facebook Page.

If you have any ideas for content or want to host something like a Reddit AMA on our Facebook drop me a line! I am always looking for new exciting things to share. Being a scientist myself, I am actually running a Facebook experiment. I started posting over the weekend, and Sunday posts seem like a winner for interaction with our Facebook fans! I’m also trying to see if changing the timing of the posts inspires more people to like, share or comment. We’ll see!

Open Humans could always use help making things more accessible. We try our best to make each piece understandable to a wide audience, but just having volunteers review the documentation and explanations of projects and goals would be welcomed!.

You did not only get one Open Humans project grant, but two. What are these two projects you have planned?

The first as you mentioned is to incorporate Google Fit data into the Open Humans platform. Google Fit ships with every Android phone that uses the Google Apps, which means as of this interview there are more than 2.6 Billion active Android phones. While Fit natively tracks things like steps and stairs, there is an entire ecosystem of products that plug into it, like Running, Biking, and even Push Up Apps! As well as many devices from glucose meters for diabetics to blood pressure cuffs, and even CPAP machines for people with sleep apnea! The second project has some overlap with the devices supported, but MS HealthVault is really a repository for millions of people to keep track of medical records, MRI images, XRays, Prescriptions, and even directly link Electronic Health Records!

Your two projects sound a bit related. How did you get the idea for them? Are you using these services yourself?

The two are related, honestly the Google Fit seemed like a logical integration I just hadn’t had time to work on it, the Healthvault occurred to be during a discussion with you on Slack about openSNP and pulling that data into Open Humans. By my using the APIs (Application programming interfaces) provided by Google and Microsoft I can allow participants to share data collected in these two “silos” with the Open Humans community and researchers. By adding these two integrations I hope to increase the appeal for researchers to look at and use the Open Humans datasets, as we have years, and in some cases in MS HealthVault, decades of data that can be accessed along with genetic sequences from those that have provided it it from services using microarrays or chips like 23andMe and Ancestry, or full genome sequences!

When I’m done with the grant work, the code should be self updating, or offer the ability for users to request data be pulled in at the push of a button. I do use both MS HealthVault and Google Fit, both directly and with attached devices and services. I started using MS HealthVault when Google Health closed, and I migrated all of the data I had put into Google Health for PGP from Google to the Microsoft platform. I’m also a long time Google Phone user, these two facts makes coding for both APIs easier as I have a ton of data in each of the respective warehouses.

Is there anything important that we didn’t cover so far that you’d like to add?

I have seen how genomics can play a huge part in one’s life. I am very open with my doctors about being lucky enough to have a sequenced genome. By being able to do a little bit of my own research and ‘checking my genome for Z’ based on doctor’s suggestions, symptoms that previously eluded diagnosis started to line up. I also lost my first wife to Ovarian Cancer that was accelerated – if not actually caused – by a family history of Prostate Cancer, something we wouldn’t know until after she had passed.

On the lighter side I’m an avid photographer, and when I was doing my PhD work in Boston went to the woods nearly every weekend, to clear my head, rejuvenate my soul, throw a tennis ball for my labrador retriever, and rack up a very high shutter count on my old DSLR. I took this picture of the first of three upcoming “Supermoons” a couple weekends ago.

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I am also an environmentalist. My background spans fisheries and oceanography, but also the hereditary traits passed on from mother to offspring of Walleye fish, the biocomplexity and interconnectedness of the Great Lakes.I was one of the four man crew who did the 9/11 impact assessment of the Hudson River Estuary from the George Washington Bridge to the Battery! I’ve been Chief Scientist on multiple cruises including working from small boats to large 115 ft, 200 ton vessels! Even though my first cruise in 1999 was one of the worst weather-wise (it was so bad the glass in the windows shattered), I still have an undying love for the oceans, being on the water or just being near it.

