Yes, You Can Reconcile The Wide Sharing Of Personal Medical Research Data With Greater Participant Control

from the this-is-how-to-do-it dept

Although the benefits of sharing big datasets are well-known, so are the privacy issues that can arise as a result. The tension between a desire to share information widely and the need to respect the wishes of those to whom it refers is probably most acute in the medical world. Although the hope is that aggregating health data on a large scale can provide new insights into diseases and their treatments, doing so makes issues of consent even trickier to deal with. A new study of Parkinson's disease from Sage Bionetworks, which describes itself as a "non-profit biomedical research organization," takes a particularly interesting approach. Unusually, it used an iPhone app to gather data directly from the participants:

The mPower app, built by Sage with support from the Robert Wood Johnson Foundation, collects data on capacities affected by Parkinson's disease, including dexterity, balance and gait, memory, and certain vocal characteristics, through tasks that make use of iPhone sensors. For example, to measure dexterity, participants complete a speed tapping exercise on their iPhone’s touchscreen. To evaluate speech, participants use their iPhone's microphone to record themselves pronouncing a vowel -- saying Aaaaah -- for 10 seconds. The app also allows participants to track when each task is completed alongside the time they take their medication, to help determine the effects of that medicine on their symptoms. Participants also complete regular surveys, rating the severity of their symptoms and what they think makes them better or worse.
That's a clever use of smartphone capabilities to allow people to become active participants in the study -- citizen scientists, almost -- but hardly a major breakthrough. What is much more impressive is the way in which the study has handled the issue of how widely the resulting data will be shared, and with whom:
Unlike traditional studies, mPower participants are able to choose who to share their data with. Sharing options include only those researchers associated with mPower, or qualified researchers worldwide. So far, over 75 percent of the more than 12,000 mPower participants chose to share their data broadly with researchers. This cutting-edge consent process, which is the driving force behind the decision to widely release the mPower data set, is outlined in a third paper published today in Nature Biotechnology, and represents a sea of change in participant control over data sharing.
There's a double benefit here. Not only are participants given direct control over how their data will be used, but a broader range of "qualified researchers" can gain access. To be classed as "qualifying," researchers must:
demonstrate their awareness and understanding of the data-sharing framework and applied ethics through a short, 18-question examination;

validate their identity to Sage Bionetworks through a variety of approved methods, such as an academic letter from a signing official, a notarized letter attesting to identity or a copy of a professional license;

make a public statement of intended data use, which we can in turn feed back to participants in the spirit of engagement and transparency;

explicitly agree to a 'contract' of data sharing, including the following: (i) downloading, initialing, signing, scanning and uploading a researcher oath to adhere to a code of behavior; (ii) complying with any data-specific conditions of use.
As you might expect, the researchers at Sage Bionetworks are great believers in openness:
As part of living our philosophy, all of our tools, platforms, and products are open source. Our software is available in Github, and our non-software creative works are licensed under the Creative Commons Attribution 3.0 Unported license except for legacy publications in closed journals.

We now dedicate funds to pay author processing charges for full Open Access on research publications and are actively working to free up our backfile of closed publications.
Open source, open access, open data released with informed consent: this is what open science looks like.

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