Tag Archives: data access
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.
Hello again! Here we are with another list of articles, links, and visualizations for you. Enjoy!
We’ve started to see a few great blog posts and articles describing the experience of attending the QS15 Conference and Expo. For the next few weeks we’ll be highlight a few here.
My Data, Your Data, Our Data by Murray Grigo-McMahon
Notes from the 2015 Quantified Self Conference by Arpit Mathur
Quantified Self 2015 by Phoebe V. Moore
QS15: Measurement with Meaning by Ben Bending
The Future of Food Data: Toward Transparency, Personalized Design, & Re-Thinking the Concept of a ‘Food Label’ by Sam Slover. We highlighted Sam’s work on visualizing his food last year and it nice to see that work is continuing. I’m interested to see where this goes.
An Evening with the Consciousness Hackers by Nellie Bowles. Brain tracking and augmentation is definitely on the rise. Great to see the Consciousness Hacking group get some attention. (We were honored to have Mikey Siegel and Ariel Garten participate at the QS15 Conference and Expo. Look for their talk soon!)
Make people the controllers of their data to help the NHS go digital by Andrew Chitty.
There’s a solution to this too. Make it the default assumption that the patient is the owner or controller of all data relating to them. They can then share this data with whichever parts of the health service they wish.
This might sound slightly outlandish but think about it: we’re increasingly going to see digitized records become the norm, with many of them self-generated by citizens as part of their self-care – which we want to encourage, not only because it engages people with their own care but because it short circuits the technical barriers around information sharing.
What if We Really Set Data Free by Elizabeth Nelson. I had the pleasure of speaking at length with Elizabeth about Quantified Self, data, and data access. Make sure to also check out this great interview with Josh Berson.
The Crying Baby and the Sympathetic Fitbit by Jocelyn Wiener. A great article by a mother with a new baby who learned how sleep tracking can be useful.
My sleep didn’t get any better just because Fitbit started quantifying how crappy it was. But I felt validated, if only by someone with a rechargeable battery for a heart. While I received plenty of clucking sympathy from family and friends, my new device gave me something arguably better: evidence.
Is drunk sleep less restful than sober sleep? How much so? Why or why not? by Justin Lawler. Not sure where I saw this, probably in the #quantifiedself stream on Twitter, but this Quora answer is pretty fantastic. Justin takes the time to explain what he found when he ran a test on how alcohol affected his sleep using his Basis watch.
Quantified home birth by Morris Villarroel. A beautiful post by our friend Morris, who describes his tracking experience during the day his son was born.
Food Chain Project by Itamar Gilboa.
The Israeli-Dutch artist kept a diary of everything he ate and drank for the duration of a year. He meticulously kept track of his daily consumption. Some three years later, the results can be seen in a sculpture installation, the Food Chain Project. His installation, a traveling pop-up supermarket consisting of more than 8,000 white plaster sculptural groceries, physically represents Gilboa’s yearly consumption.
From a Net to a Harpoon: 2014 Annual Review by Michael Anthony. I cannot stress how beautiful this annual review is. Maybe it’s the focus on running that gets to me, but the whole this is worth looking through. You can even go back in time and view Michael’s reports from 2011, 2012, and 2013.
This week on QuantifiedSelf.com
2015 QS Visualization Gallery: Part 1
2015 QS Europe Conference: Scholarship Application Now Open
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
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: 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.
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.
The 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.
Metabolomics 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.
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 hope you enjoy this week’s What We’re Reading list!
The Wow of Wearables by Joseph Kvedar. An excellent post here in the wake of the “Smartphones vs. Wearables” hype in the past weeks. Favorite part:
“I’d have to say that reports of the death of wearables have been greatly exaggerated. The power of sensor-generated data in personal health and chronic illness management is simply too powerful to ignore.”
Survival of the Fittest: Health Care Accelerators Evolve Toward Specialization by Lisa Suennen. If you’re at all interested in the recent surge in health and healthcare focused accelerators this is for you. Excellent reporting. (Thanks for sharing Maarten!)
