Topic Archives: QS Access
A year ago we released QS Access, a simple app that allows you to see your healthkit data in a table. Our idea was to make it easier for people to explore their data using familiar tools, such as Numbers, Excel, or any spreadsheet program that can open a .csv file. We’ve really enjoyed hearing its been useful, and we’ve received lots of good feedback. This week we released a new version of the QS Access App that contains some commonly requested features. You can now:
- See raw data from individual elements, such as running.
- Store the query details, so you don’t start from scratch each time.
- Choose units for many quantities.
- Get a table of your sleep data.
We’re still listening, so if you are using QS Access and have feedback for us please let us know by emailing email@example.com.
Yesterday evening I laced up my running shoes, connected my bluetooth headphones, turned on my Spotify playlist, and most importantly, hit “Go Running” on my Runkeeper app. About an hour later and I had run 6 miles at a decent pace of around 8 minutes per mile. And I knew this thanks to Runkeeper.
Founded in 2008, Runkeeper is designed to assist individuals who want to track their activities with GPS precision, whether that is walking, running, hiking, or cycling. If you’re moving outdoors, Runkeeper and similar apps, such as Strava or MapMyRun, use your smartphone’s GPS to pinpoint exactly where you are and how fast you’re moving. With all that data, you can train for your next marathon, discover new routes, and now, thanks to efforts by New York University researchers, take part in advancing public health research.
“We know from the existing literature that spatial characteristics like walkable neighborhoods and green spaces encourage exercise, but a lot of the details are still unknown.”
Last week, Dr. Rumi Chunara and her colleagues launched the Keeping Pace study. Over the next few months they hope to enroll participants who are willing to share their geo-located exercise data from Runkeeper. Because Runkeeper keeps a log of not only what you did, but where you did it, researchers hope to use the large amount of aggregated data to better understand physical activity patterns in communities around the United States.
“Typically, this type of research takes a long time and includes long, ardorous surveys or giving out GPS devices to participants,” said Dr. Chunara. “But with this type of data from apps people already use, we will be able to understand how the environment and exercise are related over more rapid and longer time periods.” With this data being contributed, the research team hope to understand differences in exercise choice between commuting and recreational activities, variation in activities among neighborhoods, and where people spend their time while being active.
Participants who enroll in Keeping Pace will be asked to complete a short demographic survey and then connect their Runkeeper account so researchers can access the type of activities they do and the GPS-based map associated with the activity. The Runkeeper data connection is being handled by a unique research platform, Open Humans.
A few years ago, Dr. Rumi Chunara was at a meeting hosted by the US Department of Health and Human Services. She was there to present and speak with colleagues about the growing importance of citizen science and crowdsourced data. There she met Jason Bobe, Executive Director of PersonalGenomes.org. They got to talking about some of their common insterests in open data, research, and new models for research participation. Later, when Dr. Chunara was designing GoViral, a project to examine how to leverage crowdsourced flu symptom information and diagnostics to predict illness risk, she ran into some issues with hosting and handling the amount of data participants were contributing. “It was obvious we needed some sort of platform to handle data,” said Dr. Chunara. She got back in touch with Jason, who helped her think about the issues and how to solve them.
This year, when it came to build out the infrastructure for the Keeping Pace study, Dr. Chunara decided to get back in touch with Jason and his colleagues, who were now developing OpenHumans.org. As we’ve written before, Open Humans represents a new way of thinking regarding researcher studies, participants, and the data being transferred between the two. The two teams, Open Humans and Dr. Chunara’s lab at NYU, worked together to develop an easy method for individuals to simultaneously allow researchers access to their Runkeeper data, and also maintain control over where and how that data flowed. Specifically, each individual who chooses to participate in the Keeping Pace study will be asked to create an Open Humans account, connect their Runkeeper account, and then authorize the Keeping Pace study to access their data. It sounds like a lot of work, but thanks to the designers and the use of Runkeeper’s API, it takes no more than five minutes to complete.
But why go through that trouble at all? Why not just have participants export their Runkeeper data and send it to the researchers? Why didn’t Dr Chunara and her colleagues build that data connection themselves?
