Research Participants’ Rights to Data Protection in the Era of Open Science
23 Pages Posted: 23 Jun 2020
Date Written: May 26, 2020
Clinical trials are increasingly using sensors embedded in wearable devices due to their capabilities to generate real-world data. These devices are able to continuously monitor, record, and store physiological metrics in response to a given therapy, which is contributing to a redesign of clinical trials around the world. Traditional clinical trials are immensely expensive and limited in testing options, as they typically entail research participants coming to designated sites for measuring responses to an investigational treatment. This process creates a costly, time-intensive pathway from discovery to market and may not produce results that future patients wish to know, particularly around improvements in activities of daily living and overall quality of life. While wearable devices present potential benefits, including a reduction in expense and time for researchers as well as burden on research participants, there are data protection concerns around the magnitude of data that is generated by these devices. Participants may not be aware of the detailed, granular-level of data being collected from them, and researchers may be in violation of collecting ‘unintended data’—that is, when the data collected does not pertain to the original research purpose. These seemingly opposing views, from individual data protection to sharing big data across populations as a common resource, would benefit from drawing on lessons learned in open science. Both data protection regulation and open science emphasize the need for transparency, access to data, security, and accountability. This Article focuses on the evolving role for research participants as they become increasingly engaged in clinical trials through participant-driven data collection, and how data protection regulation could further empower participants in the research process.
Keywords: wearables, clinical trials, privacy, data, data protection, participant-driven, data collection,
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