Your Brain Is Primed To Reach False Conclusions by Christie Aschwanden. Fascinating piece here about the nature of the “illusion of causality.”
A Few Throughs About Patient Health Data by Emil Chiauzzi. Emil, Research Director at PatientsLikeMe, lays out four point to consider when thinking about how to best use and grow self-collected patient data.
Having Parkinson’s since I was 13 has made me an expert in self-care by Sara Riggare.
I am the only person with the whole picture. To me, self-care is everything I do to stay as healthy as possible with a disease that is a difficult life companion. It entails everything from making sure I take my medication in the optimal way, to eating healthily, getting enough sleep, to making sure I stay physically active. I also make an effort to learn as much as I can about my condition; my neurologist says that I know more about Parkinson’s research than he does. I don’t find that odd, since he needs to try to stay on top of research in probably hundreds of neurological diseases, whereas I focus on just one.
From Bathroom to Healthroom: How Magical Technology will Revolutionize Human Health by Juhan Sonin. A beautifully written and illustrated essay on the design of our personal healthcare future.
Experimenting with sprints at the end of exercise routines by Gustavo M. Gustavo is a person with type 1 diabetes. After reading that post-exercise high intensity exertion might have an effect on blood glucose he put it to the test.
On Using RescueTime to Monitor Activity and Increase Productivity by Tamara Hala. Tamara walks us through the last three years of her RescueTime data and how she used that information to understand her work and productivity.
How Do You Find Time to Write? by Jamie Todd Rubin. Jamie has been writing for 576 consecutive days. How does he do it? A mixture of data and insight of course!
Say “I Love You” With Mapping by Daniel Rosner. Wonderful to see CHI papers ending up on Medium. This seems like a fun self-tracking/art project.
Cleaning up and visualizing my food log data with JMP 12 by Shannon Conners. Once again, Shannon displays a wonderful ability to wow us with her data analysis and visualization. Above is four years of food tracking data!
Two Trains: Sonification of Income Inequality on the NYC Subway by Brian Foo. Brian created this data-driven musical composition based on income data from neighborhoods the border the 2 train. Beautiful work.
Walgreens adds PatientsLikeMe data on medication side effects
How Open Data Can Reveal—And Correct—The Faults In Our Health System
Big Data is our Generation’s Civil Rights Issue, and We Don’t Know It.
We are happy to welcome this guest post by Madeleine Ball. Madeleine is the Senior Research Scientist at PersonalGenomes.org, co-founder of the upcoming Open Humans project, and the Director of Research at the Harvard Personal Genome Project. She can be found online @madprime.
Unpacking “data ownership”
It’s worth unpacking this phrase. What do we mean by “data ownership”? If we want to see changes, we need to start with a little more clarity.
Legally, data is not property. There is no copyright ownership of facts, as they are not “creative work”: the United States Supreme Court famously established this in the landmark case Feist vs. Rural. They are not patents, there is no invention. They are not trademarks. There is no “intellectual property” framework for data.
Yes, data is controlled: through security measures, access control, and data use agreements that legally restrict its usage. But it’s not owned. So let’s set aside the word “ownership” and talk about what we really want.
Control over what others do
One thing we might want is: “to control what others do with our data”. Whom they share it with, what they use it for. Practically this can be difficult to enforce, but the legal instruments exist.
Think about what you really want. Are you opposed to commercial use of your data? Look for words like “sell”, “lease”, and “commercial”. Are you concerned about privacy? Look for words like “share”, “third-parties”, and “aggregate” – and if individual data is shared, find out what that data is.
Companies won’t change if nobody is paying attention and nobody knows what they want. We can encourage change by getting specific, and by paying more attention to current policies. Raise awareness, criticize the bad actors, and praise the good ones.
Personal data access and freedom
The flip side of “data control” is our own rights: what can we do with our own data? We want access to our personal data, and the right to use it.