Thanks to the proliferation of sensors, wearables, and smartphones, the ability to generate data about our lives is rapidly expanding. Pair that data with new efforts like the Precision Medicine Initiative and it’s easy to see the potential for researchers to understand our lives and our health in new and interesting ways. But what about the people who create that data? People, like myself, who strap on their phones when they go out for a run or log onto a website to report their flu symptoms. What do they have a right to in regards to their data? This is the question many researchers and scientific institutions are grappling with. But some have already taken a stand.
“What data are collected and how varies across research studies, but the question remains, ‘Who owns it?’ If someone is spending time generating then they should have control over it.”
Dr. Chunara and her colleagues chose to work with Open Humans because they shared the same perspective — participants should be in control of their data. “Open Humans has created an infrastructure that makes it easy to share and learn while respecting the participant and their data. That’s a noble motive, and it’s important,” said Dr. Chunara. Today, Keeping Pace is the first study to use Open Humans for data access and management for a research study. If successful, researchers may not only learn about exercise and the environment, but also about how studies that place an emphasis on participants’ data access and control may engage the public in new ways.
Keeping Pace is currently enrolling participants. If you’re a Runkeeper user and want to contribute your data to research, please visit the study website to learn more.
Keeping Pace was funded as part of the Agile Projects grants by the Health Data Exploration Network. If you’re a researcher, company, or individual interested in personal health data, sign up to become a network member. Membership is free.
Quantified Self Labs is dedicated to sharing stories and insights about the role of data access for personal and public health. We invite you to share your data access stories and this article. Then follow along on quantifiedself.com and @quantifiedself.
[Editor's Note] This May we hosted our second Quantified Self Public Health Symposium in San Diego at the University of California, San Diego. With support from the Robert Wood Johnson Foundation we brought together 150 researchers, toolmakers, individuals from government institutes, and science leadership. During the meeting we had multiple conversations, talks, and show&tells that helped guide our QS Access mission – to understand and improve personal data access for person and public health. Today we’re excited to start posting videos from that meeting. If you’re inspired by what you see here and want to help us raise the conversation surrounding personal data access we invite you to get in touch and follow along on Medium and here on the QS website.
Stephen Downs, Chief Technology and Information Officer at the Robert Wood Johnson Foundation looks forward to the day when healthy choices are easy choices. That day may not be tomorrow, but identifying the early adopters, innovative thinkers, and technological disruptors are at the forefront of moving us one step closer to that healthier world. For Stephen and the Foundation, building a culture of health includes “engaging sectors that are not nominally about health.” In his presentation to the Quantified Self Public Health Symposium, Stephen shares how groups within education, community development, technology, and more have a key role to play in improving the state of our health, and health care.
On January 30th, President Obama announced the funding of a possibly groundbreaking research program — The Precision Medicine Initiative (PMI).
Launched with a $215 million investment in the President’s 2016 Budget, the Precision Medicine Initiative will pioneer 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.
Since the announcement the National Institutes of Health (NIH) have been hard at work convening a working group to build a foundation of rules, standards, and principles upon which they hope will generate meaningful outcomes: improving the health for all Americans by moving towards a more nuanced and individual view of health and wellness. As part of this project, the largest portiont of funding is being dedicated to the “development of a voluntary national research cohort of a million or more volunteers to propel our understanding of health and disease and set the foundation for a new way of doing research through engaged participants and open, responsible data sharing.”
As part of engaging this cohort the NIH is considering the role of patient generated data from mobile phones and sensors. Of course this is where we at Quantified Self Labs become intrigued. We have a long history of supporting individual’s ingenuity, insight, and expertise when it comes to personal data they collect on their own. Since 2008, we’ve been bringing together people to share their stories of self-tracking using a variety of different methods, some of which are no doubt being examined by researchers and NIH leadership for use in the proposed Precision Medicine Cohort.
We are excited to hear that the NIH is taking the time to listen to the American public through the use of online feedback forms. They are currently seeking comments on the use of mHealth for the Precision Medicine Cohort. Specifically, they want to know how people think that data generated by current and future biometric and physiologic sensors (such as heart rate and physical activity tracking devices) could be useful. Furthermore, the NIH isinterested in reactions to using smartphones to collect data on volunteer participants in the cohort. In the short description of the feedback request they highlight five key considerations:
1. Willingness of participants to carry their smartphone and wear wireless sensor devices sufficiently throughout the day so researchers can assess their health and activities.