Meet the first recipients of the Open Humans Project Grants

Open Humans is a collaborative endeavour that would be impossible without the individual support of each and every user. The whole idea behind Open Humans relies on a healthy ecosystem that flourishes with each contribution made by individuals. Our project grants are thus designed to support individual ideas that have the potential to help grow the whole Open Humans ecosystem. For example, such projects can provide new ways of analyzing and visualizing existing data sources. Or they can connect new data sources that were so far not represented in Open Humans. This open-endedness has inspired many of you. Since the initial announcement of the project grants in July 2017 we got many great submissions. And as the grants are on a rolling schedule there is no deadline to apply: You can go ahead right now!

After carefully going through the applications we got so far we are happy to now announce the first three projects. Each of them will be funded with $5,000 to support them in realizing their vision for how Open Humans should grow.

MyFitnessPal Miner

Millions of users all over the globe already use MyFitnessPal. This thriving community makes MyFitnessPal (MFP) one of the most successful apps to track both your calorie intake and your exercise. Anh Nguyet Vu’s goal is to bring this community to Open Humans. Her MyFitnessPal Miner will not only enable the import of public MFP data into Open Humans, but also generate insightful visualizations out of the MFP data. Long-term goals for the MFP Miner are the inclusion of genetic data from 23andMe and the inclusion of private data.

You can find an early prototype of the MyFitnessPal Miner on Anh Nguyet’s website, Bring Your Own Biology.

Open Humans Google Fit Integration

Google Fit allows Android users to merge activity data from different sensors, devices and apps into a single data stream. In that sense it is Google’s health-tracking answer to Apple’s HealthKit. But while Open Humans could already import data from HealthKit, there was so far no easy way to do the same for Google Fit data. Thanks to Benjamin Carr this is about to change. His Open Humans Google Fit Integration will allow both services to communicate to each other. With over 80% of all smartphones running Android this will allow a large community to put their data into Open Humans!

You can find Benjamin on Twitter and his first stab at the Google Fit Integration can already be found on GitHub.

Microsoft Healthvault Integration

Microsoft as a third big player, alongside Google and Apple, when it comes to health-related data about individuals. Microsoft’s Healthvault is a web-based personal health record that has been around for around 10 years by now. It not only takes in data from personal fitness devices, but Healthvault can also aggregate medical records and prescription fillings. This makes it go a step further than HealthKit and Google Fit and offers great potential for a connection with Open Humans. Luckily API-programming nerd Benjamin Carr – yes, the same who will work on the Google Fit Integration – did have some more time on his hands. This is why he proposed a second project that will bring together Microsoft Healthvault and Open Humans.

Find Benjamin’s current progress on this on GitHub!

What’s next?

In the next weeks we will release some short interviews our grantees so that we all can get to know them (and their projects) better. Did seeing these projects inspire you to run your own? Apply with your own project idea right now!

What is Open Humans to me?

I’m Steph, I’ve just started as a software developer at Open Humans, and in this post I want to describe what the organisation means to me.

 

I feel like the value of Open Humans can be split into three main categories, perhaps of increasing fuzziness in terms of concrete assets, but also, in my opinion, of increasing importance and rarity. Open Humans is a technological platform; it’s a vibrant community; and it’s a paradigm shift.

 

At its very core, Open Humans is a technological platform. People are increasingly finding themselves in possession of their own personal data. Whether this be from fitness tracking devices; commercial genome sequencing services; or internet search history, we are, somewhat inadvertently, gathering more and more data about ourselves.

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Fig1: people are gathering data about themselves

 

The Open Humans platform allows members to upload and store these data privately, to choose whether to share some publicly, and to use their data to contribute to research projects and learn more about themselves.

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Fig2: people can upload their data easily using data tools

 

For researchers and citizen scientists, the platform enables painless and efficient data collection from engaged research participants. It is a seamless pipeline for human subjects research, which puts the individual participants in charge of how their data is used, avoiding a one-size-fits-all ethics approach which is common in traditional research protocols.

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Fig3: data can be used to further human subjects research

 

Open Humans is defined by its vibrant community. In recent years there has been a sharp rise in production and use of personal tracking systems: wearable devices; smart scales; lifestyle logging apps (including diet, exercise, and sleep); and commercial genetic and ancestry tools. People are intrigued by their own data. For this reason, there is no single user profile: we are researchers; patients; data scientists; citizen scientists; any and all people who want to learn more about themselves. The Open Humans community have written 19 data transfer tools enabling data from external projects to be added to Open Humans by users at the touch of a button. They have contributed 9 projects to the site, where research can be done on participants’ shared data. And they have continued to be enthusiastic, motivated, and truly engaged in the work of the organisation.

