How "Future-Proof" Is Your Digital Biomarker Data?

Considerations for maximizing the long-term value of wearable sensor data

The adoption of wearable biometric sensors to capture real-world participant data within clinical research has steadily increased in recent years. As of February 2020, there were approximately 460 wearables studies underway, with analysts predicting that 70% of trials would include a wearable device by 2025. Building upon the eSource momentum within the clinical research industry, the global pandemic has accelerated the deployment of decentralized trials (DCTs) and remote data capture technologies. This has effectively redefined the role of wearables and digital biomarkers in clinical research. Sophisticated new algorithms and analytic techniques that generate novel clinical endpoints from sensor data are also emerging at a rapid pace, promising new and more precise outcomes across more therapeutic areas and patient populations in the years to come.

There are a myriad of factors to consider when evaluating wearable motionsensing technologies, including battery life, storage capacity, data access, validation, patient acceptance, privacy, and regulatory clearance, to name a few. However, one of the most important considerations - the longevity of the collected data - is frequently overlooked during this process. As the development of novel data analysis methods such as artificial intelligence and machine learning continues to accelerate, the ability to apply these new techniques to previously collected biosensor data is determined by how that data was collected and stored. Wearable technology platforms that generate raw “future-proof” data deliver long-term value by preserving comparability across multiple phases and studies, while enabling sponsors to leverage advanced methods to derive better quality outcome data.