2. Willingness of participants without smartphones to upgrade to a smartphone at no expense.
3. How often people would be willing to let researchers collect data through devices without being an inconvenience.
4. The kind of information participants might like to receive back from researchers, and how often.
5. Other ways to conveniently collect information from participants apart from smart phones or wearable devices.
We spent a little time browsing through the current crop of comments, which didn’t take long as there are only 52 at the time of this writing, to understand how people are thinking about mHealth, their data, and what it means to contribute personal data for public health research.
Privacy & Confidentiality
A common theme was a concern over what type of privacy protections would be implemented to protect volunteers who contribute their data. Comments ranged from outright fear of the government “tapping into personal computers, phones or other devices for collecting health information” to thoughts on access, control and protection of data contributed as part of this project.
As long as citizens can remain in control of the collection, flow and use of their data — and the government can guarantee anonymity, much benefit can be had from this. If it is going to be a government initiative, then standardized collection methodologies and protections will be required and the data should not “also” be collected for commercial usage. Clarity surrounding use models, sharing permissions and general privacy and security are a must.
I would also like to make sure that it is clear who ‘owns’ the data. Many times health professionals collect data about patients and then use it–often without letting the person know the data is aggregated and studied.
Diversity and Representativeness
As with any research study, there is a call to make sure that the sample being studied is representative of the population. This is especially important given the expressed interest in using different types of technology to track, measure, and engage with research participants. Those who have offered feedback have clearly picked up on the need to make sure that even those who are not current users of wearable sensors and/or smartphones are considered.
This will leave out the severely ill and disabled who are bedridden, unable to move, and definitely unable to manage a smart phone (as well as anyone whose illness causes cognitive challenges). Yet these are the patients who really need to be studied. So while it’s not a bad idea, please factor in selection bias and please, please, accommodate the most ill if you decide to implement this.
Interestingly, there are a few comments on how dependence on a smartphone may limit the diversity of the cohort.
Smartphones, etc. will limit the diversity of the sample. For example, large areas of Appalachia will be excluded. Internet access is limited in rural areas; even if available, the technology is not adequate to support many applications.
While the diversity of the sample is something that must be considered, I’m left wondering about the true impact of using smartphones for data collection as recent data from Pew suggests that nearly two-thirds of Americans own a smartphone. It appears that economic diversity may be the limiting factor when it comes to using smartphones as part of the cohort. Clearly there is a gap in smartphone ownership across income and education levels, but also when we consider geographic location.
However, one of the considerations clearly states that an “upgrade to a smartphone at no expense” may be part of this research initiative. What is the true cost of this free upgrade? It appears to be unknown, but it’s going to be important to think about the recurring costs of data plans associated with these devices, especially when data transfer is part of participating in the research.
The issue probably depends on the participant and what is being provided. I’m assuming providing the smartphone would include providing the data plan, which can be expensive.
Barriers to Using mHealth
While using wearable sensors, apps, and smartphones to collect personal health data is growing trend, there are still concerns regarding how long individuals will actively use the tools. While the Fitbits of the world are selling millions of devices, we don’t really know how long people are willing to use them, and if they’re willing to contribute that data to research (although preliminary studies are promising).
There is concern that adding devices, measurement, and data collection to the everyday lives of individuals may present a burden and that the data will suffer from inconsistent engagement. The “life gets in the way” of participating in research is a common, and justified, refrain.
In our experience, conducting a number of trials using devices, many people do not carry or use devices for very long that do not fit into their existing habits. People may use new devices provided by researchers for short periods of time, if provided research support, monitoring, and prompting, but for large scale trials and longer assessment periods, adherence will fall off considerably.
Benefits of mHealth
It’s not all doom and gloom and negativity though, there is an overwhelmingly positive outlook on the use of wearables and mobile-based data collection for informing personal and public health. From researchers to individuals already using these devices to understand their own health, the current comments are full of support for exploring new technologies, sensing capabilities, and personal data collection methods to deliver personal precision medicine.
Integration of data from personal sensors and mobile devices has the potential to change the role of the patient in their health and care, to improve the accuracy and value of behavioral data in healthcare, and to provide unparalleled insights into the how and why of behavior change in health.
