Over the past decade, the integration of digital technologies into clinical trials has fundamentally transformed the ability to monitor patients in real-time through wearable sensors, enabling the rapid capture of clinically relevant signals. This transformation has accelerated the development of medicines by providing faster, more objective measurements of therapeutic effects. Central to this technological shift is the Internet of Medical Things (IoMT) platform, with Magnol.Ai serving as a prime example. Magnol.Ai excels in continuously processing and converting vast amounts of digital data, including wearable sensor signals and electronic Patient-Reported Outcomes (ePROs), into actionable clinical insights for digital biomarker (dBM) research. Beyond its pivotal role in clinical research, Magnol.Ai demonstrates versatility as a scalable, real-time edge computing platform in other domains, such as real-time gait computing. By deploying advanced sensors and algorithms for real-time speed of movement tracking, Magnol.Ai underscores its capacity to support a broad range of applications within IoMT.

Connected clinical trials enable real-time connection with consented patients, allowing the capture of clinically meaningful signals via wearable sensors to accelerate the development of medicines. At the core of the full-stack technologies needed to support such trials is our digital data platform (DDP) — to continuously ingest, visualize, process, and transform a large amount of digital data, including wearable sensor signals and electronic Patient-Reported Outcomes (ePRO)s into meaningful clinical measures. This paper presents the integrated systems and technologies we have developed to establish such a cloud-based platform. The main advantages of the DDP include 1) interactive exploration and navigation of large volumes of digital data at scale, 2) real-time data monitoring to promptly track data quality and compliance, and 3) novel data delivery methods to allow seamless access to large-scale raw sensor signals, processed signals, and aggregated digital biomarker measures for endpoints analysis. Finally, two use scenarios developed and deployed in the DDP will be demonstrated.