Sharing digital health data responsibly: Balancing open science with participant privacy

Data sharing is essential for modern science, advancing transparency, reproducibility, and discovery. Emerging evidence shows that certain digital health data, particularly high-frequency accelerometry from body-worn sensors, carry re-identification risks even when de-identified. In 2023, the National Institutes of Health published its Data Management and Sharing (DMS) Policy formalizing a commitment to openness by requiring all funded researchers to share scientific data. However, the policy did not anticipate that raw motion signals collected by wearable devices can function as biometric identifiers. The WristPrint study demonstrated that a single day of raw accelerometry data could be used to re-identify individuals with 96% accuracy. Related research in gait detection shows that as few as ten steps may be enough to uniquely identify someone. These findings highlight gaps in current data sharing policies and the need for tailored guidance. We argue that policy updates, enforceable data use agreements, and educational initiatives are essential to align openness with protection. The path forward is not to retreat from data sharing but to share more wisely, safeguarding participant trust while sustaining scientific progress.Author summary: Data sharing fuels scientific discovery, but new evidence shows that certain types of digital health data (i.e., raw motion signals from wearable sensors) can reveal more about individuals than expected. Research now demonstrates that even a few steps of walking can uniquely identify someone, raising important privacy concerns. In this article, we argue for policies and education that both enable open science and safeguard research participants.