This idea is newer, and it has a lot of potential. This was what Tim Berners-Lee called for, when he called for data ownership last fall.
“That data that [firms] have about you isn’t valuable to them as it is to you.”
I think it’s worth listening, when the inventor of the world wide web thinks we should have a right to our data.
So let’s spell it out. Let’s turn this into a list of freedoms we demand. We should be inspired by the free software and free culture movements, which advocate for other acts of sharing with users and consumers. In particular, inspired by Richard Stallman’s “Four Freedoms” for free software, I have a suggested list.
Three Freedoms of Personal Data Rights
Raw data access – Access to digital files in standard, non-proprietary file formats.
Without raw data, we are captive to the “interface” to data that a data holder provides. Raw data is the “source code” underlying this experience. Access to raw data is fundamental to giving us the freedom to use our data in other ways.
Freedom to share - No restriction on how we share our data with others.
Typically, when data holders provide access to data, their data use agreements limit how this data may be shared. These agreements are vital to protecting user privacy rights when third parties have access, but we have the right make our own sharing decisions about our own data.
Unrestricted use – Freedom to modify and use our data for any purpose.
Data use agreements can also impose other limitations on what individuals can do with data. Any restriction imposed on our use of our data impinges on our personal data rights. Freedom for personal data means having the right to do anything we wish with data that came from us.
In the short term, access to raw data can seem obscure and irrelevant: most users cannot explore this data. But like the source code to software, access to this data has great potential: a few will be able to use it, and they can share their methods and software to create new tools.
Raw data access is also an opportunity for us to share for the greater good, on our own terms. We could share this data with research studies, to advance knowledge and technology. We could share data with developers, to develop software around it. We could share it with educators, with artists, with citizen scientists. We could even cut the red tape: dedicate our data public domain and make it a public good.
This work is licensed under a Creative Commons Attribution 4.0 International License.
As you may know, we’re very interested in how HealthKit is shaping and extending the reach of personal self-tracking data. Last week, during Apple’s quarterly earnings call, Tim Cook mentioned that “There’s also been incredible interest in HealthKit, with over 600 developers now integrating it into their apps.” (emphasis mine).
@fat32io just a heads up. All the data in HealthKit is NOT backed up into iCloud. Unless encrypted local backup all data lost on reset
— Daniel Yates (@astralpilgrim) February 3, 2015
@astralpilgrim yikes, didn’t know that.
— fat32io (@fat32io) February 3, 2015
@fat32io yup. Lost all my data history last week doing a reset. Spoke to apple Genius Bar who told me about it.
— Daniel Yates (@astralpilgrim) February 3, 2015
@fat32io it’s the encryption that is key. They said its due to future sharing of health data with docs etc, requires encryption
— Daniel Yates (@astralpilgrim) February 3, 2015
For those of you that are unfamiliar with backup options for your iOS device. Here’s a quick gif to walk you through the process of encrypting your iOS backup so that you can restore your HealthKit data if anything happens to your device:
Quantified Self Labs is dedicated to the idea that data access matters. Moving forward, we’re going to be exploring different aspects of how data access affects our personal and public lives. Stay tuned to our QS Access channel for more news, thoughts, and insights.
On January 13th Uber, a wildly popular and often scrutinized ride share company, announced they have entered into an agreement with the City of Boston to share anonymized data generated by users of the service. This is the first partnership between Uber and a local government body, but points to the ability to potentially partner with cities that want to take a peak at the vast amount of data about when and where people are traveling within their municipality. Our first reaction to this was to explore if Uber has provided any method for it’s own users to access and export their trip data. Surely if they can able to export and pass along data to a third party, they can pass that data to their own users?
