Using Crowdsourcing, Mobile Apps to Re-Think Participants’ Role in Clinical Research

Jan. 15, 2015
The federal government has put a lot of resources behind the concept of patient-centered outcomes research. So with large clinical research projects, what would patient-centric research look like? And how are big data, mobile apps and new approaches to consent involved?

The federal government has put a lot of resources behind the concept of patient-centered outcomes research. So with large clinical research projects, what would patient-centric research look like? And how are big data, mobile apps and new approaches to consent involved?

This week I had a chance to interview John Wilbanks, chief commons officer of the nonprofit Sage Bionetworks, which is getting ready to launch two projects that re-conceptualize the clinical study. With support from the Robert Wood Johnson Foundation, Sage is building Bridge as a web-based, open-source platform that will allow patients to provide their data and insights as research partners.

“The original idea for the Bridge projects was to come up with a different relationship between people and clinical study, to really focus on the idea that people shouldn’t be subjects of medical study, but that they should be participants in clinical studies,” said Wilbanks, whose career has focused on open content, open data and open innovation systems.

Working with partners in Parkinson’s disease and breast cancer research, Sage is seeking to rethink the way studies are done so they can have massive scale. “We don’t want to study 1,000 people in Norway and draw conclusions about complex diseases,” Wilbanks explained. “We want to enroll tens of thousands, if not hundreds of thousands of people in these studies. That changes the fundamental structure of the study.”

Sage decided to make mobile apps central to the projects. Rather than having patients come into the medical center four times a year to get blood drawn, fill out surveys and have the clinician do an analysis of their progression of Parkinson’s disease, for example, they plan to put apps on the phones of these people, so they can fill out the surveys on an ongoing basis.

“We can use sensors on the phone to capture some fine-grained data about gait, tremor or tapping dexterity, which are all the things the doctor would eyeball,” he said. “We can measure those on an ongoing daily, weekly, monthly basis. And we can capture other information on an ongoing basis: What did they eat today? When did they take their medicine? We are trying to get a much finer-grain picture of the way the disease variation works. Everyone’s Parkinson disease is different. And so that is what Bridge is about: thinking about ways we can do observational studies where the participants are deeply engaged in the data-generation process, not just showing up and getting data gathered about them. They own a copy of all their own data. They can pause the study or leave the study whenever they want. They can communicate pretty easily with the people analyzing the data.”

The Parkinson’s Disease Bridge Study will use microphone, accelerometer and touch-screen sensors in a mobile app to measure the fluctuations in short-term severity of the disease more consistently over time, and explore correlations with environmental factors such as sleep and exercise.

The goal is to enroll 100,000 people in each study. “I think we are going to have a lot of success enrolling people. The question is, how do we avoid the drop-off problem that plague health apps in general,” Wilbanks said. “Our hypothesis is that by engaging people in clinical study, by returning data, by returning insights, and letting people track their progress over time, not in a generic sense but in the context of a study, we think this is going to be more sticky than people who got a Fitbit for Christmas but stopped using it after 30 days. But we don't know. When we launch them, we are going to start observing them. What is it like to run a study at this kind of scale? What keeps people engaged vs. what drives them away?”

Sage also has done a lot of work to re-conceptualize the informed consent document for large-scale research. They take the most important pieces of a consent document and convey them in a different way to increase the chances that people understand them instead of just reading the document. “That has become a project we call the participant-centered consent toolkit, which is an attempt to create tools and methods for creating user interfaces to informed consent documents,” Wilbanks said.  “For the two Bridge studies, that includes icon-based visual consent, so that if you say we are going to ask you to tap on the phone, walk and vocalize, we use icons to represent those actions on the screen. It is an 11-screen process that takes less than a minute.” Participants then answer a 5-question test to proceed into the study. “Our goals are 75 to 85 pass rates.” Early returns suggest that should be easy to achieve, he said. “The goal is to develop a way to do consent that scales on mobile devices, but these are good practices for any informed consent: think about the most important pieces of information participants need, and do the patient-centric work to make sure they understand what they are signing up for.”

The Parkinson’s and breast cancer projects are low-risk, observational studies, using surveys and sensors. One of Sage’s goals for 2015 is to add support for genetic testing and EHR data to the Bridge system. “If you correlate that data with fuzzier, more lightweight, longitudinal stuff from the phone, you can start to build really interesting models of symptom variation, disease progression and drug side effects.”

Wilbanks said Sage would monitor these projects and see how well they do: what kind of enrollment they get and what kind of demand they get from the clinician side to add support for integrating EHRs or other robust clinical data.

“But we are a little nonprofit, and we have to go where the users are right now,” he said, “and our clinicians are less interested in pulling that medical record data in than they are in generating data where they know the quality and structure.”

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