Meet Quantified Flu: A community science project to find out if wearables can warn us when we’re getting sick
Can wearable devices help predict when we’re getting sick? With the COVID-19 pandemic influencing all of our lives right now, this question seems more timely than ever. But this question equally applies to the flu and the common cold. There is preliminary data which shows that biometrics from wearables (like resting heart rate, blood oxygen saturation, body temperature, and sleep quality) might be predictive of coming down with a cold.
Do you have a wearable that you use regularly? And maybe even have used it in the past, while you were sick? You can help in a participatory science project to make these data more useful for predicting and understanding our own sickness incidents! The Quantified Flu project recently received a grant from Just One Giant Lab (JOGL) as part of the OpenCovid19 Initiative, a program that breaks down barriers and empowers individuals and communities to take action together to solve the coronavirus pandemic.
To learn more about the project, we spoke with Bastian Greshake Tzovaras (Director of Research for Open Humans), Mad Price Ball (Executive Director and President of Open Humans Foundation and co-founder of Open Humans), and Gary Wolf (co-founder of the Quantified Self).
What is the big problem you’re trying to solve?
Reason is our human birthright, and everybody should have access to the tools, knowledge, and support they need to reason effectively about their own questions. Half a millennium of scientific culture has given us a rich legacy of approaches and methods, but most of the scientific toolkit remains an exclusive possession of a professional caste.
Internet access to knowledge, peer-support, and easy-to-operate tools for empirical observation have made it possible for many more people to make personal relevant discoveries. Possible. Even obviously possible. And yet, at the same time, strangely difficult. That’s the problem that interests us.
What has been the most difficult/challenging part of the journey?
Although people are motivated to do self-research by all kinds of questions, the predominant types of questions are those related to health. The biggest challenge is that empirical exploration of personal questions — which we call “personal science” — remains an outsider practice, and therefore nearly every resource to support people who want to learn about their own health with their own data has to be “borrowed” from biomedical research and healthcare industry approaches.
But there are profound differences between personal science and biomedicine that make these approaches unwieldy or inappropriate for individuals seeking their own answers. Developing a new, domain-appropriate cultural and technological kit to support personal science is a big long-term challenge that is as much about understanding and serving a community as it is about the tech bits.
What successes have you had since joining the OpenCovid19 initiative on JOGL?
We joined the OpenCovid19 initiative of JOGL with our project Quantified Flu, which tries to address the question of how we can enable personal relevant discoveries in the context of having an infection and how wearables can help in that space.
A lot of things have happened since then: The initial launch version of Quantified Flu had only support for doing retrospective analyses of sickness events and wearables from Oura and Fitbit. Since then we’ve added a lot of features: People can now do on-going symptom tracking with easy-to-use email notifications; we extended the wearable support to also cover Google Fit, Apple Health and Garmin compatible devices; and added interactive visualizations that combine the symptom logs and wearable data!
How has JOGL helped you address challenges and meet your project goals?
The OpenCovid19 initiative has been very helpful in growing our project: Through the OpenCovid19 Slack we found Lukaz Baldy, a volunteer who single-handedly programmed the iPhone application that was needed to enable the support for Apple Watches. And with the micro-grant from JOGL, we managed to support a freelance developer to add the support for Garmin devices as well as Google Fit-enabled devices!
What one big idea in your field do you wish that every non-scientist understood? Why?
How powerful the analysis of time series data by single individuals who explore their own questions can be to yield important results. This is not only true for the individuals involved but also health discovery generally. Related to this, it’s highly important to understand the context of time points on a time series, which is something we see frequently with Quantified Flu contributors.
A variance in physiological signals and even symptom reports don’t necessarily align with infections, but can have a variety of reasons which will be obvious to the individual collecting the data but would be completely missed by others!
What’s a public misconception that you wish we could correct as a community?
The quantified self community is not primarily about technology, but about learning. That’s why one of the main formats in which the quantified self community presents are the Show & Tell talks. They always focus on three questions: What did I do? How did I do it? What did I learn?
While the How did I do it? Although the answers often involve technology, the most important bits are what you’ve learned from doing a thing.
What legislation would you change to improve how science in your field is done?
Two things could help the personal science community a lot:
Having free access to scientific literature. Too often access to primary literature remains behind paywalls and thus out of reach for individuals trying to do their own inquiries.
Mandatory access to our own data in accessible digital formats, from all our digital devices. While the European General Data Protection Regulation has added some provisions for data portability, there still remains a lot of data that our digital devices store but can’t be accessed by us.
Is there any scientific topic (outside of your field of research) that you think should receive more attention? Which one?
This is perhaps a “meta-topic” but I’d like to see people interested in science pay more explicit attention to where good scientific ideas come from. There is a lot of focus on methods, and of course a lot of focus on the substantive disciplinary questions that emerge from the tradition of any particular branch. But there’s not very much attention to where the ideas come from. Since science depends fundamentally on the ability to come with good ideas, this topic seems worthy of interest!