I am 60 but use a Withing Blood Pressure Cuff, Basis Peak and Alive EKG. I am a cancer survivor of Hodgkin’s Lymphoma and Breast Cancer. Due to my treatment I am left with complications that need to be monitored. I feel more secure using these devices. Knowledge is power.
In the name of transparency, below you’ll find the full text of our comments submitted to the NIH.
The world is changing, more information is flowing across what were once impassable borders. That information is changing the way we see the world, and how we understand ourselves. Health and healthcare is a big part of this evolution in information flows. At Quantified Self, we’ve seen hundreds of examples from individuals around the world who have used personal data collected through a variety of means to impact and understand their health. It’s with these examples in mind that I’d like to share my thoughts on using mHealth for the Precision Medicine Cohort.
The considerations mentioned above are clear, but vague enough to make the only appropriate answer, “it depends.”
Will participants carry their smartphones and wear sensors? It depends. It depends on how participants are recruited and what they are being asked to contribute to. Do they have a say in what questions are being asked? Is constant data collection a requirement for participation, or can participants engage inconistently, contributing data sparsely? What activities would researchers want to understand? Some individuals may be perfectly okay with contributing physical activity data, but not geolocation data supplied by a smartphone GPS.
Will participants be willing to upgrade to a smartphone? It depends. Will they also be compensated for an increase in costs associated with data plans so that their smartphone can send data to a researcher? Will they be able to use their smartphone for personal use, downloading apps and services freely? Is the smartphone upgrade dependent on participation in research for a given amount of time, and if so, how long? Is this form of compensation coercive for those who have never been able to afford a smartphone? Including a socially and economically diverse population in the cohort while not introducing increased costs and burden will be important to consider.
Will participants allow researchers to collect data, and how often? It depends. Will participants play an active role in the data collection, or will it be passively collected through sensors and background data transfer? Will participants be engaged in the full research process, helping develop the questions, data collection methods, and even the analysis? Will participants be able to choose the frequency of data transfer that makes sense to them and their lifestyle? It’s possible to envision participants contributing infrquently over a long timescale. Will these type of contributions help push precision medicine forward?
What kind of information would participants like to receive, and how often? It depends. Are participants able to access and control the data they create? Do they get to choose what researchers or studies are able to use their data? Will they receive the same information and data that researchers have access to? All participants may not actually want their data, but I believe that the research community has an ethical obligation to be open, honest, and transparent when it comes to what we collect. This includes giving data back to participants, especially with regards to health data. Participants should also receive timely access to any and all research findings. Published work that results from Cohort data should be published in open access and freely available outlets (and open data sets should be published simultaneously).
What other ways might their be to collect information from participants? It depends. What type of information is important to researchers? Many different research groups, and even commercial entities, have discovered the power of using text messaging for data collection. Text messaging should be examined as a possible large-scale and low-cost (financial and time) method for understanding many different aspects of personal health.
Smartphones, wearable devices, and personal data applications and services represent an unprecedented look into our daily lives. The Precision Medicine Initiative should continue to explore how best to incorporate the public in the process of crafting the protocols and methods. The public will be the participants, and we should remember that this research is being conducting with them, not on them. A spirit of openness and transparency should guide this important work.
The public comment period for input on using mHealth for the Precision Medicine Cohort closes on Friday, July 24. We invite you to add your thoughts and ideas.
Howard Look is the Founder and CEO of Tidepool, a non-profit, open source development organization dedicated to reducing the burden of type 1 diabetes by building a platform that can integrate all diabetes device data in a single location. Most importantly, he is the father of a daughter with type 1 diabetes. Howard understands the challenges of effectively managing diabetes, and the essential role data plays in the minute-by-minute management this disease requires. The challenge, however, is finding a way to easily access and understand all of the data generated by the various devices a person with diabetes has at their disposal. What follows is an edited transcript of our conversation with Howard about how Tidepool has been addressing access to diabetes device data for the diabetes community.
QS: You’ve been busy over the last year – can you catch us up on the latest from Tidepool?
Howard: We made significant strides at Tidepool in 2014. Most notably, we announced partnerships with Asante, Dexcom, Insulet, Tandem, and Abbott Diabetes Care, all of whom gave us their data protocols. No money changed hands with these agreements, they simply understand that liberating diabetes device data is a good thing. You will notice the absence of the two largest diabetes device manufacturers from that list: Medtronic, and Johnson & Johnson, who make Animas and LifeScan products. We’re still having active conversations with them both and are hopeful that they will also enter into data protocol agreements with us.
