Digital health technologies such as wearable sensors are increasingly being used in clinical trials. However, the endpoints created from these useful tools are wide and varied. Often, digital health technologies such as wearable sensors are used either to collect a raw metric like “step count” or with artificial intelligence algorithms to define a biomarker for improvement. In the case of the former, improvements in such a raw metric is difficult to attribute to the patient health in a meaningful way. In the case of the latter, despite the potential predictive accuracies of machine learning and artificial intelligence approaches, the resulting biomarkers are a black box, which has limited direct interpretability to the patient's specific health concerns.
Join authors as they provide insights on how and why to place the patient at the heart of the endpoint. Discuss how, by designing trial endpoints which are measured by digital health technologies using a patient centered approach from the outset, the patient benefits from understanding the implications of approved medication for their life.