Wearable technologies and their associated informatics platforms gather, store, and process vast amounts of health-related, real-world data. These datasets can be some of the most complex used in health research. If the end-goal is to provide evidence in regulatory decision making, implementing well-defined practices to demonstrate sufficient data quality and fidelity is a must.
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When it comes to the development and discovery of novel digital biomarkers in clinical trials, one size does not fit all. Patrick Hankey, our head of strategic business development, sums up the problem very well when he explains, “If you have seen one clinical trial, you have seen one clinical trial.” In this article, we describe what we mean by “one size does not fit all” within the context of digital biomarker discovery and provide examples of how this principle is particularly relevant to the validation of wearable sensor solutions.
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The first guiding principle to the development and discovery of novel digital biomarkers is hypothesis-driven research. In this article, we will describe our approach and provide examples of how we use this principle to ensure robust digital biomarker development.
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