Additionally, we’re just starting to realize the potential for our platform in the research community when you have all of the data in one place. We’re supporting a study with the Jaeb Center for Health Research and T1D Exchange, the largest coordinating organizations for type 1 diabetes studies, by putting our software in 15 of the top diabetes clinics in the United States. Our software enables researchers to access the full range of diabetes devices in one place. They are no longer restricted to only certain insulin pumps and blood glucose meters because those were the only devices whose data they had the ability to access. Now, all diabetes data is on the table and we’re showing the true value of integrating data from multiple places.
QS: Can you speak a bit more on the idea of access and how integral that idea is to the work you’re doing?
Howard: At Tidepool, the idea of access speaks to the core of what we’re trying to accomplish and promote throughout the world of health care.
From a policy perspective, we believe that the therapy data generated by these devices does not belong to the company – it belongs to the patient. It’s their personal health data. When you consider insulin delivery, blood glucose values, basal rates, carbohydrates ingested, all of these data points related to diabetes management is patient-owned data. Simply put, if you show a number on the screen for the purpose of delivering therapy, that data belongs to the patient. This is the fundamental approach we’ve taken to conversations with device manufacturers. For other data, like internal device diagnostic data, it’s fair for a device maker to consider that proprietary and we will respect that.
This approach falls perfectly in line with one of the core tenants of HIPAA: Portability of Data. HIPAA guarantees people with diabetes the right to access to their data, which makes encounters with responses along the lines of “HIPAA regulations prevent us from giving you access to this information” particularly frustrating. There is no ambiguity in my mind that it is irresponsible to not give people with diabetes access to their data. Lack of access means you are forcing people to use terrible tools to compute their own insulin doses which has the potential for horrible outcomes, which makes things worse for everyone in the health care conversation.
By liberating data, we can create an ecosystem of software and application and devices that make managing diabetes easier.
QS: What are some of the biggest challenges you face in bringing your platform to market? Are they more technical or regulatory?
Howard: When it comes to addressing a challenge as complicated as integrating all diabetes device data in a single location, there are many technological and political barriers that put up resistance along our way. But let me be clear, regulatory matters are not one of them. Believe it or not, working on FDA documentation is not difficult, and everyone I’ve spoken with at the FDA completely understands the value in data standards and liberating data from devices.
The FDA is not the problem.
For us, the biggest barriers we face are getting access to device data protocols and funding. That’s really it. When Medtronic and Johnson & Johnson formally make their data protocols available to us, we’ll consider that a job well done, but we’re not there yet. And funding as a non-profit is a constant battle that will not surprise anyone in the Silicon Valley. We’ve done a good job addressing both of these barriers, but there’s still work to be done.
Having the support of the two biggest philanthropic organizations in the type 1 diabetes world, Juvenile Diabetes Research Foundation (JDRF) and the Helmsley Charitable Trust, has made a huge difference for us. I cannot overstate how important the support of these two groups has been to our mission. Their grants send a loud and clear message that they believe in our mission and they want this to happen. Saying things like “this is what patients want” is one thing, but when we have the backing of the JDRF and Helmsley, our mission is given much more credibility.
QS: There are a number of moving parts in the Tidepool equation – the patient community, regulatory agencies, the diabetes device manufacturers, and the researchers looking to Tidepool to make data collection easier for their work – what message do you have for these groups?
Howard: At the end of the day, my message to the patient community is keep demanding your data. It’s yours. Simple as that. Engaged patients who understand and want to be involved have better outcomes, but in order to do that you need access to data and software and tools to take care of yourself.
My message to regulators: keep doing what you’re doing. I know you understand the value in liberating data, of device data standards, of agile software development, and that access regulation gets in the way of progress and innovation, and is harmful to patients. Keep this conversation going and together we can usher in a new era of access.
To the device makers: publish your device data protocols for existing devices, adopt standards like Bluetooth Smart and IEEE 11073, adopt cloud service protocols like OAuth2 and Rest API’s to enable access to their data. I believe a rising tide raises all boats, and enabling an ecosystem of access will increase adoption of your insulin pumps and continuous glucose monitors.
