Abstract
Wearable devices have shown significant potential to capture real-time physiological and behavioral data, providing valuable insights that can enhance clinical decision-making and improve patient outcomes. These capabilities can also facilitate the discovery of new biomarkers and enable the design of more targeted and efficient clinical trials. However, many research teams face challenges due to a lack of technical expertise and analytical tools necessary to effectively integrate wearable data with clinical datasets or to develop models tailored to specific study endpoints. Existing solutions are often fragmented, proprietary, or lack standardization, forcing researchers to develop custom solutions from scratch to manage and analyze their wearable data.
To address the significant barriers researchers face in integrating wearable data into clinical trials, and to avoid the inefficiencies of individual teams independently developing similar capabilities, we propose to expand the W4H Toolkit, originally developed under the NIH/NSF SCH: Wearables for Health and Disease Knowledge (W4H) project, from a demonstration prototype to a fully operational platform that clinical teams can use on their own. More specifically, we intend to further develop and validate the W4H Toolkit through its independent use in clinical trials across various domains; this will be achieved with documentation and a plug-and-play architecture with components for data acquisition, storage, analysis, and modeling. These advancements will be validated using metrics related to trial speed, data integration efficiency, and overall workflow performance. Additionally, we will increase the toolkit's impact on translational research by promoting adoption and fostering broader use, by establishing an open-source governance and defining software development processes and automation, together with documentation that promotes community engagement.
If successful, the proposed W4H Toolkit will enhance the efficiency, quality, and impact of clinical trials with wearables, while serving as a platform to develop and share advanced machine learning models and to expand educational opportunities.