 

Open Humans is a paradigm shift: a totally new way to do humans subjects research. For me this is the most exciting way to look at Open Humans. Personal data can be sensitive: sharing can cause embarrassment, expose health concerns leading to discrimination, or lead to identity theft. Historically in the medical world, this has been handled by keeping health and human subjects research data anonymous. However as data becomes richer and more descriptive (for example, a genome, or internet search history), it is becoming easier to identify the original subject. So now we have more data than ever, and keeping it private when using it in research is becoming harder than ever.

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Fig4: the traditional human subjects research pipeline: data is handed over to scientists and generally not returned, subjects do not learn about the results and don’t get a say in how they are shared

 

Open Humans turns the traditional research pipeline on its head. It puts individual subjects at the centre of the sharing process, and in full control of how their own data is used in research. People are unique, and each will have their own reasons for wanting to keep some data private. These diverse sharing preferences call for a new system for human subjects research, that focuses on the subjects themselves, and meets their own personal privacy requirements.

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Fig5: the Open Humans research pipeline: participants have autonomy over their data and can choose which studies they share their data with

 

Giving research subjects the autonomy they deserve and at the same time increasing the efficiency of the research pipeline seems like a great idea, but the project is ambitious. Large scale open projects do have the ability to change the world (think Wikipedia), but changing the status quo in a system that has been around for a long time will always come with a lot of friction. However the vast amount of data being generated these days means that we are in new territory. This is a great time for change. Making sure that people are empowered to make their own decisions about how their data is used is an important endeavor. Working closely with our community, we hope to reach a critical mass of membership such that personal data sharing in this way becomes the standard approach. I am excited and optimistic about how Open Humans can revolutionise human subjects research, and I’m very grateful to be a part of this exciting movement.

Keeping Pace: You can advance health research with your smartphone!

Want to help advance Dr. Chunara’s study to the next phase? Anyone with a smartphone can help! Install apps to add Moves data, Runkeeper data (if you record your exercise), or HealthKit data (if you have an iPhone). The study invites Fitbit data too! You can donate any of these – or all of them – to the Keeping Pace study on Open Humans.

Click to donate data to Keeping Pace!

Keeping Pace is currently participating in the Healthy Behavior Data Challenge which is jointly run by the Centers for Disease Control and Prevention and the Department of Health and Human Services. This challenge is looking for ideas on how novel data sources and methods can support public health surveillance for healthy behaviors.

Keeping Pace was amongst the winners of Phase I of the challenge – in part thanks to 167 Open Humans members who already donated their activity data! For Phase II the goal is to further implement their proposed prototype. For this, Keeping Pace is looking for more participants.

Visit Keeping Pace!

About the projectKeeping Pace was one of the first studies to join Open Humans, back in July 2015! It is a study led by Dr. Rumi Chunara, an assistant professor at New York University. Her research interests are at the intersection of computer science and public health and make a great match to Open Humans! With Keeping Pace she aims to gain new insights into how seasons and the local environment influence our movement patterns.

Why Open Humans is an essential part of my work to change the future of healthcare research

Madeleine: I’m excited to share a Q&A with an innovator – Dana Lewis!

Dana was recently awarded a grant to further her work in patient-driven research, and she’s been using Open Humans in an exciting way. You can follow her on Twitter at @DanaMLewis.


So, what do you like about Open Humans?

Health data is important to individuals, including myself, and I think it’s important that we as a society find ways to allow individuals to be able to chose when and how we share our data. Open Humans makes that very easy, and I love being able to work with the Open Humans team to create tools like the Nightscout Data Transfer uploader tool that further anonymizes data  uploads. As an individual, this makes it easy to upload my own diabetes data (continuous glucose monitoring data, insulin dosing data, food info, and other data) and share it with projects that I trust. As a researcher, and as a partner to other researchers, it makes it easy to build Data Commons projects on Open Humans to leverage data from the DIY artificial pancreas community to further healthcare research overall.