And to the science community, this is the world of big data and open data, we’re going to do our part by asking our users if they will donate their data to an anonymized research database and if they want to donate their identified data to the T1D Exchange. We should be getting as much data into the research world as possible, while respecting privacy issues, and you should be part of that collective.
QS: Do you have any final thoughts for the Quantified Self community?
Howard: Ten years ago, you had to be a multi-million dollar device company in order to do any of the things the QS community is capable of today. Now? You can buy some parts off of sites like SparkFun and build your own medical devices now. Now it’s about acknowledging that the more we enable that interconnectivity we encourage people to tinker, even with life and death things like insulin delivery, the collective intelligence of the community is going to cause great solutions to come out of all this.
Today, we are participating in the “Data and Innovation at the Climate-Health Nexus” panel hosted by the White House Office of Science and Technology Policy. When we’ve spoken to people about this meeting the reaction we tend to receive is, “What does Quantified Self have to do with climate change?” It’s a valid question, and one we hope to answer during the panel. Today we wanted to take some time here to talk about why we’re a part of this important conversation.
It’s no surprise that data and data collection is becoming a part of the normal course of our everyday lives, from the data we choose to collect about our health and wellness to the so-called “data exhaust” we’re creating as we use different technological systems. The practice of self-tracking, collected data about yourself to answer interesting questions or help change behavior, has often been linked to narcissism or navel gazing. We know from our experience interacting with a worldwide community of self-trackers that this isn’t the case. Individuals who track, analyze, visualize, and learn from their own data also tend to do something else: share it. You just have to take a peek at our over 750 show&tell videos to see that sharing experiences, techniques, and outcomes is a core component of our work and our community. It’s the reason we hold conferences, support over 100 meetups around the world, and share on this website.
We also know that data is powerful. It can help us understand ourselves, but also the world around us. We’ve been watching closely as new citizen science, one-off projects, and commercial toolmakers have started to incorporate ways to sense and measure the personal and local environment. From air quality sensors integrated into in-home video monitors to crowdsourced DIY environmental sensing devices – we’re beginning to see the power of data for understanding the environment around us, and perhaps more importantly, how the environment plays a role in the health and wellness of our communities. A great example of this comes from our friends at Propeller Health. Recently they announced the launch of AIR Lousiville, a “first-of-its-kind data-driven collaboration among public, private and philanthropic organizations to use digital health technology to improve asthma.” By combining air quality data with geolocated asthma inhaler use data they hope to better understand and positively impact their local environment and reduce the burden of asthma in the Louisville community.
This is just one example of individuals coming together as a community to generate and contribute data about themselves, their environment, and their health to drive a much needed conversation. A conversation about the complex, and important, relationship between the environment and health. We’re hoping to see more and, to that extent, we’re excited to announce that starting at our 2015 Quantified Self Public Health Symposium we’ll be officially launching, in collaboration with with the U.S. Environmental Protection Agency, Personal & Community Environmental Data Challenges, calling on researchers and companies making wearables, sensing, data-visualization, and digital health-tools to join a national conversation about the importance of gaining a more detailed view of environmental impacts on health. This challenge is just one in a great list of commitments from leading companies and institutions designed to advance the Obama Administration’s Climate Data Initiative.
We invite you to learn more about our challenge announcement and our participation in the symposium on Data and Innovation at the Climate-Health Nexus by reading our brief press release here.
You can also learn more about national initiatives, programs, and newly released climate data from the following Fact Sheet: Administration Announces Actions To Protect Communities From The Impacts Of Climate Change
Update: The video from the panel is up and can be found here. The panel actually starts an hour and 19 minutes in to the video.
“Open Humans aims to break down data silos in human health and research. We believe data has a huge potential to live and grow beyond the boundaries a single study or program. Our online portal allows members to aggregate data from the research they participate in. By connecting individuals willing to share existing research data about themselves with researchers who are interested in using that data, data can be re-used and built upon.” — OpenHumans.org
On March 24, 2015 the Open Humans Network officially opened their virtual doors and began allowing individuals to sign up and engage in a new model of participatory research. We spoke with Co-founder & Principal Investigator of the Public Data Sharing study, Madeleine Ball, Ph.D. about Open Humans, what it means for research, and what we can look foward to from this exciting initiative. The following is an edited transcript of that conversation.