In our exploration of the mobile and web user platforms we found that Uber currently does not offer users with an easy way to access their data. As an Uber customer, you are provided with email receipts of your trips that include travel information, a route of the ride, and cost. This information is also available through their online user account page. However, it is not exportable and accessible in a method that allows individuals to store information in a consistent and machine readable format (such as a csv file). In our search for methods to assist in exporting Uber ride data, I stumbled upon this data scraper on Github developed by Josh Hunt. It’s useful to know that Uber has a standard no scraping clause in in it’s Terms of Service, but individual users accessing their own data for their own reasons is probably not what these clauses are meant to protect.
Aside from data access issues there is of course open questions about how Uber will implement privacy protections governing sensitive user data. Of course, Uber is not without fault in this space. The now infamous blog post pointing to their ability to track one-night stands (archived here) was enough for some users to question ethical standards within Uber. In their announcement, Uber touched on this issue by stating that they will provide some privacy protections by only offering anonymized aggregated data to third party partners. Protecting user privacy through data aggregation and anonymization is a step in the right direction, but there remain these open issues around data access for users. Uber and the cities they partner with will learn a lot about how we travel, but the partnership between Uber and their users could be improved by helping users (myself included) understand their own data and behavior by allowing easier access to the data we contribute when we use the service.
We’re interested to hear from our readers about their experiences using the above mentioned tool, or similar tools to access and export their Uber trip data. Please let us know. We’ve also reached out to Uber for comment.
I reached out to Uber Support over Twitter and received the following response:
“Unfortunately this is not currently a feature, however we’re always looking to improve and I’ll pass your suggestion along! *NM” (link)
We’re posting a quick note today to let you know that we’ve updated our “How To Download Your Fitbit Data” post. It now included separate instructions for both the old and new versions of Google Spreadsheets. This is just the first in a series of planned updates. We hope to post additional updates to allow you to have deeper access to your Fitbit data including, heart rate, blood pressure, and daily goal data.
If you’re using this how-to we’d love to hear from you! Are you learning something new? Making interesting data visualizations? Discussing the data with your health care team? Let us know. You can email us or post here in the comments.
As part of the Quantified Self Public Health Symposium, we invited a variety of individuals from the research and academic community. These included visionaries and new investigators in public health, human-computer interaction, and medicine. One of these was Jason Bobe, the Executive Director of the Personal Genome Project. When we think of the intersection of self-tracking and health, it’s harder to find something more definitive and personal than one’s own genetic code. The Personal Genome Project has operated since 2005 as a large scale research project that “bring together genomic, environmental and human trait data.”
We asked Jason to talk about his experience leading a remarkably different research agenda than what is commonly observed in health and medical research. From the outset, the design of the Personal Genome Project was intended to fully involve and respect the autonomy, skills, and knowledge of their participants. This is manifested most clearly one of their defining characteristics, that each participant receives a full copy of their genomic data upon participation. It may be surprising to learn that this is an anomaly in most, if not all, health research. As Jason noted at the symposium, we live in an investigator-centered research environment where participants are called on to give up their data for the greater good. In Jason’s talk below, these truths are exposed, as well as a few example and insights related to how the research community can move towards a more participant-centered design as they begin to address large amounts of personal self-tracking data being gathered around the world.
I found myself returning to this talk recently when the NIH released a new Genomic Data Sharing Policy that will be applied to all NIH-funded research proposals that generate genomic data. I spent the day attempting to read through some of the policy documents and was struck by the lack of mention of participant access to research data. After digging a bit I found the only mention was in the “NIH Points to Consider for IRBs and Institutions“:
[...] the return of individual research results to participants from secondary GWAS is expected to be a rare occurrence. Nevertheless, as in all research, the return of individual research results to participants must be carefully considered because the information can have a psychological impact (e.g., stress and anxiety) and implications for the participant’s health and well-being.
It will not be surprise to learn that the Personal Genome Project submitted public comments during the the comment period. Among these comments was a recommendation to require “researchers to give these participants access to their personal data that is shared with other researchers.” Unfortunately, this recommendation appears not to have been implemented. As Jason mentioned, we still have a long way to go.