Wait, “artificial pancreas”? What’s that?

I helped build a DIY “artificial pancreas” that is really an “automated insulin delivery system”. That means a small computer & radio device that can get data from an insulin pump & continuous glucose monitor, process the data and decide what needs to be done, and send commands to adjust the insulin dosing that the insulin pump is doing. Read, write, read, rinse, repeat!

I got into this because, as a patient, I rely on my medical equipment. I want my equipment to be better, for me and everyone else. Medical equipment often isn’t perfect. “One size fits all” really doesn’t fit all. In 2013, I built a smarter alarm system for my continuous glucose monitor to make louder alarms. In 2014, with the partnership of others like Ben West who is also a passionate advocate for understanding medical devices, I “closed the loop” and built a hybrid closed loop artificial pancreas system for myself. In early 2015, we open sourced it, launching the OpenAPS movement to make this kind of technology more broadly accessible to those who wanted it.

You must be the only one who’s doing something like this.

Actually, no. There are more than 400+ people worldwide using various types of DIY closed loop systems – and that’s a low estimate! It’s neat to live during a time when off the shelf hardware, existing medical devices, and open source software can be paired to improve our lives. There’s also half a dozen (or more) other DIY solutions in the diabetes community, and likely other examples (think 3D-printing prosthetics, etc.) in other types of communities, too. And there should be even more than there are – which is what I’m hoping to work on.


So what exactly is your project that’s being funded?

I created the OpenAPS Data Commons to address a few issues. First, to stop researchers from emailing and asking me for my individual data. I by no means represent all other DIY closed loopers or people with diabetes! Second, the Data Commons approach allows people to donate their data anonymously to research; since it’s anonymized, it is often IRB-exempt. It also makes this data available to people (patient researchers) who aren’t affiliated with an organization and don’t need IRB approval or anything fancy, and just need data to test new algorithm features or investigate theories.

But, not everyone implicitly knows how to do research. Many people learn research skills, but not everyone has the wherewithal and time to do so. Or maybe they don’t want to become a data science expert! For a variety of reasons, that’s why we decided to create an on-call data science and research team, that can provide support around forming research questions and working through the process of scientific discovery, as well as provide data science resources to expedite the research process. This portion of the project does focus on the diabetes community, since we have multiple Data Commons and communities of people donating data for research, as well as dozens of citizen scientists and researchers already in action (with more interested in getting involved).

What else does Open Humans have to do with it?

Since I’ve been administering the Nightscout and OpenAPS Data Commons, I’ve spent a lot of time on the Open Humans site as both a “participant” of research donating my data, as well as a “researcher” who is pulling down and using data for research (and working to get it to other researchers). I’ve been able to work closely with Madeleine and suggest the addition of a few features to make it easier to use for research and downloading large data sets from projects. I’ve also been documenting some tools I’ve created (like a complex json to csv converter; scripts to pull data from multiple OH download files and into a single file for analysis; plus writing up more details about how to work with data files coming from Nightscout into OH), also with the goal of facilitating more researchers to be able to dive in and do research without needing specific tool or technical experience.

It’s also great to work with a platform like Open Humans that allows us to share data or use data for multiple projects simultaneously. There’s no burdensome data collection or study procedures for individuals to be able to contribute to numerous research projects where their data is useful. People consent to share their data with the commons, fill out an optional survey (which will save them from having to repeat basic demographic-type information that every research project is interested in), and are done!

Are you *only* working with the diabetes community?

Not at all. The first part of our project does focus on learning best practices and lessons learned from the DIY diabetes communities, but with an eye toward creating open source toolkit and materials that will be of use to many other patient health communities. My goal is to help as many other patient health communities spark similar #WeAreNotWaiting projects in the areas that are of most use to them, based on their needs.

How can I find out more about this work?

Make sure to read our project announcement blog post if you haven’t already – it’s got some calls to action for people with diabetes; people interested in leading projects in other health communities; as well as other researchers interested in collaborating! Also, follow me on Twitter, for more posts about this work in progress!