It’s been a lot of work up to this point.
We’re grateful to have the funding support of two organizations to help get this off the ground, the Knight Foundation and the Robert Wood Johnson Foundation. It’s been a lot of work to get to this point, from hiring Beau Gunderson as our Senior Software Gardener to launching with our first three studies. We’re excited to be partnering with the Harvard Personal Genomes Project, the American Gut study, and the GoViral study. These are the seed studies, what we’ll build off of in the coming months and years. Today, we’re excited to start letting participants in these projects, and all individuals interested in participating in research, know about Open Humans.
This is an open invitation to join us.
We’re also working to make it easier for research partners to join the Open Humans Network. We’ve already started receiving interest from researchers that want to integrate with Open Humans or start working with our already growing public data sets. We’ve set parameters regarding how you have to behave as a study as well as how researchers looking to work with our members should engage with us. (You can find out more about that here.)
For members who sign up with us we’ve developed methods for them to control access to their data. Whether that is data from personal health devices and apps like Runkeeper (adding this to our next project), genetic data, or other data sources derived from participating studies, each individual member will have the ability to establish a peer-to-peer interaction. Members can allow access to some data, but not others. They may choose to release some or all of their data publicly, or the may choose to only share with one study. In the end it’s up to them and their individuals goals.
What excites me about Open Humans is the potential we have to transform future research studies — from how they treat data to how they think about data sharing. We’re building our system so that participants are central to the data process. A good example of this when researchers use our member’s data they must also agree to return any new data that results from their research back to the original participant. This decentralization of data is a key component of our design. No single person, researchers, or study has all the data.
We’ve also built in the ability for researchers to contact our members who contribute data. The idea that researchers must come up with all the right questions before starting a study is a recipe for failure. Researchers are not psychic, that can’t forsee what interesting questions might come up in the future. By opening up the ability for these connections to take place in the design of Open Humans, we’re creating the ability to continue asking questions of specific individuals, or groups of people, far in to the future.
I think this work is creating a new form of data sharing that will unlock a world of new exciting possibilities. Our hope is that when participants start getting data back from studies, and have the ability to use it and share it how they wish, that participation in research will be more rewarding. This model helps participants become a respected member of the evolving research conversations happening all over world. We know a lot of people don’t participate in research, even researchers who rely on participants don’t participate in studies. Hopefully this work will help move the needle.
It’s wonderful to see the long scroll of members.
As of this writing the Open Humans Network has over 200 individuals who have created member profiles. If you’re interested in participating in open research you can learn more and sign up here. If you are a researcher or personal data company interested in integrating with Open Humans you can get in touch with the team here.
Self-knowledge through numbers. Personal meaning from personal data. These are the guiding principles of the work we do here at Quantified Self Labs. Through our editorial work, our events, and our support of a worldwide network of meetups we are focused on shaping the culture of personal data and it’s impact on our lives. We realized some time ago that impact is determined not only by data analysis skills, scientific training, or even the use of new tools and technologies (although all of these play an important role). Rather, impact is directly related to our ability to access the data we’re creating and collecting during the course of our lives.
We’re happy to announce our new QS Access Program with support from the Robert Wood Johnson Foundation. We’re working together to bring issues, ideas, and insights related to personal data access for personal and public health to the forefront of this evolving conversation. We hope you join us.
You can read the full release here. Below are two quotes from the release that embody our current and future work.
“The Robert Wood Johnson Foundation is working with many partners to build a Culture of Health in the U.S., and in that culture of health, people are attuned to the factors that influence their health and the health of their communities,” said Stephen Downs, Chief Information and Technology Officer at the Robert Wood Johnson Foundation. “The explosion of data on day-to-day life creates tremendous potential for new insights about health at both the personal and population levels. To realize this potential, people need access to their data — so they can use services that surface the connections between symptoms, behaviors and community environments and so they can choose to contribute their data to important research efforts.”
“We believe that when individuals, families, and communities are able to ask their own questions of their own data, everybody benefits,” said Gary Wolf, Director of QS Labs. “We look forward to doing our part to build a culture of health with the support of the Robert Wood Johnson Foundation, and we invite anybody who has an access story to tell to get in touch.”
If you’d like to learn more or get involved. Please contact:
Quantified Self Labs
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.
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.